• Journal of Atmospheric and Environmental Optics
  • Vol. 16, Issue 6, 529 (2021)
Hongliang JIA*, Jun LUO, and Dongsheng XIAO
Author Affiliations
  • [in Chinese]
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    DOI: 10.3969/j.issn.1673-6141.2021.06.007 Cite this Article
    JIA Hongliang, LUO Jun, XIAO Dongsheng. Temporal and Spatial Distribution Characteristics of PM2.5 in Chengdu Area Based on Remote Sensing Data and GWR Model[J]. Journal of Atmospheric and Environmental Optics, 2021, 16(6): 529 Copy Citation Text show less
    References

    [1] Emili E, Popp C, Petittao M, et al. PM10 remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region[J]. Remote Sensing of Environment, 2010, 114(11): 2485-2499.

    [2] Cheng Chunying, Yin Xuebo. Source, composition, formation and hazard of PM2.5 in haze[J]. University Chemistry, 2014, 29(5): 1-6.

    [3] Suo Danfeng, Zeng Sanwu. Research on the harm of air fine particulate matter PM2.5 to various human systems[J]. Medical Information, 2019, 32(18): 32-34.

    [4] Huang Wenxi. PM2.5 Quantitative Retrieval Based on the Remote Sensing and Ground-based Air Quality Measurement Data[D]. Wuhan: China University of Geosciences, 2019.

    [5] Lu Debin, Mao Wanliu, Yang Dongyang, et al. Analysis on the trend and influencing factors of PM2.5 in China based on multi-source remote sensing data[J]. Resources and Environment in the Yangtze Basin, 2019, 28(3): 651-660.

    [6] Shen Yang, Zhang Lianpeng, Fang Xing, et al. Correlation analysis and annual cycle characteristics of aerosol optical depth and PM2.5 concentrations in the Xuzhou City[J]. Earth and Environment, 2019, 47(1): 34-42.

    [7] Li Chengcai, Mao Jietai, Liu Qihan, et al. Application of MODIS satellite remote sensing aerosol products in Beijing air pollution research[J]. Science in China, Series D: Earth Sciences, 2005, 35(SUP 1): 177-186.

    [8] Yang Lijuan, Xu Hanqiu, Jin Zhifan. Estimation of ground-levelPM2.5concentrations using MODIS satellite data in Fuzhou, China[J]. Journal of Remote Sensing, 2018, 22(1): 64-75.

    [9] Qin Wen, Wu Yunxia, Sheng Jie, et al. Study on quantitative inversion MODIS-AOD products and surface atmospheric particulate matter concentration in Nanchang city[J]. Guangzhou Chemical Industry, 2016, 44(23): 125-128.

    [10] Lin Chuyong, Deng Yujiao, Xu Jianbo, et al. Temporal variation and spatial distribution of aerosol optical depth in Guangdong Province based on modis data[J]. Journal of Tropical Meteorology, 2015, 31(6): 821-826.

    [11] Ren Yuxuan. Study on Satellite Remote Sensing Inversion Method of Ambient PM2.5 in Chengdu[D]. Chengdu: Southwest Jiaotong University, 2018.

    [12] Hou Aihua, Gao Wei, Wang Zhongting, et al. Estimation of PM2.5 concentration from GF-1 data in Kaifeng City[J]. Remote Sensing for Land & Resources, 2017, 29(4): 161-165.

    [13] Shi Lingzhi, Deng Qihong, Lu Chan, et al. Prediction of PM10 mass concentrations based on BP artificial neural network[J]. Journal of Central South University(Science and Technology), 2012, 43(5): 1969-1974.

    [14] Ma Z, Liu Y, Zhao Q, et al. Satellite-derived high resolution PM2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model[J]. Atmospheric Environment, 2016, 133: 156-164.

    [15] Fu Hongchen, Sun Yanling, Jing Yue. Estimating ground-level PM2.5 concentrations of Xinjiang based on geographically weighted regression model[J]. Journal of Tianjin Normal University (Natural Science Edition), 2019, 39(1): 63-70.

    [16] Wu Jiansheng, Wang Xi, Li Jiacheng, et al. Comparison of models on spatial sariation of PM2.5 concentration: A case of Beijing-Tianjin-Hebei region[J]. Environmental Science, 2017, 38(6): 2191-2201.

    [17] Lee H J, Liu Y, Coull B A. A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations[J]. Atmospheric Chemistry and Physics, 2011, 11(15): 7991-8002.

    [18] Fu Hongchen, Sun Yanling, Wang Bin, et al. Estimation of PM2.5 concentration in Beijing-Tianjin-Hebei region based on AOD data and GWR model[J]. China Environmental Science, 2019, 39(11): 4530-4537.

    [19] Chen Hui, Li Qing, Zhang Yuhuan, et al. Estimations of PM2.5 concentrations based on the method of geographically weighted regression[J]. Acta Scientiae Circumstantiae, 2016, 36(06): 2142-2151.

    [20] Yi Wei, Yang Dong, Li Xirong. Inversion of PM2.5 concentration in the economic zone of northern Tianshan mountain slope based on the GWR model and the temporal and spatial characteristics[J]. Earth and Environment, 2021, 49(1): 51-58.

    [21] Zhang Ying, Wang Shigong, Ni Changjian, et al. Study on an objective synoptic typing method for air pollution weather in Chengdu during winter[J]. Environmental Science & Technology, 2020, 43(5): 139-144.

    [22] Li Peirong, Xiao Tiangui. The diffusion and transport of PM2.5 under the polluted weather conditions during autumn and winter seasons in Chengdu[J]. China Environmental Science, 2020, 40(1): 63-75.

    [23] Zhang Yang. Inversion of Aerosol Optical Depth Based on the Multi-source Satellite Remote Sensing over Chengdu Region in Sichuan Province[D]. Chengdu: Chengdu University of Information Technology, 2015.

    [24] Liang Jia. MODIS Combine Ground Monitoring Station Data to Monitor the Quality Concentration and the Diffusion of PM2.5—ShuangLiu District in Chengdu[D]. Chengdu: Chengdu University of Technology, 2018.

    [25] Zhang Li, Zeng Zhiyuan. The study and implementation of extraction modis level 1B image data based on a HDF4 file[J]. Remote Sensing for Land & Resources, 2004, 16(4): 27-32.

    [26] Fu Hongchen, Sun Yanling, Chen Li, et al. Temporal and spatial distribution characteristics of PM2.5 and PM10 in Xinjiang region in 2016 based on AOD data and GWR model[J]. Acta Scientiae Circumstantiae, 2020, 40(1): 27-35.

    [27] McMillen D P. Geographically weighted regression: The analysis of spatially varying relationships[J]. American Journal of Agricultural Economics, 2004, 86(2): 554-556.

    JIA Hongliang, LUO Jun, XIAO Dongsheng. Temporal and Spatial Distribution Characteristics of PM2.5 in Chengdu Area Based on Remote Sensing Data and GWR Model[J]. Journal of Atmospheric and Environmental Optics, 2021, 16(6): 529
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