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Estimation of near-surface O3 concentration based on TROPOMI NO2， CO and HCHO reconstruction data

  • role： First author第一作者
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

  • Email：chenxj@cumt.edu.cn
  • Introduction：陈小娟，研究方向为大气环境遥感。E-mail： chenxj@cumt.edu.cn
CHEN Xiaojuan,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

  • Email：qinkai@cumt.edu.cn
  • Introduction：秦凯，研究方向为大气环境遥感。E-mail： qinkai@cumt.edu.cn
QIN Kai*,  
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

COHEN Jason,  
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

HE Qin

Resümee

Satellite remote sensing data represented by TROPOMI atmospheric composition products show good potential in the estimation of near-surface O3 concentrations. Given the limitations of cloud and inversion algorithms, data for TROPOMI atmospheric composition products are lacking, resulting in low coverage of estimation results. Therefore, the DINEOF method was used to reconstruct the missing cells of TROPOMI NO2, CO, and HCHO original data products and estimate the maximum daily 8 h average O3 concentration (MDA8 O3) of Chinese mainland high coverage from 2019 to 2021 based on XGBoost. In this study, three schemes to improve the coverage of O3 model estimation results are compared. Scheme 1 reconstructs the missing cells of TROPOMI NO2, CO, and HCHO original data products based on the DINEOF method and uses the reconstructed data to model and estimate O3. Scheme 2 is based on TROPOMI NO2, CO, and HCHO original data products, null values are assigned to their missing cells, and only other characteristic variables are entered to model and estimate O3. Scheme 3 uses a combination of modeling results containing TROPOMI NO2, CO, and HCHO original data products and modeling results that do not contain TROPOMI NO2, CO, and HCHO original data products but with other characteristic variables. Experiments show that the results of scheme 1 are the best; its 10-fold cross-validation results are R2=0.86 and RMSE=15.86 μg/m3. The model accuracy is basically the same as scheme 2 and higher than that of scheme 3, and the model accuracy in the reconstruction region is the highest (training set R2=0.82, RMSE=15.57 μg/m3). When O3 heavy pollution occurs in the reconstruction region (concentration greater than 160 μg/m3), the underestimation of the high value of the model can be remarkably improved, and the spatial distribution of the results is more reasonable. The average coverage of the near-surface MDA8 O3 estimated in scheme 1 increased from 33.6% to 97.2% from 2019 to 2021. Using TROPOMI NO2, CO, and HCHO refactor data products to model and estimate O3 can improve model performance and coverage of model results.

