esummary.ipynb

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    "# -*- coding: utf-8 -*-\n",
    "# This is a report using the data from IQAASL.\n",
    "# IQAASL was a project funded by the Swiss Confederation\n",
    "# It produces a summary of litter survey results for a defined region.\n",
    "# These charts serve as the models for the development of plagespropres.ch\n",
    "# The data is gathered by volunteers.\n",
    "# Please remember all copyrights apply, please give credit when applicable\n",
    "# The repo is maintained by the community effective January 01, 2022\n",
    "# There is ample opportunity to contribute, learn and teach\n",
    "# contact dev@hammerdirt.ch\n",
    "\n",
    "# Dies ist ein Bericht, der die Daten von IQAASL verwendet.\n",
    "# IQAASL war ein von der Schweizerischen Eidgenossenschaft finanziertes Projekt.\n",
    "# Es erstellt eine Zusammenfassung der Ergebnisse der Littering-Umfrage für eine bestimmte Region.\n",
    "# Diese Grafiken dienten als Vorlage für die Entwicklung von plagespropres.ch.\n",
    "# Die Daten werden von Freiwilligen gesammelt.\n",
    "# Bitte denken Sie daran, dass alle Copyrights gelten, bitte geben Sie den Namen an, wenn zutreffend.\n",
    "# Das Repo wird ab dem 01. Januar 2022 von der Community gepflegt.\n",
    "# Es gibt reichlich Gelegenheit, etwas beizutragen, zu lernen und zu lehren.\n",
    "# Kontakt dev@hammerdirt.ch\n",
    "\n",
    "# Il s'agit d'un rapport utilisant les données de IQAASL.\n",
    "# IQAASL était un projet financé par la Confédération suisse.\n",
    "# Il produit un résumé des résultats de l'enquête sur les déchets sauvages pour une région définie.\n",
    "# Ces tableaux ont servi de modèles pour le développement de plagespropres.ch\n",
    "# Les données sont recueillies par des bénévoles.\n",
    "# N'oubliez pas que tous les droits d'auteur s'appliquent, veuillez indiquer le crédit lorsque cela est possible.\n",
    "# Le dépôt est maintenu par la communauté à partir du 1er janvier 2022.\n",
    "# Il y a de nombreuses possibilités de contribuer, d'apprendre et d'enseigner.\n",
    "# contact dev@hammerdirt.ch\n",
    "\n",
    "# sys, file and nav packages:\n",
    "import datetime as dt\n",
    "from datetime import date, datetime, time\n",
    "from babel.dates import format_date, format_datetime, format_time, get_month_names\n",
    "import locale\n",
    "\n",
    "# math packages:\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from math import pi\n",
    "import scipy.stats as stats\n",
    "\n",
    "# charting:\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib import ticker\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import seaborn as sns\n",
    "from matplotlib import colors as mplcolors\n",
    "\n",
    "# build report\n",
    "import reportlab\n",
    "from reportlab.platypus.flowables import Flowable\n",
    "from reportlab.platypus import SimpleDocTemplate, Paragraph, PageBreak, KeepTogether\n",
    "from reportlab.lib.pagesizes import A4\n",
    "from reportlab.lib.units import cm\n",
    "from reportlab.platypus import Table, TableStyle\n",
    "\n",
    "# the module that has all the methods for handling the data\n",
    "import resources.featuredata as featuredata\n",
    "from resources.featuredata import makeAList, small_space, large_space, aSingleStyledTable, smallest_space\n",
    "from resources.featuredata import caption_style, subsection_title, title_style, block_quote_style, makeBibEntry\n",
    "from resources.featuredata import figureAndCaptionTable, tableAndCaption, aStyledTableWithTitleRow\n",
    "from resources.featuredata import sectionParagraphs, section_title, addToDoc, makeAParagraph, bold_block\n",
    "from resources.featuredata import makeAList\n",
    "\n",
    "# home brew utitilties\n",
    "import resources.chart_kwargs as ck\n",
    "import resources.sr_ut as sut\n",
    "\n",
    "# images and display\n",
    "import IPython\n",
    "from IPython.display import Markdown as md\n",
    "from IPython.display import HTML\n",
    "from PIL import Image as PILImage\n",
    "from myst_nb import glue\n",
    "\n",
    "def convertPixelToCm(file_name: str = None):\n",
    "    im = PILImage.open(file_name)\n",
    "    width, height = im.size\n",
    "    dpi = im.info.get(\"dpi\", (72, 72))\n",
    "    width_cm = width / dpi[0] * 2.54\n",
    "    height_cm = height / dpi[1] * 2.54\n",
    "    \n",
    "    return width_cm, height_cm\n",
    "\n",
    "# chart style\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# a place to save figures and a \n",
    "# method to choose formats\n",
    "save_fig_prefix = \"resources/output/\"\n",
    "\n",
    "# the arguments for formatting the image\n",
    "save_figure_kwargs = {\n",
    "    \"fname\": None,\n",
    "    \"dpi\": 300.0,\n",
    "    \"format\": \"jpeg\",\n",
    "    \"bbox_inches\": None,\n",
    "    \"pad_inches\": 0,\n",
    "    \"bbox_inches\": 'tight',\n",
    "    \"facecolor\": 'auto',\n",
    "    \"edgecolor\": 'auto',\n",
    "    \"backend\": None,\n",
    "}\n",
    "\n",
    "## !! Begin Note book variables !!\n",
    "\n",
    "# There are two language variants: german and english\n",
    "# change both: date_lang and language\n",
    "date_lang =  'de_DE.utf8'\n",
    "locale.setlocale(locale.LC_ALL, date_lang)\n",
    "\n",
    "# the date format of the survey data is defined in the module\n",
    "date_format = featuredata.date_format\n",
    "\n",
    "# the language setting use lower case: en or de\n",
    "# changing the language may require changing the unit label\n",
    "language = \"de\"\n",
    "unit_label = \"p/100 m\"\n",
    "\n",
    "# the standard date format is \"%Y-%m-%d\" if your date column is\n",
    "# not in this format it will not work.\n",
    "# these dates cover the duration of the IQAASL project\n",
    "start_date = \"2020-03-01\"\n",
    "end_date =\"2021-05-31\"\n",
    "start_end = [start_date, end_date]\n",
    "\n",
    "# the fail rate used to calculate the most common codes is\n",
    "# 50% it can be changed:\n",
    "fail_rate = 50\n",
    "\n",
    "# Changing these variables produces different reports\n",
    "# Call the map image for the area of interest\n",
    "bassin_map = \"resources/maps/all_survey_areas_summary.jpeg\"\n",
    "\n",
    "# the label for the aggregation of all data in the region\n",
    "top = \"Alle Erhebungsgebiete\"\n",
    "\n",
    "# define the feature level and components\n",
    "# the feature of interest is the all (all) at the river basin (river_bassin) level.\n",
    "# the label for charting is called 'name'\n",
    "this_feature = {'slug':'all', 'name':\"Alle Erhebungsgebiete\", 'level':'all'}\n",
    "\n",
    "# these are the smallest aggregated components\n",
    "# choices are water_name_slug=lake or river, city or location at the scale of a river bassin \n",
    "# water body or lake maybe the most appropriate\n",
    "this_level = 'river_bassin'\n",
    "\n",
    "# the doctitle is the unique name for the url of this document\n",
    "doc_title = \"lakes_rivers\"\n",
    "\n",
    "\n",
    "# # identify the lakes of interest for the survey area\n",
    "# lakes_of_interest = [\"neuenburgersee\", \"thunersee\", \"bielersee\", \"brienzersee\"]   \n",
    "\n",
    "# !! End note book variables !!\n",
    "## data\n",
    "# Survey location details (GPS, city, land use)\n",
    "dfBeaches = pd.read_csv(\"resources/beaches_with_land_use_rates.csv\")\n",
    "# set the index of the beach data to location slug\n",
    "dfBeaches.set_index(\"slug\", inplace=True)\n",
    "\n",
    "# Survey dimensions and weights\n",
    "dfDims = pd.read_csv(\"resources/corrected_dims.csv\")\n",
    "\n",
    "# code definitions\n",
    "dxCodes = pd.read_csv(\"resources/codes_with_group_names_2015.csv\")\n",
    "dxCodes.set_index(\"code\", inplace=True)\n",
    "\n",
    "# columns that need to be renamed. Setting the language will automatically\n",
    "# change column names, code descriptions and chart annotations\n",
    "columns={\"% to agg\":\"% agg\", \"% to recreation\": \"% recreation\", \"% to woods\":\"% woods\", \"% to buildings\":\"% buildings\", \"p/100m\":\"p/100 m\"}\n",
    "\n",
    "# !key word arguments to construct feature data\n",
    "# !Note the water type allows the selection of river or lakes\n",
    "# if None then the data is aggregated together. This selection\n",
    "# is only valid for survey-area reports or other aggregated data\n",
    "# that may have survey results from both lakes and rivers.\n",
    "fd_kwargs ={\n",
    "    \"filename\": \"resources/checked_sdata_eos_2020_21.csv\",\n",
    "    \"feature_name\": this_feature['slug'], \n",
    "    \"feature_level\": this_feature['level'], \n",
    "    \"these_features\": this_feature['slug'], \n",
    "    \"component\": this_level, \n",
    "    \"columns\": columns, \n",
    "    \"language\": 'de', \n",
    "    \"unit_label\": unit_label, \n",
    "    \"fail_rate\": fail_rate,\n",
    "    \"code_data\":dxCodes,\n",
    "    \"date_range\": start_end,\n",
    "    \"water_type\": None,    \n",
    "}\n",
    "\n",
    "fdx = featuredata.Components(**fd_kwargs)\n",
    "\n",
    "# call the reports and languages\n",
    "fdx.adjustForLanguage()\n",
    "fdx.makeFeatureData()\n",
    "fdx.locationSampleTotals()\n",
    "fdx.makeDailyTotalSummary()\n",
    "fdx.materialSummary()\n",
    "fdx.mostCommon()\n",
    "fdx.codeGroupSummary()\n",
    "\n",
    "# !this is the feature data!\n",
    "fd = fdx.feature_data\n",
    "\n",
    "\n",
    "\n",
    "# !keyword args to build period data\n",
    "# the period data is all the data that was collected\n",
    "# during the same period from all the other locations\n",
    "# not included in the feature data. For a survey area\n",
    "# or river bassin these_features = feature_parent and \n",
    "# feature_level = parent_level\n",
    "period_kwargs = {\n",
    "    \"period_data\": fdx.period_data,\n",
    "    \"these_features\": this_feature['slug'],\n",
    "    \"feature_level\":this_feature['level'],\n",
    "    \"feature_parent\":this_feature['slug'],\n",
    "    \"parent_level\": this_feature['level'],\n",
    "    \"period_name\": top,\n",
    "    \"unit_label\": unit_label,\n",
    "    \"most_common\": fdx.most_common.index\n",
    "}\n",
    "period_data = featuredata.PeriodResults(**period_kwargs)\n",
    "\n",
    "# the rivers are considered separately\n",
    "# select only the results from rivers\n",
    "fd_kwargs.update({\"water_type\":\"r\"})\n",
    "fdr = featuredata.Components(**fd_kwargs)\n",
    "fdr.makeFeatureData()\n",
    "fdr.adjustForLanguage()\n",
    "fdr.makeFeatureData()\n",
    "fdr.locationSampleTotals()\n",
    "fdr.makeDailyTotalSummary()\n",
    "fdr.materialSummary()\n",
    "fdr.mostCommon()\n",
    "\n",
    "# collects the summarized values for the feature data\n",
    "# use this to generate the summary data for the survey area\n",
    "# and the section for the rivers\n",
    "admin_kwargs = {\n",
    "    \"data\":fd,\n",
    "    \"dims_data\":dfDims,\n",
    "    \"label\": this_feature[\"name\"],\n",
    "    \"feature_component\": this_level,\n",
    "    \"date_range\":start_end,\n",
    "    **{\"dfBeaches\":dfBeaches}\n",
    "}\n",
    "admin_details = featuredata.AdministrativeSummary(**admin_kwargs)\n",
    "admin_summary = admin_details.summaryObject()\n",
    "\n",
    "# update the admin kwargs with river data to make the river summary\n",
    "admin_kwargs.update({\"data\":fdr.feature_data})\n",
    "admin_r_details = featuredata.AdministrativeSummary(**admin_kwargs)\n",
    "admin_r_summary = admin_r_details.summaryObject()\n",
    "\n",
    "# this defines the css rules for the note-book table displays\n",
    "header_row = {'selector': 'th:nth-child(1)', 'props': f'background-color: #FFF;'}\n",
    "even_rows = {\"selector\": 'tr:nth-child(even)', 'props': f'background-color: rgba(139, 69, 19, 0.08);'}\n",
    "odd_rows = {'selector': 'tr:nth-child(odd)', 'props': 'background: #FFF;'}\n",
    "table_font = {'selector': 'tr', 'props': 'font-size: 12px;'}\n",
    "table_css_styles = [even_rows, odd_rows, table_font, header_row]\n",
    "\n",
    "\n",
    "# pdf download is an option \n",
    "# the .pdf output is generated in parallel\n",
    "# this is the same as if it were on the backend where we would\n",
    "# have a specific api endpoint for .pdf requests. \n",
    "# reportlab is used to produce the document\n",
    "# the components of the document are captured at run time\n",
    "# the pdf link gives the name and location of the future doc\n",
    "pdf_link = f'resources/pdfs/esummary.pdf'\n",
    "\n",
    "# the components are stored in an array and collected as the script runs\n",
    "pdfcomponents = []\n",
    "\n",
    "map_caption = [\n",
    "    \"<b>Abbildung 1:</b> Karte der Erhebungsstandorte März 2020–August 2021. Die rot dargestellten Standorte betreffen Erhebungen an Fliessgewässern oder Seen, \",\n",
    "    \"die violetten Punkte sind die Standorte in den Alpen und im Jura.\"\n",
    "]\n",
    "f1cap = makeAParagraph(map_caption, featuredata.caption_style),\n",
    "\n",
    "\n",
    "def convertPixelToCm(file_name: str = None):\n",
    "    im = PILImage.open(file_name)\n",
    "    width, height = im.size\n",
    "    dpi = im.info.get(\"dpi\", (72, 72))\n",
    "    width_cm = width / dpi[0] * 2.54\n",
    "    height_cm = height / dpi[1] * 2.54\n",
    "    \n",
    "    return width_cm, height_cm\n",
    "\n",
    "astyle = \"'display:flex; align-content:center; flex-flow:column no-wrap; flex-direction:column;  box-shadow: 0px 0px 3px #d2d9db'\"\n",
    "\n",
    "\n",
    "def cardFigure(html_table: str=None, table_caption: str=None, style: str=astyle):\n",
    "    \n",
    "    \n",
    "    figure_in_a_card = f\"\"\"\n",
    "    <div style={style}> \n",
    "       <div style='padding:20px;width:80%;margin:auto'>\n",
    "            {html_table}\n",
    "        </div>\n",
    "        <div style='border-top: 1px solid #d2d9db;'>\n",
    "            <p style='margin:10px'><i>{table_caption}</i></p>\n",
    "        </div>\n",
    "    </div>\n",
    "                    \"\"\"\n",
    "    \n",
    "    return figure_in_a_card"
   ]
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     "data": {
      "application/papermill.record/text/plain": "['<b>Abbildung 1:</b> Karte der Erhebungsstandorte März 2020–August 2021. Die rot dargestellten Standorte betreffen Erhebungen an Fliessgewässern oder Seen, ',\n 'die violetten Punkte sind die Standorte in den Alpen und im Jura.']"
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       "name": "esummary_map_caption"
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   "source": [
    "glue('esummary_map_caption', map_caption, display=False)\n",
    "\n",
    "o_w, o_h = convertPixelToCm(\"resources/maps/esummary_map.jpeg\")\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\":\"resources/maps/esummary_map.jpeg\",\n",
    "    \"caption\": f1cap, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\":2,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "f1 = figureAndCaptionTable(**figure_kwargs)"
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    "(kurzfassung)=\n",
    "# Kurzfassung\n",
    "\n",
    "{Download}`Download </resources/pdfs/esummary.pdf>`\n",
    "\n",
    "\n",
    "| [Italiano](it-esum) | [Français](fr-esum) | [English](https://www.plagespropres.ch/esummary.html) |\n",
    "\n",
    "Beim Projekt «Identification, quantification and analysis of anthropogenic Swiss litter» (Identifizierung, Quantifizierung und Analyse von anthropogenem Abfall in der Schweiz, IQAASL) handelt es sich um ein vom Bundesamt für Umwelt (BAFU) in Auftrag gegebenes Projekt zur Erhebung von Daten zu sichtbaren Schadstoffen an Schweizer Seen und Fliessgewässern. Unter Verwendung von Abfallerhebungstechniken wurden sämtliche Abfallobjekte eingesammelt und identifiziert. Das Projekt wurde auf 20 Standorte in den Alpen und im Jura ausgedehnt. Insgesamt wurden 406 Proben an 163 Standorten in 95 Gemeinden entnommen.\n",
    "    \n",
    "In diesem Bericht werden die vom März 2020 bis August 2021 durchgeführten Abfallerhebungen sowie die in der Schweiz angewandten Methoden zusammengefasst und analysiert. Diese Erhebungsphase überschneidet sich mit dem Erhebungszeitraum des Swiss Litter Reports (SLR) {cite}`slr`, dessen Erhebungen von April 2017 bis März 2018 durchgeführt worden sind. Der SLR war das erste nationale Projekt, bei dem das Standardprotokoll gemäss dem Leitfaden zur Überwachung von Strandabfällen  {cite}`mlwguidance` oder eine andere vergleichbare Methode angewandt wurde. Diese Überschneidung der Erhebungsphasen ermöglicht es, die Ergebnisse der vorliegenden Studie mit denen des SLR zu vergleichen. \n",
    "\n",
    "```{card}\n",
    ":class-card: sd-text-black\n",
    ":img-top: resources/maps/esummary_map.jpeg\n",
    ":margin: auto\n",
    "+++\n",
    "__Abbildung 1:__ *Karte der Erhebungsstandorte März 2020–August 2021. Die rot dargestellten Standorte betreffen Erhebungen an Fliessgewässern oder Seen, die violetten Punkte sind die Standorte in den Alpen und im Jura.*\n",
    "```\n",
    "\n",
    "\n",
    "##  Seen und Flüsse \n",
    "\n",
    "Die Proben an Seen und Fliessgewässern wurden vom März 2020 bis Mai 2021 entnommen. Im Rahmen von 386 Erhebungen wurden insgesamt 54’744 Gegenstände eingesammelt und klassifiziert. Die Erhebungsstandorte wurden für die regionale Analyse zu Erhebungsgebieten zusammengefasst und durch die Flüsse Aare, Rhone, Ticino und Linth/Limmat definiert. Die Erhebungen fanden an 143 verschiedenen Standorten in 77 Gemeinden statt. Insgesamt wurde eine lineare Strecke von 20 km mit einer Fläche von 9 Hektaren und einer Gesamtbevölkerung von 1,7 Millionen Personen untersucht.\n",
    "\n",
    "Die meisten Erhebungen wurden an Seeufern durchgeführt (331 Proben), da Seen im Vergleich zu Fliessgewässern einen beständigeren und sichereren ganzjährigen Zugang bieten. Des Weiteren besitzen Seen grosse Flächen mit verringerter Fliessgeschwindigkeit, die von verschiedenen Flüssen, Bächen und Entwässerungssystemen gespeist werden. Damit sind sie ideale Standorte, um die Vielfalt der Gegenstände in und rund um die Gewässer zu beurteilen. Die insgesamt 316 Proben stammten aus sieben grossen Seen in drei grossen Einzugsgebieten. Zwanzig Standorte wurden für eine monatliche Probenahme während zwölf Monaten ausgewählt, mit Ausnahme des Lago Maggiore, der alle drei Monate beprobt wurde. Auch am Luganersee, am Vierwaldstättersee, am Brienzersee und am Zugersee wurden Erhebungen durchgeführt. Darüber hinaus fanden 55 Erhebungen an 16 Fliessgewässern statt.\n",
    "\n",
    "Seestandorte mit monatlichen Probenahmen:\n",
    "\n",
    "* Erhebungsgebiet Aare\n",
    "  * Thunersee: Spiez, Unterseen\n",
    "  * Bielersee: Biel/Bienne, Vinelz\n",
    "  * Neuenburgersee: Neuenburg, Cheyres-Châbles, Yverdon-les-Bains\n",
    "\n",
    "* Erhebungsgebiet Linth/Limmat\n",
    "  * Zürichsee: Zürich, Küsnacht (ZH), Rapperswil-Jona, Richterswil\n",
    "  * Walensee: Walenstadt, Weesen\n",
    "\n",
    "* Erhebungsgebiet Rhone\n",
    "  * Genfersee: Vevey, Saint-Gingolph, Genf, Préverenges, La Tour-de-Peilz\n",
    "\n",
    "* Erhebungsgebiet Ticino\n",
    "  * Lago Maggiore: Ascona, Gambarogno (dreimonatlich)"
   ]
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   "source": [
    "# start pdf\n",
    "pdf_title = Paragraph(\"Kurzfassung\", featuredata.title_style)\n",
    "\n",
    "p1 = [\n",
    "    \"Beim Projekt «Identification, quantification and analysis of anthropogenic Swiss litter» (Identifizierung, Quantifizierung und Analyse von anthropogenem \",\n",
    "    \"Abfall in der Schweiz, IQAASL) handelt es sich um ein vom Bundesamt für Umwelt (BAFU) in Auftrag gegebenes Projekt zur Erhebung von Daten über sichtbare \",\n",
    "    \"Schadstoffe an Schweizer Seen und Flüssen. Mit Hilfe von Datenerhebungen wurden Abfallobjekte gesammelt und identifiziert, alle weggeworfenen Materialien\",\n",
    "    \"wurden eingesammelt. Das Projekt wurde auf 20 Standorte in den Alpen und im Jura ausgeweitet, insgesamt wurden 406 Erhebungen von 163 Standorten in 95 Gemeinden genommen.\"\n",
    "]\n",
    "\n",
    "p2 = [\n",
    "    \"In diesem Bericht werden die vom März 2020 bis August 2021 durchgeführten Abfallerhebungen sowie die in der Schweiz angewandten Methoden zusammengefasst und analysiert. \",\n",
    "    \"Diese Erhebungsphase überschneidet sich mit dem Erhebungszeitraum des Swiss Litter Reports \",\n",
    "    '<a href=\"#Bla18\" color=\"blue\">(SLR)</a>, ',\n",
    "    \"dessen Erhebungen von April 2017 bis März 2018 durchgeführt worden sind. Der SLR war das erste nationale Projekt, bei dem das Standardprotokoll gemäss dem Leitfaden zur Überwachung von Strandabfällen\",\n",
    "    '<a href=\"#Han13\" color=\"blue\">(Han13)</a> ',\n",
    "    \"oder eine andere vergleichbare Methode angewandt wurde. Diese Überschneidung der Erhebungsphasen ermöglicht es, die Ergebnisse der vorliegenden Studie mit denen des SLR zu vergleichen.\"\n",
    "]\n",
    "\n",
    "sec1 = Paragraph(\"Seen und Fliessgewässer\", style=section_title)\n",
    "\n",
    "p3 = [\n",
    "    \"Die Proben an Seen und Fliessgewässern wurden vom März 2020 bis Mai 2021 entnommen. Im Rahmen von 386 Erhebungen wurden insgesamt 54’744 Gegenstände eingesammelt \",\n",
    "    \"und klassifiziert. Die Erhebungsstandorte wurden für die regionale Analyse zu Erhebungsgebieten zusammengefasst und durch die Flüsse Aare, Rhone, Ticino und \",\n",
    "    \"Linth/Limmat definiert. Die Erhebungen fanden an 143 verschiedenen Standorten in 77 Gemeinden statt. Insgesamt wurde eine lineare Strecke von 20 km mit einer \",\n",
    "    \"Fläche von 9 Hektaren und einer Gesamtbevölkerung von 1,7 Millionen Personen untersucht.\"\n",
    "]\n",
    "\n",
    "p1_2 = sectionParagraphs([p1, p2], smallspace=smallest_space)\n",
    "\n",
    "p4 = [\n",
    "   \"Die meisten Erhebungen wurden an Seeufern durchgeführt (331 Proben), da Seen im Vergleich zu Fliessgewässern einen beständigeren und sichereren ganzjährigen \",\n",
    "    \"Zugang bieten. Des Weiteren besitzen Seen grosse Flächen mit verringerter Fliessgeschwindigkeit, die von verschiedenen Flüssen, Bächen und Entwässerungssystemen \",\n",
    "    \"gespeist werden. Damit sind sie ideale Standorte, um die Vielfalt der Gegenstände in und rund um die Gewässer zu beurteilen. Die insgesamt 316 Proben stammten \",\n",
    "    \"aus sieben grossen Seen in drei grossen Einzugsgebieten. Zwanzig Standorte wurden für eine monatliche Probenahme während zwölf Monaten ausgewählt, mit Ausnahme \",\n",
    "    \"des Lago Maggiore, der alle drei Monate beprobt wurde. Auch am Luganersee, am Vierwaldstättersee, am Brienzersee und am Zugersee wurden Erhebungen durchgeführt. \",\n",
    "    \"Darüber hinaus fanden 55 Erhebungen an 16 Fliessgewässern statt.\"\n",
    "]\n",
    "\n",
    "\n",
    "p3_4 = sectionParagraphs([p3, p4], smallspace=smallest_space)\n",
    "\n",
    "p5 = Paragraph(\"Seestandorte mit monatlichen Probenahmen:\", style=bold_block)\n",
    "\n",
    "p6 = Paragraph(\"Erhebungsgebiet Aare\", style=bold_block)\n",
    "\n",
    "l1 = [\n",
    "    \"Thunersee: Spiez, Unterseen\",\n",
    "    \"Bielersee: Biel/Bienne, Vinelz\",\n",
    "    \"Neuenburgersee: Neuenburg, Cheyres-Châbles, Yverdon-les-Bains\"\n",
    "]\n",
    "\n",
    "l1 = makeAList(l1)\n",
    "p7 = Paragraph(\"Erhebungsgebiet Linth/Limmat\", style=bold_block)\n",
    "\n",
    "l2 = [\n",
    "    \"Zürichsee: Zürich, Küsnacht (ZH), Rapperswil-Jona, Richterswil\",\n",
    "    \"Walensee: Walenstadt, Weesen\",\n",
    "]\n",
    "l2 = makeAList(l2)\n",
    "\n",
    "p8 = Paragraph(\"Erhebungsgebiet Rhone\", style=bold_block)\n",
    "\n",
    "l3 = [\n",
    "    \"Genfersee: Vevey, Saint-Gingolph, Genf, Préverenges, La Tour-de-Peilz\",\n",
    "]\n",
    "l3 = makeAList(l3)\n",
    "\n",
    "p9 = Paragraph(\"Erhebungsgebiet Ticino\", style=bold_block)\n",
    "l4 = [\n",
    "    \"Lago Maggiore: Ascona, Gambarogno (dreimonatlich)\"\n",
    "]\n",
    "l4 = makeAList(l4)\n",
    "\n",
    "new_components = [\n",
    "     pdf_title,    \n",
    "    small_space,\n",
    "    f1,\n",
    "    smallest_space,\n",
    "    *p1_2,\n",
    "    small_space,\n",
    "    sec1,\n",
    "    small_space,\n",
    "    *p3_4,\n",
    "    smallest_space,\n",
    "    p5,\n",
    "    smallest_space,\n",
    "    p6,\n",
    "    smallest_space,\n",
    "    l1,\n",
    "    smallest_space,\n",
    "    p7,\n",
    "    smallest_space,\n",
    "    l2,\n",
    "    smallest_space,\n",
    "    p8,\n",
    "    smallest_space,\n",
    "    l3,\n",
    "    smallest_space,\n",
    "    p9,\n",
    "    smallest_space,\n",
    "    l4\n",
    "    \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "# the bibiliography entries for the article:\n",
    "\n",
    "def aBibEntry(name, team, pub):\n",
    "    # formats the bib entries according to the\n",
    "    # original version\n",
    "    return makeAParagraph(makeBibEntry(name=name, team=team, pub=pub))\n",
    "\n",
    "name = \"Han13\"\n",
    "team = \"George Hanke.\"\n",
    "pub = \"Guidance on monitoring of marine litter in european seas. Joint Research Centre of the European Commission, 2013. URL: https://indicit-europa.eu/cms/wp-content/uploads/2017/05/guidance_monitoring_marine_litter_2013.pdf.\"\n",
    "han13 = aBibEntry(name, team, pub)\n",
    "\n",
    "name = \"Kuh\"\n",
    "team = \"Gabrielle Kuhl.\"\n",
    "kuh = \"Stop plastic polution switzerland. URL: http://stoppp.org/.\"\n",
    "kuh = aBibEntry(name, team, pub)\n",
    "\n",
    "name = \"Fun\"\n",
    "team = \"World Wildlife Fund.\"\n",
    "pub = \"World wildlife fund switzerland. URL: https://www.wwf.ch/fr.\"\n",
    "fun = aBibEntry(name, team, pub)\n",
    "\n",
    "name = \"Bla18\"\n",
    "team = \"Pascal Blarer.\",\n",
    "pub = \"The swiss litter report. 2018. URL: http://stoppp.org/researchvalue.\"\n",
    "bla18 = aBibEntry(name, team, pub)\n",
    "\n",
    "name = \"vEV21\"\n",
    "team = \"T.H.M. van Emmerik and P. Vriend.\"\n",
    "pub = \"Roadmap litter monitoring in dutch rivers. Wageningen University, Report., 2021. doi:10.18174/537439.\"\n",
    "vev21 = aBibEntry(name, team, pub)\n",
    "\n",
    "HG19 = [\n",
    "    '<a name=\"HG19\" />HG19: <i> Van Loon W. Hanke G., Walvoort D.</i> Eu marine beach litter baselines. Publications Office of the European Union, 2019. doi:10.2760/16903.'\n",
    "]\n",
    "\n",
    "hg19 = makeAParagraph(HG19)\n",
    "\n",
    "references = [\n",
    "    han13,\n",
    "    smallest_space,\n",
    "    kuh,\n",
    "    smallest_space,\n",
    "    fun,\n",
    "    smallest_space,\n",
    "    bla18,\n",
    "    smallest_space,\n",
    "    vev21,\n",
    "    smallest_space,\n",
    "    hg19\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medianwert der Erhebungen\n",
    "\n",
    "Die Ergebnisse werden in der Einheit Abfallobjekte (pieces) pro 100 Meter (p/100 m) angegeben. Der Median der Erhebungsergebnisse aller Daten lag bei rund 189 p/100 m. Der höchste verzeichnete Wert betrug 6617 p/100 m (Erhebungsgebiet Rhone) und der tiefste Wert 2 p/100 m (Erhebungsgebiet Aare). Das Erhebungsgebiet Rhone wies von allen Erhebungen mit 442 p/100 m den höchsten Medianwert auf. Dies kann zum Teil durch die im Vergleich zu den anderen Erhebungsgebieten hohe Anzahl städtischer Erhebungsstandorte sowie durch die Ablagerung von fragmentierten Kunststoffen und Schaumstoffen am Ausfluss der Rhone im oberen Seebereich erklärt werden.\n",
    "\n",
    "Weiter wurde ein Referenzwert berechnet, bei dem die Ergebnisse von Proben von Uferabschnitten mit einer Länge von weniger als 10 m und Gegenständen kleiner als 2,5 cm ausgeschlossen wurden. Diese in den Marine Beach Litter Baselines der EU {cite}`eubaselines` beschriebene Methode wurde verwendet, um die Referenz- und Schwellenwerte für alle europäischen Strände für die Jahre 2015 und 2016 zu berechnen. In Anwendung dieser Methode resultierte ein Medianwert von 131 p/100 m. Die Ergebnisse des europäischen Basiswerts liegen ausserhalb des Konfidenzintervalls (KI) von 95 % von 147–213 p/100 m, das anhand der IQAASL-Daten ermittelt wurde.\n",
    "\n",
    "Die Erhebungen in der Schweiz waren im Durchschnitt kleinräumiger als in Meeresgebieten sowie an Standorten, die unter den meisten Umständen als städtisch eingestuft würden. Bisher werden See und Fliessgewässer flussaufwärts von Küstenregionen auf dem europäischen Kontinent noch nicht flächendeckend überwacht. Allerdings gibt es gemeinsame Bestrebungen einer Gruppe von Organisationen in der Schweiz und in Frankreich, um ein gemeinsames Überwachungs- und Datenaustauschprotokoll für das Rhonebecken einzuführen. Ausserdem setzt die Universität Wageningen für die Analyse von im Maas-Rhein-Delta erhobenen Daten seit Kurzem ähnliche Protokolle wie bei IQAASL ein. {cite}`meuserhine`\n",
    "\n",
    "### Die am häufigsten gefundenen Gegenstände\n",
    "\n",
    "Als die am häufigsten gefundenen Gegenstände gelten Objekte, die in mindestens der Hälfte aller Erhebungen festgestellt wurden und/oder die zu den zehn mengenmässig am häufigsten vorkommenden Gegenständen gehören. Diese häufigsten Gegenstände stellen als Gruppe 68 Prozent aller im Probezeitraum identifizierten Objekte dar. Von den häufigsten Gegenständen fallen 27 Prozent in die Bereiche Lebensmittel, Getränke und Tabakwaren und 24 Prozent stehen im Zusammenhang mit Infrastruktur und Landwirtschaft.\n",
