京津唐地区PM_(2.5)遥感估算与区域传输

近年来PM_(2.5)污染事件频发,PM_(2.5)对人类健康造成了重大威胁。各领域学者针对PM_(2.5)的研究很多,但对其来源尚存在很多争论。该文利用MODIS卫星遥感数据反演了京津唐气溶胶光学厚度(aerosol optical depth,AOD),进而估算了京津唐地区2013年1km分辨率AOD分布和PM_(2.5)时空分布,并利用研究区内AERONET观测数据对反演AOD进行验证。中国环境监测总站在京津唐地区24个PM_(2.5)实测站点监测数据对AOD-PM_(2.5)模型进行了训练和检验。结果表明,气溶胶遥感估算模型和AOD-PM_(2.5)模型精度均较高,2个气溶胶监测站点对气溶胶遥感估算模型的验证R2分别达到了0.558和0.598,AOD-PM_(2.5)模型4个季节的估算结果与实测值的相关系数R2值分别为0.36、0.64、0.56、0.62,除春季外,其他季节模型拟合精度较高。研究结果表明,利用经过高度校正和湿度校正后的气溶胶干消光系数与温度、风速等气象因子建立PM_(2.5)与AOD之间的线性回归模型,能够获得高分辨率和精度的PM_(2.5)质量浓度空间分布。结合HYSPLIT_4气流轨迹分析能够进一步分析PM_(2.5)的空间传输特征和路径,为进一步分析区域大气污染以及污染来源提供了一种有效手段。     

天宫二号数据大气细颗粒物遥感反演

针对天宫二号(TG-2)大气细颗粒物遥感反演及可行性问题,利用TG-2宽波段成像仪的可见近红外波段数据,结合MOD09A1地表反射率产品构建地表反射率库,使用6S传输模型和深蓝算法反演气溶胶光学厚度(aerosol optical depth, AOD),并结合能见度和相对湿度数据对AOD进行订正,利用统计模型估算了鲁豫皖典型地区的大气细颗粒物PM_2.5浓度,并对PM_2.5浓度的空间分布进行分析。结果表明：天宫二号数据在AOD和PM_2.5遥感反演中具有较高的可行性,拟合度分别达到0.910和0.902;经过垂直订正和湿度订正,PM_2.5和AOD之间的相关性大幅度提高;PM_2.5的空间分布呈“污染岛”特征,这在气溶胶排放和扩散研究中起着重要作用。天宫二号PM_2.5反演为区域大气污染监测提供了一种有效手段。     

中国气溶胶光学厚度与PM_(2.5)时空关联的地理学特征

针对气溶胶光学厚度(Aerosol Optical Depth,AOD)与PM_(2.5)质量浓度关系在广域地理空间的时空非平稳性,利用中国大陆地区NASA MODIS 2013年AOD产品和945个空气污染监测站点PM_(2.5)浓度数据,按气候、海拔地形和社会经济分区,在年均和季节尺度探究AOD与PM_(2.5)浓度的时空关联特征;实证分析全局和局地AOD-PM_(2.5)关联模式对区域PM_(2.5)浓度制图精度的影响。结果表明:全国范围内的AOD与PM_(2.5)具有时空关联性,但存在明显的地理学分异特点;气候分区中除中温带外,其他气候带均表现出强相关性;各海拔地形区AOD与PM_(2.5)总体上相关性较强,但海拔低于1 000m的地形区冬季相关性差;社会经济发展模式不同,而气候、海拔地形条件相似的各城市群AOD与PM_(2.5)的相关性同样呈现明显差异。研究结果对于揭示我国PM_(2.5)成因机制,指导开展基于AOD的全国或地区尺度PM_(2.5)浓度遥感制图具有重要意义。     

