关中地区PM_(2.5)地面浓度分布及来源
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摘要
大气质量与人类的生存环境息息相关,大气中的污染物质尤其是细颗粒物质直接与间接危害人类健康,因此许多城市把对细颗粒物浓度的监测放在了重要位置。利用2017年春季关中地区的MODIS高分辨率气溶胶产品(气溶胶光学厚度,AOD)与地面PM2.5浓度监测数据进行相关性分析构建模型,结合气象观测资料建立含气象要素的多元回归模型,并将二者进行比较。结果表明,含气象要素的多元回归模型对关中地区PM2.5地面浓度的空间分布拟合度更好(r2=0.768,P<0.01)。2017年春季关中地区的PM2.5空间分布总体呈现东高西低的趋势,AOD最高值出现在西安、渭南和咸阳三市,分别为1.019、0.911和0.124。通过对一次典型污染天气进行主成分分析和基于HYSPLIT向后轨迹模型模拟其潜在传输路径发现,关中地区2017年春季大气质量受多污染物、多污染源以及多途径复合污染影响,且污染气团主要来自蒙古国西南部。研究结果对关中地区的大气污染防治与生态环境保护具有一定借鉴的意义。
Atmospheric quality is closely related to human living environment. Atmospheric pollutants,especially fine particulate matter,directly and indirectly endanger human health. Therefore,many cities put the monitoring of fine particulate concentration at an important position. We analyzed the correlation between MODIS high-resolution aerosol products( aerosol optical depth,AOD) and PM2.5 concentration monitoring data in Guanzhong area in spring 2017 and constructed a direct fitting model. A multiple regression model containing meteorological elements was established by combining meteorological observation data. Those two models were compared. The results showed that the multivariate regression model with meteorological factors had better fitting degree for the spatial distribution of PM2.5 in Guanzhong area( r2= 0. 768,P < 0. 01). In the spring of 2017,the spatial distribution of PM2.5( AOD) in Guanzhong area showed a trend of being high in the east region and low in the west region. The highest value appeared in Xi'an,Weinan and Xianyang,being 1.019,0.911,and 0.124,respectively. Results from the principal component analysis of a typical polluted weather and simulation of its potential transmission path based on HYSPLIT backward trajectory model showed that atmospheric quality in Guanzhong area in spring 2017 was affected by multi-pollutants,multi-pollutant sources and multi-channel combined pollution,and the air mass mainly came from southwestern Mongolia. Our results provide reference for the prevention and control of air pollution and ecological environment protection in Guanzhong area.
Atmospheric quality is closely related to human living environment. Atmospheric pollutants,especially fine particulate matter,directly and indirectly endanger human health. Therefore,many cities put the monitoring of fine particulate concentration at an important position. We analyzed the correlation between MODIS high-resolution aerosol products( aerosol optical depth,AOD) and PM2.5 concentration monitoring data in Guanzhong area in spring 2017 and constructed a direct fitting model. A multiple regression model containing meteorological elements was established by combining meteorological observation data. Those two models were compared. The results showed that the multivariate regression model with meteorological factors had better fitting degree for the spatial distribution of PM2.5 in Guanzhong area( r2= 0. 768,P < 0. 01). In the spring of 2017,the spatial distribution of PM2.5( AOD) in Guanzhong area showed a trend of being high in the east region and low in the west region. The highest value appeared in Xi'an,Weinan and Xianyang,being 1.019,0.911,and 0.124,respectively. Results from the principal component analysis of a typical polluted weather and simulation of its potential transmission path based on HYSPLIT backward trajectory model showed that atmospheric quality in Guanzhong area in spring 2017 was affected by multi-pollutants,multi-pollutant sources and multi-channel combined pollution,and the air mass mainly came from southwestern Mongolia. Our results provide reference for the prevention and control of air pollution and ecological environment protection in Guanzhong area.
引文
范娇,郭宝峰,何宏昌.2015.基于MODIS数据的杭州地区气溶胶光学厚度反演.光学学报,35(1):9-17.
郭建平,吴业荣,张小曳,等.2013.BP网络框架下MODIS气溶胶光学厚度产品估算中国东部PM2.5.环境科学,34(3):817-825.
管理.2016.基于MODIS数据西安地区灰霾期间气溶胶的时空变化分析(硕士学位论文).西安:西北大学.
金丽娜,李雄飞,李军.2018.关中地区PM10质量浓度及MODIS气溶胶光学厚度时空特征分析.干旱气象,36(1):110-116.
