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Estimation of PM2.5 Mass Concentrations in Beijing–Tianjin–Hebei Region Based on Geographically Weighted Regression and Spatial Downscaling Method

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Abstract

Geographically Weighted Regression (GWR) is a common method to estimate mass concentrations of fine particulate matter (PM2.5). However, some shortage like spatial resolution of the raster input model still exists widely in the model. Therefore, based on GWR model, we adopted spatial downscaling (SD) method to solve this problem. GWR and SD were constructed by using Aerosol Optical Depth remote sensing data, GEOF meteorological grid data of the Goddard Earth Observing System, and PM2.5 data from the ground environmental monitoring station. In this study, GWR and SD were used to estimate monthly PM2.5 mass concentrations of the Beijing–Tianjin–Hebei (BTH) region in 2017. The results showed that: the average annual PM2.5 in 2017 estimated by GWR and SD had the characteristics of high in the south and low in the north with the boundary of 40°N in the spatial distribution. We found that the natural proximity method was the optimal choice for the treatment of residual values through verification of the estimated results. At the 95% confidence level, the determination coefficient R2 is 0.903, the mean prediction error is 7.307 μg/m3, the root mean square error is 11.62 μg/m3, and the relative prediction error is 18.35%. These results suggest that the GWR and SD method could objectively estimate PM2.5 mass concentrations in BTH region in 2017 and process raster data into the same spatial resolution.

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References

  • Beelen, R., Hoek, G., Vienneau, D., Eeftens, M., Dimakopoulou, K., Pedeli, X., et al. (2013). Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe: The ESCAPE project. Atmospheric Environment, 72(2), 10–23.

    Google Scholar 

  • Bell, M. L., Ebisu, K., & Belanger, K. (2007). Ambient air pollution and low birth weight in connecticut and massachusetts. Environmental Health Perspectives, 115(7), 1118–1124.

    Google Scholar 

  • Berrocal, V. J., Craigmile, P. F., & Guttorp, P. (2012). Regional climate model assessment using statistical upscaling and downscaling techniques. Environmetrics, 23(5), 482–492.

    Google Scholar 

  • Berrocal, V. J., Gelfand, A. E., & Holland, D. M. (2010). A spatio-temporal downscaler for output from numerical models. Journal of Agricultural, Biological, and Environmental Statistics, 15(2), 176–197.

    Google Scholar 

  • Bi, J., Huang, J., Hu, Z., Holben, B. N., & Guo, Z. (2014). Investigating the aerosol optical and radiative characteristics of heavy haze episodes in Beijing during January of 2013. Journal of Geophysical Research: Atmospheres, 119(16), 9884–9900.

    Google Scholar 

  • Brauer, M., Amann, M., Burnett, R. T., Cohen, A., Dentener, F., Ezzati, M., et al. (2012). Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution. Environmental Science and Technology, 46(2), 652–660.

    Google Scholar 

  • Chang, H. H., Hu, X., & Liu, Y. (2014). Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling. Journal of Exposure Science & Environmental Epidemiology, 24(4), 398–404.

    Google Scholar 

  • Che, H., Xia, X., Zhu, J., Li, Z., Dubovik, O., Holben, B., et al. (2014). Column aerosol optical properties and aerosol radiative forcing during a serious haze-fog month over North China Plain in 2013 based on ground-based sun photometer measurements. Atmospheric Chemistry and Physics, 14(4), 2125–2138.

    Google Scholar 

  • Chen, G., Li, S., Knibbs, L. D., Hamm, N. A. S., Cao, W., Li, T., et al. (2018). A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information. Science of the Total Environment, 636, 52–60.

    Google Scholar 

  • Chen, H., Li, Q., & Zhang, Y. H. (2016). Estimations of PM2.5 concentrations based on the method of geographically weighted regression. Acta Scientiae Circumstantiae, 36(3), 2142–2151.

    Google Scholar 

  • Chow, J. C. (2006). Health effects of fine particulate air pollution: Lines that connect. Journal of the Air and Waste Management Association, 56(6), 707–708.

    Google Scholar 

  • Dominici, F., Peng, R. D., Bell, M. L., Pham, L., McDermott, A., Zeger, S. L., et al. (2006). Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA, the Journal of the American Medical Association, 295(10), 1127–1134.

