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Received：19 July 2021，
Published：07 May 2022
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.
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