北京地区气溶胶粒径尺度谱与PM2.5浓度转换模型研究

大气细颗粒物（PM_2.5）质量浓度是重要的空气质量指标之一。为了促进区域PM_2.5浓度监测的研究,同时拓展利用CE318太阳光度计等光学传感器反演的大气气溶胶产品的应用领域,本文首先基于北京地区2014年—2017年大气气溶胶反演的粒径尺度谱分布产品,计算表征PM_2.5的粒子体积,并结合同一时间北京地区35个空气质量站点提供的PM_2.5质量浓度参考值计算转换系数,对样本区间进行划分以构建转换模型。其次,利用各CE318站点数据所得转换系数及其相对精度,对研究区PM_2.5质量浓度估算误差空间分布,以及转换系数偏差对估值误差贡献情况进行评价。研究结果表明,由CE318站PM_2.5粒子体积与其临近空气质量站PM_2.5质量浓度联合建立的转换系数是一种与气溶胶理化属性密切相关的参数指标,可将时间和空间维度上PM_2.5体积与PM_2.5质量浓度之间的关系映射到由理化属性主导的维度上,可用于对估值模型进行细化和分类,构建分段转换函数模型,使得各分段区间内具有较高的模型拟合精度。基于转换系数的北京地区PM_2.5浓度估值的相对误差多年均值介于12.8 %—28.7 %,而转换系数相对偏差对PM_2.5质量浓度估值相对误差的影响显著,二者之间具有“r”型结构。当转换系数相对偏差介于-16.3 %至24.5 %时,该偏差的出现概率约为66.5 %,使得PM_2.5质量浓度估值误差在20 %以内,表明采用此种方法对相应站点的PM_2.5质量浓度进行估值具有相当的精度和稳定性。以上研究结果可为地面观测站稀少区域利用卫星光学遥感开展空气质量大范围监测应用提供理论前提和技术支持。     

Spatiotemporal mixed effects modeling for the estimation of PM2.5 from MODIS AOD over the Indian subcontinent

ABSTRACT

The physical processes associated with the constituents of the troposphere, such as aerosols have an immediate impact on human health. This study employs a novel method to calibrate Aerosol Optical Depth (AOD) obtained from the MODerate resolution Imaging Spectrometer (MODIS – Terra satellite) for estimating surface PM_2.5 concentration. The Combined Deep Blue Deep Target daily product from the MODIS AOD data acquired across the Indian Subcontinent was used as input, and the daily averaged PM_2.5pollution level data obtained from 33 monitoring stations spread across the country was used for calibration. Mixed Effect Models (MEM) is a linear model to deal with non-independent data from multiple levels or hierarchy using fixed and random effects of dependent parameters. MEM was applied to the dataset obtained for the period from January to August 2017. The MEM considers a fixed and random component, where the random components model the daily variations of the AOD – PM_2.5 relationships, site-specific adjustment parameters, temporal (meteorological) variables such as temperature, and spatial variables such as the percentage of agricultural area, forest cover, barren land and road density with the resolution of 10 km × 10 km. Estimation accuracy was improved from an R² value of 0.66 from our earlier study (when PM_2.5 was modeled against only AOD and site-specific parameters) toR² value of 0.75 upon the inclusion of spatiotemporal (meteorological) variables with increased % within Expected Error from 18% to 35%, reduced Mean Bias Error from 3.22 to 0.11 and reduced RMSE from 29.11 to 20.09. We also found that spline interpolation performed better than IDW and Kriging inefficiently estimating the PM_2.5 concentrations wherever there were missing AOD data. The estimated minimum PM_2.5 is 93 ± 25μg/m³ which itself is in the upper limit of the hazardous level while the maximum is estimated as 170 ± 70μg/m³. The study has thus made it possible to determine the daily spatial variations of PM_2.5 concentrations across the Indian subcontinent utilizing satellite-based AOD data.     

MERSI和MODIS卫星监测京津冀及周边地区PM2.5浓度

京津冀及周边地区是中国PM_(2.5)污染最重的区域之一,利用卫星遥感技术监测大范围的PM_(2.5)时空分布变化是一种先进的重要手段。本研究首先基于暗像元算法利用FY-3B/MERSI与AQUA/MODIS对京津冀及周边区域进行了遥感AOT反演和验证分析;然后,引入气象资料和地面观测资料利用GWR模型反演了区域PM_(2.5)浓度,并对遥感反演结果进行了交叉验证评估,综合对比分析了MERSI和MODIS的气溶胶及PM_(2.5)遥感监测能力;最后,利用MERSI数据对2017年第一季度京津冀及周边区域的PM_(2.5)月均浓度时空分布变化情况进行了初步探索分析。结果表明:FY-3B/MERSI在气溶胶及PM_(2.5)遥感监测能力方面略优于AQUA/MODIS,MERSI反演的1 km分辨率AOT和PM_(2.5)与地面站点实测结果的决定系数R2分别为0.76μg/m~3和0.79μg/m~3,均方根误差分别为0.26μg/m~3和28μg/m~3,平均绝对误差分别为0.16μg/m~3和15μg/m~3,能基本满足对京津冀及周边区域PM_(2.5)的精细化监测需要。2017年第一季度京津冀及周边区域PM_(2.5)月均浓度遥感监测结果表明该区域的PM_(2.5)空间分布格局与地形地貌关系密切,高值区整体上沿太行山脉成带成片;从时间变化来看,1—3月呈逐月下降的趋势,其中3月份PM_(2.5)区域浓度较1月和2月有大幅下降。这说明FY-3\MERSI遥感反演产品能为环境质量监测和环境管理工作效果评估提供有效参考,本研究对国产卫星在大气环境遥感业务中的大力发展应用有重要参考意义。     

