收稿：2021-07-19，
纸质出版：2022-05-07
移动端阅览
许多城市建立的相对稠密的网格化监测站点，为精细化监管城市空气质量奠定了基础。本文选用徐州市网格化监测数据、地球静止卫星Himawari-8/AHI及COMS/GOCI的表观反射率和气溶胶光学厚度数据、气象和其他辅助数据，开展了徐州地区0.005°空间分辨率网格的PM
2.5
浓度精细化制图研究。本文使用了极端梯度提升（XGBoost）、随机森林（RF）及时空加权回归（GTWR）等3种方法，并选用多种特征参数组合进行对比分析。综合分析模型精度和过拟合程度，结果表明XGBoost模型表现最好，其
R
2
为0.90，RMSE为11.65 μg/m
3
。进一步将本文结果与国控站点、清华大学的TAP数据集和马里兰大学的CHAP数据集的对比分析，结果表明基于网格化站点的PM
2.5
制图结果能更好地反映城市内部不同区域的PM
2.5
浓度分布差异性，弥补因国控站点稀疏带来的缺陷，更好地服务于城市空气质量精准管控。
The dense gridded air quality monitoring sites established by local governments in China have laid the foundation for fine-tuned monitoring of urban air quality. However
whether it can help improve the capabilities of satellite-based surface PM
2.5
concentration estimation and mapping remain to be tested. To this end
we conducted a case study with Xuzhou as an example.
In order to describe the spatial distribution of PM
2.5
in detail at urban scale
this study used the PM
2.5
concentration data from 172 grid monitoring stations in Xuzhou
the apparent reflectance and aerosol optical thickness data from the Himawari-8/AHI and COMS/GOCI
meteorological data from ERA5 and other auxiliary data to carry out fine mapping study of PM2.5 concentration on the 0.005° spatial resolution grid. The machine learning and geostatistical algorithms of eXtreme Gradient Boosting (XGBoost)
Random Forest (RF) and Geographically and Temporally Weight Regression (GTWR) are applied
and a variety of characteristic parameter combinations (listed in the body of the paper) were selected for comparative analysis.
Compared with other two prediction models
the XGBoost model performed the best
in terms of model accuracy and degree of overfitting
with high correlation coefficient (0.90) and low root mean squard error (11.65 μg/m
3
). Meanwhile
the best parameter combination includes satellite data from Himawari-8 as well as GOCI and meteorological data from ERA5. These retrieved results were then compared with the measurements obtained from the national grid monitoring stations. For peer comparison
in addition
the TAP (Tracking Air Pollution in China) dataset of Tsinghua University and the CHAP (China High AirPollutants) dataset of the University of Maryland are also involved in this comparison
in the aspect of daily mean value and capacity of fine mapping of PM
2.5
in Xuzhou. Besides
these two datasets use the data from national grid monitoring stations as the validation data..
The following conclusions can be drawn:
(1) Due to its higher temporal resolution
geostationary satellite measurements can better fit hourly urban grid monitoring station data in model prediction
and is more suitable for urban PM
2.5
fine mapping than polar orbit satellite;
(2) The estimation results based on urban grid monitoring stations and satellite measurements can make up for the lack of sparse national control stations to a certain extent
better reflect the difference of PM
2.5
concentration distribution between different regions within a city
and better serve the accurate control of air pollution.
