Environmental Study on Contribution Rates of Aerosol Scale Height and Humidity in PM2.5 Inversion Based on Calipso Data

Weidong Li, Liye Dong, Sheheryar Khan

Ekoloji, 2019, Issue 107, Pages: 1185-1197, Article No: e107139


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For the study area, Introducing the humidity correction and aerosol scale height correction methods, the optimal Aerosol optical thickness (AOT) and the PM2.5 mass concentration estimation models are selected as the Monadic quadratic equation model. Based on 5 point sliding average method and accumulated variance analysis, using the cumulative slope change ratio comparison method, taking the winter as the base period without considering other factors, the relative contribution rate of relative humidity to PM2.5 in the process of retrieving PM2.5 mass concentration was calculated to be 31.60% and 48.40% respectively in spring-summer and autumn, the relative contribution rate of Aerosol scale height to AOT was 72.28% and 40.23% respectively in spring -summer and autumn, the relative contribution rate of relative humidity to AOT was 24.59% and 26.23% respectively in spring-summer and autumn. By analyzing the contribution rate of relative humidity and Aerosol scale height to AOT or PM2.5, this study reveals the sensitivity of AOT to relative humidity and Aerosol scale height and the sensitivity of PM2.5 to relative humidity. It provides a reference for a more accurate inversion of PM2.5 mass concentration in near ground surface. The National Institute for Environmental Studies (NIES) began continuous observation of the atmosphere in 1996 in Tsukuba, Japan, with a compact Mie‐scattering lidar system, and added polarization measurement capability in 1999. Similar systems were installed at Nagasaki University on 22 February 2001 and at the Sino‐Japan Friendship Center for Environmental Protection in Beijing on 1 March 2001.


CALIPSO, AOT, PM2.5 inversion, cumulative slope change ratio comparison method, contribution rate


