徐州市气溶胶光学厚度与PM_(2.5)相关性及年周期特征
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摘要
基于徐州市2014～2017年气溶胶光学厚度(AOD)、地面监测站PM_(2.5)浓度及气象数据,构建经标高订正的AOD(AOD/H)与经湿度订正的PM_(2.5)(PM_(2.5)×f_((RH)))之间的5种不同类型的拟合模型,分析两者在不同季节的相关性;同时利用经验模态分解对AOD/H与PM_(2.5)×f_((RH))进行周期变化分析。结果表明:AOD与PM_(2.5)浓度直接相关程度较低,经过订正后两者的相关程度显著提高;选取乘幂模型为最优拟合模型,利用乘幂模型估计得到的PM_(2.5)浓度与地面监测的经湿度订正的PM_(2.5)浓度呈显著正相关,相关系数在四季分别达到0.752、0.650、0.808和0.942;利用经验模态分解分析得到AOD/H与PM_(2.5)×f_((RH))具有显著的年周期变化特征,均在冬季出现高值,后逐渐降低,在6月前后出现极小值,到秋季又逐渐增大;AOD/H与PM_(2.5)×f_((RH))年变化特征表现出很高的一致性(r=0.888),表明在徐州地区AOD/H对PM_(2.5)×f_((RH))在年周期尺度变化特征研究中能起到良好的指示作用。
The relationship model of PM_(2.5) concentration and AOD(aerosol optical depth) was constructed, the annual changes of vertical corrected AOD(AOD/H) and moisture corrected PM_(2.5) concentration(PM_(2.5)×f_((RH))) and their correlation, which could be used as a reference for air quality evaluation, in the Xuzhou city were analyzed. The correlations between AODs and PM_(2.5) concentrations in different seasons were investigated on the basis of MODIS AOD, PM_(2.5) concentration dataset and related meteorological data from 2014 to 2017, and five different fitting methods were applied to explore correlations of AOD/H and PM_(2.5)× f_((RH)) in different seasons. The empirical mode decomposition was used to analyze periodic variations of AOD/H and PM_(2.5)×f_((RH)). The results showed that no correlation was found between original AODs and PM_(2.5) concentration data, but AOD/H and PM_(2.5)×f_((RH)) correlated notably. The power model was the optimal fitting model for predicting PM_(2.5) concentrations based on monitored data of PM_(2.5)×f_((RH)) in Xuzhou, the correlation coefficients were 0.752, 0.650, 0.808 and 0.942 for the four seasons, respectively. The annual trends of AOD/H and PM_(2.5)×f_((RH)) were highly covaried with r=0.888, and their highest values were showed in winter, and gradually decreased to the minimum values around June in summer, and then gradually increased throughout autumn. In summary, this study demonstrated that AOD is an effective proxy to estimate the concentration of PM_(2.5) and that AOD/H is a good index for describing the annual changes of PM_(2.5)×f_((RH) )in the Xuzhou city.
The relationship model of PM_(2.5) concentration and AOD(aerosol optical depth) was constructed, the annual changes of vertical corrected AOD(AOD/H) and moisture corrected PM_(2.5) concentration(PM_(2.5)×f_((RH))) and their correlation, which could be used as a reference for air quality evaluation, in the Xuzhou city were analyzed. The correlations between AODs and PM_(2.5) concentrations in different seasons were investigated on the basis of MODIS AOD, PM_(2.5) concentration dataset and related meteorological data from 2014 to 2017, and five different fitting methods were applied to explore correlations of AOD/H and PM_(2.5)× f_((RH)) in different seasons. The empirical mode decomposition was used to analyze periodic variations of AOD/H and PM_(2.5)×f_((RH)). The results showed that no correlation was found between original AODs and PM_(2.5) concentration data, but AOD/H and PM_(2.5)×f_((RH)) correlated notably. The power model was the optimal fitting model for predicting PM_(2.5) concentrations based on monitored data of PM_(2.5)×f_((RH)) in Xuzhou, the correlation coefficients were 0.752, 0.650, 0.808 and 0.942 for the four seasons, respectively. The annual trends of AOD/H and PM_(2.5)×f_((RH)) were highly covaried with r=0.888, and their highest values were showed in winter, and gradually decreased to the minimum values around June in summer, and then gradually increased throughout autumn. In summary, this study demonstrated that AOD is an effective proxy to estimate the concentration of PM_(2.5) and that AOD/H is a good index for describing the annual changes of PM_(2.5)×f_((RH) )in the Xuzhou city.
