Receptor modelling of air pollutants for source apportionment on coal-based anthropogenic activities

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Authors

  • Aishwarya Mishra
     ,IN
  • Sanjay Kumar Palei
     ,IN
  • Netai Chandra Karmakar
     ,IN

DOI:

https://doi.org/10.18311/jmmf/2020/27766

Keywords:

Source apportionment, air pollutants, positive matrix factorization, coal mine, thermal power plants

Abstract

Identification of sources of air pollutants plays a predominant role in coal-based anthropogenic activities to manage the air quality. Coal mining and thermal power plants (TPPs) are two multi-activity-centred coal-based sources that affect the ambient air quality of the region. Among five air pollutants under consideration, the annual average concentration of PM10 and PM2.5 exceeded the Indian Central Pollution Control Board (CPCB) prescribed annual standard limit, and the probability of exceedance of daily average concentration for PM10 was 80.3% and that of PM2.5 was 60.7%. Therefore, in this paper, pollutants' mass concentration data has been used to quantify the proportionate contribution of the sources using Positive Matrix Factorization (PMF) analysis for five pollutants. In addition, correlation analysis and literature survey has been used to designate the sources. The PMF analysis revealed that 74.4% of PM10 are emitted from coal mining and its allied activities, and 25.6% by TPP; whereas TPPs contribute 57.6% of PM2.5 emission and 42.4% is by coal mining and its allied activities. Source apportionment of pollutants can help policy-makers and company management to devise suitable short-term as well as longterm emission control measures to manage particulate pollutants.

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Published

2021-05-12

How to Cite

Mishra, A., Kumar Palei, S., & Chandra Karmakar, N. (2021). Receptor modelling of air pollutants for source apportionment on coal-based anthropogenic activities. Journal of Mines, Metals and Fuels, 68(9), 293–303. https://doi.org/10.18311/jmmf/2020/27766

Issue

Volume 68, Issue 9, September 2020

Section

Articles
Crossref
Scopus
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Europe PMC
Received 2021-05-11
Accepted 2021-05-11
Published 2021-05-12

 

References

Begum B.A., Biswas S.K., Hopke P.K. (2007): Source Apportionment of Air Particulate Matter by Chemical Mass Balance ( CMB ) and Comparison with Positive Matrix Factorization ( PMF ) Model. Aerosol Air Qual Res 7:446–468. https://doi.org/10.4209/aaqr. 2006.10.0021

Brunekreef B, Holgate S.T. (2002): Air pollution and health. Lancet 360:1233–1242. https://doi.org/10.1016/ S0140-6736(02)11274-8

Cesari D, Amato F, Pandolfi M, et al (2016): An intercomparison of PM 10 source apportionment using PCA and PMF receptor models in three European sites. Environ Sci Pollut Res 15133–15148. https://doi.org/10.1007/s11356-016-6599-z

Coal India Limited (2018): Annual Report 2017-18, Northern Coalfields Limited, Coal India Limited

Contini D, Genga A, Cesari D, et al (2010): Characterisation and source apportionment of PM10 in an urban background site in Lecce. Atmos Res 95:40–54. https://doi.org/10.1016/j.atmosres. 2009.07.010

Draxler RR, Rolph GD (2012): Evaluation of the Transfer Coefficient Matrix ( TCM ) approach to model the atmospheric radionuclide air concentrations from Fukushima. J Geophys Res 117:1–10. https://doi.org/10.1029/2011JD017205

Elbayoumi M, Ramli N.A., Md Yusof NFF, Madhoun W Al (2014): The effect of seasonal variation on indoor and outdoor carbon monoxide concentrations in Eastern Mediterranean climate. Atmos Pollut Res 5:315–324. https://doi.org/10.5094/APR.2014.037

Givehchi R, Arhami M, Tajrishy M (2013): Contribution of the Middle Eastern dust source areas to PM 10 levels in urban receptors/: Case study of Tehran , Iran. Atmos Environ 75:287–295. https://doi.org/10.1016/j.atmosenv.2013.04.039

Guttikunda S.K., Goel R (2013): Health impacts of particulate pollution in a megacity ” Delhi , India. Environ Dev 6:8–20. https://doi.org/doi.org/10.1016/j.envdev.2012.12.002

Hopke P.K. (2016): Review of receptor modelling methods for source apportionment Review of receptor modelling methods for source apportionment. J Air Waste Manage Assoc 2247:237–259. https://doi.org/10.1080/10962247.2016.1140693

Jain S, Sharma S.K', Mandal T.K., Saxena M (2018): Source apportionment of PM 10 in Delhi , India using PCA/APCS , UNMIX and PMF. Particuology 37:107– 118. https://doi.org/doi.org/10.1016/j.partic.2017.05.009

Juda-rezler K, Reizer M, Oudinet J (2011): Determination and analysis of PM 10 source apportionment during episodes of air pollution in Central Eastern European urban areas/: The case of wintertime 2006. Atmos Environ 45:6557–6566. https://doi.org/10.1016/j.atmosenv.2011.08.020

Kebin H, Qiang Z, Hong H (2002): Types and amounts of vehicular emissions. In: Li Q (ed) Point sources of pollution: Local effects and it's control. UNESCO, pp 64–84

Kim E, Hopke PK (2012): Source Identifications of Airborne Fine Particles Using Positive Matrix Factorization and U . S . Environmental Protection Agency Positive Matrix Factorization Source Identifications of Airborne Fine Particles Using Positive Matrix Factorization and U . S. J Air Waste Manage Assoc 2247:811–819. https://doi.org/10.3155/ 1047-3289.57.7.811

Kim S, Kim T, Yi S, Heo J (2018): Source apportionment of PM 2.5 using positive matrix factorization ( PMF ) at a rural site in Korea. J Environ Manage 214:325– 334. https://doi.org/doi.org/10.1016/j.jenvman. 2018.03.027

