气象要素对气溶胶光学厚度估算PM_(2.5)的影响
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摘要
地面监测得到的近地面细颗粒物PM_(2.5)浓度较为精确,但数据覆盖范围相对较小,卫星遥感反演的气溶胶光学厚度(AOD)数据可以反映污染物浓度分布,具有范围大且速度快的特点,因此,大多数学者通过建立PM_(2.5)-AOD模型来实现卫星遥感监测PM_(2.5)浓度,并通过引入气象要素来优化模型.然而,气象要素的选择与引入往往对模型的精度有较大的影响,如何有效地选择对PM_(2.5)浓度影响较大的气象要素一直是PM_(2.5)-AOD模型中的关键问题.因此,本文基于华东地区2014—2015年的MODIS AOD和地面监测站的PM_(2.5)浓度数据,结合再分析气象资料,利用多元逐步线性回归方法建立PM_(2.5)-AOD模型,从由特定时刻、高度上的气象要素与随时间、高度变化的气象要素组成的气象要素集中,筛选出对因变量PM_(2.5)浓度有显著影响的关键气象要素.结果表明:在地域与季节双重尺度下的PM_(2.5)-AOD模型精度更高;相较于特定时刻高度的气象要素,随时间和高度变化的气象要素对PM_(2.5)-AOD模型的影响更为显著;在地域与季节双重尺度下,1000～850 hPa经向风速差、世界时0:00—6:00近地面温度差、850～600 hPa温度差、6:00边界层高度、12:00—18:00近地面压强差、1000～850 hPa温度差对模型影响较大,但应依据不同季节和不同地区的具体影响程度作为选择标准.
The fine particulates( PM_(2.5)) concentrations obtained from the surface monitoring are relatively accurate but with smaller data coverage,while aerosol optical depth( AOD) data from the satellite remote sensing retrievals can reflect the concentration distribution of atmospheric pollutants,with a wide range and fast data acquisition. To effectively monitor the changes of near-surface PM_(2.5) concentrations using the satellite remote sensing,therefore,various PM_(2.5)-AOD models have been established and optimized by introducing meteorological elements. However,the selection and introduction of the meteorological elements often has a great impact on the model accuracy,and how to determine the critical meteorological element( s) has always been a key issue in developing the PM_(2.5)-AOD model. Therefore,based on the AOD data derived from the Moderate Resolution Imaging Spectroradiometer(MODIS) and the ground-level PM_(2.5) station observations in East China from 2014 to 2015,this study established the relationship between PM_(2.5) and AOD using the multivariable stepwise linear regression model in combination with meteorological reanalysis data. In constructing this PM_(2.5)-AOD model,a meteorological element dataset was built,and meteorological elements at specific time and heights were then selected to compare with other elements highly varying with time and heights in order to determine the key meteorological factors that have significant impacts on the ground-level PM_(2.5) concentrations.The main results are as follows:(1) The accuracy of the PM_(2.5)-AOD model is higher when both regional and seasonal dependences are considered.(2)Compared with the meteorological elements at a particular time or level,the meteorological elements that change with time and levels have greater impactson the PM_(2.5)-AOD model;(3)Several key meteorological factors are found to have prominent influence on the model,including the difference in meridional wind speed between 1000 hPa and 850 h Pa,the near-surface temperature difference between 00 and 06 UTC,the temperature difference at 850～600 h Pa and at 1000～850 hPa,the planetary boundary layer height at 06 UTC,and the surface pressure difference between 12 and 18 UTC. These factors should be selected in the model according to their specific impacts in different seasons and regions.
The fine particulates( PM_(2.5)) concentrations obtained from the surface monitoring are relatively accurate but with smaller data coverage,while aerosol optical depth( AOD) data from the satellite remote sensing retrievals can reflect the concentration distribution of atmospheric pollutants,with a wide range and fast data acquisition. To effectively monitor the changes of near-surface PM_(2.5) concentrations using the satellite remote sensing,therefore,various PM_(2.5)-AOD models have been established and optimized by introducing meteorological elements. However,the selection and introduction of the meteorological elements often has a great impact on the model accuracy,and how to determine the critical meteorological element( s) has always been a key issue in developing the PM_(2.5)-AOD model. Therefore,based on the AOD data derived from the Moderate Resolution Imaging Spectroradiometer(MODIS) and the ground-level PM_(2.5) station observations in East China from 2014 to 2015,this study established the relationship between PM_(2.5) and AOD using the multivariable stepwise linear regression model in combination with meteorological reanalysis data. In constructing this PM_(2.5)-AOD model,a meteorological element dataset was built,and meteorological elements at specific time and heights were then selected to compare with other elements highly varying with time and heights in order to determine the key meteorological factors that have significant impacts on the ground-level PM_(2.5) concentrations.The main results are as follows:(1) The accuracy of the PM_(2.5)-AOD model is higher when both regional and seasonal dependences are considered.(2)Compared with the meteorological elements at a particular time or level,the meteorological elements that change with time and levels have greater impactson the PM_(2.5)-AOD model;(3)Several key meteorological factors are found to have prominent influence on the model,including the difference in meridional wind speed between 1000 hPa and 850 h Pa,the near-surface temperature difference between 00 and 06 UTC,the temperature difference at 850～600 h Pa and at 1000～850 hPa,the planetary boundary layer height at 06 UTC,and the surface pressure difference between 12 and 18 UTC. These factors should be selected in the model according to their specific impacts in different seasons and regions.