李慧娟,胡列群,李帅.2018.北疆地区MODIS 3 km气溶胶光学厚度与PM10质量浓度的相关性分析.环境科学学报,38(3):1109-1116.
李霞.2006.乌鲁木齐气象要素对大气气溶胶光学特性的影响.干旱区研究,23(3):484-488.
罗云峰,周秀骥,李维亮.1998.大气气溶胶辐射强迫及气候效应的研究现状.地球科学进展,13(6):572-581.
彭艳.2015.关中地区大气气溶胶时空分布及其对气象要素的影响.2015年中国环境科学学会学术年会论文集(第二卷):3035-3046.
彭玥,赵天良,郑小波,等.2017.大气环境变化中大气颗粒物PM10的重要作用---关中平原MODIS气溶胶产品的气候分析.中国环境科学,37(7):2443-2449.
丘明艳,盛立芳,房岩松,等.2004.气象条件对青岛地区气溶胶光学特性的影响.中国海洋大学学报,34(6):925-930.
任丽新,游荣高,吕位秀,等.1999.城市大气气溶胶的物理化学特性及其对人体健康的影响.气候与环境研究,4(1):67-73.
汤天然,陈建楠,李广前,等.2013.降雨对PM2.5浓度的影响及人工降雨降低PM2.5浓度的探讨.贵州气象,37(4):35-37.
王静,杨复沫,王鼎益,等.2010.北京市MODIS气溶胶光学厚度和PM2.5质量浓度的特征及其相关性.中国科学院大学学报,27(1):10-16.
王钊,彭艳,车慧正,等.2013.近10年关中盆地MO-DIS气溶胶的时空变化特征.高原气象,32(1):234-242.
薛文博,武卫玲,付飞,等.2015.中国2013年1月PM2.5重污染过程卫星反演研究.环境科学,36(3):794-800.
杨立娟,徐涵秋,金致凡.2018.MODIS卫星遥感估计福州地区近地面PM2.5浓度.遥感学报,22(1):64-75.
于文金,于步云,谢涛,等.2016.基于MODIS气溶胶光学厚度与气象要素的苏锡常地区PM2.5地面浓度分布研究.环境科学学报,36(10):3535-3542.
周丽,徐祥德,丁国安,等.2003.北京地区气溶胶PM2.5粒子浓度的相关因子及其估算模型.气象学报,61(6):761-767.
Lau KH,Li CC,Mao JT,et al.2003.A new way of using MO-DIS data to study air pollution over the Pear River Delta.Proceedings of the SPIE,4891:105-114.
Liu Y,Sarnat JA,Kilaru V,et al.2005.Estimating groundlevel PM2.5in the eastern United States using satellite remote sensing.Environmental Science&Technology,39:3269-3278.
Liu Y,Franklin M,Kahn R,et al.2007.Using aerosol optical thickness to predict ground-level PM2.5concentrations in the St.Louis area:A comparison between MISR and MODIS.Remote Sensing of Environment,107:33-44.
Liu Y,Paciorek CJ,Koutrakis P.2009.Estimating regional spatial and temporal variability of PM2.5concentrations using satellite data,meteorology,and land use information.Environmental Health Perspectives,117:886-892.
Oscar P,Rosa M,Susana G,et al.2009.Characterization and sources assignation of PM2.5organic aerosol in a rural area of Spain.Atmospheric Environment,43:2796-2803.
Smirnov A,Holben BN,Eck TF,et al.2003.Effect of wind speed on columnar aerosol optical properties at Midway Island.Journal of Geophysical Research:Atmospheres,108:4802.
Sorek-hamer M,Strawa AW,Chatfield RB,et al.2013.Improved retrieval of PM2.5from satellite data products using non-linear methods.Environmental Pollution,182:417-423.
Suess DT,Prather KA.1999.Mass spectrometry of aerosol.Chemical Reviews,99:3007-3035.
Xie Y,Wang Y,Zhang K,et al.2015.Daily estimation of ground-level PM2.5concentrations over Beijing using 3 km resolution MODIS AOD.Environmental Science&Technology,49:12280-12288.
You W,Zang ZL,Pan XB,et al.2015.Estimating PM2.5in Xi’an,China using aerosol optical depth:A comparison between MODIS and MISR retrieval models.Science of the Total Environment,505:1156-1165.
郭建平,吴业荣,张小曳,等.2013.BP网络框架下MODIS气溶胶光学厚度产品估算中国东部PM2.5.环境科学,34(3):817-825.
管理.2016.基于MODIS数据西安地区灰霾期间气溶胶的时空变化分析(硕士学位论文).西安:西北大学.
