Estimation of ground-level NO₂ and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model

Ahmad, Naveed1; Lin, Changqing1; Lau, Alexis K. H.1,2; Kim, Jhoon3; Zhang, Tianshu4,5; Yu, Fangqun6; Li, Chengcai7; Li, Ying8; Fung, Jimmy C. H.1,9; Lao, Xiang Qian10

2024-08-30

10.5194/acp-24-9645-2024

ATMOSPHERIC CHEMISTRY AND PHYSICS   影响因子和分区

1680-7316

1680-7324

The major link between satellite-derived vertical column densities (VCDs) of nitrogen dioxide (NO2) and ground-level concentrations is theoretically the NO(2 )mixing height (NMH). Various meteorological parameters have been used as a proxy for NMH in existing studies. This study developed a nested XGBoost machine learning model to convert VCDs of NO2 into ground-level NO2 concentrations across China using Geostationary Environmental Monitoring Spectrometer (GEMS) measurements. This nested model was designed to directly incorporate NMH into the methodological framework to estimate satellite-derived ground-level NO2 concentrations. The inner machine learning model predicted the NMH from meteorological parameters, which were then input into the main XGBoost machine learning model to predict the ground-level NO2 concentrations from its VCDs. The inclusion of NMH significantly enhanced the accuracy of ground-level NO2 concentration estimates; i.e., the R2 values were improved from 0.73 to 0.93 in 10-fold cross-validation and from 0.88 to 0.99 in the fully trained model. Furthermore, NMH was identified as the second most important predictor variable, following the VCDs of NO2. Subsequently, the satellite-derived ground-level NO2 data were analyzed across subregions with varying geographic locations and urbanization levels. Highly populated areas typically experienced peak NO2 concentrations during the early morning rush hour, whereas areas categorized as lightly populated observed a slight increase in NO(2 )levels 1 or 2 h later, likely due to regional pollutant dispersion from urban sources. This study underscores the importance of incorporating NMH in estimating ground-level NO2 from satellite column measurements and highlights the significant advantages of geostationary satellites in providing detailed air pollution information at an hourly resolution.

SCI

英语

其他

NSFC-RGC Joint Research Project["42161160329","N_HKUST609/21"] ; Research Grants Council of Hong Kong["GRF 16202120","16302220"]

Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences

Environmental Sciences ; Meteorology & Atmospheric Sciences

WOS:001302951500001

COPERNICUS GESELLSCHAFT MBH

GEOSCIENCES

Web of Science

1.Hong Kong Univ Sci & Technol, Div Environm & Sustainabil, Sai Kung, Clear Water Bay, Hong Kong, Peoples R China
2.Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Sai Kung, Clear Water Bay, Hong Kong, Peoples R China
3.Yonsei Univ, Dept Atmospher Sci, Seoul 03722, South Korea
4.Hefei Comprehens Natl Sci Ctr, Inst Environm, Hefei 230000, Peoples R China
5.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, Hefei 230000, Peoples R China
6.SUNY Albany, Atmospher Sci Res Ctr, Albany, NY 12226 USA
7.Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Beijing 100871, Peoples R China
8.Southern Univ Sci & Technol, Dept Ocean Sci & Engn, Shenzhen 518055, Peoples R China
9.Hong Kong Univ Sci & Technol, Dept Math, Sai Kung, Clear Water Bay, Hong Kong, Peoples R China
10.City Univ Hong Kong, Dept Biomed Sci, Kowloon, Hong Kong, Peoples R China

Ahmad, Naveed,Lin, Changqing,Lau, Alexis K. H.,et al. Estimation of ground-level NO₂ and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2024,24(16).

Ahmad, Naveed.,Lin, Changqing.,Lau, Alexis K. H..,Kim, Jhoon.,Zhang, Tianshu.,...&Lao, Xiang Qian.(2024).Estimation of ground-level NO₂ and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model.ATMOSPHERIC CHEMISTRY AND PHYSICS,24(16).

Ahmad, Naveed,et al."Estimation of ground-level NO₂ and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model".ATMOSPHERIC CHEMISTRY AND PHYSICS 24.16(2024).