The method and the correspongding effect of ground fine partical concentration retrieved by satellite remote sensing AOD
LI Ting-yuan1,3, TAN Hao-bo1,2, WANG Chun-lin3,4, CHEN Jing-yang1, YANG Liu-lin5, HONG Ying-ying1, XU Jie1, WANG Jie-chun1
1. Guangdong Ecological Meteorological Center, Guangzhou 510640, China;
2. Foshan Meteorological Bureau, Foshan 528000, China;
3. Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai), Zhuhai 519082, China;
4. Pearl River Estuary Climate Environment and Air Quality Change Field Observation and Research Station, Guangzhou 510275, China;
5. Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China
Satellite remote sensing data (MODIS AOD) and ground meteorological data from 2015 to 2018 were used to retrieve PM2.5 concentration. The results showed that the retrieval was desirable, with R2 between satellite-retrieved and observed PM2.5 average concentration from 2015 to 2018 reaching 0.94. The retrieved results in dry season was better than that in dry season, results in Pearl River Delta (PRD) was better than that in Non-Pearl River Delta (N-PRD). The possible reasons were that the interpolation errors affected by assumption of aerosol scale height and mass extinction efficiency were larger both in wet seasons with more unstable weather system and in N-PRD with more mountains and straw burning. Also, four different spatial interpolation methods were compared based on in-situ PM2.5 measurement, using satellite-retrieved PM2.5 as the true values at those grids without measurement. The interpolation results were similar for these four methods, with the inverse distance weighted (IDW) interpolation method performing slightly better. Uneven site distribution and low site density in some areas might have a significant influence on interpolation performance, so we recommended deploying more ground PM2.5 observation stations in sparse areas.
李婷苑, 谭浩波, 王春林, 陈靖扬, 杨柳林, 洪莹莹, 徐杰, 王捷纯. 卫星遥感AOD反演地面细颗粒物浓度方法与效果[J]. 中国环境科学, 2020, 40(1): 13-23.
LI Ting-yuan, TAN Hao-bo, WANG Chun-lin, CHEN Jing-yang, YANG Liu-lin, HONG Ying-ying, XU Jie, WANG Jie-chun. The method and the correspongding effect of ground fine partical concentration retrieved by satellite remote sensing AOD. CHINA ENVIRONMENTAL SCIENCECE, 2020, 40(1): 13-23.
Ramanathan V, Chung C, Kim D, et al. Atmospheric brown clouds:Impacts on South Asian climate and hydrological cycle[J]. Proceedings of the National Academy of Sciences, 2005,102(15):5326-5333.
[2]
Tan Z, Dong J, Xiao Y, et al. Numerical simulation of diurnally varying thermal environment in a street canyon under haze-fog conditions[J]. Atmospheric Environment, 2015,119:95-106.
[3]
Gutiérrez-Avila I, Rojas-Bracho L, Riojas-Rodríguez H, et al. Cardiovascular and cerebrovascular mortality associated with acute exposure to PM2.5 in Mexico City[J]. Stroke, 2018,49(7):1734-1736.
[4]
Kollanus V, Prank M, Gens A, et al. Mortality due to vegetation fire-originated PM2.5 exposure in Europe-assessment for the years 2005 and 2008[J]. Environmental Health Perspectives, 2017,125(1):30-37.
[5]
Deng T, Huang Y Q, Li Z N, et al. Numerical simulations for the sources apportionment and control strategies of PM2.5 over Pearl River Delta, China, part II:Vertical distribution and emission reduction strategies[J]. Science of The Total Environment, 2018,634:1645-1656.
[6]
Li T Y, Deng X J, Li Y, et al. Transport paths and vertical exchange characteristics of haze pollution in Southern China[J]. Science of The Total Environment, 2018,625:1074-1087.
[7]
Wu D, Bi X Y, Deng X J, et al. Effect of atmospheric haze on the deterioration of visibility over the Pearl River Delta[J]. Acta Meteorologica Sinica, 2007,21(2):215-223.
[8]
车汶蔚,郑君瑜,钟流举.珠江三角洲机动车污染物排放特征及分担率[J]. 环境科学研究, 2009,22(4):456-461. Che W W, Zheng J Y, Zhong L J. Vehicle exhaust emission characteristics and contributions in the Pearl River Delta Region[J]. Research of Environmental Science, 2009,22(4):456-461.
[9]
吴兑,廖雪莲,邓雪娇,等.珠江三角洲霾天气的近地层输送条件研究[J]. 应用气象学报, 2008,19(1):1-9. Wu D, Bi X L, Deng X J, et al. Transport condition of surface layer under haze weather over the Pearl River Delta[J]. Journal of Applied Meteorological Science, 2009,19(1):1-9.
[10]
陈均,李婷苑,洪莹莹,等.2016年11月广东两次气溶胶污染过程的气象条件分析[J]. 广东气象, 2018,40(2):39-43. Chen J, Li T Y, Hong Y Y, et al. Meteorological conditions analysis of two aerosol pollution processes in guangdong in November 2016[J]. Guangdong Meteorology, 2018,40(2):39-43.
