Characterisation and source apportionment of atmospheric particulate matter in an industrial cluster of Western India

Document Type : Research Paper


Research Scholar Civil Engineering Department S V National Institute of Technology, Icchanath, Surat Gujarat, India


Pollution from atmospheric particulates is a severe environmental problem of universal concern. Fine and ultra-fine particulates harbour the ability to enter the bloodstream and carry with them trace metals like copper, cadmium, iron, lead, and zinc that can cause toxic and carcinogenic effects. This necessitates an increased emphasis on the detailed chemical characterisation of atmospheric particulates. The current study identified six locations in the Vapi industrial area. In these six locations, coarse particulate matter (PM10) samples were collected simultaneously for 20 days to determine the Elemental Carbon (EC), Organic Carbon (OC), Water-soluble ions (WSIs), and major and trace elements. The concentration of PM10 was observed to be in the range of 115.88 to 226.5 μg/m3, exceeding the NAAQS standard value of 100 ug/m3. The chemical analysis results suggested contributions from total carbon, water-soluble ions, and elements varied between 45 to 48%, 20 to 23%, and 29 to 33% of PM10 mass, respectively. Chemical mass balance (CMB) and Positive matrix factorisation (PMF) models were employed separately for carrying out source apportionment studies. CMB demonstrated influence from various sources: 35% from fossil fuel combustion that included industries, 22.90% from crustal or soil dust, 19.12% from biomass burning, 16.18% from vehicular emissions, and 6.79 % from secondary particulates. The PMF receptor model showed the influence from various sources as 25.75 % from fossil fuel combustion, 22.13 % from crustal or soil dust, 16.95% from vehicular emissions, 14.53% from biomass burning, 11.49% from industrial emissions, and 9.16% from secondary aerosols. Thus, this study shall help in formulating pollution abetment strategies.

Graphical Abstract

Characterisation and source apportionment of atmospheric particulate matter in an industrial cluster of Western India