Bai K X , Chang N B , Zhou J Y , Gao W and Guo J P . 2019a . Diagnosing atmospheric stability effects on the modeling accuracy of PM 2.5 /AOD relationship in eastern China using radiosonde data . Environmental Pollution , 251 : 380 - 389 [ DOI: 10.1016/j.envpol.2019.04.104 http://dx.doi.org/10.1016/j.envpol.2019.04.104 ]
Bai K X , Li K , Chang N B and Gao W . 2019b . Advancing the prediction accuracy of satellite-based PM 2.5 concentration mapping: a perspective of data mining through in situ PM 2.5 measurements . Environmental Pollution , 254 : 113047 [ DOI: 10.1016/j.envpol.2019.113047 http://dx.doi.org/10.1016/j.envpol.2019.113047 ]
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 . 2016 . An introduction to himawari-8/9— japan's new-generation geostationary meteorological satellites . Journal of the Meteorological Society of Japan , 94 ( 2 ): 151 - 183 [ DOI: 10.2151/jmsj.2016-009 http://dx.doi.org/10.2151/jmsj.2016-009 ]
Cameron M A , Jacobson M Z , Barrett S R H , Bian H , Chen C C , Eastham S D , Gettelman A , Khodayari A , Liang Q , Selkirk H B , Unger N , Wuebbles D J and Yue X . 2017 . An intercomparative study of the effects of aircraft emissions on surface air quality . Journal of Geophysical Research: Atmospheres , 122 ( 15 ): 8325 - 8344 [ DOI: 10.1002/2016jd025594 http://dx.doi.org/10.1002/2016jd025594 ]
Cao J J , Xu H M , Xu Q , Chen B H and Kan H D . 2012 . Fine particulate matter constituents and cardiopulmonary mortality in a heavily polluted Chinese city . Environmental Health Perspectives 120 ( 3 ): 373 - 378 [ DOI: 10.1289/ehp.1103671 http://dx.doi.org/10.1289/ehp.1103671 ]
Chen T Q and 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 : ACM: 785 - 794 [ DOI: 10.1145/2939672.2939785 http://dx.doi.org/10.1145/2939672.2939785 ]
Chen X F , De Leeuw G , Arola A , Liu S M , Liu Y , Li Z Q and Zhang K N . 2020 . Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: artificial neural network method . Remote Sensing of Environment , 249 : 112006 [ DOI: 10.1016/j.rse.2020.112006 http://dx.doi.org/10.1016/j.rse.2020.112006 ]
Choi M , Kim J , Lee J , Kim M , Park Y J , Jeong U , Kim W , Hong H , Holben B , Eck T F , Song C H , Lim J H and Song C K . 2016 . GOCI yonsei aerosol retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign . Atmospheric Measurement Techniques , 9 : 1377 - 1398 [ DOI: 10.5194/amt-9-1377-2016 http://dx.doi.org/10.5194/amt-9-1377-2016 ]
Fan W Z , Qin K , Cui Y L , Li D and Bilal M . 2021 . Estimation of hourly ground-level PM 2.5 concentration based on himawari-8 apparent reflectance . IEEE Transactions on Geoscience and Remote Sensing , 59 ( 1 ): 76 - 85 [ DOI: 10.1109/TGRS.2020.2990791 http://dx.doi.org/10.1109/TGRS.2020.2990791 ]
Fan W Z , Qin K , Han X , Zou J H and Li Y F . 2018 . Aerosol distribution characteristics in Xuzhou based on mobile lidar observation . China Environmental Science , 38 ( 8 ): 2857 - 2864
樊文智 , 秦凯 , 韩旭 , 邹家恒 , 李一蜚 . 2018 . 基于移动激光雷达观测的徐州市区气溶胶分布特征 . 中国环境科学 , 38 ( 8 ): 2857 - 2864 [ DOI: 10.3969/j.issn.1000-6923.2018.08.008 http://dx.doi.org/10.3969/j.issn.1000-6923.2018.08.008 ]
