Heavy metals in submicronic particulate matter (PM₁) from a Chinese metropolitan city predicted by machine learning models.

ABSTRACT: The aim of this study was to establish a method for predicting heavy metal concentrations in PM₁ (aerosol particles with an aerodynamic diameter ≤ 1.0 μm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) methods. The annual average PM₁ concentration was 26.31 μg/m³ (range: 7.00-73.40 μg/m³). The concentrations of most metals were higher in winter and lower in autumn and summer. Mn and Ni had the highest noncarcinogenic risk, and Cr the highest carcinogenic risk. The hazard index was below safe limit, and the integrated carcinogenic risk was less than precautionary value. There were no obvious differences in the simulation performances of BP-ANN and SVM models. However, in both models many elements had

SUBMITTER: Li H 

PROVIDER: S-EPMC7340598 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature