基于GTWR模型的3km京津冀PM2.5时空分布和影响因素分析

doi:10.13474/j.cnki.11-2246.2024.0615

摘要/Abstract

摘要： PM_2.5与空气质量和公众健康密切相关,许多研究使用遥感结合其他辅助数据的模型反演PM_2.5的浓度,以捕捉各地区PM_2.5时空分布。本文针对京津冀地区数据分辨率较低的问题,采用3 km分辨率的气溶胶光学厚度(AOD)数据及12个辅助变量,建立时间地理加权回归模型(GTWR),估算3 km京津冀地区2020—2022年PM_2.5浓度分布。结果表明:①GTWR模型数据的R²(0.86)均优于OLS模型数据的R²(0.66)和GWR模型数据的R²(0.78)。②在时空分布上,2020—2022年京津冀PM_2.5浓度的空间分布与地形呈负相关。低值区主要分布在地势较高的山区;高值区主要分布在地势较低的平原。③2020—2022年京津冀PM_2.5季节平均浓度差异显著,由高至低依次为冬季(60.88 μg/m³)、秋季(37.78 μg/m³)、春季(31.75 μg/m³)、夏季(22.16 μg/m³)。④PM_2.5浓度与AOD的相关性最强。研究得出3 km分辨率的AOD数据与GTWR模型相结合在反演PM_2.5浓度方面具有较好的适用性。

关键词: PM_2.5, 气溶胶光学厚度, 时空地理加权, 时空分布

Abstract: PM_2.5 is closely related to air quality and public health, and many studies use remote sensing combined with other auxiliary data models to invert PM_2.5 concentration to capture the spatial and temporal distribution of PM_2.5 in various regions. Aiming at the problem of low data resolution in the Beijing-Tianjin-Hebei region, this study adopts 3 km resolution aerosol optical depth(AOD) data and 12 auxiliary variables to establish a geographically and temporally weighted regression model (GTWR) to estimate the PM_2.5 concentration distribution in the 3 km Beijing-Tianjin-Hebei region during 2020—2022. The results show that: ①the R² of GTWR model data (0.86) is better than that of OLS model data (0.66) and GWR model data (0.78).②The spatial and temporal distribution of PM_2.5 concentration in the Beijing-Tianjin-Hebei region during 2020—2022 is negatively correlated with the terrain. The low-value area is mainly distributed in the high-lying mountainous area, and the high-value area is mainly distributed in the low-lying plain.③The seasonal mean concentration of PM_2.5 in Beijing-Tianjin-Hebei from 2020 to 2022 was significantly different as follows: winter (60.88 μg/m³), autumn (37.78 μg/m³), spring (31.75 μg/m³), summer (22.16 μg/m³).④The correlation between PM_2.5 concentration and AOD is the strongest. It is concluds that the combination of 3 km resolution AOD data and GTWR model has good applicability in retrieving PM_2.5 concentration.

Key words: PM_2.5, aerosol optical depth, GTWR, spatial and temporal distribution

中图分类号:

P208

王岩, 刘纪平, 赵阳阳, 徐婧. 基于GTWR模型的3km京津冀PM_2.5时空分布和影响因素分析[J]. 测绘通报, 2024, 0(6): 82-89.

WANG Yan, LIU Jiping, ZHAO Yangyang, XU Jing. Spatial and temporal distribution and influencing factors of PM_2.5 in 3km Beijing-Tianjin-Hebei region based on GTWR model[J]. Bulletin of Surveying and Mapping, 2024, 0(6): 82-89.