    "\n",
    "Gegenstände im Zusammenhang mit Lebensmitteln, Getränken und Tabakwaren werden häufiger an Erhebungsstandorten gefunden, an denen ein grösserer Landanteil von Gebäuden oder festen Infrastrukturen beansprucht wird, ganz im Gegensatz zu Standorten mit einem grösseren Anteil an Wäldern oder landwirtschaftlichen Flächen. Infrastrukturmaterial und fragmentierte Kunststoffe werden allerdings in allen Erhebungsgebieten unabhängig von der Landnutzung rund um die Erhebungsstandorte ähnlich häufig gefunden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
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   "outputs": [],
   "source": [
    "ssec1 = Paragraph(\"Medianwert der Erhebungen\", style=subsection_title)\n",
    "\n",
    "p10 = [\n",
    "    \"Die Ergebnisse werden in der Einheit Abfallobjekte (pieces) pro 100 Meter (p/100 m) angegeben. Der Median der Erhebungsergebnisse aller Daten lag bei rund \",\n",
    "    \"189 p/100 m. Der höchste verzeichnete Wert betrug 6617 p/100 m (Erhebungsgebiet Rhone) und der tiefste Wert 2 p/100 m (Erhebungsgebiet Aare). Das \",\n",
    "    \"Erhebungsgebiet Rhone wies von allen Erhebungen mit 442 p/100 m den höchsten Medianwert auf. Dies kann zum Teil durch die im Vergleich zu den anderen \",\n",
    "    \"Erhebungsgebieten hohe Anzahl städtischer Erhebungsstandorte sowie durch die Ablagerung von fragmentierten Kunststoffen und Schaumstoffen am Ausfluss \",\n",
    "    \"der Rhone im oberen Seebereich erklärt werden.\"\n",
    "]\n",
    "\n",
    "p11 = [\n",
    "    \"Weiter wurde ein Referenzwert berechnet, bei dem die Ergebnisse von Proben von Uferabschnitten mit einer Länge von weniger als 10 m und Gegenständen kleiner \",\n",
    "    \"als 2,5 cm ausgeschlossen wurden. Diese in den Marine Beach Litter Baselines der EU \",\n",
    "    '<a href=\"#HG19\" color=\"blue\">(HG19)</a> beschriebene Methode wurde verwendet, ',\n",
    "    \"beschriebene Methode wurde verwendet, um die Referenz- und Schwellenwerte für alle europäischen Strände für die Jahre 2015 und 2016 zu berechnen. In Anwendung \",\n",
    "    \"dieser Methode resultierte ein Medianwert von 131 p/100 m. Die Ergebnisse des europäischen Basiswerts liegen ausserhalb des Konfidenzintervalls (KI) von 95 % von \",\n",
    "    \"147–213 p/100 m, das anhand der IQAASL-Daten ermittelt wurde.\"\n",
    "]\n",
    "\n",
    "p12 = [\n",
    "    \"Die Erhebungen in der Schweiz waren im Durchschnitt kleinräumiger als in Meeresgebieten sowie an Standorten, die unter den meisten Umständen als städtisch \",\n",
    "    \"eingestuft würden. Bisher werden See und Fliessgewässer flussaufwärts von Küstenregionen auf dem europäischen Kontinent noch nicht flächendeckend überwacht. \",\n",
    "    \"Allerdings gibt es gemeinsame Bestrebungen einer Gruppe von Organisationen in der Schweiz und in Frankreich, um ein gemeinsames Überwachungs- und \",\n",
    "    \"Datenaustauschprotokoll für das Rhonebecken einzuführen. Ausserdem setzt die Universität Wageningen für die Analyse von im Maas-Rhein-Delta erhobenen Daten \",\n",
    "    \"seit Kurzem ähnliche Protokolle wie bei IQAASL ein.\",\n",
    "    '<a href=\"#vEV21\" color=\"blue\">(vEV21)</a>'\n",
    "]\n",
    "\n",
    "p10_12 = sectionParagraphs([p10, p11, p12], smallspace=smallest_space)\n",
    "\n",
    "ssec2 = Paragraph(\"Die am häufigsten gefundenen Gegenstände\", style=subsection_title)\n",
    "\n",
    "p13 = [\n",
    "    \"Als die am häufigsten gefundenen Gegenstände gelten Objekte, die in mindestens der Hälfte aller Erhebungen festgestellt wurden und/oder die zu den zehn mengenmässig \",\n",
    "    \"am häufigsten vorkommenden Gegenständen gehören. Diese häufigsten Gegenstände stellen als Gruppe 68 Prozent aller im Probezeitraum identifizierten Objekte dar. Von \",\n",
    "    \"den häufigsten Gegenständen fallen 27 Prozent in die Bereiche Lebensmittel, Getränke und Tabakwaren und 24 Prozent stehen im Zusammenhang mit Infrastruktur und Landwirtschaft.\"\n",
    "]\n",
    "\n",
    "p14 = [\n",
    "    \"Gegenstände im Zusammenhang mit Lebensmitteln, Getränken und Tabakwaren werden häufiger an Erhebungsstandorten gefunden, an denen ein grösserer Landanteil von Gebäuden oder \",\n",
    "    \"festen Infrastrukturen beansprucht wird, ganz im Gegensatz zu Standorten mit einem grösseren Anteil an Wäldern oder landwirtschaftlichen Flächen. Infrastrukturmaterial und \",\n",
    "    \"fragmentierte Kunststoffe werden allerdings in allen Erhebungsgebieten unabhängig von der Landnutzung rund um die Erhebungsstandorte ähnlich häufig gefunden.\"\n",
    "]\n",
    "\n",
    "p13_14 = sectionParagraphs([p13, p14], smallspace=smallest_space)\n",
    "\n",
    "new_components = [\n",
    "    small_space,\n",
    "    ssec1,\n",
    "    small_space,\n",
    "    *p10_12,\n",
    "    ssec2,\n",
    "    small_space,\n",
    "    *p13_14\n",
    "    \n",
    "   \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input",
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div style='display:flex; align-content:center; flex-flow:column no-wrap; flex-direction:column;  box-shadow: 0px 0px 3px #d2d9db'> \n",
       "       <div style='padding:20px;width:80%;margin:auto'>\n",
       "            <style type=\"text/css\">\n",
       "#T_0fbe5 tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_0fbe5 tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_0fbe5 tr {\n",
       "  font-size: 12px;\n",
       "}\n",
       "#T_0fbe5 th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_0fbe5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0fbe5_level0_col0\" class=\"col_heading level0 col0\" >Objekte (St.)</th>\n",
       "      <th id=\"T_0fbe5_level0_col1\" class=\"col_heading level0 col1\" >Anteil</th>\n",
       "      <th id=\"T_0fbe5_level0_col2\" class=\"col_heading level0 col2\" >Häufigkeitsrate</th>\n",
       "      <th id=\"T_0fbe5_level0_col3\" class=\"col_heading level0 col3\" >p/100 m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row0\" class=\"row_heading level0 row0\" >Zigarettenfilter</th>\n",
       "      <td id=\"T_0fbe5_row0_col0\" class=\"data row0 col0\" >8 485</td>\n",
       "      <td id=\"T_0fbe5_row0_col1\" class=\"data row0 col1\" >15%</td>\n",
       "      <td id=\"T_0fbe5_row0_col2\" class=\"data row0 col2\" >87%</td>\n",
       "      <td id=\"T_0fbe5_row0_col3\" class=\"data row0 col3\" >20,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row1\" class=\"row_heading level0 row1\" >Fragmentierte Kunststoffe</th>\n",
       "      <td id=\"T_0fbe5_row1_col0\" class=\"data row1 col0\" >7 400</td>\n",
       "      <td id=\"T_0fbe5_row1_col1\" class=\"data row1 col1\" >13%</td>\n",
       "      <td id=\"T_0fbe5_row1_col2\" class=\"data row1 col2\" >86%</td>\n",
       "      <td id=\"T_0fbe5_row1_col3\" class=\"data row1 col3\" >18,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row2\" class=\"row_heading level0 row2\" >Expandiertes Polystyrol</th>\n",
       "      <td id=\"T_0fbe5_row2_col0\" class=\"data row2 col0\" >5 563</td>\n",
       "      <td id=\"T_0fbe5_row2_col1\" class=\"data row2 col1\" >10%</td>\n",
       "      <td id=\"T_0fbe5_row2_col2\" class=\"data row2 col2\" >68%</td>\n",
       "      <td id=\"T_0fbe5_row2_col3\" class=\"data row2 col3\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row3\" class=\"row_heading level0 row3\" >Snack-Verpackungen</th>\n",
       "      <td id=\"T_0fbe5_row3_col0\" class=\"data row3 col0\" >3 325</td>\n",
       "      <td id=\"T_0fbe5_row3_col1\" class=\"data row3 col1\" >6%</td>\n",
       "      <td id=\"T_0fbe5_row3_col2\" class=\"data row3 col2\" >85%</td>\n",
       "      <td id=\"T_0fbe5_row3_col3\" class=\"data row3 col3\" >9,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row4\" class=\"row_heading level0 row4\" >Industriefolie (Kunststoff)</th>\n",
       "      <td id=\"T_0fbe5_row4_col0\" class=\"data row4 col0\" >2 534</td>\n",
       "      <td id=\"T_0fbe5_row4_col1\" class=\"data row4 col1\" >4%</td>\n",
       "      <td id=\"T_0fbe5_row4_col2\" class=\"data row4 col2\" >69%</td>\n",
       "      <td id=\"T_0fbe5_row4_col3\" class=\"data row4 col3\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row5\" class=\"row_heading level0 row5\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n",
       "      <td id=\"T_0fbe5_row5_col0\" class=\"data row5 col0\" >2 136</td>\n",
       "      <td id=\"T_0fbe5_row5_col1\" class=\"data row5 col1\" >3%</td>\n",
       "      <td id=\"T_0fbe5_row5_col2\" class=\"data row5 col2\" >65%</td>\n",
       "      <td id=\"T_0fbe5_row5_col3\" class=\"data row5 col3\" >3,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row6\" class=\"row_heading level0 row6\" >Industriepellets (Nurdles)</th>\n",
       "      <td id=\"T_0fbe5_row6_col0\" class=\"data row6 col0\" >1 968</td>\n",
       "      <td id=\"T_0fbe5_row6_col1\" class=\"data row6 col1\" >3%</td>\n",
       "      <td id=\"T_0fbe5_row6_col2\" class=\"data row6 col2\" >30%</td>\n",
       "      <td id=\"T_0fbe5_row6_col3\" class=\"data row6 col3\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row7\" class=\"row_heading level0 row7\" >Schaumstoffverpackungen/Isolierung</th>\n",
       "      <td id=\"T_0fbe5_row7_col0\" class=\"data row7 col0\" >1 702</td>\n",
       "      <td id=\"T_0fbe5_row7_col1\" class=\"data row7 col1\" >3%</td>\n",
       "      <td id=\"T_0fbe5_row7_col2\" class=\"data row7 col2\" >53%</td>\n",
       "      <td id=\"T_0fbe5_row7_col3\" class=\"data row7 col3\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row8\" class=\"row_heading level0 row8\" >Wattestäbchen/Tupfer</th>\n",
       "      <td id=\"T_0fbe5_row8_col0\" class=\"data row8 col0\" >1 406</td>\n",
       "      <td id=\"T_0fbe5_row8_col1\" class=\"data row8 col1\" >2%</td>\n",
       "      <td id=\"T_0fbe5_row8_col2\" class=\"data row8 col2\" >50%</td>\n",
       "      <td id=\"T_0fbe5_row8_col3\" class=\"data row8 col3\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row9\" class=\"row_heading level0 row9\" >Styropor < 5mm</th>\n",
       "      <td id=\"T_0fbe5_row9_col0\" class=\"data row9 col0\" >1 209</td>\n",
       "      <td id=\"T_0fbe5_row9_col1\" class=\"data row9 col1\" >2%</td>\n",
       "      <td id=\"T_0fbe5_row9_col2\" class=\"data row9 col2\" >25%</td>\n",
       "      <td id=\"T_0fbe5_row9_col3\" class=\"data row9 col3\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row10\" class=\"row_heading level0 row10\" >Kunststoff-Bauabfälle</th>\n",
       "      <td id=\"T_0fbe5_row10_col0\" class=\"data row10 col0\" >992</td>\n",
       "      <td id=\"T_0fbe5_row10_col1\" class=\"data row10 col1\" >1%</td>\n",
       "      <td id=\"T_0fbe5_row10_col2\" class=\"data row10 col2\" >52%</td>\n",
       "      <td id=\"T_0fbe5_row10_col3\" class=\"data row10 col3\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fbe5_level0_row11\" class=\"row_heading level0 row11\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n",
       "      <td id=\"T_0fbe5_row11_col0\" class=\"data row11 col0\" >700</td>\n",
       "      <td id=\"T_0fbe5_row11_col1\" class=\"data row11 col1\" >1%</td>\n",
       "      <td id=\"T_0fbe5_row11_col2\" class=\"data row11 col2\" >52%</td>\n",
       "      <td id=\"T_0fbe5_row11_col3\" class=\"data row11 col3\" >1,0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "        </div>\n",
       "        <div style='border-top: 1px solid #d2d9db;'>\n",
       "            <p style='margin:10px'><i><b>Abbildung 2:</b> Gesamtergebnisse der Erhebung für alle Seen und Fliessgewässer: die am häufigsten gefundenen Gegenstände von März 2020 bis Mai 2021. Die Menge gibt an, wie häufig ein Objekt gefunden wurde, sowie der Medianwert der Abfallobjekte pro 100 Meter (p/100 m). So wurden beispielsweise in 87 Prozent der Erhebungen insgesamt 8’485 Zigarettenfilter gefunden, was 15 Prozent aller gesammelten Gegenstände entspricht und einen Medianwert von 20 Zigarettenfiltern pro 100 m Uferlinie ergibt.</i></p>\n",
       "        </div>\n",
       "    </div>\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the most common objects results\n",
    "most_common_display = fdx.most_common\n",
    "\n",
    "# language appropriate columns\n",
    "cols_to_use = featuredata.most_common_objects_table_de\n",
    "cols_to_use.update({unit_label:unit_label})\n",
    "\n",
    "# data for display\n",
    "most_common_display.rename(columns=cols_to_use, inplace=True)\n",
    "most_common_display = most_common_display[cols_to_use.values()].copy()\n",
    "most_common_display = most_common_display.set_index(\"Objekte\", drop=True)\n",
    "\n",
    "# .pdf output\n",
    "data = most_common_display.copy()\n",
    "data[\"Anteil\"] = data[\"Anteil\"].map(lambda x: f\"{int(x)}%\")\n",
    "data['Objekte (St.)'] = data['Objekte (St.)'].map(lambda x:featuredata.thousandsSeparator(x, language))\n",
    "data['Häufigkeitsrate'] = data['Häufigkeitsrate'].map(lambda x: f\"{x}%\")\n",
    "data[unit_label] = data[unit_label].map(lambda x: featuredata.replaceDecimal(round(x,1)))\n",
    "\n",
    "# save a copy for the frnech and italian versions\n",
    "esum_most_common = data.copy()\n",
    "\n",
    "# make caption\n",
    "# get percent of total to make the caption string\n",
    "mc_caption_string = [\n",
    "    \"<b>Abbildung 2:</b> Gesamtergebnisse der Erhebung für alle Seen und Fliessgewässer: die am häufigsten gefundenen Gegenstände von März 2020 bis Mai 2021. \",\n",
    "    \"Die Menge gibt an, wie häufig ein Objekt gefunden wurde, sowie der \",\n",
    "    \"Medianwert der Abfallobjekte pro 100 Meter (p/100 m). So wurden beispielsweise in 87 Prozent der Erhebungen insgesamt 8’485 Zigarettenfilter gefunden, \",\n",
    "    \"was 15 Prozent aller gesammelten Gegenstände entspricht und einen Medianwert von 20 Zigarettenfiltern pro 100 m Uferlinie ergibt.\"\n",
    "]\n",
    "   \n",
    "\n",
    "colWidths = [5.1*cm, 2.2*cm, 2*cm, 2.8*cm, 2*cm]\n",
    "\n",
    "d_chart = aSingleStyledTable(data, colWidths=colWidths)\n",
    "d_capt = makeAParagraph(mc_caption_string, caption_style)\n",
    "mc_table = tableAndCaption(d_chart, d_capt, colWidths)\n",
    "\n",
    "most_common_display.index.name = None\n",
    "most_common_display.columns.name = None\n",
    "\n",
    "# set pandas display\n",
    "aformatter = {\n",
    "    \"Anteil\":lambda x: f\"{int(x)}%\",\n",
    "    f\"{unit_label}\": lambda x: featuredata.replaceDecimal(x, \"de\"),\n",
    "    \"Häufigkeitsrate\": lambda x: f\"{int(x)}%\",   \n",
    "    \"Objekte (St.)\": lambda x: featuredata.thousandsSeparator(int(x), \"de\")\n",
    "}\n",
    "\n",
    "mcdic = most_common_display.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "\n",
    "IPython.display.display(HTML(cardFigure(html_table=mcdic.to_html(), table_caption= \"\".join(mc_caption_string))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Industriepellets und Schaumstoffe < 5 mm kamen beide in signifikanten Mengen vor, wurden jedoch in weniger als 50 Prozent der Erhebungen festgestellt (Median = 0). Dies deutet darauf hin, dass sie an spezifischen Standorten in hoher Dichte vorkommen. Zwar handelt es sich bei beiden Gegenständen um Mikroplastik, doch unterscheiden sich ihre Verwendung, ihre Herkunft und ihre Häufigkeit je nach Region des Erhebungsgebiets. Industriepellets sind Rohstoffe, die in Spritzgussverfahren verwendet werden, während Schaumstoffkügelchen entstehen, wenn Styropor zerkleinert wird. Weitere Angaben zu Standort, Mengen und Häufigkeit einzelner Gegenstände finden sich im  [Seen und Fleisgewässer](allsurveys)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "p15 = [\n",
    "    \"Industriepellets und Schaumstoffe < 5 mm kamen beide in signifikanten Mengen vor, wurden jedoch in weniger als 50 Prozent der Erhebungen festgestellt (Median = 0). Dies deutet darauf hin, \",\n",
    "    \"dass sie an spezifischen Standorten in hoher Dichte vorkommen. Zwar handelt es sich bei beiden Gegenständen um Mikroplastik, doch unterscheiden sich ihre Verwendung, ihre Herkunft und ihre \",\n",
    "    \"Häufigkeit je nach Region des Erhebungsgebiets. Industriepellets sind Rohstoffe, die in Spritzgussverfahren verwendet werden, während Schaumstoffkügelchen entstehen, wenn Styropor zerkleinert \",\n",
    "    \"wird. Weitere Angaben zu Standort, Mengen und Häufigkeit einzelner Gegenstände finden sich im \",\n",
    "    '<a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/lakes_rivers.html\" color=\"blue\"> Seen und Fleisgewässer </a>.'\n",
    "]\n",
    "\n",
    "p15 = makeAParagraph(p15)\n",
    "\n",
    "\n",
    "\n",
    "new_components = [\n",
    "    mc_table,\n",
    "    smallest_space,\n",
    "    p15   \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "  color: #000000;\n",
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       "#T_86a16_level0_col0, #T_86a16_level0_col1, #T_86a16_level0_col2, #T_86a16_level0_col3, #T_86a16_level0_col4 {\n",
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       "<table id=\"T_86a16\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_86a16_level0_col0\" class=\"col_heading level0 col0\" >Aare</th>\n",
       "      <th id=\"T_86a16_level0_col1\" class=\"col_heading level0 col1\" >Linth</th>\n",
       "      <th id=\"T_86a16_level0_col2\" class=\"col_heading level0 col2\" >Rhone</th>\n",
       "      <th id=\"T_86a16_level0_col3\" class=\"col_heading level0 col3\" >Ticino</th>\n",
       "      <th id=\"T_86a16_level0_col4\" class=\"col_heading level0 col4\" >Alle Erhebungsgebiete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row0\" class=\"row_heading level0 row0\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n",
       "      <td id=\"T_86a16_row0_col0\" class=\"data row0 col0\" >3,0</td>\n",
       "      <td id=\"T_86a16_row0_col1\" class=\"data row0 col1\" >4,0</td>\n",
       "      <td id=\"T_86a16_row0_col2\" class=\"data row0 col2\" >2,0</td>\n",
       "      <td id=\"T_86a16_row0_col3\" class=\"data row0 col3\" >8,0</td>\n",
       "      <td id=\"T_86a16_row0_col4\" class=\"data row0 col4\" >3,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row1\" class=\"row_heading level0 row1\" >Expandiertes Polystyrol</th>\n",
       "      <td id=\"T_86a16_row1_col0\" class=\"data row1 col0\" >4,0</td>\n",
       "      <td id=\"T_86a16_row1_col1\" class=\"data row1 col1\" >3,0</td>\n",
       "      <td id=\"T_86a16_row1_col2\" class=\"data row1 col2\" >17,5</td>\n",
       "      <td id=\"T_86a16_row1_col3\" class=\"data row1 col3\" >6,0</td>\n",
       "      <td id=\"T_86a16_row1_col4\" class=\"data row1 col4\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row2\" class=\"row_heading level0 row2\" >Fragmentierte Kunststoffe</th>\n",
       "      <td id=\"T_86a16_row2_col0\" class=\"data row2 col0\" >18,5</td>\n",
       "      <td id=\"T_86a16_row2_col1\" class=\"data row2 col1\" >10,5</td>\n",
       "      <td id=\"T_86a16_row2_col2\" class=\"data row2 col2\" >48,0</td>\n",
       "      <td id=\"T_86a16_row2_col3\" class=\"data row2 col3\" >9,5</td>\n",
       "      <td id=\"T_86a16_row2_col4\" class=\"data row2 col4\" >18,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row3\" class=\"row_heading level0 row3\" >Industriefolie (Kunststoff)</th>\n",
       "      <td id=\"T_86a16_row3_col0\" class=\"data row3 col0\" >5,0</td>\n",
       "      <td id=\"T_86a16_row3_col1\" class=\"data row3 col1\" >2,0</td>\n",
       "      <td id=\"T_86a16_row3_col2\" class=\"data row3 col2\" >9,0</td>\n",
       "      <td id=\"T_86a16_row3_col3\" class=\"data row3 col3\" >3,5</td>\n",
       "      <td id=\"T_86a16_row3_col4\" class=\"data row3 col4\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row4\" class=\"row_heading level0 row4\" >Industriepellets (Nurdles)</th>\n",
       "      <td id=\"T_86a16_row4_col0\" class=\"data row4 col0\" >0,0</td>\n",
       "      <td id=\"T_86a16_row4_col1\" class=\"data row4 col1\" >0,0</td>\n",
       "      <td id=\"T_86a16_row4_col2\" class=\"data row4 col2\" >0,0</td>\n",
       "      <td id=\"T_86a16_row4_col3\" class=\"data row4 col3\" >0,0</td>\n",
       "      <td id=\"T_86a16_row4_col4\" class=\"data row4 col4\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row5\" class=\"row_heading level0 row5\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n",
       "      <td id=\"T_86a16_row5_col0\" class=\"data row5 col0\" >0,0</td>\n",
       "      <td id=\"T_86a16_row5_col1\" class=\"data row5 col1\" >1,0</td>\n",
       "      <td id=\"T_86a16_row5_col2\" class=\"data row5 col2\" >3,0</td>\n",
       "      <td id=\"T_86a16_row5_col3\" class=\"data row5 col3\" >1,0</td>\n",
       "      <td id=\"T_86a16_row5_col4\" class=\"data row5 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row6\" class=\"row_heading level0 row6\" >Kunststoff-Bauabfälle</th>\n",
       "      <td id=\"T_86a16_row6_col0\" class=\"data row6 col0\" >0,0</td>\n",
       "      <td id=\"T_86a16_row6_col1\" class=\"data row6 col1\" >0,0</td>\n",
       "      <td id=\"T_86a16_row6_col2\" class=\"data row6 col2\" >5,5</td>\n",
       "      <td id=\"T_86a16_row6_col3\" class=\"data row6 col3\" >2,5</td>\n",
       "      <td id=\"T_86a16_row6_col4\" class=\"data row6 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row7\" class=\"row_heading level0 row7\" >Schaumstoffverpackungen/Isolierung</th>\n",
       "      <td id=\"T_86a16_row7_col0\" class=\"data row7 col0\" >0,0</td>\n",
       "      <td id=\"T_86a16_row7_col1\" class=\"data row7 col1\" >0,0</td>\n",
       "      <td id=\"T_86a16_row7_col2\" class=\"data row7 col2\" >7,0</td>\n",
       "      <td id=\"T_86a16_row7_col3\" class=\"data row7 col3\" >3,0</td>\n",
       "      <td id=\"T_86a16_row7_col4\" class=\"data row7 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row8\" class=\"row_heading level0 row8\" >Snack-Verpackungen</th>\n",
       "      <td id=\"T_86a16_row8_col0\" class=\"data row8 col0\" >8,0</td>\n",
       "      <td id=\"T_86a16_row8_col1\" class=\"data row8 col1\" >6,0</td>\n",
       "      <td id=\"T_86a16_row8_col2\" class=\"data row8 col2\" >19,0</td>\n",
       "      <td id=\"T_86a16_row8_col3\" class=\"data row8 col3\" >4,0</td>\n",
       "      <td id=\"T_86a16_row8_col4\" class=\"data row8 col4\" >9,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row9\" class=\"row_heading level0 row9\" >Styropor < 5mm</th>\n",
       "      <td id=\"T_86a16_row9_col0\" class=\"data row9 col0\" >0,0</td>\n",
       "      <td id=\"T_86a16_row9_col1\" class=\"data row9 col1\" >0,0</td>\n",
       "      <td id=\"T_86a16_row9_col2\" class=\"data row9 col2\" >0,0</td>\n",
       "      <td id=\"T_86a16_row9_col3\" class=\"data row9 col3\" >0,0</td>\n",
       "      <td id=\"T_86a16_row9_col4\" class=\"data row9 col4\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row10\" class=\"row_heading level0 row10\" >Wattestäbchen/Tupfer</th>\n",
       "      <td id=\"T_86a16_row10_col0\" class=\"data row10 col0\" >0,0</td>\n",
       "      <td id=\"T_86a16_row10_col1\" class=\"data row10 col1\" >0,0</td>\n",
       "      <td id=\"T_86a16_row10_col2\" class=\"data row10 col2\" >10,5</td>\n",
       "      <td id=\"T_86a16_row10_col3\" class=\"data row10 col3\" >0,0</td>\n",
       "      <td id=\"T_86a16_row10_col4\" class=\"data row10 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86a16_level0_row11\" class=\"row_heading level0 row11\" >Zigarettenfilter</th>\n",
       "      <td id=\"T_86a16_row11_col0\" class=\"data row11 col0\" >11,0</td>\n",
       "      <td id=\"T_86a16_row11_col1\" class=\"data row11 col1\" >23,0</td>\n",
       "      <td id=\"T_86a16_row11_col2\" class=\"data row11 col2\" >41,5</td>\n",
       "      <td id=\"T_86a16_row11_col3\" class=\"data row11 col3\" >15,5</td>\n",
       "      <td id=\"T_86a16_row11_col4\" class=\"data row11 col4\" >20,0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "        </div>\n",
       "        <div style='border-top: 1px solid #d2d9db;'>\n",
       "            <p style='margin:10px'><i><b>Abbildung 3:</b> Alle Seen und Fliessgewässer nach Erhebungsgebiet: Der Medianwert der am häufigsten gefundenen Gegenstände; die Häufigkeiten variieren je nach Region des Erhebungsgebiets. So wiesen fragmentierte Kunststoffe in den Erhebungsgebieten Aare (18,5 p/100 m) und Rhone (48 p/100 m) die höchsten Medianwerte auf.</i></p>\n",
       "        </div>\n",
       "    </div>\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc_heat_map_caption = [\n",
    "    \"<b>Abbildung 3:</b> Alle Seen und Fliessgewässer nach Erhebungsgebiet: Der Medianwert der am häufigsten gefundenen Gegenstände; die Häufigkeiten variieren \",\n",
    "    \"je nach Region des Erhebungsgebiets. So wiesen fragmentierte Kunststoffe in den Erhebungsgebieten Aare (18,5 p/100 m) und Rhone (48 p/100 m) die höchsten Medianwerte auf.\"\n",
    "]\n",
    "mc_heat_map_caption = ''.join(mc_heat_map_caption)\n",
    "\n",
    "# calling componentsMostCommon gets the results for the most common codes\n",
    "# at the component level\n",
    "components = fdx.componentMostCommonPcsM()\n",
    "\n",
    "# map to proper names for features\n",
    "feature_names = featuredata.river_basin_de\n",
    "\n",
    "# pivot that and quash the hierarchal column index that is created when the table is pivoted\n",
    "mc_comp = components[[\"item\", unit_label, this_level]].pivot(columns=this_level, index=\"item\")\n",
    "mc_comp.columns = mc_comp.columns.get_level_values(1)\n",
    "\n",
    "# insert the proper columns names for display\n",
    "proper_column_names = {x : feature_names[x] for x in mc_comp.columns}\n",
    "mc_comp.rename(columns = proper_column_names, inplace=True)\n",
    "\n",
    "# the aggregated total of the feature is taken from the most common objects table\n",
    "mc_feature = fdx.most_common[unit_label]\n",
    "mc_feature = featuredata.changeSeriesIndexLabels(mc_feature, {x:fdx.dMap.loc[x] for x in mc_feature.index})\n",
    "\n",
    "# the aggregated totals of all the period data\n",
    "mc_period = period_data.parentMostCommon(parent=False)\n",
    "mc_period = featuredata.changeSeriesIndexLabels(mc_period, {x:fdx.dMap.loc[x] for x in mc_period.index})\n",
    "\n",
    "# add the feature, bassin_label and period results to the components table\n",
    "mc_comp[\"Alle Erhebungsgebiete\"]= mc_feature\n",
    "\n",
    "col_widths=[5.1*cm, *[1.2*cm]*(len(mc_comp.columns)-1)]\n",
    "\n",
    "atable = aSingleStyledTable(mc_comp, gradient=True, vertical_header=True, colWidths=col_widths)\n",
    "atable_cap = makeAParagraph(mc_heat_map_caption, style=caption_style)\n",
    "table_and_cap =tableAndCaption(atable, atable_cap, col_widths)\n",
    "\n",
    "new_components = [\n",
    "     table_and_cap\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "# notebook display style\n",
    "aformatter = {x: featuredata.replaceDecimal for x in mc_comp.columns}\n",
    "mcd = mc_comp.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "mcd = mcd.background_gradient(axis=None, vmin=mc_comp.min().min(), vmax=mc_comp.max().max(), cmap=\"YlOrBr\")\n",
    "\n",
    "# remove the index name and column name labels\n",
    "mcd.index.name = None\n",
    "mcd.columns.name = None\n",
    "\n",
    "# rotate the text on the header row\n",
    "# the .applymap_index method in the\n",
    "# df.styler module is used for this\n",
    "mcd = mcd.applymap_index(featuredata.rotateText, axis=1)\n",
    "\n",
    "# display markdown html\n",
    "IPython.display.display(HTML(cardFigure(html_table=mcd.to_html(), table_caption= \"\".join(mc_heat_map_caption))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Trends 2017–2018\n",
    "\n",
    "Ein Vergleich der IQAASL-Ergebnisse mit ähnlichen Daten für Seen und Fliessgewässer, die 2017/2018 (SLR) erhoben wurden, zeigen keine statistischen Unterschiede. Allerdings gab es Abweichungen bei der Häufigkeit der Gegenstände. So wurden in der Erhebungsperiode 2020–2021 im Allgemeinen weniger Zigaretten und Flaschendeckel gefunden. An vielen Standorten wurden jedoch keine Veränderungen beobachtet und die Menge an fragmentierten Kunst- und Schaumstoffen dürfte zugenommen haben (Details siehe [ Vergleich der Datenerhebungen seit 2018](slr-iqaasl).\n",
    "\n",
    "```{card}\n",
    ":class-card: sd-text-black\n",
    ":img-top: resources/output/slr_iqaasl_surveys.jpeg\n",
    ":margin: auto\n",
    "+++\n",
    "__Abbildung 4:__ *Vergleich der Erhebungsergebnisse von SLR (2018) und IQAASL (2021). __Oben links:__ Gesamtergebnisse der Erhebung nach Datum. __Oben rechts:__ Median der Gesamtergebnisse der monatlichen Erhebungen. __Unten links:__ Anzahl Proben im Verhältnis zum Gesamtergebnis der Erhebung. __Unten rechts:__ empirische kumulative Verteilung der Gesamtergebnisse der Erhebung.* \n",
    "``` \n",
    "\n",
    "### Die Alpen und der Jura\n",
    "\n",
    "Von den 20 Erhebungen im Erhebungsgebiet Alpen wiesen 17 eine Länge und eine Breite von über 10 m auf. Der Medianwert der Erhebungen betrug 110 p/100 m und lag damit unter dem Medianwert der anderen Erhebungsgebiete (189 p/100 m). Gegenstände aus dem Konsumbereich wie Lebensmittel und Getränke oder Tabakwaren machten einen geringeren Anteil an der Gesamtzahl aus und wiesen eine tiefere p/100 m-Rate auf im Vergleich zu den Ergebnissen von Standorten an Uferlinien. Dieser Unterschied könnte teilweise auf den geringen Verstädterungsgrad des Erhebungsgebiets Alpen im Vergleich zu allen anderen Erhebungsgebieten zurückzuführen sein sowie darauf, dass Material sich tendenziell flussabwärts bewegt. Für die Ergebungsmethodik und die Ergebnisse der Erhebung in den Alpen siehe [ Die Alpen und der Jura ](lesalpes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "sec2 = Paragraph(\"Trends 2017-2018\", style=section_title)\n",