    Google Scholar 

  • Fang, X., Zou, B., Liu, X., Sternberg, T., & Zhai, L. (2016). Satellite-based ground PM2.5 estimation using timely structure adaptive modeling. Remote Sensing of Environment, 186, 152–163.

    Google Scholar 

  • Franklin, M., Zeka, A., & Schwartz, J. (2007). Association between PM2.5 and all-cause and specific-cause mortality in 27 US communities. Journal of Exposure Science Environmental Epidemiology, 17(3), 279–287.

    Google Scholar 

  • Gent, J. F., Koutrakis, P., Belanger, K., Triche, E., Holford, T. R., Bracken, M. B., et al. (2009). Symptoms and medication use in children with asthma and traffic-related sources of fine particle pollution. Environmental Health Perspectives, 117(7), 1168–1174.

    Google Scholar 

  • Guo, Y., Feng, N., Christopher, S. A., Kang, P., Zhan, F. B., & Hong, S. (2014). Satellite remote sensing of fine particulate matter (PM2.5) air quality over Beijing using MODIS. International Journal of Remote Sensing, 35(17), 6522–6544.

    Google Scholar 

  • Guo, J. P., Zhang, X. Y., Che, H. Z., Gong, S. L., An, X., Cao, C. X., et al. (2009). Correlation between PM concentrations and aerosol optical depth in eastern China. Atmospheric Environment, 43(37), 5876–5886.

    Google Scholar 

  • He, Q., & Huang, B. (2018). Satellite-based mapping of daily high-resolution ground PM2.5, in China via space-time regression modeling. Remote Sensing of Environment, 206, 72–83.

    Google Scholar 

  • Hsu, N. C., Jeong, M. J., Bettenhausen, C., Sayer, A. M., Hansell, R., Seftor, C. S., et al. (2013). Enhanced Deep Blue aerosol retrieval algorithm: The second generation. Journal of Geophysical Research: Atmospheres, 118(16), 9296–9315.

    Google Scholar 

  • Hu, X., Waller, L. A., Al-Hamdan, M. Z., Crosson, W. L., Estes, M. G., Estes, S. M., et al. (2013). Estimating ground-level PM2.5 concentrationss in the southeastern U.S. using geographically weighted regression. Environmental Research, 121, 1–10.

    Google Scholar 

  • Hu, X., Waller, L. A., Lyapustin, A., Wang, Y., Al-Hamdan, M. Z., Crosson, W. L., et al. (2014). Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model. Remote Sensing of Environment, 140(1), 220–232.

    Google Scholar 

  • Kampa, M., & Castanas, E. (2008). Human health effects of air pollution. Environmental Pollution, 151, 362–367.

    Google Scholar 

  • Khan, M. S., Coulibaly, P., & Dibike, Y. (2006). Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 319(1–4), 357–382.

    Google Scholar 

  • Kloog, I., Koutrakis, P., Coull, B. A., Lee, H. J., & Schwartz, J. (2011). Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmospheric Environment, 45(35), 6267–6275.

    Google Scholar 

  • Koelemeijer, R. B. A., Homan, C. D., & Matthijsen, J. (2006). Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmospheric Environment, 40(27), 5304–5315.

    Google Scholar 

  • Lepeule, J., Laden, F., Dockery, D., & Schwartz, J. (2012). Chronic exposure to fine particles and mortality: An extended follow-up of the Harvard six cities study from 1974 to 2009. Environmental Health Perspectives, 120(7), 965–970.

    Google Scholar 

  • Li, Z., Zheng, F. L., Liu, W. Z., & Jiang, D. J. (2012). Spatially downscaling GCMs outputs to project changes in extreme precipitation and temperature events on the Loess Plateau of China during the 21st Century. Global and Planetary Change, 82–83, 65–73.

    Google Scholar 

  • Liang, F., Gao, M., Xiao, Q., Carmichael, G. R., Pan, X., & Liu, Y. (2017). Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China. Environmental Research, 158, 54–60.

    Google Scholar 

  • Liu, Y., Paciorek, C. J., & Koutrakis, P. (2009). Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information. Environmental Health Perspectives, 117(6), 886–892.

    Google Scholar 

  • Liu, Y., Park, R. J., Jacob, D. J., Li, Q., Kilaru, V., & Sarnat, J. A. (2004). 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), 3269–3278.

