Shenghua Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shenghua Chen has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Jinjun Liu
Department of Earth Science and Engineering, Shanxi Engineering and Technology College, Yangquan 045000, China
1 shared publication - Qinfeng Yao
Department of Earth Science and Engineering, Shanxi Engineering and Technology College, Yangquan 045000, China
1 shared publication - Sunwen Du
College of Geological and Surveying Engineering, Taiyuan University of Technology, Taiyuan 030024, China
1 shared publication - Yongxiang Ning
Department of Earth Science and Engineering, Shanxi Engineering and Technology College, Yangquan 045000, China
1 shared publication
Latest Publications
- Open Access
- 18 March 2026
- Published by MDPI AG in Atmosphere
- Vol. 17 (3) , 308
Estimation of PM2.5 Concentration in Yangquan City from 2015 to 2024 Based on MODIS Image and Meteorological Data and Analysis of Spatial and Temporal Variation
This study employed Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth data meteorological data, Digital Elevation Model (DEM), Normalized Difference Vegetation Index (NDVI), and ground monitoring data for particulate matter (PM2.5) to construct a model for estimating the PM2.5 concentration in Yangquan City, Shanxi Province, from 2015 to 2024. The spatial and temporal changes in the PM2.5 concentration were analyzed. The results revealed the following: (1) The random forest model was more accurate than the multiple linear regression model. The spring model R2 increased by 38.7%, and the Root Mean Square Error (RMSE) decreased by 92.6%. The summer model R2 increased by 65.1%, and the RMSE decreased by 92.5%. The autumn model R2 increased by 2.7%, and the RMSE decreased by 83.4%. The winter model R2 increased by 25.4%, and the RMSE decreased by 95.5%. (2) The PM2.5 concentration in Yangquan City showed an upward trend from 2015 to 2017, and then a downward trend from 2018 to 2024, with an average decrease of 18.3 μg/m3. The highest concentration of PM2.5 was 55–85 μg/m3 in winter, and the lowest concentration of PM2.5 was 25–40 μg/m3 in summer. In terms of spatial distribution, the PM2.5 concentration in Yangquan City exhibits a pattern of being lower in the northwest and higher in the southeast. The high values are primarily concentrated in the central urban areas and major industrial zones in the southeast.
- 18 March 2026
- Published by MDPI AG in Atmosphere
- Vol. 17 (3) , 308
Open AccessEstimation of PM2.5 Concentration in Yangquan City from 2015 to 2024 Based on MODIS Image and Meteorological Data and Analysis of Spatial and Temporal Variation
This study employed Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth data meteorological data, Digital Elevation Model (DEM), Normalized Difference Vegetation Index (NDVI), and ground monitoring data for particulate matter (PM2.5) to construct a model for estimating the PM2.5 concentration in Yangquan City, Shanxi Province, from 2015 to 2024. The spatial and temporal changes in the PM2.5 concentration were analyzed. The results revealed the following: (1) The random forest model was more accurate than the multiple linear regression model. The spring model R2 increased by 38.7%, and the Root Mean Square Error (RMSE) decreased by 92.6%. The summer model R2 increased by 65.1%, and the RMSE decreased by 92.5%. The autumn model R2 increased by 2.7%, and the RMSE decreased by 83.4%. The winter model R2 increased by 25.4%, and the RMSE decreased by 95.5%. (2) The PM2.5 concentration in Yangquan City showed an upward trend from 2015 to 2017, and then a downward trend from 2018 to 2024, with an average decrease of 18.3 μg/m3. The highest concentration of PM2.5 was 55–85 μg/m3 in winter, and the lowest concentration of PM2.5 was 25–40 μg/m3 in summer. In terms of spatial distribution, the PM2.5 concentration in Yangquan City exhibits a pattern of being lower in the northwest and higher in the southeast. The high values are primarily concentrated in the central urban areas and major industrial zones in the southeast.