[11]
Lin C Q, Li Y, Yuan Z B, et al. Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5[J]. Remote Sensing of Environment, 2015,156:117-128.
[12]
Liu Y, Park R J, Jacob D J, et al. Mapping annual mean ground-level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States[J]. Journal of Geophysical Research:Atmospheres banner, 2004,109:1-10.
[13]
Wang J, Christopher S A. Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass:Implications for air quality studies[J]. Geophysical Research Letters, 2003,30(21):2095.
[14]
Engel-Cox J A, Holloman C H, Coutant B W, et al. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality[J]. Atmospheric Environment, 2004,38:2495-2509.
[15]
Koelemeijer R, Homan C, Matthijsen J. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe[J]. Atmospheric Environment, 2006,40:5304-5315.
[16]
Li C, Hsu N C, Tsay S C. A study on the potential applications of satellite data in air quality monitoring and forecasting[J]. Atmospheric Environment, 2011,45(22):3663-3675.
[17]
Kloog I, Nordio F, Coull B A, et al. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states[J]. Environmental Science & Technology, 2012,46(21):11913-11921.
[18]
Hu X, Waller L A, Al-Hamdan M Z, et al. Estimating ground-level PM2.5 concentrations in the southeastern US using Geographically weighted regression[J]. Environment Research, 2013,121:1-10.
[19]
Gupta P, Christopher S A. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products:Multiple regression approach[J]. Journal of Geophysical Research, 2009,114(D14):1-13.
[20]
Benas N, Beloconi A, Chrysoulakis N. Estimation of urban PM10 concentration, based on MODIS and MERIS/AATSR synergistic observations[J]. Atmospheric Environment, 2013,79:448-454.
[21]
Guo J, Zhang X, Che H, et al. Correlation between PM concentrations and aerosol optical depth in eastern China[J]. Atmospheric Environment, 2009,43(37):5876-5886.
[22]
Li J, Carlson B E, Lacis A A. How well do satellite AOD observations represent the spatial and temporal variability of PM2.5 concentration for the United States[J]. Atmospheric Environment, 2015,102:260-273.
[23]
Zheng C W, Zhao C F, Zhu Y L, et al. Analysis of influential factors for the relationship between PM2.5 and AOD in Beijing[J]. Atmospheric Chemistry and Physics, 2017,17:13473-13489.
[24]
Zhang T H, Zhu Z M, Gong W, et al. Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160m Gaofen-1AOD retrievals[J]. Remote Sensing of Environment, 2018,216:91-104.
[25]
Fu D S, Xia X G, Wang J, et al. Synergy of AERONET and MODIS AOD products in the estimation of PM2.5 concentrations in Beijing[J]. Scientific Reports, 2018,8(1):1-8.
[26]
Fu D, Xia X, Duan M, et al. Mapping nighttime PM2.5 from VIIRS DNB using a linear mixed-effect model[J]. Atmospheric Environment, 2018,178:214-222.
[27]
Wang J, Aegerter C, Xu X, et al. Potential application of VIIRS Day/Night Band for monitoring nighttime surface PM2.5 air quality from space[J]. Atmospheric Environment, 2016,124:55-63.
[28]
McHardy T M, Zhang J, Reid J S, et al. An improved method for retrieving nighttime aerosol optical thickness from the VIIRS Day/Night Band[J]. Atmospheric Measurement Techniques Discussions, 2015,8(5):5147-5178.
[29]
李成才,毛节泰,刘启汉.利用MODIS遥感大气气溶胶及气溶胶产品的应用[J]. 北京大学学报:自然科学版, 2003(增刊):108-117. Li C C, Mao J T, Liu Q H. Remote sensing aerosol with MODIS and the application of MODIS aerosol products[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2003(supplementary issue):108-117.
[30]
van Donkelaar A, Martin R V, Park R J. Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing. Journal of Geophysical Research, 2006,111,D21201.
[31]
QX/T 412-2017卫星遥感监测技术导则霾[S]. QX/T 412-2017 Technical directives for haze monitoring by satellite remote sensing[S].
[32]
Liu Y, Sarnat J A, Kilaru V, et al. Estimating ground-level PM2.5 in the Eastern United States using satellite remote sensing[J]. Environmental Science & Technology, 2005,39(9):3269-3278.
[33]
He Q S, Li C C, Mao J T, et al. Analysis of aerosol vertical distribution and variability in Hong Kong[J]. Journal of Geophysical Research, 2008,113:D14211.
[34]
郑晓伍.石河子市大气颗粒物理化性质与消光特性研究[D]. 石河子,石河子大学, 2017:44-45. Zheng X W. Physicochemical and light extinction characteristics in atmospheric particulates of Shihezi, Xinjiang, China[D]. Shihezi, Shihezi University, 2017:44-45.
[35]
Kotchenruther R A, Hobbs P V, Hegg D A. Humidification factors for atmospheric aerosols off the mid-Atlantic coast of the United States[J]. Journal of Geophysical Research:Atmospheres, 1999,104(D2):2239-2251.
