The Development and Application of Machine Learning in Atmospheric Environment Studies

Machine learning (ML) plays an important role in atmospheric environment prediction, having been widely applied in atmospheric science with significant progress in algorithms and hardware. In this paper, we present a brief overview of the development of ML models as well as their application to atmospheric environment studies. ML model performance is then compared based on the main air pollutants (i.e., PM2.5, O-3, and NO2) and model type. Moreover, we identify the key driving variables for ML models in predicting particulate matter (PM) pollutants by quantitative statistics. Additionally, a case study for wet nitrogen deposition estimation is carried out based on ML models. Finally, the prospects of ML for atmospheric prediction are discussed.

Automated summary

This paper provides an overview of the important role of machine learning in atmospheric environment prediction, compares the performance of ML models, identifies key variables for predicting particulate matter pollutants, presents a case study on wet nitrogen deposition estimation, and discusses the prospects of ML for atmospheric prediction.