Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5333-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-5333-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Aug 2021
Research article |
| 04 Aug 2021
| 04 Aug 2021
Estimation of PM2.5 concentration in China using linear hybrid machine learning model
Zhihao Song
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,
China
Bin Chen
CORRESPONDING AUTHOR
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,
China
Yue Huang
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,
China
Li Dong
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,
China
Tingting Yang
Gansu Seed General Station, Lanzhou 730030, China
Related authors
Zhihao Song and Bin Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2194, https://doi.org/10.5194/egusphere-2025-2194, 2025
Short summary
Short summary
This study reveals that 2015–2023 PM (particulate matter) pollution in eastern China declined faster in urban than rural areas. Temperature and interannual variability were key drivers, with urban reductions concentrated in urban core zones and suburbs. The effect of interannual variability on PM decreased significantly, while other factors showed periodic fluctuations.
Zhihao Song and Bin Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2194, https://doi.org/10.5194/egusphere-2025-2194, 2025
Short summary
Short summary
This study reveals that 2015–2023 PM (particulate matter) pollution in eastern China declined faster in urban than rural areas. Temperature and interannual variability were key drivers, with urban reductions concentrated in urban core zones and suburbs. The effect of interannual variability on PM decreased significantly, while other factors showed periodic fluctuations.
Cited articles
Adams, M. D., Massey, F., Chastko, K., and Cupini, C.: Spatial modelling of particulate matter air pollution sensor measurements collected by community scientists while cycling, land use regression with spatial cross-validation, and applications of machine learning for data correction, Atmos. Environ., 230, https://doi.org/10.1016/j.atmosenv.2020.117479, 2020.
Apte, J. S., Marshall, J. D., Cohen, A. J., and Brauer, M.: Addressing Global Mortality from Ambient PM2.5, Environ. Sci. Technol., 49, 8057–8066, https://doi.org/10.1021/acs.est.5b01236, 2015.
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., and Yoshida, R.: An Introduction to Himawari-8/9-Japan's New-Generation Geostationary Meteorological Satellites, J. Meteorol. Soc. Jpn., 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
CGIAR Consortium for Spatial Information: SRTM3, available at: https://srtm.csi.cgiar.org/srtmdata/, CGIAR [data set], last access: 1 July 2021.
Chen, B. J., You, S. X., Ye, Y., Fu, Y. Y., Ye, Z. R., Deng, J. S., Wang, K., and Hong, Y.: An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China, Sci. Total Environ., 768, 144724, https://doi.org/10.1016/j.scitotenv.2020.144724, 2021.
Chen, J. P., Yin, J. H., Zang, L., Zhang, T. X., and Zhao, M. D.: Stacking machine learning model for estimating hourly PM2.5 in China based on Himawari 8 aerosol optical depth data, Sci. Total Environ., 697, 134021, https://doi.org/10.1016/j.scitotenv.2019.134021, 2019.
China: Ambient air quality standards, GB 3095-2012, Environmental Science Press, Beijing, China, available at: http://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/dqhjbh/dqhjzlbz/201203/W020120410330232398521.pdf (last access: 1 July 2021), 2012.
China National Environmental Monitoring Centre (CNEMC): Homepage, available at: http://www.cnemc.cn, last access: 1 July 2021.
Diederik, P. K. and Jimmy, B.: Adam: A Method for Stochastic Optimization,
arXiv [preprint], arXiv:1412.6980, 22 December 2014.
ECMWF: ERA5, ECMWF [data set], available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=form, last access: 1 July 2021.
Emili, E., Popp, C., Petitta, M., Riffler, M., Wunderle, S., and Zebisch, M.: PM10 remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region, Remote Sens. Environ., 114, 2485–2499, https://doi.org/10.1016/j.rse.2010.05.024, 2010.
Engel-Cox, J. A., Holloman, C. H., Coutant, B. W., and Hoff, R. M.: Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality, Atmos. Environ., 38, 2495–2509, https://doi.org/10.1016/j.atmosenv.2004.01.039, 2004.