    Google Scholar 

  • Liu, Y., Sarnat, J. A., Kilaru, V., Jacob, D. J., & Koutrakis, P. (2005). Estimating Ground-Level PM2.5 in the Eastern United States using satellite remote sensing. Environmental Science and Technology, 39(9), 3269–3278.

    Google Scholar 

  • Lucchesi R. (2013). File Specification for GEOS-5 FP (Forward Processing). GMAO Office Note No.4 (Version 1.0). Available: http://gmao.gsfc.nasa.gov/pubs/docs/Lucchesi617.pdf. Accessed 9 July 2018.

  • Ma, Z., Hu, X., Huang, L., Bi, J., & Liu, Y. (2014). Estimating ground-level PM2.5 in China using satellite remote sensing. Environmental Science and Technology, 48(13), 7436–7444.

    Google Scholar 

  • Ma, Z., Hu, X., Sayer, A. M., Levy, R., Zhang, Q., Xue, Y., et al. (2016). Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013. Environmental Health Perspectives, 124(2), 184–192.

    Google Scholar 

  • Madrigano, J., Kloog, I., Goldberg, R., Coull, B. A., Mittleman, M. A., & Schwartz, J. (2013). Long-term exposure to PM2.5 and incidence of acute myocardial infarction. Environmental Health Perspectives, 121(2), 192–196.

    Google Scholar 

  • Miller, K. A., Siscovick, D. S., Sheppard, L., Shepherd, K., Sullivan, J. H., Anderson, G. L., et al. (2007). Long-term exposure to air pollution and incidence of cardiovascular events in women. New England Journal of Medicine, 356(5), 447–458.

    Google Scholar 

  • Ming, L., Jin, L., Li, J., Fu, P., Yang, W., Liu, D., et al. (2017). PM2.5 in the Yangtze River Delta, China: Chemical compositions, seasonal variations, and regional pollution events. Environmental Pollution, 223, 200–212.

    Google Scholar 

  • Paciorek, C. J., & Liu, Y. (2009). Limitations of remotely sensed aerosol as a spatial proxy for fine particulate matter. Environmental Health Perspectives, 117(6), 904–909.

    Google Scholar 

  • Paciorek, C. J., Liu, Y., Moreno-Macias, H., & Kondragunta, S. (2008). Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5. Environmental Science and Technology, 42(15), 5800–5806.

    Google Scholar 

  • Park, M. E., Song, C. H., Park, R. S., Lee, J., Kim, J., Lee, S., et al. (2014). New approach to monitor transboundary particulate pollution over Northeast Asia. Atmospheric Chemistry and Physics, 14(2), 659–674.

    Google Scholar 

  • Pope, C. A., III, Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., et al. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA, the Journal of the American Medical Association, 287(9), 1132–1141.

    Google Scholar 

  • Pope, C. A., Ezzati, M., & Dockery, D. W. (2009). Fine-particulate air pollution and life expectancy in the United States. New England Journal of Medicine, 360(4), 376–386.

    Google Scholar 

  • Qi, Y., Ge, J., & Huang, J. (2013). Spatial and temporal distribution of MODIS and MISR aerosol optical depth over northern China and comparison with AERONET. Chinese Science Bulletin, 58(20), 2497–2506.

    Google Scholar 

  • Reich, B. J., Chang, H. H., & Foley, K. M. (2014). A spectral method for spatial downscaling. Biometrics, 70(4), 932–942.

    Google Scholar 

  • Sarnat, J. A., Schwartz, J., & Suh, H. H. (2001). Fine particulate air pollution and mortality in 20 U.S. Cities. The New England Journal of Medicine, 344(16), 1253–1254.

    Google Scholar 

  • Schwartz, J., Dockery, D. W., & Neas, L. M. (1996). Is daily mortality associated specifically with fine particles? Journal of the Air and Waste Management Association, 46(10), 927–939.

    Google Scholar 

  • Slama, R., Morgenstern, V., Cyrys, J., Zutavern, A., Herbarth, O., Wichmann, H. E., et al. (2007). Traffic-related atmospheric pollutants levels during pregnancy and offspring’s term birth weight: A study relying on a land-use regression exposure model. Environmental Health Perspectives, 115(9), 1283–1292.

    Google Scholar 

  • Strawa, A. W., Chatfield, R. B., Legg, M., Scarnato, B., & Esswein, R. (2013). Improving retrievals of regional fine particulate matter concentrationss from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) multisatellite observations. Journal of the Air and Waste Management Association, 63(12), 1434–1446.

