PEPPER - Predict Environmental Pollutant PERsistence

PEPPER is a package developed by the Fenner Labs for analyzing and modeling persistence of micropollutants in different environments.

Installation

The PEPPER library may be installed using:

pip install pepper-lab

Projects

Follow these steps to reproduce the workflows and results from previous publications:

Clone the repository

git clone https://github.com/FennerLabs/pepper
cd pepper

Fetch the files from github

git lfs fetch --all
git lfs pull

We also recommend creating a dedicated virtual environment with python 3.12 as base

python -m venv pepper_env
source pepper_env/bin/activate

We have included all requirements in the pyproject.toml file so all dependencies may be installed as follows

pip install .

Bayesian inference for soil biotransformation half-lives - Hafner et al., 2023

Here's how to reproduce the data and the figures from the publication:

cd scripts
python bayesian_inference_main.py

Current Opportunities and Limitations in Predicting Micropollutant Removal in Wastewater Treatment based on Molecular Structure - Cordero et al., 2025

In this project we include methods to model the breakthrough of micropollutants in wastewater treatment plants. Main results can be reproduced as follows:

cd scripts
predict_breakthrough_wwtp.py

Please refer to the main publication for further details

Confidently uncertain: Probabilistic machine learning to predict soil biotransformation half-lives - Salz et al., 2026

Code to reproduce the predictions from the publication:

cd scripts
python predict_soil_halflives.py

The code to reproduce analyses, tables and figures can be found in the jupyter notebook:

jupyter notebook soil_half_lives_additional_analyses.ipynb

Related Projects

We also have a pepper_app to predict several endpoints of interest related to environmental persistence