Modeling arsenic pollution from cropland soil management in data-scarce areas: a Zhangjiang river basin case study

Agricultural arsenic pollution poses increasing environmental and public health challenges. Making evidence-based conservation strategy is key for effective pollution control, but is challenged by data scarcity which is common in China. To address the scarcity of monitoring data, we developed an integrated methodology combining the Soil and Water Assessment Tool (SWAT) and the Load Estimator (LOADEST) to assess long-term variations in the arsenic load within the Zhangjiang River (ZR) watershed, China. Our findings suggest that approximately 1% of the urbanized area may contribute to up to 75% of the current stream arsenic load (a preliminary inference based on load differences between GTDK and upstream sites), though this conclusion is constrained by data limitations (e.g., stream flow parameters transferred from an adjacent watershed, limited arsenic monitoring scope, and low NSE at GTDK). This area could be a potential pollution hotspot, while diffuse arsenic pollution across the watershed is on the rise due to expanding agriculture, increased contaminated manure usage and the shifting hydroclimatic condition. Results showed that recycling arsenic-rich animal waste as manure could have the unintended consequence of building up an arsenic storage pool in farmland soils, turning croplands into pollution sources and increasing the risk of diffuse arsenic pollution, thus calling for adjustment in current agricultural management strategy. The proposed modeling method proves as a promising tool for investigating arsenic pollution in data-sparse region, supporting the assessment and optimization of agricultural management practices and policies for arsenic pollution control.