Schlüsselwort

remote sensing;near-surface O3;TROPOMI;DINEOF;XGBoost;Data refactoring

References

  1. 1.
    Ainsworth E A, Yendrek C R, Sitch S, Collins W J and Emberson L D. 2012. The effects of tropospheric ozone on net primary productivity and implications for climate change. Annual Review of Plant Biology, 63: 637-661
  2. 2.
    Alvera-Azcárate A, Barth A, Rixen M and Beckers J M. 2005. Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature. Ocean Modelling, 9(4): 325-346
  3. 3.
    Alvera-Azcárate A, Barth A, Parard G and Beckers J M. 2016. Analysis of SMOS sea surface salinity data using DINEOF. Remote Sensing of Environment, 180: 137-145
  4. 4.
    Cakmak S, Hebbern C, Pinault L, Lavigne E, Vanos J, Crouse D L and Tjepkema M. 2018. Associations between long-term PM2.5 and ozone exposure and mortality in the Canadian Census Health and Environment Cohort (CANCHEC), by spatial synoptic classification zone. Environment International, 111: 200-211
  5. 5.
    Chen L, Liang S, Li X L, Mao J, Gao S, Zhang H, Sun Y L, Vedal S, Bai Z P, Ma Z X, Haiyu N and Azzi M. 2021. A hybrid approach to estimating long-term and short-term exposure levels of ozone at the national scale in China using land use regression and Bayesian maximum entropy. Science of the Total Environment, 752: 141780
  6. 6.
    Chen T Q, Guestrin C. 2016. XGBoost: a scalable tree boosting system//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, USA: ACM: 785-794
  7. 7.
    Chen Y J, Zhang F, Du Z H and Li R Y. 2019. Reconstruction of geostationary satellite ocean color data based on DINEOF. Journal of Marine Sciences, 37(4): 14-23
  8. 8.
    Cheng L J, Wang S, Gong Z Y, Li H, Yang Q and Wang Y Y. 2018. Regionalization based on spatial and seasonal variation in ground-level ozone concentrations across China. Journal of Environmental Sciences, 67: 179-190
  9. 9.
    De Hoogh K, Saucy A, Shtein A, Schwartz J, West E A, Strassmann A, Puhan M, Röösli M, Stafoggia M and Kloog I. 2019. Predicting fine-scale daily NO2 for 2005-2016 incorporating OMI satellite data across Switzerland. Environmental Science and Technology, 53(17): 10279-10287
  10. 10.
    Feng R, Zheng H J, Zhang A R, Huang C, Gao H and Ma Y C. 2019b. Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison: a case study in Hangzhou, China. Environmental Pollution, 252: 366-378
  11. 11.
    Feng Z Z, De Marco A, Anav A, Gualtieri M, Sicard P, Tian H Q, Fornasier F, Tao F L, Guo A H and Paoletti E. 2019a. Economic losses due to ozone impacts on human health, forest productivity and crop yield across China. Environment International, 131: 104966
  12. 12.
    Guo J R, Song J, Bao X W and Li J. 2016. The method study of remote sensing data reconstruction in multi-scale variations of chlorophyll in East China Sea. Remote Sensing Technology and Application, 31(5): 939-949
  13. 13.
    Gupta M and Mohan M. 2015. Validation of WRF/Chem model and sensitivity of chemical mechanisms to ozone simulation over megacity Delhi. Atmospheric Environment, 122: 220-229
  14. 14.
    Hashim N M, Noor N M, Ul-Saufie A Z, Sandu A V, Vizureanu P, Deák G and Kheimi M. 2022. Forecasting daytime ground-level ozone concentration in urbanized areas of Malaysia using predictive models. Sustainability, 14(13): 7936
  15. 15.
    He Q, Qin K, Cohen J B, Loyola D, Li D, Shi J C and Xue Y. 2020. Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements. Environmental Research Letters, 15(12): 125011
  16. 16.
    Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, De Chiara G, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan R J, Hólm E, Janisková M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, De Rosnay P, Rozum I, Vamborg F, Villaume S and Thépaut J N. 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730): 1999-2049
  17. 17.
    Hogrefe C, Pouliot G, Wong D, Torian A, Roselle S, Pleim J and Mathur R. 2015. Annual application and evaluation of the online coupled WRF-CMAQ system over North America under AQMEII phase 2. Atmospheric Environment, 115: 683-694
  18. 18.
    Hoshyaripour G, Brasseur G, Andrade M F, Gavidia-Calderón M, Bouarar I and Ynoue R Y. 2016. Prediction of ground-level ozone concentration in São Paulo, Brazil: deterministic versus statistic models. Atmospheric Environment, 145: 365-375
  19. 19.
    Jiang Y T, Gao Z K, He J Y, Wu J P and Christakos G. 2022. Application and analysis of XCO2 data from OCO satellite using a synthetic DINEOF-BME spatiotemporal interpolation framework. Remote Sensing, 14(17): 4422