参考文献

[1] SUN Jiyao,ZHANG Nan,YAN Xiaona,et al. The effect of ambient fine particulate matter (PM_2.5) on respiratory diseases in China: a systematic review and meta-analysis[J]. Stochastic Environmental Research and Risk Assessment,2020,34(3): 593-610.
[2] HU Xuefei,WALLER L A,LYAPUSTIN A,et al. Estimating ground-level PM_2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model[J]. Remote Sensing of Environment,2014,140: 220-232.
[3] 刘浩,高小明,谢志英,等. 京津冀晋鲁区域气溶胶光学厚度的时空特征[J]. 环境科学学报,2015,35(5): 1506-1511.
[4] 李珂,白开旭. 融合星地多源数据资料的长三角地区高分辨率时空无缝PM_2.5浓度数据[J]. 遥感学报,2022,26(5): 1002-1014.
[5] LV Baolei,HU Yongtao,CHANG H,et al. Daily estimation of ground-level PM_2.5 concentrations at 4km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations[J]. The Science of the Total Environment,2017,580: 235-244.
[6] 张仲伍,魏凯艳,孙九林,等. 基于MODIS数据的山西省PM_2.5浓度估算研究[J]. 浙江大学学报(理学版),2022,49(5): 606-612.
[7] NI Xiliang,CAO Chunxiang,ZHOU Yuke,et al. Spatio-temporal pattern estimation of PM_2.5 in Beijing-Tianjin-Hebei region based on MODIS AOD and meteorological data using the back propagation neural network[J]. Atmosphere,2018,9(3): 105.
[8] 沈焕锋,李同文. 大气PM_2.5遥感制图研究进展[J]. 测绘学报,2019,48(12): 1624-1635.
[9] 杨晓辉,宋春杰,范丽行,等. 京津冀地区高分辨率PM_2.5浓度时空变化模拟与分析[J]. 环境科学,2021,42(9): 4083-4094.
[10] 吴迪,杜宁,王莉,等. 基于GTWR-XGBoost模型的四川省PM_2.5小时浓度估算[J]. 环境科学,2023,44(7): 3738-3748.
[11] GUO Bin,WANG Xiaoxia,PEI Lin,et al. Identifying the spatiotemporal dynamic of PM_2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015-2018[J]. Science of the Total Environment,2021,751: 141765.
[12] LEVY R C,MATTOO S,MUNCHAK L A,et al. The Collection 6 MODIS aerosol products over land and ocean[J]. Atmospheric Measurement Techniques,2013,6(11): 2989-3034.
[13] LYAPUSTIN A,WANG Y,XIONG X,et al. Scientific impact of MODIS C5 calibration degradation and C6+improvements[J].Atmospheric Measurement Techniques,2014,7(12): 4353-4365.
[14] RANDLES C A,DA SILVA A M,BUCHARD V,et al. The MERRA-2 aerosol reanalysis,1980-onward,part I: system description and data assimilation evaluation[J]. Journal of Climate,2017,30(17): 6823-6850.
[15] WANG Suxian,GAO Jiangbo,GUO Linghui,et al. Meteorological influences on spatiotemporal variation of PM_2.5 concentrations in atmospheric pollution transmission channel cities of the Beijing-Tianjin-Hebei region,China[J].International Journal of Environmental Research and Public Health,2022,19(3):1607.
[16] HE Qingqing,HUANG Bo. Satellite-based high-resolution PM_2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model[J]. Environmental Pollution,2018,236: 1027-1037.
[17] JEONG J I,PARK R J,YEH S W,et al. Statistical predictability of wintertime PM_2.5 concentrations over East Asia using simple linear regression[J]. Science of the Total Environment,2021,776: 146059.
[18] BRUNSDON C,FOTHERINGHAM A S,CHARLTON M E. Geographically weighted regression: a method for exploring spatial nonstationarity[J]. Geographical Analysis,1996,28(4): 281-298.
[19] HUANG Bo,WU Bo,BARRY M. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices[J]. International Journal of Geographical Information Science,2010,24(3): 383-401.
[20] JIA Mengwei,ZHAO Tianliang,CHENG Xinghong,et al. Inverse relations of PM_2.5 and O₃ in air compound pollution between cold and hot seasons over an urban area of East China[J]. Atmosphere,2017,8(3): 59.
[21] BALAMURUGAN V,CHEN Jia,QU Zhen,et al. Secondary PM_2.5 decreases significantly less than NO₂ emission reductions during COVID lockdown in Germany[J]. Atmospheric Chemistry and Physics,2022,22(11): 7105-7129.