Advertisement

Vertical Profiling of Aerosol and Aerosol Types Using Space-Borne Lidar

  • Alaa Mhawish
  • K. S. Vinjamuri
  • Nandita Singh
  • Manish Kumar
  • Tirthankar BanerjeeEmail author
Chapter
Part of the Energy, Environment, and Sustainability book series (ENENSU)

Abstract

Aerosol remote sensing has become a powerful tool to characterize the optical and microphysical properties of aerosols. Several satellite sensors such as MODIS, MISR, OMI, Tropomi and PARASOL utilize the solar electromagnetic radiation for retrieving aerosol properties from space. These instruments have high spatial coverage and can provide aerosol properties globally on a repeated basis. However, these passive sensors mostly lack the information regarding vertical distribution of aerosol and its types. Using active remote sensing technique however, provide valuable information to understand the vertical distribution of aerosols which is very useful to predict the lifetime of atmospheric aerosols, long-range transport and subsequent interaction with cloud droplets. CALIOP is an active sensor flying on board CALIPSO satellite provide height-dependent aerosol extinction repeatedly on a global basis. CALIOP aerosol retrieval algorithm retrieves aerosol information in 5 km horizontal resolution and 30–60 m vertical resolution. The latest updated CALIOP aerosol retrieval algorithm version 4 (V4) has the ability to identify ten aerosol subtypes; six for tropospheric aerosols and four for stratospheric aerosols. In this context, the annual, seasonal and diurnal variation of smoke aerosol have been investigated over central Indo-Gangetic Plain (IGP), South Asia using ten years V4 CALIOP profile data. We noted that for all the seasons, the highest smoke aerosol extinction observed near surface and contributed 40–60% to the total aerosol extinction during winter (DJF) and postmonsoon seasons (ON). In premonsoon (MAM) and monsoon (JJAS) seasons the highest contribution of smoke to the total extinction coefficient found at relatively higher altitude (premonsoon: 60% at 7–9 km, monsoon: 75% at 5–8 km). The day-night occurrence frequency of smoke aerosol found higher during the day time in winter at 4 km, while during monsoon the occurrence of the smoke was found higher at night time.

Keywords

Aerosol Smoke CALIPSO Vertical profile South Asia 

Notes

Acknowledgements

The research is ASEAN-India Science and Technology Development Fund (CRD/2018/000011) under ASEAN-India Collaborative R&D Scheme, Government of India support from University Grants Commission (UGC) under UGC-Israel Science Foundation bilateral project (6-11/2018 IC).

