Remote Sensing Model for Estimating Atmospheric PM2.5 Concentration in the Guangdong-Hong Kong-Macao Greater Bay Area

被引：0

作者：

Dai Y.-Y. ^{[1
]}

Gong S.-Q. ^{[1
]}

Zhang C.-J. ^{[2
]}

Min A.-L. ^{[1
]}

Wang H.-J. ^{[1
]}

机构：

[1] School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing

[2] National Climate Center, Beijing

来源：

Huanjing Kexue/Environmental Science | 2024年 / 45卷 / 01期

关键词：

BP neural network model(BPNN); geographically and temporally weighted regression model(GTWR); Guangdong-Hong Kong-Macao Greater Bay Area; MAIAC AOD; PM[!sub]2.5[!/sub; random forest model(RF); support vector machine regression model(SVR);

D O I：

10.13227/j.hjkx.202302237

中图分类号：

学科分类号：

摘要：

PM2.5 is extremely harmful to the atmospheric environment and human health, and a timely and accurate understanding of PM2.5 with high spatial and temporal resolution plays an important role in the prevention and control of air pollution. Based on multi-angle implementation of atmospheric correction algorithm (MAIAC), 1 km AOD products, ERA5 meteorological data, and pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5)in the Guangdong-Hong Kong-Macao Greater Bay Area during 2015-2020, a geographically and temporally weighted regression model (GTWR), BP neural network model (BPNN), support vector machine regression model (SVR), and random forest model (RF) were established, respectively, to estimate PM2.5 concentration. The results showed that the estimation ability of the RF model was better than that of the BPNN, SVR, and GTWR models. The correlation coefficients of the BPNN, SVR, GTWR, and RF models were 0.922, 0.920, 0.934, and 0.981, respectively. The RMSE values were 7.192, 7.101, 6.385, and 3.670 μg·m−3. The MAE values were 5.482, 5.450, 4.849, and 2.323 μg·m−3, respectively. The RF model had the best effect during winter, followed by that during summer, and again during spring and autumn, with correlation coefficients above 0.976 in the prediction of different seasons. The RF model could be used to predict the PM2.5 concentration in the Greater Bay Area. In terms of time, the daily ρ (PM2.5)of cities in the Greater Bay Area showed a trend of "decreasing first and then increasing" in 2021, with the highest values ranging from 65.550 μg·m−3 to 112.780 μg·m−3 and the lowest values ranging from 5.000 μg·m−3 to 7.899 μg·m−3. The monthly average concentration showed a U-shaped distribution, and the concentration began to decrease in January and gradually increased after reaching a trough in June. Seasonally, it was characterized by the highest concentration during winter, the lowest during summer, and the transition during spring and autumn. The annual average ρ(PM2.5)of the Greater Bay Area was 28.868 μg·m−3, which was lower than the secondary concentration limit. Spatially, there was a "northwest to southeast"decreasing distribution of PM2.5 in 2021, and the high-pollution areas clustered in the central part of the Greater Bay Area, represented by Foshan. Low concentration areas were mainly distributed in the eastern part of Huizhou, Hong Kong, Macao, Zhuhai, and other coastal areas. The spatial distribution of PM2.5 in different seasons also showed heterogeneity and regionality. The RF model estimated the PM2.5 concentration with high accuracy, which provides a scientific basis for the health risk assessment associated with PM2.5 pollution in the Greater Bay Area. © 2024 Science Press. All rights reserved.

引用

页码：8 / 22

页数：14

共 45 条

[1] Shang Y J, Mao Y H, Liao H, Et al., Response of PM2.5 and O3 to reductions of emissions in Nanjing based on random forest, Environmental Science, 44, 8, pp. 4250-4261, (2023)
[2] Xia X S, Chen J J, Wang J J, Et al., PM2.5 concentration influencing factors in China based on the random forest model [J], Environmental Science, 41, 5, pp. 2057-2065, (2020)
[3] Lu F, Yang Y X, Yang J., Spatiotemporal distribution characteristics and relationship analysis of aerosol optical depth and PM2.5 concentration:taking the“2+26”urban agglomeration as an example, Acta Ecologica Sinica, 43, 1, pp. 153-165, (2023)
[4] Jin J N, Yang X C, Yan X, Et al., MAIAC AOD and PM2.5 mass concentrations characteristics and correlation analysis in Beijing−Tianjin−Hebei and surrounding areas [J], Environmental Science, 42, 6, pp. 2604-2615, (2021)
[5] Shen H F, Li T W., Progress of remote sensing mapping of atmospheric PM2.5 [J], Acta Geodaetica et Cartographica Sinica, 48, 12, pp. 1624-1635, (2019)
[6] Xiang J, Tao M H, Guo L, Et al., Progress of near-surface PM2.5 concentration retrieve based on satellite remote sensing, National Remote Sensing Bulletin, 26, 9, pp. 1757-1776, (2022)
[7] Liu Y, Park R J, Jacob D J, Et al., Mapping annual mean ground-level PM2.5 concentrations using multiangle imaging spectroradiometer aerosol optical thickness over the contiguous United States, Journal of Geophysical Research: Atmospheres, 109, D22, (2004)
[8] Li Z Q, Zhang Y, Shao J, Et al., Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground:Method validation using ground-based measurements[J], Remote Sensing of Environment, 173, pp. 59-68, (2016)
[9] Sun C, Wang W, Liu F T, Et al., Spatial-temporal simulation of PM2.5 concentration in Hebei province based on linear mixed effects model, Research of Environmental Sciences, 32, 9, pp. 1500-1509, (2019)
[10] Pan C, Kang Z M., Impact of meteorological conditions on PM2.5 in Jiangsu province from 2001 to 2019, Environmental Science, 43, 2, pp. 649-662, (2022)

← 1 2 3 4 5 →