{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Uncertainty calculation of plastic emission fate factors (FF)**\n",
    "\n",
    "In this notebook the uncertainty and distribution of plastic emission fate factors is conducted. Therefore, the following approach is followed:\n",
    "1. Import and handling/parsing of the excel sheet named *Supplementary Material 2.xlsx*\n",
    "2. Calculation of the [coefficient of variation](https://en.wikipedia.org/wiki/Coefficient_of_variation) and cleaning of the dataframe\n",
    "3. Setting a dataframe called (df_SSDR_new) which includes all the necessary stuff for calculation\n",
    "4. Calculation of the average SSDR for all polymer types and compartments where\n",
    "    * column 'SSDR expert judgment based on' not equals Exp+compatment leading to df_SSDR_basic\n",
    "    * column 'SSDR expert judgment based on' equals Exp+compartment lead to df_exp\n",
    "    * merging of the to dataframes to retrieve the SSDR with uncertainty dataframe (df_SSDR)\n",
    "5. Calculation the GSD for the SSDRs and storing the values in an excel sheet\n",
    "6. Calculation of the average lifetimes and storing the values in an excel sheet\n",
    "7. Retrieving redistribution values from excel sheet named *Supplementary Material 2.xlsx*\n",
    "8. Calculation of plastic emissions fate factor\n",
    "9. Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the usual stuff\n",
    "import random\n",
    "import scipy\n",
    "from scipy import stats, optimize, interpolate\n",
    "from scipy.stats import skew, gmean, gstd\n",
    "from statistics import geometric_mean\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "import re\n",
    "import seaborn as sns\n",
    "from openpyxl import load_workbook\n",
    "import matplotlib.gridspec as gridspec\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "TodaysDate = time.strftime(\"%Y%m%d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining the function for calculating the coefficient of variation (CV) using the predefined data quality indicators and calculating CVs for each data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining function for CV calculation\n",
    "def CV(rel, comp1, comp2, temp, geo, mea, exp):\n",
    "    # function depended on the DQIS for reliability, completeness 1,\n",
    "    # completeness2, temporal correlation, geographical correlation,\n",
    "    # and mearuement method. Expert judgments are handled differently\n",
    "    a_rel = 0.75  # a parameters b parameter defined as explained in manuscript\n",
    "    a_comp1 = 1.5\n",
    "    a_comp2 = 1.5\n",
    "    a_temp = 0.375\n",
    "    a_geo = 1.5\n",
    "    a_mea = 1.5\n",
    "    a_exp = 2.5\n",
    "    b = 1.105\n",
    "    if exp == 0:\n",
    "        # calculates all CVs for the data points expect for the ones which\n",
    "        # are based on expert judgment\n",
    "        CV_rel = a_rel*math.exp(b*rel)\n",
    "        if comp1 == 1:\n",
    "            # if comp1 is 1 then CV is 0 this is the case as it is assumed\n",
    "            # that there is no uncertainty when DQIS is 1 in the comp1\n",
    "            CV_comp1 = 0\n",
    "        else:\n",
    "            CV_comp1 = a_comp1*math.exp(b*(comp1-1))\n",
    "        if comp2 == 1:\n",
    "            CV_comp2 = 0\n",
    "        else:\n",
    "            CV_comp2 = a_comp2*math.exp(b*(comp2-1))\n",
    "        if temp == 1:\n",
    "            CV_temp = 0\n",
    "        else:\n",
    "            CV_temp = a_temp*math.exp(b*(temp-1))\n",
    "        if geo == 1:\n",
    "            CV_geo = 0\n",
    "        else:\n",
    "            CV_geo = a_geo*math.exp(b*(geo-1))\n",
    "        CV_mea = a_mea*math.exp(b*mea)\n",
    "        # sum of all\n",
    "        return math.sqrt(((CV_rel/100)**2+1) * ((CV_comp1/100)**2+1) *\n",
    "                         ((CV_comp2/100)**2+1) * ((CV_temp/100)**2+1) *\n",
    "                         ((CV_geo/100)**2+1) * ((CV_mea/100)**2+1)-1)\n",
    "    # CV squares and the square root to calculate the total CV for\n",
    "    # the data point under investigation\n",
    "    else:\n",
    "        # calulation of CV if an expert judgment\n",
    "        # was applied for data generation\n",
    "        return a_exp*math.exp(b*exp)/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading excel file with name \"Supplementary Material 2.xlsx\"\n",
    "# and calculating CV relying on DQIS\n",
    "df = pd.read_excel('Supplementary Material 2.xlsx',\n",
    "                   sheet_name='DQ of SSDR', skiprows=2)\n",
    "df['CV'] = df.apply(lambda row: CV(float(row['DQIS_rel']),\n",
    "                                   float(row['DQIS_comp1']),\n",
    "                                   float(row['DQIS_comp2']),\n",
    "                                   float(row['DQIS_temp']),\n",
    "                                   float(row['DQIS_geo']),\n",
    "                                   float(row['DQIS_mea']),\n",
    "                                   float(row['DQIS_exp'])), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating an excel file with the original data and the calculated CVs\n",
    "excel_name = TodaysDate + '_fate_factor_calculation.xlsx'\n",
    "writer = pd.ExcelWriter(excel_name, engine='openpyxl')\n",
    "df.to_excel(writer, sheet_name='Original_with_CV', index=False)\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Polymer type</th>\n",
       "      <th>Compartment</th>\n",
       "      <th>SSDR</th>\n",
       "      <th>CV</th>\n",
       "      <th>SSDR expert judgment based on</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HDPE</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>11.703125</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>EXP+marine water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HDPE</td>\n",
       "      <td>marine water</td>\n",
       "      <td>11.699359</td>\n",
       "      <td>0.630773</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HDPE</td>\n",
       "      <td>river sediment</td>\n",
       "      <td>11.703125</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>EXP+marine water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HDPE</td>\n",
       "      <td>soil</td>\n",
       "      <td>11.703125</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>EXP+marine water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LDPE</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>0.706833</td>\n",
       "      <td>0.472005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>PVC</td>\n",
       "      <td>soil</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>2.077407</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>42.740047</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>EXP+soil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>marine water</td>\n",
       "      <td>101.290255</td>\n",
       "      <td>0.146425</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>river sediment</td>\n",
       "      <td>42.740047</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>EXP+soil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>soil</td>\n",
       "      <td>42.740047</td>\n",
       "      <td>0.240464</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Polymer type      Compartment        SSDR        CV  \\\n",
       "0           HDPE  marine sediment   11.703125  0.227893   \n",
       "1           HDPE     marine water   11.699359  0.630773   \n",
       "2           HDPE   river sediment   11.703125  0.227893   \n",
       "3           HDPE             soil   11.703125  0.227893   \n",
       "4           LDPE  marine sediment    0.706833  0.472005   \n",
       "..           ...              ...         ...       ...   \n",
       "91           PVC             soil    0.001000  2.077407   \n",
       "92  Starch-blend  marine sediment   42.740047  0.227893   \n",
       "93  Starch-blend     marine water  101.290255  0.146425   \n",
       "94  Starch-blend   river sediment   42.740047  0.227893   \n",
       "95  Starch-blend             soil   42.740047  0.240464   \n",
       "\n",
       "   SSDR expert judgment based on  \n",
       "0               EXP+marine water  \n",
       "1                            NaN  \n",
       "2               EXP+marine water  \n",
       "3               EXP+marine water  \n",
       "4                            NaN  \n",
       "..                           ...  \n",
       "91                           NaN  \n",
       "92                      EXP+soil  \n",
       "93                           NaN  \n",
       "94                      EXP+soil  \n",
       "95                           NaN  \n",
       "\n",
       "[96 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a list of all polymers used in the excel file\n",
    "poly = df['Polymer type'].unique().tolist()\n",
    "# get a list of all compartments in the excel file and remove\n",
    "# fresh water as it is not considered in further calculations\n",
    "comp = df['Compartment'].unique().tolist()\n",
    "# comp.remove('fresh water')\n",
    "# create a new dataframe with only the colums of\n",
    "# polymer type, compartment, SSDR, CV and Uncertainty\n",
    "df_SSDR_new = pd.DataFrame(columns=['Polymer type', 'Compartment', 'SSDR',\n",
    "                                    'CV', 'SSDR expert judgment based on'])\n",
    "# generating a reduced df (df_poly_reduced) for each polymer\n",
    "# and each compartment containing polymer type, compartment,\n",
    "# SSDR, CV, Uncertainty\n",
    "for polymer in poly:\n",
    "    df_poly_reduced = df.loc[(df['Polymer type'] == polymer), ['Polymer type',\n",
    "                                                               'Compartment',\n",
    "                                                               'SSDR',\n",
    "                                                               'CV',\n",
    "                                                               'SSDR expert judgment based on']]\n",
    "    for compartment in comp:\n",
    "        df_poly_comp_reduced = df_poly_reduced.loc[(\n",
    "            df_poly_reduced['Compartment'] == compartment), :]\n",
    "        # finding the lowest CV\n",
    "        min_CV = df_poly_comp_reduced['CV'].min()\n",
    "        # finding the number how often the minimal CV occurs\n",
    "        num_min_CV = df_poly_comp_reduced.CV.value_counts()[min_CV]\n",
    "        # Difference between cases were the number of minimal\n",
    "        # CVs occurs is equal to one or not\n",
    "        if num_min_CV == 1:\n",
    "            # retrieving SSDR for min_CV value from dataframe (df_poly_reduced)\n",
    "            # and adding this to the dataframe (df_SSDR_new)\n",
    "            df_poly_comp_reduced = df_poly_comp_reduced.loc[(\n",
    "                df_poly_comp_reduced['CV'] == min_CV), :]\n",
    "            df_SSDR_new.loc[len(df_SSDR_new.index)] = [polymer, compartment,\n",
    "                                                       df_poly_comp_reduced.values[0][2],\n",
    "                                                       df_poly_comp_reduced.values[0][3],\n",
    "                                                       df_poly_comp_reduced.values[0][4]]\n",
    "        else:\n",
    "            # same as in the if statetment but using mean of SSDR\n",
    "            # because SSDR is occuring several times with the same min_CV value\n",
    "            df_poly_comp_reduced = df_poly_comp_reduced.loc[(\n",
    "                df_poly_comp_reduced['CV'] == min_CV), :]\n",
    "            SSDR = scipy.stats.gmean(df_poly_comp_reduced['SSDR'])\n",
    "            df_SSDR_new.loc[len(df_SSDR_new.index)] = [\n",
    "                polymer, compartment, SSDR, min_CV, 'NaN']\n",
    "df_SSDR_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Polymer type</th>\n",
       "      <th>Compartment</th>\n",
       "      <th>SSDR</th>\n",
       "      <th>CV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HDPE</td>\n",
       "      <td>marine water</td>\n",
       "      <td>11.699359</td>\n",
       "      <td>0.630773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LDPE</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>0.706833</td>\n",
       "      <td>0.472005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LDPE</td>\n",
       "      <td>marine water</td>\n",
       "      <td>5.657500</td>\n",
       "      <td>0.253505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NR/SBR</td>\n",
       "      <td>marine water</td>\n",
       "      <td>43.650000</td>\n",
       "      <td>0.629620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PA</td>\n",