引文
[1] Chafe Z A, Brauer M, Klimont Z, et al. Household cooking with solid fuels contributes to ambient PM2.5 air pollution and the burden of disease [J]. Environmental Health Perspectives, 2014, 122(12):1314-1320.
[2] Lelieveld J, Evans J S, Fnais M, et al. The contribution of outdoor air pollution sources to premature mortality on a global scale [J]. Nature, 2015, 525(7569): 367-371.
[3] Song C, He J, Wu L, et al. Health burden attributable to ambient PM2.5 in China [J]. Environmental Pollution, 2017, 223: 575-586.
[4] Fang D, Wang Q, Li H, et al. Mortality effects assessment of ambient PM2.5 pollution in the 74 leading cities of China [J]. Science of the Total Environment, 2016, 569-570: 1545-1552.
[5] Etchie T O, Sivanesan S, Adewuyi G O, et al. The health burden and economic costs averted by ambient PM2.5 pollution reductions in Nagpur, India [J]. Environment International, 2017, 102: 145-156.
[6] 李成才. MODIS遥感气溶胶光学厚度及应用于区域环境大气污染研究[D]. 北京: 北京大学, 2002.
[7] Chu D A, Kaufman Y J, Zibordi G, et al. Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS) [J]. Journal of Geophysical Research Atmospheres, 2003, 108(D21): 4661-4678.
[8] Chew B N, Campbell J R, Hyer E J, et al. Relationship between aerosol optical depth and particulate matter over Singapore: Effects of aerosol vertical distributions [J]. Aerosol and Air Quality Research, 2016, 16(11): 2818-2830.
[9] 吴健生,王茜. 基于AOD数据反演地面PM2.5浓度研究进展[J]. 环境科学与技术, 2017, 40(8): 68-76.
[10] Wu J, Yao F, Li W, et al. VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing-Tianjin-Hebei: A spatiotemporal statistical model [J]. Remote Sensing of Environment, 2016, 184: 316-328.
[11] He Q, Geng F, Li C, et al. Long-term characteristics of satellite-based PM2.5 over East China [J]. Science of The Total Environment, 2018, 612: 1417-1423.
[12] Bilal M, Nichol J E, Spak S N. A new approach for estimation of fine particulate concentrations using satellite aerosol optical depth and binning of meteorological variables [J]. Aerosol and Air Quality Research, 2017, 17(2): 1-37.
[13] Jung C, Hwang B, Chen W. Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015 [J]. Environmental Pollution, 2017, 237: 1-11.
[14] 贺婧婧,张敏,陈显尧,等. 香港气溶胶光学厚度与PM10质量浓度的季节变异及相关性[J]. 中国科学(地球科学), 2015, 45(4): 444-454.
[15] 吴立新,吕鑫,秦凯,等. 基于太阳光度计地基观测的徐州气溶胶光学特性变化分析[J]. 科学通报, 2016, 61(20): 2287-2298.
[16] 白杨,秦凯,吴立新,等. 徐州市区主干道路黑炭气溶胶浓度移动观测实验[J]. 地理与地理信息科学, 2014, 30(1): 45-49.
[17] 吴立新,吕鑫,秦凯,等. 秸秆焚烧期间徐州市空气污染物时空分布特征分析[J]. 地理与地理信息科学, 2014, 30(1): 18-22.