金丽娜,李雄飞,李军.2018.关中地区PM10质量浓度及MODIS气溶胶光学厚度时空特征分析.干旱气象,36(1):110-116.
李慧娟,胡列群,李帅.2018.北疆地区MODIS 3 km气溶胶光学厚度与PM10质量浓度的相关性分析.环境科学学报,38(3):1109-1116.
李霞.2006.乌鲁木齐气象要素对大气气溶胶光学特性的影响.干旱区研究,23(3):484-488.
罗云峰,周秀骥,李维亮.1998.大气气溶胶辐射强迫及气候效应的研究现状.地球科学进展,13(6):572-581.
彭艳.2015.关中地区大气气溶胶时空分布及其对气象要素的影响.2015年中国环境科学学会学术年会论文集(第二卷):3035-3046.
彭玥,赵天良,郑小波,等.2017.大气环境变化中大气颗粒物PM10的重要作用---关中平原MODIS气溶胶产品的气候分析.中国环境科学,37(7):2443-2449.
丘明艳,盛立芳,房岩松,等.2004.气象条件对青岛地区气溶胶光学特性的影响.中国海洋大学学报,34(6):925-930.
任丽新,游荣高,吕位秀,等.1999.城市大气气溶胶的物理化学特性及其对人体健康的影响.气候与环境研究,4(1):67-73.
汤天然,陈建楠,李广前,等.2013.降雨对PM2.5浓度的影响及人工降雨降低PM2.5浓度的探讨.贵州气象,37(4):35-37.
王静,杨复沫,王鼎益,等.2010.北京市MODIS气溶胶光学厚度和PM2.5质量浓度的特征及其相关性.中国科学院大学学报,27(1):10-16.
王钊,彭艳,车慧正,等.2013.近10年关中盆地MO-DIS气溶胶的时空变化特征.高原气象,32(1):234-242.
薛文博,武卫玲,付飞,等.2015.中国2013年1月PM2.5重污染过程卫星反演研究.环境科学,36(3):794-800.
杨立娟,徐涵秋,金致凡.2018.MODIS卫星遥感估计福州地区近地面PM2.5浓度.遥感学报,22(1):64-75.
于文金,于步云,谢涛,等.2016.基于MODIS气溶胶光学厚度与气象要素的苏锡常地区PM2.5地面浓度分布研究.环境科学学报,36(10):3535-3542.
周丽,徐祥德,丁国安,等.2003.北京地区气溶胶PM2.5粒子浓度的相关因子及其估算模型.气象学报,61(6):761-767.
Lau KH,Li CC,Mao JT,et al.2003.A new way of using MO-DIS data to study air pollution over the Pear River Delta.Proceedings of the SPIE,4891:105-114.
Liu Y,Sarnat JA,Kilaru V,et al.2005.Estimating groundlevel PM2.5in the eastern United States using satellite remote sensing.Environmental Science&Technology,39:3269-3278.
Liu Y,Franklin M,Kahn R,et al.2007.Using aerosol optical thickness to predict ground-level PM2.5concentrations in the St.Louis area:A comparison between MISR and MODIS.Remote Sensing of Environment,107:33-44.
Liu Y,Paciorek CJ,Koutrakis P.2009.Estimating regional spatial and temporal variability of PM2.5concentrations using satellite data,meteorology,and land use information.Environmental Health Perspectives,117:886-892.
Oscar P,Rosa M,Susana G,et al.2009.Characterization and sources assignation of PM2.5organic aerosol in a rural area of Spain.Atmospheric Environment,43:2796-2803.
Smirnov A,Holben BN,Eck TF,et al.2003.Effect of wind speed on columnar aerosol optical properties at Midway Island.Journal of Geophysical Research:Atmospheres,108:4802.
Sorek-hamer M,Strawa AW,Chatfield RB,et al.2013.Improved retrieval of PM2.5from satellite data products using non-linear methods.Environmental Pollution,182:417-423.
Suess DT,Prather KA.1999.Mass spectrometry of aerosol.Chemical Reviews,99:3007-3035.
Xie Y,Wang Y,Zhang K,et al.2015.Daily estimation of ground-level PM2.5concentrations over Beijing using 3 km resolution MODIS AOD.Environmental Science&Technology,49:12280-12288.
You W,Zang ZL,Pan XB,et al.2015.Estimating PM2.5in Xi’an,China using aerosol optical depth:A comparison between MODIS and MISR retrieval models.Science of the Total Environment,505:1156-1165.
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