基于GOCI卫星的大气细颗粒物PM2.5的遥感反演及其时空分布规律研究

随着我国经济的快速发展和城市化进程的加快,大气细颗粒物PM_2.5已经成为影响我国大气环境污染的主要因素之一。利用静止卫星数据可以获取大范围的面状PM_2.5信息,为我国大气环境的监测、治理、预测等提供了不可替代的数据源。以江苏省为研究区,利用静止卫星GOCI数据,在反演逐时气溶胶光学厚度（AOD）的基础上,结合气象因子,利用多元统计分析进行了研究区PM_2.5的遥感反演研究。结果表明：基于AOD的多元统计模型,在估计的PM_2.5浓度和观测值之间表现出良好的一致性,拟合度R ²为0.665 2。在对AOD进行湿度订正后得到的dry AOD进行多元统计建模,预测的PM_2.5浓度与观测值之间的拟合度R ²达到了0.702 6,证明了经过湿度订正后的“干”AOD与PM_2.5之间建立的关系更加可靠。使用GOCI反演的AOD计算PM_2.5浓度,在空间分辨率和时间分辨率上充分体现了GOCI作为静止卫星监测PM_2.5的优势。在空间分分辨率上,基于GOCI卫星获取AOD的空间分辨率为500 m,优于MODIS 10 km的AOD产品;时间分辨率上,基于GOCI获取AOD实现每日自9:00~16:00逐小时监测,优于MODIS每日两次的AOD产品。     

包头市细颗粒物遥感监测混合线性模型

以PM_(2.5)为主的颗粒物污染是影响我国城市空气质量的首要因素之一。针对城市尺度,高时间分辨率的MODIS气溶胶产品空间分辨率不足,且气溶胶光学厚度(AOD)与PM_(2.5)回归精度受到季节因素的影响而精度不足的问题,以Landsat-8OLI为数据源,选取季节因子为影响AOD变化的随机效应,以风速、地表温度、AOD等为固定效应,提出AOD与实测PM_(2.5)关系的混合线性模型。结果表明,与线性模型相比,该模型有较好的回归精度;包头市PM_(2.5)浓度时空差别明显,PM_(2.5)浓度冬季浓度水平最高,春季最低;从空间分布来看昆都仑区污染较高,青山区北部次之,东河区和九原区最低。     

基于随机森林的高分辨率 PM2.5 遥感反演 ——以广东省为例

细颗粒物(PM2.5)监测是大气污染治理的重要手段,受限于地面观测点的数量,从遥感反演 PM2.5 是常规地面观测的有效补充,是当前的研究热点。通常遥感反演 PM2.5 的思路是先反演大气气溶胶光学厚度,然后基于统计关系由大气气溶胶光学厚度反演 PM2.5。该方法容易造成误差传递,从而 导致反演模型的不稳定。该文提出了一种基于随机森林算法(一种机器学习算法)的 PM2.5 遥感反演方法,直接建立中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer,MODIS)影像与地 面实测 PM2.5 的关系,可以避免传统反演 PM2.5 时先反演大气气溶胶光学厚度带来的误差,最终得到精度更高的 PM2.5 反演结果。该方法先用随机森林算法对 MODIS 影像和经过克里金插值后的地面监测站PM2.5 数据进行训练和测试;然后,根据测试的均方根误差从多个模型中选取最优(均方根误差最小)的模型;最后,将此模型用于整幅 MODIS 影像,得到整个区域的 PM2.5 反演结果。实验选取了广东省 四个季节多幅 MODIS 影像数据进行验证,并通过决定系数和均方根误差两个表现指标进行对比和分析,验证了所提算法的优越性。     

高时空分辨率PM_(2.5)浓度土地利用回归模拟与制图

针对传统地面监测手段稀疏获取PM2.5浓度的缺陷,该研究在利用中分辨率成像光谱仪(MODIS)遥感影像反演500m空间分辨率气溶胶光学厚度(AOD)的基础上,辅以人口密度、植被叶面积指数等地理特征要素,构建了研究区时均尺度PM2.5浓度土地利用回归(LUR)模型,并与经典克里格插值方法(OK)对比进行精度评价。结果表明:(1)研究区内部AOD存在明显空间分异,中心城区AOD值高于周边地区;(2)融合AOD要素的最优LUR模型相关系数可达0.51;(3)相比OK方法,LUR模型生成的PM2.5浓度图可客观反映研究区空气污染的空间变化。研究表明,融合遥感气溶胶数据的LUR模型可在短时间尺度准确模拟地面高空间分辨率PM2.5浓度。     

