Accurate and efficient prediction of atmospheric PM, PM, PM, and O concentrations using a customized software package based on a machine-learning algorithm.

Particulate matter (PM) and ozone (O) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O are secondary pollutants, and there remain significant challenges in accurately and efficiently predicting their concentrations in the atmosphere. In this study, one-year monitoring data of PM, PM, PM, and O concentrations as well as meteorological parameters and concentrations of various precursors (i.

Machine learning and deep learning modeling and simulation for predicting PM2.5 concentrations.

Particulate matter (PM) pollution greatly endanger human physical and mental health, and it is of great practical significance to predict PM concentrations accurately. This study measured one-year monitoring data of six main meteorological parameters and PM2.5 concentrations independently at two monitoring sites in central China's Hunan Province.