Source characterization of PM10 using CMB receptor modeling for the western industrial area of India
,
Abstract
Introduction: Receptor models use the chemical characterisation of particulate matter to determine the source and analyse the source contributions. The main aim of this study is to carry out source apportionment
of PM10 for industrial locations of Vapi and Ankleshwar in Gujarat, using the Chemical Mass Balance (CMB) receptor model.
Materials and methods: At six distinct locations of Ankleshwar and Vapi, respirable dust samplers were used to collect particulate matter on quartz filter sheets for the current study. Filter papers containing PM10 mass were subsequently examined for Water Soluble Ions (WSIs), major and trace elements, elemental and organic carbon followed by source apportionment study.
Results: Using CMB, the contributions obtained for Ankleshwar are 27.85% for crustal or soil dust, 26.31% for fossil fuel combustion, 21.06% for vehicle emissions, 14.20% for secondary aerosols, 9.30% for biomass, and 1.20% for industrial emissions. CMB for Vapi revealed the chief source signatures as fossil fuel combustion including industries contributing 35%, crustal or soil dust contributing 22.90%, biomass burning contributing
19.12%, vehicular emissions contributing 16.18%, and secondary aerosols contributing 6.79%.
Conclusion: By applying the CMB model, the primary source is found to be crustal or soil dust followed by burning fossil fuels, vehicular emissions, and secondary aerosols for Ankleshwar and Vapi, respectively. A quantitative assessment of source contributions to particulate matter is required to create emission control measures. The findings of this study will be beneficial for the environmental management of particle concentrations in the study region.
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Environmental monitoring and assessment. 2013
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Air Quality Research. 2014 Jun;14(6):1685-700.
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coarse and fine particle sources in Delhi by the
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Hazardous Materials. 2007 Jun 1;144(1-2):283-
91.
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India; 2010.
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Samadi SZ. Source apportionment of particulate
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Atmospheric Research. 2011 Aug 1;101(3):752-
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28. Guttikunda SK, Kopakka RV, Dasari
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Hyderabad, India. Environmental monitoring and
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India. Indian Institute of Technology Kanpur,
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30. 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 Apr 2;142(1-2):279-
87.
31. Keerthi R, Selvaraju N, Alen Varghese
L, Anu N. Source apportionment studies for
particulates (PM10) in Kozhikode, South Western
India using a combined receptor model. Chemistry
and Ecology. 2018 Oct 21;34(9):797-817.
32. Chelani AB, Gajghate DG, Devotta S.
Source apportionment of PM10 in Mumbai, India
using CMB model. Bulletin of environmental
contamination and toxicology. 2008 Aug;81:190-
5.
33. Pipalatkar P, Khaparde VV, Gajghate DG,
Bawase MA. Source apportionment of PM2. 5
using a CMB model for a centrally located Indian
city. Aerosol and Air Quality Research. 2014
Mar;14(3):1089-99.
34. Li TC, Yuan CS, Huang HC, Lee CL,
Wu SP, Tong C. Inter-comparison of seasonal
variation, chemical characteristics, and source
identification of atmospheric fine particles on
both sides of the Taiwan Strait. Scientific reports.
2016 Mar 14;6(1):1-6
Sikdar PK, Webster RD, Wang X. Trace element
composition of PM2.5 and PM10 from Kolkata–a
heavily polluted Indian metropolis. Atmospheric
Pollution Research. 2015 Sep 1;6(5):742-50.
2. Belis CA, Karagulian F, Larsen BR,
Hopke PK. Critical review and meta-analysis of
ambient particulate matter source apportionment
using receptor models in Europe. Atmospheric
Environment. 2013 Apr 1;69:94-108.
3. Nabizadeh R, Yousefi M, Azimi F. Study
of particle number size distributions at Azadi
terminal in Tehran, comparing high-traffic and
no traffic area. MethodsX. 2018 Jan 1;5:1549-55.
4. Nihalani SA, Khambete AK, Jariwala
ND. Receptor modelling for particulate matter:
review of Indian scenario. Asian Journal of
Water, Environment and Pollution. 2020 Jan
1;17(1):105-12.
5. Saradhi IV, Prathibha P, Hopke PK,
Pandit GG, Puranik VD. Source apportionment of coarse and fine particulate matter at Navi
Mumbai, India. Aerosol and Air Quality Research.
2008;8(4):423-36.
6. Hopke PK, Ito K, Mar T, Christensen WF,
Eatough DJ, Henry RC, Kim E, Laden F, Lall
R, Larson TV, Liu H. PM source apportionment
and health effects: 1. Intercomparison of source
apportionment results. Journal of exposure
science & environmental epidemiology. 2006
May;16(3):275-86.
7. Pant P, Harrison RM. Critical review of
receptor modelling for particulate matter: a case
study of India. Atmospheric Environment. 2012
Mar 1;49:1-2.
8. Parthasarathy K, Sahu SK, Pandit GG.
Comparison of two receptor model techniques
for the size fractionated particulate matter
source apportionment. Aerosol and Air Quality
Research. 2016 Jun;16(6):1497-508.
9. Viana M, Kuhlbusch TA, Querol X,
Alastuey A, Harrison RM, Hopke PK, Winiwarter
W, Vallius M, Szidat S, Prévôt AS, Hueglin C.
Source apportionment of particulate matter in
Europe: a review of methods and results. Journal
of aerosol science. 2008 Oct 1;39(10):827-49.
10. Sudheer AK, Rengarajan R. Atmospheric
mineral dust and trace metals over urban
environment in western India during winter.
Aerosol and Air Quality Research. 2012
May;12(5):923-33.
11. Sharma SK, Mandal TK, Jain S, Sharma
A, Saxena M. Source apportionment of PM2.5
in Delhi, India using PMF model. Bulletin of
environmental contamination and toxicology.
