diff --git a/scripts/build-site.sh b/scripts/build-site.sh
index d52e2a88..7d8b4bc9 100755
--- a/scripts/build-site.sh
+++ b/scripts/build-site.sh
@@ -11,21 +11,19 @@ IFS=$'\n\t'
 
 TIMESTAMP=`date`
 
+# RKI Abwasser ----------------------------------------
 svn checkout --username $SVN_USER --password $SVN_PASSWORD --no-auth-cache --depth infinity \
     https://svn.vsp.tu-berlin.de/repos/public-svn/matsim/scenarios/countries/de/episim/original-data/Abwasser/
 
-pip install html_to_json
-pip install bs2json
+# fetch rki sewage data from GitHub into Abwasser/amelag_einzelstandorte.tsv
+wget https://raw.githubusercontent.com/robert-koch-institut/Abwassersurveillance_AMELAG/main/amelag_einzelstandorte.tsv -O Abwasser/amelag_einzelstandorte.tsv
+
+# add additional Abwasser cities here:
 python3 scripts/sewage_data_parser.py -c Köln
-python3 scripts/sewage_data_parser.py -a True
-python3 scripts/sewage_data_parser.py -g True
-# ls
-# pwd
-# cp ./Abwasser/*sewage_data* Abwasser/
-# cp ./Abwasser/sewage_combined_data.csv* Abwasser/
 
 svn commit --username $SVN_USER --password $SVN_PASSWORD  --no-auth-cache -m "autobuild: $TIMESTAMP" Abwasser
 
+# RKI Infection Data ---------------------------------
 echo BUILD: Getting RKI_FILE
 
 RKI_FILE=https://media.githubusercontent.com/media/robert-koch-institut/SARS-CoV-2-Infektionen_in_Deutschland/main/Aktuell_Deutschland_SarsCov2_Infektionen.csv
diff --git a/scripts/sewage_data_parser.py b/scripts/sewage_data_parser.py
index e188e973..a76e9d15 100644
--- a/scripts/sewage_data_parser.py
+++ b/scripts/sewage_data_parser.py
@@ -2,39 +2,11 @@
 import json
 import os
 import shutil
-from matplotlib import pyplot as plt
 import pandas as pd
-import requests
-from bs4 import BeautifulSoup
-from bs2json import BS2Json
+import numpy as np
 import csv
-# import pandas as pd
-# import matplotlib.pyplot as plt
-
-def fetch_data_from_website(url):
-    # Send an HTTP request to the website
-    response = requests.get(url)
-    response.raise_for_status()  # Check for any HTTP errors
-    return response.text
-
-def parse_html(html_content):
-    # Parse HTML content using BeautifulSoup
-    soup = BeautifulSoup(html_content, 'html.parser')
-    return soup
-
-def find_matching_scripts(soup, text_to_find):
-    # Find all <script> tags on the page
-    script_tags = soup.find_all("script")
-
-    # Search for the text "2022" in the <script> tags
-    matching_scripts = [tag for tag in script_tags if text_to_find in tag.text]
-    return matching_scripts
-
-def convert_script_to_json(script):
-    # Convert a <script> tag containing JSON data to a Python dictionary
-    bs2json_converter = BS2Json(script)
-    json_data = bs2json_converter.convert()["script"]["text"]
-    return json.loads(json_data)
+
+RAW_TSV = './Abwasser/amelag_einzelstandorte.tsv'
 
 def extract_plot_data(parsed_data, index):
     # Extract data points from the plot (specified by index)
@@ -69,144 +41,62 @@ def combine_csv_files(folder_path, output_file):
     # Ergebnisse in eine neue CSV-Datei schreiben
     combined_data.to_csv(output_file, index=False)
 
