Total views : 246

Air Quality Management through Receptor Modelling


  • School of Civil and Chemical Engineering, VIT-University, Vellore - 632014, Tamil Nadu, India


Background: One of the major risk factors to human health is air pollution. It contains a complex mixture of gaseous and particulate pollutants which possesses dynamic properties due to the combination of anthropogenic activities and meteorological conditions. The precise characteristics of the ambient air pollutants in a given locale depend on the source origin, which in-turn is a function of economic, social and technological factors. Hence it is very essential to identify the source origins. Methods: Receptor models have been widely used in source identification and their contribution of Airborne Particulate Matter (APM). Chemical Mass Balance model (CMB) is one among them. This paper presents a critical brief reappraisal of the source apportionment of APM through CMB model. Findings: The review shows that CMB model have been frequently used and proved to be an important tool in source apportionment studies without any pre requisite of source and meteorological data sets. Applications: Source apportionment through CMB model is one among the way to control and manage the PM10 and PM2.5 emissions in an informed way.


Air Quality, CMB Model, Management, Particulate Matter, Receptor Modelling, Source Apportionment.

Full Text:

 |  (PDF views: 241)


  • CPCB. Air quality monitoring, Emission inventory & source apportionment studies for Indian cities, Central Pollution Control Board, New Delhi; 2010.
  • Larssen S, Gronskei KE, Hanegraaf MC, Jansen H, Kuik OJ, Oosterhuis FH, Olsthoorn XA. Urban air quality management strategy in Asia–Guidebook. URBAIR. World Bank Publications; 1997.
  • Longhurst JWS, Elsom DM. A theoretical perspective on air quality management in the United Kingdom. Baldasano JM, Brebbia CA, Power H, Zannetti P, editors. Air pollutionII, Vol. 2, Computational Mechanics Inc. Southampton, Boston; 1997.
  • Nagendra SMS, Khare M. Principal component analysis of urban traffic characteristic and meteorological data. Journal of Transportation Research. 2003; 8:285–97.
  • U.S.EPA. Air quality criteria for particulate matter. Vol-1, 2 and 3. Research Triangle Park, NC; 2004.
  • Watson JG, Zhu T, Chow JC, Engelbrecht J, Fujita EM, Wilson WE. Receptor modeling application framework for particle source apportionment. Chemosphere. 2002; 49:1093–136.
  • Belis CA. European guide on air pollution source apportionment with receptor models joint research centre report EUR 26080 EN; 2014.
  • Colvile RN, Gómez-Perales JE, Nieuwenhuijsen MJ. Use of dispersion modelling to assess road-user exposure to PM2.5 and its source apportionment. Atmospheric Environment. 2003; 37(20):2773–82.
  • Laupsa H, Denby B, Larssen S, Schaug J. Source apportionment of particulate matter (PM2.5) in an urban area using dispersion, receptor and inverse modelling. Atmospheric Environment. 2009; 43(31):4733–44.
  • Henry RC, Lewis CW, Hopke PK, Williamson HJ. Review of receptor model fundamentals. Atmospheric Environment. 1984; 18:1507–15.
  • Hopke PK. Receptor modeling for air quality management. Amsterdam; 1991.
  • Lai CH, Chen KS, Ho YT, Peng YP, Chou Y. Receptor modeling of source contributions to atmospheric hydrocarbons in urban Kaohsiung, Taiwan. Atmospheric Environment. 2005; 39:4543–59.
  • Gu J, Pitz M, Schenlle-Kreis J, Diemer J, Reller A, Zimmermann R, Soentgen J, Stoelzel M, Wichmann HE, Peters A, Cyrys J. Source apportionment of ambient particles: comparison of positive matrix factorization analysis applied to particle size distribution and chemical composition data. Atmospheric Environment. 2011; 45:1849–57.
  • Cooper JA, Watson JG. Receptor oriented methods of air particulate source apportionment. Journal of Air Pollution Control Association. 1980; 30(10):1116–25.
  • Ramadan Z, Eickhout B, Song Xin-Hua, Buydens LMC, Hopke PK. Comparison of positive matrix factorization and multilinear engine for the source apportionment of particulate pollutants. Chemometrics and Intelligent Laboratory Systems. 2003; 66:15–28.
