基于遥感与模式技术的华东地区可吸入颗粒物预报方法研究
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摘要
颗粒物(particulate matter, PM)作为主要空气污染物之一,对人体健康具有显著危害,其中,空气动力学粒径小于10μm的PM10和小于2.51μm的PM2.5已成为大气环境研究的主要对象。利用极轨卫星上中分辨率遥感传感器空间覆盖大、光谱波段多、重访周期短等特点开展PM监测具有重要意义和广阔前景,但同时仍有若干关键问题有待解决。本文以上海及周边地区为主要研究区,开展PM10与PM25、PM10与气溶胶光学厚度(Aerosol Optical Depth, AOD)和气象/环境参数、PM2.5与AOD等之间的相关性分析和相关增强处理。在此基础上,以位于华东师范大学的直接广播极轨卫星遥感数据接收系统为中心,研究一套以快速反演获取的AOD和全球预报系统(Global Forecast System, GFS)气象场为主要数据源,以气块轨迹模式为驱动的遥感空气质量动态监测方案。本文主要得到以下几个方面的结论：
(1)基于单站获取的和全市多站点平均获取的PM10与PM2.5日均浓度都呈现显著相关,两者浓度均具有冬季高、夏季低的季节特征,颗粒物污染来源以本地源为主。
(2)PM10与AOD的空间分布均呈现由北向南递减的趋势,但两者的季节特征略有差异,研究表明,在不同季节采用不同的湿度或标高订正方法,对提高两者的相关性会产生较明显的影响；同时,多分辨率分析结果显示,PM10与AOD在长时间序列上具有一致的周期性,而PM2.5与AOD相关性分析也显示二者显著相关,从而为利用卫星遥感开展近地层PM10与PM2.5监测提供了可行性。
(3)PM10与主要气象因子的相关性在空间上(不同城市)或时间上(不同季节)均呈现一定差异,其中在空间上呈现北高南低的特点；将卫星遥感获取的AOD作为自变量开展地理加权回归分析的结果表明,AOD在时间和空间上都能较显著地提高PM10与相关气象因子的相关性,但仍存在空间分异。
(4)利用多元逐步回归和小波变换的方法,构建了不同尺度的PM10增强型估算模型,其中,结合各城市具体特征建立的小尺度城市估算模型与整个华东区域的中尺度估算模型相比,具有更高的准确率和精度；无论在哪个尺度下,AOD均是PM10估算的最重要变量。
(5)集成实时接收并反演的卫星遥感数据与GFS气象场数据,在颗粒物扩散模式的基础上,实现了可吸入颗粒物的48小时遥感动态跟踪监测,并利用Hysplit模式分析得出上海地区颗粒物远距离输送的敏感高度在1500m附近。
(6)通过可吸入颗粒物重污染过程的模拟研究,对遥感动态跟踪监测方法进行验证,结果显示,本文方法的结果与Hysplit后向轨迹模拟结果一致,与星载雷达Calipso资料反演的垂直方向气溶胶组成一致。
本文主要创新点：
(1)通过研发以实时接收并快速反演的卫星遥感为主要数据源,结合气象预报场与气块轨迹模式的可吸入颗粒物区域动态监测方法,为开展快速、动态、大范围的空气质量监测提供了一种新方法。
(2)在PM10与AOD相关性分析的研究中,发现了二者的相关性对湿度订正、标高订正的季节敏感性,同时通过已有大量长期积累的PM10和AOD数据,建立了一种PM10的增强型估算方法,不仅提高了其遥感估算精度,还为今后高精度PM2.5遥感估算提供了借鉴。
(3)在传统颗粒物本地源解析的基础上,引入后向轨迹模式对颗粒物远距离输送过程进行定量模拟,并实现了在500m、1000m和1500m三个高度层上颗粒物输送的贡献率及敏感高度的判断。
Particulate matter (PM) is one of the main pollutants in the atmosphere, which is harmful to human. In which, PM10(atmospheric dynamics equivalent diameter less than10μm) and PM2.5(atmospheric dynamics equivalent diameter less than2.5um) are the main subject attracting more and more interest. It is very important to use the moderate resolution remote sensor on polar orbital satellite in PM researches, as it has many spectral bands with larger space coverage and shorter revisit cycle. However, there are still many key problems to be solved in the near future work. In this study, we focused on PM10, PM2.5and AOD (Aerosol Optical Depth) in Shanghai and its surrounding areas. Correlation between each two of them was analyzed. Meanwhile, enhanced PM10forecast model was built based on remote sensing derived AOD and corresponding meteorological parameters. What's more, a dynamic air quality monitory scheme was built based on real time remote sensing data. This dynamic system combined the real time AOD from the direct broadcast polar orbital satellite remote sensing receiving system at East China Normal University and the meteorological fields from Global Forecast System (GFS), then drive air parcel trajectory model to forecast. The main conclusions can be summarized as follows:
(1) PM10and PM2.5had significant correlation no matter data obtained from single monitoring station or averaged data from many stations in the city. PM10and PM2.5had higher concentration in winter and lower concentration in summer. The main sources of PM were the local emissions.