    Google Scholar 

  • Tian, D., Martinez, C. J., Graham, W. D., & Hwang, S. (2014). Statistical downscaling multimodel forecasts for seasonal precipitation and surface temperature over the southeastern United States. Journal of Climate, 27(22), 8384–8411.

    Google Scholar 

  • Van Donkelaar, A., Martin, R. V., Levy, R. C., da Silva, A. M., Krzyzanowski, M., Chubarova, N. E., et al. (2011). Satellite-based estimates of ground-level fine particulate matter during extreme events: A case study of the Moscow fires in 2010. Atmospheric Environment, 45(34), 6225–6232.

    Google Scholar 

  • Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., et al. (2020). Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees. Atmospheric Chemistry and Physics, 20, 3273–3289.

    Google Scholar 

  • Wu, J. S., Wang, Q., Li, J. C., & Tu, Y. J. (2017). Comparison of models on spatial variation of PM2.5 concentration: A case of Beijing-Tianjin-Hebei Region. Environmental Science, 38(6), 2191–2201.

    Google Scholar 

  • Wyzga, R. E. (2003). Commentary on the HEI reanalysis of two cohort studies of particulate air pollution and mortality. Journal of Toxicology and Environmental Health, Part A, 66(16–19), 1701–1704.

    Google Scholar 

  • Xie, Y., Wang, Y., Zhang, K., Dong, W., Lv, B., & Bai, Y. (2015). Daily Estimation of Ground-Level PM2.5 concentrations over Beijing Using 3 km resolution MODIS AOD. Environmental Science and Technology, 49(20), 12280–12288.

    Google Scholar 

  • Xu, J. W., Martin, R. V., van Donkelaar, A., Kim, J., Choi, M., Zhang, Q., et al. (2015). Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument. Atmospheric Chemistry and Physics, 15(22), 13133–13144.

    Google Scholar 

  • Zang, Z., Wang, W., You, W., Li, Y., Ye, F., & Wang, C. (2016). Estimating ground-level PM2.5 concentrations in Beijing, China using aerosol optical depth and parameters of the temperature inversion layer. Science of the Total Environment, 575, 1219–1227.

    Google Scholar 

  • Zhang, X. C. (2005). Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion. Agricultural and Forest Meteorology, 135(1), 215–229.

    Google Scholar 

  • Zhang, Y. L., & Cui, X. M. (2020). A comparative study of MODIS 3 km aerosol optical depth products and 10 km products in the Beijing-Tianjin-Hebei region. Acta Scientiae Circumstantiae, 40(2), 429–437.

    Google Scholar 

  • Zheng, Y., Zhang, Q., Liu, Y., Geng, G., & He, K. (2016). Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements. Atmospheric Environment, 124, 232–242.

    Google Scholar 

  • Zhou, S. D., Ouyang, W. Q., & Ge, J. H. (2017). Study on the main influencing factors and their intrinsic relations of PM2.5 in Beijing-Tianjin-Hebei. China Population, Resources and Environment, 27(4), 102–109.

    Google Scholar 

  • Zhou, L., Wu, J. J., Jia, R. J., Liang, N., Zhang, F. Y., Ni, Y., et al. (2016). Investigation of temporal-spatial characteristics and underlying risk factors of PM2.5 pollution in Beijing-Tianjin-Hebei area. Research of Environmental Sciences, 29(4), 483–493.

    Google Scholar 

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Funding

This study was funded by the National Natural Science Foundation of China (Nos. 41661025, 42071216) and Research Capacity Promotion Program for Young Teachers of Northwest Normal University (No. NWNU-LKQN-16-7].

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  1. College of Geography and Environmental Science, Northwest Normal University, No. 967 Anning East Road, Lanzhou, 730070, Gansu Province, People’s Republic of China

    Lianglin Zhang & Jinghu Pan

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  1. Lianglin Zhang
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Correspondence to Jinghu Pan.

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Zhang, L., Pan, J. Estimation of PM2.5 Mass Concentrations in Beijing–Tianjin–Hebei Region Based on Geographically Weighted Regression and Spatial Downscaling Method. J Indian Soc Remote Sens 48, 1691–1703 (2020). https://doi.org/10.1007/s12524-020-01193-6

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