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Conceptualizing How Severe Haze Events Are Impacting Long-Term Satellite-Based Trend Studies of Aerosol Optical Thickness over Asia

  • Zhao Yang Zhang
  • Man Sing Wong
  • James R. Campbell
Chapter
Part of the Springer Remote Sensing/Photogrammetry book series (SPRINGERREMO)

Abstract

Error budgets derived for aerosol trend analysis from satellite-based datasets always consider sensor calibration, cloud contamination, and sampling scale. Here, we also consider and characterize an additional uncertainty induced by severe haze events. Such events were evaluated using MODerate resolution Imaging Spectroradiometer (MODIS) daily aerosol optical thickness (AOT) products (Terra MOD04 and Aqua MYD04) and AErosol RObotic Network (AERONET) AOT measurements over two regions subject to relatively high anthropogenic pollution loadings (and, as such, those conditions whereby the hygroscopic enhancement of local particulate mass is more likely): Beijing, China; and Kanpur, India. Further, the data are analyzed for trend analysis using two methods: linear and non-linear regression techniques. The latter, considered using the relatively new ensemble empirical mode decomposition (EEMD) methodology, allows for better representation of trends in non-linear time series, which is more practical for considering aerosol global trends overall. Our work shows that the severe haze events exhibit a significant impact on the AOT trends derived from these two regions. AOT trends from both the Terra and Aqua MODIS platforms over Kanpur are consistent with and without haze AOT using the linear method. Slight decreasing AOT trends were observed at Beijing from Terra and Aqua using both linear and non-linear methods. Case studies show the practical influence of severe haze events on the over- and under-estimate of MODIS AOT in these urban areas and how ground-based instrumentation critically assist interpretation of satellite-based aerosol observations.

Keywords

Haze Aerosol optical thickness Aerosol optical properties Aerosol trends 

Notes

Acknowledgements

The authors thank the anonymous reviewers for valuable suggestions and comments. We also thank the NASA Goddard Space Flight Center and Langley Data Centers for MODIS and CALIPSO data, visibility and relative humidity data from the National Centers for Environmental Information, and the NASA AERONET project and principal investigators/staff responsible for establishing and maintaining the AERONET sites used here. This research was supported in part by the grant of General Research Fund (project id: 15205515), the grant HKU9/CRF/12G of Collaborative Research Fund from the Research Grants Council of Hong Kong; the grant PolyU 1-ZVAJ from the Faculty of Construction and Environment, the Hong Kong Polytechnic University.

