Anterior|Siguiente

Synergistic fusion of multisource AOD and air quality measurements for spatially contiguous PM2.5 concentration mapping in the Yangtze River Delta

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China

    School of Geographic Sciences, East China Normal University, Shanghai 200241, China

  • Email：kli997126@gmail.com
  • Introduction：李珂，研究方向为气溶胶遥感，多源数据融合。E-mail： kli997126@gmail.com
LI Ke,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China

    School of Geographic Sciences, East China Normal University, Shanghai 200241, China

  • Email：kxbai@geo.ecnu.edu.cn
  • Introduction：白开旭，研究方向为大气环境遥感，多源数据融合，遥感信息挖掘。E-mail： kxbai@geo.ecnu.edu.cn
BAI Kaixu*

resumen

Monitoring concentrations of atmospheric particulate matters is essential to regional haze pollution prevention and control. Satellite-based Aerosol Optical Depth (AOD) data have been frequently used to map regional PM2.5 concentrations. However, the resultant PM2.5 concentration maps are always spatially incomplete due to significant data gaps in satellite-based AOD retrievals. This study aims to fill data gaps in AOD imageries to support spatially contiguous PM2.5 concentration mapping on an hourly basis in the Yangtze River Delta.An integrated data fusion approach was developed to seamlessly gear up the missing AOD imputation and multimodal data fusion approaches. Specifically, all available Himawari-8 AOD observations during the daytime were fused to maximize hourly AOD coverage in each single snapshot. To further tackle data gaps in fused AOD maps, a virtual AOD monitoring network was constructed by estimating AOD at each state-controlled air quality monitoring station based on ground measured air pollutant concentration. This way enables us to extend the sparsely distributed aerosol monitoring network nationwide, which significantly improves the spatial coverage of AOD. Subsequently, the reconstructed satellite AOD and PM inferred AOD were fused with AOD simulations from MERRA-2 using the optimal interpolation method to generate spatially contiguous yet far more accurate AOD reanalysis. Spatially complete PM2.5 concentration maps were finally generated on hourly basis over the study region using the random forest method.Ground validation results indicate that AOD values inferred from air quality measurements agree well with in situ AOD measurements, with R of 0.90 and RMSE of 0.13. The analyzed spatially complete AOD dataset has a correlation of 0.86 and RMSE of 0.16 compared with in situ AOD data, which is much higher than that of raw Himawari-8 AOD. The estimated PM2.5 concentration data also have a promising accuracy, with R of 0.9 and mean absolute error of 9.87 μg m-3 compared with in situ PM2.5 measurements.Compared with sparsely distributed in situ PM2.5 measurements, this spatially contiguous PM2.5 concentration dataset has great advantages in assessing PM2.5 variations in space and time in the Yangtze River Delta. Statistically significant decreasing trend over the whole study area also highlights the effectiveness of clean air actions in reducing PM2.5 loadings across China. Overall, the proposed method can be practically used for future PM2.5 mapping practices and the generated spatially contiguous PM2.5 concentration dataset is a promising data source for the assessment of the human exposure risk to haze pollution.

palabra clave

remote sensing;PM2.5 concentration mapping;aerosol optical depth;multimodal data fusion;missing value imputation;air quality;Yangtze River Delta

