Earth Systems and Environment

, Volume 3, Issue 3, pp 551–562 | Cite as

Analysis of Absorption Characteristics and Source Apportionment of Carbonaceous Aerosol in Arid Region of Western India

  • M. Sateesh
  • V. K. Soni
  • P. V. S. RajuEmail author
  • V. S. Prasad
Original Article


The present work analyses the equivalent Black Carbon (EBC) data obtained using Aethalometer (AE-33) located at India Meteorological Department, Jodhpur, Rajasthan during the year 2016. The annual mean EBC concentration is 5.76 µg m−3 and the monthly mean concentration is maximum (12.12 µg m−3) in January and minimum (1.27 µg m−3) in December. The seasonal mean of wind speeds are 1.94, 2.02, 1.34, 1.02 m s−1 and the calm percentages are 7, 5.7, 28.7, 25.7% during pre-monsoon (MAM), monsoon (JJAS), post-monsoon (ON) and winter (DJF), respectively. The night time EBC concentrations are more than the day time concentrations due to the shallowness of the boundary layer and local anthropogenic activities. The Concentrated Weighted Trajectories (CWT) are calculated using back trajectories ending at 100 m above ground level at Jodhpur station using National Centre for Medium Range Weather Forecast (NCMRWF) Global Forecast System (GFS) based reanalysis T574 data. The CWT, directional source region analysis reveals the effect of long-range transport in the winter season with a 60% of probability of source regions from the W, NW direction of observational site. Source apportionment also carried out by assuming alpha (at 470-, 950-nm wavelengths) close to 1 for anthropogenic emissions and alpha close to 2 for biomass burning aerosols. The monthly mean biomass burning concentration is found maximum (2.58 µg m−3) in November and minimum (0.22 µg m−3) in July.


Equivalent black carbon Seasonal variation Angstrom exponent Ventilation coefficient Concentrated weighted trajectories 



Authors are thankful to NOAA for providing offline HYSPLIT trajectory model used in this study. The authors are also thankful to Director General of Meteorology and Head of NCMRWF for encouragement to carry out this work. The authors are also thankful to the unknown reviewers for their valuable suggestions.


