Estimating ground-level PM 2.5 concentrations by developing and optimizing machine learning and statistical models using 3  km MODIS AODs: case study of Tehran, Iran

ConclusionsWhile the best shape of LME and GPM had similar and reliable performances in predicting ground-level PM2.5 values during the cross-validation, GPM was able to predict extreme values of ground-level PM2.5 concentrations, which was the weakness of LME models and was an important issue in urban polluted environments. In this respect, GPM could be a good alternative for LME models for high levels of PM2.5 concentrations. The spatial distribution of estimated PM2.5 values represented that central parts of Tehran were the most polluted area over the studied region which was consistent with the ground-level recording PM2.5 data over monitoring stations.
Source: Journal of Environmental Health Science and Engineering - Category: Environmental Health Source Type: research