References

  1. Banerjee T, Kumar M, Singh N (2018) Aerosol, climate, and Sustainability. In: Della Sala DA, Goldstein MI (eds) The encyclopedia of the anthropocene, vol 2. Elsevier, Oxford, pp 419–428CrossRefGoogle Scholar
  2. Burney J, Ramanathan V (2014) Recent climate and air pollution impacts on Indian agriculture. Proc Natl Acad Sci 111(46):16319–16324CrossRefGoogle Scholar
  3. Chang D, Song Y (2010) Estimates of biomass burning emissions in tropical Asia based on satellite-derived data. Atmos Chem Phys 10(5):2335–2351CrossRefGoogle Scholar
  4. Chowdhury S, Dey S, Di Girolamo L, Smith KR, Pillarisetti A, Lyapustin A (2019) Tracking ambient PM2.5 build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset. Atmos Environ 204:142–150CrossRefGoogle Scholar
  5. Cusworth DH, Mickley LJ, Sulprizio MP, Liu T, Marlier ME, DeFries RS, Guttikunda SK, Gupta P (2018) Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi India. Environ Res Lett 13(4):044018CrossRefGoogle Scholar
  6. Dey S, Di Girolamo L (2010) A climatology of aerosol optical and microphysical properties over the Indian subcontinent from 9 years (2000–2008) of Multiangle Imaging Spectroradiometer (MISR) data. J Geophys Res 115:D15204CrossRefGoogle Scholar
  7. Evans J, van Donkelaar A, Martin RV, Burnett R, Rainham DG, Birkett NJ, Krewski D (2013) Estimates of global mortality attributable to particulate air pollution using satellite imagery. Environ Res 120:33–42CrossRefGoogle Scholar
  8. Gadhavi H, Jayaraman A (2006) Airborne lidar study of the vertical distribution of aerosols over Hyderabad, an urban site in central India, and its implication for radiative forcing calculations. Ann Geophys 24(10):2461–2470CrossRefGoogle Scholar
  9. Gautam R, Hsu NC, Lau, KM (2010) Premonsoon aerosol characterization and radiative effects over the Indo‐Gangetic Plains: implications for regional climate warming. J Geophys Res 115(D17)Google Scholar
  10. Gautam R, Hsu NC, Tsay SC, Lau KM, Holben B, Bell S, Payra S et al (2011) Accumulation of aerosols over the Indo-Gangetic plains and southern slopes of the Himalayas: distribution, properties and radiative effects during the 2009 pre-monsoon season. Atmos Chem Phys 11(24):12841–12863CrossRefGoogle Scholar
  11. Jethva H, Chand D, Torres O, Gupta P, Lyapustin A, Patadia F (2018) Agricultural burning and air quality over northern India: a synergistic analysis using NASA’s A-train satellite data and ground measurements. Aerosol Air Qual Res 18:1756–1773CrossRefGoogle Scholar
  12. Kaskaoutis DG, Kumar S, Sharma D, Singh RP, Kharol SK, Sharma M, Singh AK, Singh S, Singh A, Singh D (2014) Effects of crop residue burning on aerosol properties, plume characteristics, and long-range transport over northern India. J Geophys Res: Atmos 119(9):5424–5444Google Scholar
  13. Kim M-H, Omar AH, Tackett JL, Vaughan MA, Winker DM, Trepte CR, Hu Y, Liu Z, Poole LR, Pitts MC, Kar J, Magill BE (2018) The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm. Atmos Meas Tech 11:6107–6135.  https://doi.org/10.5194/amt-11-6107-2018CrossRefGoogle Scholar
  14. Kumar M, Tiwari S, Murari V, Singh AK, Banerjee T (2015) Wintertime characteristics of aerosols at middle Indo-Gangetic Plain: impacts of regional meteorology and long range transport. Atmos Environ 104:162–175CrossRefGoogle Scholar
  15. Kumar M, Singh RK, Murari V, Singh AK, Singh RS, Banerjee T (2016) Fireworks induced particle pollution: a spatio-temporal analysis. Atmos Res 180:78–91CrossRefGoogle Scholar
  16. Kumar M, Raju MP, Singh RK, Singh AK, Singh RS, Banerjee T (2017a) Wintertime characteristics of aerosols over middle Indo-Gangetic Plain: vertical profile, transport and radiative forcing. Atmos Res 183:268–282CrossRefGoogle Scholar
  17. Kumar M, Raju MP, Singh RS, Banerjee T (2017b) Impact of drought and normal monsoon scenarios on aerosol induced radiative forcing and atmospheric heating rate in Varanasi over middle Indo-Gangetic Plain. J Aerosol Sci 113:95–107CrossRefGoogle Scholar
  18. Kumar M, Parmar KS, Kumar DB, Mhawish A, Broday DM, Mall RK, Banerjee T (2018) Long-term aerosol climatology over Indo-Gangetic Plain: trend, prediction and potential source fields. Atmos Environ 180:37–50CrossRefGoogle Scholar
  19. Mehta M, Singh N (2018) Global trends of columnar and vertically distributed properties of aerosols with emphasis on dust, polluted dust and smoke-inferences from 10-year long CALIOP observations. Remote Sens Environ 208:120–132CrossRefGoogle Scholar
  20. Mhawish A, Banerjee T, Broday DM, Misra A, Tripathi SN (2017) Evaluation of MODIS collection 6 aerosol retrieval algorithms over Indo-Gangetic Plain: implications of aerosols types and mass loading. Remote Sens Environ 201:297–313CrossRefGoogle Scholar