基于地理加权回归克里金的中国PM_(2.5)浓度空间制图方法北大核心CSCD

空气细颗粒物健康暴露风险等研究需要准确的PM_(2.5)浓度时空分布信息作为健康评估的重要输入。然而,由于监测台站稀疏分布,通常需要融合遥感等辅助信息,通过空间制图模型得到PM_(2.5)浓度的分布状况。如何在估计模型中将PM_(2.5)浓度的空间分布特征融入制图模型将是提高PM_(2.5)制图精度的关键。发展了一种融合地理加权回归和克里金插值方法的混合模型:地理加权回归克里金(Geographically Weighted Regression-Kriging,GWRK),地理加权回归模型考虑PM_(2.5)浓度分布的空间异质性,克里金模型对回归后的残差中存在的空间自相关性进行建模。基于该方法,利用中国空气质量监测站数据,采用遥感、模式模拟数据作为辅助信息,对2017年中国逐月的PM_(2.5)浓度分布进行估计空间制图。交叉验证结果表明,GWRK相较于传统制图方法(最小二乘回归、地理加权回归、回归克里金)具有更高的精度,决定系数R2为0.824,平均绝对误差为6.96μg/m3,均方根误差为10.94μg/m3。2017年逐月的PM_(2.5)浓度制图结果显示,在时间上,冬季是PM_(2.5)污染最严重的时段,夏季最轻,空间上,东部经济较为发达的城市如长三角地区是污染严重区,西南地区污染程度较轻。     

利用支持向量机遥感估算京津冀细颗粒物浓度

针对传统地面监测手段难以获取全面的细颗粒物(fine particulate matter,PM_(2.5))浓度信息,以2015年3月份京津冀地区PM_(2.5)污染状况为研究对象,将卫星遥感产品气溶胶光学厚度(aerosol optical depth,AOD)作为单变量输入构建支持向量机回归模型,得到的预测值与真实值的R~2为0.525,相对误差为44.6%。鉴于相对湿度(relative humidity,RH)和边界层高度(planetary boundary layer height,PBLH)是PM_(2.5)形成机制的重要影响因素,遂将RH、PBLH与AOD一起作为输入特征构建支持向量机回归模型预测PM2.5,得到的结果 R~2为0.729,相对误差为33.3%。研究结果表明:基于AOD、RH、PBLH为输入特征构建的支持向量机回归模型能够较好地从空间层次预测PM_(2.5)质量浓度。     

乌海矿区AOD时空变化特征及影响因素

针对大气低空间分辨卫星遥感数据不能准确反映长时间序列矿区小尺度区域的大气环境问题,利用MODIS MAIAC高分辨率数据,分析2003—2018年乌海矿区气溶胶光学厚度(aerosol optical depth,AOD)时空变化特征,采用Theil-Sen median趋势分析、Mann-Kendall检验方法、Pearson相关系数法,分析像元尺度AOD空间变化趋势和AOD与6个影响因子的相关性。结果表明,乌海矿区年际和季度AOD空间分布有明显的差异;乌海矿区年际AOD均值整体呈下降趋势,季度AOD变化秋季最高,春季和冬季较高,夏季最低;大尺度AOD与人为因子呈正相关关系,与自然因子无显著相关性;像元尺度AOD与人为因子和气温因子呈正相关性分布,在中部煤矿开采区及工业园区,与风速和降水因子呈强负相关性分布,在南部,与植被指数的相关性呈分散分布。     

基于MODIS影像的PM10反演——以北京市为例

提出了一种从MODIS影像上反演可吸入颗粒物浓度(PM10)的方法。该方法的基础为从MODIS影像上反演得到的3个可见光波段气溶胶光学厚度(AOD)计算的ngstrm-α。ngstrm-α与颗粒物粒径有关,根据ngstrm-α能够得到颗粒物有效半径,进而估算颗粒物浓度。反演的气溶胶光学厚度由AERONET北京站与香河站验证。PM10反演结果由北京市环保局发布的AQI反插得到的PM10(AQI)进行验证。结果表明:从MODIS影像上反演的3个可见光波段AOD与AERONET基站AOD具有良好的相关性,相关系数为0.923,均方根误差为0.149。该方法反演的PM10与PM10(AQI)相关系数为0.794,均方根误差为48.34(μg/m3)。     

Remote Sensing Retrieval of Atmospheric Fine Particle PM2.5 based on GOCI Satellite and Its Temporal and Spatial Distribution