Gong P , Wang J , Yu L , Zhao Y C , Zhao Y Y , Liang L , Niu Z G , Huang X M , Fu H H , Liu S , Li C C , Li X Y , Fu W , Liu C X , Xu Y , Wang X Y , Cheng Q , Hu L Y , Yao W B , Zhang H , Zhu P , Zhao Z Y , Zhang H Y , Zheng Y M , Ji L Y , Zhang Y W , Chen H , Yan A , Guo J H , Yu L , Wang L , Liu X J , Shi T T , Zhu M H , Chen Y L , Yang G W , Tang P , Xu B , Giri C , Clinton N , Zhu Z L , Chen J and Chen J . 2013 . Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data . International Journal of Remote Sensing , 34 ( 7 ): 2607 - 2654 [ DOI: 10.1080/01431161.2012.748992 http://dx.doi.org/10.1080/01431161.2012.748992 ]
Guo J P , Wu Y T , Zhang X Y and Li X W . 2013 . Estimation of PM 2.5 over Eastern China from MODIS aerosol optical depth using the back propagation neural network . Environmental Science , 34 ( 3 ): 817 - 825
郭建平 , 吴业荣 , 张小曳 , 李小文 . 2013 . BP网络框架下MODIS气溶胶光学厚度产品估算中国东部PM 2.5 . 环境科学 , 34 ( 3 ): 817 - 825 [ DOI: 10.13227/j.hjkx.2013.03.024 http://dx.doi.org/10.13227/j.hjkx.2013.03.024 ]
Guo J P , Xue Y , Cao C X , Zhang H , Guang J , Zhang X Y and Li X W . 2009 . A synergic algorithm for retrieval of aerosol optical depth over land . Advances in Atmospheric Sciences , 26 ( 5 ): 973 - 983 [ DOI: 10.1007/s00376-009-7218-4 http://dx.doi.org/10.1007/s00376-009-7218-4 ]
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 , 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 , 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 [ DOI: 10.1002/qj.3803 http://dx.doi.org/10.1002/qj.3803 ]
Hsu N C , Tsay S C , King M D and Herman J R . 2004 . Aerosol properties over bright-reflecting source regions . IEEE Transactions on Geoscience and Remote Sensing , 42 ( 3 ): 557 - 569 [ DOI: 10.1109/tgrs.2004.824067 http://dx.doi.org/10.1109/tgrs.2004.824067 ]
Hu Z Y . 2009 . Spatial analysis of MODIS aerosol optical depth, PM 2.5 , and chronic coronary heart disease . International Journal of Health Geographics , 8 : 27 [ DOI: 10.1186/1476-072X-8-27 http://dx.doi.org/10.1186/1476-072X-8-27 ]
Huang B , Wu B and Barry M . 2010 . Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices . International Journal of Geographical Information Science , 24 ( 3 ): 383 - 401 [ DOI: 10.1080/13658810802672469 http://dx.doi.org/10.1080/13658810802672469 ]
Koelemeijer R B A , Homan C D and Matthijsen J . 2006 . Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe . Atmospheric Environment , 40 ( 27 ): 5304 - 5315 [ DOI: 10.1016/j.atmosenv.2006.04.044 http://dx.doi.org/10.1016/j.atmosenv.2006.04.044 ]
Li R , Cui L L , Fu H B , Meng Y , Li J L and Guo J P . 2020 . Estimating high-resolution PM 1 concentration from Himawari-8 combining extreme gradient boosting-geographically and temporally weighted regression (XGBoost-GTWR) . Atmospheric Environment , 229 : 117434 [ DOI: 10.1016/j.atmosenv.2020.117434 http://dx.doi.org/10.1016/j.atmosenv.2020.117434 ]
Lloyd C T , Chamberlain H , Kerr D , Yetman G , Pistolesi L , Stevens F R , Gaughan A E , Nieves J J , Hornby G , Macmanus K , Sinha P , Bondarenko M , Sorichetta A and Tatem A J . 2019 . Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets . Big Earth Data , 3 ( 2 ): 108 - 139 [ DOI: 10.1080/20964471.2019.1625151 http://dx.doi.org/10.1080/20964471.2019.1625151 ]