    "\n",
    "p16 = [\n",
    "    \"Ein Vergleich der IQAASL-Ergebnisse mit ähnlichen Daten für Seen und Fliessgewässer, die 2017/2018 (SLR) erhoben wurden, zeigen keine statistischen Unterschiede. \",\n",
    "    \"Allerdings gab es Abweichungen bei der Häufigkeit der Gegenstände. So wurden in der Erhebungsperiode 2020–2021 im Allgemeinen weniger Zigaretten und Flaschendeckel \",\n",
    "    \"gefunden. An vielen Standorten wurden jedoch keine Veränderungen beobachtet und die Menge an fragmentierten Kunst- und Schaumstoffen dürfte zugenommen haben \",\n",
    "    '(Details siehe <a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/slr-iqaasl.html\" color=\"blue\"> Vergleich der Datenerhebungen seit 2018</a>)'\n",
    "\n",
    "]\n",
    "p16 = makeAParagraph(p16)\n",
    "\n",
    "\n",
    "f2cap = [\n",
    "   \"Vergleich der Erhebungsergebnisse von SLR (2018) und IQAASL (2021). <b>Oben links:</b> Gesamtergebnisse der Erhebung nach Datum. <b>Oben rechts:</b> Median \",\n",
    "    \"der Gesamtergebnisse der monatlichen Erhebungen. <b>Unten links: </b> Anzahl Proben im Verhältnis zum Gesamtergebnis der Erhebung. <b>Unten rechts:</b> \",\n",
    "    \"empirische kumulative Verteilung der Gesamtergebnisse der Erhebung.\",\n",
    "]\n",
    "\n",
    "f2cap = makeAParagraph(f2cap, style=caption_style)\n",
    "\n",
    "sec3 = Paragraph(\"Die Alpen und der Jura\", style=section_title)\n",
    "\n",
    "p17 = [\n",
    "    \"Von den 20 Erhebungen im Erhebungsgebiet Alpen wiesen 17 eine Länge und eine Breite von über 10 m auf. Der Medianwert der Erhebungen betrug 110 p/100 m und \",\n",
    "    \"lag damit unter dem Medianwert der anderen Erhebungsgebiete (189 p/100 m). Gegenstände aus dem Konsumbereich wie Lebensmittel und Getränke oder Tabakwaren \",\n",
    "    \"machten einen geringeren Anteil an der Gesamtzahl aus und wiesen eine tiefere p/100 m-Rate auf im Vergleich zu den Ergebnissen von Standorten an Uferlinien. \",\n",
    "    \"Dieser Unterschied könnte teilweise auf den geringen Verstädterungsgrad des Erhebungsgebiets Alpen im Vergleich zu allen anderen Erhebungsgebieten zurückzuführen \",\n",
    "    \"sein sowie darauf, dass Material sich tendenziell flussabwärts bewegt. Für die Ergebungsmethodik und die Ergebnisse der Erhebung in den Alpen siehe  \",\n",
    "    '<a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/alpes_valaisannes.html\" color=\"blue\">Die Alpen und der Jura</a>.'\n",
    "]\n",
    "p17 = makeAParagraph(p17)\n",
    "\n",
    "o_w, o_h = convertPixelToCm(\"resources/output/slr_iqaasl_surveys.jpeg\")\n",
    "figure_kwargs = {\n",
    "    \"image_file\":\"resources/output/slr_iqaasl_surveys.jpeg\",\n",
    "    \"caption\": f2cap, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\":2.1,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "f2 = figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "new_components = [\n",
    "   small_space,\n",
    "    sec2,\n",
    "    small_space,\n",
    "    p16,\n",
    "    small_space,\n",
    "    f2,\n",
    "    small_space,\n",
    "    sec3,\n",
    "    small_space,\n",
    "    p17,\n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kommunikation der Ergebnisse\n",
    "\n",
    "Für die Kommunikation von Verschmutzungsmengen ist es naheliegend, die Ergebnisse in eine einfache Einheit mit der durchschnittlichen Anzahl pro 100 m umzuwandeln, da der Durchschnitt meist höher ist und nur selten null beträgt. Allerdings kann der Durchschnitt bei Extremwerten doppelt so hoch ausfallen wie der Median, was Verwirrung in Bezug auf den Unterschied zwischen den beobachteten und den gemeldeten Ergebnissen stiften kann. Deshalb ist es informativer und reproduzierbarer, die Bandbreite wahrscheinlicher Werte oder die Wahrscheinlichkeit, einen Gegenstand zu finden, anzugeben, wenn ähnliche Protokolle befolgt werden. So etwa bei der Interpretation der Mengen an Industriepellets, die am Genfersee gefunden wurden:\n",
    "\n",
    "> 1 387 Produktionspellets aus Kunststoff wurden festgestellt, was 5 Prozent aller am Genfersee identifizierten Gegenstände entspricht. Die Anzahl Pellets pro 100 m schwankt je nach Region zwischen 0 und 1’033. Beim See besteht im Allgemeinen eine Wahrscheinlichkeit von 40 Prozent, in einer Erhebung irgendwo mindestens auf ein Pellet zu stossen. An einigen Standorten, wie Genf (Wahrscheinlichkeit von 60 %) oder Préverenges (Wahrscheinlichkeit von 80 %), werden Produktionspellets regelmässig an den Ufern gefunden. Werte zwischen 3 p/100 m und 56 p/100 m sind üblich.\n",
    "\n",
    "### Schlussfolgerungen\n",
    "\n",
    "Auf nationaler Ebene waren die Erhebungsergebnisse des IQAASL-Projekts im Vergleich zu den Erhebungen, die 2017 im Rahmen der SLR-Studie durchgeführt wurden, stabil. Allerdings war ein allgemeiner Rückgang der Menge an Gegenständen im Zusammenhang mit Lebensmitteln, Getränken und Tabakwaren feststellbar. Die Anzahl der identifizierten Infrastrukturobjekte sowie fragmentierten Kunststoffe und Schaumstoffe ging hingegen nicht zurück, an einigen Standorten könnten gar starke Zunahmen verzeichnet worden sein. Die pandemiebedingten Einschränkungen, die grosse Menschenansammlungen im Freien begrenzten, könnten sich positiv ausgewirkt und zu einem Rückgang von Objekten aus den Bereichen Lebensmittel, Getränke und Tabakwaren geführt haben. Die grössten Zunahmen bei infrastrukturbezogenen Gegenständen waren im Wallis, in der Waadt und in Brienz zu beobachten, also an Standorten in der Nähe von Flusseinträgen von Rhone und Aare.\n",
    "\n",
    "Die Landnutzung rund um einen Erhebungsstandort hat einen messbaren Effekt auf das Vorkommen bestimmter Gegenstände: Je höher die Anzahl Gebäude und fester Infrastrukturen ist, desto mehr Tabakwaren und Lebensmittelprodukte werden gefunden. Diese Korrelation gilt nicht für Gegenstände wie fragmentierte Kunststoffe und Industriefolien – sie werden unabhängig von der Landnutzung zu fast gleichen Teilen festgestellt, wobei sie bei Eintragsstellen von Flüssen und Kanälen häufiger anzutreffen sind.\n",
    "\n",
    "Derzeit werden drei der vier IQAASL-Erhebungsgebiete von Forschungs- und Regierungsstellen flussabwärts der Schweiz aktiv überwacht. Diese verwenden ähnliche Methoden wie die in diesem Bericht vorgestellten. Zudem streben regionale Organisationen in der Schweiz mit Partnerorganisationen in der EU eine Standardisierung von Berichterstattung und Protokollen an.\n",
    "\n",
    "IQAASL ist ein Bürgerwissenschaftsprojekt, das ausschliesslich Open-Source-Tools nutzt und Daten mit einer GNU Public License teilt. So ist eine Zusammenarbeit mit Stakeholdern möglich. Am Ende des Mandats per 31. Dezember 2021 übernimmt Hammerdirt die Verantwortung für die Pflege des Code- und Daten-Repositorys, das öffentlich auf Github gehostet wird.\n",
    "\n",
    "Die Organisationen, die sich an IQAASL beteiligt haben, suchen aktiv nach Möglichkeiten, den Datenerhebungsprozess und/oder die Ergebnisse in ihr eigenes Geschäftsmodell zu integrieren. Allerdings gibt es in vielen regionalen Organisationen zu wenige Datenwissenschaftlerinnen und Datenwissenschaftler, was den Integrationsprozess verlängern und die Innovationsrate auf der Ebene, auf der sie am nötigsten wäre, drosseln könnte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "sec4 = Paragraph(\"Kommunikation der Ergebnisse\", style=section_title)\n",
    "\n",
    "p18 = [\n",
    "    \"Für die Kommunikation von Verschmutzungsmengen ist es naheliegend, die Ergebnisse in eine einfache Einheit mit der durchschnittlichen Anzahl pro 100 \",\n",
    "    \"m umzuwandeln, da der Durchschnitt meist höher ist und nur selten null beträgt. Allerdings kann der Durchschnitt bei Extremwerten doppelt so hoch \",\n",
    "    \"ausfallen wie der Median, was Verwirrung in Bezug auf den Unterschied zwischen den beobachteten und den gemeldeten Ergebnissen stiften kann. Deshalb \",\n",
    "    \"ist es informativer und reproduzierbarer, die Bandbreite wahrscheinlicher Werte oder die Wahrscheinlichkeit, einen Gegenstand zu finden, anzugeben, \",\n",
    "    \"wenn ähnliche Protokolle befolgt werden. So etwa bei der Interpretation der Mengen an Industriepellets, die am Genfersee gefunden wurden:\"\n",
    "]\n",
    "\n",
    "p19 = [\n",
    "    \"1’387 Produktionspellets aus Kunststoff wurden festgestellt, was 5 Prozent aller am Genfersee identifizierten Gegenstände entspricht. Die Anzahl \",\n",
    "    \"Pellets pro 100 m schwankt je nach Region zwischen 0 und 1’033. Beim See besteht im Allgemeinen eine Wahrscheinlichkeit von 40 Prozent, in einer \",\n",
    "    f'Erhebung irgendwo mindestens auf ein Pellet zu stossen. An einigen Standorten, wie Genf (Wahrscheinlichkeit von 60 %) oder Préverenges (Wahrscheinlichkeit von {80} {\"%\"}), ', \n",
    "    f'werden Produktionspellets regelmässig an den Ufern gefunden. Werte zwischen 3 {unit_label} und 56 {unit_label} m sind üblich.'\n",
    "]\n",
    "\n",
    "p18 = makeAParagraph(p18)\n",
    "p19 = makeAParagraph(p19, style=block_quote_style)\n",
    "\n",
    "sec5 = Paragraph(\"Schlussfolgerungen\", style=section_title)\n",
    "p20 = [\n",
    "    \"Auf nationaler Ebene waren die Erhebungsergebnisse des IQAASL-Projekts im Vergleich zu den Erhebungen, die 2017 im Rahmen der SLR-Studie durchgeführt wurden, stabil. \",\n",
    "    \"Allerdings war ein allgemeiner Rückgang der Menge an Gegenständen im Zusammenhang mit Lebensmitteln, Getränken und Tabakwaren feststellbar. Die Anzahl der identifizierten \",\n",
    "    \"Infrastrukturobjekte sowie fragmentierten Kunststoffe und Schaumstoffe ging hingegen nicht zurück, an einigen Standorten könnten gar starke Zunahmen verzeichnet worden sein. \",\n",
    "    \"Die pandemiebedingten Einschränkungen, die grosse Menschenansammlungen im Freien begrenzten, könnten sich positiv ausgewirkt und zu einem Rückgang von Objekten aus den \",\n",
    "    \"Bereichen Lebensmittel, Getränke und Tabakwaren geführt haben. Die grössten Zunahmen bei infrastrukturbezogenen Gegenständen waren im Wallis, in der Waadt und in Brienz \",\n",
    "    \"zu beobachten, also an Standorten in der Nähe von Flusseinträgen von Rhone und Aare.\"\n",
    "]\n",
    "\n",
    "\n",
    "p21 = [\n",
    "    \"Die Landnutzung rund um einen Erhebungsstandort hat einen messbaren Effekt auf das Vorkommen bestimmter Gegenstände: Je höher die Anzahl Gebäude und fester Infrastrukturen \",\n",
    "    \"ist, desto mehr Tabakwaren und Lebensmittelprodukte werden gefunden. Diese Korrelation gilt nicht für Gegenstände wie fragmentierte Kunststoffe und Industriefolien – sie werden \",\n",
    "    \"unabhängig von der Landnutzung zu fast gleichen Teilen festgestellt, wobei sie bei Eintragsstellen von Flüssen und Kanälen häufiger anzutreffen sind.\"\n",
    "]\n",
    "\n",
    "p22 = [\n",
    "    \"Derzeit werden drei der vier IQAASL-Erhebungsgebiete von Forschungs- und Regierungsstellen flussabwärts der Schweiz aktiv überwacht. Diese verwenden ähnliche Methoden wie die in \",\n",
    "    \"diesem Bericht vorgestellten. Zudem streben regionale Organisationen in der Schweiz mit Partnerorganisationen in der EU eine Standardisierung von Berichterstattung und Protokollen an.\"\n",
    "]\n",
    "\n",
    "p23 = [\n",
    "    \"IQAASL ist ein Bürgerwissenschaftsprojekt, das ausschliesslich Open-Source-Tools nutzt und Daten mit einer GNU Public License teilt. So ist eine Zusammenarbeit mit Stakeholdern \",\n",
    "    \"möglich. Am Ende des Mandats per 31. Dezember 2021 übernimmt Hammerdirt die Verantwortung für die Pflege des Code- und Daten-Repositorys, das öffentlich auf Github gehostet wird.\"\n",
    "]\n",
    "\n",
    "p24 = [\n",
    "    \"Die Organisationen, die sich an IQAASL beteiligt haben, suchen aktiv nach Möglichkeiten, den Datenerhebungsprozess und/oder die Ergebnisse in ihr eigenes Geschäftsmodell zu \",\n",
    "    \"integrieren. Allerdings gibt es in vielen regionalen Organisationen zu wenige Datenwissenschaftlerinnen und Datenwissenschaftler, was den Integrationsprozess verlängern und \",\n",
    "    \"die Innovationsrate auf der Ebene, auf der sie am nötigsten wäre, drosseln könnte.\"\n",
    "]\n",
    "\n",
    "p20_24 = sectionParagraphs([p20, p21, p22, p23, p24], smallspace=smallest_space)\n",
    "\n",
    "new_components = [\n",
    "    small_space,\n",
    "    sec4,\n",
    "    small_space,\n",
    "    p18,\n",
    "    smallest_space,\n",
    "    p19,\n",
    "    small_space,\n",
    "    sec5,\n",
    "    small_space,\n",
    "    *p20_24,\n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Empfehlungen\n",
    "#### Überwachung und Berichterstattung\n",
    "\n",
    "Effizienzgewinne bei Datenaustausch und Berichterstattung könnten durch die Festlegung eines Standardberichtsformats umgehend erzielt werden. Dies würde es für regionale Verwaltungen einfacher machen, andere Stakeholder über die Prioritäten zu informieren, was wiederum Überwachungsstrategien erleichtern und die Definition von Reduktionszielen unterstützen würde. Hierzu könnten folgende Massnahmen umgesetzt werden:\n",
    "\n",
    ">Ein Netz von Organisationen aufbauen, die für die Erhebung und die Berichterstattung über die Ergebnisse zuständig sind.\n",
    ">Ein Standardberichtsformat schaffen, um die Kommunikation zwischen Verwaltungen auf Ebene Gemeinde, Kanton und Region/Bezirk zu erleichtern und die Koordination regionaler und lokaler Reduktionsstrategien zu verbessern.\n",
    ">Die nächste Probenahmeperiode oder das nächste Probenahmeintervall festlegen.\n",
    "\n",
    "Akademische Kreise formell in Planung, Probenahme und Analyse einbeziehen. Dieses Projekt wurde durch die Zusammenarbeit von Professorinnen und Professoren von ETH, UNIGE, EPFL, PSI und FHNW geprägt. Universitäre Partner wären ideal, um die Analysemethoden weiterzuentwickeln. Das Citizen Science Center (ETH) und das Citizen Cyberlab (UNIGE) verfügen über die Erfahrung und die Infrastruktur, um bürgerwissenschaftliche Überwachungsprojekte mit Forschungstätigkeiten zu verknüpfen. Auf diese Weise kann ein äusserst anpassungsfähiger und effizienter Überwachungsplan erarbeitet werden.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
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     "remove-input"
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   "outputs": [],
   "source": [
    "sec6 = Paragraph(\"Empfehlungen\", style=section_title)\n",
    "ssec3 = Paragraph(\"Überwachung und Berichterstattung\", style=subsection_title)\n",
    "\n",
    "p25 = [\n",
    "    \"Effizienzgewinne bei Datenaustausch und Berichterstattung könnten durch die Festlegung eines Standardberichtsformats umgehend \",\n",
    "    \"erzielt werden. Dies würde es für regionale Verwaltungen einfacher machen, andere Stakeholder über die Prioritäten zu informieren, \",\n",
    "    \"was wiederum Überwachungsstrategien erleichtern und die Definition von Reduktionszielen unterstützen würde. Hierzu könnten folgende \",\n",
    "    \"Massnahmen umgesetzt werden:\"\n",
    "]\n",
    "\n",
    "p25 = makeAParagraph(p25)\n",
    "l5 = [\n",
    "    \"Ein Netz von Organisationen aufbauen, die für die Erhebung und die Berichterstattung über die Ergebnisse zuständig sind.\",\n",
    "    \"Ein Standardberichtsformat schaffen, um die Kommunikation zwischen Verwaltungen auf Ebene Gemeinde, Kanton und Region/Bezirk zu erleichtern und die Koordination regionaler und lokaler Reduktionsstrategien zu verbessern.\",\n",
    "    \"Die nächste Probenahmeperiode oder das nächste Probenahmeintervall festlegen.\"\n",
    "]\n",
    "l5 = makeAList(l5)\n",
    "\n",
    "p26 = [\n",
    "    \"Akademische Kreise formell in Planung, Probenahme und Analyse einbeziehen. Dieses Projekt wurde durch die Zusammenarbeit \",\n",
    "    \"von Professorinnen und Professoren von ETH, UNIGE, EPFL, PSI und FHNW geprägt. Universitäre Partner wären ideal, um die \",\n",
    "    \"Analysemethoden weiterzuentwickeln. Das Citizen Science Center (ETH) und das Citizen Cyberlab (UNIGE) verfügen über die \",\n",
    "    \"Erfahrung und die Infrastruktur, um bürgerwissenschaftliche Überwachungsprojekte mit Forschungstätigkeiten zu verknüpfen.  \",\n",
    "    \"Auf diese Weise kann ein äusserst anpassungsfähiger und effizienter Überwachungsplan erarbeitet werden.\"\n",
    "]\n",
    "p26 = makeAParagraph(p26)\n",
    "\n",
    "new_components = [\n",
    "    small_space,\n",
    "    sec6,\n",
    "    smallest_space,\n",
    "    ssec3,\n",
    "    small_space,\n",
    "    p25,\n",
    "    smallest_space,\n",
    "    l5,\n",
    "    smallest_space,\n",
    "    p26\n",
    "]\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Beseitigung und Reduktion\n",
    "\n",
    "Bei Strategien zur Beseitigung oder Reduktion von Abfällen im Wasser sollte zunächst deren Quelle berücksichtigt werden.\n",
    "\n",
    "__Gegenstände im Zusammenhang mit Landnutzungsmerkmalen__\n",
    "\n",
    "Die Ergebnisse weisen auf einen positiven Zusammenhang zwischen der Anzahl Gebäude und der Menge an Gegenständen aus den Bereichen Lebensmittel, Getränke und Tabakwaren hin. Dies legt nahe, dass Strategien zur Verringerung dieser Gegenstände in Gebieten ansetzen sollten, die eine hohe Konzentration an Infrastrukturen in Ufernähe aufweisen. Die Ergebnisse aus dem Erhebungsgebiet Rhone lassen vermuten, dass sich lokale Sensibilisierungskampagnen positiv auswirken könnten siehe [Seen und Fleisgewässer](allsurveys). Auch wenn alle Gegenstände im Zusammenhang mit Lebensmitteln, Getränken und Tabakwaren, auf die Sensibilisierungskampagnen für Abfall in der Regel abzielen, beseitigt würden, würde dies die Gesamtmengen zwar signifikant reduzieren, doch würden immer noch 64 Prozent des Materials liegen bleiben.\n",
    "\n",
    "Weitere gängige Reduktionsstrategien sind u. a. die folgenden:\n",
    "\n",
    "* Angemessene Bereitstellung von wetter- und tierbeständigen Abfallbehältern\n",
    "* Enger getakteter Zeitplan für Abfallentsorgung und Reinigung\n",
    "* Reduktion von Einwegprodukten aus Kunststoff\n",
    "\n",
    "Viele Länder haben bereits begonnen, Einschränkungen für bestimmte Gegenstände einzuführen. So dürfen beispielsweise Einwegteller und -besteck aus Plastik, Strohhalme, Ballonstäbe und Wattestäbchen aus Kunststoff seit dem 3. Juli 2021 in den EU-Mitgliedstaaten nicht mehr verkauft werden. In Frankreich werden in Regenwasserkanalisationen erfolgreich Rückhaltenetze eingesetzt, damit Abfälle nicht mehr in Seen und Fliessgewässer gelangen, was jedoch Investitionen in Infrastruktur, Ausrüstung und Arbeitskräfte bedingt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
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   "outputs": [],
   "source": [
    "sec7 = Paragraph(\"Beseitigung und Reduktion\", style=section_title)\n",
    "\n",
    "p27 = [\"Bei Strategien zur Beseitigung oder Reduktion von Abfällen im Wasser sollte zunächst deren Quelle berücksichtigt werden.\"]\n",
    "\n",
    "p28 = [\n",
    "    \"Gegenstände im Zusammenhang mit Landnutzungsmerkmalen\"\n",
    "]\n",
    "\n",
    "p29 = [\n",
    "    \"Die Ergebnisse weisen auf einen positiven Zusammenhang zwischen der Anzahl Gebäude und der Menge an Gegenständen aus den Bereichen \",\n",
    "    \"Lebensmittel, Getränke und Tabakwaren hin. Dies legt nahe, dass Strategien zur Verringerung dieser Gegenstände in Gebieten ansetzen \",\n",
    "    \"sollten, die eine hohe Konzentration an Infrastrukturen in Ufernähe aufweisen. Die Ergebnisse aus dem Erhebungsgebiet Rhone lassen \",\n",
    "    \"vermuten, dass sich lokale Sensibilisierungskampagnen positiv auswirken könnten \",\n",
    "    '<a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/lakes_rivers.html\" color=\"blue\"> Seen und Fleisgewässer </a>. Auch ',\n",
    "    \"wenn alle Gegenstände im Zusammenhang mit Lebensmitteln, Getränken und Tabakwaren, auf die Sensibilisierungskampagnen für Abfall in der \",\n",
    "    \"Regel abzielen, beseitigt würden, würde dies die Gesamtmengen zwar signifikant reduzieren, doch würden immer noch 64 Prozent des \",\n",
    "    \"Materials liegen bleiben.\"\n",
    "]\n",
    "\n",
    "p30 = [\"Weitere gängige Reduktionsstrategien sind u. a. die folgenden:\"]\n",
    "l6 = [\n",
    "    \"Angemessene Bereitstellung von wetter- und tierbeständigen Abfallbehältern\",\n",
    "    \"Enger getakteter Zeitplan für Abfallentsorgung und Reinigung\",\n",
    "    \"Reduktion von Einwegprodukten aus Kunststoff\",\n",
    "]\n",
    "\n",
    "l6 = makeAList(l6)\n",
    "\n",
    "p31 = [\n",
    "    \"Viele Länder haben bereits begonnen, Einschränkungen für bestimmte Gegenstände einzuführen. So dürfen beispielsweise Einwegteller und -besteck aus Plastik, \",\n",
    "    \"Strohhalme, Ballonstäbe und Wattestäbchen aus Kunststoff seit dem 3. Juli 2021 in den EU-Mitgliedstaaten nicht mehr verkauft werden. In Frankreich werden in \",\n",
    "    \"Regenwasserkanalisationen erfolgreich Rückhaltenetze eingesetzt, damit Abfälle nicht mehr in Seen und Fliessgewässer gelangen, was jedoch Investitionen in \",\n",
    "    \"Infrastruktur, Ausrüstung und Arbeitskräfte bedingt.\"\n",
    "]\n",
    "\n",
    "p27 = makeAParagraph(p27)\n",
    "p28 = makeAParagraph(p28, style=bold_block)\n",
    "p29 = makeAParagraph(p29)\n",
    "p30 = makeAParagraph(p30)\n",
    "p31 = makeAParagraph(p31)\n",
    "new_components = [\n",
    "    PageBreak(),\n",
    "    sec7,\n",
    "    small_space,\n",
    "    p27,\n",
    "    smallest_space,\n",
    "    p28,\n",
    "    smallest_space,\n",
    "    p29,\n",
    "    smallest_space,\n",
    "    p30,\n",
    "    smallest_space,\n",
    "    l6,\n",
    "    smallest_space,\n",
    "    p31\n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nicht mit der Landnutzung verbundene Gegenstände\n",
    "\n",
    "Zur Verringerung der Anzahl Gegenstände, die keine Korrelation mit der Landnutzung aufweisen, ist zumindest auf Ebene des Sees oder des Fliessgewässers sowie bei allen Standorten, die oberhalb der geplanten Erhebungsorte liegen, ein koordiniertes Vorgehen notwendig. Zu den häufigsten Gegenständen zählen die folgenden:\n",
    "\n",
    "* Fragmentierte Kunststoffe\n",
    "* Fragmentierte Schaumstoffe\n",
    "* Baukunststoffe\n",
    "* Industriepellets\n",
    "* Wattestäbchen\n",
    "* Industriefolien\n",
    "\n",
    "Diese Gegenstände machen 40 Prozent des gesamten identifizierten Materials aus. Viele Gegenstände werden in der Industrie oder für die Körperpflege verwendet und haben normalerweise nichts mit Tätigkeiten am Ufer zu tun. Eine Ausweitung der Sensibilisierungskampagnen, die auf eine Vermeidung von Materialverlusten an der Quelle aus bestimmten Sektoren abzielen, könnte dafür sorgen, dass weniger Gegenstände wie Industriepellets, die für Spritzgussverfahren verwendet werden, gefunden werden. Einige Gegenstände wie Wattestäbchen aus Kunststoff und andere Gegenstände aus Plastik, die über Toilettenspülungen entsorgt werden, gelangen über Abwasserreinigungsanlagen in Seen und Fliessgewässer\n",
    "\n",
    "Reduktionsstrategien könnten Folgendes umfassen:\n",
    "\n",
    "* Aufrüstung von Abwasserreinigungsanlagen zur Verringerung von Materialverlusten\n",
    "* Sensibilisierungskampagnen für spezifische Gegenstände oder Produkte\n",
    "* Sensibilisierungskampagnen für spezifische Branchen\n",
    "\n",
    "Beseitigungs- oder Reduktionsstrategien erfordern regionale Massnahmen, um auch die Gemeinden oberhalb der Erhebungsstandorte miteinzubeziehen. Wahrscheinlich würde eine weniger starke Abhängigkeit von Einwegkunststoffen, Schaumstoffen, Baukunststoffen und Industriefolien dazu führen, dass kleinere Mengen dieser Materialien in die Umwelt gelangen. Da diese Materialien günstige Wegwerfwaren sind, wurden sie in allen Sektoren immer häufiger eingesetzt, wodurch eine immer grössere Abhängigkeit entstand. Das geringe Gewicht und ihre Abbaueigenschaften erleichtern die Fragmentierung dieser Materialien sowie ein Entweichen in die Umwelt, insbesondere, wenn sie länger im Freien verbleiben. Kunststoffschadstoffe sind ein weltweites Problem und immer mehr Länder ergreifen Massnahmen, um weniger von Einwegkunststoffen und -schaumstoffen wie Styropor abhängig zu sein."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "jupyter": {
     "source_hidden": true
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     "remove-input"
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   "source": [
    "sec7 = Paragraph(\"Nicht mit der Landnutzung verbundene Gegenstände\", style=section_title)\n",
    "p32 = [\n",
    "    \"Zur Verringerung der Anzahl Gegenstände, die keine Korrelation mit der Landnutzung aufweisen, ist zumindest auf Ebene des Sees oder des Fliessgewässers sowie bei \",\n",
    "    \"allen Standorten, die oberhalb der geplanten Erhebungsorte liegen, ein koordiniertes Vorgehen notwendig. Zu den häufigsten Gegenständen zählen die folgenden:\"\n",
    "]\n",
    "\n",
    "p32 = makeAParagraph(p32)\n",
    "\n",
    "l8 = [\n",
    "    \"Fragmentierte Kunststoffe\",\n",
    "    \"Fragmentierte Schaumstoffe\",\n",
    "    \"Baukunststoffe\",\n",
    "    \"Industriepellets\",\n",
    "    \"Wattestäbchen\",\n",
    "    \"Industriefolien\"\n",
    "]\n",
    "l8 = makeAList(l8)\n",
    "\n",
    "p33 = [\n",
    "    \"Diese Gegenstände machen 40 Prozent des gesamten identifizierten Materials aus. Viele Gegenstände werden in der Industrie oder für die Körperpflege verwendet \",\n",
    "    \"und haben normalerweise nichts mit Tätigkeiten am Ufer zu tun. Eine Ausweitung der Sensibilisierungskampagnen, die auf eine Vermeidung von Materialverlusten an \",\n",
    "    \"der Quelle aus bestimmten Sektoren abzielen, könnte dafür sorgen, dass weniger Gegenstände wie Industriepellets, die für Spritzgussverfahren verwendet werden, \",\n",
    "    \"gefunden werden. Einige Gegenstände wie Wattestäbchen aus Kunststoff und andere Gegenstände aus Plastik, die über Toilettenspülungen entsorgt werden, gelangen \",\n",
    "    \"über Abwasserreinigungsanlagen in Seen und Fliessgewässer.\"\n",
    "]\n",
    "\n",
    "p34 = [\"Reduktionsstrategien könnten Folgendes umfassen:\"]\n",
    "p33_34 = sectionParagraphs([p33, p34], smallspace=smallest_space)\n",
    "\n",
    "l9 = [\n",
    "    \"Aufrüstung von Abwasserreinigungsanlagen zur Verringerung von Materialverlusten\",\n",
    "    \"Sensibilisierungskampagnen für spezifische Gegenstände oder Produkte\",\n",
    "    \"Sensibilisierungskampagnen für spezifische Branchen\",\n",
    "]\n",
    "l9 = makeAList(l9)\n",
    "\n",
    "p35 = [\n",
    "    \"Beseitigungs- oder Reduktionsstrategien erfordern regionale Massnahmen, um auch die Gemeinden oberhalb der Erhebungsstandorte miteinzubeziehen. Wahrscheinlich \"\n",
    "    \"würde eine weniger starke Abhängigkeit von Einwegkunststoffen, Schaumstoffen, Baukunststoffen und Industriefolien dazu führen, dass kleinere Mengen dieser Materialien \",\n",
    "    \"in die Umwelt gelangen. Da diese Materialien günstige Wegwerfwaren sind, wurden sie in allen Sektoren immer häufiger eingesetzt, wodurch eine immer grössere Abhängigkeit \",\n",
    "    \"entstand. Das geringe Gewicht und ihre Abbaueigenschaften erleichtern die Fragmentierung dieser Materialien sowie ein Entweichen in die Umwelt, insbesondere, wenn sie \",\n",
    "    \"länger im Freien verbleiben. Kunststoffschadstoffe sind ein weltweites Problem und immer mehr Länder ergreifen Massnahmen, um weniger von Einwegkunststoffen und \",\n",
    "    \"-schaumstoffen wie Styropor abhängig zu sein.\"\n",
    "]\n",
    "\n",
    "p35 = makeAParagraph(p35)\n",
    "\n",
    "new_components = [\n",
    "    small_space,\n",
    "    sec7,\n",
    "    small_space,\n",
    "    p32,\n",
    "    smallest_space,\n",
    "    l8,\n",
    "    smallest_space,\n",
    "    *p33_34,\n",
    "    l9,\n",
    "    smallest_space,\n",
    "    p35,\n",
    "   \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(it-esum)=\n",
    "## Sommario esecutivo\n",
    "\n",
    "| [Deutsch](kurzfassung) | [Français](fr-esum)|\n",
    "\n",
    "IQAASL (Identificazione, quantificazione e analisi dei rifiuti antropogenici in Svizzera) è un progetto commissionato dall’Ufficio federale dell’ambiente (UFAM) per raccogliere dati sugli inquinanti visibili lungo le rive di laghi e fiumi svizzeri. Tutti i materiali scaricati nell’ambiente sono stati raccolti e identificati con l’ausilio di tecniche d’indagine sui rifiuti. Il progetto è stato ampliato al fine di includere 20 ubicazioni nelle Alpi e nel Giura, per un totale di 406 campioni prelevati da 163 ubicazioni in 95 comuni.\n",
    "\n",
    "Il presente rapporto costituisce una sintesi e un’analisi sia delle indagini condotte sui rifiuti, sia dei metodi utilizzati in Svizzera da marzo 2020 fino a fine agosto 2021. Questo arco temporale si sovrappone al periodo di rilevamento dell’indagine Swiss Litter Report {cite}`slr`, condotta da aprile 2017 a marzo 2018. Lo Swiss Litter Report è stato il primo progetto a livello nazionale a utilizzare il protocollo standard descritto nella Guida al monitoraggio del littering delle spiagge {cite}`mlwguidance`, o qualsiasi altro metodo comparabile. Poiché il periodo di indagine del presente studio si sovrappone a quello dello SLR è possibile fare una comparazione tra i risultati.\n",
    "\n",
    "```{card}\n",
    ":class-card: sd-text-black\n",
    ":img-top: resources/maps/esummary_mapit.jpeg\n",
    ":margin: auto\n",
    "+++\n",