With the rapid development of China's economy and the acceleration of urbanization, PM_2.5 has become one of the major factors affecting atmospheric environmental pollution in China. The use of geostationary satellite data can obtain a wide range of regional PM_2.5information, providing irreplaceable data sources for China's atmospheric environment monitoring, control, and forecasting. This paper uses the geostationary satellite GOCI data, based on Aerosol Optical Depth (AOD) retrieveal, combined with meteorological factors, and uses multivariate statistical analysis to study the remote sensing retrieval of PM_2.5 in the study area. The results show that the multivariate statistical model based on AOD shows a good agreement between the estimated PM_2.5 concentration and the observed values, and the fitting degreeR ² is 0.665 2. After multivariate statistical modeling of dry AOD obtained after moisture correction of AOD, the fitting degree R² between the predicted concentration of PM_2.5 and the observed value reached 0.702 6, which proved the relationship established betweenthe “dry” AOD after the humidity correction and PM_2.5 is more reliable.The use of GOCI-retrieved AOD to calculate PM_2.5 concentration fully reflects the advantages of GOCI as a geostationary satellite in spatial resolution and temporal resolution. In terms of spatial resolution, the spatial resolution of AOD based on GOCI satellite reachs to 500 meters, which is better than MODIS 10 km AOD product.In terms of temporal resolution,hourly AOD monitoring from 9:00 to 16:00 based on GOCI can be obtained,which is better than MODIS twice daily AOD products.     

PM10 remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region

The subject of this study is to investigate the capability of spaceborne remote sensing data to predict ground concentrations of PM₁₀ over the European Alpine region using satellite derived Aerosol Optical Depth (AOD) from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the polar-orbiting MODerate resolution Imaging Spectroradiometer (MODIS). The spatial and temporal resolutions of these aerosol products (10 km and 2 measurements per day for MODIS, ∼ 25 km and observation intervals of 15 min for SEVIRI) permit an evaluation of PM estimation from space at different spatial and temporal scales. Different empirical linear relationships between coincident AOD and PM₁₀ observations are evaluated at 13 ground-based PM measurement sites, with the assumption that aerosols are vertically homogeneously distributed below the planetary Boundary Layer Height (BLH). The BLH and Relative Humidity (RH) variability are assessed, as well as their impact on the parameterization. The BLH has a strong influence on the correlation of daily and hourly time series, whilst RH effects are less clear and smaller in magnitude. Despite its lower spatial resolution and AOD accuracy, SEVIRI shows higher correlations than MODIS (r_SEV∼ 0.7, r_MOD∼ 0.6) with regard to daily averaged PM₁₀. Advantages from MODIS arise only at hourly time scales in mountainous locations but lower correlations were found for both sensors at this time scale (r∼ 0.45). Moreover, the fraction of days in 2008 with at least one satellite observation was 27% for SEVIRI and 17% for MODIS. These results suggest that the frequency of observations plays an important role in PM monitoring, while higher spatial resolution does not generally improve the PM estimation. Ground-based Sun Photometer (SP) measurements are used to validate the satellite-based AOD in the study region and to discuss the impact of aerosols' micro-physical properties in the empirical models. A lower error limit of 30 to 60% in the PM₁₀ assessment from space is estimated in the study area as a result of AOD uncertainties, variability of aerosols properties and the heterogeneity of ground measurement sites. It is concluded that SEVIRI has a similar capacity to map PM as sensors on board polar-orbiting platforms, with the advantage of a higher number of observations. However, the accuracy represents a serious limitation to the applicability of satellites for ground PM mapping, especially in mountainous areas.     

基于HJ-1卫星数据反演长江三角洲地区气溶胶光学厚度

探索利用我国HJ-1卫星CCD数据,运用深蓝算法开展长江三角洲地区气溶胶光学厚度反演的可行性,并将结果与其他气溶胶光学厚度产品进行比较。针对HJ-1A和HJ-1B数据,反演结果分别与MODIS气溶胶光学厚度产品以及AERONET地基观测数据进行对比验证。结果表明:深蓝算法得到A星、B星的反演结果与MODIS气溶胶产品呈显著相关,但在数值上普遍高于MODIS产品;反演结果与AERONET站点数据之间的误差介于0.008~0.364之间,研究时段内站点数据缺乏,未对误差进行严格的统计分析。基于深蓝算法的HJ-1卫星数据反演结果虽然在数值上与MODIS气溶胶光学厚度产品存在系统性偏差,但在空间上能够较好地反映长江三角洲地区大气气溶胶分布状况,且具有空间分辨率高的优势。     