       "      <td>marine water</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>0.146425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>PU</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>0.517929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>PU</td>\n",
       "      <td>river sediment</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>0.517929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>PU</td>\n",
       "      <td>soil</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>0.517929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>42.740047</td>\n",
       "      <td>0.335799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>river sediment</td>\n",
       "      <td>42.740047</td>\n",
       "      <td>0.335799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Polymer type      Compartment        SSDR        CV\n",
       "1           HDPE     marine water   11.699359  0.630773\n",
       "4           LDPE  marine sediment    0.706833  0.472005\n",
       "5           LDPE     marine water    5.657500  0.253505\n",
       "9         NR/SBR     marine water   43.650000  0.629620\n",
       "13            PA     marine water  292.000000  0.146425\n",
       "..           ...              ...         ...       ...\n",
       "43            PU  marine sediment  193.000000  0.517929\n",
       "44            PU   river sediment  193.000000  0.517929\n",
       "45            PU             soil  193.000000  0.517929\n",
       "46  Starch-blend  marine sediment   42.740047  0.335799\n",
       "47  Starch-blend   river sediment   42.740047  0.335799\n",
       "\n",
       "[96 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setting a list which includes only expert estimates\n",
    "# and the compartment which they rely on and build dataframes\n",
    "exp = ['EXP+marine water', 'EXP+marine sediment',\n",
    "       'EXP+soil', 'EXP+river sediment']\n",
    "df_SSDR_basic = df_SSDR_new.loc[~df_SSDR_new['SSDR expert judgment based on']\n",
    "                                .isin(exp)]\n",
    "df_SSDR_exp = df_SSDR_new.loc[df_SSDR_new['SSDR expert judgment based on']\n",
    "                              .isin(exp)]\n",
    "\n",
    "# creating of several dictionaries which will be\n",
    "# filled for the expert judgment uncertainty\n",
    "CV_dict_exp = dict()\n",
    "CV_dict_basic = dict()\n",
    "SSDR_dict_exp = dict()\n",
    "\n",
    "# filling the CV_dict_basic dictionary with the names\n",
    "# and SSDR for already set polymer types and compartments\n",
    "for i in range(0, len(df_SSDR_basic)):\n",
    "    name_basic = df_SSDR_basic.iloc[i]['Polymer type'] + \\\n",
    "        ', ' + df_SSDR_basic.iloc[i]['Compartment']\n",
    "    CV_dict_basic[name_basic] = (\n",
    "        df_SSDR_basic.iloc[i]['SSDR'], df_SSDR_basic.iloc[i]['CV']**2)\n",
    "\n",
    "# filling the CV_dict_exp dictionaries with the name and SSDR of the\n",
    "# expert judgments and calculating the new CV for the expert judgments\n",
    "# consiting of the already calculated CV for the expert judgment and the\n",
    "# basic uncertainty of the addtional uncertainy coming from the inital\n",
    "# value the SSDR is taken from\n",
    "for i in range(0, len(df_SSDR_exp)):\n",
    "    name_exp = df_SSDR_exp.iloc[i]['Polymer type'] + ', ' + df_SSDR_exp.iloc[i]['Compartment'] + \\\n",
    "        ', ' + \\\n",
    "        re.findall('\\+(.+)', df_SSDR_exp.iloc[i]\n",
    "                   ['SSDR expert judgment based on'])[0]\n",
    "    SSDR_dict_exp[name_exp] = CV_dict_basic.get(\n",
    "        name_exp.split(', ')[0] + ', ' + name_exp.split(', ')[2], 0)[0]\n",
    "    CV_dict_exp[name_exp] = (df_SSDR_exp.iloc[i]['CV'])**2\n",
    "    CV_dict_exp[name_exp] = math.sqrt((CV_dict_exp.get(name_exp, 0)+1) * (\n",
    "        CV_dict_basic.get(name_exp.split(', ')[0] + ', ' + name_exp.split(', ')[2], 0)[1]+1)-1)\n",
    "\n",
    "# merging of the two dictionaries consisting of the SSDRs and CVs\n",
    "# for the expert estimates (SSDR_dict_exp and CV_dict_exp)\n",
    "dcts = [SSDR_dict_exp, CV_dict_exp]\n",
    "\n",
    "# organizing of the new dataframe (df_SSDR_exp)\n",
    "df_SSDR_exp = pd.DataFrame(dcts).T\n",
    "df_SSDR_exp.reset_index(level=0, inplace=True)\n",
    "cols = {'columns': {'index': 'Polymer', 0: 'SSDR', 1: 'CV'}}\n",
    "df_SSDR_exp = df_SSDR_exp.rename(**cols)\n",
    "df_SSDR_exp\n",
    "df_SSDR_exp[['Polymer type', 'Compartment', 'Exp']\n",
    "            ] = df_SSDR_exp.Polymer.str.split(', ', expand=True)\n",
    "df_SSDR_exp = df_SSDR_exp[['Polymer type', 'Compartment', 'SSDR', 'CV']]\n",
    "\n",
    "# merging of the two dataframes consisting either the\n",
    "# already set SSDRs and CVs and expert values\n",
    "frames = [df_SSDR_basic, df_SSDR_exp]\n",
    "df_SSDR = pd.concat(frames)\n",
    "df_SSDR = df_SSDR[['Polymer type', 'Compartment', 'SSDR', 'CV']]\n",
    "df_SSDR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculationg of standard deviation based on the CV for each data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculation of the geometric standard deviation\n",
    "# according to Muller et al. (2016)\n",
    "def GSD(CV):\n",
    "    return math.exp(math.sqrt(math.log((CV**2)+1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Application of the GSD function to all rows and SSDRs and sorting by polymer type and compartment\n",
    "df_SSDR['GSD'] = df_SSDR.apply(lambda row: GSD(float(row['CV'])), axis=1)\n",
    "df_SSDR = df_SSDR.sort_values(by=['Polymer type', 'Compartment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building of a dictionary containing the name as key (composed of polymer type and compartment) and a tuple as value\n",
    "# containing the SSDR and GSD\n",
    "SSDR_dict = {}\n",
    "\n",
    "for i in range(0, len(df_SSDR)):\n",
    "    name = df_SSDR.iloc[i]['Polymer type'] + \\\n",
    "        ', ' + df_SSDR.iloc[i]['Compartment']\n",
    "    SSDR_dict[name] = df_SSDR.iloc[i]['SSDR'], df_SSDR.iloc[i]['GSD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating an excel file with the SSDR and uncertainty based on df_SSDR\n",
    "book = load_workbook(excel_name)\n",
    "writer = pd.ExcelWriter(excel_name, engine='openpyxl')\n",
    "writer.book = book\n",
    "df_SSDR.to_excel(writer, sheet_name='SSDR_with_Uncertainty', index=False)\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fate factor calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining elements (polymer type, compartment, shape, characterisitc length) for the calculation of the fate factor\n",
    "polymer_type = df_SSDR['Polymer type'].unique().tolist()\n",
    "compartment = ['air', 'fresh water', 'marine water', 'soil']\n",
    "shape_dict = {'film': 2, 'fiber': 4, 'particle': 6}\n",
    "shape = list(shape_dict.keys())\n",
    "char_length = ['0.01-0.1 mm', '0.1-1 mm', '1-10 mm']\n",
    "\n",
    "# building a list of all environmental flows containing the above describe elements\n",
    "flows = list()\n",
    "for polymer in polymer_type:\n",
    "    for form in shape:\n",
    "        for length in char_length:\n",
    "            for comp in compartment:\n",
    "                flows.append('DE: ' + polymer + ' ' + form + ' ' + length +\n",
    "                            ' ' + '(emission to ' + comp + ')')\n",
    "# setting up a dictionary for the elementary flows\n",
    "flows_dict = {}\n",
    "# building of a dictionary containing the environmental flow as key and a tuple containing the polymer type, shape,\n",
    "# characterisitc lenght and initial compartment as value\n",
    "for flow in flows:\n",
    "    names = re.findall('^DE: (.+)', flow)\n",
    "    polymer_type = names[0].split(' ')[0]\n",
    "    shape = names[0].split(' ')[1]\n",
    "    characteristic_length = float(names[0].split(\n",
    "        ' ')[2].split('-')[1].replace(',', '.'))\n",
    "    initial_compartment = re.findall(\n",
    "        'to (.+)', re.findall('\\((.*?)\\)', names[0])[-1])[0]\n",
    "    flows_dict[names[0]] = polymer_type, shape, characteristic_length, initial_compartment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining a function calculating the CV for expert judgments only, as this is necessary for the further calculation\n",
    "def CV_exp(exp):\n",
    "    a_exp = 2.5\n",
    "    b = 1.105\n",
    "    return a_exp*math.exp(b*exp)/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# setting up a dictionary for the average lifetime calculation\n",
    "t_i_dict = {}\n",
    "length = [0.1, 1.0, 10.0]\n",
    "# looping through the dataframe df_SSDR to get name, SSDR, and GSDi\n",
    "for i in range(0, len(df_SSDR)):\n",
    "    namei = df_SSDR.iloc[i]['Polymer type'] + \\\n",
    "        ', ' + df_SSDR.iloc[i]['Compartment']\n",
    "    SSDRi = df_SSDR.iloc[i]['SSDR']\n",
    "    GSDi = df_SSDR.iloc[i]['GSD']\n",
    "    for l in length:  # looping through elements in lenght and shape_dict to retrieve the lenght and shape values\n",
    "        for s in shape_dict:\n",
    "            t_l = l*1000/(2*SSDRi)\n",
    "            # calculation of the average lifetime (formular given in manuscript)\n",
    "            t_i = t_l/(1+shape_dict[s]/2)\n",
    "            # building dictionary with names of envionmental flows as keys and average lifetime and GSD as values\n",
    "            t_i_dict[namei + ', ' + s + ', ' + str(l) + ', ' + 'inf'] = t_i, GSDi, t_l                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Polymer type</th>\n",
       "      <th>Compartment</th>\n",
       "      <th>Shape</th>\n",
       "      <th>Characteristic length</th>\n",
       "      <th>Time horizon</th>\n",
       "      <th>Average lifetime</th>\n",
       "      <th>GSD</th>\n",
       "      <th>Lifetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HDPE</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>film</td>\n",
       "      <td>0.1</td>\n",
       "      <td>inf</td>\n",
       "      <td>2.136869</td>\n",
       "      <td>1.860703</td>\n",
       "      <td>4.273738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HDPE</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>fiber</td>\n",
       "      <td>0.1</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.424579</td>\n",
       "      <td>1.860703</td>\n",
       "      <td>4.273738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HDPE</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>particle</td>\n",
       "      <td>0.1</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.068435</td>\n",
       "      <td>1.860703</td>\n",
       "      <td>4.273738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HDPE</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>film</td>\n",
       "      <td>1.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>21.368692</td>\n",
       "      <td>1.860703</td>\n",
       "      <td>42.737384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HDPE</td>\n",
       "      <td>marine sediment</td>\n",
       "      <td>fiber</td>\n",
       "      <td>1.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>14.245795</td>\n",
       "      <td>1.860703</td>\n",
       "      <td>42.737384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>soil</td>\n",
       "      <td>fiber</td>\n",
       "      <td>1.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>3.899543</td>\n",
       "      <td>1.267558</td>\n",
       "      <td>11.698630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>soil</td>\n",
       "      <td>particle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>2.924658</td>\n",
       "      <td>1.267558</td>\n",
       "      <td>11.698630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>soil</td>\n",
       "      <td>film</td>\n",
       "      <td>10.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>58.493151</td>\n",
       "      <td>1.267558</td>\n",
       "      <td>116.986301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>soil</td>\n",
       "      <td>fiber</td>\n",
       "      <td>10.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>38.995434</td>\n",
       "      <td>1.267558</td>\n",
       "      <td>116.986301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>soil</td>\n",
       "      <td>particle</td>\n",
       "      <td>10.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>29.246575</td>\n",
       "      <td>1.267558</td>\n",
       "      <td>116.986301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>864 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Polymer type      Compartment     Shape  Characteristic length  \\\n",
       "0            HDPE  marine sediment      film                    0.1   \n",
       "1            HDPE  marine sediment     fiber                    0.1   \n",