    "__Figura 1:__ *Mappa delle ubicazioni oggetto d’indagine da marzo 2020 ad agosto 2021. Le ubicazioni contrassegnate in rosso indicano le indagini su fiumi o laghi e i punti in viola designano le ubicazioni sulle Alpi e nel Giura.*\n",
    "```\n",
    "### Laghi e fiumi\n",
    "\n",
    "Laghi e fiumi sono stati campionati da marzo 2020 a maggio 2021, per un totale di 54 744 oggetti prelevati e classificati nel corso di 386 indagini. Le ubicazioni sono state divise in aree di indagine per l’analisi regionale e definite in funzione dei bacini dei fiumi Aare, Rodano, Ticino e Linth/Limmat. Le indagini sono state condotte presso 143 diverse ubicazioni distribuite su 77 comuni. È stata presa in esame una distanza lineare totale di 20 km comprendente una superficie di 9 ettari e una popolazione totale di 1,7 milioni di abitanti.\n",
    "\n",
    "La maggior parte delle indagini si è svolta lungo le rive dei laghi (331 campioni), in quanto questi specchi d’acqua offrono tutto l’anno un accesso più costante e sicuro rispetto ai fiumi. Inoltre i laghi sono grandi aree con scorrimento ridotto che hanno come immissari fiumi, ruscelli e sistemi di drenaggio. Sulla scorta di queste caratteristiche, si configurano come ubicazioni ideali per valutare la varietà degli oggetti nei corpi idrici e sulle loro sponde. In totale, 316 campioni sono stati raccolti in sette laghi di grandi dimensioni distribuiti lungo tre bacini fluviali principali. Venti località sono state selezionate per un campionamento con cadenza mensile sull’arco di dodici mesi, con l’eccezione del Lago Maggiore che è stato campionato ogni tre mesi. Sono state condotte indagini anche sul lago di Lugano, sul lago dei Quattro Cantoni, sul lago di Brienz e sul lago di Zugo. Sono state inoltre effettuate 55 indagini su 16 fiumi.\n",
    "\n",
    "In totale 316 campioni provenivano da sette laghi principali in 3 maggiori bacini fluviali. Venti località sono state selezionate per essere campionate mensilmente per un periodo di dodici mesi ad eccezione del Lago Maggiore, che è stato campionato ogni tre mesi. Sono state condotte indagini anche sul Lago di Lugano, Lago dei Quattro Cantoni, Lago di Brienz e Lago di Zugo. Inoltre, ci sono state 55 indagini su 16 fiumi.\n",
    "\n",
    "__Ubicazioni lacustri con campionamento mensile:__\n",
    "\n",
    "* Area d’indagine dell’Aare\n",
    "  * Lago di Thun: Spiez, Unterseen\n",
    "  * Lago di Biel/Bienne: Biel/Bienne, Vinelz\n",
    "  * Lago di Neuchâtel: Neuchâtel, Cheyres-Châbles, Yverdon-les-Bains\n",
    "* Area d’indagine della Linth/Limmat\n",
    "  * Lago di Zurigo: Zurigo, Küsnacht (ZH), Rapperswil-Jona, Richterswil\n",
    "  * Walensee: Walenstadt, Weesen\n",
    "* Area d’indagine del Rodano\n",
    "  * Lago Lemano: Vevey, Saint-Gingolph, Ginevra, Préverenges, La Tour-de-Peilz\n",
    "* Area d’indagine del Ticino\n",
    "  * Lago Maggiore: Ascona, Gambarogno (frequenza trimestrale)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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     "source_hidden": true
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     "remove-input"
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   "source": [
    "p41 = makeAParagraph([\"Area d’indagine dell’Aare\"], style=bold_block)\n",
    "l2i = [\n",
    "    \"Lago di Thun: Spiez, Unterseen\",\n",
    "    \"Lago di Biel/Bienne: Biel/Bienne, Vinelz\",\n",
    "    \"Lago di Neuchâtel: Neuchâtel, Cheyres-Châbles, Yverdon-les-Bains\"\n",
    "]\n",
    "l2i = makeAList(l2i)\n",
    "p42 = makeAParagraph([\"Area d’indagine della Linth/Limmat\"], style=bold_block)\n",
    "l3i = [\n",
    "    \"Lago di Zurigo: Zurigo, Küsnacht (ZH), Rapperswil-Jona, Richterswil\",\n",
    "    \"Walensee: Walenstadt, Weesen\"\n",
    "]\n",
    "l3i=makeAList(l3i)\n",
    "p43 = makeAParagraph([\"Area d’indagine del Rodano\"], style=bold_block)\n",
    "\n",
    "l4i = [\n",
    "    \"Lago Lemano: Vevey, Saint-Gingolph, Ginevra, Préverenges, La Tour-de-Peilz\"\n",
    "]\n",
    "l4i = makeAList(l4i)\n",
    "p44 = makeAParagraph([\"Area d’indagine del Ticino\"], style=bold_block)\n",
    "l5i = [\"Lago Maggiore: Ascona, Gambarogno (frequenza trimestrale)\"]\n",
    "l5i = makeAList(l5i)\n",
    "\n",
    "seci = Paragraph(\"Sommario esecutivo\", style=section_title)\n",
    "\n",
    "p36 = [\n",
    "    \"IQAASL (Identificazione, quantificazione e analisi dei rifiuti antropogenici in Svizzera) è un progetto commissionato dall’Ufficio \",\n",
    "    \"federale dell’ambiente (UFAM) per raccogliere dati sugli inquinanti visibili lungo le rive di laghi e fiumi svizzeri. Tutti i materiali \",\n",
    "    \"scaricati nell’ambiente sono stati raccolti e identificati con l’ausilio di tecniche d’indagine sui rifiuti. Il progetto è stato ampliato \",\n",
    "    \"al fine di includere 20 ubicazioni nelle Alpi e nel Giura, per un totale di 406 campioni prelevati da 163 ubicazioni in 95 comuni.\"\n",
    "]\n",
    "\n",
    "p37 = [\n",
    "    \"Il presente rapporto costituisce una sintesi e un’analisi sia delle indagini condotte sui rifiuti, sia dei metodi utilizzati in \",\n",
    "    \"Svizzera da marzo 2020 fino a fine agosto 2021. Questo arco temporale si sovrappone al periodo di rilevamento dell’indagine Swiss \",\n",
    "    'Litter Report <a href=\"#Bla18\" color=\"blue\">(SLR)</a>, condotta da aprile 2017 a marzo 2018. Lo Swiss Litter Report è stato il primo progetto a livello ',\n",
    "    'nazionale a utilizzare il protocollo standard descritto nella Guida al monitoraggio del littering delle spiagge <a href=\"#Han13\" color=\"blue\">(Han13)</a>, ',\n",
    "    \"o qualsiasi altro metodo comparabile. Poiché il periodo di indagine del presente studio si sovrappone a quello dello SLR è possibile fare \"\n",
    "    \"una comparazione tra i risultati.\"\n",
    "]\n",
    "seci2 = Paragraph(\"Laghi e fiumi\", style=section_title)\n",
    "\n",
    "p36_37 = sectionParagraphs([p36, p37], smallspace=smallest_space)\n",
    "\n",
    "p38 = [\n",
    "    \"Laghi e fiumi sono stati campionati da marzo 2020 a maggio 2021, per un totale di 54 744 oggetti prelevati e classificati nel corso di 386 indagini. \",\n",
    "    \"Le ubicazioni sono state divise in aree di indagine per l’analisi regionale e definite in funzione dei bacini dei fiumi Aare, Rodano, Ticino e \",\n",
    "    \"Linth/Limmat. Le indagini sono state condotte presso 143 diverse ubicazioni distribuite su 77 comuni. È stata presa in esame una distanza lineare \",\n",
    "    \"totale di 20 km comprendente una superficie di 9 ettari e una popolazione totale di 1,7 milioni di abitanti.\"\n",
    "]\n",
    "\n",
    "p39 = [\n",
    "    \"La maggior parte delle indagini si è svolta lungo le rive dei laghi (331 campioni), in quanto questi specchi d’acqua offrono tutto l’anno un accesso più costante \",\n",
    "    \"e sicuro rispetto ai fiumi. Inoltre i laghi sono grandi aree con scorrimento ridotto che hanno come immissari fiumi, ruscelli e sistemi di drenaggio. Sulla scorta \",\n",
    "    \"di queste caratteristiche, si configurano come ubicazioni ideali per valutare la varietà degli oggetti nei corpi idrici e sulle loro sponde. In totale, 316 campioni \",\n",
    "    \"sono stati raccolti in sette laghi di grandi dimensioni distribuiti lungo tre bacini fluviali principali. Venti località sono state selezionate per un campionamento \",\n",
    "    \"con cadenza mensile sull’arco di dodici mesi, con l’eccezione del Lago Maggiore che è stato campionato ogni tre mesi. Sono state condotte indagini anche sul lago di \",\n",
    "    \"Lugano, sul lago dei Quattro Cantoni, sul lago di Brienz e sul lago di Zugo. Sono state inoltre effettuate 55 indagini su 16 fiumi.\"\n",
    "]\n",
    "p40 = [\n",
    "    \"In totale 316 campioni provenivano da sette laghi principali in 3 maggiori bacini fluviali. Venti località sono state selezionate per essere campionate \",\n",
    "    \"mensilmente per un periodo di dodici mesi ad eccezione del Lago Maggiore, che è stato campionato ogni tre mesi. Sono state condotte indagini anche sul \",\n",
    "    \"Lago di Lugano, Lago dei Quattro Cantoni, Lago di Brienz e Lago di Zugo. Inoltre, ci sono state 55 indagini su 16 fiumi.\"\n",
    "]\n",
    "\n",
    "p38_40 = sectionParagraphs([p38, p39, p40], smallspace=smallest_space)\n",
    "\n",
    "\n",
    "\n",
    "o_w, o_h = convertPixelToCm(\"resources/maps/esummary_map.jpeg\")\n",
    "\n",
    "f1icap = [\n",
    "    \"Mappa delle ubicazioni oggetto d’indagine da marzo 2020 ad agosto 2021. Le ubicazioni contrassegnate in rosso indicano le indagini su fiumi \",\n",
    "    \"o laghi e i punti in viola designano le ubicazioni sulle Alpi e nel Giura.\"\n",
    "]\n",
    "\n",
    "f1icap = makeAParagraph(f1icap, style=caption_style)\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\":\"resources/maps/esummary_map.jpeg\",\n",
    "    \"caption\": f1icap, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\":1.5,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "f1i = figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "new_components = [\n",
    "    PageBreak(),\n",
    "    seci,\n",
    "    small_space,\n",
    "    *p36_37,\n",
    "    f1i,\n",
    "    small_space,\n",
    "    seci2,\n",
    "    small_space,\n",
    "    *p38_40,\n",
    "    smallest_space,\n",
    "    p41,\n",
    "    smallest_space,\n",
    "    l2i,\n",
    "    smallest_space,\n",
    "    p42,\n",
    "    smallest_space,\n",
    "    l3i,\n",
    "    smallest_space,\n",
    "    p43,\n",
    "    smallest_space,\n",
    "    l4i,\n",
    "    smallest_space,\n",
    "    p44,\n",
    "    smallest_space,\n",
    "    l5i    \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Totale mediano dell’indagine\n",
    "\n",
    "I risultati sono espressi in unità di pezzi di rifiuti per 100 metri lineari (p/100 m). Il risultato mediano dell’indagine per tutti i dati è stato di circa 189 p/100 m. Il valore massimo registrato è stato di 6617 p/100 m (area d’indagine del Rodano), mentre il minimo è stato di 2 p/100 m (area d’indagine dell’Aare). L’area d’indagine del Rodano ha evidenziato il totale d’indagine mediano più alto, pari a 442 p/100 m. Questa incidenza può essere spiegata in parte sia dall’elevato numero di campioni d’indagine in contesti urbani rispetto alle altre aree d’indagine, sia dal deposito di plastiche frammentate e plastiche espanse nella zona di immissione del Rodano nella regione superiore del Lago Lemano.\n",
    "\n",
    "Un valore di riferimento è stato calcolato escludendo i risultati dei campioni provenienti da sezioni di rive inferiori a 10 m e da oggetti di diametro inferiore a 2,5 cm. Questo metodo, descritto nella pubblicazione EU Marine Beach Litter Baselines {cite}`eubaselines` è stato utilizzato per calcolare i valori di riferimento e di soglia per tutte le spiagge europee nel 2015 e nel 2016, con un valore mediano di 131 p/100 m. I risultati del valore di baseline europeo si collocano al di fuori dell’intervallo di confidenza (CI) del 95 per cento, pari a 147-213 p/100 m, definito utilizzando i dati IQAASL.\n",
    "\n",
    "Le indagini in Svizzera hanno evidenziato in media una portata più ridotta rispetto agli ambienti marini e sono state condotte in ubicazioni che nella maggior parte delle circostanze sarebbero considerate urbane. Finora, nel continente europeo il monitoraggio generalizzato di laghi e fiumi a monte delle regioni costiere non è stato una pratica generalizzata. È tuttavia in atto un’iniziativa coordinata, condotta da un gruppo di associazioni in Svizzera e in Francia, per definire un protocollo comune di monitoraggio e scambio di dati per il bacino del Rodano. L’Università e istituto di ricerca di Wageningen, Paesi Bassi, ha altresì iniziato ad analizzare i dati raccolti nel delta della Mosa e del Reno utilizzando protocolli analoghi a quelli IQAASL {cite}`meuserhine`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "sseci1 = Paragraph(\"Totale mediano dell’indagine\", style=subsection_title)\n",
    "\n",
    "p45 = [\n",
    "    \"I risultati sono espressi in unità di pezzi di rifiuti per 100 metri lineari (p/100 m). Il risultato mediano dell’indagine per tutti i dati è stato \",\n",
    "    \"di circa 189 p/100 m. Il valore massimo registrato è stato di 6617 p/100 m (area d’indagine del Rodano), mentre il minimo è stato di 2 p/100 m \",\n",
    "    \"(area d’indagine dell’Aare). L’area d’indagine del Rodano ha evidenziato il totale d’indagine mediano più alto, pari a 442 p/100 m. Questa incidenza \",\n",
    "    \"può essere spiegata in parte sia dall’elevato numero di campioni d’indagine in contesti urbani rispetto alle altre aree d’indagine, sia dal deposito \",\n",
    "    \"di plastiche frammentate e plastiche espanse nella zona di immissione del Rodano nella regione superiore del Lago Lemano.\"\n",
    "]\n",
    "\n",
    "p46 = [\n",
    "    \"Un valore di riferimento è stato calcolato escludendo i risultati dei campioni provenienti da sezioni di rive inferiori a 10 m e da oggetti di diametro inferiore \",\n",
    "    'a 2,5 cm. Questo metodo, descritto nella pubblicazione EU Marine Beach Litter Baselines <a href=\"#HG19\" color=\"blue\">(HG19)</a> è stato utilizzato per calcolare i valori di riferimento e ',\n",
    "    \"di soglia per tutte le spiagge europee nel 2015 e nel 2016, con un valore mediano di 131 p/100 m. I risultati del valore di baseline europeo si collocano al di fuori \",\n",
    "    \"dell’intervallo di confidenza (CI) del 95 per cento, pari a 147-213 p/100 m, definito utilizzando i dati IQAASL.\"\n",
    "]\n",
    "\n",
    "p47 = [ \n",
    "    \"Le indagini in Svizzera hanno evidenziato in media una portata più ridotta rispetto agli ambienti marini e sono state condotte in ubicazioni che nella maggior parte delle \",\n",
    "    \"circostanze sarebbero considerate urbane. Finora, nel continente europeo il monitoraggio generalizzato di laghi e fiumi a monte delle regioni costiere non è stato una pratica \",\n",
    "    \"generalizzata. È tuttavia in atto un’iniziativa coordinata, condotta da un gruppo di associazioni in Svizzera e in Francia, per definire un protocollo comune di monitoraggio e \",\n",
    "    \"scambio di dati per il bacino del Rodano. L’Università e istituto di ricerca di Wageningen, Paesi Bassi, ha altresì iniziato ad analizzare i dati raccolti nel delta della Mosa e \",\n",
    "    'del Reno utilizzando protocolli analoghi a quelli IQAASL.  <a href=\"#vEV21\" color=\"blue\">(vEV21)</a>'\n",
    "]\n",
    "\n",
    "p45_47 = sectionParagraphs([p45, p46, p47], smallspace=smallest_space)\n",
    "new_components = [\n",
    "    small_space,\n",
    "    sseci1,\n",
    "    small_space,\n",
    "    *p45_47,\n",
    "    \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gli oggetti più comuni\n",
    "\n",
    "Gli oggetti più comuni sono definiti come quegli elementi censiti in almeno il 50 per cento di tutte le indagini e/o che si collocano tra i dieci più frequenti per quantità. Come gruppo, gli oggetti più comuni rappresentano il 68 per cento di tutti gli elementi censiti nel periodo di campionamento. Nel novero degli elementi più comuni, il 27 per cento è correlato al consumo di prodotti alimentari, bevande e tabacco, mentre il 24 per cento proviene da infrastrutture e agricoltura.\n",
    "\n",
    "Gli oggetti correlati al consumo di prodotti alimentari, bevande e tabacco vengono censiti con percentuali più elevate nelle zone d’indagine con una quota maggiore di edifici o infrastrutture fisse. Una tendenza inversa si riscontra nelle ubicazioni con una percentuale maggiore di terreni ricoperti da boschi o dedicati ad attività agricole. Materiali per infrastrutture e plastiche frammentate sono invece reperiti con una frequenza analoga in tutte le aree d’indagine, indipendentemente dalla destinazione d’uso dei terreni circostanti."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div style='display:flex; align-content:center; flex-flow:column no-wrap; flex-direction:column;  box-shadow: 0px 0px 3px #d2d9db'> \n",
       "       <div style='padding:20px;width:80%;margin:auto'>\n",
       "            <style type=\"text/css\">\n",
       "#T_bd508 tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_bd508 tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_bd508 tr {\n",
       "  font-size: 12px;\n",
       "}\n",
       "#T_bd508 th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_bd508\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_bd508_level0_col0\" class=\"col_heading level0 col0\" >quantita</th>\n",
       "      <th id=\"T_bd508_level0_col1\" class=\"col_heading level0 col1\" >% del totale</th>\n",
       "      <th id=\"T_bd508_level0_col2\" class=\"col_heading level0 col2\" >tasso di occorrenza</th>\n",
       "      <th id=\"T_bd508_level0_col3\" class=\"col_heading level0 col3\" >p/100 m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row0\" class=\"row_heading level0 row0\" >Filtri di sigarette</th>\n",
       "      <td id=\"T_bd508_row0_col0\" class=\"data row0 col0\" >8 485</td>\n",
       "      <td id=\"T_bd508_row0_col1\" class=\"data row0 col1\" >15%</td>\n",
       "      <td id=\"T_bd508_row0_col2\" class=\"data row0 col2\" >87%</td>\n",
       "      <td id=\"T_bd508_row0_col3\" class=\"data row0 col3\" >20,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row1\" class=\"row_heading level0 row1\" >Plastica frammentata</th>\n",
       "      <td id=\"T_bd508_row1_col0\" class=\"data row1 col0\" >7 400</td>\n",
       "      <td id=\"T_bd508_row1_col1\" class=\"data row1 col1\" >13%</td>\n",
       "      <td id=\"T_bd508_row1_col2\" class=\"data row1 col2\" >86%</td>\n",
       "      <td id=\"T_bd508_row1_col3\" class=\"data row1 col3\" >18,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row2\" class=\"row_heading level0 row2\" >Polistirolo espanso</th>\n",
       "      <td id=\"T_bd508_row2_col0\" class=\"data row2 col0\" >5 563</td>\n",
       "      <td id=\"T_bd508_row2_col1\" class=\"data row2 col1\" >10%</td>\n",
       "      <td id=\"T_bd508_row2_col2\" class=\"data row2 col2\" >68%</td>\n",
       "      <td id=\"T_bd508_row2_col3\" class=\"data row2 col3\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row3\" class=\"row_heading level0 row3\" >Incarti di cibo; caramelle, snack</th>\n",
       "      <td id=\"T_bd508_row3_col0\" class=\"data row3 col0\" >3 325</td>\n",
       "      <td id=\"T_bd508_row3_col1\" class=\"data row3 col1\" >6%</td>\n",
       "      <td id=\"T_bd508_row3_col2\" class=\"data row3 col2\" >85%</td>\n",
       "      <td id=\"T_bd508_row3_col3\" class=\"data row3 col3\" >9,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row4\" class=\"row_heading level0 row4\" >Telo industriale</th>\n",
       "      <td id=\"T_bd508_row4_col0\" class=\"data row4 col0\" >2 534</td>\n",
       "      <td id=\"T_bd508_row4_col1\" class=\"data row4 col1\" >4%</td>\n",
       "      <td id=\"T_bd508_row4_col2\" class=\"data row4 col2\" >69%</td>\n",
       "      <td id=\"T_bd508_row4_col3\" class=\"data row4 col3\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row5\" class=\"row_heading level0 row5\" >Bottiglie per bevande in vetro, pezzi</th>\n",
       "      <td id=\"T_bd508_row5_col0\" class=\"data row5 col0\" >2 136</td>\n",
       "      <td id=\"T_bd508_row5_col1\" class=\"data row5 col1\" >3%</td>\n",
       "      <td id=\"T_bd508_row5_col2\" class=\"data row5 col2\" >65%</td>\n",
       "      <td id=\"T_bd508_row5_col3\" class=\"data row5 col3\" >3,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row6\" class=\"row_heading level0 row6\" >Pellet industriali (nurdles)</th>\n",
       "      <td id=\"T_bd508_row6_col0\" class=\"data row6 col0\" >1 968</td>\n",
       "      <td id=\"T_bd508_row6_col1\" class=\"data row6 col1\" >3%</td>\n",
       "      <td id=\"T_bd508_row6_col2\" class=\"data row6 col2\" >30%</td>\n",
       "      <td id=\"T_bd508_row6_col3\" class=\"data row6 col3\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row7\" class=\"row_heading level0 row7\" >Schiume isolanti</th>\n",
       "      <td id=\"T_bd508_row7_col0\" class=\"data row7 col0\" >1 702</td>\n",
       "      <td id=\"T_bd508_row7_col1\" class=\"data row7 col1\" >3%</td>\n",
       "      <td id=\"T_bd508_row7_col2\" class=\"data row7 col2\" >53%</td>\n",
       "      <td id=\"T_bd508_row7_col3\" class=\"data row7 col3\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row8\" class=\"row_heading level0 row8\" >Bastoncini di cotton fioc/ tampone</th>\n",
       "      <td id=\"T_bd508_row8_col0\" class=\"data row8 col0\" >1 406</td>\n",
       "      <td id=\"T_bd508_row8_col1\" class=\"data row8 col1\" >2%</td>\n",
       "      <td id=\"T_bd508_row8_col2\" class=\"data row8 col2\" >50%</td>\n",
       "      <td id=\"T_bd508_row8_col3\" class=\"data row8 col3\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row9\" class=\"row_heading level0 row9\" >Schiume espanse < 5mm</th>\n",
       "      <td id=\"T_bd508_row9_col0\" class=\"data row9 col0\" >1 209</td>\n",
       "      <td id=\"T_bd508_row9_col1\" class=\"data row9 col1\" >2%</td>\n",
       "      <td id=\"T_bd508_row9_col2\" class=\"data row9 col2\" >25%</td>\n",
       "      <td id=\"T_bd508_row9_col3\" class=\"data row9 col3\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row10\" class=\"row_heading level0 row10\" >Rifiuti plastici da costruzione</th>\n",
       "      <td id=\"T_bd508_row10_col0\" class=\"data row10 col0\" >992</td>\n",
       "      <td id=\"T_bd508_row10_col1\" class=\"data row10 col1\" >1%</td>\n",
       "      <td id=\"T_bd508_row10_col2\" class=\"data row10 col2\" >52%</td>\n",
       "      <td id=\"T_bd508_row10_col3\" class=\"data row10 col3\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bd508_level0_row11\" class=\"row_heading level0 row11\" >Tappi e coperchi di bottiglia in metallo</th>\n",
       "      <td id=\"T_bd508_row11_col0\" class=\"data row11 col0\" >700</td>\n",
       "      <td id=\"T_bd508_row11_col1\" class=\"data row11 col1\" >1%</td>\n",
       "      <td id=\"T_bd508_row11_col2\" class=\"data row11 col2\" >52%</td>\n",
       "      <td id=\"T_bd508_row11_col3\" class=\"data row11 col3\" >1,0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "        </div>\n",
       "        <div style='border-top: 1px solid #d2d9db;'>\n",
       "            <p style='margin:10px'><i><b>Figura 2:</b> Totale delle indagini per tutti i laghi e i fiumi: gli oggetti più comuni censiti da marzo 2020 a maggio 2021. Il tasso di occorrenza (indicato come “tasso di insuccesso” in questo rapporto) è il rapporto tra il numero di volte in cui un oggetto è stato censito con almeno un’occorrenza rispetto al numero totale di indagini. La quantità è il numero totale di elementi raccolti per un oggetto censito e la mediana dei pezzi di rifiuti per 100 metri lineari (p/100 m). Per esempio, un totale di 8 485 mozziconi di sigarette è stato censito nell’87 per cento delle indagini, pari al 15 per cento degli elementi totali raccolti e con un valore mediano di 20 mozziconi per 100 metri lineari di linea rivierasca.</i></p>\n",
       "        </div>\n",
       "    </div>\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sseci2 = Paragraph(\"Gli oggetti più comuni\", style=subsection_title)\n",
    "\n",
    "p48 = [\n",
    "    \"Gli oggetti più comuni sono definiti come quegli elementi censiti in almeno il 50 per cento di tutte le indagini e/o che \",\n",
    "    \"si collocano tra i dieci più frequenti per quantità. Come gruppo, gli oggetti più comuni rappresentano il 68 per cento di \",\n",
    "    \"tutti gli elementi censiti nel periodo di campionamento. Nel novero degli elementi più comuni, il 27 per cento è correlato \",\n",
    "    \"al consumo di prodotti alimentari, bevande e tabacco, mentre il 24 per cento proviene da infrastrutture e agricoltura.\"\n",
    "]\n",
    "\n",
    "p49 = [\n",
    "    \"Gli oggetti correlati al consumo di prodotti alimentari, bevande e tabacco vengono censiti con percentuali più elevate nelle \",\n",
    "    \"zone d’indagine con una quota maggiore di edifici o infrastrutture fisse. Una tendenza inversa si riscontra nelle ubicazioni con \",\n",
    "    \"una percentuale maggiore di terreni ricoperti da boschi o dedicati ad attività agricole. Materiali per infrastrutture e plastiche \",\n",
    "    \"frammentate sono invece reperiti con una frequenza analoga in tutte le aree d’indagine, indipendentemente dalla destinazione d’uso \",\n",
    "    \"dei terreni circostanti.\"\n",
    "]\n",
    "\n",
    "p48_49 = sectionParagraphs([p48, p49], smallspace=smallest_space)\n",
    "\n",
    "new_components = [\n",
    "    PageBreak(),\n",
    "    sseci2,\n",
    "    small_space,\n",
    "    *p48_49,\n",
    "    \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "\n",
    "ital = {\n",
    "    \"Zigarettenfilter\": \"Filtri di sigarette\" ,\n",
    "    \"Fragmentierte Kunststoffe\":\"Plastica frammentata\",\n",
    "    \"Expandiertes Polystyrol\": \"Polistirolo espanso\",\n",
    "    \"Snack-Verpackungen\":\"Incarti di cibo; caramelle, snack\",\n",
    "    \"Industriefolie (Kunststoff)\": \"Telo industriale\",\n",
    "    \" Getränkeflaschen aus Glas, Glasfragmente\":\"Bottiglie per bevande in vetro, pezzi\",\n",
    "    \"Industriepellets (Nurdles)\":\"Pellet industriali (nurdles)\",\n",
    "    \"Schaumstoffverpackungen/Isolierung\": \"Schiume isolanti\",\n",
    "    \"Wattestäbchen/Tupfer\":\"Bastoncini di cotton fioc/ tampone\",\n",
    "    \"Styropor < 5mm\":\"Schiume espanse < 5mm\",\n",
    "    \"Kunststoff-Bauabfälle\":\"Rifiuti plastici da costruzione\",\n",
    "    \"Kronkorken, Lasche von Dose/Ausfreisslachen\":\"Tappi e coperchi di bottiglia in metallo\",\n",
    "    \"Packaging films nonfood or unknown\":\"Pellicole da imballaggio non alimentari o sconosciute\"\n",
    "}\n",
    "\n",
    "\n",
    "fren = {\n",
    "    \"Zigarettenfilter\": \"Filtres à cigarettes\",  \n",
    "    \"Fragmentierte Kunststoffe\": \"Plastiques fragmentés\",\n",
    "    \"Expandiertes Polystyrol\": \"Polystyrène expansé\",\n",
    "    \"Snack-Verpackungen\":\"Emballages alimentaires, bonbons\",\n",
    "    \"Industriefolie (Kunststoff)\":\"film plastique épais\",\n",
    "    \" Getränkeflaschen aus Glas, Glasfragmente\":\"Bouteilles pour boissons, morceaux\",\n",
    "    \"Industriepellets (Nurdles)\":\"Granules Plastique industriels (GPI)\",\n",
    "    \"Schaumstoffverpackungen/Isolierung\":\"Isolation : y compris les mousses en spray\",\n",
    "    \"Wattestäbchen/Tupfer\":\"Bâtonnets de coton-tige\",\n",
    "    \"Styropor < 5mm\":\"Mousses expansées < 5 mm\",\n",
    "    \"Kunststoff-Bauabfälle\":\"Déchets plastiques de construction\",\n",
    "    \"Kronkorken, Lasche von Dose/Ausfreisslachen\":\"Bouchons et couvercles de bouteilles en métal\", \n",
    "    \"Packaging films nonfood or unknown\":\"Films d'emballage non alimentaires ou inconnus\",\n",
    "}\n",
    "\n",
    "# the most common objects results\n",
    "most_common_display = fdx.most_common\n",
    "\n",
    "# language appropriate columns\n",
    "cols_to_use = featuredata.most_common_objects_table_de\n",
    "cols_to_use.update({unit_label:unit_label})\n",
    "\n",
    "# data for display\n",
    "most_common_display.rename(columns=cols_to_use, inplace=True)\n",
    "most_common_display = most_common_display[cols_to_use.values()].copy()\n",
    "most_common_display = most_common_display.set_index(\"Objekte\", drop=True)\n",
    "\n",
    "# .pdf output\n",
    "data = most_common_display.copy()\n",
    "data[\"Anteil\"] = data[\"Anteil\"].map(lambda x: f\"{int(x)}%\")\n",
    "data['Objekte (St.)'] = data['Objekte (St.)'].map(lambda x:featuredata.thousandsSeparator(x, language))\n",
    "data['Häufigkeitsrate'] = data['Häufigkeitsrate'].map(lambda x: f\"{x}%\")\n",
    "data[unit_label] = data[unit_label].map(lambda x: featuredata.replaceDecimal(round(x,1)))\n",
    "new_cols = {'Objekte (St.)':\"quantita\", \"Anteil\":\"% del totale\", 'Häufigkeitsrate':'tasso di occorrenza'}\n",
    "data.rename(columns=new_cols, inplace=True)\n",
    "\n",
    "data[\"Index\"] = data.index.map(lambda x: ital[x])\n",
    "data.set_index(\"Index\", drop=True, inplace=True)\n",
    "\n",
    "# save a copy for the frnech and italian versions\n",
    "esum_most_common = data.copy()\n",
    "\n",
    "# make caption\n",
    "# get percent of total to make the caption string\n",
    "mc_caption_string = [\n",
    "    \"<b>Figura 2:</b> Totale delle indagini per tutti i laghi e i fiumi: gli oggetti più comuni censiti da marzo 2020 a maggio 2021. Il tasso \",\n",
    "    \"di occorrenza (indicato come “tasso di insuccesso” in questo rapporto) è il rapporto tra il numero di volte in cui un \",\n",
    "    \"oggetto è stato censito con almeno un’occorrenza rispetto al numero totale di indagini. La quantità è il numero totale \",\n",
    "    \"di elementi raccolti per un oggetto censito e la mediana dei pezzi di rifiuti per 100 metri lineari (p/100 m). Per esempio, \",\n",
    "    \"un totale di 8 485 mozziconi di sigarette è stato censito nell’87 per cento delle indagini, pari al 15 per cento degli elementi \",\n",
    "    \"totali raccolti e con un valore mediano di 20 mozziconi per 100 metri lineari di linea rivierasca.\"\n",
    "]\n",
    "   \n",
    "\n",
    "colWidths = [5.1*cm, 2.2*cm, 2*cm, 2.8*cm, 2*cm]\n",
    "\n",
    "d_chart = aSingleStyledTable(data, colWidths=colWidths)\n",
    "d_capt = makeAParagraph(mc_caption_string, caption_style)\n",
    "mc_table = tableAndCaption(d_chart, d_capt, colWidths)\n",