深蓝算法应用于GF-1 16m相机反演陆地气溶胶

高分一号卫星是我国发展的新一代高分辨率对地观测卫星,如何应用该数据进行环境空气监测是目前迫切需要解决的问题。在深蓝算法基础上,根据GF-1星16 m相机的波段特征,借助MODIS的地表反射率产品去除地表贡献,从蓝波段数据反演了陆地气溶胶,实现了深蓝算法在GF-1星16m相机的应用。在此基础上,收集了2014年8~11月过境北京地区的GF-1星16m相机数据进行了反演实验,结果表明:该算法获取的气溶胶反演结果较好地反映了气溶胶的空间分布。同时,利用同期的AERONET/PHOTONS北京站的地面监测数据进行了算法验证,结果表明,本算法与地面数据有较好的相关性,相关系数大约为0.9,但该算法结果明显高于地面观测结果,可能是MODIS与GF-1星16m相机的波段响应不同导致的结果。     

Remote sensing of ambient particles in Delhi and its environs: estimation and validation

Recent advances in atmospheric remote sensing offer a unique opportunity to compute indirect estimates of air quality, particularly for developing countries that lack adequate spatial-temporal coverage of air pollution monitoring. The present research establishes an empirical relationship between satellite-based aerosol optical depth (AOD) and ambient particulate matter (PM) in Delhi and its environs. The PM data come from two different sources. Firstly, a field campaign was conducted to monitor airborne particles ≤ 2.5 μm and ≤10 μm in aerodynamic diameter (PM(2.5) and PM(10) respectively) at 113 spatially dispersed sites from July to December 2003 using photometric samplers. Secondly, data on eight hourly PM(10) and total suspended particulate (TSP) matter, collected using gravimetric samplers, from 2000 to 2005 were acquired from the Central Pollution Control Board (CPCB). The aerosol optical depths were estimated from MODIS data, acquired from NASA's Goddard Space Flight Center Earth Sciences Distributed Active Archive Center from 2000 to 2005. Both the PM and AOD data were collocated by time and space: PM mass ± 150 min of AOD time, and ± 2.5 and 5 km radius (separately) of the centroid of the AOD pixel for the 5 and 10 km AOD, respectively. The analysis here shows that PM correlates positively with the 5 km AOD; a 1% change in the AOD explains 0.52% ± 0.20% and 0.39% ± 0.15% changes in PM(2.5) within 45 and 150 min intervals (of AOD data) respectively. At a coarser spatial resolution, however, the relationship between AOD and PM is relatively weak. But, the relationship turns significantly stronger when monthly estimates are analysed over a span of six years (2000 to 2005), especially for the winter months, which have relatively stable meteorological conditions.     

基于MODIS大气产品的天宫一号高光谱成像仪数据大气校正研究

天宫一号(TG-1)搭载的高光谱成像仪获取了大量的高光谱数据,可用于国土资源、农林业和油气矿产等领域的研究。但由于遥感成像时会受到大气的干扰,因此需要首先进行大气校正,消除大气的影响,才能进行遥感定量分析与应用。利用准同步的中分辨率成像光谱仪MODIS(Moderate Resolution Imaging Spectroradiometer)大气参数产品,结合6S辐射传输模型对天宫一号高光谱成像仪数据进行大气校正,并利用地面测量光谱和同步MODIS反射率数据对结果进行了验证。结果表明:经过大气校正后,天宫一号高光谱成像仪数据和地面测量光谱一致性较好,所有样点的相关系数都大于0.97,最大均方根误差为0.088。和MODIS反射相比,各波段回归直线的斜率接近1,且R²都大于0.8。     

基于K最近邻模型的青藏高原CMORPH日降水数据的订正研究

青藏高原的降水数据主要由遥感产品和多源观测数据融合产生,由于青藏高原的观测站点分布稀疏不均,遥感数据误差较大,因此常用的CMORPH(Climate Prediction Center Morphing Technique)等降水数据集精度有限。通过K最近邻(K-Nearest Neighbor,简称KNN)模型,可以建立环境(海拔、坡度、坡向、植被)、气象因子(气温、湿度、风速)和日降水量的关系,从而订正青藏高原的CMORPH日降水数据集,提高数据精度。对CMORPH日降水数据的误差分析表明,采用KNN模型订正后的CMORPH降水数据优于原始数据和采用PDF(Probability Density Function Matching Method)法订正的CMORPH数据,且空间分布较好地符合青藏高原的降水分布特征。