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首页> 外文期刊>Tellus, Series B. Chemical and Physical Meteorology >Monitoring of urban air pollution from MODIS aerosol data: effect of meteorological parameters
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Monitoring of urban air pollution from MODIS aerosol data: effect of meteorological parameters

机译：从MODIS气溶胶数据监测城市空气污染：气象参数的影响

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Remote sensors designed specifically for studying the atmosphere have been widely used to derive timely information on air pollution at various scales. Whether the satellite-generated aerosol optical thickness (AOT) data can be used to monitor air pollution, however, is subject to the effect of a number of meteorological parameters. This study analyses the influence of four meteorological parameters (air pressure, air temperature, relative humidity, and wind velocity) on estimating particulate matter (PM) from MODIS AOT data for the city of Nanjing, China during 2004-2006. After the PM data were correlated with the AOT data that had been divided into four chronological seasons, a minimum correlation coefficient of 0.47 was found for the winter season, but a much stronger correlation (r > 0.80) existed in summer and autumn. Similar analyses were carried out after all observations were clustered into four groups based on their meteorological similarity using the K-Means analysis. Grouping caused more observations to be useable in the monitoring of air pollution than season-based analysis. Of the four groups, three had a correlation coefficient higher than 0.60. Grouping-based analysis enables the pollution level to be determined more accurately from MODIS AOT data at a higher temperature and relative humidity, but a lower air pressure and wind velocity. The accuracy of monitoring air pollution is inversely related to the pollution level. Thus, remote sensing monitoring of air pollution has its limits.
机译：专为研究大气而设计的远程传感器已被广泛用于及时获取各种规模的空气污染信息。但是，卫星生成的气溶胶光学厚度（AOT）数据是否可用于监视空气污染，受到许多气象参数的影响。这项研究分析了四个气象参数（气压，气温，相对湿度和风速）对根据MODIS AOT数据估算的中国南京市2004-2006年颗粒物（PM）的影响。将PM数据与分为四个时间季节的AOT数据进行关联后，冬季的最小关联系数为0.47，而夏季和秋季则存在更强的关联（r> 0.80）。使用K均值分析将所有观测值根据气象相似性分为四类后，进行了相似的分析。与基于季节的分析相比，分组使更多的观测值可用于监视空气污染。在这四组中，三组的相关系数高于0.60。基于分组的分析可以在较高温度和相对湿度，较低气压和风速的情况下，根据MODIS AOT数据更准确地确定污染水平。监测空气污染的准确性与污染水平成反比。因此，遥感监测空气污染有其局限性。

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