[18] 张永宏,朱灵龙,阚希,等. 基于卫星遥感和气象数据的2015年徐州市PM2.5时空特征[J]. 科学技术与工程, 2017, 17(36): 124-129.
[19] 朱忠敏,龚威,余娟,等. 水平能见度与气溶胶光学厚度转换模型的适用性分析[J]. 武汉大学学报(信息科学版), 2010, 35(9): 1086-1090.
[20] 李成才,毛节泰,刘启汉,等. MODIS卫星遥感气溶胶产品在北京市大气污染研究中的应用[J]. 中国科学(地球科学), 2005, 35(S1): 177-186.
[21] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[22] 叶海彬,杨顶田,杨超宇. 经验模态与小波分解在光学遥感内波参数提取中的应用[J]. 热带海洋学报, 2012, 31(2): 47-54.
[23] 张婷慧,陈报章,王瑾,等. 基于CAMx的徐州市2016年冬季PM2.5污染过程及来源分析[J]. 环境科学学报, 2017, 37(10): 3918-3925.
[24] Shen Y, Zhang L, Fang X, et al. Long-term analysis of aerosol optical depth over the Huaihai Economic Region (HER): Possible causes and implications[J]. Atmosphere, 2018, 9(3): 93-111.
[25] Zhang Y M, Zhang X Y, Sun J Y, et al. Characterization of new particle and secondary aerosol formation during summertime in Beijing, China[J]. Tellus B: Chemical and Physical Meteorology, 2011, 63(3): 382-394.
[26] 李昌龙,王静怡,高媛媛. 徐州市区PM2.5浓度与气象要素的相关性分析——以2015年冬为例[J]. 环保科技, 2017, 23(2): 15-20.
[27] Li H. Agricultural fire impacts on the air quality of Shanghai during summer harvesttime [J]. Aerosol and Air Quality Research, 2010, 10: 95-101.
[28] Wang S, Li G, Gong Z, et al. Spatial distribution, seasonal variation and regionalization of PM2.5 concentrations in China [J]. Science China Chemistry, 2015, 58(9): 1435-1443.
[29] 杭鑫,李亚春,张明明,等. 基于遥感的秸秆焚烧对江苏省气溶胶光学厚度时空分布的影响研究[J]. 生态环境学报, 2017, 26(1): 111-118.
[2] Lelieveld J, Evans J S, Fnais M, et al. The contribution of outdoor air pollution sources to premature mortality on a global scale [J]. Nature, 2015, 525(7569): 367-371.
[3] Song C, He J, Wu L, et al. Health burden attributable to ambient PM2.5 in China [J]. Environmental Pollution, 2017, 223: 575-586.
[4] Fang D, Wang Q, Li H, et al. Mortality effects assessment of ambient PM2.5 pollution in the 74 leading cities of China [J]. Science of the Total Environment, 2016, 569-570: 1545-1552.
[5] Etchie T O, Sivanesan S, Adewuyi G O, et al. The health burden and economic costs averted by ambient PM2.5 pollution reductions in Nagpur, India [J]. Environment International, 2017, 102: 145-156.
[6] 李成才. MODIS遥感气溶胶光学厚度及应用于区域环境大气污染研究[D]. 北京: 北京大学, 2002.
[7] Chu D A, Kaufman Y J, Zibordi G, et al. Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS) [J]. Journal of Geophysical Research Atmospheres, 2003, 108(D21): 4661-4678.
[8] Chew B N, Campbell J R, Hyer E J, et al. Relationship between aerosol optical depth and particulate matter over Singapore: Effects of aerosol vertical distributions [J]. Aerosol and Air Quality Research, 2016, 16(11): 2818-2830.
[9] 吴健生,王茜. 基于AOD数据反演地面PM2.5浓度研究进展[J]. 环境科学与技术, 2017, 40(8): 68-76.