Fang, X., Zou, B., Liu, X. P., Sternberg, T., and Zhai, L.: Satellite-based ground PM2.5 estimation using timely structure adaptive modeling, Remote Sens. Environ., 186, 152–163, https://doi.org/10.1016/j.rse.2016.08.027, 2016.
Friedman, J. H.: Greedy function approximation: A gradient boosting machine, Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Gao, M., Guttikunda, S. K., Carmichael, G. R., Wang, Y. S., Liu, Z. R., Stanier, C. O., Saide, P. E., and Yu, M.: Health impacts and economic losses assessment of the 2013 severe haze event in Beijing area, Sci. Total Environ., 511, 553–561, https://doi.org/10.1016/j.scitotenv.2015.01.005, 2015.
Gui, K., Che, H. Z., Wang, Y. Q., Wang, H., Zhang, L., Zhao, H. J., Zheng, Y., Sun, T. Z., and Zhang, X. Y.: Satellite-derived PM2.5 concentration trends over Eastern China from 1998 to 2016: Relationships to emissions and meteorological parameters, Environ. Pollut., 247, 1125–1133, https://doi.org/10.1016/j.envpol.2019.01.056, 2019.
Guo, B., Zhang, D. M., Pei, L., Su, Y., Wang, X. X., Bian, Y., Zhang, D. H., Yao, W. Q., Zhou, Z. X., and Guo, L. Y.: Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017, Sci. Total Environ., 778, 146288, https://doi.org/10.1016/j.scitotenv.2021.146288, 2021.
Guo, J. P., Xia, F., Zhang, Y., Liu, H., Li, J., Lou, M. Y., He, J., Yan, Y., Wang, F., Min, M., and Zhai, P. M.: Impact of diurnal variability and meteorological factors on the PM2.5 – AOD relationship: Implications for PM2.5 remote sensing, Environ. Pollut., 221, 94–104, https://doi.org/10.1016/j.envpol.2016.11.043, 2017.
Han, Y., Wu, Y. H., Wang, T. J., Zhuang, B. L., Li, S., and Zhao, K.: Impacts of elevated-aerosol-layer and aerosol type on the correlation of AOD and particulate matter with ground-based and satellite measurements in Nanjing, southeast China, Sci. Total Environ., 532, 195–207, https://doi.org/10.1016/j.scitotenv.2015.05.136, 2015.
Hoff, R. M. and Christopher, S. A.: Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?, J. Air Waste Manage., 59, 645–675, https://doi.org/10.3155/1047-3289.59.6.645, 2009.
Hu, X. F., Waller, L. A., Al-Hamdan, M. Z., Crosson, W. L., Estes, M. G., Estes, S. M., Quattrochi, D. A., Sarnat, J. A., and Liu, Y.: Estimating ground-level PM2.5 concentrations in the southeastern US using geographically weighted regression, Environ. Res., 121, 1–10, https://doi.org/10.1016/j.envres.2012.11.003, 2013.
JAXA: Himawari-8 AOD, JAXA [data set], available at: https://www.eorc.jaxa.jp/ptree/, last access: 1 July 2021.
Jiang, Y., Yang, K., Shao, C., Zhou, X., Zhao, L., Chen, Y., and Wu, H.: A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis, Atmos. Res., 256, 105574, https://doi.org/10.1016/j.atmosres.2021.105574, 2021.
Johnson, N. E., Bonczak, B., and Kontokosta, C. E.: Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment, Atmos. Environ., 184, 9–16, https://doi.org/10.1016/j.atmosenv.2018.04.019, 2018.
Lee, H. J., Coull, B. A., Bell, M. L., and Koutrakis, P.: Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations, Environ. Res., 118, 8–15, https://doi.org/10.1016/j.envres.2012.06.011, 2012.
Li, T. W., Shen, H. F., Zeng, C., Yuan, Q. Q., and Zhang, L. P.: Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment, Atmos. Environ., 152, 477–489, https://doi.org/10.1016/j.atmosenv.2017.01.004, 2017a.