Ordieres J B , Vergara E P , Capuz R S and Salazar R E . 2005 . Neural network prediction model for fine particulate matter (PM 2.5 ) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua) . Environmental Modelling & Software , 20 ( 5 ): 547 - 559 [ DOI: 10.1016/j.envsoft.2004.03.010 http://dx.doi.org/10.1016/j.envsoft.2004.03.010 ]
Pan J X , Yan P Z , Sun F , Li Y T , Liu B X , Wang Z S and Dong R . 2019 . Application of ensemble forecast and linear regression method in improving PM 2.5 forecast over Beijing area . Environmental Monitoring in China , 35 ( 2 ): 43 - 52
潘锦秀 , 晏平仲 , 孙峰 , 李云婷 , 刘保献 , 王占山 , 董瑞 . 2019 . 多元线性回归方法对北京地区PM 2.5 预报的改进应用 . 中国环境监测 , 35 ( 2 ): 43 - 52 [ DOI: 10.19316/j.issn.1002-6002.2019.02.06 http://dx.doi.org/10.19316/j.issn.1002-6002.2019.02.06 ]
Ryu J H , Han H J , Cho S , Park Y J and Ahn Y H . 2012 . Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS) . Ocean Science Journal , 47 ( 3 ): 223 - 233 [ DOI: 10.1007/s12601-012-0024-4 http://dx.doi.org/10.1007/s12601-012-0024-4 ]
Saffari A , Daher N , Shafer M M , Schauer J J and Sioutas C . 2014 . Global perspective on the oxidative potential of airborne particulate matter: a synthesis of research findings . Environmental Science & Technology , 48 ( 13 ): 7576 - 7583 [ DOI: 10.1021/es500937x http://dx.doi.org/10.1021/es500937x ]
Shen H F , Li T W , Yuan Q Q and Zhang L P . 2018 . Estimating regional ground-level PM 2.5 directly from satellite top-of-atmosphere reflectance using deep belief networks . Journal of Geophysical Research: Atmospheres , 123 ( 24 ): 13875 - 13886 [ DOI: 10.1029/2018 jd028759 http://dx.doi.org/10.1029/2018jd028759 ]
Stevens F R , Gaughan A E , Linard C and Tatem A J . 2015 . Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data . PLoS One , 10 ( 2 ): e 0107042 [ DOI: 10.1371/journal.pone.0107042 http://dx.doi.org/10.1371/journal.pone.0107042 ]
Tang D , Liu D R , Tang Y L , Seyler B C , Deng X F and Zhan Y . 2019 . Comparison of GOCI and Himawari-8 aerosol optical depth for deriving full-coverage hourly PM 2.5 across the Yangtze River Delta . Atmospheric Environment , 217 : 116973 [ DOI: 10.1016/j.atmosenv.2019.116973 http://dx.doi.org/10.1016/j.atmosenv.2019.116973 ]
Wang J and Christopher S A . 2003 . Intercomparison between satellite-derived aerosol optical thickness and PM 2.5 mass: Implications for air quality studies . Geophysical Research Letters , 30 ( 21 ): 2095 [ DOI: 10.1029/2003gl018174 http://dx.doi.org/10.1029/2003gl018174 ]
Wang W , Mao F Y , Du L , Pan Z X , Gong W , andShenghui Fang . 2017 . Deriving hourly PM 2.5 concentrations from himawari-8 AODs over Beijing-Tianjin-Hebei in China . Remote Sensing , 9 ( 8 ): 858 [ DOI: 10.3390/rs9080858 http://dx.doi.org/10.3390/rs9080858 ]
Wang Y L , Chen H S , Wu Q Z , Chen X S , Wang H , Gbaguidi A , Wang W and Wang Z F . 2018 . Three-year, 5 km resolution China PM 2.5 simulation: Model performance evaluation . Atmospheric Research , 207 : 1 - 13 [ DOI: 10.1016/j.atmosres.2018.02.016 http://dx.doi.org/10.1016/j.atmosres.2018.02.016 ]
Wei J , Li Z Q , Lyapustin A , Sun L , Peng Y R , Xue W H , Su T N and Cribb M . 2021 . Reconstructing 1-km-resolution high-quality PM 2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications . Remote Sensing of Environment , 252 : 112136 [ DOI: 10.1016/j.rse.2020.112136 http://dx.doi.org/10.1016/j.rse.2020.112136 ]