[36]
Koelemeijer R B A, Homan C D, Matthijsen J. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe[J]. Atmospheric Environment, 2006,40(27):5304-5315.
[37]
林良勋,冯业荣,黄忠,等.广东省天气预报技术手册[M]. 北京:气象出版社, 2006:9-10,202-203. Lin L X, Feng Y R, Huang Z, et al. Technical manual on weather forecasting of Guangdong province[M]. Beijing:China Meteorological Press, 2006:9-10,202-203.
[38]
He Q S, Zhou G Q, Geng F H, et al. Spatial distribution of aerosol hygroscopicity and its effect on PM2.5 retrieval in East China[J]. Atmospheric Research, 2016,170:161-167.
[39]
刘显通,李菲,谭浩波,等.基于卫星遥感资料监测地面细颗粒物的敏感性分析[J]. 中国环境科学, 2014,34(7):1649-1659. Liu X T, Li F, Tan H B, et al. Analysis of sensitivity of monitored ground PM2.5 concentrations based on satellite remote sensing data[J]. China Environmental Science, 2014,34(7):1649-1659.
[40]
夏丰.中国地区卫星遥感反演地面PM2.5相关问题研究[D]. 北京:中国气象科学研究院, 2017:46-47. Xia F. Study on several issue concerning PM2.5 remote sensing in China from MODIS[J]. Being, Chinese Academy of Meteorological Sciences, 2017,46-47.
[41]
周爽,王春林,孙睿,等.基于LME/BME的珠江三角洲PM2.5星地融合技术研究[J]. 中国环境科学, 2019,39(5):1869-1878. Zhou S, Wang C L, Sun R, et al. Fusion of satellite data and ground observed PM2.5 in Pearl River Delta region with linear mixed effect and Bayesian maximum entropy method[J]. China Environmental Science, 2019,39(5):1869-1878.
[42]
黄嘉佑,李庆祥.气象数据统计分析方法[M]. 北京:气象出版社, 2015:494-495. Huang J Y, Li Q X. Statistical analysis method of meteorological data[M]. Beijing:China Meteorological Press, 2015:494-495.
[43]
孙西勃,廖程浩,曾武涛,等.广东省秸秆燃烧大气污染物及VOCs物种排放清单[J]. 环境科学, 2018,39(9):3995-4001. Sun X B, Liao C H, Zeng W T, et al. Emission inventory of atmospheric pollutants and VOC species from crop residue burning in Guangdong province[J]. Environmental Science, 2018,39(9):3995-4001.
[44]
Parrish D F, Derber J C. The national meteorological center's spectral statistical interpolation analysis system[J]. Monthly Weather Review, 1992,120(8):1747-1763.
[45]
Tang Y, Pagowski M, Chai T, et al. A case study of aerosol data assimilation with the community multi-scale air quality model over the contiguous United States using 3D-Var and optimal interpolation methods[J]. Geoscientific Model Development, 2017,10(12):4743-4758.
[46]
曹文静,李强子,蒙继华,等.基于GIS的气温插值方法比较[J]. 中国农业气象, 2007,28(增刊):175-178. Cao W J, Li Q Z, Meng J H, et al. Comparison on air temperature interpolation methods based on GIS[J]. Chinese Journal of Agrometeorology, 2007,28(supplementary issue):175-178.
[47]
何由,高晶,姚檀栋,等.利用不同插值方法对青藏高原降水稳定同位素空间分布分析[J]. 冰川冻土, 2015,37(2):351-359. He Y, Gao J, Yao T D, et al. Spatial distribution of stable isotope in precipitation upon the Tibetan Plateau analyzed with various interpolation methods[J]. Journal of Glaciology and Geocryology, 2015,37(2):351-359.
[48]
梁颖珊.广州市降水量空间插值方法研究[J]. 广东水利水电, 2017, 10:11-16. Liang Y S. Study on spatial interpolation of precipitation in Guangzhou[J]. Guangdong Water Resources and hydropower, 2017, 10:11-16.
[49]
李增涛,高鹏,张三敏,等.克里金插值参数设置对网格化结果的影响[J]. 物探化探计算技术, 2015,37(5):666-670. Li Z T, Gao P, Zhang S M, et al. The influence of parameter setting about gridded data generated by Kriging method[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2015,37(5):666-670.
[50]
齐静.径向基函数插值若干问题研究[D]. 重庆:重庆师范大学, 2016:5-15. Qi J. Research for some problems of the radial basis function interpolation[D]. Chongqing, Chongqing Normal University, 2016:5-15.
[51]
黄昊,胡永峰.基于采样点局部密度的径向基函数核宽度确定方法及其在装备设计中的应用范例[J]. 军事运筹与系统工程, 2016, 30(4):58-64. Huang H, Hu Y F. Radial basis function kernel width determination method based on local density of sampling points and its application in equipment design[J]. Military Operations Research and Systems Engineering, 2016,30(4):58-64.