引文
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黄思,唐晓,徐文帅,等.2015.利用多模式集合和多元线性回归改进北京PM10预报[J].环境科学学报,35(1):56-64
靳全锋,马祥庆,王文辉,等.2017.华东地区2000-2014年间秸秆燃烧排放PM2.5时空动态变化[J].环境科学学报,37(2):460-468
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Gupta P,Christopher S A,Wang J,et al.2006.Satellite remote sensing of particulate matter and air quality assessment over global cities[J].Atmospheric Environment,40(30):5880-5892
Li P,Xin J,Wang Y,et al.2013.The acute effects of fine particles on respiratory mortality and morbidity in Beijing,2004-2009[J].Environmental Science and Pollution Research,20(9):6433-6444
Liu Y,Paciorek C J,Koutrakis P.2009.Estimating regional spatial and temporal variability of PM2.5concentrations using satellite data,meteorology,and land use information[J].Environmental health perspectives,117(6):886-892
Kumar N,Chu A,Foster A.2007.An empirical relationship between PM2.5and aerosol optical depth in Delhi Metropolitan[J].Atmospheric Environment,41(21):4492-4503
马艳,黄容,时晓曚,等.2018.青岛冬季持续PM2.5重污染天气的大气边界层特征[J].环境科学研究,31(1):42-52
穆泉,张世秋.2013.2013年1月中国大面积雾霾事件直接社会经济损失评估[J].中国环境科学,33(11):2087-2094
Pope III C A,Burnett R T,Thun M J,et al.2002.Lung cancer,cardiopulmonary mortality,and long-term exposure to fine particulate air pollution[J].Jama,287(9):1132-1141
Tsai T C,Jeng Y J,Chu D A,et al.2011.Analysis of the relationship between MODIS aerosol optical depth and particulate matter from2006 to 2008[J].Atmospheric Environment,45(27):4777-4788
王静.2016.基于卫星遥感的长三角主要城市PM2.5估算[D].上海:华东师范大学
王耀庭,李威,张小玲,等.2012.北京城区夏季静稳天气下大气边界层与大气污染的关系[J].环境科学研究,25(10):1092-1098
Wang Z F,Chen L F,Tao J H,et al.2010.Satellite-based estimation of regional particulate matter(PM)in Beijing using vertical-and-RHcorrecting method[J].Remote Sensing of Environment,114(1):50-63
吴健生,王茜,李嘉诚,等.2017.PM2.5浓度空间分异模拟模型对比:以京津冀地区为例[J].环境科学,38(6):2191-2201
Zheng S,Pozzer A,Cao C X,et al.2014.Long-term(2001-2012)fine particulate matter(PM2.5)and the impact on human health in Beijing,China[J].Atmospheric Chemistry&Physics Discussions,14(21):5715-5725
孙友敏,李少洛,陈春竹,等.2017.济南市机动车排气污染特征及对市区PM2.5的影响研究[J].环境科学学报,37(4):1384-1391
徐晓斌.2016.我国霾和光化学污染观测研究进展[J].应用气象学报,27(5):604-619
游士兵,严研.2017.逐步回归分析法及其应用[J].统计与决策,(14):31-35
郁珍艳,高大伟,李正泉,等.2017.华东区域PM2.5变化背景下浙江省人口经济暴露水平评估[J].环境科学,38(12):4924-4931
张悦,樊曙先,李皓,等.2016.气溶胶辐射效应在华东地区一次雾霾过程中的作用[J].气象学报,74(3):465-478
赵普生,冯银厂,金晶,等.2009.建筑施工扬尘特征与监控指标[J].环境科学学报,29(8):1618-1623
周峤.2015.雾霾天气的成因[J].中国人口资源与环境,(S1):211-212
周志华.2016.机器学习[M].北京:清华大学出版社.25-26
陈朝晖,程水源,苏福庆,等.2007.北京地区一次重污染过程的大尺度天气型分析[J].环境科学研究,20(2):99-105
邓君俊.2011.长三角地区霾天气形成机理和预报方法研究[D]南京:南京大学
杜荣光,齐冰,郭惠惠,等.2011.杭州市大气逆温特征及对空气污染物浓度的影响[J].气象与环境学报,27(4):49-53
顾为东.2014.中国雾霾特殊形成机理研究[J].宏观经济研究,6(3):3-7;123
