城市大气污染的定量遥感监测方法研究
摘要
20世纪80年代以来,我国工业迅猛发展,城市化进程大大加快,由此带来诸多环境问题,城市大气污染尤其受到人们的关注。虽然最近十年我国注重清洁生产,污染排放强度逐渐降低,大气污染仍是我国面临的最严峻挑战之一。由于受到监测站点分布和监测费用的影响,大气污染的常规监测难以获得大范围、长时序的大气污染信息。而遥感数据以其宏观性、经济性、实时性等特点,可以大范围地对大气污染情况进行动态监测。
本文基于大气辐射传输理论,应用Terra/MODIS (moderate resolution imaging spectro-radiometer,中分辨率成像光谱仪)数据,对郑州市2006～2011年大气污染进行了定量遥感动态监测试验研究,提出了一套切实可行的城市大气污染监测方法,得出如下结论：
(1)提出了反演城市地区气溶胶光学厚度(AOD)的改进算法。首先运用地表温度对遥感辐射率进行热力学温度定标,并通过与地基遥感数据进行相关分析确定参与反演的波段组合,在回归方法上对自变量进行了非线性转换,并根据NDVI (Normalized difference Vegetation Index,归一化植被指数)值的大小对数据进行分组,反演精度得到很大提高。
(2)经过气溶胶标高(ASH)、Angstrom波长指数、大气水汽含量(WVA)校正的AOD模型较好地模拟了大气污染指数(API),应用此模型可以绘出每日的API分布图,弥补了传统大气监测以点带面的不足。
(3)基于MODIS数据的城市地表温度反演,在覃志豪等人算法的基础上,根据城市地物的辐射特征,对大气透过率和地表比辐射率的计算方法进行了改进,反演精度有了明显提高。
(4)本文对基于MODIS数据反演大气水汽含量的两通道和三通道方法进行改进,使得反演的精度有了较大的提高,适用于城市地区大气水汽含量的反演。
(5)本文运用MODIS数据,分析了郑州市2006～2011年AOD、Angstrom波长指数、气溶胶标高和大气污染指数的动态变化。结果表明：①除2009年外,郑州市每年均是夏季AOD水平最高,春、秋次之,冬季最低。②建成区和农业用地气溶胶Angstrom波长指数四季变化趋势一致,都是夏季最高,冬季最低,春、秋次之。城市中心地区气溶胶Angstrom波长指数低于周边地区。③郑州市气溶胶标高呈明显的上升趋势,只在2009年略微下降。每年均是夏季最高,冬季最低,春秋次之。气溶胶标高的分布基本与光学厚度一致。郑州市郊区气溶胶标高越来越接近中心城区。④郑州市大气污染的高值区分布在城市中心区,而且中心区的污染有逐渐增大的趋势,而周边地区则有反复,总的来说有逐渐减小的趋势。
Since1980s, the process of industralization and urbanization has been speed up in China. Because of deficiency of environmental treatment, air pollution has been becoming more and more serious in urban area. Despite of clean production and lower intensity of pollution discharge in past10years, air pollution is still one of the most serious challenges to sustainable developement. However, monitoring pollution status routinely in a large area is difficult because of limit monitoring sites. Large scale monitoring can be carried out using remotely sensed data because of its macroscopic charactoristics, real-time performance and low cost in economy.
Based on the atmospheric radiative transfer theory, a set of practical and feasible methods are put forward to moniter urban air pollution through the experimental researches on dynamicly monitering air pollution in Zhengzhou city in2006-2011by quantitatively remote sensing methods using Terra/MODIS(moderate resolution imaging spectroradiometer) data, and following conclusions can been drawn:
1) A modified algorythm is developed to retrieve aerosol optical depth (AOD) in urban area. First, the radiance is scaled with thermodynamic temperature retrieved with simultaneous remote sensing data and band combination defined according to the correlation analysis with ground-based measurements. Indepent variables are nonlinear-transformed while regression analyses are carried out. Meanwhile, the data are grouped according to NDVI (normalized difference vegetation index) values.
2) The regression model between AOD and air pollution index (API) has high accuracy after corrected by aerosol scale height (ASH), Angstrom wavelength exponent and water vapor amount (WVA). Using the model, API distribution maps can be drawn daily, which make up the defect of routine air monitoring.
3) Retrieval of urban land surface temperature (LST) using MODIS data is worked out by a modified algorithm based on Qin's algorithm. The accuracy of retrieval improves markedly after modifing the computing methods of atmospheric transmittance and land surface emissivity according to the radiance characteristics of urban land objects.
4) The retrieval algorythm of water vapor amount (WVA) using MODIS data is improved at the base of two-three method. It is opt to retrieve WVA in urban areas.
5) The dynamic changes of AOD, Anstrom wavelength exponent, aerosol scale height (ASH) and API are analized in Zhengzhou city from2006to2011, which indicates:
*AOD is highest in summer, spring and autumn following and minimum in winter, annually except for2009.
*The seasonal variation trend of Angstrom wavelength exponent in built-up area is consistent with agricultural land, highest in summer, spring and autumn following and lowest in winter. Overall, aerosol Angstrom exponent is lower in the urban center area than the surrounding area.
