With the increasing threaten from anthropogenic activities and climate change, addressing the widespread deterioration of river ecosystems has become a pressing concern. The combination of water shortage and water pollution has exacerbated the number of people exposed to dangerous levels of water security, leading to health risks and reduced quality of life. Previous studies have shown nearly 80% (4.8 billion) of world’s population (for 2000) lives in areas with a high incidence of threat (> 75%) to human water security. The urgency to mitigate these risks has prompted a research endeavor aimed to diagnose the threats to river water quality over a broad range of temporal and spatial scales, remedy their underlying causes, and limit the threats from the source to protect river freshwater resources.
The study uses a stacking machine learning model to accurately simulate and predict the monthly variations in 613 sub-watersheds of the nation’s 10 major river basins during the period of 1980 to 2018, and then uses two future scenarios (SSP2-RCP4.5 and SSP5-RCP8.5) to predict the decadal trends in water quality between 2020-2050. Comparison between measured and predicted values in the 10 large river basins shows that our stacking machine-learning models are generally able to recreate TN, TP, NH3-N, and CODMn concentrations at a significance level of p<0.01 and with a low predictive bias.
One significant finding of the research is the recent decreases in TP, NH3-N, and CODMn concentrations in most rivers, indicating the effectiveness of China’s nutrient control measures. While the proportions of sampling sites with TN concentration greater than 1.5 mg L-1 was 62.3%, which suggests that TN represents a relatively serious and urgent pollution problem in most regions of China under the current water quality standards. The results of future prediction under SSP2-RCP4.5 and SSP5-RCP8.5 scenario show that human activities and climate change will significantly influence riverine nutrient concentrations, especially within the Yellow, Huaihe, and Haihe rivers (p<0.01).
It is noteworthy that anthropogenic factors were found to be the dominant controls compared with climatic and geographical drivers for TN, TP, and NH3-N concentrations. The reverse was true for CODMn concentration, which may be attributed to increased input of terrestrial organic matter through runoff influenced by precipitation.
The implication of this study extends beyond the realm of academia. Decision makers can utilize the findings to formulate water quality protection strategies for achieving the Sustainable Development Goals. The water resources, water environment, aquatic ecology and water risk should be considered together to achieve good ecological status in China’s rivers. In formulating future policies, special attention needs to be paid to pollution discharge, sewage systems and climate change regarding their economic, societal, institutional and technical feasibility in ensuring the effectiveness of the policies in pollution control.
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