       "2            HDPE  marine sediment  particle                    0.1   \n",
       "3            HDPE  marine sediment      film                    1.0   \n",
       "4            HDPE  marine sediment     fiber                    1.0   \n",
       "..            ...              ...       ...                    ...   \n",
       "859  Starch-blend             soil     fiber                    1.0   \n",
       "860  Starch-blend             soil  particle                    1.0   \n",
       "861  Starch-blend             soil      film                   10.0   \n",
       "862  Starch-blend             soil     fiber                   10.0   \n",
       "863  Starch-blend             soil  particle                   10.0   \n",
       "\n",
       "    Time horizon  Average lifetime       GSD    Lifetime  \n",
       "0            inf          2.136869  1.860703    4.273738  \n",
       "1            inf          1.424579  1.860703    4.273738  \n",
       "2            inf          1.068435  1.860703    4.273738  \n",
       "3            inf         21.368692  1.860703   42.737384  \n",
       "4            inf         14.245795  1.860703   42.737384  \n",
       "..           ...               ...       ...         ...  \n",
       "859          inf          3.899543  1.267558   11.698630  \n",
       "860          inf          2.924658  1.267558   11.698630  \n",
       "861          inf         58.493151  1.267558  116.986301  \n",
       "862          inf         38.995434  1.267558  116.986301  \n",
       "863          inf         29.246575  1.267558  116.986301  \n",
       "\n",
       "[864 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# building a dataframe (df_average_lifetime) based on the t_i_dict\n",
    "df_average_lifetime = pd.DataFrame(t_i_dict).T\n",
    "df_average_lifetime.reset_index(level=0, inplace=True)\n",
    "cols = {'columns': {'index': 'Environmental_flow', 0: 'Average lifetime', 1: 'GSD', 2: 'Lifetime'}}\n",
    "df_average_lifetime = df_average_lifetime.rename(**cols)\n",
    "\n",
    "df_average_lifetime[['Polymer type', 'Compartment', 'Shape', 'Characteristic length',\n",
    "                  'Time horizon']] = df_average_lifetime.Environmental_flow.str.split(', ', expand=True)\n",
    "df_average_lifetime = df_average_lifetime[['Polymer type', 'Compartment',\n",
    "                                     'Shape', 'Characteristic length', 'Time horizon', 'Average lifetime', 'GSD', 'Lifetime']]\n",
    "df_average_lifetime['Characteristic length'] = df_average_lifetime['Characteristic length'].astype(\n",
    "    float)\n",
    "df_average_lifetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thni\\.conda\\envs\\py39\\lib\\site-packages\\openpyxl\\workbook\\child.py:99: UserWarning: Title is more than 31 characters. Some applications may not be able to read the file\n",
      "  warnings.warn(\"Title is more than 31 characters. Some applications may not be able to read the file\")\n"
     ]
    }
   ],
   "source": [
    "# Creating an excel file with the average lifetime and uncertainty based on df_average_lifetime\n",
    "book = load_workbook(excel_name)\n",
    "writer = pd.ExcelWriter(excel_name, engine='openpyxl')\n",
    "writer.book = book\n",
    "df_average_lifetime.to_excel(\n",
    "    writer, sheet_name='Average_lifetime_with_Uncertainty', index=False)\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving the redistribution factors of plastic emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Emission</th>\n",
       "      <th>Initial compartment</th>\n",
       "      <th>Redistribution to soil</th>\n",
       "      <th>Redistribution to marine water</th>\n",
       "      <th>Redistribution to river sediment</th>\n",
       "      <th>Redistribution to marine sediment</th>\n",
       "      <th>Data quality expert judgment</th>\n",
       "      <th>CV</th>\n",
       "      <th>GSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NR/SBR</td>\n",
       "      <td>soil</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NR/SBR</td>\n",
       "      <td>fresh water</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.111</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NR/SBR</td>\n",
       "      <td>marine water</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NR/SBR</td>\n",
       "      <td>air</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Polymers with a density ≥1 g/cm3</td>\n",
       "      <td>soil</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.003</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Polymers with a density ≥1 g/cm3</td>\n",
       "      <td>fresh water</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.111</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Polymers with a density ≥1 g/cm3</td>\n",
       "      <td>marine water</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Polymers with a density ≥1 g/cm3</td>\n",
       "      <td>air</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Polymers with a density &lt;1 g/cm3</td>\n",
       "      <td>soil</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Polymers with a density &lt;1 g/cm3</td>\n",
       "      <td>fresh water</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Polymers with a density &lt;1 g/cm3</td>\n",
       "      <td>marine water</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Polymers with a density &lt;1 g/cm3</td>\n",
       "      <td>air</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227893</td>\n",
       "      <td>1.252341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Emission Initial compartment  \\\n",
       "1                             NR/SBR                soil   \n",
       "3                             NR/SBR         fresh water   \n",
       "5                             NR/SBR        marine water   \n",
       "7                             NR/SBR                 air   \n",
       "9   Polymers with a density ≥1 g/cm3                soil   \n",
       "11  Polymers with a density ≥1 g/cm3         fresh water   \n",
       "13  Polymers with a density ≥1 g/cm3        marine water   \n",
       "15  Polymers with a density ≥1 g/cm3                 air   \n",
       "17  Polymers with a density <1 g/cm3                soil   \n",
       "19  Polymers with a density <1 g/cm3         fresh water   \n",
       "21  Polymers with a density <1 g/cm3        marine water   \n",
       "23  Polymers with a density <1 g/cm3                 air   \n",
       "\n",
       "   Redistribution to soil Redistribution to marine water  \\\n",
       "1                       1                              0   \n",
       "3                       0                              0   \n",
       "5                       0                              0   \n",
       "7                   0.948                              0   \n",
       "9                    0.97                              0   \n",
       "11                      0                              0   \n",
       "13                      0                              0   \n",
       "15                  0.948                              0   \n",
       "17                   0.97                           0.03   \n",
       "19                      0                              1   \n",
       "21                      0                              1   \n",
       "23                  0.948                          0.052   \n",
       "\n",
       "   Redistribution to river sediment Redistribution to marine sediment  \\\n",
       "1                                 0                                 0   \n",
       "3                             0.889                             0.111   \n",
       "5                                 0                                 1   \n",
       "7                             0.047                             0.005   \n",
       "9                             0.027                             0.003   \n",
       "11                            0.889                             0.111   \n",
       "13                                0                                 1   \n",
       "15                            0.047                             0.005   \n",
       "17                                0                                 0   \n",
       "19                                0                                 0   \n",
       "21                                0                                 0   \n",
       "23                                0                                 0   \n",
       "\n",
       "    Data quality expert judgment        CV       GSD  \n",
       "1                            2.0  0.227893  1.252341  \n",
       "3                            2.0  0.227893  1.252341  \n",
       "5                            2.0  0.227893  1.252341  \n",
       "7                            2.0  0.227893  1.252341  \n",
       "9                            2.0  0.227893  1.252341  \n",
       "11                           2.0  0.227893  1.252341  \n",
       "13                           2.0  0.227893  1.252341  \n",
       "15                           2.0  0.227893  1.252341  \n",
       "17                           2.0  0.227893  1.252341  \n",
       "19                           2.0  0.227893  1.252341  \n",
       "21                           2.0  0.227893  1.252341  \n",
       "23                           2.0  0.227893  1.252341  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading excel file with name \"Supplementary Material 2.xlsx\" and sheet_name = 'DQ of transfer coefficients' for\n",
    "# retrieving the data quality score for the redistribution factors and storing them in the dataframe df_redistribution\n",
    "df_redistribution = pd.read_excel('Supplementary Material 2.xlsx', sheet_name='DQ of transfer coefficients', skiprows=16)\n",
    "df_redistribution['Emission'] = df_redistribution['Emission'].fillna(\n",
    "    method='ffill')\n",
    "df_redistribution = df_redistribution.rename(columns={'Redistribution to …': 'Redistribution to soil', 'Unnamed: 4': 'Redistribution to marine water',\n",
    "                                                      'Unnamed: 5': 'Redistribution to river sediment', 'Unnamed: 6': 'Redistribution to marine sediment'})\n",
    "empty_rows = df_redistribution.loc[pd.isna(\n",
    "    df_redistribution['Initial compartment']), :].index.tolist()\n",
    "df_redistribution = df_redistribution.drop(empty_rows)\n",
    "df_redistribution = df_redistribution[['Emission', 'Initial compartment', 'Redistribution to soil', 'Redistribution to marine water',\n",
    "                                       'Redistribution to river sediment', 'Redistribution to marine sediment', 'Data quality expert judgment']]\n",
    "df_redistribution['Initial compartment'] = df_redistribution['Initial compartment'].str.lower()\n",
    "# calculation of the CV and GSD based on the defined functions\n",
    "df_redistribution['CV'] = df_redistribution.apply(\n",
    "    lambda row: CV_exp(float(row['Data quality expert judgment'])), axis=1)\n",
    "df_redistribution['GSD'] = df_redistribution.apply(\n",
    "    lambda row: GSD(float(row['CV'])), axis=1)\n",
    "\n",
    "df_redistribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation of plastic emissions fate factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "runs = 10000\n",
    "ff_total_dict = {}\n",
    "#defining list for low and high density polymers\n",
    "low_density_poly = ['HDPE', 'LDPE', 'PE', 'PEA', 'PES', 'PP']\n",
    "high_density_poly = ['PA', 'PBAT', 'PBS', 'PBSA', 'PBSe', 'PBSeT', 'PC', 'PCL', 'PET', 'PHA', 'PHB', 'PHBV', 'PLA(-blend)', 'PS', 'PU', 'PVC', 'Starch-blend']\n",
    "#beginning of the fate factor calculation\n",
    "for key, value in flows_dict.items():#getting items of the flows_dict as key and value as the basis\n",
    "    #retrieving the compartment, average lifetime and GSD for the environmental flows\n",
    "    t_i = df_average_lifetime.loc[(df_average_lifetime['Polymer type'] == value[0]) & (df_average_lifetime['Shape'] == value[1]) \n",
    "                     & (df_average_lifetime['Characteristic length'] == value[2]),\n",
    "                        ['Compartment', 'Average lifetime', 'GSD', 'Lifetime']].to_numpy()\n",
    "    #first case distinction for polymers which have a low density\n",
    "    if value[0] in low_density_poly:\n",
    "        #retrieving redistribution factors from data frame (df_redistribution)\n",
    "        redistribution = df_redistribution.loc[(df_redistribution['Emission'] == 'Polymers with a density <1 g/cm3') \n",
    "                                               & (df_redistribution['Initial compartment'] == value[3]), \n",
    "                                               ['Redistribution to soil', 'Redistribution to marine water', \n",