-def write_data_to_csv(filename, fields, data, city_data):
-    with open(filename, 'w', newline='') as file:
-        writer = csv.writer(file)
-        writer.writerow(fields)
-
-        for num in city_data:
-            avg_date, avg_value, dot_value = "", "", ""
-
-            if num in data[1]:
-                index_avg = data[1].index(num)
-                avg_date = data[0][index_avg]
-                avg_value = data[2][index_avg]
-
-                if num in data[4]:
-                    index_dot = data[4].index(num)
-                    dot_value = data[5][index_dot]
-
-            if avg_date:
-                writer.writerow([avg_date, avg_value, dot_value])
-
-def write_complete_germany_data_to_csv(filename, fields, data):
-    with open(filename, 'w', newline='') as file:
-        writer = csv.writer(file)
-        writer.writerow(fields)
-
-        for i in range(len(data[0])):
-
-            date_avg = data[0][i]
-            virusload_avg = data[2][i]
-            virusload_dots = ""
-
-            if date_avg in data[3]:
-                index = data[3].index(date_avg)
-                virusload_dots = data[5][index]
-
-            writer.writerow([date_avg, virusload_avg, virusload_dots]) # date_values_avg, map_values_avg, virus_loads_avg, date_values_dots, map_values_dots, virus_loads_dots
-
-def plot_data(data_frame):
-    # Plot the data
-    plt.figure(figsize=(12, 6))
-    plt.plot(data_frame['date'], data_frame['virusload_avg'], color='black', label='virusload_avg')
-    plt.scatter(data_frame['date'], data_frame['virusload'], color='gray', label='virusload', marker='o', alpha=0.5)
-
-    # Format the x-axis date format
-    plt.gca().xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter('%Y-%m-%d'))
-
-    # Add labels and legend
-    plt.xlabel('Date')
-    plt.ylabel('Virusload')
-    plt.title('Virusload Data')
-    plt.legend()
-
-    # Display the plot
-    plt.grid(True)
-    plt.tight_layout()
-    plt.show()
-
-def main(city_name, parse_all_cities, germany):
-    # Define the URL of the website containing the data
-    URL = 'https://www.rki.de/DE/Content/Institut/OrgEinheiten/Abt3/FG32/Abwassersurveillance/Bericht_Abwassersurveillance.html?__blob=publicationFile'
-
-    # Define CSV file fields
-    fields = ["date", "virusload_avg", "virusload"]
-
-    try:
-        # Fetch data from the website
-        html_content = fetch_data_from_website(URL)
-
-        # Parse HTML content
-        soup = parse_html(html_content)
-
-        # Search for scripts containing the desired data
-        matching_scripts = find_matching_scripts(soup, '2022')
-
-        # Convert scripts to JSON and extract data
-        parsed_data = convert_script_to_json(matching_scripts[1 if germany else 3])
-        date_values_avg, map_values_avg, virus_loads_avg = extract_plot_data(parsed_data, 4)
-        date_values_dots, map_values_dots, virus_loads_dots = extract_plot_data(parsed_data, 2)
-
-        os.makedirs("./Abwasser", exist_ok=True)
-        if germany:
-            write_complete_germany_data_to_csv('./Abwasser/complete_germany_sewage_data.csv', fields, [date_values_avg, map_values_avg, virus_loads_avg,
-                                                                            date_values_dots, map_values_dots, virus_loads_dots])
-
-        else:
-            # Convert the second <script> tag containing city data
-            json_data_city = convert_script_to_json(matching_scripts[2])
-            
-
-            if parse_all_cities:
-                os.makedirs("./Abwasser/sewage", exist_ok=True)
-                for city_name in json_data_city["map"]:
-                    city_data = json_data_city["map"][city_name]
-                    
-                    # Write data to a CSV file
-                    file_city_name = city_name.replace('ä', 'ae')
-                    file_city_name = file_city_name.replace('ö', 'oe')
-                    file_city_name = file_city_name.replace('ü', 'ue')
-                    file_city_name = file_city_name.replace('ß', 'ss')
-                    file_city_name = file_city_name.replace(' ', '_')
-                    file_city_name = file_city_name.replace('(', '')
-                    file_city_name = file_city_name.replace(')', '')
-                    file_city_name = file_city_name.replace('-', '_')
-                    file_city_name = file_city_name.lower()