(2) PM10and AOD had similar spatial distribution, both of which showed a decrease from north to south, but they had different seasonal characteristics. Suitable validation (Relative humidity validation or Boundary layer height validation) method should be considered to improve the correlation between PM10and AOD in different seasons. The multi-resolution analysis showed that PM10and AOD had the same periodicity. Meanwhile, a significant correlation was found between PM2.5and AOD. Thus, it is feasible to use satellite derived data for PM10and PM2.5monitoring.
(3) Correlations between PM10and related meteorological factors were different at different cities as well as in different seasons, which was a little higher in the north. What's more, correlations between PM10and related meteorological factors were improved when adding the satellite derived AOD as an important independent variable into the geographically weighted regression, however, the spatial non-stationary was also generated.
(4) An enhanced PM10forecast model was built for different scale based on multi-stepwise regression and wavelet analysis. The correct ratio and accuracy rate obtained from the small scale model (based on the local city characteristics) were higher than the medium model (based on the regional). Satellite derived AOD was the most important variable for model construction.
(5) Combining the direct broadcasted remote sensing data with the GFS meteorological fields, the dynamic trajectories of the inhalable particulate matter in near48hours were forecasted via air parcel trajectory model. In Shanghai, the sensitivity height for PM transportation from long distance was found at1500m.
(6) The dynamic monitoring method was validated by a case study about heavy inhalable particulate matter pollution. The simulated results of dynamic system were basically identical to simulation outputs from HYSPLIT. Meanwhile, the vertical distributions about aerosol component retrieved from Calipso can also reflect the processes showed by dynamic monitoring.
The innovations of this study can be summarized as follows:
(1) A new method was built for quickly and dynamically monitoring the air quality with large spatial coverage during the research and development of the regional inhalable particulate matter dynamic monitoring system. The dynamic monitoring system was built based on a combination among the remote sensing data, the real-time retrieval algorithm, the meteorological forecasting fields and the air parcel trajectory model.
(2) During PM10and AOD correlation analysis, we found that their correlation had a seasonal sensitivity to different validation methods (Relative humidity validation and Boundary layer height validation). Meanwhile, an enhanced PM10forecast method was built with long term accumulated PM10and AOD data. On the one hand, the accuracy of PM10forecast was improved by the enhancement. On the other hand, it also provides some reference for high precision PM2.5forecast.
(3) With all around local source analysis, we also analyzed the pollution sources from long distance with the backward trajectory model. Especially, we calculated the contribution rate in the height of500m,1000m, and1500m from long distance transportation sources in quantitative. Finally, the most sensitive height for long distance transportation was found.
(1)基于单站获取的和全市多站点平均获取的PM10与PM2.5日均浓度都呈现显著相关,两者浓度均具有冬季高、夏季低的季节特征,颗粒物污染来源以本地源为主。
(2)PM10与AOD的空间分布均呈现由北向南递减的趋势,但两者的季节特征略有差异,研究表明,在不同季节采用不同的湿度或标高订正方法,对提高两者的相关性会产生较明显的影响；同时,多分辨率分析结果显示,PM10与AOD在长时间序列上具有一致的周期性,而PM2.5与AOD相关性分析也显示二者显著相关,从而为利用卫星遥感开展近地层PM10与PM2.5监测提供了可行性。
(3)PM10与主要气象因子的相关性在空间上(不同城市)或时间上(不同季节)均呈现一定差异,其中在空间上呈现北高南低的特点；将卫星遥感获取的AOD作为自变量开展地理加权回归分析的结果表明,AOD在时间和空间上都能较显著地提高PM10与相关气象因子的相关性,但仍存在空间分异。
(4)利用多元逐步回归和小波变换的方法,构建了不同尺度的PM10增强型估算模型,其中,结合各城市具体特征建立的小尺度城市估算模型与整个华东区域的中尺度估算模型相比,具有更高的准确率和精度；无论在哪个尺度下,AOD均是PM10估算的最重要变量。
(5)集成实时接收并反演的卫星遥感数据与GFS气象场数据,在颗粒物扩散模式的基础上,实现了可吸入颗粒物的48小时遥感动态跟踪监测,并利用Hysplit模式分析得出上海地区颗粒物远距离输送的敏感高度在1500m附近。
(6)通过可吸入颗粒物重污染过程的模拟研究,对遥感动态跟踪监测方法进行验证,结果显示,本文方法的结果与Hysplit后向轨迹模拟结果一致,与星载雷达Calipso资料反演的垂直方向气溶胶组成一致。
本文主要创新点：
(1)通过研发以实时接收并快速反演的卫星遥感为主要数据源,结合气象预报场与气块轨迹模式的可吸入颗粒物区域动态监测方法,为开展快速、动态、大范围的空气质量监测提供了一种新方法。
(2)在PM10与AOD相关性分析的研究中,发现了二者的相关性对湿度订正、标高订正的季节敏感性,同时通过已有大量长期积累的PM10和AOD数据,建立了一种PM10的增强型估算方法,不仅提高了其遥感估算精度,还为今后高精度PM2.5遥感估算提供了借鉴。
(3)在传统颗粒物本地源解析的基础上,引入后向轨迹模式对颗粒物远距离输送过程进行定量模拟,并实现了在500m、1000m和1500m三个高度层上颗粒物输送的贡献率及敏感高度的判断。