References

  1. Badarinath KVS, Kharol SK, Latha KM, Chand TR, Prasad VK, Jyothsna AN, Samatha K (2007) Multiyear ground-based and satellite observations of aerosol properties over a tropical urban area in India. Atmos Sci Lett 8(1):7–13CrossRefGoogle Scholar
  2. Badarinath KVS, Kharol SK, Krishna Prasad V, Kaskaoutis DG, Kambezidis HD (2008) Variation in aerosol properties over Hyderabad, India during intense cyclonic conditions. Int J Remote Sens 29(15):4575–4597CrossRefGoogle Scholar
  3. Badarinath KVS, Sharma AR, Kharol SK, Prasad VK (2009) Variations in CO, O3 and black carbon aerosol mass concentrations associated with planetary boundary layer (PBL) over tropical urban environment in India. J Atmos Chem 62(1):73–86CrossRefGoogle Scholar
  4. Boucher O, Randall AD, Bretherton P, Feingold C, Forster G, Kerminen P, Kondo V, Liao Y, Lohmann H, Rasch U (2013) Clouds and aerosols in climate change 2013. In: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  5. Campbell JR, Reid JS, Westphal DL, Zhang J, Tackett JL, Chew BN, Welton EJ, Shimizu A, Sugimoto N, Aoki K, Winker DM (2013) Characterizing the vertical profile of aerosol particle extinction and linear depolarization over Southeast Asia and the Maritime Continent: the 2007–2009 view from CALIOP. Atmos Res 122:520–543.  https://doi.org/10.1016/j.atmosres.2012.05.007 CrossRefGoogle Scholar
  6. Campbell JR, Vaughan MA, Oo M, Holz RE, Lewis JR, Welton EJ (2015) Distinguishing cirrus cloud presence in autonomous lidar measurements. Atmos Meas Tech 8:435–449.  https://doi.org/10.5194/amt-8-435-2015 CrossRefGoogle Scholar
  7. Capparelli V, Franzke C, Vecchio A, Freeman MP, Watkins NW, Carbone V (2013) A spatiotemporal analysis of U.S. station temperature trends over the last century. J Geophys Res Atmos 118:7427–7434.  https://doi.org/10.1002/jgrd.50551 CrossRefGoogle Scholar
  8. Che H, Zhang X, Li Y, Zhou Z, Qu JJ, Hao X (2008) Haze trends over the capital cities of 31 provinces in China, 1981–2005. Theor Appl Climatol 97:235–242.  https://doi.org/10.1007/s00704-008-0059-8 CrossRefGoogle Scholar
  9. Chew BN, Campbell JR, Reid JS, Giles DM, Welton EJ, Salinas SV, Liew SC (2011) Tropical cirrus cloud contamination in sun photometer data. Atmos Environ 45:6724–6731.  https://doi.org/10.1016/j.atmosenv.2011.08.017 CrossRefGoogle Scholar
  10. Cnossen I, Franzke C (2014) The role of the Sun in long-term change in the F2 peak ionosphere: new insights from Ensemble Empirical Mode Decomposition (EEMD) and numerical modelling. J Geophys Res Space Phys 119:8610–8623.  https://doi.org/10.1002/2014JA020048 CrossRefGoogle Scholar
  11. Cook J, Highwood EJ (2004) Climate response to tropospheric absorbing aerosols in an intermediate general-circulation model. Q J Roy Meteorol Soc 130:175–191.  https://doi.org/10.1256/qj.03.64 CrossRefGoogle Scholar
  12. Dey S, Tripathi SN, Singh RP, Holben BN (2005) Seasonal variability of the aerosol parameters over Kanpur, an urban site in Indo-Gangetic basin. Adv Space Res 36:778–782.  https://doi.org/10.1016/j.asr.2005.06.040 CrossRefGoogle Scholar
  13. Ensor DS, Porch WM, Pilat MJ, Charlson RJ (1971) Influence of the atmospheric aerosol on albedo. J Appl Meteorol 10:1303–1306CrossRefGoogle Scholar
  14. Field CB, Barros VR, Mach K, Mastrandrea M (2014) Climate change 2014: impacts, adaptation, and vulnerability. In: Working group II contribution to the IPCC 5th assessment report-technical summary, pp 1–76Google Scholar
  15. Franzke C (2009) Multi-scale analysis of teleconnection indices: climate noise and nonlinear trend analysis. Nonlinear Processes Geophys 16:65–76.  https://doi.org/10.5194/npg-16-65-2009 CrossRefGoogle Scholar
  16. Franzke CLE (2014) Warming trends: nonlinear climate change. Nature Climate Change 4:423–424.  https://doi.org/10.1038/nclimate2245 CrossRefGoogle Scholar