Li, T. W., Shen, H. F., Yuan, Q. Q., Zhang, X. C., and Zhang, L. P.: Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach, Geophys. Res. Lett., 44, 11985–11993, https://doi.org/10.1002/2017gl075710, 2017b.
Li, Z. Q., Zhang, Y., Shao, J., Li, B. S., Hong, J., Liu, D., Li, D. H., Wei, P., Li, W., Li, L., Zhang, F. X., Guo, J., Deng, Q., Wang, B. X., Cui, C. L., Zhang, W. C., Wang, Z. Z., Lv, Y., Xu, H., Chen, X. F., Li, L., and Qie, L. L.: Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground: Method validation using ground-based measurements, Remote Sens. Environ., 173, 59–68, https://doi.org/10.1016/j.rse.2015.11.019, 2016.
Lim, C. H., Ryu, J., Choi, Y., Jeon, S. W., and Lee, W. K.: Understanding global PM2.5 concentrations and their drivers in recent decades (1998–2016), Environ. Int., 144, 106011, https://doi.org/10.1016/j.envint.2020.106011, 2020.
Liu, Y., Sarnat, J. A., Kilaru, A., Jacob, D. J., and Koutrakis, P.: Estimating ground-level PM2.5 in the eastern united states using satellite remote sensing, Environ. Sci. Technol., 39, 3269–3278, https://doi.org/10.1021/es049352m, 2005.
Liu, Y., Cao, G. F., Zhao, N. Z., Mulligan, K., and Ye, X. Y.: Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach, Environ. Pollut., 235, 272–282, https://doi.org/10.1016/j.envpol.2017.12.070, 2018.
Lv, B. L., Hu, Y. T., Chang, H. H., Russell, A. G., Cai, J., Xu, B., and Bai, Y. Q.: Daily estimation of ground-level PM2.5 concentrations at 4 km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations, Sci. Total Environ., 580, 235–244, https://doi.org/10.1016/j.scitotenv.2016.12.049, 2017.
Mao, F. Y., Hong, J., Min, Q. L., Gong, W., Zang, L., and Yin, J. H.: Estimating hourly full-coverage PM2.5 over China based on TOA reflectance data from the Fengyun-4A satellite, Environ. Pollut., 270, 116119, https://doi.org/10.1016/j.envpol.2020.116119, 2021.
Miao, Y. C., Liu, S. H., Guo, J. P., Huang, S. X., Yan, Y., and Lou, M. Y.: Unraveling the relationships between boundary layer height and PM2.5 pollution in China based on four-year radiosonde measurements, Environ. Pollut., 243, 1186–1195, https://doi.org/10.1016/j.envpol.2018.09.070, 2018.
Pan, Z. X., Mao, F. Y., Wang, W., Zhu, B., Lu, X., and Gong, W.: Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season, Remote Sens.-Basel, 10, 651, https://doi.org/10.3390/rs10040651, 2018.
Pun, V. C., Kazemiparkouhi, F., Manjourides, J., and Suh, H. H.: Long-Term PM2.5 Exposure and Respiratory, Cancer, and Cardiovascular Mortality in Older US Adults, Am. J. Epidemiol., 186, 961–969, https://doi.org/10.1093/aje/kwx166, 2017.
Qin, K., Wang, L. Y., Wu, L. X., Xu, J., Rao, L. L., Letu, H., Shi, T. W., and Wang, R. F.: A campaign for investigating aerosol optical properties during winter hazes over Shijiazhuang, China, Atmos. Res., 198, 113–122, https://doi.org/10.1016/j.atmosres.2017.08.018, 2017.
Schonlau, M.: Boosted regression (boosting): An introductory tutorial and a Stata plugin, Stata J., 5, 330–354, https://doi.org/10.1177/1536867x0500500304, 2005.
SEDAC: Gridded Population of the World (GPW), v4, available at: http://sedac.ciesin.columbia.edu/data/collection/gpw-v4/documentation, NASA [data set], last access: 1 July 2021.