  1. Ashbaugh LL, Malm WC, Sadeh WZ (1985) A residence time probability analysis of sulfur concentrations at grand Canyon National Park. Atmos Environ 19:1263–1270CrossRefGoogle Scholar
  2. Babu SS, Manoj MR, Moorthy KK et al (2013) Trends in aerosol optical depth over Indian region: potential causes and impact indicators. J Geophys Res Atmos 118:11794–11806CrossRefGoogle Scholar
  3. Bäumer D, Vogel B, Versick S, Rinke R, Möhler O, Schnaiter M (2008) Relationship of visibility, aerosol optical thickness and aerosol size distribution in an ageing air mass over South-West Germany. Atmos Environ 42:989–998CrossRefGoogle Scholar
  4. Bond TC, Habib G, Bergstrom RW (2006) Limitations in the enhancement of visible light absorption due to mixing state. J Geophys Res 111:D20211CrossRefGoogle Scholar
  5. Bond TC et al (2013) Bounding the role of black carbon in the climate system: a scientific assessment. J Geophys Res Atmos 118:5380–5552CrossRefGoogle Scholar
  6. Carslaw D (2015) The openair manual open-source tools for analysing air pollution data. King’s College, London, p 287Google Scholar
  7. Carslaw DC, Beevers SD (2013) Characterising and understanding emission sources using bivariate polar plots and k-means clustering. Environ Model Softw 40:325–329CrossRefGoogle Scholar
  8. Carslaw DC, Ropkins K (2012) openair - an R package for air quality data analysis. Environ Model Softw 27–28:52–61CrossRefGoogle Scholar
  9. Chan L, Deng QH, Liu WW, Huang BL, Shi LZ (2012) Characteristics of ventilation coefficient and its impact on urban air pollution. J Cent South Univ Technol 19:615–622CrossRefGoogle Scholar
  10. D’Almeida GA (1986) A model for Saharan dust transport. J Clim Appl Meteorol 25:903–916CrossRefGoogle Scholar
  11. Draxler RR (1998) An Overview of the HYSPLIT_4 Modelling System for Trajectories, Dispersion, and Deposition. Aust Meteorol Mag 47:295–308Google Scholar
  12. Drinovec L, Močnik G, Ježek I, Petit JE, Sciare J, Favez O, Zotter P, Wolf R, Prévôt ASH, Hansen ADA (2013) Indication of aerosol aging by Aethalometer optical absorption measurements 32. In: AAAR annual conference, Sep 2013, Portland, United StatesGoogle Scholar
  13. Drinovec L, Močni G, Zotter P, Prévôt ASH, Ruckstuhl C, Coz E, Rupakheti M, Sciare J, Müller T, Wiedensohler A, Hansen ADA (2015) The “dual-spot” Aethalometer: an improved measurement of aerosol black carbon with real-time loading compensation. Atmos Meas Tech 8:1965–1979CrossRefGoogle Scholar
  14. Drinovec L, Gregoric A, Zotter P et al (2017) The filter-loading effect by ambient aerosols in filter absorption photometers depends on the coating of the sampled particles. Atmos Meas Tech 10:1043–1059CrossRefGoogle Scholar
  15. Eck TF, Holben BN, Sinyuk A, Pinker T, Goloub P, Chen H, Chatenet B, Li Z, Singh RP, Tripathi SN, Reid JS, Giles DM, Dubovik O, O’Neill NT, Smirnov A, Wang P, Xia X (2010) Climatological aspects of the optical properties of fine/coarse mode aerosol mixtures. J Geophys Res 115(D19):1–20CrossRefGoogle Scholar
  16. Engelstaedter S, Tegen I, Washington R (2006) North African dust emissions and transport. Earth Sci Rev 79(1–2):77–100Google Scholar
  17. Ensor DS, Porch WM, Pilat MJ, Charlson RJ (1971) Influence of the atmospheric Aerosol on Albedo. J Appl Meteorol 10:1303–1306CrossRefGoogle Scholar
  18. Fleming ZL, Monks PS, Manning AJ (2012) Review: untangling the influence of air-mass history in interpreting observed atmospheric composition. Atmos Res 104–105:1–39CrossRefGoogle Scholar
  19. Ghiaus C, Allard F, Santamouris M, Georgakis C, Nicol F (2006) Urban environment influence on natural ventilation potential. Build Environ 41:395–406CrossRefGoogle Scholar
  20. Goudie AS, Middleton NJ (2001) Saharan dust storms: nature and consequences. Earth Sci Rev. CrossRefGoogle Scholar
  21. Grange SK, Lewis AC, Carslaw DC (2016) Source apportionment advances using polar plots of bivariate correlation and regression statistics. Atmos Environ 145:128–134CrossRefGoogle Scholar
  22. Haywood JM, Ramaswamy V (1998) Global sensitivity studies of the direct radiative forcing due to anthropogenic sulfate and black carbon aerosols. J Geophys Res Atmos 103:6043–6058CrossRefGoogle Scholar
  23. Healy RM, Sofowote U, Su Y, Debosz J, Noble M, Jeong CH, Wang JM, Hilker N, Evans GJ, Doerksen G, Jones K, Munoz A (2017) Ambient measurements and source apportionment of fossil fuel and biomass burning black carbon in Ontario. Atmos Environ 161:34–47CrossRefGoogle Scholar
  24. Henriksson SV, Pietikäinen JP, Hyvärinen AP, Räisänen R, Kupiainen K, Tonttila J, Hooda R, Lihavainen H, O’Donnell D, Backman L, Klimont Z, Laaksonen A (2014) Spatial distributions and seasonal cycles of aerosol climate effects in India seen in a global climate–aerosol model. Atmos Chem Phys 14:10177–10192CrossRefGoogle Scholar
  25. Henry RC, Chang YS, Spiegelman CH (2002) Locating nearby sources of air pollution by nonparametric regression of atmospheric concentrations on wind direction. Atmos Environ 36(13):2237–2244CrossRefGoogle Scholar
  26. Kaufman YJ, Koren I (2006) Smoke and pollution aerosol effect on cloud cover. Science 313:655–658CrossRefGoogle Scholar
  27. Kirchstetter TW, Novakov T, Hobbs PV (2004) Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon. J Geophys Res D Atmos 109(D21):1–12CrossRefGoogle Scholar
  28. Lack DA, Cappa CD (2010) Impact of brown and clear carbon on light absorption enhancement, single scatter albedo and absorption wavelength dependence of black carbon. Atmos ChemPhys 10:4207–4220CrossRefGoogle Scholar