2016 Aug;97:286-93.
12. Matawle J, Pervez S, Dewangan S, Tiwari
S, Bisht DS, Pervez YF. PM2.5 chemical source
profiles of emissions resulting from industrial
and domestic burning activities in India. Aerosol
and Air Quality Research. 2014 Jul;14(7):2051-
66.
13. Srimuruganandam B, Nagendra SS.
Source characterization of PM10 and PM2. 5 mass
using a chemical mass balance model at urban
roadside. Science of the total environment. 2012
Sep 1;433:8-19.
14. CSIR-NEERI, PM10 and PM2.5 source
apportionment study and development of
emission inventory of Twin cities Kolkata and
Howrah of west Bengal, 2019.
15. Clements AL, Fraser MP, Herckes P,
Solomon PA. Chemical mass balance source
apportionment of fine and PM10 in the Desert
Southwest, USA. AIMS Environmental Science.
2016;3(1):115-32.
16. Gadkari N, Pervez S. Source
apportionment of personal exposure of fine
particulates among school communities in India.
Environmental monitoring and assessment. 2008
Jul;142:227-41.
17. Selvaraju N, Pushpavanam S, Anu N.
A holistic approach combining factor analysis,
positive matrix factorization, and chemical
mass balance applied to receptor modeling.
Environmental monitoring and assessment. 2013
Dec;185:10115-29.
18. Chowdhury Z, Zheng M, Schauer JJ,
Sheesley RJ, Salmon LG, Cass GR, Russell
AG. Speciation of ambient fine organic carbon
particles and source apportionment of PM2.5 in
Indian cities. Journal of Geophysical Research:
Atmospheres. 2007 Aug 16;112(D15).
19. Thammadi SP, Pisini SK, Shukla SK.
Estimation of PM2.5 emissions and source
apportionment using receptor and dispersion
models. International Journal of Environmental
and Ecological Engineering. 2018 Jun
1;12(7):513-9.
20. Behera SN, Sharma M. Reconstructing
primary and secondary components of PM2.5
composition for an urban atmosphere. Aerosol Science
and Technology. 2010 Sep 30;44(11):983-92.
21. Behera SN, Sharma M. Reconstructing
primary and secondary components of PM2.5
composition for an urban atmosphere. Aerosol
Science and Technology. 2010 Sep 30;44(11):983-
92.
22. Pipal AS, Jan R, Satsangi PG, Tiwari S,
Taneja A. Study of surface morphology, elemental
composition and origin of atmospheric aerosols
(PM2.5 and PM10) over Agra, India. Aerosol and
Air Quality Research. 2014 Jun;14(6):1685-700.
23. Chakraborty A, Gupta T. Chemical
characterization and source apportionment of
submicron (PM1) aerosol in Kanpur region,
India. Aerosol and Air Quality Research. 2010
May;10(5):433-45.
24. CPCB, In: C.P.C.B (Ed.), Air Quality
Assessment, Emissions Inventory, and Source
Apportionment Studies Bangalore, India. The
Energy and Resources Institute, India. 2010.
25. Srivastava A, Jain VK. Seasonal trends in
coarse and fine particle sources in Delhi by the
chemical mass balance receptor model. Journal of
Hazardous Materials. 2007 Jun 1;144(1-2):283-
91.
26. CPCB, In: C.P.C.B (Ed.), Air Quality
Assessment, Emissions Inventory, and Source
Apportionment Studies Delhi, India. National
Environmental Engineering Research Institute,
India; 2010.
27. Gummeneni S, Yusup YB, Chavali M,
Samadi SZ. Source apportionment of particulate
matter in the ambient air of Hyderabad city, India.
Atmospheric Research. 2011 Aug 1;101(3):752-
64.
28. Guttikunda SK, Kopakka RV, Dasari
P, Gertler AW. Receptor model-based source
apportionment of particulate pollution in
Hyderabad, India. Environmental monitoring and
assessment. 2013 Jul;185:5585-93.
29. CPCB, In: C.P.C.B (Ed.), Air Quality
Assessment, Emissions Inventory and Source
Apportionment Studies for Kanpur City,
India. Indian Institute of Technology Kanpur,
India;2010.
30. 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 Apr 2;142(1-2):279-
87.
31. Keerthi R, Selvaraju N, Alen Varghese
L, Anu N. Source apportionment studies for
particulates (PM10) in Kozhikode, South Western
India using a combined receptor model. Chemistry
and Ecology. 2018 Oct 21;34(9):797-817.
32. Chelani AB, Gajghate DG, Devotta S.
Source apportionment of PM10 in Mumbai, India
using CMB model. Bulletin of environmental
contamination and toxicology. 2008 Aug;81:190-
5.
33. Pipalatkar P, Khaparde VV, Gajghate DG,
Bawase MA. Source apportionment of PM2. 5
using a CMB model for a centrally located Indian
city. Aerosol and Air Quality Research. 2014
Mar;14(3):1089-99.
34. Li TC, Yuan CS, Huang HC, Lee CL,
Wu SP, Tong C. Inter-comparison of seasonal
variation, chemical characteristics, and source
identification of atmospheric fine particles on
both sides of the Taiwan Strait. Scientific reports.
2016 Mar 14;6(1):1-6
| Files | ||
| Issue | Vol 8 No 1 (2023): Winter 2023 | |
| Section | Original Research | |
| DOI | https://doi.org/10.18502/japh.v8i1.12029 | |
| Keywords | ||
| Particulate matter; Vehicle emissions; aerosols; Ankleshwar; Vapi | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Nihalani S, Khambete A, Jariwala N. Source characterization of PM10 using CMB receptor modeling for the western industrial area of India. JAPH. 2023;8(1):43-58.
