Unveiling the Role of Wetland Strategies in Antibiotic Risk Reduction across China by Machine Learning.

Pervasive antibiotic pollution in water environments has emerged as a serious threat to global ecosystem functions and public health. While wetland expansion─including protection, restoration, and construction, is widely promoted for sustainable water quality improvement, its effectiveness in mitigating antibiotic pollution remains poorly understood. Here, we develop a machine learning model based on a compiled data set of 337 experimental observations to quantify antibiotic removal and map risk distribution in wetlands across 2,833 counties/districts in mainland China. Between 2010 and 2020, the wetland area across China expanded by 34.7%, yet antibiotic removal improved by only 0.1%, failing to meaningfully reduce the risk. We find that antibiotic removal in wetlands is primarily constrained by input magnitudes rather than the wetland area. To address this, we proposed a multistage wetland management strategy to enhance antibiotic removal by 27.6% in 2020 and high-risk area reduction by 90.6% under optimal policies by 2035. Furthermore, we further identified the importance of wetland management strategies through an interpretable model. Our findings provide novel wetland strategy insights for policymakers and highlight the fact that wetland expansion without targeted management is insufficient for controlling antibiotic pollution, although it is an important cornerstone characteristic for water quality improvement.