Identifying causal pathways and risk-decision rules for nitrous oxide emission hot moments in wastewater treatment plants using probabilistic causal machine learning

Description

This dataset contains the Python scripts used to reproduce the data preprocessing, feature engineering, and statistical and machine-learning analyses presented in the manuscript. The code package includes scripts for: Cleaneddata – data preprocessing and preparation of the cleaned dataset used for subsequent analyses; Temporal characteristics – analysis of the temporal characteristics and patterns of N₂O emissions and associated operational variables; XGBoost – XGBoost model development and analysis of N₂O emissions, including model training and evaluation; Cohort SHAP – cohort-based SHAP analysis for interpreting feature contributions under different operational conditions; Copula – copula-based joint probability analysis of key operational-variable combinations and N₂O emission risks; LiNGAM – exploratory causal analysis using the LiNGAM framework to identify potential causal relationships among operational variables and N₂O emissions. These scripts provide the computational workflow underlying the results reported in the manuscript and are provided to facilitate transparency, reproducibility, and independent verification of the analyses.

Institutions

• Northwest University

  Shaanxi, Xi'an

Categories

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