  • Aaron VD, Martin RV, Michael B, et al. (2010) Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application. Environ Health Perspect, 118(6): 847-855.
  • Alam K, Khan R, Ali S, et al. (2015) Variability of aerosol optical depth over Swat in Northern Pakistan based on satellite data. Arabian Journal of Geosciences, 8(1): 547-555.
  • Bilal M, Nichol JE, Spak SN (2017) A New Approach for Estimation of Fine Particulate Concentrations Using Satellite Aerosol Optical Depth and Binning of Meteorological Variables. Aerosol & Air Quality Research, 17(2).
  • Chen C, Zhu ZJ, Liu D, Wang YY, Shen JC (2013) Pollution Characteristics and Source Apportionment of PM_(2.5) of Ambient Air in Zhengzhou. Environmental Monitoring in China, 29(5): 47-52.
  • Chen YZ, Shi RH, Wang C, Chen YY, Gao W (2013) Estimating Aerosol Optical Depth Based on Regional Optimized Peterson Model. Journal of Geo-Information Science, 15(2): 241-248.
  • Chitranshi S, Sharma SP, Dey S (2015) Spatio-temporal variations in the estimation of PM 10, from MODIS-derived aerosol optical depth for the urban areas in the Central Indo-Gangetic Plain. Meteorology & Atmospheric Physics, 127(1): 107-121.
  • Choi YS, Park RJ, Ho CH (2009) Estimates of ground-level aerosol mass concentrations using achemical transport model with Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol observations over East Asia. Journal of Geophysical Research Atmospheres, 114(D4): 83-84.
  • Chudnovsky AA, Lee HJ, Kostinski A, et al. (2012) Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite (GOES). Journal of the Air & Waste Management Association, 62(9): 1022-31.
  • Donkelaar AV, Martin RV, Brauer M, et al. (2010) Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application. Environmental Health Perspectives, 118(6): 847-855.
  • Donkelaar AV, Martin RV, Park RJ (2006) Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing. Geophysics Research, 111(D21): 1-10.
  • Engel-Cox JA, Hoff RM, Rogers R, et al. (2006) Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization. Atmospheric Environment, 40(40): 8056-8067.
  • Garnier A, Pelon J, Vaughan MA, et al. (2015) Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans. Atmospheric Measurement Techniques, 8(7): 2759-2774.
  • Guo J, Xia F, Zhang Y, et al. (2017) Impact of diurnal variability and meteorological factors on the PM2.5 - AOT relationship: Implications for PM2.5 remote sensing. Environmental Pollution, 221(94): 94.
  • Gupta P, Christopher SA, Wang J (2006) Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment, 40: 5880-5892.
  • Hu X, Waller LA, Lyapustin A, et al. (2014) Estimating ground-level PM 2.5, concentrations in the Southeastern United States using MAIAC AOT retrievals and a two-stage model. Remote Sensing of Environment, 140(1): 220-232.
  • Hutchison KD, Smith FS (2008) Improving correlations between MODIS aerosol optical thickness and ground-based PM_(2.5) observations through 3D spatial analyses. Atmospheric Environment, 42(3): 530-543.
  • Jiang QJ, Hua LI, Wang SP, Fu CL (2017) Study on Causes and Countermeasures of Haze in Zhengzhou. Guangzhou Chemical Industry, 45(16): 136-137.
  • Kang HX, Na XD, Zang SY (2016) Advance in Ground-level PM_(2.5) Prediction Using Remote Sensing data(AOT). Environmental Science & Management, 41(2): 30-34.
  • Kato S, Rose FG, Sun-Mack S, et al. (2011) Improvements of top‐of‐atmosphere and surface irradiance computations with CALIPSO‐, CloudSat‐, and MODIS‐derived cloud and aerosol properties. Journal of Geophysical Research Atmospheres, 116(D19): D19209.
  • Koschmieder H (1924) Theorie der horizontalen sichtweite. Beitr Phys.d.freien Atm, 12: 171-181.
  • Lary DJ, Faruque FS, Malakar N, et al. (2014) Estimating the global abundance of ground level presence of particulate matter (PM2.5). Geospatial Health, 8(3): S611.
  • Lau AKH (2005) Application of MODIS satellite products to the air pollution research in Beijing. Science in China Ser. D Earth Sciences, 48(z2): 209-219.
  • Lee HJ, Liu Y, Coull BA, et al. (2011) A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations. Atmospheric Chemistry & Physics, 11(11): 9769-9795.
  • Li J, Carlson BE, Lacis AA (2015) How well do satellite AOD observations represent the spatial and temporal variability of PM 2.5, concentration for the United States?. Atmospheric Environment, 102: 260-273.
  • Liu C, Shen X, Gao W, et al. (2014) Evaluation of CALIPSO aerosol optical depth using AERONET and MODIS data over China. SPIE Optical Engineering + Applications. International Society for Optics and Photonics, 92210F-92210F-13.
  • Liu JY (2015) Study on The Correlation between The MODIS AOT and The Concentration of PM2.5 in Beijing City. Chengdu University of Technology.
  • Liu Y, Sarnat JA, Kilaru V, et al. (2005a), Estimating ground-level PM2.5 in the Eastern United States using satellite remote sensing. Environment Science Technology, 39(9): 3269-3278.
  • Liu Z, Zheng YF, Liu JJ, Xie MQ (2015b) Research on the distribution of the northern region of China aerosol based on A-trian satellite. China Environmental Science, 35(10): 2891-2898.
  • Ma L, Liu TX, Ma L, Sun M, Ding T, Xin XH (2015) The effect of climate change and human activities on the runoff in the upper and middle reaches of the Liaohe River,Inner Mongolia. Journal of Glaciology & Geocryology, 37(2): 470-479.
  • Man SW, Shahzad M, Nichol J, et al. (2013), Validation of MODIS, MISR, OMI, and CALIPSO aerosol optical thickness using ground-based sunphotometers in Hong Kong. International Journal of Remote Sensing, 34(3): 897-918.
  • Martinelli N, Olivieri O, Girelli D (2013) Air particulate matter and cardiovascular disease: a narrative review. European Journal of Internal Medicine, 24(4): 295-302.
  • Pei YX, Guo M (2001) The Fundamental Principle and Application of Sliding Average Method. J. Journal of Gun Launch & Control, 1: 21-23.
  • Peterson JT, Fee CJ (1981) Visibility-atmospheric turbidity dependence at Raleigh, North Carolina. Atmospheric Environment, 15(12): 2561-2563.
  • Qi H, Chen WZ (2015) Correlation between Aerosol Layer Optical Depth from CALIPSO Satellite Lidar and Air Pollution Index in Qingdao. Journal of Atmospheric & Environmental Optics, 10(06): 463-471.
  • Seung-Jae L, Serre ML, Aaron VD, et al. (2012) Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM2.5Concentrations across the Continental United States. Environmental Health Perspectives, 120(12): 1727.
  • Singh MK, Venkatachalam P (2014) Merging of aerosol optical depth data from multiple remote sensing sensors. Geoscience & Remote Sensing Symposium IEEE International: 4173-4175.
  • Strawa AW, Chatfield RB, Legg M, et al. (2013) Improving retrievals of regional fine particulate matter concentrations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) multisatellite observations. Journal of the Air & Waste Management Association, 63(12): 1434-46.
  • Tao JH, Zhang MG, Chen LF, Wang ZF, Su L, Ge C, Han X, Zou MM (2013) A method to estimate concentrations of surface-level particulate matter using satellite-based aerosol optical thickness. Science China Earth Sciences, 56(8): 1422-1433.
  • Tripathi SN, Dey S, Chandel A, et al. (2005) Comparison of MODIS and AERONET derived aerosol optical depth over the Ganga Basin, India. Annales Geophysicae, 23(4): 1093-1101.
  • Van Donkelaar A, Martin RV, Park RJ (2006) Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing. Journal of Geophysical Research Atmospheres, 111(D21): 1-10.
  • Wang J, Christopher SA (2003) Intercomparison between satellite-derives aerosol optical thickness and PM2.5 mass: Implication for air quality studies. Geophysical Research Letters, 30(21): 465-471.
  • Wang J, Yang FM, Wang DY, He KB (2010) Characteristics and relationship of aerosol optical thickness and PM_(2.5) concentration over Beijing. Journal of the Graduate School of the Chinese Academy of Sciences, 27(1): 10-16.
  • Wang SJ, Yan YX, Yan M, Zhao XK (2012) Contributions of precipitation and human activities to the runoff change of the Huangfuchuan drainage basin: Application of comparative method of the slope changing ratio of cumulative quantity. Acta Geographica Sinica, 67(3): 388-397.
  • White WH, Roberts PT (1977) On the nature and origins of visibility-reducing aerosols in the los angeles air basin. Atmospheric Environment, 11(9): 803-812.
  • Winker DM, Pelon J, Coakley JAJ, et al. (2010) The CALIPSO Mission: A Global 3D View of Aerosols and Clouds. Bulletin of the American Meteorological Society, 91(9): 1211-1229.
  • Zhu YL, Ni CJ, Sun HH, Tu CY (2017) A new inversion model of surface ‘wet’ extinction coefficient based on MODIS AOT and its application. Acta Scientiae Circumstantiae, 37(7): 2468-2473.