diff --git a/scripts/build-site.sh b/scripts/build-site.sh
index 88f76e2f..1a3ae00b 100755
--- a/scripts/build-site.sh
+++ b/scripts/build-site.sh
@@ -18,8 +18,8 @@ svn checkout --username $SVN_USER --password $SVN_PASSWORD --no-auth-cache --dep
     https://svn.vsp.tu-berlin.de/repos/public-svn/matsim/scenarios/countries/de/episim/original-data/Abwasser/
 
 pip install html_to_json
-python3 scripts/sewage-data-parser.py
-cp *abwassersurveillance* Abwasser/
+python3 scripts/sewage_data_parser.py Köln
+cp *sewage_data* Abwasser/
 
 svn commit --username $SVN_USER --password $SVN_PASSWORD  --no-auth-cache -m "autobuild: $TIMESTAMP" Abwasser
 
diff --git a/scripts/sewage-data-parser.py b/scripts/sewage-data-parser.py
deleted file mode 100644
index 4ce6ca59..00000000
--- a/scripts/sewage-data-parser.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import urllib.request
-import html_to_json
-import json
-import csv
-
-# download and parse the Abwassersurveillance from the rki
-opener = urllib.request.FancyURLopener({})
-url = "https://www.rki.de/DE/Content/Institut/OrgEinheiten/Abt3/FG32/Abwassersurveillance/Bericht_Abwassersurveillance.html?__blob=publicationFile"
-f = opener.open(url)
-content = f.read()
-
-# convert the html-string to json, get the data, convert the data to json and get the x- and y-data
-output_json = html_to_json.convert(content)
-current = output_json["html"][0]["body"][0]["div"][0]["script"][1]["_value"]
-current = json.loads(current)["x"]["data"]
-dates = current[3]["x"]
-viruslast = current[3]["y"]
-
-# write the data to a .csv file
-with open('abwassersurveillance.csv', 'w', newline='') as file:
-    writer = csv.writer(file)
-    field = ["date", "viruslast"]
-
-    writer.writerow(field)
-
-    for i in range(len(dates)):
-        writer.writerow([dates[i], viruslast[i]])
\ No newline at end of file
diff --git a/scripts/sewage_data_parser.py b/scripts/sewage_data_parser.py
new file mode 100644
index 00000000..f50e43dc
--- /dev/null
+++ b/scripts/sewage_data_parser.py
@@ -0,0 +1,131 @@
+import argparse
+import json
+import requests
+from bs4 import BeautifulSoup
+from bs2json import BS2Json
+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)
+
+def extract_plot_data(parsed_data, index):
+    # Extract data points from the plot (specified by index)
+    data_point = parsed_data["x"]["data"][index]
+    date_values = data_point["x"]
+    map_values = data_point["key"]
+    virus_loads = data_point["y"]
+    return date_values, map_values, virus_loads
+
+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 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):
+    # 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[2])
+        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)
+
+        # Convert the second <script> tag containing city data
+        json_data_city = convert_script_to_json(matching_scripts[1])
+        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.lower()
+        write_data_to_csv(f'{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(f'{city_name}_abwassersurveillance_avg.csv', parse_dates=['date'])
+
+        # Plot the data
+        # plot_data(data_frame)
+
+    except Exception as e:
+        print(f"An error occurred: {str(e)}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Fetch and analyze virusload data from RKI website.")
+    parser.add_argument("city_name", help="Name of the city for which to analyze virusload data.")
+    args = parser.parse_args()
+    main(args.city_name)