摘要
可吸入颗粒物PM10浓度是表征空气质量最为重要的污染指标之一,快速了解其浓度和大范围的分布状况,有利于控制PM10质量浓度,并最终提高空气质量。对可吸入颗粒物在地面建立观测站,并进行全天候连续观测能够直接得到反映PM10地面浓度及其时间变化较为准确的信息。但是仅采用有限的地面观测站点资料尚不能全面反映可吸入颗粒物的空间分布,因此近年来开展了采用大面积覆盖卫星遥感数据进行可吸入颗粒物监测的研究以弥补地面观测的这一局限性。当前,卫星数据已在气溶胶产品光学厚度反演、污染物监测、突发性大气污染事件监测以及污染物的区域输送等方面开展了广泛应用。
常见的可吸入颗粒物遥感监测方法,首先通过多光谱遥感影像(如MODIS)反演出气溶胶光学厚度产品AOD,然后建立AOD与可吸入颗粒物PM10质量浓度的关系。但是MODIS的业务化反演产品的空间分辨率较低,难以反映PM10地面分布的细节。如果采用日平均数据建立模型,两者更难保证较高的相关性。
本文针对上述问题,以浙江省为例,采用时空序列分析,统计学习方法,以及基于大气辐射传输模型的大气校正方法,开展可吸入物浓度的MODIS数据遥感监测方法研究,并取得了以下主要成果：(1)探索性地采用图像特征技术,结合小波分析技术,对遥感子图像的空间信息进行定量描述,通过对图像的灰度、边缘能量等多种特征向量与空气环境质量进行对比分析,找到这些特征向量与空气环境质量的对应关系,识别当地的大气污染状况,进行大气污染的遥感监测。(2)结合地面气溶胶遥感资料对MODIS气溶胶光学厚度产品进行对比订正,建立气溶胶光学厚度与地面PM1o浓度的回归关系并对其进行反演。(3)针对NASA的气溶胶光学厚度的业务化反演算法,结合浙江省的实际情况,对算法进行了改进,提高了反演结果的分辨率,并在此基础上对地面PM10浓度进行了反演(4)通过对辐射传输方程的分析,选取大气纠正差值为自变量,建立了基于SVR的PM10反演模型,为PM10的遥感监测提供了新的技术手段。
concentration is one of the most important quantitative indicators to air quality. Efficient collection of the concentration and distribution information of PM10 at a large scale benefits to control and improve air quality. PM10 can be measured using ground instruments that provide accurate information reflecting the spatial and temporal change of pollutants in a single location. However, ground observation is impractical if measurements are to be made over relatively large areas or for continuous monitoring. Therefore, satelite remote sensing has been administered to compensate for the limitations of ground instruments. Currently, the satellite data has been successfully applied atmosphere research, e.g. inversion of aerosol products, pollutant monitoring, study of sudden atmospheric pollution events, study of pollutant regional transportation etc.
Conventional PM10 inversion algorithms are executed in two steps. Firstly, aerosol optical depth (AOD) data is extracted from satellite images; then, PM10 is estimated by modeling between AOD and ground measured data. However,due to nasa's operatinal aod product has low spatial resolution,the PM10 inversion result based on this can not reflect the details of the distribution of ground's PM10. Therefore, it is difficult to guarantee a high correlation between them while using the average daily as input for the model.
Aim to provide new solutions to the above issue, a new approach for monitoring PM10 concentration using satellite data (e.g. MODIS) is proposed. In this approach, two analysis methods i.e. time-frequency analysis, statistical learning and an atmospheric correction algorithm based on radiative transfer model are evaluated. Major contributions of this thesis are as follows:(1) image (including images after wavelet analysis) characteristics, e.g. grayscale, edge energy etc. together are quantitatively measured; and the correlations between these measurements and PM10 concentration are studied; (2) generating the regression relationship between AOD and filed measured PM10 concentration via using filed-based aerosol remote sensing data to modify MODIS AOD product. An AOD inversion procedure is then followed. (3) considering the atmospheric conditions of Zhejiang Province, NASA's operational AO-D inversion algorithm is modified to a better output with higher spatial resolution. Base-d on this modified algorithm, field PM10 concentration is then inversed. (4) modeling the difference map (difference between before and after atmospheric correction) from MODIS imagery with PM10 concentration using support vector machine regression (SVR).
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