MODIS和OMI数据评估阅兵期间北京市大气减排成效

卫星观测不仅能反映区域宏观大气污染状况,也能从城市尺度上监测大气污染物的变化。基于以上优势,本文利用MODIS气溶胶光学厚度(AOD)和OMI对流层NO_2垂直柱浓度数据,比较2015年与2012年—2014年以及2015年3个时期(减排前、减排中、减排后)AOD和NO_2柱浓度的变化,定性分析了阅兵期间华北平原地区污染物减排效果,重点定量评估北京市联控减排措施的效果。研究发现2015年减排中华北平原重污染地区AOD和NO_2柱浓度相比于前3年同期有明显降低。定量分析北京市的减排效果得到:2015年减排中较前3年同期而言,AOD降低59%,NO_2柱浓度降低41%;较2015年减排前而言,AOD降低73%,NO_2柱浓度降低30%,去除气象条件影响后,AOD下降43%,NO_2柱浓度下降21%,说明严格的联控减排措施有效地改善了空气质量,气象条件也起到积极的作用。减排措施结束后,AOD和NO_2柱浓度比减排中分别增加159%和71%。研究结果表明,卫星遥感与地基监测评估效果相当,能反映北京地面污染物排放能力;它既能观测区域尺度大气污染变化,又可评估城市尺度大气污染减排。随着卫星技术水平的提高,期望未来卫星遥感可作为一种独立手段来定量评估区域及城市尺度空气质量减排措施的效果。     

MODIS卫星遥感估计福州地区近地面PM2.5浓度

卫星遥感反演气溶胶光学厚度已被广泛应用于近地面空气污染遥感监测。为揭示福州地区细颗粒物污染的空间分异趋势,利用2014年—2015年的地基监测细颗粒物(PM_(2.5))浓度数据、MODIS 3 km气溶胶光学厚度(AOD)卫星数据以及GEOS-FP气象数据,分别构建了估计福州地区近地面PM2.5浓度的日校正模型和站点一日校正模型,并利用十折交叉验证方法对2个模型进行评价验证。结果表明:(1)日校正模型和站点一日校正模型分别能够解释福州地区PM2.5浓度76.2%和81.4%的变异,反演的2014年—2015年福州地区近地面PM2.5浓度和地面实测站点数据之间的相关性R~2分别为0.724(RMSE=10.993μg·m~(-3))和0.781(RMSE=9.687μg.m~(-3));(2)分别针对不同下垫面环境的城市站点和县郊站点数据进行模型拟合验证,两个模型反演的PM2.5浓度值与地面实测值之间皆具有良好的相关性,R~2最高可达0.808;(3)将模型反演的PM2.5浓度季均值与地面实测季均值进行对比分析,结果也显示二者高度相关,据此反演的2015年福州地区年平均PM2.5浓度分布图可清晰地揭示福州地区PM_(2.5)浓度分布的空间变化情况。由此可见,基于MODIS 3 km AOD产品和气象数据建立的近地面PM_(2.5)浓度遥感估算模型能够很好地反演出福州地区近地面PM2.5浓度分布情况。     

地球静止卫星和网格化站点支持下的PM2.5精细制图

许多城市建立的相对稠密的网格化监测站点,为精细化监管城市空气质量奠定了基础。本文选用徐州市网格化监测数据、地球静止卫星Himawari-8/AHI及COMS/GOCI的表观反射率和气溶胶光学厚度数据、气象和其他辅助数据,开展了徐州地区0.005°空间分辨率网格的PM_2.5浓度精细化制图研究。本文使用了极端梯度提升（XGBoost）、随机森林（RF）及时空加权回归（GTWR）等3种方法,并选用多种特征参数组合进行对比分析。综合分析模型精度和过拟合程度,结果表明XGBoost模型表现最好,其R²为0.90,RMSE为11.65 μg/m³。进一步将本文结果与国控站点、清华大学的TAP数据集和马里兰大学的CHAP数据集的对比分析,结果表明基于网格化站点的PM_2.5制图结果能更好地反映城市内部不同区域的PM_2.5浓度分布差异性,弥补因国控站点稀疏带来的缺陷,更好地服务于城市空气质量精准管控。     

Use of earth observation data for applications in public health

The Earth Observation (EO) data with their advantages in spectral, spatial and temporal resolutions have demonstrated their great value in providing information about many of the components that comprise environmental systems and ecosystems for decades that are crucial to the understating of public health issues. This literature review shows that in conjunction with in situ data collection, EO data have been used to observe, monitor, measure and model many environmental variables that are associated with disease vectors. Furthermore, satellite derived aerosol optical depth has been increasingly employed to estimate ground-level PM_2.5 concentrations, which have been found to associate with various health outcomes such as cardiovascular and respiratory diseases. It is suggested that Landsat-like imagery data may provide important data sources to analyse and understand contagious and infectious diseases at the local and regional scales, which are tied to urbanisation and associated impacts on the environment. There is also a great need of data products from coarse resolution imagery, such as those from moderate resolution imaging spectrometer, multiangle imaging spectroradiometer and geostationary operational environmental satellite , to model and characterise infectious diseases at the continental and global scales. The infectious diseases at greater geographical scales have become unprecedentedly significant as global climate change and the process of globalisation intensify. The relationship between infectious diseases and environmental characteristic have been explored by using statistical, geostatistical and physical models, with recent emphasis on the use of machine-learning techniques such as artificial neural networks. Lastly, we suggest that the planned HyspIRI mission is crucial for observing, measuring and modelling environmental variables impacting various diseases as it will improve both spectral resolution and revisit time, thus contributing to better prediction of occurrence of infectious diseases, target intervention and tracking of epidemic events.