  20. 20.
    Kang Y, Choi H, Im J, Park S, Shin M, Song C K and Kim S. 2021. Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia. Environmental Pollution, 288: 117711
  21. 21.
    Kazemiparkouhi F, Eum K D, Wang B Y, Manjourides J and Suh H H. 2020. Long-term ozone exposures and cause-specific mortality in a US Medicare cohort. Journal of Exposure Science and Environmental Epidemiology, 30(4): 650-658
  22. 22.
    Li T W and Cheng X. 2021. Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach. International Journal of Applied Earth Observation and Geoinformation, 101: 102356
  23. 23.
    Li T W, Wu J G, Chen J J and Shen H F. 2022. An enhanced geographically and temporally weighted neural network for remote sensing estimation of surface ozone. IEEE Transactions on Geoscience and Remote Sensing, 60: 4108813
  24. 24.
    Li Y F, Qin K, Li D, Fan W Z and He Q. 2020. Estimation of ground-level ozone concentration based on GBRT. China Environmental Science, 40(3): 997-1007
  25. 25.
    Liu R Y, Ma Z W, Liu Y, Shao Y C, Zhao W and Bi J. 2020. Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: a machine learning approach. Environment International, 142: 105823
  26. 26.
    Matyssek R, Sandermann H, Wieser G, Booker F, Cieslik S, Musselman R and Ernst D. 2008. The challenge of making ozone risk assessment for forest trees more mechanistic. Environmental Pollution, 156(3): 567-582
  27. 27.
    Mitchell R and Frank E. 2017. Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science, 3: e127
  28. 28.
    Singh M K, Venkatachalam P and Gautam R. 2017. 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
  29. 29.
    Song G, Li S W, Xing J, Yang J, Dong L C, Lin H, Teng M F, Hu S L, Qin Y M and Zeng X Y. 2022. Surface UV-assisted retrieval of spatially continuous surface ozone with high spatial transferability. Remote Sensing of Environment, 274: 112996
  30. 30.
    Song W J, Zhang P, Sun L, Tang S H and Zhou F C. 2022. Reconstruction of sea surface temperature from DINEOF-based FY polar-orbiting meteorological satellite. Journal of Marine Sciences, 40(2): 10-18
  31. 31.
    Tatem A J. 2017. WorldPop, open data for spatial demography. Scientific Data, 4(1): 170004
  32. 32.
    Turner M C, Jerrett M, Pope III C A, Krewski D, Gapstur S M, Diver W R, Beckerman B S, Marshall J D, Su J, Crouse D L and Burnett R T. 2016. Long-term ozone exposure and mortality in a large prospective study. American Journal of Respiratory and Critical Care Medicine, 193(10): 1134-1142
  33. 33.
    Veefkind J P, Aben I, McMullan K, Förster H, De Vries J, Otter G, Claas J, Eskes H J, De Haan J F, Kleipool Q, Van Weele M, Hasekamp O, Hoogeveen R, Landgraf J, Snel R, Tol P, Ingmann P, Voors R, Kruizinga B, Vink R, Visser H and Levelt P F. 2012. TROPOMI on the ESA sentinel-5 precursor: a GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment, 120: 70-83
  34. 34.
    Wang C E and Prinn R G. 2000. On the roles of deep convective clouds in tropospheric chemistry. Journal of Geophysical Research: Atmospheres, 105(D17): 22269-22297
  35. 35.
    Wang T, Xue L K, Brimblecombe P, Lam Y F, Li L and Zhang L. 2017. Ozone pollution in China: a review of concentrations, meteorological influences, chemical precursors, and effects. Science of the Total Environment, 575: 1582-1596
  36. 36.
    Wang W H, Liu X, Bi J Z and Liu Y. 2022. A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology. Environment International, 158: 106917
  37. 37.
    Xu W Y, Zhao C S, Ran L, Deng Z Z, Liu P F, Ma N, Lin W L, Xu X B, Yan P, He X, Yu J, Liang W D and Chen L L. 2011. Characteristics of pollutants and their correlation to meteorological conditions at a suburban site in the North China Plain. Atmospheric Chemistry and Physics, 11(9): 4353-4369
  38. 38.
    Zhang X Y, Zhao L M, Cheng M M and Chen D M. 2020. Estimating ground-level ozone concentrations in Eastern China using satellite-based precursors. IEEE Transactions on Geoscience and Remote Sensing, 58(7): 4754-4763
  39. 39.
    Zhao N and Lu Y M. 2022. Remote-sensing estimation of near-surface ozone concentration based on XGBoost. Acta Scientiae Circumstantiae, 42(5): 95-108
  40. 40.
    Zhao W, Fan S J, Guo H, Gao B, Sun J R and Chen L G. 2016. Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models. Atmospheric Environment, 144: 182-193

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