*ASH takes a clear upward trend, only a slight decrease in2009. It is highest in summer and lowest in winter annually. ASH spatial pattern is consistent with AOD but ASH has little difference, no more than30m. ASH in suburb is getting closer to the center city.
*API is higher in the urban center area than the surrounding area and increasing gradually in the urban center area and decreasing in the surrounding area.
本文基于大气辐射传输理论,应用Terra/MODIS (moderate resolution imaging spectro-radiometer,中分辨率成像光谱仪)数据,对郑州市2006～2011年大气污染进行了定量遥感动态监测试验研究,提出了一套切实可行的城市大气污染监测方法,得出如下结论：
(1)提出了反演城市地区气溶胶光学厚度(AOD)的改进算法。首先运用地表温度对遥感辐射率进行热力学温度定标,并通过与地基遥感数据进行相关分析确定参与反演的波段组合,在回归方法上对自变量进行了非线性转换,并根据NDVI (Normalized difference Vegetation Index,归一化植被指数)值的大小对数据进行分组,反演精度得到很大提高。
(2)经过气溶胶标高(ASH)、Angstrom波长指数、大气水汽含量(WVA)校正的AOD模型较好地模拟了大气污染指数(API),应用此模型可以绘出每日的API分布图,弥补了传统大气监测以点带面的不足。
(3)基于MODIS数据的城市地表温度反演,在覃志豪等人算法的基础上,根据城市地物的辐射特征,对大气透过率和地表比辐射率的计算方法进行了改进,反演精度有了明显提高。
(4)本文对基于MODIS数据反演大气水汽含量的两通道和三通道方法进行改进,使得反演的精度有了较大的提高,适用于城市地区大气水汽含量的反演。
(5)本文运用MODIS数据,分析了郑州市2006～2011年AOD、Angstrom波长指数、气溶胶标高和大气污染指数的动态变化。结果表明：①除2009年外,郑州市每年均是夏季AOD水平最高,春、秋次之,冬季最低。②建成区和农业用地气溶胶Angstrom波长指数四季变化趋势一致,都是夏季最高,冬季最低,春、秋次之。城市中心地区气溶胶Angstrom波长指数低于周边地区。③郑州市气溶胶标高呈明显的上升趋势,只在2009年略微下降。每年均是夏季最高,冬季最低,春秋次之。气溶胶标高的分布基本与光学厚度一致。郑州市郊区气溶胶标高越来越接近中心城区。④郑州市大气污染的高值区分布在城市中心区,而且中心区的污染有逐渐增大的趋势,而周边地区则有反复,总的来说有逐渐减小的趋势。
Since1980s, the process of industralization and urbanization has been speed up in China. Because of deficiency of environmental treatment, air pollution has been becoming more and more serious in urban area. Despite of clean production and lower intensity of pollution discharge in past10years, air pollution is still one of the most serious challenges to sustainable developement. However, monitoring pollution status routinely in a large area is difficult because of limit monitoring sites. Large scale monitoring can be carried out using remotely sensed data because of its macroscopic charactoristics, real-time performance and low cost in economy.
Based on the atmospheric radiative transfer theory, a set of practical and feasible methods are put forward to moniter urban air pollution through the experimental researches on dynamicly monitering air pollution in Zhengzhou city in2006-2011by quantitatively remote sensing methods using Terra/MODIS(moderate resolution imaging spectroradiometer) data, and following conclusions can been drawn:
1) A modified algorythm is developed to retrieve aerosol optical depth (AOD) in urban area. First, the radiance is scaled with thermodynamic temperature retrieved with simultaneous remote sensing data and band combination defined according to the correlation analysis with ground-based measurements. Indepent variables are nonlinear-transformed while regression analyses are carried out. Meanwhile, the data are grouped according to NDVI (normalized difference vegetation index) values.
2) The regression model between AOD and air pollution index (API) has high accuracy after corrected by aerosol scale height (ASH), Angstrom wavelength exponent and water vapor amount (WVA). Using the model, API distribution maps can be drawn daily, which make up the defect of routine air monitoring.
3) Retrieval of urban land surface temperature (LST) using MODIS data is worked out by a modified algorithm based on Qin's algorithm. The accuracy of retrieval improves markedly after modifing the computing methods of atmospheric transmittance and land surface emissivity according to the radiance characteristics of urban land objects.
4) The retrieval algorythm of water vapor amount (WVA) using MODIS data is improved at the base of two-three method. It is opt to retrieve WVA in urban areas.
5) The dynamic changes of AOD, Anstrom wavelength exponent, aerosol scale height (ASH) and API are analized in Zhengzhou city from2006to2011, which indicates:
*AOD is highest in summer, spring and autumn following and minimum in winter, annually except for2009.
*The seasonal variation trend of Angstrom wavelength exponent in built-up area is consistent with agricultural land, highest in summer, spring and autumn following and lowest in winter. Overall, aerosol Angstrom exponent is lower in the urban center area than the surrounding area.
*ASH takes a clear upward trend, only a slight decrease in2009. It is highest in summer and lowest in winter annually. ASH spatial pattern is consistent with AOD but ASH has little difference, no more than30m. ASH in suburb is getting closer to the center city.
*API is higher in the urban center area than the surrounding area and increasing gradually in the urban center area and decreasing in the surrounding area.
引文
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