    "                                                'Redistribution to river sediment', 'Redistribution to marine sediment', \n",
    "                                                'GSD']].to_numpy()\n",
    "        #Running the Monte-Carlo simulation for each redistribtuion type excluding zero values\n",
    "        if redistribution[0][0] != 0:\n",
    "            rng = np.random.default_rng(seed=42)\n",
    "            red_soil = rng.lognormal(math.log(redistribution[0][0]), math.log(redistribution[0][4]), runs)\n",
    "        else:\n",
    "            red_soil = 0\n",
    "        if redistribution[0][1] != 0:\n",
    "            rng = np.random.default_rng(seed=45)\n",
    "            red_marine_water = rng.lognormal(math.log(redistribution[0][1]), math.log(redistribution[0][4]), runs)\n",
    "        else:\n",
    "            red_marine_water = 0\n",
    "        if redistribution[0][2] != 0:\n",
    "            rng = np.random.default_rng(seed=50)\n",
    "            red_river_sediment = rng.lognormal(math.log(redistribution[0][2]), math.log(redistribution[0][4]), runs)\n",
    "        else:\n",
    "            red_river_sediment = 0\n",
    "        if redistribution[0][3] != 0:\n",
    "            rng = np.random.default_rng(seed=9)\n",
    "            red_marine_sediment = rng.lognormal(math.log(redistribution[0][3]), math.log(redistribution[0][4]), runs)\n",
    "        else:\n",
    "            red_marine_sediment = 0\n",
    "        #summing up the redistribution factors to a total value\n",
    "        red_tot = red_soil + red_marine_water + red_river_sediment + red_marine_sediment\n",
    "        #normalizing the redistribution factors as the sum should not be greater than 1 (percentages!)\n",
    "        red_soil = red_soil/red_tot\n",
    "        red_marine_water = red_marine_water/red_tot\n",
    "        red_river_sediment = red_river_sediment/red_tot\n",
    "        red_marine_sediment = red_marine_sediment/red_tot\n",
    "        #Running the Monte-Carlo simulation for each average lifetime\n",
    "        rng = np.random.default_rng(seed=5)\n",
    "        t_i_marine_sediment = rng.lognormal(math.log(t_i[0][1]), math.log(t_i[0][2]), runs)\n",
    "        t_i_marine_water = rng.lognormal(math.log(t_i[1][1]), math.log(t_i[1][2]), runs)\n",
    "        t_i_river_sediment = rng.lognormal(math.log(t_i[2][1]), math.log(t_i[2][2]), runs)\n",
    "        t_i_soil = rng.lognormal(math.log(t_i[3][1]), math.log(t_i[3][2]), runs)\n",
    "        #calculating the fate factors for each compartment\n",
    "        FF_marine_sediment = t_i_marine_sediment*red_marine_sediment\n",
    "        FF_marine_water =  t_i_marine_water*red_marine_water\n",
    "        FF_river_sediment =  t_i_river_sediment*red_river_sediment \n",
    "        FF_soil = t_i_soil*red_soil\n",
    "        #calculating the total fate factors        \n",
    "        FF_total = FF_marine_sediment + FF_marine_water + FF_river_sediment + FF_soil\n",
    "        if np.sum(FF_marine_sediment) != 0:\n",
    "            FF_marine_sediment = geometric_mean(FF_marine_sediment)\n",
    "        else:\n",
    "            FF_marine_sediment = 0\n",
    "        if np.sum(FF_marine_water) != 0:\n",
    "            FF_marine_water = geometric_mean(FF_marine_water)\n",
    "        else:\n",
    "            FF_marine_water = 0\n",
    "        if np.sum(FF_river_sediment) != 0:\n",
    "            FF_river_sediment = geometric_mean(FF_river_sediment)\n",
    "        else:\n",
    "            FF_river_sediment = 0\n",
    "        if np.sum(FF_soil) != 0:\n",
    "            FF_soil = geometric_mean(FF_soil)\n",
    "        else:\n",
    "            FF_soil = 0\n",
    "        FF_total_sum = FF_marine_sediment + FF_marine_water + FF_river_sediment + FF_soil\n",
    "\n",
    "        #creating a dictionary containing the geometric mean of each fate factors compartment and the GSD for the total fate factor\n",
    "        flows_dict[key] = flows_dict[key] + (FF_marine_sediment,\n",
    "                                             FF_marine_water,\n",
    "                                             FF_river_sediment,\n",
    "                                             FF_soil,\n",
    "                                             FF_total_sum,\n",
    "                                             scipy.stats.gstd(FF_total),\n",
    "                                             t_i[0][3],\n",
    "                                             t_i[1][3],\n",
    "                                             t_i[2][3],\n",
    "                                             t_i[3][3])\n",
    "    #second case distinction for polymers which have a high density\n",
    "    elif value[0] in high_density_poly:\n",
    "        redistribution = df_redistribution.loc[(df_redistribution['Emission'] == 'Polymers with a density ≥1 g/cm3') \n",
    "                                               & (df_redistribution['Initial compartment'] == value[3]), \n",
    "                                               ['Redistribution to soil', 'Redistribution to marine water', \n",
    "                                                'Redistribution to river sediment', 'Redistribution to marine sediment', \n",
    "                                                'GSD']].to_numpy()\n",
    "        \n",
    "        if redistribution[0][0] != 0:\n",
    "            rng = np.random.default_rng(seed=42)\n",
    "            red_soil = rng.lognormal(math.log(redistribution[0][0]), math.log(redistribution[0][4]), runs)\n",
    "        else:\n",
    "            red_soil = 0\n",
    "        if redistribution[0][1] != 0:\n",
    "            rng = np.random.default_rng(seed=45)\n",
    "            red_marine_water = rng.lognormal(math.log(redistribution[0][1]), math.log(redistribution[0][4]), runs)\n",
    "        else:\n",
    "            red_marine_water = 0\n",
    "        if redistribution[0][2] != 0:\n",
    "            rng = np.random.default_rng(seed=50)\n",
    "            red_river_sediment = rng.lognormal(math.log(redistribution[0][2]), math.log(redistribution[0][4]), runs)\n",
    "        else:\n",
    "            red_river_sediment = 0\n",
    "        if redistribution[0][3] != 0:\n",
    "            rng = np.random.default_rng(seed=9)\n",
    "            red_marine_sediment = rng.lognormal(math.log(redistribution[0][3]), math.log(redistribution[0][4]), runs)\n",
    "        else:\n",
    "            red_marine_sediment = 0\n",
    "        red_tot = red_soil + red_marine_water + red_river_sediment + red_marine_sediment\n",
    "        \n",
    "        red_soil = red_soil/red_tot\n",
    "        red_marine_water = red_marine_water/red_tot\n",
    "        red_river_sediment = red_river_sediment/red_tot\n",
    "        red_marine_sediment = red_marine_sediment/red_tot\n",
    "        \n",
    "        rng = np.random.default_rng(seed=5)\n",
    "        t_i_marine_sediment = rng.lognormal(math.log(t_i[0][1]), math.log(t_i[0][2]), runs)\n",
    "        t_i_marine_water = rng.lognormal(math.log(t_i[1][1]), math.log(t_i[1][2]), runs)\n",
    "        t_i_river_sediment = rng.lognormal(math.log(t_i[2][1]), math.log(t_i[2][2]), runs)\n",
    "        t_i_soil = rng.lognormal(math.log(t_i[3][1]), math.log(t_i[3][2]), runs)\n",
    "\n",
    "        FF_marine_sediment = t_i_marine_sediment*red_marine_sediment\n",
    "        FF_marine_water =  t_i_marine_water*red_marine_water\n",
    "        FF_river_sediment =  t_i_river_sediment*red_river_sediment \n",
    "        FF_soil = t_i_soil*red_soil\n",
    "        \n",
    "        FF_total = FF_marine_sediment + FF_marine_water + FF_river_sediment + FF_soil\n",
    "        if np.sum(FF_marine_sediment) != 0:\n",
    "            FF_marine_sediment = geometric_mean(FF_marine_sediment)\n",
    "        else:\n",
    "            FF_marine_sediment = 0\n",
    "        if np.sum(FF_marine_water) != 0:\n",
    "            FF_marine_water = geometric_mean(FF_marine_water)\n",
    "        else:\n",
    "            FF_marine_water = 0\n",
    "        if np.sum(FF_river_sediment) != 0:\n",
    "            FF_river_sediment = geometric_mean(FF_river_sediment)\n",
    "        else:\n",
    "            FF_river_sediment = 0\n",
    "        if np.sum(FF_soil) != 0:\n",
    "            FF_soil = geometric_mean(FF_soil)\n",
    "        else:\n",
    "            FF_soil = 0\n",
    "        FF_total_sum = FF_marine_sediment + FF_marine_water + FF_river_sediment + FF_soil\n",
    "            \n",
    "        flows_dict[key] = flows_dict[key] + (FF_marine_sediment,\n",
    "                                             FF_marine_water,\n",
    "                                             FF_river_sediment,\n",
    "                                             FF_soil,\n",
    "                                             FF_total_sum,\n",
    "                                             scipy.stats.gstd(FF_total),\n",
    "                                             t_i[0][3],\n",
    "                                             t_i[1][3],\n",
    "                                             t_i[2][3],\n",
    "                                             t_i[3][3])\n",
    "    #third case distinction for polymers which are rubbers\n",
    "    else:\n",
    "        redistribution = df_redistribution.loc[(df_redistribution['Emission'] == 'NR/SBR') \n",
    "                                               & (df_redistribution['Initial compartment'] == value[3]), \n",
    "                                               ['Redistribution to soil', 'Redistribution to marine water', \n",
    "                                                'Redistribution to river sediment', 'Redistribution to marine sediment', \n",
    "                                                'GSD']].to_numpy()\n",
    "        if redistribution.size == 0:\n",
    "            redistribution = np.array([[0, 0, 0, 1]])\n",
    "        else:\n",
    "            if redistribution[0][0] != 0:\n",
    "                rng = np.random.default_rng(seed=42)\n",
    "                red_soil = rng.lognormal(math.log(redistribution[0][0]), math.log(redistribution[0][4]), runs)\n",
    "            else:\n",
    "                red_soil = 0\n",
    "            if redistribution[0][1] != 0:\n",
    "                rng = np.random.default_rng(seed=45)\n",
    "                red_marine_water = rng.lognormal(math.log(redistribution[0][1]), math.log(redistribution[0][4]), runs)\n",
    "            else:\n",
    "                red_marine_water = 0\n",
    "            if redistribution[0][2] != 0:\n",
    "                rng = np.random.default_rng(seed=50)\n",
    "                red_river_sediment = rng.lognormal(math.log(redistribution[0][2]), math.log(redistribution[0][4]), runs)\n",
    "            else:\n",
    "                red_river_sediment = 0\n",
    "            if redistribution[0][3] != 0:\n",
    "                rng = np.random.default_rng(seed=9)\n",
    "                red_marine_sediment = rng.lognormal(math.log(redistribution[0][3]), math.log(redistribution[0][4]), runs)\n",
    "            else:\n",
    "                red_marine_sediment = 0\n",
    "            red_tot = red_soil + red_marine_water + red_river_sediment + red_marine_sediment\n",
    "\n",
    "            red_soil = red_soil/red_tot\n",
    "            red_marine_water = red_marine_water/red_tot\n",
    "            red_river_sediment = red_river_sediment/red_tot\n",
    "            red_marine_sediment = red_marine_sediment/red_tot\n",
    "            \n",
    "            rng = np.random.default_rng(seed=5)\n",
    "            t_i_marine_sediment = rng.lognormal(math.log(t_i[0][1]), math.log(t_i[0][2]), runs)\n",
    "            t_i_marine_water = rng.lognormal(math.log(t_i[1][1]), math.log(t_i[1][2]), runs)\n",
    "            t_i_river_sediment = rng.lognormal(math.log(t_i[2][1]), math.log(t_i[2][2]), runs)\n",
    "            t_i_soil = rng.lognormal(math.log(t_i[3][1]), math.log(t_i[3][2]), runs)\n",
    "\n",
    "            FF_marine_sediment = t_i_marine_sediment*red_marine_sediment\n",
    "            FF_marine_water =  t_i_marine_water*red_marine_water\n",
    "            FF_river_sediment =  t_i_river_sediment*red_river_sediment \n",
    "            FF_soil = t_i_soil*red_soil\n",
    "\n",
    "            FF_total = FF_marine_sediment + FF_marine_water + FF_river_sediment + FF_soil\n",
    "            if np.sum(FF_marine_sediment) != 0:\n",
    "                FF_marine_sediment = geometric_mean(FF_marine_sediment)\n",
    "            else:\n",
    "                FF_marine_sediment = 0\n",
    "            if np.sum(FF_marine_water) != 0:\n",