  29. Moosmüller H, Chakrabarty RK, Arnott WP (2009) Aerosol light absorption and its measurement: a review. J Quant Spectrosc Radiat Transf. 110:844–878CrossRefGoogle Scholar
  30. Moosmüller H, Chakrabarty RK, Ehlers KM, Arnott WP (2011) Absorption Ångström coefficient, brown carbon, and aerosols: basic concepts, bulk matter, and spherical particles. Atmos Chem Phys 11:1217–1225CrossRefGoogle Scholar
  31. Otto S, Bierwirth E, Weinzierl B, Kandler K, Esselborn M, Tesche M, Schladitz A, Wendisch M, Trautmann T (2009) Solar radiative effects of a Saharan dust plume observed during SAMUM assuming spheroidal model particles. Tellus B: Chem Phys Meteorol 61(1):270–296CrossRefGoogle Scholar
  32. Petzold A, Ogren JA, Fiebig M et al (2013) Recommendations for the interpretation of “black carbon” measurements. Atmos Chem Phys Discuss 13:9485–9517CrossRefGoogle Scholar
  33. Prasad VS, Johny CJ, Mali P, Singh SK, Rajagopal EN (2017) Global retrospective analysis using NGFS for the period 2000–2011. Curr Sci 112(2):370–377CrossRefGoogle Scholar
  34. Prather KA, Hatch CD, Grassian VH (2008) Analysis of atmospheric aerosols. Annu Rev Anal Chem 1:485–514CrossRefGoogle Scholar
  35. Prospero JM, Ginoux P, Torres O et al (2002) Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev Geophys 40(1):2.1–2.31CrossRefGoogle Scholar
  36. Ramanathan V, Li F, Ramana MV et al (2007) Atmospheric brown clouds: hemispherical and regional variations in long-range transport, absorption, and radiative forcing. J Geophys Res Atmos 112:1–26CrossRefGoogle Scholar
  37. Russell PB, Bergstrom RW, Shinozuka Y, Clarke AD, DeCarlo PF, Jimenez JL, Livingston JM, Redemann J, Dubovik O, Strawa A (2010) Absorption Angstrom Exponent in AERONET and related data as an indicator of aerosol composition. Atmos Chem Phys 10(3):1155–1169CrossRefGoogle Scholar
  38. Sandradewi J, Prévôt SH, Alfarra MR, Szidat S, Wehrli MN, Ruff M, Weimer S, Lanz VA, Weingartner E, Perron N, Caseiro A, Kasper-Giebl A, Puxbaum H, Wacker L, Baltensperger U (2008a) Comparison of several wood smoke markers and source apportionment methods for wood burning particulate mass. Atmos Chem Phys Discuss 8:8091–8118CrossRefGoogle Scholar
  39. Sandradewi J, Prévôt ASH, Weingartner E et al (2008b) A study of wood burning and traffic aerosols in an Alpine valley using a multi-wavelength Aethalometer. Atmos Environ 42:101–112CrossRefGoogle Scholar
  40. Sateesh M, Soni VK, Raju PVS (2018) Effect of Diwali firecrackers on air quality and aerosol optical properties over a mega city (Delhi) in India. Earth Syst Environ 2(2):293–304CrossRefGoogle Scholar
  41. Singh A, Dey S (2012) Influence of aerosol composition on visibility in megacity Delhi. Atmos Environ 62:367–373CrossRefGoogle Scholar
  42. Singhvi AK, Kar A (2004) The aeolian sedimentation record of the Thar desert. Proc Indian Acad Sci Earth Planet Sci 113(3):371–401Google Scholar
  43. Sirois A, Bottenheim JW (1995) Use of backward trajectories to interpret the 5-year record of PAN and O”SUB 3” ambient air concentrations at Kejimkujik National Park, Nova Scotia. J Geophys Res 100(D2):2867–2881CrossRefGoogle Scholar
  44. Sokolik IN, Toon OB (1996) Direct radiative forcing by anthropogenic airborne mineral aerosols. Nature 381:681–683CrossRefGoogle Scholar
  45. Soni VK, Pandithurai G, Pai DS (2016) Is there a transition of solar radiation from dimming to brightening over India? Atmos Res 169:209–224CrossRefGoogle Scholar
  46. Stein AF, Draxler RR, Rolph GD, Stunder BJB, Cohen MD, Ngan F (2015) NOAA’s hysplit atmospheric transport and dispersion modeling system. Bull Am Meteorol Soc 96:2059–2077CrossRefGoogle Scholar
  47. Stier P, Seinfeld JH, Kinne S, Boucher O (2007) Aerosol absorption and radiative forcing. Atmos Chem Phys Discuss 7:7171–7233CrossRefGoogle Scholar
  48. Stohl A (1996) Trajectory statistics-A new method to establish source-receptor relationships of air pollutants and its application to the transport of particulate sulfate in Europe. Atmos Environ 30:579–587CrossRefGoogle Scholar
  49. Stohl A (1998) Computation, accuracy and applications of trajectories—a review and bibliography. Atmos Environ 32(6):945–1140CrossRefGoogle Scholar
  50. Tegen I, Hollrig P, Chin M, Fung I, Jacob D, Penner J (1997) Contribution of different aerosol species to the global aerosol extinction optical thickness: estimates from model results. J Geophys Res Atmos 102(D20):23895–23915CrossRefGoogle Scholar
  51. Virkkula A, Chi X, Ding A, Shen Y, Nie W, Qi X, Zheng L, Huang X, Xie Y, Wang J, Petäjä T, Kulmala M (2015) On the interpretation of the loading correction of the aethalometer. Atmos Meas Tech 8:4415–4427CrossRefGoogle Scholar
  52. Yu H (2002) Radiative effects of aerosols on the evolution of the atmospheric boundary layer. J Geophys Res 107:4142–4146CrossRefGoogle Scholar
  53. Zotter P, Herich H, Gysel M, El-Haddad I, Zhang Y, Močnik G, Hüglin C, Baltensperger U, Szidat S, Prévôt ASH (2017) Evaluation of the absorption Ångström exponents for traffic and wood burning in the Aethalometer-based source apportionment using radiocarbon measurements of ambient aerosol. Atmos Chem Phys 17:4229–4249CrossRefGoogle Scholar

Copyright information

© King Abdulaziz University and Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Ocean-Atmospheric Science and Technology (COAST)Amity University RajasthanJaipurIndia
  2. 2.Environmental Monitoring and Research CenterIndia Meteorological DepartmentNew DelhiIndia
  3. 3.National Centre for Medium Range Weather ForecastingNoidaIndia

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