Machine learning-based source apportionment and source-oriented probabilistic ecological risk assessment of heavy metals in urban green spaces
Jun Li, J. Y. Lu, Xin-Ying Tuo, Chao Wang, Junzhuo Liu, Zhan-Dong Gao, C. Yu, Fei Zang
Abstract
). Notably, exceedance rates for Cd, Hg, Pb, and Zn were 90.91 %, 94.95 %, 80.81 %, and 87.88 %, respectively. Elevated concentrations, particularly of Zn, Cd, Pb, and Hg, displayed distinct spatial patterns linked to industrial activities and urban development. Overall contamination reached moderate levels, primarily driven by Cd and Hg. Source apportionment identified traffic emissions, industrial activities, and coal combustion as the principal HM sources. Probabilistic ecological risk assessment confirmed that Cd and Hg pose the greatest ecological risks, primarily stemming from industrial activities and coal combustion. These findings provide important insights for developing source-specific remediation to mitigate and manage HM pollution in urban green spaces.