Deciphering pollution loads in the Middle-Lower Yangtze River by coupling water quality models with machine learning

Abstract

Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river’s pollution load remains challenging due to limited monitoring and unclear spatiotemporal distribution of pollution sources. Specifically, anthropogenic activities’ contribution to the pollution have been underestimated in previous research. Here, we coupled a hydrodynamic-based water quality (HWQ) model with a machine learning (ML) model, namely attention-based Gated Recurrent Unit, to decipher the daily pollution loads (i.e., chemical oxygen demand, COD; total phosphorus, TP) and their sources in the Middle-Lower Yangtze River from 2014 to 2018. The coupled HWQ-ML model outperformed the standalone ML model with KGE values ranging 0.77–0.91 for COD and 0.47–0.64 for TP, while also reducing parameter uncertainty. When examining the relative contributions at the Middle Yangtze River Hankou cross-section, we observed that the main stream and tributaries, lateral anthropogenic activities, and parameter uncertainty contributed 15%, 66%, and 19% to COD, and 58%, 35%, and 7% to TP, respectively. For the Lower Yangtze River Datong cross-section, the contributions were 6%, 69%, and 25% for COD and 41%, 42%, and 17% for TP. The primary drivers of the anthropogenic pollution sources, in decreasing order of importance, were temperature (reflecting seasonality), date, and precipitation. This study emphasizes the synergy between physical modeling and machine learning, offering new insights into pollution load dynamics in the Yangtze River.