    "                FF_marine_water = geometric_mean(FF_marine_water)\n",
    "            else:\n",
    "                FF_marine_water = 0\n",
    "            if np.sum(FF_river_sediment) != 0:\n",
    "                FF_river_sediment = geometric_mean(FF_river_sediment)\n",
    "            else:\n",
    "                FF_river_sediment = 0\n",
    "            if np.sum(FF_soil) != 0:\n",
    "                FF_soil = geometric_mean(FF_soil)\n",
    "            else:\n",
    "                FF_soil = 0\n",
    "            FF_total_sum = FF_marine_sediment + FF_marine_water + FF_river_sediment + FF_soil\n",
    "\n",
    "            flows_dict[key] = flows_dict[key] + (FF_marine_sediment,\n",
    "                                                 FF_marine_water,\n",
    "                                                 FF_river_sediment,\n",
    "                                                 FF_soil,\n",
    "                                                 FF_total_sum,\n",
    "                                                 scipy.stats.gstd(FF_total),\n",
    "                                                 t_i[0][3],\n",
    "                                                 t_i[1][3],\n",
    "                                                 t_i[2][3],\n",
    "                                                 t_i[3][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Environmental_flow</th>\n",
       "      <th>Polymer type</th>\n",
       "      <th>Shape</th>\n",
       "      <th>Length</th>\n",
       "      <th>Compartment</th>\n",
       "      <th>FF_marine_sediment</th>\n",
       "      <th>FF_marine_water</th>\n",
       "      <th>FF_river_sediment</th>\n",
       "      <th>FF_soil</th>\n",
       "      <th>FF_total</th>\n",
       "      <th>GSD</th>\n",
       "      <th>Lifetime_marine_sediment</th>\n",
       "      <th>Lifetime_marine_water</th>\n",
       "      <th>Lifetime_river_sediment</th>\n",
       "      <th>Lifetime_soil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HDPE film 0.01-0.1 mm (emission to air)</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>film</td>\n",
       "      <td>0.1</td>\n",
       "      <td>air</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111558</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.030544</td>\n",
       "      <td>2.142101</td>\n",
       "      <td>1.783331</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HDPE film 0.01-0.1 mm (emission to fresh water)</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>film</td>\n",
       "      <td>0.1</td>\n",
       "      <td>fresh water</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.145031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.145031</td>\n",
       "      <td>1.792868</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HDPE film 0.01-0.1 mm (emission to marine water)</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>film</td>\n",
       "      <td>0.1</td>\n",
       "      <td>marine water</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.145031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.145031</td>\n",
       "      <td>1.792868</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HDPE film 0.01-0.1 mm (emission to soil)</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>film</td>\n",
       "      <td>0.1</td>\n",
       "      <td>soil</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064432</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.079991</td>\n",
       "      <td>2.144423</td>\n",
       "      <td>1.812068</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HDPE film 0.1-1 mm (emission to air)</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>film</td>\n",
       "      <td>1.0</td>\n",
       "      <td>air</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.115576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.305437</td>\n",
       "      <td>21.421013</td>\n",
       "      <td>1.783331</td>\n",
       "      <td>42.737384</td>\n",
       "      <td>42.737384</td>\n",
       "      <td>42.737384</td>\n",
       "      <td>42.737384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>Starch-blend particle 0.1-1 mm (emission to soil)</td>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>particle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>soil</td>\n",
       "      <td>0.008855</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078905</td>\n",
       "      <td>2.837959</td>\n",
       "      <td>2.925719</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>11.698630</td>\n",
       "      <td>4.936309</td>\n",
       "      <td>11.698630</td>\n",
       "      <td>11.698630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>Starch-blend particle 1-10 mm (emission to air)</td>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>particle</td>\n",
       "      <td>10.0</td>\n",
       "      <td>air</td>\n",
       "      <td>0.147421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.372026</td>\n",
       "      <td>27.705542</td>\n",
       "      <td>29.224989</td>\n",
       "      <td>1.250641</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>49.363090</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>116.986301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>Starch-blend particle 1-10 mm (emission to fre...</td>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>particle</td>\n",
       "      <td>10.0</td>\n",
       "      <td>fresh water</td>\n",
       "      <td>3.258542</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.839069</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.097611</td>\n",
       "      <td>1.332288</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>49.363090</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>116.986301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>Starch-blend particle 1-10 mm (emission to mar...</td>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>particle</td>\n",
       "      <td>10.0</td>\n",
       "      <td>marine water</td>\n",
       "      <td>29.453380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.453380</td>\n",
       "      <td>1.389288</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>49.363090</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>116.986301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>Starch-blend particle 1-10 mm (emission to soil)</td>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>particle</td>\n",
       "      <td>10.0</td>\n",
       "      <td>soil</td>\n",
       "      <td>0.088550</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.789049</td>\n",
       "      <td>28.379589</td>\n",
       "      <td>29.257189</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>49.363090</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>116.986301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>864 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Environmental_flow  Polymer type  \\\n",
       "0              HDPE film 0.01-0.1 mm (emission to air)          HDPE   \n",
       "1      HDPE film 0.01-0.1 mm (emission to fresh water)          HDPE   \n",
       "2     HDPE film 0.01-0.1 mm (emission to marine water)          HDPE   \n",
       "3             HDPE film 0.01-0.1 mm (emission to soil)          HDPE   \n",
       "4                 HDPE film 0.1-1 mm (emission to air)          HDPE   \n",
       "..                                                 ...           ...   \n",
       "859  Starch-blend particle 0.1-1 mm (emission to soil)  Starch-blend   \n",
       "860    Starch-blend particle 1-10 mm (emission to air)  Starch-blend   \n",
       "861  Starch-blend particle 1-10 mm (emission to fre...  Starch-blend   \n",
       "862  Starch-blend particle 1-10 mm (emission to mar...  Starch-blend   \n",
       "863   Starch-blend particle 1-10 mm (emission to soil)  Starch-blend   \n",
       "\n",
       "        Shape  Length   Compartment  FF_marine_sediment  FF_marine_water  \\\n",
       "0        film     0.1           air            0.000000         0.111558   \n",
       "1        film     0.1   fresh water            0.000000         2.145031   \n",
       "2        film     0.1  marine water            0.000000         2.145031   \n",
       "3        film     0.1          soil            0.000000         0.064432   \n",
       "4        film     1.0           air            0.000000         1.115576   \n",
       "..        ...     ...           ...                 ...              ...   \n",
       "859  particle     1.0          soil            0.008855         0.000000   \n",
       "860  particle    10.0           air            0.147421         0.000000   \n",
       "861  particle    10.0   fresh water            3.258542         0.000000   \n",
       "862  particle    10.0  marine water           29.453380         0.000000   \n",
       "863  particle    10.0          soil            0.088550         0.000000   \n",
       "\n",
       "     FF_river_sediment    FF_soil   FF_total       GSD  \\\n",
       "0             0.000000   2.030544   2.142101  1.783331   \n",
       "1             0.000000   0.000000   2.145031  1.792868   \n",
       "2             0.000000   0.000000   2.145031  1.792868   \n",
       "3             0.000000   2.079991   2.144423  1.812068   \n",
       "4             0.000000  20.305437  21.421013  1.783331   \n",
       "..                 ...        ...        ...       ...   \n",
       "859           0.078905   2.837959   2.925719  1.257224   \n",
       "860           1.372026  27.705542  29.224989  1.250641   \n",
       "861          25.839069   0.000000  29.097611  1.332288   \n",
       "862           0.000000   0.000000  29.453380  1.389288   \n",
       "863           0.789049  28.379589  29.257189  1.257224   \n",
       "\n",
       "     Lifetime_marine_sediment  Lifetime_marine_water  Lifetime_river_sediment  \\\n",
       "0                    4.273738               4.273738                 4.273738   \n",
       "1                    4.273738               4.273738                 4.273738   \n",
       "2                    4.273738               4.273738                 4.273738   \n",
       "3                    4.273738               4.273738                 4.273738   \n",
       "4                   42.737384              42.737384                42.737384   \n",
       "..                        ...                    ...                      ...   \n",
       "859                 11.698630               4.936309                11.698630   \n",
       "860                116.986301              49.363090               116.986301   \n",
       "861                116.986301              49.363090               116.986301   \n",
       "862                116.986301              49.363090               116.986301   \n",
       "863                116.986301              49.363090               116.986301   \n",
       "\n",
       "     Lifetime_soil  \n",
       "0         4.273738  \n",
       "1         4.273738  \n",
       "2         4.273738  \n",
       "3         4.273738  \n",
       "4        42.737384  \n",
       "..             ...  \n",
       "859      11.698630  \n",
       "860     116.986301  \n",
       "861     116.986301  \n",
       "862     116.986301  \n",
       "863     116.986301  \n",
       "\n",
       "[864 rows x 15 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FF = pd.DataFrame.from_dict(flows_dict, orient='index')\n",
    "df_FF.reset_index(level=0, inplace=True)\n",
    "cols = {'columns': {'index': 'Environmental_flow', 0: 'Polymer type', 1: 'Shape', 2: 'Length', 3: 'Compartment',\n",
    "                    4: 'FF_marine_sediment', 5: 'FF_marine_water',\n",
    "                    6: 'FF_river_sediment', 7: 'FF_soil', 8: 'FF_total', 9: 'GSD',\n",
    "                    10: 'Lifetime_marine_sediment', 11: 'Lifetime_marine_water',\n",
    "                    12: 'Lifetime_river_sediment', 13: 'Lifetime_soil'}}\n",
    "df_FF = df_FF.rename(**cols)\n",
    "df_FF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Environmental_flow</th>\n",
       "      <th>Polymer type</th>\n",
       "      <th>Shape</th>\n",
       "      <th>Length</th>\n",
       "      <th>Compartment</th>\n",
       "      <th>FF_marine_sediment</th>\n",
       "      <th>FF_marine_water</th>\n",
       "      <th>FF_river_sediment</th>\n",
       "      <th>FF_soil</th>\n",
       "      <th>FF_total</th>\n",
       "      <th>GSD</th>\n",
       "      <th>Lifetime_marine_sediment</th>\n",
       "      <th>Lifetime_marine_water</th>\n",
       "      <th>Lifetime_river_sediment</th>\n",
       "      <th>Lifetime_soil</th>\n",
       "      <th>Time horizon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HDPE film 0.01-0.1 mm (emission to air)</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>film</td>\n",
