wOtter is a new tool used to predict micro contamination of rivers as a consequence of wastewater discharge. This github page presents the user-friendly and fast code that researchers and practitioners alike can use and adapt. The tool is written in Python, and models rivers as graph networks with each node representing a section of a river. Each node can store and retrieve information, allowing for simple manipulation of the river network and simple extraction of data. Additionally, wOtter includes a library of functions for pre and post processing of data. The library facilitates extraction of information from the model and facilitates representing model outcomes. The source code is designed to run swiftly, such that simulations and calibrations that require many executions can be performed on a standard laptop.
This should probably be your first step. Go to examples/ and open the .html file to visualize the map created with the current source code configuration. This map contains all the European WWTPs and the pollution level of the rivers.
To create the environment with all packages needed, simply run
conda env create -f environment.yml
conda activate otter
pip install -e .
Then, download the compressed data here, and unzip the file at the main directory.
Finally, create a folder at the main directory called results.
Before starting to do anything, make sure you have activated the conda environment. Please, keep in mind that a computer with minimum 16GB of RAM is needed.
conda activate otter
If you want to perform all the steps of the simulation, modify the source code or change the raster files, you will have to download the data here and unzip it inside your previously downloaded and unzipped data/ folder (Warning: the unzipped file weighs 40GB, and the simulation takes about 3 hours). Then, from your root folder, run
python .\runall.py
Once the simulation is over, generate the pollutant map.
In the notebooks/run_graph.ipynb notebook, you will find how to visualize the results of the simulation on a dynamic map. You can open the file by simply running
jupyter notebook
and selecting the file.
Run all the cells, and some files will be created at the results/ folder. These files have to be uploaded to the kepler.gl web page, which is the tool used to create this map. Then, customize your map following the instructions/ folder.
To replicate the tables and figures in the article, run the scripts located in the Replication/ folder. You will find more detailed information inside.