  17. Hayasaka H, Noguchi I, Putra EI, Yulianti N, Vadrevu K (2014) Peat-fire-related air pollution in Central Kalimantan, Indonesia. Environ Pollut 195):257–266CrossRefGoogle Scholar
  18. Holben BN, Eck TF, Slutsker I, Tanre D, Buis JP, Setzer A, Vermote E, Reagan JA, Kaufman YJ, Nakajima T, Lavenu F, Jankowiak I, Smirnov A (1998) AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sens Environ 66:1–16.  https://doi.org/10.1016/S0034-4257(98)00031-5 CrossRefGoogle Scholar
  19. Hsu NC, Si-Chee T, King MD, Herman JR (2004) Aerosol properties over bright-reflecting source regions. IEEE Trans Geosci Remote Sens 42:557–569.  https://doi.org/10.1109/TGRS.2004.824067 CrossRefGoogle Scholar
  20. Hsu NC, Gautam R, Sayer AM, Bettenhausen C, Li C, Jeong MJ, Tsay SC, Holben BN (2012) Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010. Atmos Chem Phys 12:8037–8053.  https://doi.org/10.5194/acp-12-8037-2012 CrossRefGoogle Scholar
  21. Huang J, Hsu NC, Tsay S-C, Jeong M-J, Holben BN, Berkoff TA, Welton EJ (2011) Susceptibility of aerosol optical thickness retrievals to thin cirrus contamination during the BASE-ASIA campaign. J Geophys Res Atmos 116:D08214.  https://doi.org/10.1029/2010JD014910 CrossRefGoogle Scholar
  22. Kant Y, Ghosh AB, Sharma MC, Gupta PK, Prasad VK, Badarinath KVS, Mitra AP (2000) Studies on aerosol optical depth in biomass burning areas using satellite and ground-based observations. Infrared Phys Technol 41(1):21–28CrossRefGoogle Scholar
  23. Kim YJ, Kim KW, Kim SD, Lee BK, Han JS (2006) Fine particulate matter characteristics and its impact on visibility impairment at two urban sites in Korea: Seoul and Incheon. Atmos Environ 40(Suppl 2):593–605.  https://doi.org/10.1016/j.atmosenv.2005.11.076 CrossRefGoogle Scholar
  24. Le TH, Nguyen TNT, Lasko K, Ilavajhala S, Vadrevu KP, Justice C (2014) Vegetation fires and air pollution in Vietnam. Environ Pollut 195:267–275CrossRefGoogle Scholar
  25. Levy RC, Remer LA, Kleidman RG, Mattoo S, Ichoku C, Kahn R, Eck TF (2010) Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmos Chem Phys 10:10399–10420.  https://doi.org/10.5194/acp-10-10399-2010 CrossRefGoogle Scholar
  26. Levy RC, Mattoo S, Munchak LA, Remer LA, Sayer AM, Patadia F, Hsu NC (2013) The Collection 6 MODIS aerosol products over land and ocean. Atmos Meas Tech 6:2989–3034.  https://doi.org/10.5194/amt-6-2989-2013 CrossRefGoogle Scholar
  27. Li Z (2009) Toward understanding the climatic effects of aerosols under hazy environments: an overview of field observations in China. AIP Conf Proc 1100:478–481.  https://doi.org/10.1063/1.3117025 CrossRefGoogle Scholar
  28. Li S, Chen L, Xiong X, Tao J, Su L, Han D, Liu Y (2013) Retrieval of the haze optical thickness in North China Plain using MODIS data. IEEE Trans Geosci Remote Sens 51:2528–2540.  https://doi.org/10.1109/TGRS.2012.2214038 CrossRefGoogle Scholar
  29. Li J, Carlson BE, Lacis AA (2014) Application of spectral analysis techniques to the intercomparison of aerosol data—part 4: synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis. Atmos Meas Tech 7:2531–2549.  https://doi.org/10.5194/amt-7-2531-2014 CrossRefGoogle Scholar
  30. Liberti GL, Chéruy F (2002) Tropospheric aerosols by passive radiometry. In: Marzano FS, Visconti G (eds) Remote sensing of atmosphere and ocean from space: models, instruments and techniques. Springer, Dordrecht, pp 145–162.  https://doi.org/10.1007/0-306-48150-2_10 CrossRefGoogle Scholar
  31. Lindeman JD, Boybeyi Z, Gultepe I (2011) An examination of the aerosol semi-direct effect for a polluted case of the ISDAC field campaign. J Geophys Res Atmos 116:D00T10.  https://doi.org/10.1029/2011JD015649 CrossRefGoogle Scholar
