An assessment of air-quality monitoring station locations based on satellite observations
An assessment of air-quality monitoring station locations based on satellite observations
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DOI：
10.1080/01431161.2018.1460505
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发表时间：
2018-04
影响因子：
3.4
通讯作者：
T. Yu;Wen Wang;P. Ciren;R. Sun
中科院分区：
文献类型：
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作者：
T. Yu;Wen Wang;P. Ciren;R. Sun
空气质量监测站的位置优化对于为区域空气污染监测提供高质量的数据具有重要意义。为了评估现有空气质量监测站位置的代表性，我们提出了一种基于卫星观测的分层抽样方法。与传统方法依赖于通过弥散模型模拟空气污染物的空间分布不同，我们通过遥感观测获得采样总体。首先，基于卫星观测得到的地面颗粒物（空气动力直径小于10 μm， PM10）、细颗粒物（空气动力直径小于2.5 μm, PM2.5）、二氧化氮（NO2）和二氧化硫（SO2）的浓度，获得了综合空气质量的空间分布。其次，采用分层抽样法评价空气质量监测站位置的代表性。结果表明：京津冀地区空气质量监测站集中在空气重污染地区，而空气质量高污染地区空气质量监测站数量不足；优化后的最小相对误差仅为6.77%。结果表明，将遥感数据与分层抽样方法相结合，在评价空气质量监测站的空间代表性方面具有很大的潜力。
ABSTRACT Optimization of the locations of air quality monitoring stations has great importance in providing high-quality data for regional air pollution monitoring. To assess the representativeness of the locations of the current air quality monitoring stations, we propose a new method based on satellite observations by applying the stratified sampling approach. Unlike the traditional method, which relies on the simulated spatial distribution of air pollutants from dispersion models, we obtained the sampling population through observations from remote sensing. As a first step, the spatial distribution of aggregated air quality was obtained based on ground concentrations of particulate matter (aerodynamic diameters of less than 10 μm, PM10), fine particulate matter (aerodynamic diameters of less than 2.5 μm, PM2.5), nitrogen dioxide (NO2), and sulphur dioxide (SO2) derived from satellite observations. Second, the representativeness of locations of air quality monitoring stations was assessed using the stratified sampling method. The results demonstrated that air quality monitoring stations in Beijing-Tianjin-Hebei were clustered in areas with heavily polluted air, whereas the number of air quality monitoring stations was insufficient in areas with higher air quality. After optimization, the minimum relative error was only 6.77%. It is indicated that combing remote-sensing data with the stratified sampling approach has great potential in assessing the spatial representativeness of air quality monitoring stations.