Stafoggia, M., Bellander, T., Bucci, S., Davoli, M., de Hoogh, K., de'Donato, F., Gariazzo, C., Lyapustin, A., Michelozzi, P., Renzi, M., Scortichini, M., Shtein, A., Viegi, G., Kloog, I., and Schwartz, J.: Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model, Environ. Int., 124, 170–179, https://doi.org/10.1016/j.envint.2019.01.016, 2019.
Tian, J. and Chen, D. M.: A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements, Remote Sens. Environ., 114, 221–229, https://doi.org/10.1016/j.rse.2009.09.011, 2010.
Wang, W., Mao, F. Y., Du, L., Pan, Z. X., Gong, W., and Fang, S. H.: Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing-Tianjin-Hebei in China, Remote Sens.-Basel, 9, 858, https://doi.org/10.3390/rs9080858, 2017.
Wang, X. H., Zhong, S. Y., Bian, X. D., and Yu, L. J.: Impact of 2015–2016 El Nino and 2017–2018 La Nina on PM2.5 concentrations across China, Atmos. Environ., 208, 61–73, https://doi.org/10.1016/j.atmosenv.2019.03.035, 2019a.
Wang, X. P. and Sun, W. B.: Meteorological parameters and gaseous pollutant concentrations as predictors of daily continuous PM2.5 concentrations using deep neural network in Beijing-Tianjin-Hebei, China, Atmos. Environ., 211, 128–137, https://doi.org/10.1016/j.atmosenv.2019.05.004, 2019.
Wang, X. Q., Wei, W., Cheng, S. Y., Yao, S., Zhang, H. Y., and Zhang, C.: Characteristics of PM2.5 and SNA components and meteorological factors impact on air pollution through 2013–2017 in Beijing, China, Atmos. Pollut. Res., 10, 1976–1984, https://doi.org/10.1016/j.apr.2019.09.004, 2019b.
Wei, J., Huang, W., Li, Z. Q., Xue, W. H., Peng, Y. R., Sun, L., and Cribb, M.: Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach, Remote Sens. Environ., 231, 111221, https://doi.org/10.1016/j.rse.2019.111221, 2019a.
Wei, J., Li, Z., Sun, L., Peng, Y., Zhang, Z., Li, Z., Su, T., Feng, L., Cai, Z., and Wu, H.: Evaluation and uncertainty estimate of next-generation geostationary meteorological Himawari-8/AHI aerosol products, Sci. Total Environ., 692, 879–891, https://doi.org/10.1016/j.scitotenv.2019.07.326, 2019b.
Wei, J., Li, Z., Pinker, R. T., Wang, J., Sun, L., Xue, W., Li, R., and Cribb, M.: Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM), Atmos. Chem. Phys., 21, 7863–7880, https://doi.org/10.5194/acp-21-7863-2021, 2021a.
Wei, J., Li, Z. Q., Lyapustin, A., Sun, L., Peng, Y. R., Xue, W. H., Su, T. N., and Cribb, M.: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications, Remote Sens. Environ., 252, 112136, https://doi.org/10.1016/j.rse.2020.112136, 2021b.
Wolpert, D. H.: Stacked Generalization, Neural Networks, 5, 241–259, https://doi.org/10.1016/S0893-6080(05)80023-1, 1992.
Xu, J. H., Lindqvist, H., Liu, Q. F., Wang, K., and Wang, L.: Estimating the spatial and temporal variability of the ground-level NO2 concentration in China during 2005–2019 based on satellite remote sensing, Atmos. Pollut. Res., 12, 57–67, https://doi.org/10.1016/j.apr.2020.10.008, 2021.
Yang, X. C., Jiang, L., Zhao, W. J., Xiong, Q. L., Zhao, W. H., and Yan, X.: Comparison of Ground-Based PM2.5 and PM10 Concentrations in China, India, and the US, Int. J. Env. Res. Pub. He., 15, 1382, https://doi.org/10.3390/ijerph15071382, 2018.
