PMHMs_dataset

The absence of nationwide distribution data regarding heavy metal flux in the atmosphere poses a significant constraint in environmental research and public health assessment. In response to the critical data deficiency, we have established a dataset covering Cr, Cd, As, and Pb flux in the atmosphere (PMHMs) across 367 municipalities in China. Initially, we collected PMHMs data and covariates such as industrial emissions, vehicle emissions, meteorological variables, among other 10 indicators. Following this, nine machine learning models, including Linear Regression (LR), Ridge, Bayesian Ridge (Bayesian), K-Neighbors Regressor (KNN), MLP Regressor (MLP), Random Forest Regressor (RF), LGBM Regressor (LGBM), Lasso, and ElasticNet, were assessed using R², RMSE, and MAE on the testing dataset. RF and LGBM models were chosen, due to their favorable predictive performance (R²: 0.58–0.77, lower RMSE/MAE), confirming their robustness in modelling. Subsequently, using predicted data, distribution maps of PMHMs at the city level were generated. This dataset serves as a valuable resource for informing environmental policies, monitoring air quality, conducting environmental assessments, and facilitating academic research.