  32. Mace GG (2010) Cloud properties and radiative forcing over the maritime storm tracks of the Southern Ocean and North Atlantic derived from A-Train. J Geophys Res 115(D10)Google Scholar
  33. Malm WC, Sisler JF, Huffman D, Eldred RA, Cahill TA (1994) Spatial and seasonal trends in particle concentration and optical extinction in the United States. J Geophys Res Atmos 99:1347–1370.  https://doi.org/10.1029/93JD02916 CrossRefGoogle Scholar
  34. Mao KB, Ma Y, Xia L, Chen WY, Shen XY, He TJ, Xu TR (2014) Global aerosol change in the last decade: an analysis based on MODIS data. Atmos Environ 94:680–686.  https://doi.org/10.1016/j.atmosenv.2014.04.053 CrossRefGoogle Scholar
  35. Mishchenko MI, Geogdzhayev IV, Rossow WB, Cairns B, Carlson BE, Lacis AA, Liu L, Travis LD (2007) Long-term satellite record reveals likely recent aerosol trend. Science 315:1543.  https://doi.org/10.1126/science.1136709 CrossRefGoogle Scholar
  36. Prasad VK, Kant Y, Gupta PK, Elvidge C, Badarinath KVS (2002) Biomass burning and related trace gas emissions from tropical dry deciduous forests of India: a study using DMSP-OLS data and ground-based measurements. Int J Remote Sens 23(14):2837–2851CrossRefGoogle Scholar
  37. Prasad VK, Lata M, Badarinath KVS (2003) Trace gas emissions from biomass burning from northeast region in India—estimates from satellite remote sensing data and GIS. Environmentalist 23(3):229–236CrossRefGoogle Scholar
  38. Qian C, Fu C, Wu Z, Yan Z (2009) On the secular change of spring onset at Stockholm. Geophys Res Lett 36:L12706.  https://doi.org/10.1029/2009gl038617 CrossRefGoogle Scholar
  39. Ramanathan V, Feng Y (2009) Air pollution, greenhouse gases and climate change: global and regional perspectives. Atmos Environ 43:37–50.  https://doi.org/10.1016/j.atmosenv.2008.09.063 CrossRefGoogle Scholar
  40. Remer LA, Kaufman YJ, Tanré D, Mattoo S, Chu DA, Martins JV, Li R-R, Ichoku C, Levy RC, Kleidman RG, Eck TF, Vermote E, Holben BN (2005) The MODIS aerosol algorithm, products, and validation. J Atmos Sci 62:947–973.  https://doi.org/10.1175/JAS3385.1 CrossRefGoogle Scholar
  41. Sakaeda N, Wood R, Rasch PJ (2011) Direct and semidirect aerosol effects of southern African biomass burning aerosol. J Geophys Res Atmos 116:D12205.  https://doi.org/10.1029/2010JD015540 CrossRefGoogle Scholar
  42. Sayer AM, Hsu NC, Bettenhausen C, Jeong MJ (2013) Validation and uncertainty estimates for MODIS collection 6 “Deep Blue” aerosol data. J Geophys Res Atmos 118:7864–7872.  https://doi.org/10.1002/jgrd.50600 CrossRefGoogle Scholar
  43. Smith A, Lott N, Vose R (2011) The Integrated Surface Database: recent developments and partnerships. Bull Am Meteorol Soc 92:704–708.  https://doi.org/10.1175/2011BAMS3015.1 CrossRefGoogle Scholar
  44. Song H, Zhang K, Piao S, Wan S (2016) Spatial and temporal variations of spring dust emissions in northern China over the last 30 years. Atmos Environ 126:117–127.  https://doi.org/10.1016/j.atmosenv.2015.11.052 CrossRefGoogle Scholar
  45. Tao M, Chen L, Su L, Tao J (2012) Satellite observation of regional haze pollution over the North China Plain. J Geophys Res Atmos 117:D12203.  https://doi.org/10.1029/2012JD017915 CrossRefGoogle Scholar
  46. Tosca M, Campbell J, Garay M, Lolli S, Seidel F, Marquis J, Kalashnikova O (2017) Attributing accelerated summertime warming in the Southeast United States to recent reductions in aerosol burden: indications from vertically-resolved observations. Remote Sensing 9(7):674CrossRefGoogle Scholar
  47. Vadrevu KP, Ellicott E, Badarinath KVS, Vermote E (2011) MODIS derived fire characteristics and aerosol optical depth variations during the agricultural residue burning season, north India. Environ Pollut 159(6):1560–1569CrossRefGoogle Scholar