  • Harrison RM, Smith DJT, Luhana L. Source apportionment of atmospheric polycyclic aromatic hydrocarbons collected from an urban location in Birmingham, U.K. Environmental Science and Technology. 1996; 30:825–32.
  • Robinson AL, Subramanian R, Donahue NM, BernardoBricker A, Rogge WF. Source apportionment of molecular markers and organic aerosol. Environmental Science and Technology. 2006; 40:7811–19.
  • Winchester JW, Nilfong GD. Water pollution in Lake Michigan by trace elements from aerosol fallout. Water Air Soil Pollution. 1971; 1:50–64
  • Hidy GM, Friedlander SK. The nature of Los Angeles aerosol. Proceedings of 2nd IUAPPA Clean Air Congress, Washington DC; 1972.
  • Kneip TJ, Kleinman MT, Eisenbud M. Relative contribution of emission sources to the total airborne particulates in New York city. Proceedings of 3rd IUAPPA Clean Air Congress; 1972.
  • Gordon GE. Receptor models. Environmental Science and Technology. 1980; 14:792–800.
  • Chow JC, Watson JG, Ono DM, Mathai CV. PM10 standards and nontraditional particulate source controls: A summary of the A&WMA;/EPA international specialty conference. Journal of Air and Waste Management Association. 1993; 43:74–84.
  • Coulter CT. EPA-CMB8.2-User’s manual, Air Quality Modeling Group Emissions, Monitoring and Analysis Division Office of Air Quality Planning and Standards; Research Triangle Park, NC; 2004.
  • Srimuruganandam B, Nagendra SMS. Source characterization of PM10 and PM2.5 mass using a chemical mass balance model at urban roadside. Science of the Total Environment. 2012; 433:8–19.
  • Abu-Allaban M, Gertler AW, Lowenthal DH. A preliminary apportionment of the sources of ambient PM10, PM2.5 and VOCs in Cairo. Atmospheric Environment. 2002; 36:5549– 57.
  • Samara C, Kouimtzis TH, Tsitouridou R, Kanias G, Simeonov V. Chemical mass balance source apportionment of PM10 in an industrialized urban area of Northern Greece. Atmospheric Environment. 2003; 37:41–54.
  • Sawant AA, Na K, Zhu X, Cocker DR. Chemical characterization of outdoor PM2.5, gas-phase compounds in Mira Loma, California. Atmospheric Environment. 2004; 38:5517–28.
  • Zheng M, Salmon LG, Schauer JJ, Zeng L, Kiang CS, Zhang Y, Cass GR. Seasonal trends in PM2.5 source contributions in Beijing, China. Atmospheric Environment. 2005; 39:3967–76.
  • Park SS, Kim YJ. Source contributions to fine particulate matter in an urban atmosphere. Chemosphere. 2005; 59:217–26.
  • Bi X, Feng Y, Wu J, Wang Y, Zhu T. Source apportionment of PM10 in six cities of northern China. Atmospheric Environment. 2007; 41:903–12.
  • Srivastava A, Jain K. Seasonal trends in coarse and fine particle sources in Delhi by the chemical mass balance receptor model. Journal of Hazardous Materials. 2007; 144:283–91.
  • Gupta AK, Karar K, Srivastava A. Chemical mass balance source apportionment of PM10 and TSP in residential and industrial sites of an urban region of Kolkata, India. Journal of Hazardous Materials. 2007; 142:279–87.
  • Srivastava A, Jain VK. Source apportionment of suspended particulate in a clean area of Delhi: a note. Transportation Research, Part D. 2008; 13:59–63.
  • Vianaa M, Pandolfi M, Minguillon MC, Querol X, Alastuey A, Monfort E, Celades I. Inter-comparison of receptor models for PM source apportionment: Case study in an industrial area. Atmospheric Environment. 2008; 42:3820– 32.
  • Yatkin S, Bayram A. Source apportionment of PM10 and PM2.5 using positive matrix factorization and chemical mass balance in Izmir, Turkey. Science of the Total Environment. 2008; 390:109–123.
  • Gummeneni S, Yusup YB, Chavali M, Samadi SZ. Source apportionment of particulate matter in the ambient air of Hyderabad city, India. Atmospheric Research. 2011; 101(3):752–64.
  • Hua Y, Cheng Z, Wang S, Jiang J, Chen D, Cai S, Fu X, Fu Q, Chen C, Xu B, Yu J. Characteristics and source apportionment of PM2.5 during a fall heavy haze episode in the Yangtze River Delta of China. Atmospheric Environment; 2015 Dec; 123(B):380–91.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.