Linking water quality assessment to source apportionment with machine learning-assisted WQI, PMF, and SOM: A case study of the Jinma River basin

Rapid urbanisation intensifies multiple pollution pressures on river ecosystems, generating heterogeneous and nonlinear spatiotemporal water quality dynamics that challenge conventional evaluation methods. Although machine-learning (ML) models are increasingly used for water-quality assessment and prediction, most applications remain evaluation-centric and rarely link their outputs to receptor-based source apportionment or explicit spatial zoning, which limits interpretability and management relevance. Here, we proposed an integrated framework that couples ML-assisted water quality index (WQI) optimisation with receptor modelling and unsupervised spatial clustering. Using monthly observations of ten water quality indicators from 19 sites in the Jinma River Basin (2018–2022), we trained an eXtreme Gradient Boosting (XGBoost) model to derive optimised weights of water quality indicators for WQI aggregation. We then applied positive matrix factorisation (PMF) to resolve latent source factors and quantify their contributions, and used self-organising maps (SOM) to cluster monitoring sites into spatially coherent zones based on both WQI status and source composition. Four dominant contributors to the basin water pollution were identified: seasonal hydrological influences (28.16%), domestic sewage (27.36%), agricultural runoff (27.30%) and industrial emissions (17.18%). Integrating the XGBoost-optimised WQI, PMF-resolved source contributions, and SOM clusters delineated three functional management zones, specifically, forested headwaters with high WQI and minimal anthropogenic influence, midstream transition reaches dominated by nutrient-enriched agricultural runoff, and urban downstream corridors affected by combined industrial and domestic inputs. This modular, code-driven workflow translates routine multi-indicator monitoring data into management outputs and can be retrained for other river basins facing complex pollution regimes. • Optimised weights of ten water quality indicators obtained by XGBoost for WQI aggregation. • Pollution source apportionment and spatial zoning with integration of WQI, PMF and SOM. • Four pollution contributors identified and three managerial zones proposed for the study area.