       "      <td>0.1</td>\n",
       "      <td>air</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111558</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.030544</td>\n",
       "      <td>2.142101</td>\n",
       "      <td>1.783331</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HDPE film 0.01-0.1 mm (emission to fresh water)</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>film</td>\n",
       "      <td>0.1</td>\n",
       "      <td>fresh water</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.145031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.145031</td>\n",
       "      <td>1.792868</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HDPE film 0.01-0.1 mm (emission to marine water)</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>film</td>\n",
       "      <td>0.1</td>\n",
       "      <td>marine water</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.145031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.145031</td>\n",
       "      <td>1.792868</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HDPE film 0.01-0.1 mm (emission to soil)</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>film</td>\n",
       "      <td>0.1</td>\n",
       "      <td>soil</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064432</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.079991</td>\n",
       "      <td>2.144423</td>\n",
       "      <td>1.812068</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>4.273738</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HDPE film 0.1-1 mm (emission to air)</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>film</td>\n",
       "      <td>1.0</td>\n",
       "      <td>air</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.115576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.305437</td>\n",
       "      <td>21.421013</td>\n",
       "      <td>1.783331</td>\n",
       "      <td>42.737384</td>\n",
       "      <td>42.737384</td>\n",
       "      <td>42.737384</td>\n",
       "      <td>42.737384</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>Starch-blend particle 0.1-1 mm (emission to soil)</td>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>particle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>soil</td>\n",
       "      <td>0.008855</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078905</td>\n",
       "      <td>2.837959</td>\n",
       "      <td>2.925719</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>11.698630</td>\n",
       "      <td>4.936309</td>\n",
       "      <td>11.698630</td>\n",
       "      <td>11.698630</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>Starch-blend particle 1-10 mm (emission to air)</td>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>particle</td>\n",
       "      <td>10.0</td>\n",
       "      <td>air</td>\n",
       "      <td>0.147421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.372026</td>\n",
       "      <td>27.705542</td>\n",
       "      <td>29.224989</td>\n",
       "      <td>1.250641</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>49.363090</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>Starch-blend particle 1-10 mm (emission to fre...</td>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>particle</td>\n",
       "      <td>10.0</td>\n",
       "      <td>fresh water</td>\n",
       "      <td>3.258542</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.839069</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.097611</td>\n",
       "      <td>1.332288</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>49.363090</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>Starch-blend particle 1-10 mm (emission to mar...</td>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>particle</td>\n",
       "      <td>10.0</td>\n",
       "      <td>marine water</td>\n",
       "      <td>29.453380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.453380</td>\n",
       "      <td>1.389288</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>49.363090</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>Starch-blend particle 1-10 mm (emission to soil)</td>\n",
       "      <td>Starch-blend</td>\n",
       "      <td>particle</td>\n",
       "      <td>10.0</td>\n",
       "      <td>soil</td>\n",
       "      <td>0.088550</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.789049</td>\n",
       "      <td>28.379589</td>\n",
       "      <td>29.257189</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>49.363090</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>116.986301</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3456 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Environmental_flow  Polymer type  \\\n",
       "0              HDPE film 0.01-0.1 mm (emission to air)          HDPE   \n",
       "1      HDPE film 0.01-0.1 mm (emission to fresh water)          HDPE   \n",
       "2     HDPE film 0.01-0.1 mm (emission to marine water)          HDPE   \n",
       "3             HDPE film 0.01-0.1 mm (emission to soil)          HDPE   \n",
       "4                 HDPE film 0.1-1 mm (emission to air)          HDPE   \n",
       "..                                                 ...           ...   \n",
       "859  Starch-blend particle 0.1-1 mm (emission to soil)  Starch-blend   \n",
       "860    Starch-blend particle 1-10 mm (emission to air)  Starch-blend   \n",
       "861  Starch-blend particle 1-10 mm (emission to fre...  Starch-blend   \n",
       "862  Starch-blend particle 1-10 mm (emission to mar...  Starch-blend   \n",
       "863   Starch-blend particle 1-10 mm (emission to soil)  Starch-blend   \n",
       "\n",
       "        Shape  Length   Compartment  FF_marine_sediment  FF_marine_water  \\\n",
       "0        film     0.1           air            0.000000         0.111558   \n",
       "1        film     0.1   fresh water            0.000000         2.145031   \n",
       "2        film     0.1  marine water            0.000000         2.145031   \n",
       "3        film     0.1          soil            0.000000         0.064432   \n",
       "4        film     1.0           air            0.000000         1.115576   \n",
       "..        ...     ...           ...                 ...              ...   \n",
       "859  particle     1.0          soil            0.008855         0.000000   \n",
       "860  particle    10.0           air            0.147421         0.000000   \n",
       "861  particle    10.0   fresh water            3.258542         0.000000   \n",
       "862  particle    10.0  marine water           29.453380         0.000000   \n",
       "863  particle    10.0          soil            0.088550         0.000000   \n",
       "\n",
       "     FF_river_sediment    FF_soil   FF_total       GSD  \\\n",
       "0             0.000000   2.030544   2.142101  1.783331   \n",
       "1             0.000000   0.000000   2.145031  1.792868   \n",
       "2             0.000000   0.000000   2.145031  1.792868   \n",
       "3             0.000000   2.079991   2.144423  1.812068   \n",
       "4             0.000000  20.305437  21.421013  1.783331   \n",
       "..                 ...        ...        ...       ...   \n",
       "859           0.078905   2.837959   2.925719  1.257224   \n",
       "860           1.372026  27.705542  29.224989  1.250641   \n",
       "861          25.839069   0.000000  29.097611  1.332288   \n",
       "862           0.000000   0.000000  29.453380  1.389288   \n",
       "863           0.789049  28.379589  29.257189  1.257224   \n",
       "\n",
       "     Lifetime_marine_sediment  Lifetime_marine_water  Lifetime_river_sediment  \\\n",
       "0                    4.273738               4.273738                 4.273738   \n",
       "1                    4.273738               4.273738                 4.273738   \n",
       "2                    4.273738               4.273738                 4.273738   \n",
       "3                    4.273738               4.273738                 4.273738   \n",
       "4                   42.737384              42.737384                42.737384   \n",
       "..                        ...                    ...                      ...   \n",
       "859                 11.698630               4.936309                11.698630   \n",
       "860                116.986301              49.363090               116.986301   \n",
       "861                116.986301              49.363090               116.986301   \n",
       "862                116.986301              49.363090               116.986301   \n",
       "863                116.986301              49.363090               116.986301   \n",
       "\n",
       "     Lifetime_soil Time horizon  \n",
       "0         4.273738          inf  \n",
       "1         4.273738          inf  \n",
       "2         4.273738          inf  \n",
       "3         4.273738          inf  \n",
       "4        42.737384          inf  \n",
       "..             ...          ...  \n",
       "859      11.698630         1000  \n",
       "860     116.986301         1000  \n",
       "861     116.986301         1000  \n",
       "862     116.986301         1000  \n",
       "863     116.986301         1000  \n",
       "\n",
       "[3456 rows x 16 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FF['Time horizon'] = 'inf'\n",
    "df_FF = df_FF.append(df_FF.iloc[0:864])\n",
    "df_FF = df_FF.append(df_FF.iloc[0:864])\n",
    "df_FF = df_FF.append(df_FF.iloc[0:864])\n",
    "df_FF.iloc[864:2*864+1, 15] = 100\n",
    "df_FF.iloc[2*864:3*864+1, 15] = 500\n",
    "df_FF.iloc[3*864:4*864+1, 15] = 1000\n",
    "\n",
    "df_FF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FF_time(shape, time_horizon, lifetime, FF, poly, init_comp, comp):\n",
    "    if time_horizon == 'inf' or time_horizon > lifetime:\n",
    "        return FF \n",
    "    else:\n",
    "        time_horizon = int(time_horizon)\n",
    "        if shape == 'film':\n",
    "            a = 1\n",
    "        elif shape == 'fiber':\n",
    "            a = 2\n",
    "        elif shape == 'particle':\n",
    "            a = 3\n",
    "        if poly in low_density_poly:\n",
    "            red = df_redistribution.loc[(df_redistribution['Emission'] == 'Polymers with a density <1 g/cm3') \n",
    "                                           & (df_redistribution['Initial compartment'] == init_comp), \n",
    "                                           ['Redistribution to soil', 'Redistribution to marine water', \n",
    "                                            'Redistribution to river sediment', 'Redistribution to marine sediment']].to_numpy()\n",
    "            if comp == 'marine_sediment':\n",
    "                red = red[0][3]                    \n",
    "            elif comp == 'marine_water':\n",
    "                red = red[0][1]                    \n",
    "            elif comp == 'river_sediment':\n",
    "                red = red[0][2]\n",
    "            elif comp == 'soil':\n",
    "                red = red[0][0]\n",
    "            return ((lifetime/(a+1)) * (1-((1-(time_horizon/lifetime))**(a+1))))*red    \n",
    "        elif poly in high_density_poly:\n",
    "            red = df_redistribution.loc[(df_redistribution['Emission'] == 'Polymers with a density ≥1 g/cm3') \n",
    "                                           & (df_redistribution['Initial compartment'] == init_comp), \n",
    "                                           ['Redistribution to soil', 'Redistribution to marine water', \n",
    "                                            'Redistribution to river sediment', 'Redistribution to marine sediment']].to_numpy()\n",
    "            if comp == 'marine_sediment':\n",
    "                red = red[0][3]                    \n",
    "            elif comp == 'marine_water':\n",
    "                red = red[0][1]                    \n",
    "            elif comp == 'river_sediment':\n",
    "                red = red[0][2]\n",
    "            elif comp == 'soil':\n",
    "                red = red[0][0]\n",
    "            return ((lifetime/(a+1)) * (1-((1-(time_horizon/lifetime))**(a+1))))*red\n",
    "        else:\n",
    "            red = df_redistribution.loc[(df_redistribution['Emission'] == 'NR/SBR') \n",
    "                                           & (df_redistribution['Initial compartment'] == init_comp), \n",
    "                                           ['Redistribution to soil', 'Redistribution to marine water', \n",
    "                                            'Redistribution to river sediment', 'Redistribution to marine sediment']].to_numpy()\n",