Main Subjects

[1] Gummeneni, S., Yusup, Y. B., Chavali, M., Samadi, S. Z. (2011). Source apportionment of particulate matter in the ambient air of Hyderabad city, India. Atmospheric Research, 101(3), 752-764.
[2] CPCB, 2010. In: C.P.C.B (Ed.), Air Quality Assessment, Emissions Inventory, and Source Apportionment Studies Bangalore, India. The Energy and Resources Institute, India.
[3] CPCB, 2010. In: C.P.C.B (Ed.), Air Quality Assessment, Emissions Inventory, and Source Apportionment Studies Delhi, India. National Environmental Engineering Research Institute, India.
[4] Assessment, A. Q. (2010). Emission Inventory and Source Apportionment Studies: Mumbai. National Environmental Engineering Research Institute, CPCB: New Delhi, India.
[5] Gupta, A. K., Karar, K., Srivastava, A. (2007). 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,142(1-2), 279-287.
[6] Agarwal, A., Satsangi, A., Lakhani, A., Kumari, K. M. (2020). Seasonal and spatial variability of secondary inorganic aerosols in PM2. 5 at Agra: Source apportionment through receptor models. Chemosphere, 242, 125132.
[7] Nihalani, S. A., Khambete, A. K., Jariwala, N. D. (2020). Receptor modelling for particulate matter: review of Indian scenario. Asian Journal of Water, Environment and Pollution,17(1), 105-112.
[8] Jain, S., Sharma, S. K., Choudhary, N., Masiwal, R., Saxena, M., Sharma, A., Sharma, C. (2017). Chemical characteristics and source apportionment of PM 2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India. Environmental Science and Pollution Research, 24, 14637-14656.
[9] Banerjee, T., Murari, V., Kumar, M., & Raju, M. P. (2015). Source apportionment of airborne particulates through receptor modeling: Indian scenario. Atmospheric Research, 164, 167-187.
[10] Hopke, P. K., Ito, K., Mar, T., Christensen, W. F., Eatough, D. J., Henry, R. C., Thurston, G. D. (2006). PM source apportionment and health effects: 1. Intercomparison of source apportionment results. Journal of Exposure Science and Environmental Epidemiology, 16(3), 275-286.
[11] Pant, P., Harrison, R. M. (2012). Critical review of receptor modelling for particulate matter: a case study of India. Atmospheric Environment, 49, 1-12.
[12] GoG (2022), Performance Audit of Air Pollution Control, Comptroller and Auditor General of India, Government of Gujarat.
[13] GoG (2017) State of Environment, industrial report, Government of Gujarat.
[14] Chelani, A. B., Gajghate, D. G., Devotta, S. (2008). Source apportionment of PM10 in Mumbai, India using the CMB model. Bulletin of Environmental Contamination and Toxicology, 81(2), 190-195.
[15] Police, S., Sahu, S. K., Pandit, G. G. (2016). Chemical characterization of atmospheric particulate matter and their source apportionment at an emerging industrial coastal city, Visakhapatnam, India. Atmospheric Pollution Research, 7(4), 725-733.
[16] Parthasarathy, K., Sahu, S. K., Pandit, G. G. (2016). Comparison of two receptor model techniques for the size-fractionated particulate matter source apportionment. Aerosol and Air Quality Research, 16(6), 1497-1508.
[17] Guttikunda, S. K., Kopakka, R. V., Dasari, P., Gertler, A. W. (2013). Receptor model-based source apportionment of particulate pollution in Hyderabad, India. Environmental Monitoring and Assessment, 185(7), 5585-5593.
[18] Keerthi, R., Selvaraju, N., Alen Varghese, L., Anu, N. (2018). Source apportionment studies for particulates (PM10) in Kozhikode, South Western India using a combined receptor model. Chemistry and Ecology, 34(9), 797-817.
[19] Pipalatkar, P., Khaparde, V. V., Gajghate, D. G., Bawase, M. A. (2014). Source apportionment of PM2. 5 using a CMB model for a centrally located Indian city. Aerosol and Air Quality Research, 14, 1089–1099.
[20] Belis C. A., Karagulian, F., Larsen, B. R., Hopke, P. K. (2013). Critical review and meta-analysis of ambient particulate matter source apportionment using receptor models in Europe. Atmospheric Environment, 69, 94-108.
[21] Srimuruganandam, B., Nagendra, S. S. (2012). Application of positive matrix factorization in characterization of PM10 and PM2.5 emission sources at the urban roadside. Chemosphere, 88(1), 120-130.
[22] Selvaraju, N., Pushpavanam, S., Anu, N. (2013). A holistic approach combining factor analysis, positive matrix factorization, and chemical mass balance applied to receptor modeling. Environmental Monitoring and Assessment,185, 10115-10129
[23] Rai, P., Furger, M., El Haddad, I., Kumar, V., Wang, L., Singh, A., Prévôt, A. S. (2020). Real-time measurement and source apportionment of elements in Delhi's atmosphere. Science of the Total Environment, 742, 140332.
[24] Gupta, I., Salunkhe, A., Kumar, R. (2012). Source apportionment of PM 10 by positive matrix factorization in urban area of Mumbai, India. The Scientific World Journal, 585791.
[25] Jain, S., Sharma, S.K., Srivastava, M.K., Chaterjee, A., Singh, R.K., Saxena, M., and Mandal, T.K., (2019). Source apportionment of PM10 over three tropical urban atmospheres at indo-gangetic plain of India: an approach using different receptor models. Archives of Environmental Contamination and Toxicology, 76(1), pp.114-128.
[26] Jain, S., Sharma, S. K., Vijayan, N., Mandal, T. K. (2020). Seasonal characteristics of aerosols (PM2. 5 and PM10) and their source apportionment using PMF: A four-year study over Delhi, India. Environmental Pollution, 262, 114337.
[27] Sharma, S. K., Sharma, A., Saxena, M., Choudhary, N., Masiwal, R., Mandal, T. K., & Sharma, C. (2016). Chemical characterization and source apportionment of aerosol at an urban area of Central Delhi, India. Atmospheric Pollution Research, 7(1), 110-121. 2015.08.002
[28] Raman, R. S., Ramachandran, S., & Rastogi, N. (2010). Source identification of ambient aerosols over an urban region in western India. Journal of Environmental Monitoring, 12(6), 1330-1340.
[29] Saggu, G. S., Mittal, S. K. (2020). Source apportionment of PM10 by positive matrix factorization model at a source region of biomass burning. Journal of Environmental Management,266, 110545.
[30] Sharma, S.K., Mandal, T.K., Jain, S., Sharma, A., and Saxena, M., (2016). Source apportionment of PM 2.5 in Delhi, India using PMF model Bulletin of Environmental Contamination and Toxicology, 97(2), pp.286-293.
[31] Soni, A., Kumar, U., Prabhu, V., Shridhar, V. (2020). Characterization, source apportionment, and carcinogenic risk assessment of atmospheric particulate matter at Dehradun, situated in the Foothills of the Himalayas. Journal of Atmospheric and Solar-Terrestrial Physics, 199, 105205.
[32] Sharma, S. K., Mandal, T. K., Saxena, M., Sharma, A., Gautam, R. (2014). Source apportionment of PM10 by using positive matrix factorization at an urban site in Delhi, India. Urban climate, 10, 656-670.
[33] Sharma, S. K., Mandal, T. K. (2017). Chemical composition of fine mode particulate matter (PM2.5) in an urban area of Delhi, India and its source apportionment. Urban Climate, 21, 106-122.
[34] TERI (2021), Source Apportionment Study and Preparation of Air Quality Action Plan for Surat City, The Energy and Resources Institute (TERI), New Delhi.
[35] CPCB (2009), National Ambient Air Quality Standards, Central Pollution Control Board.
[36] Srivastava, A., Jain, V. K. (2007). Seasonal trends in coarse and fine particle sources in Delhi by the chemical mass balance receptor model. Journal of Hazardous Materials, 144(1-2), 283-291.
[37] Sudheer, A. K., Rengarajan, R. (2012). Atmospheric mineral dust and trace metals over urban environment in western India during winter. Aerosol and Air Quality Research,12(5), 923-933.
[38] Tiwari, S., Pervez, S., Cinzia, P., Bisht, D. S., Kumar, A., Chate, D. M. (2013). Chemical characterization of atmospheric particulate matter in Delhi, India, Part II: Source apportionment studies using PMF 3.0.
[39] Singhai, A., Habib, G., Raman, R.S., and Gupta, T., (2017). Chemical characterization of PM 1.0 aerosol in Delhi and source apportionment using positive matrix factorization. Environmental Science and Pollution Research, 24(1), pp.445-462.