Accurate prediction of pollution and health risks of iodinated X-ray contrast media in Taihu Lake with machine learning and revealing key environmental factors.

Iodinated X-ray contrast media (ICM) are commonly detected at considerable concentrations in aquatic environments. The long-term pollution trends in ICM at the whole lake/river scale have not yet been investigated; therefore, the risks associated with ICM and the influences of environmental factors remain understudied. Herein, the occurrence and distribution of ICM in the surface water of Taihu Lake were comprehensively investigated. In addition, the accuracy and applicability of different machine learning models for predicting ICM pollution and associated health risk were compared using meteorological and water quality parameters as inputs. The results revealed that the ΣICM concentration ranged from 10.8 to 454.6 ng/L, exhibiting significant spatial and seasonal variations, which reflected the influence of hydrodynamics and climatic conditions. Among the nine models, the RF model achieved the most accurate prediction of ICM, with R ≥ 0.92. Via feature importance ranking and linear relationship analysis, TN, NH-N, S, PS, SUVA, UV, and pH were identified as important factors affecting ICM. This study provides a hybrid framework that includes environmental pollution prediction, health risk analysis, and key environmental factor identification for ICM, providing scientific techniques for the application of machine learning in the analysis of trace organic contaminants.