-                    write_data_to_csv(f'Abwasser/sewage/{file_city_name}_sewage_data.csv', fields, [date_values_avg, map_values_avg, virus_loads_avg,
-                                                                            date_values_dots, map_values_dots, virus_loads_dots], city_data)
-                    
-                combine_csv_files("./Abwasser/sewage", "./Abwasser/sewage_combined_data.csv")    
-                shutil.rmtree("./Abwasser/sewage")            
-            elif city_name:
-                city_data = json_data_city["map"][city_name]
-                
-                # Write data to a CSV file
-                file_city_name = city_name.replace('ä', 'ae')
-                file_city_name = file_city_name.replace('ö', 'oe')
-                file_city_name = file_city_name.replace('ü', 'ue')
-                file_city_name = file_city_name.replace('ß', 'ss')
-                file_city_name = file_city_name.replace(' ', '_')
-                file_city_name = file_city_name.replace('(', '')
-                file_city_name = file_city_name.replace(')', '')
-                file_city_name = file_city_name.replace('-', '_')
-                file_city_name = file_city_name.lower()
-                write_data_to_csv(f'Abwasser/{file_city_name}_sewage_data.csv', fields, [date_values_avg, map_values_avg, virus_loads_avg,
-                                                                        date_values_dots, map_values_dots, virus_loads_dots], city_data)
-
-        # Read the CSV file into a DataFrame
-        # data_frame = pd.read_csv('/Users/friedrichvoelkers/GIT/covid-sim/Abwasser/sewage_combined_data.csv', parse_dates=['date'])
-
-        # Plot the data
-        # plot_data(data_frame)
-
-    except Exception as e:
-        print(f"An error occurred: {str(e)}")
+# Deliberately removing try/catch, because we want script to fail loudly if something goes wrong
+def main(city_name):
+    # Load raw data
+    df = pd.read_csv(RAW_TSV, delimiter = '\t')
+
+    # Filter just the selected city
+    df = df[ df['standort'] == city_name]
+
+    # Filter N/A
+    df = df[ df['loess_vorhersage'].notna()]
+
+        # # weekly average -- need a DatetimeIndex and resample using weekly frequency
+        # df['date_index'] = pd.to_datetime(df['datum'])
+        # df.set_index('date_index', inplace=True)
+        # # 'W' means weekend (Sunday)
+        # df['weekly_avg'] = df['viruslast'].resample('W').mean()
+
+    # Scale: log10
+    df['scaled'] = np.log10(df['loess_vorhersage'])
+
+    # rename columns
+    df = df.rename(columns={'datum': 'date', 'scaled': 'virusload_avg' })
+    print(df)
+
+    # Write data to a CSV file
+    file_city_name = city_name.replace('ä', 'ae')
+    file_city_name = file_city_name.replace('ö', 'oe')
+    file_city_name = file_city_name.replace('ü', 'ue')
+    file_city_name = file_city_name.replace('ß', 'ss')
+    file_city_name = file_city_name.replace(' ', '_')
+    file_city_name = file_city_name.replace('(', '')
+    file_city_name = file_city_name.replace(')', '')
+    file_city_name = file_city_name.replace('-', '_')
+    file_city_name = file_city_name.lower()
+
+    output_file = f'Abwasser/{file_city_name}_sewage_data.csv'
+
+    df.to_csv(
+        output_file,
+        columns=['date', 'virusload_avg'],
+        header=True,
+        sep=',',
+        encoding='utf-8',
+        index=False,
+        decimal='.'
+    )
+
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Fetch and analyze virusload data from RKI website.")
+
+    # Script now only handles one city at a time.
     parser.add_argument('-c', '--cityName', help="Name of the city for which to analyze virusload data.")
-    parser.add_argument('-a', '--allCities', help="If you want to parse only one city, set this to False and set city_nmame to the city you want to parse. If you want to parse all cities, set this to True.")
-    parser.add_argument('-g', '--germany', help="If you want the aggregated data for Germany, set this to True.")
     args = parser.parse_args()
-    main(args.cityName, args.allCities, args.germany)
+    main(args.cityName)
+
+    # parser.add_argument('-a', '--allCities', help="If you want to parse only one city, set this to False and set city_nmame to the city you want to parse. If you want to parse all cities, set this to True.")
+    # parser.add_argument('-g', '--germany', help="If you want the aggregated data for Germany, set this to True.")
+    # main(args.cityName, args.allCities, args.germany)