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Fine mapping of PM2.5 based on measurements from geostationary satellites and grid monitoring stations

  • role： First author第一作者
  • Affiliation：

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

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：fandonghao21@mails.ucas.ac.cn
  • Introduction：范东浩，研究方向为大气遥感。E-mail： fandonghao21@mails.ucas.ac.cn
FAN Donghao12,  
  • 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、遥感大数据与机器学习。E-mail：qinkai@cumt.edu.cn
QIN Kai1*,  
  • Affiliation：

    Xuzhou Environmental Monitoring Center, Jiangsu Province, Xuzhou 221002, China

DU Juan3,  
  • Affiliation：

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

HE Qin1,  
  • Affiliation：

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

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

XIN Shiji12,  
  • Affiliation：

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

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LIU Dingyi12

resumen

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 PM2.5 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 PM2.5 in detail at urban scale , this study used the PM2.5 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/m3). 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 PM2.5 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 PM2.5 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 PM2.5 concentration distribution between different regions within a city, and better serve the accurate control of air pollution.

palabra clave

remote sensing;PM2.5;grid site;fine drawing;Aerosol Optical Depth (AOD);apparent reflectance

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