Evaluation of different machine learning approaches and aerosol optical depth in PM2.5 prediction

Hamed Karimian; Yaqian Li; Youliang Chen; Zhaoru Wang

doi:10.1016/j.envres.2022.114465

Evaluation of different machine learning approaches and aerosol optical depth in PM_2.5 prediction

Environ Res. 2023 Jan 1;216(Pt 2):114465. doi: 10.1016/j.envres.2022.114465. Epub 2022 Oct 12.

Authors

Hamed Karimian¹, Yaqian Li¹, Youliang Chen², Zhaoru Wang³

Affiliations

¹ School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China.
² School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China; School of Geosciences and Info Physics, Central South University, Changsha, China. Electronic address: 9120010023@jxust.edu.cn.
³ School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China.

PMID: 36241075
DOI: 10.1016/j.envres.2022.114465

Abstract

Atmospheric Aerosol Optical Depth (AOD), derived from polar-orbiting satellites, has shown potential in PM_2.5 predictions. However, this important source of data suffers from low temporal resolution. Recently, geostationary satellites provide AOD data in high temporal and spatial resolution. However, the feasibility of these data in PM_2.5 prediction needs further study. In this paper, we analyzed the impact of AOD derived from Himawari-8 in PM_2.5 predictions. Moreover, by combining wavelet, machine learning techniques, and minimum redundancy maximum relevance (mRMR), a novel hybrid model was proposed. The results showed that AOD missing rate over Yangtze River Delta region is the highest in Nanjing, Hefei, and Maanshan. In addition, missing rates are the lowest in winter and summer (∼80%). Moreover, we found that considering AOD, as an auxiliary variable in the model, could not improve the accuracy of PM_2.5 predictions, and in some cases decreased it slightly. In comparison with other models, our proposed hybrid model showed higher prediction accuracy, R² is improved by 11.64% on average, and root mean square error, mean absolute error, and mean absolute percentage error is reduced by 26.82%, 27.24%, and 29.88% respectively. This research provides a general overview of the availability of Himawari-8 AOD data and its feasibility in PM_2.5 predictions. In addition, it evaluates different machine learning approaches in PM_2.5 predictions. Our proposed framework can be used in other regions to predict different air pollutants concentrations and can be used as an aid for air pollution controlling programs.

Keywords: AOD; Air pollution; Himawari-8; Remote sensing; Wavelet transform.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aerosols / analysis
Air Pollutants* / analysis
Air Pollution* / analysis
Environmental Monitoring / methods
Machine Learning
Particulate Matter / analysis

Substances

Particulate Matter
Aerosols
Air Pollutants