    "            if comp == 'marine_sediment':\n",
    "                red = red[0][3]                    \n",
    "            elif comp == 'marine_water':\n",
    "                red = red[0][1]                    \n",
    "            elif comp == 'river_sediment':\n",
    "                red = red[0][2]\n",
    "            elif comp == 'soil':\n",
    "                red = red[0][0]\n",
    "            return ((lifetime/(a+1)) * (1-((1-(time_horizon/lifetime))**(a+1))))*red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FF_total(FF_marine_sediment, FF_marine_water, FF_river_sediment, FF_soil, time_horizon, FF):\n",
    "    if time_horizon == 'inf':\n",
    "        return FF\n",
    "    else:\n",
    "        return FF_marine_sediment + FF_marine_water + FF_river_sediment + FF_soil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_FF['FF_marine_sediment'] = df_FF.apply(lambda row : FF_time(row['Shape'], row['Time horizon'], row['Lifetime_marine_sediment'], row['FF_marine_sediment'], row['Polymer type'], row['Compartment'], 'marine_sediment'), axis = 1)\n",
    "df_FF['FF_marine_water'] = df_FF.apply(lambda row : FF_time(row['Shape'], row['Time horizon'], row['Lifetime_marine_water'], row['FF_marine_water'], row['Polymer type'], row['Compartment'], 'marine_water'), axis = 1)\n",
    "df_FF['FF_river_sediment'] = df_FF.apply(lambda row : FF_time(row['Shape'], row['Time horizon'], row['Lifetime_river_sediment'], row['FF_river_sediment'], row['Polymer type'], row['Compartment'], 'river_sediment'), axis = 1)\n",
    "df_FF['FF_soil'] = df_FF.apply(lambda row : FF_time(row['Shape'], row['Time horizon'], row['Lifetime_soil'], row['FF_soil'], row['Polymer type'], row['Compartment'], 'soil'), axis = 1)\n",
    "df_FF['FF_total'] = df_FF.apply(lambda row : FF_total(row['FF_marine_sediment'], row['FF_marine_water'], row['FF_river_sediment'], row['FF_soil'], row['Time horizon'], row['FF_total']), axis = 1)\n",
    "df_FF = df_FF[['Environmental_flow', 'Time horizon', 'FF_marine_sediment', 'FF_marine_water', 'FF_river_sediment', 'FF_soil', 'FF_total', 'GSD']]\n",
    "df_FF = df_FF.sort_values(by=['Environmental_flow', 'Time horizon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thni\\.conda\\envs\\py39\\lib\\site-packages\\openpyxl\\workbook\\child.py:99: UserWarning: Title is more than 31 characters. Some applications may not be able to read the file\n",
      "  warnings.warn(\"Title is more than 31 characters. Some applications may not be able to read the file\")\n"
     ]
    }
   ],
   "source": [
    "# creation of excel file consisting of enviornmental flows and fate factors\n",
    "book = load_workbook(excel_name)\n",
    "writer = pd.ExcelWriter(excel_name, engine='openpyxl')\n",
    "writer.book = book\n",
    "df_FF.to_excel(writer, sheet_name='FF_with_uncertainty', index=False)\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-24-ddae8a4ca35f>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_FF_reduced['Polymer_type'] = df_FF_reduced.Environmental_flow.apply(\n"
     ]
    }
   ],
   "source": [
    "df_FF_reduced = df_FF[['Environmental_flow', 'Time horizon',\n",
    "                       'FF_marine_sediment', 'FF_marine_water', 'FF_river_sediment', 'FF_soil']]\n",
    "df_FF_reduced['Polymer_type'] = df_FF_reduced.Environmental_flow.apply(\n",
    "    lambda x: x.split()[0])\n",
    "df_FF_reduced['Shape'] = df_FF_reduced.Environmental_flow.apply(\n",
    "    lambda x: x.split()[1])\n",
    "df_FF_reduced['Characteristic_length'] = df_FF_reduced.Environmental_flow.apply(\n",
    "    lambda x: x.split()[2] + ' ' + x.split()[3])\n",
    "df_FF_reduced['Initial_compartment'] = df_FF_reduced.Environmental_flow.apply(lambda x: x.split(\n",
    ")[4].replace('(', '').capitalize() + ' ' + x.split()[5] + ' ' + x.split()[6].replace(')', ''))\n",
    "df_FF_reduced.loc[(df_FF_reduced.Initial_compartment == 'Emission to fresh'),\n",
    "                  'Initial_compartment'] = 'Emission to fresh water'\n",
    "df_FF_reduced.loc[(df_FF_reduced.Initial_compartment == 'Emission to marine'),\n",
    "                  'Initial_compartment'] = 'Emission to marine water'\n",
    "df_FF_reduced[['FF_marine_sediment', 'FF_marine_water', 'FF_river_sediment', 'FF_soil']] = df_FF_reduced[[\n",
    "    'FF_marine_sediment', 'FF_marine_water', 'FF_river_sediment', 'FF_soil']].apply(lambda x: x*100/sum(x), axis=1)\n",
    "\n",
    "df_FF_reduced_limited = df_FF_reduced.loc[(df_FF_reduced['Characteristic_length'] == '0.01-0.1 mm') & (df_FF_reduced['Shape'] == 'particle') & (\n",
    "    df_FF_reduced['Time horizon'] == 'inf'), ['Environmental_flow', 'FF_marine_sediment', 'FF_marine_water', 'FF_river_sediment', 'FF_soil', 'Polymer_type', 'Initial_compartment']]\n",
    "df_FF_reduced_limited.set_index('Environmental_flow', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 421.2x298.8 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comp = ['air', 'fresh water', 'marine water', 'soil']\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "fig = plt.figure(figsize=(11.7/2, 8.3/2))\n",
    "plt.xlabel('test')\n",
    "G = gridspec.GridSpec(ncols=2, nrows=3, figure=fig)\n",
    "plt.rc('font', size=12)\n",
    "color = ['#dfc27d', '#018571', '#80cdc1', '#a6611a']\n",
    "axes_5 = plt.subplot(G[2, 0])\n",
    "axes_6 = plt.subplot(G[2, 1])\n",
    "axes_1 = plt.subplot(G[0, 0], sharex=axes_5)\n",
    "axes_2 = plt.subplot(G[0, 1], sharex=axes_6)\n",
    "axes_3 = plt.subplot(G[1, 0], sharex=axes_5)\n",
    "axes_4 = plt.subplot(G[1, 1], sharex=axes_6)\n",
    "\n",
    "#axes_1 = Group_1\n",
    "axes_1.text(-0.1, 1.05, 'A', transform=axes_1.transAxes,\n",
    "            size=12, weight='bold')\n",
    "df_FF_reduced_limited[df_FF_reduced_limited.Polymer_type == 'HDPE'].plot(kind='barh', rot=0, ax=axes_1, stacked=True,\n",
    "                                                                         legend=False, color=color)\n",
    "axes_1.set(facecolor = \"white\")\n",
    "axes_1.set_yticklabels(comp)\n",
    "axes_1.set_ylabel((None))\n",
    "axes_1.set_xlim(0, 100)\n",
    "#axes_2 = Group_2\n",
    "axes_2.text(-.1, 1.05, 'B', transform=axes_2.transAxes, size=12, weight='bold')\n",
    "df_FF_reduced_limited[df_FF_reduced_limited.Polymer_type == 'PE'].plot(kind='barh', rot=0, ax=axes_2, stacked=True,\n",
    "                                                                       legend=False, color=color)\n",
    "axes_2.set(facecolor = \"white\")\n",
    "axes_2.set_ylabel((None))\n",
    "axes_2.set_xlim(0, 100)\n",
    "plt.setp(axes_2.get_yticklabels(), visible=False)\n",
    "#axes_3 = Group_3\n",
    "axes_3.text(-0.1, 1.05, 'C', transform=axes_3.transAxes,\n",
    "            size=12, weight='bold')\n",
    "df_FF_reduced_limited[df_FF_reduced_limited.Polymer_type == 'NR/SBR'].plot(kind='barh', rot=0, ax=axes_3, stacked=True,\n",
    "                                                                           legend=False, color=color)\n",
    "axes_3.set(facecolor = \"white\")\n",
    "axes_3.set_yticklabels(comp)\n",
    "axes_3.set_ylabel((None))\n",
    "axes_3.set_ylabel(('Emission to'))\n",
    "axes_3.set_xlim(0, 100)\n",
    "#axes_4 = Group_4\n",
    "axes_4.text(-.1, 1.05, 'D', transform=axes_4.transAxes, size=12, weight='bold')\n",
    "df_FF_reduced_limited[df_FF_reduced_limited.Polymer_type == 'PBS'].plot(kind='barh', rot=0, ax=axes_4, stacked=True,\n",
    "                                                                        legend=False, color=color)\n",
    "axes_4.set(facecolor = \"white\")\n",
    "axes_4.set_ylabel((None))\n",
    "axes_4.set_xlim(0, 100)\n",
    "plt.setp(axes_4.get_yticklabels(), visible=False)\n",
    "#axes_5 = Group_5\n",
    "axes_5.text(-0.1, 1.05, 'E', transform=axes_5.transAxes,\n",
    "            size=12, weight='bold')\n",
    "df_FF_reduced_limited[df_FF_reduced_limited.Polymer_type == 'PHA'].plot(kind='barh', rot=0, ax=axes_5, stacked=True,\n",
    "                                                                        legend=False, color=color)\n",
    "axes_5.set(facecolor = \"white\")\n",
    "axes_5.set_yticklabels(comp)\n",
    "axes_5.set_ylabel((None))\n",
    "axes_5.set_xlim(0, 100)\n",
    "#axes_6 = Group_6\n",
    "axes_6.text(-.1, 1.05, 'F', transform=axes_6.transAxes, size=12, weight='bold')\n",
    "df_FF_reduced_limited[df_FF_reduced_limited.Polymer_type == 'PLA(-blend)'].plot(kind='barh', rot=0, ax=axes_6,\n",
    "                                                                                stacked=True, legend=False, color=color)\n",
    "axes_6.set(facecolor = \"white\")\n",
    "axes_6.set_ylabel((None))\n",
    "plt.xlabel(\n",
    "    \"Share of compartment-specific fate factors to the total fate factor [%]\", x=0)\n",
    "axes_6.set_xlim(0, 100)\n",
    "plt.setp(axes_6.get_yticklabels(), visible=False)\n",
    "lines, labels = fig.axes[-1].get_legend_handles_labels()\n",
    "fig.legend(lines, ('marine sediment', 'marine water', 'river sediment', 'soil'),\n",
    "           loc='center left', title='Persistance in', bbox_to_anchor=(1, 0.48, 0.1, 0.1), frameon=False, fontsize='medium')\n",
    "fig.savefig('FF_contribution_small.png', bbox_inches='tight', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FF_dict = dict()\n",
    "\n",
    "for i in range(0, len(df_FF)):\n",
    "    name = df_FF.iloc[i]['Environmental_flow'].split()[0]\n",
    "    GSDi = df_FF.loc[(\n",
    "        df_FF['Environmental_flow'].str.startswith(name + ' ')), ['GSD']]\n",
    "    GSDi = GSDi['GSD'].values.tolist()\n",
    "    FF_dict[name] = GSDi\n",
    "FF_GSD_boxplot = pd.DataFrame.from_dict(FF_dict)\n",
    "sorted_index = FF_GSD_boxplot.median().sort_values().index\n",
    "FF_GSD_boxplot_sorted = FF_GSD_boxplot[sorted_index]\n",
    "\n",
    "FF_GSD_boxplot_sorted = FF_GSD_boxplot_sorted.rename(columns={'Starch-blend': 'Starch\\n-blend',\n",
    "                                                              'PLA(-blend)': 'PLA\\n(-blend)',\n",
    "                                                              'NR/SBR': 'NR/\\nSBR'})\n",
    "\n",
    "df_new = df.rename(columns={'A': 'Col_1'}, index={'ONE': 'Row_1'})\n",
    "sns.set(font_scale=1.9)\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "g = sns.catplot(data=FF_GSD_boxplot_sorted, orient=\"v\",\n",
    "                kind=\"box\", color='#018571')\n",
    "g.set(xlabel='Plastic type', ylabel='Geometric standard deviation of fate factors')\n",
    "# g.set_xticklabels(rotation=30)\n",
    "g.fig.set_figwidth(30)\n",
    "g.fig.set_figheight(10)\n",
    "\n",
    "g.savefig('FF_GSD_boxplot_sorted.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PBSeT</th>\n",
       "      <th>PBSe</th>\n",
       "      <th>PEA</th>\n",
       "      <th>PHA</th>\n",
       "      <th>PA</th>\n",
       "      <th>PHBV</th>\n",
       "      <th>Starch\\n-blend</th>\n",
       "      <th>PBS</th>\n",
       "      <th>PBSA</th>\n",
       "      <th>PCL</th>\n",
       "      <th>...</th>\n",
       "      <th>PLA\\n(-blend)</th>\n",
       "      <th>PP</th>\n",
       "      <th>PBAT</th>\n",
       "      <th>HDPE</th>\n",
       "      <th>PC</th>\n",
       "      <th>PE</th>\n",
       "      <th>NR/\\nSBR</th>\n",
       "      <th>PET</th>\n",
       "      <th>PVC</th>\n",
       "      <th>PS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.198034</td>\n",
       "      <td>1.199458</td>\n",
       "      <td>1.314871</td>\n",
       "      <td>1.366625</td>\n",
       "      <td>1.287344</td>\n",
       "      <td>1.250641</td>\n",
       "      <td>1.250641</td>\n",
       "      <td>1.216773</td>\n",
       "      <td>1.273265</td>\n",
       "      <td>1.314632</td>\n",
       "      <td>...</td>\n",
       "      <td>1.598978</td>\n",
       "      <td>1.573799</td>\n",
       "      <td>1.712965</td>\n",
       "      <td>1.783331</td>\n",
       "      <td>1.781498</td>\n",
       "      <td>1.876427</td>\n",
       "      <td>1.780156</td>\n",
       "      <td>2.593003</td>\n",
       "      <td>3.151354</td>\n",
       "      <td>3.151354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.198034</td>\n",
       "      <td>1.199458</td>\n",
       "      <td>1.314871</td>\n",
       "      <td>1.366625</td>\n",
       "      <td>1.287344</td>\n",
       "      <td>1.250641</td>\n",
       "      <td>1.250641</td>\n",
       "      <td>1.216773</td>\n",
       "      <td>1.273265</td>\n",
       "      <td>1.314632</td>\n",
       "      <td>...</td>\n",
       "      <td>1.598978</td>\n",
       "      <td>1.573799</td>\n",
       "      <td>1.712965</td>\n",
       "      <td>1.783331</td>\n",
       "      <td>1.781498</td>\n",
       "      <td>1.876427</td>\n",
       "      <td>1.780156</td>\n",
       "      <td>2.593003</td>\n",
       "      <td>3.151354</td>\n",