Wu C B , Li K and Bai K X . 2020 . Validation and Calibration of CAMS PM 2.5 Forecasts Using In Situ PM 2.5 Measurements in China and United States . Remote Sensing , 12 ( 22 ): 3813 [ DOI: 10.3390/rs12223813 http://dx.doi.org/10.3390/rs12223813 ]
Wu Y R , Guo J P , Zhang X Y , Tian X , Zhang J H , Wang Y Q , Duan J and Li X W . 2012 . Synergy of satellite and ground based observations in estimation of particulate matter in eastern China . Science of The Total Environment , 433 : 20 - 30 [ DOI: 10.1016/j.scitotenv.2012.06.033 http://dx.doi.org/10.1016/j.scitotenv.2012.06.033 ]
Xiao Q Y , Geng G N , Cheng J , Liang F C , Li R , Meng X , Xue T , Huang X M , Kan H D , Zhang Q and He K B . 2021 . Evaluation of gap-filling approaches in satellite-based daily PM 2.5 prediction models . Atmospheric Environment , 244 : 117921 [ DOI: 10.1016/j.atmosenv.2020.117921 http://dx.doi.org/10.1016/j.atmosenv.2020.117921 ]
Xu Z , Xia X P , Liu X N and Qian Z G . 2015 . Combining DMSP/OLS nighttime light with echo state network for prediction of daily PM 2.5 average concentrations in Shanghai, China . Atmosphere , 6 ( 10 ): 1507 - 1520 [ DOI: 10.3390/atmos6101507 http://dx.doi.org/10.3390/atmos6101507 ]
Yan X , Zang Z , Luo N N , Jiang Y Z and Li Z Q . 2020 . New interpretable deep learning model to monitor real-time PM 2.5 concentrations from satellite data . Environment International , 144 : 106060 [ DOI: 10.1016/j.envint.2020.106060 http://dx.doi.org/10.1016/j.envint.2020.106060 ]
Yin J H , Mao F Y , Zang L , Chen J P , Lu X and Hong J . 2021 . Retrieving PM 2.5 with high spatio-temporal coverage by TOA reflectance of Himawari-8 . Atmospheric Pollution Research , 12 ( 4 ): 14 - 20 [ DOI: 10.1016/j.apr.2021.02.007 http://dx.doi.org/10.1016/j.apr.2021.02.007 ]
Zhang Y and Li Z Q . 2013 . Estimation of PM 2.5 from fine-mode aerosol optical depth . National Remote Sensing Bulletin , 17 ( 4 ): 929 - 943
张莹 , 李正强 . 2013 . 利用细模态气溶胶光学厚度估计PM 2.5 . 遥感学报 , 17 ( 4 ): 929 - 943 [ DOI: 10.11834/jrs.20133063 http://dx.doi.org/10.11834/jrs.20133063 ]
Zhang Y , Li Z Q , Bai K X , Wei Y Y , Xie Y S , Zhang Y X , Ou Y , Cohen J , Zhang Y H , Peng Z R , Zhang X Y , Chen C , Hong J , Xu H , Guang J , Lv Y , Li K T and Li D H . 2021 . Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives . Fundamental Research , 1 ( 3 ): 240 - 258 [ DOI: 10.1016/j.fmre.2021.04.007 http://dx.doi.org/10.1016/j.fmre.2021.04.007 ]
Zhang Y J , Zhao T L , Yin C Z , Wang Z F , Ge B Z , Liu D Y and Du X X . 2019 . Seasonal variation of the relationship between surface PM 2.5 and O 3 concentrations in Xuzhou . China Environmental Science , 39 ( 6 ): 2267 - 2272
张宇静 , 赵天良 , 殷翀之 , 王自发 , 葛宝珠 , 刘端阳 , 杜欣欣 . 2019 . 徐州市大气PM 2.5 与O 3 作用关系的季节变化 . 中国环境科学 , 39 ( 6 ): 2267 - 2272 [ DOI: 10.3969/j.issn.1000-6923.2019.06.004 http://dx.doi.org/10.3969/j.issn.1000-6923.2019.06.004 ]
Zou B , Pu Q , Bilal M , Weng Q , Zhai L and Nichol J . 2017 . High-resolution satellite mapping of fine particulates based on geographically weighted regression . IEEE Geoscience and Remote Sensing Letters , 13 ( 4 ): 495 - 499 [ DOI: 10.1109/LGRS.2016.2520480 http://dx.doi.org/10.1109/LGRS.2016.2520480 ]
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