References

  1. 1.
    Bai K X, Li K, Chang N B and Gao W. 2019. Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: a perspective of data mining through in situ PM2.5 measurements. Environmental Pollution, 254: 113047.
  2. 2.
    Bai K X, Li K, Guo J P, Yang Y J and Chang N B. 2020a. Filling the gaps of in situ hourly PM2.5 concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles. Atmospheric Measurement Techniques, 13(3): 1213-1226
  3. 3.
    Bai K X, Li K, Wu C B, Chang N B and Guo J P. 2020b. A homogenized daily in situ PM2.5 concentration dataset from the national air quality monitoring network in China. Earth System Science Data, 12(4): 3067-3080
  4. 4.
    Bi J Z, Belle J H, Wang Y J, Lyapustin A I, Wildani A and Liu Y. 2019. Impacts of snow and cloud covers on satellite-derived PM2.5 levels. Remote Sensing of Environment, 221: 665-674
  5. 5.
    Buchard V, Randles C A, da Silva A M, Darmenov A, Colarco P R, Govindaraju R, Ferrare R, Hair J, Beyersdorf A J, Ziemba L D and Yu H. 2017. The MERRA-2 aerosol reanalysis, 1980 onward. Part II: evaluation and case studies. Journal of Climate, 30(17): 6851-6872
  6. 6.
    Feng F and Wang K C. 2019. Does the modern-era retrospective analysis for research and applications-2 aerosol reanalysis introduce an improvement in the simulation of surface solar radiation over China? International Journal of Climatology, 39(3): 1305-1318
  7. 7.
    Hong J, Mao F Y, Min Q L, Pan Z X, Wang W, Zhang T H and Gong W. 2020. Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations. Environmental Pollution, 263: 114451
  8. 8.
    Jiang T T, Chen B, Chan K K Y and Xu B. 2019. Himawari-8/AHI and MODIS aerosol optical depths in China: evaluation and comparison. Remote Sensing, 11(9): 1011
  9. 9.
    Li D, Qin K, Wu L X, Mei L L, de Leeuw G, Xue Y, Shi Y N and Li Y F. 2020a. Himawari-8-derived aerosol optical depth using an improved time series algorithm over eastern China. Remote Sensing, 12(6): 978
  10. 10.
    Li L F, Franklin M, Girguis M, Lurmann F, Wu J, Pavlovic N, Breton C, Gilliland F and Habre R. 2020b. Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling. Remote Sensing of Environment, 237: 111584
  11. 11.
    Li T W, Shen H F, Yuan Q Q and Zhang L P. 2020c. Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5. ISPRS Journal of Photogrammetry and Remote Sensing, 167: 178-188
  12. 12.
    Li Z Q, Xu H, Li K T, Li D H, Xie Y S, Li L, Zhang Y, Gu X F, Zhao W, Tian Q J, Deng R R, Su X L, Huang B, Qiao Y L, Cui W Y, Hu Y, Gong C L, Wang Y Q, Wang X F, Wang J P, Du W B, Pan Z Q, Li Z Z and Bu D. 2018. Comprehensive study of optical, physical, chemical, and radiative properties of total columnar atmospheric aerosols over China: an overview of sun–sky radiometer observation network (SONET) measurements. Bulletin of the American Meteorological Society, 99(4): 739-755
  13. 13.
    Niu X J, Tang J K, Zhang Z L, Cui L L, Xing W P and Song Y. 2019. The method of aerosol retrieval using Himawari-8 satellite data and its application in monitoring haze process. Journal of University of Chinese Academy of Sciences, 36(5): 671-681
  14. 14.
    Park Y, Kwon B, Heo J, Hu X F, Liu Y and Moon T. 2020. Estimating PM2.5 concentration of the conterminous United States via interpretable convolutional neural networks. Environmental Pollution, 256: 113395
  15. 15.
    Shen F Z, Zhang L, Jiang L, Tang M Q, Gai X Y, Chen M D and Ge X L. 2020. Temporal variations of six ambient criteria air pollutants from 2015 to 2018, their spatial distributions, health risks and relationships with socioeconomic factors during 2018 in China. Environment International, 137: 105556
  16. 16.
    Shen H F and Li T W. 2019. Progress of remote sensing mapping of atmospheric PM2.5. Acta Geodaetica et Cartographica Sinica, 48(12): 1624-1635
  17. 17.
    Singh M K, Gautam R and Venkatachalam P. 2017a. Bayesian merging of MISR and MODIS aerosol optical depth products using error distributions from AERONET. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12): 5186-5200
  18. 18.
    Singh M K, Venkatachalam P and Gautam R. 2017b. Geostatistical methods for filling gaps in level-3 monthly-mean aerosol optical depth data from multi-angle imaging spectroradiometer. Aerosol and Air Quality Research, 17(8): 1963-1974
  19. 19.
    Tang Q X, Bo Y C and Zhu Y X. 2016. Spatiotemporal fusion of multiple-satellite aerosol optical depth (AOD) products using Bayesian maximum entropy method. Journal of Geophysical Research, 121(8): 4034-4048
  20. 20.
    Tao J H, Zhang M G, Chen L F, Wang Z F, Su L, Ge C, Han X and Zou M M. 2013. A method to estimate concentrations of surface-level particulate matter using satellite-based aerosol optical thickness. Science China Earth Sciences, 56(8): 1422-1433
  21. 21.
    Wang Y, Yuan Q Q, Li T W, Shen H F, Zheng L and Zhang L P. 2019. Large-scale MODIS AOD products recovery: spatial-temporal hybrid fusion considering aerosol variation mitigation. ISPRS Journal of Photogrammetry and Remote Sensing, 157: 1-12
  22. 22.
    Wang Z F, Zeng Q L, Chen L F, Tao J H, Fan M and Zhang Z Y. 2019. Research progress of methodology and applications of PM2.5 estimation using satellite remote sensing. Environmental Monitoring and Forewarning, 11(5): 33-38
  23. 23.
    Wei J, Li Z X, Pinker R T, Wang J, Sun L, Xue W H, Li R Z and Cribb M. 2021. Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM). Atmospheric Chemistry and Physics, 21(10): 7863-7880
  24. 24.
    Wei X L, Chang N B, Bai K X and Gao W. 2020. Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives. Critical Reviews in Environmental Science and Technology, 50(16): 1640-1725
  25. 25.
    Werner M, Kryza M, Pagowski M and Guzikowski J. 2019. Assimilation of PM2.5 ground base observations to two chemical schemes in WRF-Chem-The results for the winter and summer period. Atmospheric Environment, 200: 178-189
  26. 26.
    Xie Y, Dai H C, Dong H J, Hanaoka T and Masui T. 2016. Economic impacts from PM2.5 pollution-related health effects in China: a provincial-level analysis. Environmental Science and Technology, 50(9): 4836-4843
  27. 27.
    Xu J H and Jiang H. 2015. Estimation of PM2.5 concentration over the Yangtze delta using remote sensing: analysis of spatial and temporal variations. Environmental Science, 36(9): 3119-3127
  28. 28.
    Yoshida M, Kikuchi M, Nagao T M, Murakami H, Nomaki T and Higurashi A. 2018. Common retrieval of aerosol properties for imaging satellite sensors. Journal of the Meteorological Society of Japan, 96B: 193-209
  29. 29.
    Zhang L P and Shen H F. 2016. Progress and future of remote sensing data fusion. Journal of Remote Sensing, 20(5): 1050-1061
  30. 30.
    Zhang Z Y, Wu W L, Fan W, Tao W H, Wei J, Jin J, Tan Y H and Wang Q. 2019. Validation of Himawari-8 aerosol optical depth retrievals over China. Atmospheric Environment, 199: 32-44
  31. 31.
    Zou B, Liu N, Wang W, Feng H H, Liu X P and Lin Y. 2020. An effective and efficient enhanced fixed rank smoothing method for the spatiotemporal fusion of multiple-satellite aerosol optical depth products. Remote Sensing, 12(7): 1102

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website