Guo J P,Zhang X Y,Che H Z,et al.2009.Correlation between PMconcentrations and aerosol optical depth in eastern China[J].Atmospheric Environment,43(37):5876-5886
黄思,唐晓,徐文帅,等.2015.利用多模式集合和多元线性回归改进北京PM10预报[J].环境科学学报,35(1):56-64
靳全锋,马祥庆,王文辉,等.2017.华东地区2000-2014年间秸秆燃烧排放PM2.5时空动态变化[J].环境科学学报,37(2):460-468
Li C,Mao J,Lau A K H,et al.2005.Application of MODIS satellite products to the air pollution research in Beijing[J].Science in China Series D(Earth Sciences),48:209-219
Gupta P,Christopher S A,Wang J,et al.2006.Satellite remote sensing of particulate matter and air quality assessment over global cities[J].Atmospheric Environment,40(30):5880-5892
Li P,Xin J,Wang Y,et al.2013.The acute effects of fine particles on respiratory mortality and morbidity in Beijing,2004-2009[J].Environmental Science and Pollution Research,20(9):6433-6444
Liu Y,Paciorek C J,Koutrakis P.2009.Estimating regional spatial and temporal variability of PM2.5concentrations using satellite data,meteorology,and land use information[J].Environmental health perspectives,117(6):886-892
Kumar N,Chu A,Foster A.2007.An empirical relationship between PM2.5and aerosol optical depth in Delhi Metropolitan[J].Atmospheric Environment,41(21):4492-4503
马艳,黄容,时晓曚,等.2018.青岛冬季持续PM2.5重污染天气的大气边界层特征[J].环境科学研究,31(1):42-52
穆泉,张世秋.2013.2013年1月中国大面积雾霾事件直接社会经济损失评估[J].中国环境科学,33(11):2087-2094
Pope III C A,Burnett R T,Thun M J,et al.2002.Lung cancer,cardiopulmonary mortality,and long-term exposure to fine particulate air pollution[J].Jama,287(9):1132-1141
Tsai T C,Jeng Y J,Chu D A,et al.2011.Analysis of the relationship between MODIS aerosol optical depth and particulate matter from2006 to 2008[J].Atmospheric Environment,45(27):4777-4788
王静.2016.基于卫星遥感的长三角主要城市PM2.5估算[D].上海:华东师范大学
王耀庭,李威,张小玲,等.2012.北京城区夏季静稳天气下大气边界层与大气污染的关系[J].环境科学研究,25(10):1092-1098
Wang Z F,Chen L F,Tao J H,et al.2010.Satellite-based estimation of regional particulate matter(PM)in Beijing using vertical-and-RHcorrecting method[J].Remote Sensing of Environment,114(1):50-63
吴健生,王茜,李嘉诚,等.2017.PM2.5浓度空间分异模拟模型对比:以京津冀地区为例[J].环境科学,38(6):2191-2201
Zheng S,Pozzer A,Cao C X,et al.2014.Long-term(2001-2012)fine particulate matter(PM2.5)and the impact on human health in Beijing,China[J].Atmospheric Chemistry&Physics Discussions,14(21):5715-5725
孙友敏,李少洛,陈春竹,等.2017.济南市机动车排气污染特征及对市区PM2.5的影响研究[J].环境科学学报,37(4):1384-1391
徐晓斌.2016.我国霾和光化学污染观测研究进展[J].应用气象学报,27(5):604-619
游士兵,严研.2017.逐步回归分析法及其应用[J].统计与决策,(14):31-35
郁珍艳,高大伟,李正泉,等.2017.华东区域PM2.5变化背景下浙江省人口经济暴露水平评估[J].环境科学,38(12):4924-4931
张悦,樊曙先,李皓,等.2016.气溶胶辐射效应在华东地区一次雾霾过程中的作用[J].气象学报,74(3):465-478
赵普生,冯银厂,金晶,等.2009.建筑施工扬尘特征与监控指标[J].环境科学学报,29(8):1618-1623
周峤.2015.雾霾天气的成因[J].中国人口资源与环境,(S1):211-212
周志华.2016.机器学习[M].北京:清华大学出版社.25-26