       "      <td>3.151354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.198034</td>\n",
       "      <td>1.199458</td>\n",
       "      <td>1.314871</td>\n",
       "      <td>1.366625</td>\n",
       "      <td>1.287344</td>\n",
       "      <td>1.250641</td>\n",
       "      <td>1.250641</td>\n",
       "      <td>1.216773</td>\n",
       "      <td>1.273265</td>\n",
       "      <td>1.314632</td>\n",
       "      <td>...</td>\n",
       "      <td>1.598978</td>\n",
       "      <td>1.573799</td>\n",
       "      <td>1.712965</td>\n",
       "      <td>1.783331</td>\n",
       "      <td>1.781498</td>\n",
       "      <td>1.876427</td>\n",
       "      <td>1.780156</td>\n",
       "      <td>2.593003</td>\n",
       "      <td>3.151354</td>\n",
       "      <td>3.151354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.198034</td>\n",
       "      <td>1.199458</td>\n",
       "      <td>1.314871</td>\n",
       "      <td>1.366625</td>\n",
       "      <td>1.287344</td>\n",
       "      <td>1.250641</td>\n",
       "      <td>1.250641</td>\n",
       "      <td>1.216773</td>\n",
       "      <td>1.273265</td>\n",
       "      <td>1.314632</td>\n",
       "      <td>...</td>\n",
       "      <td>1.598978</td>\n",
       "      <td>1.573799</td>\n",
       "      <td>1.712965</td>\n",
       "      <td>1.783331</td>\n",
       "      <td>1.781498</td>\n",
       "      <td>1.876427</td>\n",
       "      <td>1.780156</td>\n",
       "      <td>2.593003</td>\n",
       "      <td>3.151354</td>\n",
       "      <td>3.151354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.237266</td>\n",
       "      <td>1.237266</td>\n",
       "      <td>1.203538</td>\n",
       "      <td>1.237266</td>\n",
       "      <td>1.265816</td>\n",
       "      <td>1.332288</td>\n",
       "      <td>1.332288</td>\n",
       "      <td>1.374323</td>\n",
       "      <td>1.325239</td>\n",
       "      <td>1.290780</td>\n",
       "      <td>...</td>\n",
       "      <td>1.445240</td>\n",
       "      <td>1.792868</td>\n",
       "      <td>1.711223</td>\n",
       "      <td>1.792868</td>\n",
       "      <td>1.712426</td>\n",
       "      <td>1.778615</td>\n",
       "      <td>1.711223</td>\n",
       "      <td>1.322321</td>\n",
       "      <td>2.963819</td>\n",
       "      <td>2.963819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1.196659</td>\n",
       "      <td>1.196526</td>\n",
       "      <td>1.323735</td>\n",
       "      <td>1.338495</td>\n",
       "      <td>1.295295</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>1.197508</td>\n",
       "      <td>1.277433</td>\n",
       "      <td>1.323494</td>\n",
       "      <td>...</td>\n",
       "      <td>1.603478</td>\n",
       "      <td>1.574622</td>\n",
       "      <td>1.738881</td>\n",
       "      <td>1.812068</td>\n",
       "      <td>1.811089</td>\n",
       "      <td>1.852258</td>\n",
       "      <td>1.857257</td>\n",
       "      <td>2.689507</td>\n",
       "      <td>3.284080</td>\n",
       "      <td>3.284080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.196659</td>\n",
       "      <td>1.196526</td>\n",
       "      <td>1.323735</td>\n",
       "      <td>1.338495</td>\n",
       "      <td>1.295295</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>1.197508</td>\n",
       "      <td>1.277433</td>\n",
       "      <td>1.323494</td>\n",
       "      <td>...</td>\n",
       "      <td>1.603478</td>\n",
       "      <td>1.574622</td>\n",
       "      <td>1.738881</td>\n",
       "      <td>1.812068</td>\n",
       "      <td>1.811089</td>\n",
       "      <td>1.852258</td>\n",
       "      <td>1.857257</td>\n",
       "      <td>2.689507</td>\n",
       "      <td>3.284080</td>\n",
       "      <td>3.284080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.196659</td>\n",
       "      <td>1.196526</td>\n",
       "      <td>1.323735</td>\n",
       "      <td>1.338495</td>\n",
       "      <td>1.295295</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>1.197508</td>\n",
       "      <td>1.277433</td>\n",
       "      <td>1.323494</td>\n",
       "      <td>...</td>\n",
       "      <td>1.603478</td>\n",
       "      <td>1.574622</td>\n",
       "      <td>1.738881</td>\n",
       "      <td>1.812068</td>\n",
       "      <td>1.811089</td>\n",
       "      <td>1.852258</td>\n",
       "      <td>1.857257</td>\n",
       "      <td>2.689507</td>\n",
       "      <td>3.284080</td>\n",
       "      <td>3.284080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1.196659</td>\n",
       "      <td>1.196526</td>\n",
       "      <td>1.323735</td>\n",
       "      <td>1.338495</td>\n",
       "      <td>1.295295</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>1.257224</td>\n",
       "      <td>1.197508</td>\n",
       "      <td>1.277433</td>\n",
       "      <td>1.323494</td>\n",
       "      <td>...</td>\n",
       "      <td>1.603478</td>\n",
       "      <td>1.574622</td>\n",
       "      <td>1.738881</td>\n",
       "      <td>1.812068</td>\n",
       "      <td>1.811089</td>\n",
       "      <td>1.852258</td>\n",
       "      <td>1.857257</td>\n",
       "      <td>2.689507</td>\n",
       "      <td>3.284080</td>\n",
       "      <td>3.284080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>1.197347</td>\n",
       "      <td>1.197992</td>\n",
       "      <td>1.259204</td>\n",
       "      <td>1.287881</td>\n",
       "      <td>1.291320</td>\n",
       "      <td>1.294756</td>\n",
       "      <td>1.294756</td>\n",
       "      <td>1.295548</td>\n",
       "      <td>1.301336</td>\n",
       "      <td>1.319063</td>\n",
       "      <td>...</td>\n",
       "      <td>1.601228</td>\n",
       "      <td>1.683745</td>\n",
       "      <td>1.725923</td>\n",
       "      <td>1.792868</td>\n",
       "      <td>1.796294</td>\n",
       "      <td>1.815437</td>\n",
       "      <td>1.818707</td>\n",
       "      <td>1.985126</td>\n",
       "      <td>3.217717</td>\n",
       "      <td>3.217717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           PBSeT      PBSe       PEA       PHA        PA      PHBV  \\\n",
       "0       1.198034  1.199458  1.314871  1.366625  1.287344  1.250641   \n",
       "1       1.198034  1.199458  1.314871  1.366625  1.287344  1.250641   \n",
       "2       1.198034  1.199458  1.314871  1.366625  1.287344  1.250641   \n",
       "3       1.198034  1.199458  1.314871  1.366625  1.287344  1.250641   \n",
       "4       1.237266  1.237266  1.203538  1.237266  1.265816  1.332288   \n",
       "...          ...       ...       ...       ...       ...       ...   \n",
       "140     1.196659  1.196526  1.323735  1.338495  1.295295  1.257224   \n",
       "141     1.196659  1.196526  1.323735  1.338495  1.295295  1.257224   \n",
       "142     1.196659  1.196526  1.323735  1.338495  1.295295  1.257224   \n",
       "143     1.196659  1.196526  1.323735  1.338495  1.295295  1.257224   \n",
       "median  1.197347  1.197992  1.259204  1.287881  1.291320  1.294756   \n",
       "\n",
       "        Starch\\n-blend       PBS      PBSA       PCL  ...  PLA\\n(-blend)  \\\n",
       "0             1.250641  1.216773  1.273265  1.314632  ...       1.598978   \n",
       "1             1.250641  1.216773  1.273265  1.314632  ...       1.598978   \n",
       "2             1.250641  1.216773  1.273265  1.314632  ...       1.598978   \n",
       "3             1.250641  1.216773  1.273265  1.314632  ...       1.598978   \n",
       "4             1.332288  1.374323  1.325239  1.290780  ...       1.445240   \n",
       "...                ...       ...       ...       ...  ...            ...   \n",
       "140           1.257224  1.197508  1.277433  1.323494  ...       1.603478   \n",
       "141           1.257224  1.197508  1.277433  1.323494  ...       1.603478   \n",
       "142           1.257224  1.197508  1.277433  1.323494  ...       1.603478   \n",
       "143           1.257224  1.197508  1.277433  1.323494  ...       1.603478   \n",
       "median        1.294756  1.295548  1.301336  1.319063  ...       1.601228   \n",
       "\n",
       "              PP      PBAT      HDPE        PC        PE  NR/\\nSBR       PET  \\\n",
       "0       1.573799  1.712965  1.783331  1.781498  1.876427  1.780156  2.593003   \n",
       "1       1.573799  1.712965  1.783331  1.781498  1.876427  1.780156  2.593003   \n",
       "2       1.573799  1.712965  1.783331  1.781498  1.876427  1.780156  2.593003   \n",
       "3       1.573799  1.712965  1.783331  1.781498  1.876427  1.780156  2.593003   \n",
       "4       1.792868  1.711223  1.792868  1.712426  1.778615  1.711223  1.322321   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "140     1.574622  1.738881  1.812068  1.811089  1.852258  1.857257  2.689507   \n",
       "141     1.574622  1.738881  1.812068  1.811089  1.852258  1.857257  2.689507   \n",
       "142     1.574622  1.738881  1.812068  1.811089  1.852258  1.857257  2.689507   \n",
       "143     1.574622  1.738881  1.812068  1.811089  1.852258  1.857257  2.689507   \n",
       "median  1.683745  1.725923  1.792868  1.796294  1.815437  1.818707  1.985126   \n",
       "\n",
       "             PVC        PS  \n",
       "0       3.151354  3.151354  \n",
       "1       3.151354  3.151354  \n",
       "2       3.151354  3.151354  \n",
       "3       3.151354  3.151354  \n",
       "4       2.963819  2.963819  \n",
       "...          ...       ...  \n",
       "140     3.284080  3.284080  \n",
       "141     3.284080  3.284080  \n",
       "142     3.284080  3.284080  \n",
       "143     3.284080  3.284080  \n",
       "median  3.217717  3.217717  \n",
       "\n",
       "[145 rows x 24 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FF_GSD_boxplot_sorted.loc['median'] = FF_GSD_boxplot_sorted.median()\n",
    "FF_GSD_boxplot_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Polymer type'] = df['Polymer type'].replace(\n",
    "    ['Starch-blend', 'PLA(-blend)', 'NR/SBR'], ['Starch\\n-blend', 'PLA\\n(-blend)', 'NR/\\nSBR'])\n",
    "non_expert = df[df['SSDR expert judgment based on'].isna() & (\n",
    "    df['Source'] != \"Experts' estimate\")]\n",
    "expert = df[(df['SSDR expert judgment based on'].notnull())\n",
    "            | (df['Source'] == \"Experts' estimate\")]\n",
    "color = ['#018571', '#dfc27d', '#a6611a', '#80cdc1']\n",
    "legend_elements = [Line2D([0], [0], linewidth=0, marker='o', color='#dfc27d', label='marine sediment', markersize=8),\n",
    "                   Line2D([0], [0], linewidth=0, marker='o',\n",
    "                          color='#018571', label='marine water', markersize=8),\n",
    "                   Line2D([0], [0], linewidth=0, marker='o',\n",
    "                          color='#80cdc1', label='river sediment', markersize=8),\n",
    "                   Line2D([0], [0], linewidth=0, marker='o',\n",
    "                          color='#a6611a', label='soil', markersize=8),\n",
    "                   Line2D([0], [0], linewidth=0, marker='^', color='#dfc27d',\n",
    "                          label='marine sediment', markersize=8),\n",
    "                   Line2D([0], [0], linewidth=0, marker='^',\n",
    "                          color='#018571', label='marine water', markersize=8),\n",
    "                   Line2D([0], [0], linewidth=0, marker='^',\n",
    "                          color='#80cdc1', label='river sediment', markersize=8),\n",
    "                   Line2D([0], [0], linewidth=0, marker='^', color='#a6611a', label='soil', markersize=8)]\n",
    "df_sorted = df.sort_values(by='SSDR')\n",
    "poly_sorted_by_SSDR = df_sorted['Polymer type'].unique().tolist()\n",
    "poly_sorted_by_SSDR\n",
    "\n",
    "sns.set(font_scale=2, rc={'figure.figsize': (30, 10)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "g = sns.stripplot(x=\"Polymer type\", y=\"SSDR\", hue='Compartment', palette=sns.color_palette(color),\n",
    "                  data=non_expert, size=10, jitter=0.2, order=poly_sorted_by_SSDR)\n",
    "n = sns.stripplot(x=\"Polymer type\", y=\"SSDR\", hue='Compartment', palette=sns.color_palette(color),\n",
    "                  data=expert, marker='^', size=10, jitter=0.2, order=poly_sorted_by_SSDR)\n",
    "n.set(xlabel='Plastic type',\n",
    "      ylabel='SSDR [µ*$a^{-1}$]', ylim=(10**(-3.5), 10**4))\n",
    "plot = g.get_figure()\n",
    "n.set_yscale('log')\n",
    "n.legend(title='Literature values              Expert estimates',\n",
    "         handles=legend_elements, ncol=2, frameon=True)\n",
    "poly_sorted_by_SSDR[1] = 'PLA\\n(-blend)'\n",
    "plot = n.get_figure()\n",
    "\n",
    "plot.savefig('SSDR.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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