    "\n",
    "new_components = [\n",
    "    smallest_space,\n",
    "    mc_table,\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "most_common_display[\"Index\"] = most_common_display.index.map(lambda x: ital[x])\n",
    "most_common_display.set_index(\"Index\", inplace=True, drop=True)\n",
    "most_common_display.index.name = None\n",
    "most_common_display.columns.name = None\n",
    "\n",
    "most_common_display.rename(columns=new_cols, inplace=True)\n",
    "\n",
    "# set pandas display\n",
    "aformatter = {\n",
    "    \"% del totale\":lambda x: f\"{int(x)}%\",\n",
    "    f\"{unit_label}\": lambda x: featuredata.replaceDecimal(x, \"de\"),\n",
    "    'tasso di occorrenza': lambda x: f\"{int(x)}%\",   \n",
    "    \"quantita\": lambda x: featuredata.thousandsSeparator(int(x), \"de\")\n",
    "}\n",
    "\n",
    "mcdic = most_common_display.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "IPython.display.display(HTML(cardFigure(html_table=mcdic.to_html(), table_caption= \"\".join(mc_caption_string))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Granulati di plastica di provenienza industriale e schiume espanse < 5 mm hanno evidenziato entrambi occorrenze in quantità significative, ma sono stati censiti in meno del 50 per cento delle indagini (mediana di 0), indicando conteggi elevati in ubicazioni specifiche. Pur trattandosi in entrambi i casi di microplastiche, le loro peculiarità in termini di impiego, origine e tasso di occorrenza sono diverse a seconda della regione dell’area d’indagine. I granulati di plastica di provenienza industriale sono materie prime usate nei processi di stampaggio a iniezione e le palline di plastica espansa derivano dalla frammentazione del polistirolo espanso. Per maggiori informazioni su ubicazione, quantità e tasso di occorrenza dei singoli oggetti, si veda la sezione [Laghi e fiumi - deutsch](allsurveys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
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       "</style>\n",
       "<table id=\"T_69075\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_69075_level0_col0\" class=\"col_heading level0 col0\" >Aare</th>\n",
       "      <th id=\"T_69075_level0_col1\" class=\"col_heading level0 col1\" >Linth</th>\n",
       "      <th id=\"T_69075_level0_col2\" class=\"col_heading level0 col2\" >Rhone</th>\n",
       "      <th id=\"T_69075_level0_col3\" class=\"col_heading level0 col3\" >Ticino</th>\n",
       "      <th id=\"T_69075_level0_col4\" class=\"col_heading level0 col4\" >Tute le aree di indagine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row0\" class=\"row_heading level0 row0\" >Bottiglie per bevande in vetro, pezzi</th>\n",
       "      <td id=\"T_69075_row0_col0\" class=\"data row0 col0\" >3,0</td>\n",
       "      <td id=\"T_69075_row0_col1\" class=\"data row0 col1\" >4,0</td>\n",
       "      <td id=\"T_69075_row0_col2\" class=\"data row0 col2\" >2,0</td>\n",
       "      <td id=\"T_69075_row0_col3\" class=\"data row0 col3\" >8,0</td>\n",
       "      <td id=\"T_69075_row0_col4\" class=\"data row0 col4\" >3,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row1\" class=\"row_heading level0 row1\" >Polistirolo espanso</th>\n",
       "      <td id=\"T_69075_row1_col0\" class=\"data row1 col0\" >4,0</td>\n",
       "      <td id=\"T_69075_row1_col1\" class=\"data row1 col1\" >3,0</td>\n",
       "      <td id=\"T_69075_row1_col2\" class=\"data row1 col2\" >17,5</td>\n",
       "      <td id=\"T_69075_row1_col3\" class=\"data row1 col3\" >6,0</td>\n",
       "      <td id=\"T_69075_row1_col4\" class=\"data row1 col4\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row2\" class=\"row_heading level0 row2\" >Plastica frammentata</th>\n",
       "      <td id=\"T_69075_row2_col0\" class=\"data row2 col0\" >18,5</td>\n",
       "      <td id=\"T_69075_row2_col1\" class=\"data row2 col1\" >10,5</td>\n",
       "      <td id=\"T_69075_row2_col2\" class=\"data row2 col2\" >48,0</td>\n",
       "      <td id=\"T_69075_row2_col3\" class=\"data row2 col3\" >9,5</td>\n",
       "      <td id=\"T_69075_row2_col4\" class=\"data row2 col4\" >18,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row3\" class=\"row_heading level0 row3\" >Telo industriale</th>\n",
       "      <td id=\"T_69075_row3_col0\" class=\"data row3 col0\" >5,0</td>\n",
       "      <td id=\"T_69075_row3_col1\" class=\"data row3 col1\" >2,0</td>\n",
       "      <td id=\"T_69075_row3_col2\" class=\"data row3 col2\" >9,0</td>\n",
       "      <td id=\"T_69075_row3_col3\" class=\"data row3 col3\" >3,5</td>\n",
       "      <td id=\"T_69075_row3_col4\" class=\"data row3 col4\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row4\" class=\"row_heading level0 row4\" >Pellet industriali (nurdles)</th>\n",
       "      <td id=\"T_69075_row4_col0\" class=\"data row4 col0\" >0,0</td>\n",
       "      <td id=\"T_69075_row4_col1\" class=\"data row4 col1\" >0,0</td>\n",
       "      <td id=\"T_69075_row4_col2\" class=\"data row4 col2\" >0,0</td>\n",
       "      <td id=\"T_69075_row4_col3\" class=\"data row4 col3\" >0,0</td>\n",
       "      <td id=\"T_69075_row4_col4\" class=\"data row4 col4\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row5\" class=\"row_heading level0 row5\" >Tappi e coperchi di bottiglia in metallo</th>\n",
       "      <td id=\"T_69075_row5_col0\" class=\"data row5 col0\" >0,0</td>\n",
       "      <td id=\"T_69075_row5_col1\" class=\"data row5 col1\" >1,0</td>\n",
       "      <td id=\"T_69075_row5_col2\" class=\"data row5 col2\" >3,0</td>\n",
       "      <td id=\"T_69075_row5_col3\" class=\"data row5 col3\" >1,0</td>\n",
       "      <td id=\"T_69075_row5_col4\" class=\"data row5 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row6\" class=\"row_heading level0 row6\" >Rifiuti plastici da costruzione</th>\n",
       "      <td id=\"T_69075_row6_col0\" class=\"data row6 col0\" >0,0</td>\n",
       "      <td id=\"T_69075_row6_col1\" class=\"data row6 col1\" >0,0</td>\n",
       "      <td id=\"T_69075_row6_col2\" class=\"data row6 col2\" >5,5</td>\n",
       "      <td id=\"T_69075_row6_col3\" class=\"data row6 col3\" >2,5</td>\n",
       "      <td id=\"T_69075_row6_col4\" class=\"data row6 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row7\" class=\"row_heading level0 row7\" >Schiume isolanti</th>\n",
       "      <td id=\"T_69075_row7_col0\" class=\"data row7 col0\" >0,0</td>\n",
       "      <td id=\"T_69075_row7_col1\" class=\"data row7 col1\" >0,0</td>\n",
       "      <td id=\"T_69075_row7_col2\" class=\"data row7 col2\" >7,0</td>\n",
       "      <td id=\"T_69075_row7_col3\" class=\"data row7 col3\" >3,0</td>\n",
       "      <td id=\"T_69075_row7_col4\" class=\"data row7 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row8\" class=\"row_heading level0 row8\" >Incarti di cibo; caramelle, snack</th>\n",
       "      <td id=\"T_69075_row8_col0\" class=\"data row8 col0\" >8,0</td>\n",
       "      <td id=\"T_69075_row8_col1\" class=\"data row8 col1\" >6,0</td>\n",
       "      <td id=\"T_69075_row8_col2\" class=\"data row8 col2\" >19,0</td>\n",
       "      <td id=\"T_69075_row8_col3\" class=\"data row8 col3\" >4,0</td>\n",
       "      <td id=\"T_69075_row8_col4\" class=\"data row8 col4\" >9,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row9\" class=\"row_heading level0 row9\" >Schiume espanse < 5mm</th>\n",
       "      <td id=\"T_69075_row9_col0\" class=\"data row9 col0\" >0,0</td>\n",
       "      <td id=\"T_69075_row9_col1\" class=\"data row9 col1\" >0,0</td>\n",
       "      <td id=\"T_69075_row9_col2\" class=\"data row9 col2\" >0,0</td>\n",
       "      <td id=\"T_69075_row9_col3\" class=\"data row9 col3\" >0,0</td>\n",
       "      <td id=\"T_69075_row9_col4\" class=\"data row9 col4\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row10\" class=\"row_heading level0 row10\" >Bastoncini di cotton fioc/ tampone</th>\n",
       "      <td id=\"T_69075_row10_col0\" class=\"data row10 col0\" >0,0</td>\n",
       "      <td id=\"T_69075_row10_col1\" class=\"data row10 col1\" >0,0</td>\n",
       "      <td id=\"T_69075_row10_col2\" class=\"data row10 col2\" >10,5</td>\n",
       "      <td id=\"T_69075_row10_col3\" class=\"data row10 col3\" >0,0</td>\n",
       "      <td id=\"T_69075_row10_col4\" class=\"data row10 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69075_level0_row11\" class=\"row_heading level0 row11\" >Filtri di sigarette</th>\n",
       "      <td id=\"T_69075_row11_col0\" class=\"data row11 col0\" >11,0</td>\n",
       "      <td id=\"T_69075_row11_col1\" class=\"data row11 col1\" >23,0</td>\n",
       "      <td id=\"T_69075_row11_col2\" class=\"data row11 col2\" >41,5</td>\n",
       "      <td id=\"T_69075_row11_col3\" class=\"data row11 col3\" >15,5</td>\n",
       "      <td id=\"T_69075_row11_col4\" class=\"data row11 col4\" >20,0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "        </div>\n",
       "        <div style='border-top: 1px solid #d2d9db;'>\n",
       "            <p style='margin:10px'><i><b>Figura 3:</b> Tutti i laghi e i fiumi per area di indagine: totale mediano dell’indagine degli oggetti più comuni censiti; le percentuali variano a seconda della regione dell’area d’indagine. Per esempio, le plastiche frammentate hanno evidenziato il valore mediano maggiore nelle aree d’indagine di Aare (18,5 p/100 m) e Rodano (48 p/100 m).</i></p>\n",
       "        </div>\n",
       "    </div>\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p50 = [\n",
    "    \"Granulati di plastica di provenienza industriale e schiume espanse < 5 mm hanno evidenziato entrambi occorrenze in quantità significative, \",\n",
    "    \"ma sono stati censiti in meno del 50 per cento delle indagini (mediana di 0), indicando conteggi elevati in ubicazioni specifiche. Pur \",\n",
    "    \"trattandosi in entrambi i casi di microplastiche, le loro peculiarità in termini di impiego, origine e tasso di occorrenza sono diverse a \",\n",
    "    \"seconda della regione dell’area d’indagine. I granulati di plastica di provenienza industriale sono materie prime usate nei processi di \",\n",
    "    \"stampaggio a iniezione e le palline di plastica espansa derivano dalla frammentazione del polistirolo espanso. Per maggiori informazioni su \"\n",
    "    \"ubicazione, quantità e tasso di occorrenza dei singoli oggetti, si veda la sezione \",\n",
    "    '<a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/lakes_rivers.html\" color=\"blue\">Laghi e fiumi -deutsch</a>.'\n",
    "]\n",
    "\n",
    "p50 = makeAParagraph(p50)\n",
    "\n",
    "new_components = [\n",
    "    smallest_space,\n",
    "    p50,\n",
    "    smallest_space \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "mc_heat_map_caption = [\n",
    "    \"<b>Figura 3:</b> Tutti i laghi e i fiumi per area di indagine: totale mediano dell’indagine degli oggetti più comuni censiti; le percentuali \",\n",
    "    \"variano a seconda della regione dell’area d’indagine. Per esempio, le plastiche frammentate hanno evidenziato il valore mediano \",\n",
    "    \"maggiore nelle aree d’indagine di Aare (18,5 p/100 m) e Rodano (48 p/100 m).\",\n",
    "]\n",
    "mc_heat_map_caption = ''.join(mc_heat_map_caption)\n",
    "\n",
    "# calling componentsMostCommon gets the results for the most common codes\n",
    "# at the component level\n",
    "components = fdx.componentMostCommonPcsM()\n",
    "\n",
    "# map to proper names for features\n",
    "feature_names = featuredata.river_basin_de\n",
    "\n",
    "# pivot that and quash the hierarchal column index that is created when the table is pivoted\n",
    "mc_comp = components[[\"item\", unit_label, this_level]].pivot(columns=this_level, index=\"item\")\n",
    "mc_comp.columns = mc_comp.columns.get_level_values(1)\n",
    "\n",
    "# insert the proper columns names for display\n",
    "proper_column_names = {x : feature_names[x] for x in mc_comp.columns}\n",
    "mc_comp.rename(columns = proper_column_names, inplace=True)\n",
    "\n",
    "# the aggregated total of the feature is taken from the most common objects table\n",
    "mc_feature = fdx.most_common[unit_label]\n",
    "mc_feature = featuredata.changeSeriesIndexLabels(mc_feature, {x:fdx.dMap.loc[x] for x in mc_feature.index})\n",
    "\n",
    "# the aggregated totals of all the period data\n",
    "mc_period = period_data.parentMostCommon(parent=False)\n",
    "mc_period = featuredata.changeSeriesIndexLabels(mc_period, {x:fdx.dMap.loc[x] for x in mc_period.index})\n",
    "\n",
    "# add the feature, bassin_label and period results to the components table\n",
    "mc_comp[\"Tute le aree di indagine\"] = mc_feature\n",
    "\n",
    "mc_comp[\"Index\"] = mc_comp.index.map(lambda x: ital[x])\n",
    "mc_comp.set_index(\"Index\", inplace=True, drop=True)\n",
    "\n",
    "col_widths=[5.1*cm, *[1.2*cm]*(len(mc_comp.columns)-1)]\n",
    "\n",
    "atable = aSingleStyledTable(mc_comp, gradient=True, vertical_header=True, colWidths=col_widths)\n",
    "atable_cap = makeAParagraph(mc_heat_map_caption, style=caption_style)\n",
    "table_and_cap =tableAndCaption(atable, atable_cap, col_widths)\n",
    "\n",
    "new_components = [\n",
    "     table_and_cap\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "# notebook display style\n",
    "aformatter = {x: featuredata.replaceDecimal for x in mc_comp.columns}\n",
    "mcd = mc_comp.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "mcd = mcd.background_gradient(axis=None, vmin=mc_comp.min().min(), vmax=mc_comp.max().max(), cmap=\"YlOrBr\")\n",
    "\n",
    "# remove the index name and column name labels\n",
    "mcd.index.name = None\n",
    "mcd.columns.name = None\n",
    "\n",
    "# rotate the text on the header row\n",
    "# the .applymap_index method in the\n",
    "# df.styler module is used for this\n",
    "mcd = mcd.applymap_index(featuredata.rotateText, axis=1)\n",
    "\n",
    "# display markdown html\n",
    "IPython.display.display(HTML(cardFigure(html_table=mcd.to_html(), table_caption= \"\".join(mc_heat_map_caption))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tendenze in atto dal 2017-2018\n",
    "\n",
    "Dati analoghi relativi a un’indagine su laghi e fiumi raccolti nel periodo 2017-2018 (SLR) non hanno evidenziato differenze statistiche nel raffronto con i risultati IQAASL. Sono state tuttavia registrate variazioni a livello di quantità degli oggetti. In generale, nel periodo di indagine 2020-2021 è stato censito un numero inferiore di mozziconi di sigarette e tappi di bottiglia, ma per molte ubicazioni non si è registrata alcuna variazione e si è avuto probabilmente un aumento delle plastiche e delle schiume espanse frammentate. Per i dettagli in merito, si veda [Aumento e diminuzione dei rifiuti dal 2017 -deutsch](slr-iqaasl).\n",
    "\n",
    "\n",
    "```{card}\n",
    ":class-card: sd-text-black\n",
    ":img-top: resources/output/slr_iqaasl_surveys_it.jpeg\n",
    ":margin: auto\n",
    "+++\n",
    "__Figura 4:__ *Raffronto dei risultati d’indagine tra SRL (2018) e IQAASL (2021). __In alto a sinistra:__ totali delle indagini per data. __In alto a destra:__ totale mediano dell’indagine mensile. __In basso a sinistra:__ numero di campioni rispetto al totale dell’indagine. __In basso a sinistra:__ distribuzione cumulativa empirica dei totali delle indagini.* \n",
    "``` \n",
    "\n",
    "### Alpi e Giura\n",
    "\n",
    "Sulle venti indagini condotte nell’area delle Alpi, 17 hanno soddisfatto i criteri di lunghezza e larghezza superiori a 10 m. Il valore mediano delle indagini è stato di 110 p/100m, inferiore al valore mediano di tutte le altre aree d’indagine (189 p/100 m). Gli oggetti correlati ai consumi personali, per esempio di prodotti alimentari e bevande o tabacco, hanno evidenziato una percentuale più bassa rispetto al totale, con un tasso di p/100 m inferiore rispetto ai risultati delle ubicazioni rivierasche. Questa differenza potrebbe essere in parte dovuta sia ai bassi livelli di urbanizzazione che caratterizzano l’area d’indagine delle Alpi rispetto a tutte le altre aree d’indagine, sia alla tendenza del materiale a fluire a valle. Per quanto concerne la metodologia di indagine adottata per le Alpi e i relativi risultati, si veda la sezione [ Alpi e Giura -deutsch ](lesalpes).\n",
    "\n",
    "### Comunicazione dei risultati¶\n",
    "\n",
    "Per comunicare le quantità di agenti inquinanti è utile convertire i risultati in una semplice parametrazione di pezzi medi per 100 m lineari, in quanto la media è generalmente più elevata e raramente risulta pari a 0. Quando si considerano valori estremi, tuttavia, __la media può risultare anche doppia rispetto alla mediana creando confusione circa la differenza tra i risultati osservati e quelli riportati__. Quando si adottano protocolli di questo tipo, comunicare la gamma di valori possibili o la probabilità di reperire un oggetto costituisce un fattore più informativo e ripetibile. Per esempio, in relazione all’interpretazione delle quantità di granulati di plastica di provenienza industriale censiti sul Lago Lemano.\n",
    "\n",
    "\n",
    "> Sono stati reperiti 1 387 granulati in plastica (GPI), pari al 5 per cento di tutti gli oggetti censiti sul Lago Lemano. Il numero di unità di granulati per 100 m lineari varia da 0 a 1033 a seconda della regione. Per il lago, in generale, sussiste ovunque una probabilità pari a circa il 40 per cento di trovare almeno un granulato durante un rilevamento. In alcune località come Ginevra (probabilità del 60%) o Préverenges (probabilità dell’80%), i granulati di plastica industriale costituiscono elementi costanti sulle sponde del lago e quantità tra 3 p/100 m e 56 p/100 m sono del tutto comuni."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "seci3 = Paragraph(\"Tendenze in atto dal 2017-2018\", style=section_title)\n",
    "\n",
    "p51 = [\n",
    "    \"Dati analoghi relativi a un’indagine su laghi e fiumi raccolti nel periodo 2017-2018 (SLR) non hanno evidenziato differenze statistiche nel \",\n",
    "    \"raffronto con i risultati IQAASL. Sono state tuttavia registrate variazioni a livello di quantità degli oggetti. In generale, nel periodo di \",\n",
    "    \"indagine 2020-2021 è stato censito un numero inferiore di mozziconi di sigarette e tappi di bottiglia, ma per molte ubicazioni non si è \",\n",
    "    \"registrata alcuna variazione e si è avuto probabilmente un aumento delle plastiche e delle schiume espanse frammentate. Per i dettagli in \",\n",
    "    'merito, si veda <a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/slr-iqaasl.html\" color=\"blue\">Aumento e diminuzione dei rifiuti dal 2017 -deutsch </a>.',\n",
    "\n",
    "]\n",
    "\n",
    "p51 = makeAParagraph(p51)\n",
    "\n",
    "seci4 = Paragraph(\"Alpi e Giura\", style=section_title)\n",
    "\n",
    "p52 = [\n",
    "    \"Sulle venti indagini condotte nell’area delle Alpi, 17 hanno soddisfatto i criteri di lunghezza e larghezza superiori a 10 m. Il valore mediano delle \",\n",
    "    \"indagini è stato di 110 p/100m, inferiore al valore mediano di tutte le altre aree d’indagine (189 p/100 m). Gli oggetti correlati ai consumi personali, \",\n",
    "    \"per esempio di prodotti alimentari e bevande o tabacco, hanno evidenziato una percentuale più bassa rispetto al totale, con un tasso di p/100 m inferiore \",\n",
    "    \"rispetto ai risultati delle ubicazioni rivierasche. Questa differenza potrebbe essere in parte dovuta sia ai bassi livelli di urbanizzazione che \",\n",
    "    \"caratterizzano l’area d’indagine delle Alpi rispetto a tutte le altre aree d’indagine, sia alla tendenza del materiale a fluire a valle. Per quanto concerne \",\n",
    "    \"la metodologia di indagine adottata per le Alpi e i relativi risultati, si veda la sezione \",\n",
    "    '<a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/alpes_valaisannes.html\" color=\"blue\">Alpi e Giura -deutsch</a>.'\n",
    "]\n",
    "\n",
    "p52 = makeAParagraph(p52) \n",
    "\n",
    "fig4icap = [\n",
    "    \"<b>Figura 4:</b> Raffronto dei risultati d’indagine tra SRL (2018) e IQAASL (2021). <b>In alto a sinistra:</b> totali delle indagini per data. \",\n",
    "    \"<b>In alto a destra:</b> totale mediano dell’indagine mensile. <b>In basso a sinistra:</b> numero di campioni rispetto al totale dell’indagine. \",\n",
    "    \"<b>In basso a destra:</b> distribuzione cumulativa empirica dei totali delle indagini.\"\n",
    "]\n",
    "\n",
    "seci5 = Paragraph(\"Comunicazione dei risultati\", style=section_title)\n",
    "\n",
    "p53 = [\n",
    "    \"Per comunicare le quantità di agenti inquinanti è utile convertire i risultati in una semplice parametrazione di pezzi medi per 100 m \",\n",
    "    \"lineari, in quanto la media è generalmente più elevata e raramente risulta pari a 0. Quando si considerano valori estremi, tuttavia, \",\n",
    "    \"la media può risultare anche doppia rispetto alla mediana creando confusione circa la differenza tra i risultati osservati e quelli \",\n",
    "    \"riportati. Quando si adottano protocolli di questo tipo, comunicare la gamma di valori possibili o la probabilità di reperire un \",\n",
    "    \"oggetto costituisce un fattore più informativo e ripetibile. Per esempio, in relazione all’interpretazione delle quantità di granulati \",\n",
    "    \"di plastica di provenienza industriale censiti sul Lago Lemano.\"\n",
    "]\n",
    "\n",
    "p54 = [\n",
    "    \"Sono stati reperiti 1387 granulati in plastica (GPI), pari al 5 per cento di tutti gli oggetti censiti sul Lago Lemano. Il numero di \",\n",
    "    \"unità di granulati per 100 m lineari varia da 0 a 1033 a seconda della regione. Per il lago, in generale, sussiste ovunque una probabilità \",\n",
    "    \"pari a circa il 40 per cento di trovare almeno un granulato durante un rilevamento. In alcune località come Ginevra (probabilità del 60%) o \",\n",
    "    \"Préverenges (probabilità dell’80%), i granulati di plastica industriale costituiscono elementi costanti sulle sponde del lago e quantità tra \",\n",
    "    \"3 p/100 m e 56 p/100 m sono del tutto comuni.\"\n",
    "]\n",
    "\n",
    "p53 = makeAParagraph(p53)\n",
    "\n",
    "p54 = makeAParagraph(p54, style=block_quote_style)\n",
    "\n",
    "\n",
    "fig4icap = makeAParagraph(fig4icap, style=caption_style)\n",
    "\n",
    "o_w, o_h = convertPixelToCm(\"resources/output/slr_iqaasl_surveys.jpeg\")\n",
    "figure_kwargs = {\n",
    "    \"image_file\":\"resources/output/slr_iqaasl_surveys.jpeg\",\n",
    "    \"caption\": fig4icap, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\":2,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "f4i = figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "\n",
    "new_components = [\n",
    "    small_space,\n",
    "    seci3,\n",
    "    small_space,\n",
    "    p51,\n",
    "    smallest_space,\n",
    "    f4i,\n",
    "    small_space,\n",
    "    seci4,\n",
    "    small_space,\n",
    "    p52,\n",
    "    small_space,\n",
    "    seci5,\n",
    "    small_space,\n",
    "    p53,\n",
    "    smallest_space,\n",
    "    p54,\n",
    "    smallest_space,\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusioni\n",
    "\n",
    "Su scala nazionale, i risultati dell’indagine IQAASL riflettono una certa stabilità rispetto ai dati rilevati nel 2017 dallo studio SLR. Tuttavia, è stata registrata una diminuzione generalizzata della quantità di oggetti correlati al consumo di prodotti alimentari, bevande e tabacco. Gli oggetti legati alle infrastrutture e alle plastiche e schiume espanse frammentate non sono diminuiti e alcune ubicazioni hanno eventualmente registrato forti incrementi. Le misure di contenimento della pandemia, che limitano i grandi assembramenti all’aperto, hanno probabilmente prodotto un effetto favorevole sulla riduzione degli oggetti legati al consumo di prodotti alimentari, bevande e tabacco. I maggiori aumenti per quanto concerne gli oggetti legati alle infrastrutture sono stati registrati nei Cantoni Vallese e Vaud, nonché a Brienz: tutte ubicazioni in prossimità dei punti di immissione dei fiumi Rodano e Aare.\n",
    "\n",
    "La destinazione d’uso del terreno prospiciente a un luogo d’indagine produce un effetto misurabile sul deposito di determinati oggetti. Il numero di edifici e infrastrutture presenti è direttamente proporzionale al reperimento di rifiuti correlati al consumo di prodotti alimentari e tabacco. Oggetti come plastiche frammentate e teli industriali non presentano invece lo stesso grado di correlazione e sono censiti con un’occorrenza percentuale approssimativamente analoga a prescindere dalla destinazione d’uso del terreno, con picchi di frequenza in prossimità dei punti di immissione di fiumi/canali.\n",
    "\n",
    "Attualmente tre delle quattro aree di indagine nell’IQAASL sono monitorate in modo attivo da agenzie governative e di ricerca a valle della Svizzera, con l’utilizzo di metodologie analoghe a quelle illustrate nel presente rapporto. Le associazioni regionali in Svizzera stanno inoltre promuovendo attivamente una rendicontazione e dei protocolli standard con organizzazioni partner nell’UE.\n",
    "\n",
    "QAASL è un progetto di «citizen science» che si avvale esclusivamente di strumenti open-source e condivide i dati su licenza pubblica GNU, permettendo la collaborazione con le parti coinvolte. Alla fine del mandato, fissata in data 31 dicembre 2021, Hammerdirt si assumerà la responsabilità per la gestione del codice e dell’archivio di dati ospitato pubblicamente su Github.\n",
    "\n",
    "Le associazioni che hanno partecipato al progetto IQAASL sono alla ricerca attiva di modalità per integrare il processo di raccolta dei dati e/o i risultati nel rispettivo modello di business. All’interno di molte associazioni regionali sussiste tuttavia una carenza di data scientist e ciò potrebbe tradursi in un allungamento del processo di integrazione e in un’inibizione del tasso di innovazione proprio negli ambiti in cui questo aspetto risulta più necessario.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "seci6 = Paragraph(\"Conclusioni\", style=section_title)\n",
    "\n",
    "p55 = [\n",
    "    \"Su scala nazionale, i risultati dell’indagine IQAASL riflettono una certa stabilità rispetto ai dati rilevati nel 2017 dallo studio SLR. Tuttavia, \",\n",
    "    \"è stata registrata una diminuzione generalizzata della quantità di oggetti correlati al consumo di prodotti alimentari, bevande e tabacco. Gli oggetti \",\n",
    "    \"legati alle infrastrutture e alle plastiche e schiume espanse frammentate non sono diminuiti e alcune ubicazioni hanno eventualmente registrato forti \",\n",
    "    \"incrementi. Le misure di contenimento della pandemia, che limitano i grandi assembramenti all’aperto, hanno probabilmente prodotto un effetto favorevole \",\n",
    "    \"sulla riduzione degli oggetti legati al consumo di prodotti alimentari, bevande e tabacco. I maggiori aumenti per quanto concerne gli oggetti legati \",\n",
    "    \"alle infrastrutture sono stati registrati nei Cantoni Vallese e Vaud, nonché a Brienz: tutte ubicazioni in prossimità dei punti di immissione dei \",\n",
    "    \"fiumi Rodano e Aare.\"\n",
    "]\n",
    "\n",
    "p56 = [\n",
    "    \"La destinazione d’uso del terreno prospiciente a un luogo d’indagine produce un effetto misurabile sul deposito di determinati oggetti. \",\n",
    "    \"Il numero di edifici e infrastrutture presenti è direttamente proporzionale al reperimento di rifiuti correlati al consumo di prodotti \",\n",
    "    \"alimentari e tabacco. Oggetti come plastiche frammentate e teli industriali non presentano invece lo stesso grado di correlazione e \",\n",
    "    \"sono censiti con un’occorrenza percentuale approssimativamente analoga a prescindere dalla destinazione d’uso del terreno, con picchi di frequenza \",\n",
    "    \"in prossimità dei punti di immissione di fiumi/canali.\"\n",
    "]\n",
    "\n",
    "p57 = [\n",
    "    \"Attualmente tre delle quattro aree di indagine nell’IQAASL sono monitorate in modo attivo da agenzie governative e di ricerca a valle \",\n",
    "    \"della Svizzera, con l’utilizzo di metodologie analoghe a quelle illustrate nel presente rapporto. Le associazioni regionali in Svizzera \",\n",
    "    \"stanno inoltre promuovendo attivamente una rendicontazione e dei protocolli standard con organizzazioni partner nell’UE.\",\n",
    "]\n",
    "\n",
    "p58 = [\n",
    "    \"IQAASL è un progetto di «citizen science» che si avvale esclusivamente di strumenti open-source e condivide i dati su licenza pubblica GNU, \",\n",
    "    \"permettendo la collaborazione con le parti coinvolte. Alla fine del mandato, fissata in data 31 dicembre 2021, Hammerdirt si assumerà la \",\n",
    "    \"responsabilità per la gestione del codice e dell’archivio di dati ospitato pubblicamente su Github.\"\n",
    "]\n",
    "\n",
    "\n",
    "p59 = [\n",
    "    \"Le associazioni che hanno partecipato al progetto IQAASL sono alla ricerca attiva di modalità per integrare il processo di raccolta dei dati \",\n",
    "    \"e/o i risultati nel rispettivo modello di business. All’interno di molte associazioni regionali sussiste tuttavia una carenza di data scientist \",\n",
    "    \"e ciò potrebbe tradursi in un allungamento del processo di integrazione e in un’inibizione del tasso di innovazione proprio negli ambiti in cui \",\n",
    "    \"questo aspetto risulta più necessario.\"\n",
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    "### Raccomandazioni\n",
    "#### Monitoraggio e rendicontazione\n",