[10] Wu J, Yao F, Li W, et al. VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing-Tianjin-Hebei: A spatiotemporal statistical model [J]. Remote Sensing of Environment, 2016, 184: 316-328.
[11] He Q, Geng F, Li C, et al. Long-term characteristics of satellite-based PM2.5 over East China [J]. Science of The Total Environment, 2018, 612: 1417-1423.
[12] Bilal M, Nichol J E, Spak S N. A new approach for estimation of fine particulate concentrations using satellite aerosol optical depth and binning of meteorological variables [J]. Aerosol and Air Quality Research, 2017, 17(2): 1-37.
[13] Jung C, Hwang B, Chen W. Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015 [J]. Environmental Pollution, 2017, 237: 1-11.
[14] 贺婧婧,张敏,陈显尧,等. 香港气溶胶光学厚度与PM10质量浓度的季节变异及相关性[J]. 中国科学(地球科学), 2015, 45(4): 444-454.
[15] 吴立新,吕鑫,秦凯,等. 基于太阳光度计地基观测的徐州气溶胶光学特性变化分析[J]. 科学通报, 2016, 61(20): 2287-2298.
[16] 白杨,秦凯,吴立新,等. 徐州市区主干道路黑炭气溶胶浓度移动观测实验[J]. 地理与地理信息科学, 2014, 30(1): 45-49.
[17] 吴立新,吕鑫,秦凯,等. 秸秆焚烧期间徐州市空气污染物时空分布特征分析[J]. 地理与地理信息科学, 2014, 30(1): 18-22.
[18] 张永宏,朱灵龙,阚希,等. 基于卫星遥感和气象数据的2015年徐州市PM2.5时空特征[J]. 科学技术与工程, 2017, 17(36): 124-129.
[19] 朱忠敏,龚威,余娟,等. 水平能见度与气溶胶光学厚度转换模型的适用性分析[J]. 武汉大学学报(信息科学版), 2010, 35(9): 1086-1090.
[20] 李成才,毛节泰,刘启汉,等. MODIS卫星遥感气溶胶产品在北京市大气污染研究中的应用[J]. 中国科学(地球科学), 2005, 35(S1): 177-186.
[21] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[22] 叶海彬,杨顶田,杨超宇. 经验模态与小波分解在光学遥感内波参数提取中的应用[J]. 热带海洋学报, 2012, 31(2): 47-54.
[23] 张婷慧,陈报章,王瑾,等. 基于CAMx的徐州市2016年冬季PM2.5污染过程及来源分析[J]. 环境科学学报, 2017, 37(10): 3918-3925.
[24] Shen Y, Zhang L, Fang X, et al. Long-term analysis of aerosol optical depth over the Huaihai Economic Region (HER): Possible causes and implications[J]. Atmosphere, 2018, 9(3): 93-111.
[25] Zhang Y M, Zhang X Y, Sun J Y, et al. Characterization of new particle and secondary aerosol formation during summertime in Beijing, China[J]. Tellus B: Chemical and Physical Meteorology, 2011, 63(3): 382-394.
[26] 李昌龙,王静怡,高媛媛. 徐州市区PM2.5浓度与气象要素的相关性分析——以2015年冬为例[J]. 环保科技, 2017, 23(2): 15-20.
[27] Li H. Agricultural fire impacts on the air quality of Shanghai during summer harvesttime [J]. Aerosol and Air Quality Research, 2010, 10: 95-101.
[28] Wang S, Li G, Gong Z, et al. Spatial distribution, seasonal variation and regionalization of PM2.5 concentrations in China [J]. Science China Chemistry, 2015, 58(9): 1435-1443.
[29] 杭鑫,李亚春,张明明,等. 基于遥感的秸秆焚烧对江苏省气溶胶光学厚度时空分布的影响研究[J]. 生态环境学报, 2017, 26(1): 111-118.