Identifying spatial drivers of soil heavy metal pollution risk integrating positive matrix factorization, machine learning, and multi-scale geographically weighted regression.

Soil heavy metal (HMs) contamination poses significant ecological and health risks, yet the spatial drivers of HMs pollution remain poorly understood. This study integrates pollution risk assessment, positive matrix factorization, machine learning, and multi-scale geographically weighted regression to develop a framework for identifying the spatial drivers of soil HMs contamination risk in Yangtze River New City, China. Analysis of 7152 samples revealed that although average HMs concentrations were below national standards, As, Cd, Cr, Cu, Hg, and Ni exceeded local background levels.

[Simulation of Pollution Apportionment and Optimization of Control Methods in Watershed Scale: A Case Study of the Shun'an Watershed in Tongling City].

The protection of the Yangtze River is an important national strategy in China, but it faces many problems such as difficult water environment protection, unclear pollution sources, and low integration of measures. Aimed at addressing watershed scale multi-source pollution together with facing the bottleneck method, by combining research data analysis, mechanism model, and intelligent algorithm optimization, this study built the framework for accurate pollution apportionment, measures evaluation, and overall measure optimization. Shun'an watershed in Tongling City of Anhui Province was set as an example for the application.

Copula-based analysis of socio-economic impact on water quantity and quality: A case study of Yitong River, China.

Socio-economic development has a significant impact on both water quantity and quality. However, few studies have considered the complex relationship between water quantity and quality when evaluating such impact. In this study, three indicators based on copula model were proposed, namely, water quantity improvement degree (WQID), water quality improvement degree (WQID) and water quantity and quality joint improvement degree (WQJID).

New method for scaling nonpoint source pollution by integrating the SWAT model and IHA-based indicators.

Nonpoint source (NPS) pollution shows spatial scaling effects because it is affected by topography, river networks, and many other factors. Currently, the lack of an integrated methodology for quantifying the scaling effect has become a crucial barrier in evaluating NPS pollution. In this study, a new method was proposed for scaling NPS pollution by integrating hydrological model and hydrological alteration indicators.

A water quality management methodology for optimizing best management practices considering changes in long-term efficiency.

Optimizing long-term best management practices (BMPs) is of vital importance for water quality management, especially for nonpoint source (NPS) pollution. However, changes in the efficiency of BMPs over time have not been incorporated and a proper method for determining long-term BMP configuration strategies is still lacking. In this study, the long-term BMP optimization method (LBMP-OM) was developed for recommending the BMP maintenance-replacement strategies and optimizing the BMP configuration.