    "\n",
    "L’aumento dell’efficienza a livello di scambio di dati e di reportistica potrebbe essere ottenuto con effetto immediato definendo un formato di rendicontazione standard. In questo modo, per le amministrazioni regionali sarebbe più agevole comunicare le priorità alle altre parti coinvolte, agevolando le strategie di monitoraggio e contribuendo a definire gli obiettivi di riduzione. In quest’ottica, si potrebbero adottare le seguenti misure:\n",
    "\n",
    "> Sviluppo di una rete di associazioni responsabili per la raccolta e la rendicontazione dei risultati.\n",
    "\n",
    "> Creazione di un formato di rendicontazione standard per agevolare la comunicazione tra amministrazioni a livello comunale, cantonale e regionale/distrettuale, con contestuale miglioramento del coordinamento delle strategie di riduzione a livello regionale e locale.\n",
    "\n",
    "> Definizione del successivo periodo di campionamento e dei relativi intervalli.\n",
    "\n",
    "Coinvolgimento formale del mondo accademico nelle attività di pianificazione, campionamento e analisi. Questo progetto è stato creato con la collaborazione di professori delle seguenti università: Politecnico federale di Zurigo (ETH), Università di Ginevra (UNIGE), Scuola politecnica federale di Losanna (EPFL), Istituto Paul Scherrer (PSI) e Scuola universitaria professionale della Svizzera nordoccidentale (FHNW). La collaborazione di partner universitari è auspicabile per la prosecuzione dello sviluppo di metodologie analitiche. Il Citizen Science Center (ETH) e il Citizen Cyberlab (UNIGE) dispongono dell’esperienza e delle infrastrutture per collegare i progetti di monitoraggio di «citizen science» con le attività di ricerca, dando così vita a un piano di monitoraggio molto flessibile ed efficiente.\n"
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    "    \"L’aumento dell’efficienza a livello di scambio di dati e di reportistica potrebbe essere ottenuto con effetto immediato definendo un formato di rendicontazione \",\n",
    "    \"standard. In questo modo, per le amministrazioni regionali sarebbe più agevole comunicare le priorità alle altre parti coinvolte, agevolando le strategie \",\n",
    "    \"di monitoraggio e contribuendo a definire gli obiettivi di riduzione. In quest’ottica, si potrebbero adottare le seguenti misure:\"\n",
    "]\n",
    "\n",
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    "\n",
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    "    \"Sviluppo di una rete di associazioni responsabili per la raccolta e la rendicontazione dei risultati.\",\n",
    "    \"Creazione di un formato di rendicontazione standard per agevolare la comunicazione tra amministrazioni a livello comunale, cantonale e regionale/distrettuale, con contestuale miglioramento del coordinamento delle strategie di riduzione a livello regionale e locale.\",\n",
    "    \"Definizione del successivo periodo di campionamento e dei relativi intervalli.\"\n",
    "]\n",
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    "\n",
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    "    \"Coinvolgimento formale del mondo accademico nelle attività di pianificazione, campionamento e analisi. Questo progetto è stato creato con la collaborazione di \",\n",
    "    \"professori delle seguenti università: Politecnico federale di Zurigo (ETH), Università di Ginevra (UNIGE), Scuola politecnica federale di Losanna (EPFL), \",\n",
    "    \"Istituto Paul Scherrer (PSI) e Scuola universitaria professionale della Svizzera nordoccidentale (FHNW). La collaborazione di partner universitari è auspicabile per \",\n",
    "    \"la prosecuzione dello sviluppo di metodologie analitiche. Il Citizen Science Center (ETH) e il Citizen Cyberlab (UNIGE) dispongono dell’esperienza e delle infrastrutture \",\n",
    "    \"per collegare i progetti di monitoraggio di «citizen science» con le attività di ricerca, dando così vita a un piano di monitoraggio molto flessibile ed efficiente.\"\n",
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    "### Eliminazione e riduzione\n",
    "\n",
    "Le strategie per l’eliminazione o la riduzione dei rifiuti lungo le rive di specchi e corsi d’acqua dovrebbero innanzitutto considerare le fonti di provenienza degli agenti inquinanti.\n",
    "\n",
    "__Oggetti associati a fattori di uso del suolo__\n",
    "\n",
    "I risultati indicano una correlazione positiva tra il numero di edifici presenti e la quantità di rifiuti correlati al consumo di prodotti alimentari, bevande e tabacco. Si evince che le strategie di riduzione per questi oggetti dovrebbero iniziare nelle aree che presentano alte concentrazioni di infrastrutture in prossimità della linea rivierasca. I risultati dell’area di indagine del Rodano indicano che la conduzione di campagne di sensibilizzazione a livello locale potrebbe produrre effetti positivi. Si veda a riguardo la Figura 1.9 [Laghi e fiumi - deutsch](allsurveys) . Anche se l’eliminazione di tutti gli oggetti correlati al consumo di generi alimentari, bevande e tabacco, sui quali di norma si concentrano le campagne di sensibilizzazione sui rifiuti, contribuirebbe in maniera significativa alla riduzione delle quantità totali, __rimarrebbe comunque il 64 per cento del materiale complessivo__.\n",
    "\n",
    "Altre strategie comunemente diffuse di riduzione delle immissioni di rifiuti comprendono:\n",
    "\n",
    "* una predisposizione adeguata di contenitori per l’immondizia resistenti alle intemperie e agli animali;\n",
    "* dei miglioramenti nei programmi di ritiro dei rifiuti e di pulizia delle strade;\n",
    "* la riduzione dei contenitori di plastica monouso.\n",
    "\n",
    "Molti Paesi hanno già messo in atto procedure di restrizione di elementi mirati. Per esempio, a partire dal 3 luglio 2021 non è più consentito immettere sui mercati degli Stati membri dell’UE articoli di plastica monouso quali piatti, posate, bastoncini per palloncini e bastoncini di ovatta. Reti di contenimento per intercettare i rifiuti nei sistemi di drenaggio dell’acqua piovana prima dell’immissione in laghi e fiumi sono state impiegate con successo in Francia, ma richiedono investimenti in infrastrutture, attrezzature e manodopera.\n"
   ]
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    "    \"Le strategie per l’eliminazione o la riduzione dei rifiuti lungo le rive di specchi e corsi d’acqua dovrebbero innanzitutto considerare le fonti di provenienza degli agenti inquinanti.\"\n",
    "]\n",
    "\n",
    "p64 = [\n",
    "    \"<b>Oggetti associati a fattori di uso del suolo</b>\"\n",
    "]\n",
    "\n",
    "p65 = [\n",
    "    \"I risultati indicano una correlazione positiva tra il numero di edifici presenti e la quantità di rifiuti correlati al consumo di prodotti alimentari, bevande \",\n",
    "    \"e tabacco. Si evince che le strategie di riduzione per questi oggetti dovrebbero iniziare nelle aree che presentano alte concentrazioni di infrastrutture in \",\n",
    "    \"prossimità della linea rivierasca. I risultati dell’area di indagine del Rodano indicano che la conduzione di campagne di sensibilizzazione a livello locale \",\n",
    "    \"potrebbe produrre effetti positivi. Si veda a riguardo la Figura 1.9 \",\n",
    "    '<a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/lakes_rivers.html\" color=\"blue\">Laghi e fiumi -deutsch</a>. ',\n",
    "    \"Anche se l’eliminazione di tutti gli oggetti correlati al \",\n",
    "    \"consumo di generi alimentari, bevande e tabacco, sui quali di norma si concentrano le campagne di sensibilizzazione sui rifiuti, contribuirebbe in maniera \",\n",
    "    \"significativa alla riduzione delle quantità totali, <b>rimarrebbe comunque il 64 per cento del materiale complessivo</b>.\"\n",
    "]\n",
    "\n",
    "p66 = [\n",
    "    \"Altre strategie comunemente diffuse di riduzione delle immissioni di rifiuti comprendono:\"\n",
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    "\n",
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    "\n",
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    "    \"una predisposizione adeguata di contenitori per l’immondizia resistenti alle intemperie e agli animali\",\n",
    "    \"dei miglioramenti nei programmi di ritiro dei rifiuti e di pulizia delle strade;\",\n",
    "    \"la riduzione dei contenitori di plastica monouso.\"\n",
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    "\n",
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    "Oggetti non associati all’uso del suolo\n",
    "\n",
    "Gli oggetti che non presentano una correlazione positiva con l’uso del suolo richiedono un intervento coordinato almeno a livello di lago o di fiume, nonché per tutte le località a monte delle ubicazioni di rilevamento previste. Il novero degli oggetti più comunemente diffusi comprende:\n",
    "\n",
    "* plastiche frammentate\n",
    "* schiume espanse frammentate\n",
    "* plastiche da costruzione\n",
    "* granulati di plastica di provenienza industriale\n",
    "* bastoncini di ovatta\n",
    "* teli industriali\n",
    "\n",
    "Questi oggetti costituiscono il 40 per cento di tutto il materiale censito. Molti hanno applicazioni industriali e di igiene personale, tipicamente non associate ad attività rivierasche. L’intensificazione delle campagne di sensibilizzazione mirate alla prevenzione di perdite di materiale da settori specifici può ridurre l’occorrenza di oggetti come i granulati industriali usati per lo stampaggio a iniezione di articoli in plastica. Alcuni oggetti come i bastoncini di ovatta e altre plastiche scaricate dal water vengono immessi nei laghi e nei fiumi attraverso gli impianti di trattamento delle acque.\n",
    "\n",
    "Le strategie di riduzione possono comprendere:\n",
    "\n",
    "* il miglioramento degli impianti di trattamento delle acque reflue per ridurre gli sversamenti di materiale\n",
    "* l’attuazione di campagne di sensibilizzazione per oggetti o prodotti specifici\n",
    "* l’attuazione di campagne di sensibilizzazione per settori produttivi specifici\n",
    "\n",
    "Le strategie di eliminazione o di riduzione richiedono interventi coordinati a livello regionale, al fine di coinvolgere le comunità a monte delle ubicazioni di indagine.\n",
    "\n",
    "Una minore dipendenza da plastiche monouso, plastiche espanse, plastiche da costruzione come pure da fogli e pellicole industriali ridurrebbe probabilmente in ampia misura i volumi di questi inquinanti che finiscono nell’ambiente. Il basso costo e la natura «usa e getta» di questi materiali si sono tradotti in un’abbondanza e una dipendenza sempre maggiori in tutti i settori. Le caratteristiche di leggerezza e degradabilità di questi materiali agevolano la frammentazione e la dispersione nei sistemi naturali, soprattutto in caso di esposizione esterna prolungata. Gli inquinanti plastici costituiscono un problema globale e un numero sempre maggiore di Paesi sta portando avanti un processo di riduzione della dipendenza dalle plastiche monouso e dalle plastiche espanse come il polistirolo."
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    "    \"Gli oggetti che non presentano una correlazione positiva con l’uso del suolo richiedono un intervento coordinato almeno a livello di lago o di fiume, \",\n",
    "    \"nonché per tutte le località a monte delle ubicazioni di rilevamento previste. Il novero degli oggetti più comunemente diffusi comprende:\"\n",
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    "\n",
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    "    \"plastiche da costruzione\",\n",
    "    \"granulati di plastica di provenienza industriale\",\n",
    "    \"bastoncini di ovatta\",\n",
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    "\n",
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    "    \"Questi oggetti costituiscono il 40 per cento di tutto il materiale censito. Molti hanno applicazioni industriali e di igiene personale, tipicamente \",\n",
    "    \"non associate ad attività rivierasche. L’intensificazione delle campagne di sensibilizzazione mirate alla prevenzione di perdite di materiale da \",\n",
    "    \"settori specifici può ridurre l’occorrenza di oggetti come i granulati industriali usati per lo stampaggio a iniezione di articoli in plastica. \",\n",
    "    \"Alcuni oggetti come i bastoncini di ovatta e altre plastiche scaricate dal water vengono immessi nei laghi e nei fiumi attraverso gli impianti di \",\n",
    "    \"trattamento delle acque.\"\n",
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    "    \"l’attuazione di campagne di sensibilizzazione per oggetti o prodotti specifici\",\n",
    "    \"l’attuazione di campagne di sensibilizzazione per settori produttivi specifici\"\n",
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    "\n",
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    "    \"ampia misura i volumi di questi inquinanti che finiscono nell’ambiente. Il basso costo e la natura «usa e getta» di questi materiali si sono tradotti in \",\n",
    "    \"un’abbondanza e una dipendenza sempre maggiori in tutti i settori. Le caratteristiche di leggerezza e degradabilità di questi materiali agevolano la \",\n",
    "    \"frammentazione e la dispersione nei sistemi naturali, soprattutto in caso di esposizione esterna prolungata. Gli inquinanti plastici costituiscono un \",\n",
    "    \"problema globale e un numero sempre maggiore di Paesi sta portando avanti un processo di riduzione della dipendenza dalle plastiche monouso e dalle plastiche \",\n",
    "    \"espanse come il polistirolo.\"\n",
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    "(fr-esum)=\n",
    "## Résumé\n",
    "\n",
    "| [Italiano](it-esum) | [Deutsch](kurzfassung) |\n",
    "\n",
    "L’IQAASL (Identification, quantification and analysis of anthropogenic Swiss litter) est un projet mandaté par l’Office fédéral de l’environnement (OFEV) dans le but de recueillir des données sur les déchets visibles sur les rivages des lacs et des cours d’eau suisses. Tous les déchets collectés ont été identifiés à l’aide de techniques d’inventaire spécifiques. Le projet a été élargi pour inclure 20 sites supplémentaires situés dans les Alpes et le Jura. Au total, 406 inventaires ont été réalisés sur 163 sites dans 95 communes.\n",
    "\n",
    "Ce rapport résume et analyse les inventaires effectués en Suisse de mars 2020 à août 2021 et explicite les méthodes de travail utilisées. Cette phase d’échantillonnage se chevauche avec la période d’enquête du Swiss Litter Report (SLR) {cite}`slr`, qui s’est déroulée d’avril 2017 à mars 2018. Le SLR est le premier projet d’envergure nationale dans le cadre duquel le protocole standard décrit dans le Guide sur la surveillance des déchets marins dans les mers européennes {cite}`mlwguidance`, ou toute autre méthode comparable, a été utilisé. Ce chevauchement permet la comparaison des résultats de la présente étude avec celle du SLR.\n",
    "\n",
    "```{card}\n",
    ":class-card: sd-text-black\n",
    ":img-top: resources/maps/esummary_mapfr.jpeg\n",
    ":margin: auto\n",
    "+++\n",
    "__Figure 1:__ *Cartes des zones de relevé étudiées entre mars 2020 et août 2021. Les sites signalés en rouge correspondent aux inventaires réalisés sur les plages de lacs et de cours d’eau, ceux en violet aux relevés effectués dans les Alpes et le Jura.*\n",
    "``` \n",
    "\n",
    "### Lacs et cours d’eau\n",
    "\n",
    "Les lacs et les cours d’eau ont fait l’objet de prélèvements de mars 2020 à mai 2021. Au total, ce sont 54 744 objets qui ont été collectés et classés dans le cadre de 386 inventaires. Afin de permettre une analyse régionale, les sites ont été attribués à des périmètres d’étude définis en fonction des cours d’eau auxquels ils se rattachent (Aar, Linth/Limmat, Rhône et Tessin). Les inventaires ont été réalisés sur 143 sites répartis sur 77 communes. Au total, la surface étudiée représente une distance linéaire de 20 km et une superficie de 9 hectares pour une population de 1,7 million d’habitants.\n",
    "\n",
    "La plupart des relevés (331 inventaires) ont été effectués sur les rives des lacs, qui offrent tout au long de l’année un accès plus sûr et plus constant que les cours d’eau. Ils constituent par ailleurs de vastes étendues à faible débit qui reçoivent l’apport de multiples cours d’eau et systèmes de drainage, ce qui en fait des endroits idéaux pour évaluer la variété des objets prévalant dans les plans d’eau et autour de ceux-ci.\n",
    "\n",
    "Au total, 316 inventaires ont été analysés autour de 7 lacs principaux alimentés par 3 grands bassins fluviaux. Dans ce cadre, 20 sites ont fait l’objet de prélèvements mensuels sur une période de 12 mois – à l’exception du lac Majeur, où seuls des prélèvements trimestriels ont été réalisés. Des relevés ont également été effectués sur les berges des lacs de Lugano, des Quatre-Cantons, de Brienz et de Zoug. En outre, 55 inventaires ont été menés le long de 16 cours d’eau.\n",
    "\n",
    "Sites au bord des lacs ayant fait l’objet de prélèvements mensuels :\n",
    "\n",
    "* Périmètre d’étude de l’Aar\n",
    "  * Lac de Thoune : Spiez, Unterseen\n",
    "  * Lac de Bienne : Bienne, Vinelz\n",
    "  * Lac de Neuchâtel : Neuchâtel, Cheyres-Châbles, Yverdon-les-Bains\n",
    "* Périmètre d’étude de la Linth/Limmat\n",
    "  * Lac de Zurich : Zurich, Küsnacht (ZH), Rapperswil-Jona, Richterswil\n",
    "  * Lac de Walenstadt : Walenstadt, Weesen\n",
    "* Périmètre d’étude du Rhône\n",
    "  * Lac Léman : Vevey, Saint-Gingolph, Genève, Préverenges, La Tour-de-Peilz\n",
    "* Périmètre d’étude du Tessin\n",
    "  * Lac Majeur : Ascona, Gambarogno (prélèvements trimestriels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "p41 = makeAParagraph([\"Périmètre d’étude de l’Aar\"], style=bold_block)\n",
    "l2i = [\n",
    "    \"Lac de Thoune : Spiez, Unterseen\",\n",
    "    \"Lac de Bienne : Bienne, Vinelz\",\n",
    "    \"Lac de Neuchâtel : Neuchâtel, Cheyres-Châbles, Yverdon-les-Bains\"\n",
    "]\n",
    "l2i = makeAList(l2i)\n",
    "p42 = makeAParagraph([\"Périmètre d’étude de la Linth/Limmat\"], style=bold_block)\n",
    "l3i = [\n",
    "    \"Lac de Zurich : Zurich, Küsnacht (ZH), Rapperswil-Jona, Richterswil\",\n",
    "    \"Lac de Walenstadt : Walenstadt, Weesen\"\n",
    "]\n",
    "l3i=makeAList(l3i)\n",
    "p43 = makeAParagraph([\"Périmètre d’étude du Rhône\"], style=bold_block)\n",
    "\n",
    "l4i = [\n",
    "    \"Lac Léman : Vevey, Saint-Gingolph, Genève, Préverenges, La Tour-de-Peilz\"\n",
    "]\n",
    "l4i = makeAList(l4i)\n",
    "p44 = makeAParagraph([\"Périmètre d’étude du Tessin\"], style=bold_block)\n",
    "l5i = [\"Lac Majeur : Ascona, Gambarogno (prélèvements trimestriels)\"]\n",
    "l5i = makeAList(l5i)\n",
    "\n",
    "seci = Paragraph(\"Résumé\", style=section_title)\n",
    "\n",
    "p36 = [\n",
    "    \"L’IQAASL (Identification, quantification and analysis of anthropogenic Swiss litter) est un projet mandaté par \",\n",
    "    \"l’Office fédéral de l’environnement (OFEV) dans le but de recueillir des données sur les déchets visibles sur les \",\n",
    "    \"rivages des lacs et des cours d’eau suisses. Tous les déchets collectés ont été identifiés à l’aide de techniques \",\n",
    "    \"d’inventaire spécifiques. Le projet a été élargi pour inclure 20 sites supplémentaires situés dans les Alpes et le \",\n",
    "    \"Jura. Au total, 406 inventaires ont été réalisés sur 163 sites dans 95 communes.\"   \n",
    "]\n",
    "\n",
    "p37 = [\n",
    "    \"Ce rapport résume et analyse les inventaires effectués en Suisse de mars 2020 à août 2021 et explicite les méthodes de travail utilisées. \",\n",
    "    \"Cette phase d’échantillonnage se chevauche avec la période d’enquête du Swiss Litter Report (SLR) \",\n",
    "    '<a href=\"#Bla18\" color=\"blue\">(SLR)</a>, qui s’est déroulée d’avril 2017 à mars 2018. Le SLR est le ',\n",
    "    'premier projet d’envergure nationale dans le cadre duquel le protocole standard décrit dans le Guide sur la surveillance des ',\n",
    "    'déchets marins dans les mers européennes <a href=\"#Han13\" color=\"blue\">(Han13)</a>, ',\n",
    "    'ou toute autre méthode comparable, a été utilisé. Ce chevauchement permet la comparaison des résultats de la présente étude avec celle du SLR.'\n",
    "]\n",
    "seci2 = Paragraph(\"Lacs et cours d’eau\", style=section_title)\n",
    "\n",
    "p36_37 = sectionParagraphs([p36, p37], smallspace=smallest_space)\n",
    "\n",
    "p38 = [\n",
    "    \"Les lacs et les cours d’eau ont fait l’objet de prélèvements de mars 2020 à mai 2021. Au total, ce sont \",\n",
    "    \"54 744 objets qui ont été collectés et classés dans le cadre de 386 inventaires. Afin de permettre une analyse \",\n",
    "    \"régionale, les sites ont été attribués à des périmètres d’étude définis en fonction des cours d’eau auxquels ils \",\n",
    "    \"se rattachent (Aar, Linth/Limmat, Rhône et Tessin). Les inventaires ont été réalisés sur 143 sites répartis sur 77 \",\n",
    "    \"communes. Au total, la surface étudiée représente une distance linéaire de 20 km et une superficie de 9 hectares \",\n",
    "    \"pour une population de 1,7 million d’habitants.\"\n",
    "]\n",
    "\n",
    "p39 = [\n",
    "    \"La plupart des relevés (331 inventaires) ont été effectués sur les rives des lacs, qui offrent tout au long de l’année \",\n",
    "    \"un accès plus sûr et plus constant que les cours d’eau. Ils constituent par ailleurs de vastes étendues à faible débit \",\n",
    "    \"qui reçoivent l’apport de multiples cours d’eau et systèmes de drainage, ce qui en fait des endroits idéaux pour évaluer \",\n",
    "    \"la variété des objets prévalant dans les plans d’eau et autour de ceux-ci.\"\n",
    "]\n",
    "p40 = [\n",
    "    \"Au total, 316 inventaires ont été analysés autour de 7 lacs principaux alimentés par 3 grands bassins fluviaux. Dans ce cadre, \",\n",
    "    \"20 sites ont fait l’objet de prélèvements mensuels sur une période de 12 mois – à l’exception du lac Majeur, où seuls des \",\n",
    "    \"prélèvements trimestriels ont été réalisés. Des relevés ont également été effectués sur les berges des lacs de Lugano, des \",\n",
    "    \"Quatre-Cantons, de Brienz et de Zoug. En outre, 55 inventaires ont été menés le long de 16 cours d’eau.\"\n",
    "]\n",
    "\n",
    "p38_40 = sectionParagraphs([p38, p39, p40], smallspace=smallest_space)\n",
    "\n",
    "\n",
    "\n",
    "o_w, o_h = convertPixelToCm(\"resources/maps/esummary_map.jpeg\")\n",
    "\n",
    "f1icap = [\n",
    "    \"Cartes des zones de relevé étudiées entre mars 2020 et août 2021. Les sites signalés en rouge correspondent \",\n",
    "    \"aux inventaires réalisés sur les plages de lacs et de cours d’eau, ceux en violet aux relevés effectués dans \",\n",
    "    \"les Alpes et le Jura.\"\n",
    "]\n",
    "\n",
    "f1icap = makeAParagraph(f1icap, style=caption_style)\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\":\"resources/maps/esummary_mapfr.jpeg\",\n",
    "    \"caption\": f1icap, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\":1.5,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "f1i = figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "new_components = [\n",
    "    PageBreak(),\n",
    "    seci,\n",
    "    small_space,\n",
    "    *p36_37,\n",
    "    f1i,\n",
    "    small_space,\n",
    "    seci2,\n",
    "    small_space,\n",
    "    *p38_40,\n",
    "    smallest_space,\n",
    "    p41,\n",
    "    smallest_space,\n",
    "    l2i,\n",
    "    smallest_space,\n",
    "    p42,\n",
    "    smallest_space,\n",
    "    l3i,\n",
    "    smallest_space,\n",
    "    p43,\n",
    "    smallest_space,\n",
    "    l4i,\n",
    "    smallest_space,\n",
    "    p44,\n",
    "    smallest_space,\n",
    "    l5i    \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Résultats médians\n",
    "\n",
    "Les résultats sont exprimés en nombre d’éléments (pieces) par transect de 100 m (p/100m). Le résultat médian de l’ensemble des données atteint approximativement 189p/100m. La valeur maximale enregistrée est de 6617p/100m (périmètre d’étude du Rhône) et la valeur minimale de 2p/100m (périmètre d’étude de l’Aar). C’est dans la région du Rhône qu’a été enregistrée la valeur médiane la plus élevée (442p/100m). Ce chiffre s’explique en partie par le nombre important de sites de prélèvement en milieu urbain qu’elle abrite par rapport aux autres zones étudiées et par les dépôts de plastiques fragmentés et de mousses expansées qui s’accumulent à l’embouchure du Rhône, dans la partie supérieure du lac Léman.\n",
    "\n",
    "Une valeur de référence a été calculée en excluant les échantillonnages effectués sur des rivages d’une longueur de moins de 10 m ainsi que les objets d’une taille inférieure à 2,5 cm. Cette méthode, décrite dans le document EU Marine Beach Litter Baselines {cite}`eubaselines`, a été utilisée en 2015 et 2016 pour calculer les valeurs de référence et les valeurs seuils pour toutes les plages européennes et a abouti à une valeur médiane de 131p/100m. Cette valeur de référence européenne se situe en dehors de l’intervalle de confiance (IC) de 147 – 213p/100m qui a été établi en utilisant les données de l’IQAASL.\n",
    "\n",
    "En Suisse, les relevés étaient globalement de plus faible envergure que ceux réalisés en milieu marin et concernaient des endroits qui seraient considérés comme urbains dans la plupart des circonstances. Jusqu’à présent, la surveillance des lacs et des cours d’eau en amont des régions côtières ne s’est pas généralisée sur le continent européen. Un groupe d’associations suisses et françaises s’efforce toutefois d’établir un protocole commun de surveillance et d’échange de données pour le bassin du Rhône. En outre, l’Institut de recherche de l’Université de Wageningen (NL) a commencé à analyser les données recueillies dans le delta de la Meuse et du Rhin en utilisant des protocoles similaires à ceux de l’IQAASL. {cite}`meuserhine`\n",
    "\n",
    "\n",
    "### Objets les plus courants\n",
    "\n",
    "Ce terme est appliqué aux objets qui ont été identifiés dans au moins 50 % des relevés et/ou qui font partie des dix objets les plus représentés en termes de quantité. En tant que sous-groupe, les objets les plus courants représentent 68 % de tous les objets identifiés durant la période d’inventaire. Parmi eux, 27 % sont des déchets issus de la consommation d’aliments, de boissons et de tabac et 24 % des déchets liés aux infrastructures et à l’agriculture.\n",
    "\n",
    "L’occurrence des déchets issus de la consommation d’aliments, de boissons et de tabac est plus importante dans les zones où les sols sont surtout affectés aux constructions et aux infrastructures. L’observation inverse se vérifie dans celles où un pourcentage plus élevé des terres est dédié aux surfaces forestières ou à l’exploitation agricole. En ce qui concerne les matériaux infrastructurels et les plastiques fragmentés, il est à noter que des volumes similaires ont été identifiés dans tous les périmètres d’étude, indifféremment de l’utilisation des sols autour des zones de relevé."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "sseci1 = Paragraph(\"Résultats médians\", style=subsection_title)\n",
    "\n",
    "p45 = [\n",
    "    \"Les résultats sont exprimés en nombre d’éléments (pieces) par transect de 100 m (p/100m). Le résultat médian de l’ensemble des \",\n",
    "    \"données atteint approximativement 189p/100m. La valeur maximale enregistrée est de 6617p/100m (périmètre d’étude du Rhône) et \",\n",
    "    \"la valeur minimale de 2p/100m (périmètre d’étude de l’Aar). C’est dans la région du Rhône qu’a été enregistrée la valeur médiane \",\n",
    "    \"la plus élevée (442p/100m). Ce chiffre s’explique en partie par le nombre important de sites de prélèvement en milieu urbain qu’elle \",\n",
    "    \"abrite par rapport aux autres zones étudiées et par les dépôts de plastiques fragmentés et de mousses expansées qui s’accumulent à \",\n",
    "    \"l’embouchure du Rhône, dans la partie supérieure du lac Léman.\"\n",
    "]\n",
    "\n",
    "p46 = [\n",
    "    \"Une valeur de référence a été calculée en excluant les échantillonnages effectués sur des rivages d’une longueur de moins de 10 m ainsi \",\n",
    "    \"que les objets d’une taille inférieure à 2,5 cm. Cette méthode, décrite dans le document EU Marine Beach Litter Baselines \",\n",
    "    '<a href=\"#HG19\" color=\"blue\">(HG19)</a>, a été utilisée en 2015 et 2016 pour calculer les valeurs de référence et les valeurs ',\n",
    "    \"seuils pour toutes les plages européennes et a abouti à une valeur médiane de 131p/100m. Cette valeur de référence européenne se \",\n",
    "    \"situe en dehors de l’intervalle de confiance (IC) de 147 – 213p/100m qui a été établi en utilisant les données de l’IQAASL.\"\n",
    "]\n",
    "\n",
    "p47 = [ \n",
    "    \"En Suisse, les relevés étaient globalement de plus faible envergure que ceux réalisés en milieu marin et concernaient des endroits \",\n",
    "    \"qui seraient considérés comme urbains dans la plupart des circonstances. Jusqu’à présent, la surveillance des lacs et des cours d’eau \",\n",
    "    \"en amont des régions côtières ne s’est pas généralisée sur le continent européen. Un groupe d’associations suisses et françaises s’efforce \",\n",
    "    \"toutefois d’établir un protocole commun de surveillance et d’échange de données pour le bassin du Rhône. En outre, l’Institut de recherche \",\n",