  21. Mhawish A, Kumar M, Mishra AK, Srivastava PK, Banerjee T (2018) Remote sensing of aerosols from space: retrieval of properties and applications. In: Remote Sensing of Aerosols, Clouds, and Precipitation. Elsevier Inc, pp 1–38.  https://doi.org/10.1016/B978-0-12-810437-8.00003-7CrossRefGoogle Scholar
  22. Mhawish A, Banerjee T, Sorek-Hamer M, Lyapustin AI, Broday DM, Chatfield R (2019) Comparison and evaluation of MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia. Remote Sens Environ 224:12–28.  https://doi.org/10.1016/j.rse.2019.01.033CrossRefGoogle Scholar
  23. Pal DK, Bhattacharyya T, Srivastava P, Chandran P, Ray SK, (2009) Soils of the Indo-Gangetic plains: their historical perspective and management. Curr Sci 1193–1202Google Scholar
  24. Rajput P, Sarin MM, Sharma D, Singh D (2014) Characteristics and emission budget of carbonaceous species from post-harvest agricultural-waste burning in source region of the Indo-Gangetic Plain. Tellus B 66:21026.  https://doi.org/10.3402/tellusb.v66.21026CrossRefGoogle Scholar
  25. Schulz M, Textor C, Kinne S, Balkanski Y, Bauer S, Berntsen T. et al (2006) Radiative forcing by aerosols as derived from the AeroCom present-day and pre-industrial simulations. Atmos Chem Phys 6(12):5225–5246CrossRefGoogle Scholar
  26. Sharma AR, Kharol SK, Badarinath KVS, Singh D (2010) Impact of agriculture crop residue burning on atmospheric aerosol loading–a study over Punjab State, India. Ann Geophys 28(2) (09927689)Google Scholar
  27. Singh N, Murari V, Kumar M, Barman SC, Banerjee T (2017a) Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor model. Environ Pollut 223:121–136CrossRefGoogle Scholar
  28. Singh N, Mhawish A, Deboudt K, Singh RS, Banerjee T (2017b) Organic aerosols over Indo-Gangetic plain: sources, distributions and climatic implications. Atmos EnvironGoogle Scholar
  29. Singh N, Banerjee T, Raju MP, Deboudt K, Sorek-Hamer M, Singh RS, Mall RK (2018) Aerosol chemistry, transport, and climatic implications during extreme biomass burning emissions over the Indo-Gangetic Plain. Atmos Chem & Phys 18(19)Google Scholar
  30. Singh N, Mhawish A, Ghosh S, Banerjee T and Mall RK (2019) Attributing mortality from temperature extremes: a time series analysis in Varanasi, India. Sci Total Environ 665:453-464CrossRefGoogle Scholar
  31. Soni K, Parmar KS, Kapoor S (2015) Time series model prediction and trend variability of aerosol optical depth over coal mines in India. Environ Sci Pollut Res 22(5):3652–3671CrossRefGoogle Scholar
  32. Sorek-Hamer M, Cohen A, Levy RC, Ziv B, Broday DM (2013a) Classification of dust days by satellite remotely sensed aerosol products. Int J Remote Sens 34(8):2672–2688CrossRefGoogle Scholar
  33. Sorek-Hamer M, Strawa AW, Chatfield RB, Esswein R, Cohen A, Broday DM (2013b) Improved retrieval of PM2.5 from satellite data products using non-linear methods. Environ Pollut 182:417–423CrossRefGoogle Scholar
  34. 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
  35. Vaishya A, Babu SNS, Jayachandran V, Gogoi MM, Lakshmi NB, Moorthy KK, Satheesh SK (2018) Large contrast in the vertical distribution of aerosol optical properties and radiative effects across the Indo-Gangetic Plain during the SWAAMI–RAWEX campaign. Atmos Chem Phys 18(23):17669–17685CrossRefGoogle Scholar
  36. Van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R, Verduzco C, Villeneuve PJ (2010) Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ Health Perspect 118(6):847CrossRefGoogle Scholar
  37. Vinjamuri KS, Mhawish A, Banerjee T, Sorek-Hamer M, Broday DM, Mall RK, Latif MT (2019) Vertical distribution of smoke aerosols over upper Indo-Gangetic Plain. Under review in Environmental PollutionGoogle Scholar
  38. Wan X, Kang S, Li Q, Rupakheti D, Zhang Q, Guo J, Chen P, Tripathee L, Rupakheti M, Panday AK, Wang W (2017) Organic molecular tracers in the atmospheric aerosols from Lumbini, Nepal, in the northern Indo-Gangetic Plain: influence of biomass burning. Atmos Chem Phys 17:8867–8885.  https://doi.org/10.5194/acp-17-8867-2017CrossRefGoogle Scholar
  39. 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 Ocean Tech 26:2310–2323CrossRefGoogle Scholar
  40. Young A, Vaughan MA (2009) The retrieval of profiles of particulate extinction from Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Data: algorithm description. J Atmos Ocean Tech 26:1105–1119CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Alaa Mhawish
    • 1
  • K. S. Vinjamuri
    • 2
  • Nandita Singh
    • 1
  • Manish Kumar
    • 1
  • Tirthankar Banerjee
    • 1
    Email author
  1. 1.Institute of Environment and Sustainable DevelopmentBanaras Hindu UniversityVaranasiIndia
  2. 2.DST-Mahamana Centre of Excellence in Climate Change ResearchBanaras Hindu UniversityVaranasiIndia

Personalised recommendations