Particulate matter (PM) is one of the main pollutants in the atmosphere, which is harmful to human. In which, PM10(atmospheric dynamics equivalent diameter less than10μm) and PM2.5(atmospheric dynamics equivalent diameter less than2.5um) are the main subject attracting more and more interest. It is very important to use the moderate resolution remote sensor on polar orbital satellite in PM researches, as it has many spectral bands with larger space coverage and shorter revisit cycle. However, there are still many key problems to be solved in the near future work. In this study, we focused on PM10, PM2.5and AOD (Aerosol Optical Depth) in Shanghai and its surrounding areas. Correlation between each two of them was analyzed. Meanwhile, enhanced PM10forecast model was built based on remote sensing derived AOD and corresponding meteorological parameters. What's more, a dynamic air quality monitory scheme was built based on real time remote sensing data. This dynamic system combined the real time AOD from the direct broadcast polar orbital satellite remote sensing receiving system at East China Normal University and the meteorological fields from Global Forecast System (GFS), then drive air parcel trajectory model to forecast. The main conclusions can be summarized as follows:
(1) PM10and PM2.5had significant correlation no matter data obtained from single monitoring station or averaged data from many stations in the city. PM10and PM2.5had higher concentration in winter and lower concentration in summer. The main sources of PM were the local emissions.
(2) PM10and AOD had similar spatial distribution, both of which showed a decrease from north to south, but they had different seasonal characteristics. Suitable validation (Relative humidity validation or Boundary layer height validation) method should be considered to improve the correlation between PM10and AOD in different seasons. The multi-resolution analysis showed that PM10and AOD had the same periodicity. Meanwhile, a significant correlation was found between PM2.5and AOD. Thus, it is feasible to use satellite derived data for PM10and PM2.5monitoring.
(3) Correlations between PM10and related meteorological factors were different at different cities as well as in different seasons, which was a little higher in the north. What's more, correlations between PM10and related meteorological factors were improved when adding the satellite derived AOD as an important independent variable into the geographically weighted regression, however, the spatial non-stationary was also generated.
(4) An enhanced PM10forecast model was built for different scale based on multi-stepwise regression and wavelet analysis. The correct ratio and accuracy rate obtained from the small scale model (based on the local city characteristics) were higher than the medium model (based on the regional). Satellite derived AOD was the most important variable for model construction.
(5) Combining the direct broadcasted remote sensing data with the GFS meteorological fields, the dynamic trajectories of the inhalable particulate matter in near48hours were forecasted via air parcel trajectory model. In Shanghai, the sensitivity height for PM transportation from long distance was found at1500m.
(6) The dynamic monitoring method was validated by a case study about heavy inhalable particulate matter pollution. The simulated results of dynamic system were basically identical to simulation outputs from HYSPLIT. Meanwhile, the vertical distributions about aerosol component retrieved from Calipso can also reflect the processes showed by dynamic monitoring.
The innovations of this study can be summarized as follows:
(1) A new method was built for quickly and dynamically monitoring the air quality with large spatial coverage during the research and development of the regional inhalable particulate matter dynamic monitoring system. The dynamic monitoring system was built based on a combination among the remote sensing data, the real-time retrieval algorithm, the meteorological forecasting fields and the air parcel trajectory model.
(2) During PM10and AOD correlation analysis, we found that their correlation had a seasonal sensitivity to different validation methods (Relative humidity validation and Boundary layer height validation). Meanwhile, an enhanced PM10forecast method was built with long term accumulated PM10and AOD data. On the one hand, the accuracy of PM10forecast was improved by the enhancement. On the other hand, it also provides some reference for high precision PM2.5forecast.
(3) With all around local source analysis, we also analyzed the pollution sources from long distance with the backward trajectory model. Especially, we calculated the contribution rate in the height of500m,1000m, and1500m from long distance transportation sources in quantitative. Finally, the most sensitive height for long distance transportation was found.
引文
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