    "    \"de l’Université de Wageningen (NL) a commencé à analyser les données recueillies dans le delta de la Meuse et du Rhin en utilisant des \",\n",
    "    'protocoles similaires à ceux de l’IQAASL.  <a href=\"#vEV21\" color=\"blue\">(vEV21)</a>'\n",
    "]\n",
    "\n",
    "p45_47 = sectionParagraphs([p45, p46, p47], smallspace=smallest_space)\n",
    "new_components = [\n",
    "    small_space,\n",
    "    sseci1,\n",
    "    small_space,\n",
    "    *p45_47,\n",
    "    \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div style='display:flex; align-content:center; flex-flow:column no-wrap; flex-direction:column;  box-shadow: 0px 0px 3px #d2d9db'> \n",
       "       <div style='padding:20px;width:80%;margin:auto'>\n",
       "            <style type=\"text/css\">\n",
       "#T_9e43c tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_9e43c tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_9e43c tr {\n",
       "  font-size: 12px;\n",
       "}\n",
       "#T_9e43c th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9e43c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9e43c_level0_col0\" class=\"col_heading level0 col0\" >Quantité</th>\n",
       "      <th id=\"T_9e43c_level0_col1\" class=\"col_heading level0 col1\" >% du total</th>\n",
       "      <th id=\"T_9e43c_level0_col2\" class=\"col_heading level0 col2\" >taux d'occurrence</th>\n",
       "      <th id=\"T_9e43c_level0_col3\" class=\"col_heading level0 col3\" >p/100 m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row0\" class=\"row_heading level0 row0\" >Filtri di sigarette</th>\n",
       "      <td id=\"T_9e43c_row0_col0\" class=\"data row0 col0\" >8 485</td>\n",
       "      <td id=\"T_9e43c_row0_col1\" class=\"data row0 col1\" >15%</td>\n",
       "      <td id=\"T_9e43c_row0_col2\" class=\"data row0 col2\" >87%</td>\n",
       "      <td id=\"T_9e43c_row0_col3\" class=\"data row0 col3\" >20,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row1\" class=\"row_heading level0 row1\" >Plastica frammentata</th>\n",
       "      <td id=\"T_9e43c_row1_col0\" class=\"data row1 col0\" >7 400</td>\n",
       "      <td id=\"T_9e43c_row1_col1\" class=\"data row1 col1\" >13%</td>\n",
       "      <td id=\"T_9e43c_row1_col2\" class=\"data row1 col2\" >86%</td>\n",
       "      <td id=\"T_9e43c_row1_col3\" class=\"data row1 col3\" >18,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row2\" class=\"row_heading level0 row2\" >Polistirolo espanso</th>\n",
       "      <td id=\"T_9e43c_row2_col0\" class=\"data row2 col0\" >5 563</td>\n",
       "      <td id=\"T_9e43c_row2_col1\" class=\"data row2 col1\" >10%</td>\n",
       "      <td id=\"T_9e43c_row2_col2\" class=\"data row2 col2\" >68%</td>\n",
       "      <td id=\"T_9e43c_row2_col3\" class=\"data row2 col3\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row3\" class=\"row_heading level0 row3\" >Incarti di cibo; caramelle, snack</th>\n",
       "      <td id=\"T_9e43c_row3_col0\" class=\"data row3 col0\" >3 325</td>\n",
       "      <td id=\"T_9e43c_row3_col1\" class=\"data row3 col1\" >6%</td>\n",
       "      <td id=\"T_9e43c_row3_col2\" class=\"data row3 col2\" >85%</td>\n",
       "      <td id=\"T_9e43c_row3_col3\" class=\"data row3 col3\" >9,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row4\" class=\"row_heading level0 row4\" >Telo industriale</th>\n",
       "      <td id=\"T_9e43c_row4_col0\" class=\"data row4 col0\" >2 534</td>\n",
       "      <td id=\"T_9e43c_row4_col1\" class=\"data row4 col1\" >4%</td>\n",
       "      <td id=\"T_9e43c_row4_col2\" class=\"data row4 col2\" >69%</td>\n",
       "      <td id=\"T_9e43c_row4_col3\" class=\"data row4 col3\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row5\" class=\"row_heading level0 row5\" >Bottiglie per bevande in vetro, pezzi</th>\n",
       "      <td id=\"T_9e43c_row5_col0\" class=\"data row5 col0\" >2 136</td>\n",
       "      <td id=\"T_9e43c_row5_col1\" class=\"data row5 col1\" >3%</td>\n",
       "      <td id=\"T_9e43c_row5_col2\" class=\"data row5 col2\" >65%</td>\n",
       "      <td id=\"T_9e43c_row5_col3\" class=\"data row5 col3\" >3,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row6\" class=\"row_heading level0 row6\" >Pellet industriali (nurdles)</th>\n",
       "      <td id=\"T_9e43c_row6_col0\" class=\"data row6 col0\" >1 968</td>\n",
       "      <td id=\"T_9e43c_row6_col1\" class=\"data row6 col1\" >3%</td>\n",
       "      <td id=\"T_9e43c_row6_col2\" class=\"data row6 col2\" >30%</td>\n",
       "      <td id=\"T_9e43c_row6_col3\" class=\"data row6 col3\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row7\" class=\"row_heading level0 row7\" >Schiume isolanti</th>\n",
       "      <td id=\"T_9e43c_row7_col0\" class=\"data row7 col0\" >1 702</td>\n",
       "      <td id=\"T_9e43c_row7_col1\" class=\"data row7 col1\" >3%</td>\n",
       "      <td id=\"T_9e43c_row7_col2\" class=\"data row7 col2\" >53%</td>\n",
       "      <td id=\"T_9e43c_row7_col3\" class=\"data row7 col3\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row8\" class=\"row_heading level0 row8\" >Bastoncini di cotton fioc/ tampone</th>\n",
       "      <td id=\"T_9e43c_row8_col0\" class=\"data row8 col0\" >1 406</td>\n",
       "      <td id=\"T_9e43c_row8_col1\" class=\"data row8 col1\" >2%</td>\n",
       "      <td id=\"T_9e43c_row8_col2\" class=\"data row8 col2\" >50%</td>\n",
       "      <td id=\"T_9e43c_row8_col3\" class=\"data row8 col3\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row9\" class=\"row_heading level0 row9\" >Schiume espanse < 5mm</th>\n",
       "      <td id=\"T_9e43c_row9_col0\" class=\"data row9 col0\" >1 209</td>\n",
       "      <td id=\"T_9e43c_row9_col1\" class=\"data row9 col1\" >2%</td>\n",
       "      <td id=\"T_9e43c_row9_col2\" class=\"data row9 col2\" >25%</td>\n",
       "      <td id=\"T_9e43c_row9_col3\" class=\"data row9 col3\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row10\" class=\"row_heading level0 row10\" >Rifiuti plastici da costruzione</th>\n",
       "      <td id=\"T_9e43c_row10_col0\" class=\"data row10 col0\" >992</td>\n",
       "      <td id=\"T_9e43c_row10_col1\" class=\"data row10 col1\" >1%</td>\n",
       "      <td id=\"T_9e43c_row10_col2\" class=\"data row10 col2\" >52%</td>\n",
       "      <td id=\"T_9e43c_row10_col3\" class=\"data row10 col3\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e43c_level0_row11\" class=\"row_heading level0 row11\" >Tappi e coperchi di bottiglia in metallo</th>\n",
       "      <td id=\"T_9e43c_row11_col0\" class=\"data row11 col0\" >700</td>\n",
       "      <td id=\"T_9e43c_row11_col1\" class=\"data row11 col1\" >1%</td>\n",
       "      <td id=\"T_9e43c_row11_col2\" class=\"data row11 col2\" >52%</td>\n",
       "      <td id=\"T_9e43c_row11_col3\" class=\"data row11 col3\" >1,0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "        </div>\n",
       "        <div style='border-top: 1px solid #d2d9db;'>\n",
       "            <p style='margin:10px'><i><b>Figure 2:</b> Total des relevés de tous les lacs et cours d’eau : objets les plus courants identifiés de mars 2020 à mai 2021. Le taux d’occurrence (celui-ci est dénommé comme « taux d’échec » dans le présent rapport) est le rapport entre le nombre de fois où un objet a été identifié au moins une fois et le nombre total de relevés réalisés. La quantité indique le nombre total d’objets identifiés. La valeur médiane correspond au nombre d’éléments collectés par transect de 100 m (p/100m). À titre d’exemple, 8485 filtres de cigarettes ont au total été identifiés dans 87 % des relevés, ce qui représente 15 % de l’ensemble des objets collectés et correspond à une valeur médiane de 20 filtres de cigarettes par 100 m de rivage</i></p>\n",
       "        </div>\n",
       "    </div>\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sseci2 = Paragraph(\"Objets les plus courants\", style=subsection_title)\n",
    "\n",
    "p48 = [\n",
    "    \"Ce terme est appliqué aux objets qui ont été identifiés dans au moins 50 % des relevés et/ou qui font partie des dix objets \",\n",
    "    \"les plus représentés en termes de quantité. En tant que sous-groupe, les objets les plus courants représentent 68 % de tous \",\n",
    "    \"les objets identifiés durant la période d’inventaire. Parmi eux, 27 % sont des déchets issus de la consommation d’aliments, \",\n",
    "    \"de boissons et de tabac et 24 % des déchets liés aux infrastructures et à l’agriculture.\"\n",
    "]\n",
    "\n",
    "p49 = [\n",
    "    \"L’occurrence des déchets issus de la consommation d’aliments, de boissons et de tabac est plus importante dans les zones où les \",\n",
    "    \"sols sont surtout affectés aux constructions et aux infrastructures. L’observation inverse se vérifie dans celles où un pourcentage \",\n",
    "    \"plus élevé des terres est dédié aux surfaces forestières ou à l’exploitation agricole. En ce qui concerne les matériaux infrastructurels \",\n",
    "    \"et les plastiques fragmentés, il est à noter que des volumes similaires ont été identifiés dans tous les périmètres d’étude, \",\n",
    "    \"indifféremment de l’utilisation des sols autour des zones de relevé.\"\n",
    "]\n",
    "\n",
    "p48_49 = sectionParagraphs([p48, p49], smallspace=smallest_space)\n",
    "\n",
    "new_components = [\n",
    "    PageBreak(),\n",
    "    sseci2,\n",
    "    small_space,\n",
    "    *p48_49,\n",
    "    \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "\n",
    "# the most common objects results\n",
    "most_common_display = fdx.most_common\n",
    "\n",
    "# language appropriate columns\n",
    "cols_to_use = featuredata.most_common_objects_table_de\n",
    "cols_to_use.update({unit_label:unit_label})\n",
    "\n",
    "# data for display\n",
    "most_common_display.rename(columns=cols_to_use, inplace=True)\n",
    "most_common_display = most_common_display[cols_to_use.values()].copy()\n",
    "most_common_display = most_common_display.set_index(\"Objekte\", drop=True)\n",
    "\n",
    "# .pdf output\n",
    "data = most_common_display.copy()\n",
    "data[\"Anteil\"] = data[\"Anteil\"].map(lambda x: f\"{int(x)}%\")\n",
    "data['Objekte (St.)'] = data['Objekte (St.)'].map(lambda x:featuredata.thousandsSeparator(x, language))\n",
    "data['Häufigkeitsrate'] = data['Häufigkeitsrate'].map(lambda x: f\"{x}%\")\n",
    "data[unit_label] = data[unit_label].map(lambda x: featuredata.replaceDecimal(round(x,1)))\n",
    "new_cols = {'Objekte (St.)':\"Quantité\", \"Anteil\":\"% du total\", 'Häufigkeitsrate':\"taux d'occurrence\"}\n",
    "data.rename(columns=new_cols, inplace=True)\n",
    "\n",
    "data[\"Index\"] = data.index.map(lambda x: ital[x])\n",
    "data.set_index(\"Index\", drop=True, inplace=True)\n",
    "\n",
    "# save a copy for the frnech and italian versions\n",
    "esum_most_common = data.copy()\n",
    "\n",
    "# make caption\n",
    "# get percent of total to make the caption string\n",
    "mc_caption_string = [\n",
    "    \"<b>Figure 2:</b> Total des relevés de tous les lacs et cours d’eau : objets les plus courants identifiés de mars 2020 à mai 2021. Le \",\n",
    "    \"taux d’occurrence (celui-ci est dénommé comme « taux d’échec » dans le présent rapport) est le rapport entre le nombre de fois où un \",\n",
    "    \"objet a été identifié au moins une fois et le nombre total de relevés réalisés. La quantité indique le nombre total d’objets identifiés. \",\n",
    "    \"La valeur médiane correspond au nombre d’éléments collectés par transect de 100 m (p/100m). À titre d’exemple, 8485 filtres de cigarettes \",\n",
    "    \"ont au total été identifiés dans 87 % des relevés, ce qui représente 15 % de l’ensemble des objets collectés et correspond à une valeur \",\n",
    "    \"médiane de 20 filtres de cigarettes par 100 m de rivage\"\n",
    "]\n",
    "   \n",
    "\n",
    "colWidths = [5.1*cm, 2.2*cm, 2*cm, 2.8*cm, 2*cm]\n",
    "\n",
    "d_chart = aSingleStyledTable(data, colWidths=colWidths)\n",
    "d_capt = makeAParagraph(mc_caption_string, caption_style)\n",
    "mc_table = tableAndCaption(d_chart, d_capt, colWidths)\n",
    "\n",
    "new_components = [\n",
    "    smallest_space,\n",
    "    mc_table,\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "most_common_display[\"Index\"] = most_common_display.index.map(lambda x: ital[x])\n",
    "most_common_display.set_index(\"Index\", inplace=True, drop=True)\n",
    "most_common_display.index.name = None\n",
    "most_common_display.columns.name = None\n",
    "\n",
    "most_common_display.rename(columns=new_cols, inplace=True)\n",
    "\n",
    "# set pandas display\n",
    "aformatter = {\n",
    "    \"% du total\":lambda x: f\"{int(x)}%\",\n",
    "    f\"{unit_label}\": lambda x: featuredata.replaceDecimal(x, \"de\"),\n",
    "    \"taux d'occurrence\": lambda x: f\"{int(x)}%\",   \n",
    "    \"Quantité\": lambda x: featuredata.thousandsSeparator(int(x), \"de\")\n",
    "}\n",
    "\n",
    "mcdic = most_common_display.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "IPython.display.display(HTML(cardFigure(html_table=mcdic.to_html(), table_caption= \"\".join(mc_caption_string))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Des quantités importantes de granulés plastiques industriels (GPI) et de mousses expansées < 5 mm ont été retrouvées, mais ceci seulement sur certains sites spécifiques. Ces déchets sont présents dans moins de 50 % des relevés, ce qui se traduit par une valeur médiane égale à zéro. Si les GPI et les mousses expansées entrent tous les deux dans la catégorie des microplastiques, leur utilisation, leur origine et leur taux d’occurrence varient en fonction de la région étudiée. Les GPI sont des matières premières utilisées dans les processus de moulage par injection tandis que les billes de mousse expansée sont le résultat de la fragmentation du polystyrène. Pour de plus amples informations sur les zones de relevé, les quantités et les taux d’occurrence des objets individuels, voir [Lacs et rivières -deutsch](allsurveys)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
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       "<table id=\"T_10496\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_10496_level0_col0\" class=\"col_heading level0 col0\" >Aare</th>\n",
       "      <th id=\"T_10496_level0_col1\" class=\"col_heading level0 col1\" >Linth</th>\n",
       "      <th id=\"T_10496_level0_col2\" class=\"col_heading level0 col2\" >Rhone</th>\n",
       "      <th id=\"T_10496_level0_col3\" class=\"col_heading level0 col3\" >Ticino</th>\n",
       "      <th id=\"T_10496_level0_col4\" class=\"col_heading level0 col4\" >Tous périmètres confondus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row0\" class=\"row_heading level0 row0\" >Bouteilles pour boissons, morceaux</th>\n",
       "      <td id=\"T_10496_row0_col0\" class=\"data row0 col0\" >3,0</td>\n",
       "      <td id=\"T_10496_row0_col1\" class=\"data row0 col1\" >4,0</td>\n",
       "      <td id=\"T_10496_row0_col2\" class=\"data row0 col2\" >2,0</td>\n",
       "      <td id=\"T_10496_row0_col3\" class=\"data row0 col3\" >8,0</td>\n",
       "      <td id=\"T_10496_row0_col4\" class=\"data row0 col4\" >3,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row1\" class=\"row_heading level0 row1\" >Polystyrène expansé</th>\n",
       "      <td id=\"T_10496_row1_col0\" class=\"data row1 col0\" >4,0</td>\n",
       "      <td id=\"T_10496_row1_col1\" class=\"data row1 col1\" >3,0</td>\n",
       "      <td id=\"T_10496_row1_col2\" class=\"data row1 col2\" >17,5</td>\n",
       "      <td id=\"T_10496_row1_col3\" class=\"data row1 col3\" >6,0</td>\n",
       "      <td id=\"T_10496_row1_col4\" class=\"data row1 col4\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row2\" class=\"row_heading level0 row2\" >Plastiques fragmentés</th>\n",
       "      <td id=\"T_10496_row2_col0\" class=\"data row2 col0\" >18,5</td>\n",
       "      <td id=\"T_10496_row2_col1\" class=\"data row2 col1\" >10,5</td>\n",
       "      <td id=\"T_10496_row2_col2\" class=\"data row2 col2\" >48,0</td>\n",
       "      <td id=\"T_10496_row2_col3\" class=\"data row2 col3\" >9,5</td>\n",
       "      <td id=\"T_10496_row2_col4\" class=\"data row2 col4\" >18,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row3\" class=\"row_heading level0 row3\" >film plastique épais</th>\n",
       "      <td id=\"T_10496_row3_col0\" class=\"data row3 col0\" >5,0</td>\n",
       "      <td id=\"T_10496_row3_col1\" class=\"data row3 col1\" >2,0</td>\n",
       "      <td id=\"T_10496_row3_col2\" class=\"data row3 col2\" >9,0</td>\n",
       "      <td id=\"T_10496_row3_col3\" class=\"data row3 col3\" >3,5</td>\n",
       "      <td id=\"T_10496_row3_col4\" class=\"data row3 col4\" >5,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row4\" class=\"row_heading level0 row4\" >Granules Plastique industriels (GPI)</th>\n",
       "      <td id=\"T_10496_row4_col0\" class=\"data row4 col0\" >0,0</td>\n",
       "      <td id=\"T_10496_row4_col1\" class=\"data row4 col1\" >0,0</td>\n",
       "      <td id=\"T_10496_row4_col2\" class=\"data row4 col2\" >0,0</td>\n",
       "      <td id=\"T_10496_row4_col3\" class=\"data row4 col3\" >0,0</td>\n",
       "      <td id=\"T_10496_row4_col4\" class=\"data row4 col4\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row5\" class=\"row_heading level0 row5\" >Bouchons et couvercles de bouteilles en métal</th>\n",
       "      <td id=\"T_10496_row5_col0\" class=\"data row5 col0\" >0,0</td>\n",
       "      <td id=\"T_10496_row5_col1\" class=\"data row5 col1\" >1,0</td>\n",
       "      <td id=\"T_10496_row5_col2\" class=\"data row5 col2\" >3,0</td>\n",
       "      <td id=\"T_10496_row5_col3\" class=\"data row5 col3\" >1,0</td>\n",
       "      <td id=\"T_10496_row5_col4\" class=\"data row5 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row6\" class=\"row_heading level0 row6\" >Déchets plastiques de construction</th>\n",
       "      <td id=\"T_10496_row6_col0\" class=\"data row6 col0\" >0,0</td>\n",
       "      <td id=\"T_10496_row6_col1\" class=\"data row6 col1\" >0,0</td>\n",
       "      <td id=\"T_10496_row6_col2\" class=\"data row6 col2\" >5,5</td>\n",
       "      <td id=\"T_10496_row6_col3\" class=\"data row6 col3\" >2,5</td>\n",
       "      <td id=\"T_10496_row6_col4\" class=\"data row6 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row7\" class=\"row_heading level0 row7\" >Isolation : y compris les mousses en spray</th>\n",
       "      <td id=\"T_10496_row7_col0\" class=\"data row7 col0\" >0,0</td>\n",
       "      <td id=\"T_10496_row7_col1\" class=\"data row7 col1\" >0,0</td>\n",
       "      <td id=\"T_10496_row7_col2\" class=\"data row7 col2\" >7,0</td>\n",
       "      <td id=\"T_10496_row7_col3\" class=\"data row7 col3\" >3,0</td>\n",
       "      <td id=\"T_10496_row7_col4\" class=\"data row7 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row8\" class=\"row_heading level0 row8\" >Emballages alimentaires, bonbons</th>\n",
       "      <td id=\"T_10496_row8_col0\" class=\"data row8 col0\" >8,0</td>\n",
       "      <td id=\"T_10496_row8_col1\" class=\"data row8 col1\" >6,0</td>\n",
       "      <td id=\"T_10496_row8_col2\" class=\"data row8 col2\" >19,0</td>\n",
       "      <td id=\"T_10496_row8_col3\" class=\"data row8 col3\" >4,0</td>\n",
       "      <td id=\"T_10496_row8_col4\" class=\"data row8 col4\" >9,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row9\" class=\"row_heading level0 row9\" >Mousses expansées < 5 mm</th>\n",
       "      <td id=\"T_10496_row9_col0\" class=\"data row9 col0\" >0,0</td>\n",
       "      <td id=\"T_10496_row9_col1\" class=\"data row9 col1\" >0,0</td>\n",
       "      <td id=\"T_10496_row9_col2\" class=\"data row9 col2\" >0,0</td>\n",
       "      <td id=\"T_10496_row9_col3\" class=\"data row9 col3\" >0,0</td>\n",
       "      <td id=\"T_10496_row9_col4\" class=\"data row9 col4\" >0,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row10\" class=\"row_heading level0 row10\" >Bâtonnets de coton-tige</th>\n",
       "      <td id=\"T_10496_row10_col0\" class=\"data row10 col0\" >0,0</td>\n",
       "      <td id=\"T_10496_row10_col1\" class=\"data row10 col1\" >0,0</td>\n",
       "      <td id=\"T_10496_row10_col2\" class=\"data row10 col2\" >10,5</td>\n",
       "      <td id=\"T_10496_row10_col3\" class=\"data row10 col3\" >0,0</td>\n",
       "      <td id=\"T_10496_row10_col4\" class=\"data row10 col4\" >1,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10496_level0_row11\" class=\"row_heading level0 row11\" >Filtres à cigarettes</th>\n",
       "      <td id=\"T_10496_row11_col0\" class=\"data row11 col0\" >11,0</td>\n",
       "      <td id=\"T_10496_row11_col1\" class=\"data row11 col1\" >23,0</td>\n",
       "      <td id=\"T_10496_row11_col2\" class=\"data row11 col2\" >41,5</td>\n",
       "      <td id=\"T_10496_row11_col3\" class=\"data row11 col3\" >15,5</td>\n",
       "      <td id=\"T_10496_row11_col4\" class=\"data row11 col4\" >20,0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "        </div>\n",
       "        <div style='border-top: 1px solid #d2d9db;'>\n",
       "            <p style='margin:10px'><i><b>Figure 3:</b> Ensemble des lacs et cours d’eau par périmètre d’étude : valeur médiane globale des objets les plus courants identifiés. Les taux varient en fonction du périmètre d’étude dans lequel se situe la zone de relevé. Les plastiques fragmentés présentaient par exemple la valeur médiane la plus élevée dans les périmètres de l’Aar (18,5p/100m) et du Rhône (48p/100m).</i></p>\n",
       "        </div>\n",
       "    </div>\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p50 = [\n",
    "    \"Des quantités importantes de granulés plastiques industriels (GPI) et de mousses expansées < 5 mm ont été retrouvées, mais ceci seulement sur certains sites \",\n",
    "    \"spécifiques. Ces déchets sont présents dans moins de 50 % des relevés, ce qui se traduit par une valeur médiane égale à zéro. Si les GPI et les mousses \",\n",
    "    \"expansées entrent tous les deux dans la catégorie des microplastiques, leur utilisation, leur origine et leur taux d’occurrence varient en fonction de \",\n",
    "    \"la région étudiée. Les GPI sont des matières premières utilisées dans les processus de moulage par injection tandis que les billes de mousse expansée \",\n",
    "    \"sont le résultat de la fragmentation du polystyrène. Pour de plus amples informations sur les zones de relevé, les quantités et les taux d’occurrence des objets\"\n",
    "    'individuels, voir <a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/lakes_rivers.html\" color=\"blue\">Lacs et rivières -deutsch</a>.'\n",
    "]\n",
    "\n",
    "p50 = makeAParagraph(p50)\n",
    "\n",
    "new_components = [\n",
    "    smallest_space,\n",
    "    p50,\n",
    "    smallest_space \n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "mc_heat_map_caption = [\n",
    "    \"<b>Figure 3:</b> Ensemble des lacs et cours d’eau par périmètre d’étude : valeur médiane globale des objets les plus courants identifiés. Les taux varient en fonction \",\n",
    "    \"du périmètre d’étude dans lequel se situe la zone de relevé. Les plastiques fragmentés présentaient par exemple la valeur médiane la plus élevée dans les périmètres de \",\n",
    "    \"l’Aar (18,5p/100m) et du Rhône (48p/100m).\"\n",
    "]\n",
    "\n",
    "mc_heat_map_caption = ''.join(mc_heat_map_caption)\n",
    "\n",
    "# calling componentsMostCommon gets the results for the most common codes\n",
    "# at the component level\n",
    "components = fdx.componentMostCommonPcsM()\n",
    "\n",
    "# map to proper names for features\n",
    "feature_names = featuredata.river_basin_de\n",
    "\n",
    "# pivot that and quash the hierarchal column index that is created when the table is pivoted\n",
    "mc_comp = components[[\"item\", unit_label, this_level]].pivot(columns=this_level, index=\"item\")\n",
    "mc_comp.columns = mc_comp.columns.get_level_values(1)\n",
    "\n",
    "# insert the proper columns names for display\n",
    "proper_column_names = {x : feature_names[x] for x in mc_comp.columns}\n",
    "mc_comp.rename(columns = proper_column_names, inplace=True)\n",
    "\n",
    "# the aggregated total of the feature is taken from the most common objects table\n",
    "mc_feature = fdx.most_common[unit_label]\n",
    "mc_feature = featuredata.changeSeriesIndexLabels(mc_feature, {x:fdx.dMap.loc[x] for x in mc_feature.index})\n",
    "\n",
    "# the aggregated totals of all the period data\n",
    "mc_period = period_data.parentMostCommon(parent=False)\n",
    "mc_period = featuredata.changeSeriesIndexLabels(mc_period, {x:fdx.dMap.loc[x] for x in mc_period.index})\n",
    "\n",
    "# add the feature, bassin_label and period results to the components table\n",
    "mc_comp[\"Tous périmètres confondus\"] = mc_feature\n",
    "\n",
    "mc_comp[\"Index\"] = mc_comp.index.map(lambda x: fren[x])\n",
    "mc_comp.set_index(\"Index\", inplace=True, drop=True)\n",
    "\n",
    "col_widths=[5.1*cm, *[1.2*cm]*(len(mc_comp.columns)-1)]\n",
    "\n",
    "atable = aSingleStyledTable(mc_comp, gradient=True, vertical_header=True, colWidths=col_widths)\n",
    "atable_cap = makeAParagraph(mc_heat_map_caption, style=caption_style)\n",
    "table_and_cap =tableAndCaption(atable, atable_cap, col_widths)\n",
    "\n",
    "new_components = [\n",
    "     table_and_cap\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "# notebook display style\n",
    "aformatter = {x: featuredata.replaceDecimal for x in mc_comp.columns}\n",
    "mcd = mc_comp.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "mcd = mcd.background_gradient(axis=None, vmin=mc_comp.min().min(), vmax=mc_comp.max().max(), cmap=\"YlOrBr\")\n",
    "\n",
    "# remove the index name and column name labels\n",
    "mcd.index.name = None\n",
    "mcd.columns.name = None\n",
    "\n",
    "# rotate the text on the header row\n",
    "# the .applymap_index method in the\n",
    "# df.styler module is used for this\n",
    "mcd = mcd.applymap_index(featuredata.rotateText, axis=1)\n",
    "\n",
    "# display markdown html\n",
    "IPython.display.display(HTML(cardFigure(html_table=mcd.to_html(), table_caption= \"\".join(mc_heat_map_caption))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tendances depuis 2017-2018\n",
    "\n",
    "Une comparaison entre les résultats de l’IQAASL et des données relatives aux lacs et aux cours d’eau de nature similaire recueillies entre 2017 et 2018 (SLR) ne fait pas ressortir de différences statistiques, même si des variations sont à noter en ce qui concerne les quantités d’objets collectés. Si moins de filtres de cigarettes et de bouchons de bouteille ont de manière générale été identifiés sur la période 2020-2021, aucun changement n’était à signaler sur de nombreux sites, si ce n’est une légère augmentation des plastiques fragmentés et des mousses expansées, voir [Plus ou moins de déchets depuis 2017 -deutsch](slr-iqaasl).\n",
    "\n",
    "```{card}\n",
    ":class-card: sd-text-black\n",
    ":img-top: resources/output/slr_iqaasl_surveys_fr.jpeg\n",
    ":margin: auto\n",
    "+++\n",
    "__Figure 4 :__ *Comparaison des résultats des inventaires SLR (2018) et IQAASL (2021). __En haut à gauche :__ totaux des inventaires par date. __En haut à droite :__ totaux des valeurs médianes mensuelles. __En bas à gauche :__ nombre total d’échantillons par rapport au nombre de relevés. __En bas à droite :__ répartition empirique cumulative des résultats totaux des inventaires.*\n",
    "``` \n",
    "\n",
    "\n",
    "### Les Alpes et le Jura\n",
    "\n",
    "Parmi les 20 inventaires réalisés dans le périmètre des Alpes, 17 remplissaient des critères de longueur et de largeur supérieures à 10 m. De l’ordre de 110p/100m, la valeur médiane était inférieure à celle de tous les autres périmètres d’étude (189p/100m). Les objets liés à la consommation d’aliments, de boissons ou de tabac constituaient un pourcentage plus faible du total et présentaient un taux d’occurrence (p/100m) inférieur par rapport aux résultats relevés sur les rivages. Ces différences pourraient en partie être dues au taux d’urbanisation réduit qui caractérise le périmètre d’étude des Alpes par rapport à tous les autres périmètres d’étude et à la tendance qu’ont les matériaux de s’écouler vers l’aval. Pour de plus amples détails sur la méthodologie et les résultats, voir [Les Alpes et le Jura -deutsch](lesalpes).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Communication des résultats\n",
    "\n",
    "Pour communiquer sur les degrés de pollution, il apparaît pertinent de convertir les résultats en une simple mesure du nombre moyen d’éléments collectés par transect de 100 m, étant donné que ce chiffre est généralement plus élevé et rarement égal à zéro. Cependant, la moyenne peut s’avérer deux fois supérieure à la valeur médiane lorsque des valeurs extrêmes interviennent, si bien qu’il en résulte une confusion quant à la différence existant entre les résultats observés et les valeurs rapportées. Communiquer la plage de valeurs probables, ou la probabilité de trouver un objet, est plus informatif et plus facilement reproductible lorsque des protocoles similaires sont appliqués. Les quantités de granulés plastiques industriels retrouvées sur les bords du lac Léman permettent par exemple l’interprétation suivante.\n",
    "\n",
