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利用遥感技术估算长江三角洲地区PM2.5浓度:时空变化分析

[Estimation of PM2.5 Concentration over the Yangtze Delta Using Remote Sensing: Analysis of Spatial and Temporal Variations].

作者信息

Xu Jian-hui, Jiang Hong

出版信息

Huan Jing Ke Xue. 2015 Sep;36(9):3119-27.

Abstract

Satellite remote sensing retrieved aerosol optical thickness is widely used to monitor surface PM2.5 concentration. In order to monitor PM2.5 by remote sensing in the Yangtze delta, estimate model of PM2.5 concentration was constructed based on MODIS AOT, PM2.5 concentration data of the 36 ground air quality observation sites and meteorological data in 2013. Afterwards, the model estimated PM2.5 was validated by PM2.5 concentration data from the 17 ground air quality observation sites, and the results showed that the model estimation was higher. The correlation coefficient value of R2 between model estimation of PM2.5 concentration and the value of the ground monitoring of spring, summer, autumn and winter were 0. 45, 0. 50, 0. 58 and 0. 52, respectively. The variation characteristics of temporal and spatial was analyzed based on the long time PM2.5 data together with model estimated, and an increase trend of PM2.5 concentration was observed from 2000 to 2013, with the maximum concentration of PM2.5 (66. 2 µg.m-3 ± 19. 3 µg.m-3) in February and minimum in December (22.6 µg.m-3 ± 5. 9 µg.m-3). In addition, it was found that the distribution of PM2.5 concentration was of obvious features, displaying high value in south and low in north. Mass concentration of PM2.5 was peaked in the zone of urban agglomeration which was grouped to a delta-shaped region by Shanghai, Hangzhou and Nanjing, while the low value areas were in the forest away from city. The result suggested that MODIS AOT and meteorological data can be used to monitor regional PM2.5 by the established multi-linear regression model.

摘要

卫星遥感反演的气溶胶光学厚度被广泛用于监测地表PM2.5浓度。为了在长江三角洲地区通过遥感监测PM2.5,基于2013年的MODIS气溶胶光学厚度(AOT)、36个地面空气质量观测站点的PM2.5浓度数据以及气象数据,构建了PM2.5浓度估算模型。之后,利用17个地面空气质量观测站点的PM2.5浓度数据对模型估算的PM2.5进行验证,结果表明模型估算值偏高。春季、夏季、秋季和冬季PM2.5浓度的模型估算值与地面监测值之间的R2相关系数分别为0.45、0.50、0.58和0.52。基于长时间的PM2.5数据以及模型估算结果,分析了其时空变化特征,观测到2000年至2013年PM2.5浓度呈上升趋势,2月PM2.5浓度最高(66.2µg·m-3±19.3µg·m-3),12月最低(22.6µg·m-3±5.9µg·m-3)。此外,发现PM2.5浓度分布具有明显特征,呈现南高北低。PM2.5质量浓度在以上海、杭州和南京为三角区域的城市群地带达到峰值,而低值区域位于远离城市的森林地区。结果表明,利用建立的多元线性回归模型,MODIS AOT和气象数据可用于监测区域PM2.5。

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