Raising wastewater collection or discharge standards? Identifying priority pathway to mitigate riverine greenhouse gas emissions

Description

This dataset supports the manuscript entitled “Raising wastewater collection or discharge standards? Identifying priority pathway to mitigate riverine greenhouse gas emissions”. It contains the data and R scripts used to quantify riverine greenhouse gas (GHG) emissions, develop machine-learning prediction models, and evaluate wastewater management scenarios. The file “riverine GHG emissions.xlsx” contains the compiled riverine GHG flux dataset used for model development and analysis. The dataset includes CH4, N2O, and CO2 flux records and paired environmental variables, including NH4+, TP, DO, NO3-, TN, DOC, TC, TOC, pH, water temperature, wind speed, flow velocity, and related metadata. The accompanying “Read me” sheet provides variable descriptions and units. The file “other_data.xlsx” contains scenario input data and supporting environmental data. It includes provincial uncollected wastewater estimates, wastewater treatment plant effluent scenario inputs, water quality class statistics, provincial effluent standard shares, and data source descriptions. These data were used to construct baseline and management scenarios, including increased centralized wastewater collection rates, effluent standard upgrading, and their combined management pathways. The script files provide the analytical workflow used in the study. “CH4 flux.txt”, “N2O flux.txt”, and “CO2 flux.txt” contain the gas-specific machine-learning modeling procedures for predicting CH4, N2O, and CO2 fluxes. “RFE.txt” contains the recursive feature elimination workflow used for feature selection across machine-learning models. “Shapley.txt” contains the SHAP and GAM-based interpretability analysis used to evaluate nonlinear responses of key environmental drivers. “CAI.txt” contains the Monte Carlo scenario simulation and comparative advantage indicator workflow used to assess provincial wastewater management priorities. Together, these files document the data sources, model inputs, feature selection, model interpretation, and scenario simulation procedures used to compare the riverine GHG mitigation potential of wastewater collection improvement and effluent standard upgrading in China.

Institutions

• Tongji University

  Shanghai, Shanghai

Categories

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