Yesilkanat, C. M.: Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm, Chaos Soliton. Fract., 140, 110210, https://doi.org/10.1016/j.chaos.2020.110210, 2020.
Yin, J. H., Mao, F. Y., Zang, L., Chen, J. P., Lu, X., and Hong, J.: Retrieving PM2.5 with high spatio-temporal coverage by TOA reflectance of Himawari-8, Atmos. Pollut. Res., 12, 14–20, https://doi.org/10.1016/j.apr.2021.02.007, 2021.
Yoshida, M., Kikuchi, M., Nagao, T. M., Murakami, H., Nomaki, T., and Higurashi, A.: Common Retrieval of Aerosol Properties for Imaging Satellite Sensors, J. Meteorol. Soc. Jpn. Ser. II, 96B, 193–209, https://doi.org/10.2151/jmsj.2018-039, 2018.
Yumimoto, K., Nagao, T. M., Kikuchi, M., Sekiyama, T. T., Murakami, H., Tanaka, T. Y., Ogi, A., Irie, H., Khatri, P., Okumura, H., Arai, K., Morino, I., Uchino, O., and Maki, T.: Aerosol data assimilation using data from Himawari-8, a next-generation geostationary meteorological satellite, Geophys. Res. Lett., 43, 5886–5894, https://doi.org/10.1002/2016gl069298, 2016.
Zang, L., Mao, F. Y., Guo, J. P., Gong, W., Wang, W., and Pan, Z. X.: Estimating hourly PM1 concentrations from Himawari-8 aerosol optical depth in China, Environ. Pollut., 241, 654–663, https://doi.org/10.1016/j.envpol.2018.05.100, 2018.
Zhang, L., Guo, X. M., Zhao, T. L., Gong, S. L., Xu, X. D., Li, Y. Q., Luo, L., Gui, K., Wang, H. L., Zheng, Y., and Yin, X. F.: A modelling study of the terrain effects on haze pollution in the Sichuan Basin, Atmos. Environ., 196, 77–85, https://doi.org/10.1016/j.atmosenv.2018.10.007, 2019.
Zhang, T. H., Zhu, Z. M., Gong, W., Zhu, Z. R., Sun, K., Wang, L. C., Huang, Y. S., Mao, F. Y., Shen, H. F., Li, Z. W., and Xu, K.: Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals, Remote Sens. Environ., 216, 91–104, https://doi.org/10.1016/j.rse.2018.06.030, 2018.
Zhang, T. X., Zang, L., Wan, Y. C., Wang, W., and Zhang, Y.: Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8, Sci. Total Environ., 676, 535–544, https://doi.org/10.1016/j.scitotenv.2019.04.299, 2019.
Zhang, Z., Wu, W., Fan, M., Tao, M., Wei, J., Jin, J., Tan, Y., and Wang, Q.: Validation of Himawari-8 aerosol optical depth retrievals over China, Atmos. Environ., 199, 32–44, https://doi.org/10.1016/j.atmosenv.2018.11.024, 2019.
Zheng, C., Zhao, C., Zhu, Y., Wang, Y., Shi, X., Wu, X., Chen, T., Wu, F., and Qiu, Y.: Analysis of influential factors for the relationship between PM2.5 and AOD in Beijing, Atmos. Chem. Phys., 17, 13473–13489, https://doi.org/10.5194/acp-17-13473-2017, 2017.
Short summary
The linear hybrid machine learning model achieves the expected target well. The overall inversion accuracy (R2) of the model is 0.84, and the RMSE is 12.92 µg m−3. R2 was above 0.7 in more than 70 % of the sites, whereas RMSE and mean absolute error were below 20 and 15 µg m−3, respectively. There was severe pollution in winter with an average PM2.5 concentration of 62.10 µg m−3. However, there was only slight pollution in summer with an average PM2.5 concentration of 47.39 µg m−3.
The linear hybrid machine learning model achieves the expected target well. The overall...