    "> Au total, 1 387 granulés plastiques industriels (GPI) ont été collectés, soit 5 % de tous les objets identifiés sur le lac Léman. Le nombre d’éléments par 100 m varie de 0 à 1033 selon les régions. En ce qui concerne le lac de manière générale, la probabilité de trouver au moins un granulé lors d’un inventaire est d’environ 40 %, quel que soit le lieu concerné. Dans certains endroits, comme sur les plages de Genève (probabilité de 60 %) ou de Préverenges (probabilité de 80 %), les GPI constituent des déchets caractéristiques et il est courant de relever des quantités comprises entre 3p/100m et 56p/100m.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "seci3 = Paragraph(\"Tendances depuis 2017-2018\", style=section_title)\n",
    "\n",
    "p51 = [\n",
    "    \"Une comparaison entre les résultats de l’IQAASL et des données relatives aux lacs et aux cours d’eau de nature similaire recueillies entre 2017 et 2018 \",\n",
    "    \"(SLR) ne fait pas ressortir de différences statistiques, même si des variations sont à noter en ce qui concerne les quantités d’objets collectés. Si moins \",\n",
    "    \"de filtres de cigarettes et de bouchons de bouteille ont de manière générale été identifiés sur la période 2020-2021, aucun changement n’était à signaler \",\n",
    "    \"sur de nombreux sites, si ce n’est une légère augmentation des plastiques fragmentés et des mousses expansées, \",\n",
    "    'voir <a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/slr-iqaasl.html\" color=\"blue\">Plus ou moins de déchets depuis 2017 -deutsch</a>.',\n",
    "\n",
    "]\n",
    "\n",
    "p51 = makeAParagraph(p51)\n",
    "\n",
    "seci4 = Paragraph(\"Les Alpes et le Jura\", style=section_title)\n",
    "\n",
    "p52 = [\n",
    "    \"Parmi les 20 inventaires réalisés dans le périmètre des Alpes, 17 remplissaient des critères de longueur et de largeur supérieures à 10 m. \",\n",
    "    \"De l’ordre de 110p/100m, la valeur médiane était inférieure à celle de tous les autres périmètres d’étude (189p/100m). Les objets liés à la \",\n",
    "    \"consommation d’aliments, de boissons ou de tabac constituaient un pourcentage plus faible du total et présentaient un taux d’occurrence (p/100m) \",\n",
    "    \"inférieur par rapport aux résultats relevés sur les rivages. Ces différences pourraient en partie être dues au taux d’urbanisation réduit qui \",\n",
    "    \"caractérise le périmètre d’étude des Alpes par rapport à tous les autres périmètres d’étude et à la tendance qu’ont les matériaux de s’écouler vers \",\n",
    "    \"l’aval. Pour de plus amples détails sur la méthodologie et les résultats, voir \",\n",
    "    '<a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/alpes_valaisannes.html\" color=\"blue\">Les Alpes et le Jura -deutsch</a>.'\n",
    "]\n",
    "\n",
    "p52 = makeAParagraph(p52) \n",
    "\n",
    "fig4icap = [\n",
    "    \"Comparaison des résultats des inventaires SLR (2018) et IQAASL (2021). <b>En haut à gauche :</b> totaux des inventaires par date. <b>En haut à droite :</b> \",\n",
    "    \"totaux des valeurs médianes mensuelles. <b>En bas à gauche :</b> nombre total d’échantillons par rapport au nombre de relevés. <b>En bas à droite :</b> \",\n",
    "    \"répartition empirique cumulative des résultats totaux des inventaires.\"\n",
    "]\n",
    "\n",
    "seci5 = Paragraph(\"Communication des résultats\", style=section_title)\n",
    "\n",
    "p53 = [\n",
    "    \"Pour communiquer sur les degrés de pollution, il apparaît pertinent de convertir les résultats en une simple mesure du nombre moyen d’éléments collectés \",\n",
    "    \"par transect de 100 m, étant donné que ce chiffre est généralement plus élevé et rarement égal à zéro. Cependant, la moyenne peut s’avérer deux fois \",\n",
    "    \"supérieure à la valeur médiane lorsque des valeurs extrêmes interviennent, si bien qu’il en résulte une confusion quant à la différence existant entre \",\n",
    "    \"les résultats observés et les valeurs rapportées. Communiquer la plage de valeurs probables, ou la probabilité de trouver un objet, est \",\n",
    "    \"plus informatif et plus facilement reproductible lorsque des protocoles similaires sont appliqués. Les quantités de granulés plastiques industriels \",\n",
    "    \"retrouvées sur les bords du lac Léman permettent par exemple l’interprétation suivante.\"\n",
    "]\n",
    "\n",
    "p54 = [\n",
    "    \"Au total, 1 387 granulés plastiques industriels (GPI) ont été collectés, soit 5 % de tous les objets identifiés sur le lac Léman. Le nombre d’éléments \",\n",
    "    \"par 100 m varie de 0 à 1033 selon les régions. En ce qui concerne le lac de manière générale, la probabilité de trouver au moins un granulé lors d’un \",\n",
    "    \"inventaire est d’environ 40 %, quel que soit le lieu concerné. Dans certains endroits, comme sur les plages de Genève (probabilité de 60 %) ou de \",\n",
    "    \"Préverenges (probabilité de 80 %), les GPI constituent des déchets caractéristiques et il est courant de relever des quantités comprises entre \",\n",
    "    \"3p/100m et 56p/100m.\"\n",
    "]\n",
    "\n",
    "p53 = makeAParagraph(p53)\n",
    "\n",
    "p54 = makeAParagraph(p54, style=block_quote_style)\n",
    "\n",
    "\n",
    "fig4icap = makeAParagraph(fig4icap, style=caption_style)\n",
    "\n",
    "o_w, o_h = convertPixelToCm(\"resources/output/slr_iqaasl_surveys.jpeg\")\n",
    "figure_kwargs = {\n",
    "    \"image_file\":\"resources/output/slr_iqaasl_surveys.jpeg\",\n",
    "    \"caption\": fig4icap, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\":2,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "f4i = figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "\n",
    "new_components = [\n",
    "    small_space,\n",
    "    seci3,\n",
    "    small_space,\n",
    "    p51,\n",
    "    smallest_space,\n",
    "    f4i,\n",
    "    small_space,\n",
    "    seci4,\n",
    "    small_space,\n",
    "    p52,\n",
    "    small_space,\n",
    "    seci5,\n",
    "    small_space,\n",
    "    p53,\n",
    "    smallest_space,\n",
    "    p54,\n",
    "    smallest_space,\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "\n",
    "À l’échelle nationale, les résultats de l’IQAASL sont stables par rapport aux relevés qui avaient été effectués en 2017 dans le cadre de l’étude SLR. Une diminution globale de la quantité de déchets issus de la consommation d’aliments, de boissons et de tabac est néanmoins à noter. Les objets liés aux infrastructures ainsi que les plastiques fragmentés et mousses expansées n’ont pas diminué et sont même en forte augmentation à certains endroits. En limitant les grands rassemblements en plein air, les restrictions liées à la pandémie sont susceptibles d’avoir influé positivement sur la baisse des déchets issus de la consommation d’aliments, de boissons et de tabac. Les objets liés aux infrastructures ont enregistré les plus fortes augmentations dans les cantons du Valais et de Vaud ainsi qu’à Brienz, c’est-à-dire sur des sites proches des apports fluviaux du Rhône et de l’Aar.\n",
    "\n",
    "L’utilisation des sols observée autour d’une zone de relevé exerce un effet mesurable quant au dépôt de certains objets. Plus il existe de bâtiments et d’infrastructures à proximité, plus on retrouve de déchets issus de la consommation de tabac et de produits alimentaires. Cette corrélation n’est pas observée pour les objets comme les plastiques fragmentés et les films plastiques industriels. Ces derniers sont en effet retrouvés à des taux à peu près égaux quelle que soit l’utilisation des sols, si ce n’est que leur concentration augmente près des exutoires des cours d’eau et des canaux.\n",
    "\n",
    "Actuellement, trois des quatre périmètres d’étude de l’IQAASL font l’objet d’une surveillance active assurée en aval de la Suisse par des organismes de recherche et des institutions étatiques qui s’appuient sur des méthodes similaires à celles décrites dans le présent rapport. En Suisse, des associations régionales collaborent par ailleurs activement avec des organisations partenaires au sein de l’UE afin de standardiser rapports et protocoles.\n",
    "\n",
    "L’IQAASL est un projet scientifique citoyen qui recourt exclusivement à des outils open source et partage les données recueillies via une licence publique GNU, permettant ainsi une collaboration avec les parties prenantes. Depuis que le mandat a pris fin le 31 décembre 2021, Hammerdirt assure la maintenance du code et de la base de données, qui est hébergée sur Github et librement accessible au public.\n",
    "\n",
    "Les associations qui ont participé au projet IQAASL recherchent activement des moyens d’intégrer le processus de collecte de données et/ou les résultats des inventaires dans leur propre mode de fonctionnement. Cependant, de nombreuses associations régionales font face à une pénurie de spécialistes des données, ce qui est susceptible de retarder ce processus d’intégration et de freiner l’innovation au niveau où elle s’avère le plus nécessaire."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "seci6 = Paragraph(\"Conclusions\", style=section_title)\n",
    "\n",
    "p55 = [\n",
    "    \"À l’échelle nationale, les résultats de l’IQAASL sont stables par rapport aux relevés qui avaient été effectués en 2017 dans le cadre \",\n",
    "    \"de l’étude SLR. Une diminution globale de la quantité de déchets issus de la consommation d’aliments, de boissons et de tabac est néanmoins \",\n",
    "    \"à noter. Les objets liés aux infrastructures ainsi que les plastiques fragmentés et mousses expansées n’ont pas diminué et sont même en forte \",\n",
    "    \"augmentation à certains endroits. En limitant les grands rassemblements en plein air, les restrictions liées à la pandémie sont susceptibles \",\n",
    "    \"d’avoir influé positivement sur la baisse des déchets issus de la consommation d’aliments, de boissons et de tabac. Les objets liés aux \",\n",
    "    \"infrastructures ont enregistré les plus fortes augmentations dans les cantons du Valais et de Vaud ainsi qu’à Brienz, c’est-à-dire sur des \",\n",
    "    \"sites proches des apports fluviaux du Rhône et de l’Aar.\"\n",
    "]\n",
    "\n",
    "p56 = [\n",
    "    \"L’utilisation des sols observée autour d’une zone de relevé exerce un effet mesurable quant au dépôt de certains objets. Plus il existe de \",\n",
    "    \"bâtiments et d’infrastructures à proximité, plus on retrouve de déchets issus de la consommation de tabac et de produits alimentaires. Cette \",\n",
    "    \"corrélation n’est pas observée pour les objets comme les plastiques fragmentés et les films plastiques industriels. Ces derniers sont en effet \",\n",
    "    \"retrouvés à des taux à peu près égaux quelle que soit l’utilisation des sols, si ce n’est que leur concentration augmente près des exutoires des \",\n",
    "    \"cours d’eau et des canaux.\"\n",
    "]\n",
    "\n",
    "p57 = [\n",
    "    \"Actuellement, trois des quatre périmètres d’étude de l’IQAASL font l’objet d’une surveillance active assurée en aval de la Suisse par des organismes \",\n",
    "    \"de recherche et des institutions étatiques qui s’appuient sur des méthodes similaires à celles décrites dans le présent rapport. En Suisse, \",\n",
    "    \"des associations régionales collaborent par ailleurs activement avec des organisations partenaires au sein de l’UE afin de standardiser rapports et protocoles.\"\n",
    "]\n",
    "\n",
    "p58 = [\n",
    "    \"L’IQAASL est un projet scientifique citoyen qui recourt exclusivement à des outils open source et partage les données recueillies via une licence publique \",\n",
    "    \"GNU, permettant ainsi une collaboration avec les parties prenantes. Depuis que le mandat a pris fin le 31 décembre 2021, Hammerdirt assure la maintenance \",\n",
    "    \"du code et de la base de données, qui est hébergée sur Github et librement accessible au public.\"\n",
    "]\n",
    "\n",
    "\n",
    "p59 = [\n",
    "    \"Les associations qui ont participé au projet IQAASL recherchent activement des moyens d’intégrer le processus de collecte de données et/ou les résultats \",\n",
    "    \"des inventaires dans leur propre mode de fonctionnement. Cependant, de nombreuses associations régionales font face à une pénurie de spécialistes des données, \",\n",
    "    \"ce qui est susceptible de retarder ce processus d’intégration et de freiner l’innovation au niveau où elle s’avère le plus nécessaire.\"\n",
    "]\n",
    "\n",
    "p55_59 = sectionParagraphs([p55, p56,p57, p58, p59], smallspace=smallest_space)\n",
    "\n",
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    "### Recommandations\n",
    "#### Suivi et rapports\n",
    "\n",
    "Le partage de données et la production de rapports gagneraient immédiatement en efficacité si un format de rapport standard était défini. Les institutions régionales pourraient ainsi communiquer plus facilement leurs priorités aux autres parties prenantes, ce qui faciliterait les stratégies de suivi et aiderait à définir des objectifs de réduction. Pour ce faire, les mesures suivantes pourraient être mises en place :\n",
    "\n",
    "> Développement d’un réseau d’associations chargées de l’échantillonnage et de la communication des résultats.\n",
    "> Création d’un format de rapport standardisé permettant de faciliter les échanges entre les communes, cantons, régions et districts et d’améliorer la coordination des stratégies de réduction à l’échelon régional et local.\n",
    "> Planification de la prochaine période d’échantillonnage et définition de l’intervalle optimal entre deux inventaires.\n",
    "\n",
    "En sus, il serait pertinent d’associer le milieu universitaire aux processus de planification, échantillonnage et analyse des résultats. Ce projet a bénéficié jusqu’ici de la collaboration de professeurs des EPFL, de l’UNIGE, du PSI et de la FHNW. Des partenaires universitaires apporteraient une contribution idéale pour continuer à développer des méthodes analytiques. Le Citizen Science Center (EPF) et le Citizen Cyberlab (UNIGE) disposent en particulier de l’expérience et des infrastructures nécessaires pour concilier projets citoyens de suivi scientifique et efforts de recherche. Il en résulterait un plan de surveillance très adaptatif et très efficace."
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    "\n",
    "p60 = [\n",
    "    \"Le partage de données et la production de rapports gagneraient immédiatement en efficacité si un format de rapport standard était défini. \",\n",
    "    \"Les institutions régionales pourraient ainsi communiquer plus facilement leurs priorités aux autres parties prenantes, ce qui faciliterait \",\n",
    "    \"les stratégies de suivi et aiderait à définir des objectifs de réduction. Pour ce faire, les mesures suivantes pourraient être mises en place :\"\n",
    "]\n",
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    "li7 = [\n",
    "    \"Développement d’un réseau d’associations chargées de l’échantillonnage et de la communication des résultats.\",\n",
    "    \"Création d’un format de rapport standardisé permettant de faciliter les échanges entre les communes, cantons, régions et districts et d’améliorer la coordination des stratégies de réduction à l’échelon régional et local.\",\n",
    "    \"Planification de la prochaine période d’échantillonnage et définition de l’intervalle optimal entre deux inventaires.\"\n",
    "]\n",
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    "p61 = [\n",
    "    \"En sus, il serait pertinent d’associer le milieu universitaire aux processus de planification, échantillonnage et analyse des résultats. Ce projet a bénéficié jusqu’ici \",\n",
    "    \"de la collaboration de professeurs des EPFL, de l’UNIGE, du PSI et de la FHNW. Des partenaires universitaires apporteraient une contribution idéale pour continuer à \",\n",
    "    \"développer des méthodes analytiques. Le Citizen Science Center (EPF) et le Citizen Cyberlab (UNIGE) disposent en particulier de l’expérience et des infrastructures \",\n",
    "    \"nécessaires pour concilier projets citoyens de suivi scientifique et efforts de recherche. Il en résulterait un plan de surveillance très adaptatif et très efficace.\"\n",
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    "### Élimination et réduction\n",
    "\n",
    "Les stratégies visant à éliminer ou à réduire les déchets sauvages doivent d’abord tenir compte de la source.\n",
    "\n",
    "__Objets associés à l’utilisation des sols__\n",
    "\n",
    "Les résultats indiquent qu’il existe une corrélation entre le nombre de bâtiments et la quantité de déchets liés à la consommation d’aliments, de boissons et de tabac. Il semblerait donc logique que les stratégies de réduction soient tout d’abord déployées dans les zones où l’on observe une concentration élevée d’infrastructures à proximité des rivages. Les résultats recueillis dans le périmètre d’étude du Rhône suggèrent que des campagnes locales de sensibilisation pourraient exercer un effet positif, cf. figure 1.9 [Lacs et rivières -deutsch](allsurveys). Si éliminer tous les objets liés à la consommation d’aliments, de boissons et de tabac couramment ciblés lors des campagnes de sensibilisation permet de réduire considérablement les quantités totales de déchets, 64 % des matériaux subsisteront encore.\n",
    "\n",
    "Autres stratégies de réduction courantes :\n",
    "\n",
    "* Mise à disposition d’un nombre adéquat de poubelles résistantes aux animaux et aux intempéries\n",
    "* Hausse du rythme d’enlèvement des ordures et de balayage\n",
    "* Réduction des plastiques à usage unique\n",
    "\n",
    "De nombreux pays ont commencé à restreindre la production des articles susvisés. La vente des assiettes, couverts, pailles, bâtonnets de ballon et cotons-tiges en plastique à usage unique est ainsi interdite au sein des États membres de l’UE depuis le 3 juillet 2021. En France, des filets de rétention ont été utilisés avec succès afin de filtrer les déchets transportés par les réseaux d’eaux pluviales avant qu’ils ne parviennent dans les lacs et les cours d’eau, mais cette approche nécessite des investissements supplémentaires en matière d’infrastructures, d’équipements et de main-d’œuvre.\n",
    "\n",
    "__Objets non associés à l’utilisation des sols__\n",
    "\n",
    "Les objets dont la présence n’est pas en corrélation avec l’utilisation des sols nécessitent que des actions concertées soient mises en place au moins au niveau du lac ou du cours d’eau et dans tous les lieux situés en amont des zones de relevé prévues. Les objets les plus courants sont :\n",
    "\n",
    "* les plastiques fragmentés,\n",
    "* les mousses expansées,\n",
    "* les déchets plastiques issus du secteur de la construction,\n",
    "* les granulés plastiques industriels,\n",
    "* les cotons-tiges et\n",
    "* les films plastiques industriels.\n",
    "\n",
    "Ils représentent 40 % de tous les matériaux identifiés. Beaucoup ont des applications industrielles ou hygiéniques qui ne sont généralement pas associées aux activités de plage. Le développement de campagnes de sensibilisation visant à prévenir en interne les pertes de matériaux dans des secteurs spécifiques pourrait induire une réduction des objets tels que les granulés plastiques industriels utilisés pour le moulage par injection. Certains objets tels que les cotons-tiges en plastique et autres matières plastiques éliminées dans les toilettes pénètrent dans les lacs et les cours d’eau par l’intermédiaire des stations d’épuration.\n",
    "\n",
    "Les stratégies de réduction pourraient inclure :\n",
    "\n",
    "* la modernisation des stations d’épuration afin de réduire les pertes de déchets,\n",
    "* des campagnes de sensibilisation ciblées sur des objets ou des produits spécifiques et\n",
    "* des campagnes de sensibilisation à l’intention de secteurs industriels spécifiques.\n",
    "\n",
    "Les stratégies d’élimination ou de réduction nécessitent une action coordonnée au niveau régional qui englobe les communautés situées en amont des zones de relevé. Réduire la dépendance aux plastiques à usage unique, aux plastiques expansés, aux plastiques de construction et aux films plastiques et industriels serait susceptible de réduire considérablement les volumes qui s’accumulent dans l’environnement. Le faible coût et la nature jetable de ces matériaux ont entraîné leur prolifération et induit une dépendance sans cesse croissante dans tous les secteurs. Leur légèreté et leur dégradabilité caractéristiques facilitent leur fragmentation et leur dispersion dans les milieux naturels, en particulier lors d’une exposition extérieure prolongée. La pollution induite par les matières plastiques constitue un problème mondial si bien que de plus en plus de pays réduisent leur dépendance à l’égard des plastiques à usage unique et des plastiques expansés tels que le polystyrène."
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    "seci8 = Paragraph(\"Élimination et réduction\", style=section_title)\n",
    "\n",
    "p63 = [\n",
    "    \"Les stratégies visant à éliminer ou à réduire les déchets sauvages doivent d’abord tenir compte de la source.\"\n",
    "]\n",
    "\n",
    "p64 = [\n",
    "    \"<b>Objets associés à l’utilisation des sols</b>\"\n",
    "]\n",
    "\n",
    "p65 = [\n",
    "    \"Les résultats indiquent qu’il existe une corrélation entre le nombre de bâtiments et la quantité de déchets liés à la consommation \",\n",
    "    \"d’aliments, de boissons et de tabac. Il semblerait donc logique que les stratégies de réduction soient tout d’abord déployées dans \",\n",
    "    \"les zones où l’on observe une concentration élevée d’infrastructures à proximité des rivages. Les résultats recueillis dans le périmètre \",\n",
    "    \"d’étude du Rhône suggèrent que des campagnes locales de sensibilisation pourraient exercer un effet positif, cf. figure 1.9 \",\n",
    "    '<a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/lakes_rivers.html\" color=\"blue\">Lacs et rivières -deutsch</a>. ',\n",
    "    \"Si éliminer tous les objets liés à la consommation d’aliments, de boissons et de tabac couramment ciblés lors des campagnes de sensibilisation \",\n",
    "    \"permet de réduire considérablement les quantités totales de déchets, 64 % des matériaux subsisteront encore.\"\n",
    "]\n",
    "\n",
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    "    \"Mise à disposition d’un nombre adéquat de poubelles résistantes aux animaux et aux intempéries\",\n",
    "    \"Hausse du rythme d’enlèvement des ordures et de balayage\",\n",
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    "\n",
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    "    \"Les objets dont la présence n’est pas en corrélation avec l’utilisation des sols nécessitent que des actions concertées soient \",\n",
    "    \"mises en place au moins au niveau du lac ou du cours d’eau et dans tous les lieux situés en amont des zones de relevé prévues. \",\n",
    "    \"Les objets les plus courants sont :\"\n",
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    "    \"les plastiques fragmentés,\",\n",
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    "    \"les déchets plastiques issus du secteur de la construction,\",\n",
    "    \"les granulés plastiques industriels,\",\n",
    "    \"les cotons-tiges et\",\n",
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    "    \"Ils représentent 40 % de tous les matériaux identifiés. Beaucoup ont des applications industrielles ou hygiéniques \",\n",
    "    \"qui ne sont généralement pas associées aux activités de plage. Le développement de campagnes de sensibilisation visant \",\n",
    "    \"à prévenir en interne les pertes de matériaux dans des secteurs spécifiques pourrait induire une réduction des objets \",\n",
    "    \"tels que les granulés plastiques industriels utilisés pour le moulage par injection. Certains objets tels que les cotons-tiges \",\n",
    "    \"en plastique et autres matières plastiques éliminées dans les toilettes pénètrent dans les lacs et les cours d’eau par \",\n",
    "    \"l’intermédiaire des stations d’épuration.\"\n",
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    "\n",
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    "    \"la modernisation des stations d’épuration afin de réduire les pertes de déchets,\",\n",
    "    \"des campagnes de sensibilisation ciblées sur des objets ou des produits spécifiques et\",\n",
    "    \"des campagnes de sensibilisation à l’intention de secteurs industriels spécifiques.\"\n",
    "]\n",
    "\n",
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    "p81 = [\n",
    "    \"Les stratégies d’élimination ou de réduction nécessitent une action coordonnée au niveau régional qui englobe les communautés situées en amont des zones de relevé.\"\n",
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    "\n",
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    "    \"Réduire la dépendance aux plastiques à usage unique, aux plastiques expansés, aux plastiques de construction et aux films plastiques et industriels serait susceptible \",\n",
    "    \"de réduire considérablement les volumes qui s’accumulent dans l’environnement. Le faible coût et la nature jetable de ces matériaux ont entraîné leur prolifération et \",\n",
    "    \"induit une dépendance sans cesse croissante dans tous les secteurs. Leur légèreté et leur dégradabilité caractéristiques facilitent leur fragmentation et leur dispersion \",\n",
    "    \"dans les milieux naturels, en particulier lors d’une exposition extérieure prolongée. La pollution induite par les matières plastiques constitue un problème mondial si \",\n",
    "    \"bien que de plus en plus de pays réduisent leur dépendance à l’égard des plastiques à usage unique et des plastiques expansés tels que le polystyrène.\"\n",
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    "## Acronyms\n",
    "\n",
    "(ABS) Acrylonitrile Butadiene Styrene\n",
    "\n",
    "(agg) Agriculture\n",
    "\n",
    "(BCa) Bias-corrected and accelerated confidence intervals\n",
    "\n",
    "(BVs) Baseline Values\n",
    "\n",
    "(CG) Contributed Group\n",
    "\n",
    "(CI) Confidence intervals\n",
    "\n",
    "(DG) Distributed Group\n",
    "\n",
    "(ECDF) Empirical Cumulative Distribution Function\n",
    "\n",
    "(EPFL) Swiss Federal Institute of Technology Lausanne\n",
    "\n",
    "(EPS) Expanded Polystyrene Foam\n",
    "\n",
    "(EPR) Extended Producer Responsibility\n",
    "\n",
    "(ETH) Swiss Federal Institute of Technology Zürich\n",
    "\n",
    "(EU) European Union\n",
    "\n",
    "(EVs) Extreme Values\n",
    "\n",
    "(EVT) Extreme Value Threshold\n",
    "\n",
    "(FHNW) University of Applied Sciences and Arts Northwestern Switzerland\n",
    "\n",
    "(FTIR) Fourier Transform Infrared\n",
    "\n",
    "(GNU) General Public License\n",
    "\n",
    "(GPI) Plastic production pellet\n",
    "\n",
    "(GPS) Global Positioning System\n",
    "\n",
    "(HDPE2) High-Density Polyethylene\n",
    "\n",
    "(IOC)Intergovernmental Oceanographic Commission\n",
    "\n",
    "(IQAASL) Identification, Quantification and Analysis of Swiss Litter\n",
    "\n",
    "(JRC) Joint Research Centre\n",
    "\n",
    "(LDPE4)Low-density Polyethylene\n",
    "\n",
    "(LPE)Loi sur la Protection de l’Environnement\n",
    "\n",
    "(LWS) Landwirtshaft\n",
    "\n",
    "(MLE) Maximum Likelihood Estimation\n",
    "\n",
    "(MCBP) Montreux Clean Beach Project\n",
    "\n",
    "(MOM) Method of Moments\n",
    "\n",
    "(MSFD) Marine Strategy Framework Directive\n",
    "\n",
    "(OLED) Ordinance on the Limitation and Disposal of Waste\n",
    "\n",
    "(OSPAR) Oslo/Paris convention (for the Protection of the Marine Environment of the North-East Atlantic)\n",
    "\n",
    "(PSI) Paul Scherrer Institute\n",
    "\n",
    "(pcs) pieces\n",
    "\n",
    "(p/100m) Pieces per 100 meters\n",
    "\n",
    "(PVC3) Polyvinyl Chloride\n",
    "\n",
    "(PET) Polyethylene Terephthalate\n",
    "\n",
    "(PP5) Polypropylene\n",
    "\n",
    "(PPL) Precious Plastic Léman\n",
    "\n",
    "(PS6) Polystyrene\n",
    "\n",
    "(QGIS) Quantum Geographic Information System\n",
    "\n",
    "(SLR) Swiss Litter Report\n",
    "\n",
    "(STOPPP) Stop Plastic Pollution Switzerland\n",
    "\n",
    "(UNEP) United Nations Environment Program\n",
    "\n",
    "(UNIGE) University of Geneva\n",
    "\n",
    "(VD) Vaud\n",
    "\n",
    "(WWF) World Wildlife Fund\n",
    "\n",
    "(XPS) Extruded Polystyrene Foam\n"
   ]
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    "new_components = [\n",
    "    PageBreak(),\n",
    "    *references\n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "doc = SimpleDocTemplate(pdf_link, pagesize=A4, leftMargin=2.5*cm, rightMargin=2.5*cm, topMargin=2.5*cm, bottomMargin=1.5*cm)\n",
    "pageinfo = f'IQAASL/Zusammengefasste/Kurzfassung'\n",
    "source_prefix = \"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/\"\n",
    "source = \"esummary.html\"\n",
    "\n",
    "link_to_source = f'{source_prefix}{source}'\n",
    "\n",
    "def myLaterPages(canvas, doc):\n",
    "    canvas.saveState()\n",
    "    canvas.setLineWidth(.001*cm)\n",
    "    canvas.setFillAlpha(.8)\n",
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    "    canvas.setFont('Times-Roman',9)\n",
    "    canvas.drawString(2.5*cm, 1*cm, link_to_source)\n",
    "    canvas.drawString(18.5*cm, 1*cm,  \"S.%d \" % (doc.page,))\n",
    "    canvas.drawString(2.5*cm, 27.7*cm, pageinfo)\n",
    "    canvas.restoreState()\n",
    "    \n",
    "doc.build(pdfcomponents,  onFirstPage=myLaterPages, onLaterPages=myLaterPages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}