  48. Vadrevu KP, Ellicott E, Giglio L, Badarinath KVS, Vermote E, Justice C, Lau WK (2012) Vegetation fires in the himalayan region–aerosol load, black carbon emissions and smoke plume heights. Atmos Environ 47:241–251CrossRefGoogle Scholar
  49. Vadrevu KP, Lasko K, Giglio L, Justice C (2014) Analysis of Southeast Asian pollution episode during June 2013 using satellite remote sensing datasets. Environ Pollut 195:245–256CrossRefGoogle Scholar
  50. Vadrevu KP, Lasko K, Giglio L, Justice C (2015) Vegetation fires, absorbing aerosols and smoke plume characteristics in diverse biomass burning regions of Asia. Environ Res Lett 10(10):105003CrossRefGoogle Scholar
  51. Wang Y, Zhuang G, Sun Y, An Z (2006) The variation of characteristics and formation mechanisms of aerosols in dust, haze, and clear days in Beijing. Atmos Environ 40:6579–6591.  https://doi.org/10.1016/j.atmosenv.2006.05.066 CrossRefGoogle Scholar
  52. Wilcox EM (2012) Direct and semi-direct radiative forcing of smoke aerosols over clouds. Atmos Chem Phys 12:139–149.  https://doi.org/10.5194/acp-12-139-2012 CrossRefGoogle Scholar
  53. Winker DM, Vaughan MA, Omar A, Hu Y, Powell KA, Liu Z, Hunt WH, Young SA (2009) Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms. J Atmos Oceanic Technol 26:2310–2323.  https://doi.org/10.1175/2009JTECHA1281.1 CrossRefGoogle Scholar
  54. Wu D, Bi X, Deng X, Li F, Tan H, Liao G, Huang J (2007) Effect of atmospheric haze on the deterioration of visibility over the Pearl River Delta. Acta Meteorol Sin 21:215Google Scholar
  55. Wu Z, Huang NE, Chen X (2009) The multi-dimensional ensemble empirical mode decomposition method. Adv Adapt Data Anal 1:339–372.  https://doi.org/10.1142/S1793536909000187 CrossRefGoogle Scholar
  56. Wu Y, Wang R, Zhou Y, Lin B, Fu L, He K, Hao J (2010) On-road vehicle emission control in Beijing: past, present, and future. Environ Sci Technol 45:147–153.  https://doi.org/10.1021/es1014289 CrossRefGoogle Scholar
  57. Wu Z, Huang NE, Wallace JM (2014) Adaptive and local analysis of climate data. Engineering 1:41–45Google Scholar
  58. Xia X, Eck TF, Holben BN, Phillippe G, Chen H (2008) Analysis of the weekly cycle of aerosol optical depth using AERONET and MODIS data. J Geophys Res Atmos 113:D14217.  https://doi.org/10.1029/2007JD009604 CrossRefGoogle Scholar
  59. Zhang J, Reid JS (2010) A decadal regional and global trend analysis of the aerosol optical depth using a data-assimilation grade over-water MODIS and level 2 MISR aerosol products. Atmos Chem Phys 10:10949–10963.  https://doi.org/10.5194/acp-10-10949-2010 CrossRefGoogle Scholar
  60. Zhang Z, Wong M, Nichol J (2015) Global trends of aerosol optical thickness using ensemble empirical mode decomposition method. Int J Climatol 36(13):4358–4372CrossRefGoogle Scholar
  61. Zhao P, Zhang X, Xu X, Zhao X (2011) Long-term visibility trends and characteristics in the region of Beijing, Tianjin, and Hebei, China. Atmos Res 101:711–718.  https://doi.org/10.1016/j.atmosres.2011.04.019 CrossRefGoogle Scholar
  62. Zhou M, Okada K, Qian F, PM W, Su L, Casareto BE, Shimohara T (1996) Characteristics of dust-storm particles and their long-range transport from China to Japan—case studies in April 1993. Atmos Res 40:19–31.  https://doi.org/10.1016/0169-8095(95)00023-2 CrossRefGoogle Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2018

Authors and Affiliations

  • Zhao Yang Zhang
    • 1
  • Man Sing Wong
    • 1
  • James R. Campbell
    • 2
  1. 1.Department of Land Surveying and Geo-InformaticsThe Hong Kong Polytechnic UniversityKowloonHong Kong
  2. 2.Aerosol and Radiation Sciences Section, Marine Meteorology DivisionNaval Research LaboratoryMontereyUSA

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