Kumar A.V., Patil R.S., Nambi K.S.V. (2001): Source apportionment of suspended particulate matter at two traffic junctions in Mumbai , India. Atmos Environ 35:4245–4251. https://doi.org/10.1016/S1352- 2310(01)00258-8

Lee E, Chan C.K., Paatero P (1999): Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong. Atmos Environ 33:3201–3212. https://doi.org/10.1016/S1352- 2310(99)00113-2

Lippmann M (1998): The 1997 US EPA Standards for Particulate Matter and Ozone. R Soc Chem 10:75–99. https://doi.org/doi.org/10.1039/9781847550095-00075

Mallik C, Mahapatra P.S., Kumar P, et al (2019): Influence of regional emissions on SO2 concentrations over Bhubaneswar, a capital city in eastern India downwind of the Indian SO2 hotspots. Atmos Environ 209:220–232. https://doi.org/10.1016/j.atmosenv. 2019.04.006

Mansha M, Ghauri B, Rahman S, Amman A (2012): Characterization and source apportionment of ambient air particulate matter (PM2.5) in Karachi. Sci Total Environ 425:176–183. https://doi.org/10.1016/j.scitotenv.2011.10.056

Nelson P.F. (2007): Trace Metal Emissions in Fine Particles from Coal Combustion. Energy & Fuels 21:477–484. https://doi.org/10.1021/ef060405q

Norris G, Duvall R, Brown S, Bai S (2014): EPA Positive Matrix Factorization (PM F) 5.0 Fundamentals and User Guide. U.S. Environmental Protection Agency Office of Research and Development Washington, DC 20460

Ostro B.D., Hurley S, Lipsett M.J. (1999): Air pollution and daily mortality in the Coachella valley, california: A study of PM10 dominated by coarse particles. Environ Res 81:231–238. https://doi.org/10.1006/enrs.1999.3978

Paatero P, Eberly S, Brown S.G., Norris G.A. (2014): Methods for estimating uncertainty in factor analytic solutions. Atmos Meas Tech 781–797. https://doi.org/10.5194/amt-7-781-2014

Paatero P, Hopke P.K., Begum B.A., Biswas S.K. (2005): A graphical diagnostic method for assessing the rotation in factor analytical models of atmospheric pollution. Atmos Environ 39:193–201. https://doi.org/10.1016/j.atmosenv.2004.08.018

Paatero P, Hopke P.K., Song XH, Ramadan Z (2002): Understanding and controlling rotations in factor analytic models. Chemom Intell Lab Syst 60:253–264. https://doi.org/10.1016/S0169-7439(01)00200-3

Paatero P, Tappert U (1994): Positive Matrix Factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5:111–126. https://doi.org/10.1002/env.3170050203

Peng X, Liu X, Shi X, et al (2019): Source apportionment using receptor model based on aerosol mass spectra and 1 h resolution chemical dataset in Tianjin , China. Atmos Environ 198:387–397. https://doi.org/doi.org/10.1016/j.atmosenv.2018.11.018

Putaud J, Raes F, Dingenen R Van, et al (2004): A European aerosol phenomenology – 2/: chemical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe. Atmos Environ 38:2579–2595. https://doi.org/10.1016/j.atmosenv. 2004.01.041

Querol X, Pey J, Pandolfi M, et al (2009): African dust contributions to mean ambient PM 10 mass-levels across the Mediterranean Basin. Atmos Environ 43:4266–4277. https://doi.org/10.1016/j.atmosenv. 2009.06.013

Rai P, Chakraborty A, Kumar A, Gupta T (2016): Composition and source apportionment of PM 1 at urban site Kanpur in India using PMF coupled with CBPF. Atmos Res 179:506–520. https://doi.org/doi.org/10.1016/j.atmosres. 2016.04.015

Rastogi A, Rajan A V., Mukherjee M (2017): A Review of Vehicular Pollution and Control Measures in India. In: Advances in Health and Environment Safety. Springer, Singapore, pp 237–245

Reff A, Eberly SI, Bhave P V, et al (2007): Receptor Modelling of Ambient Particulate Matter Data Using Positive Matrix Factorization/: Review of Existing Methods. J Air Waste Manag 57:146–154. https://doi.org/10.1080/10473289.2007.10465319

Rizzo M.J., Scheff P.A. (2007): Fine particulate source apportionment using data from the USEPA speciation trends network in Chicago, Illinois/: Comparison of two source apportionment models. Atmos Environ 41:6276– 6288. https://doi.org/10.1016/j.atmosenv.2007.03.055

Sahu S.P., Patra A.K., Kolluru SSR (2018): Spatial and temporal variation of respirable particles around a surface coal mine in India. Atmos Pollut Res 9:662–679. https://doi.org/10.1016/j.apr.2018.01.010

Sharma S.K., Mandal T.K., Saxena M, et al (2014): Source apportionment of PM10 by using positive matrix factorization at an urban site of Delhi, India. Urban Clim 10:656–670. https://doi.org/doi.org/10.1016/j.uclim.2013.11.002

Sharma S.K., Sharma A, Saxena M, et al (2016): Chemical characterization and source apportionment of aerosol at an urban area of Central Delhi, India. Atmos Pollut Res 7:110–121. https://doi.org/doi.org/10.1016/ j.apr.2015.08.002

Vargas F.A., Rojas N.Y., Pachon J.E., Russell A.G. (2012): PM 10 characterization and source apportionment at two residential areas in Bogota. Atmos Pollut Res 3:72– 80. https://doi.org/10.5094/APR.2012.006

Volchyn I.A., Haponych L.S. (2014): Estimate of the Sulphur dioxide concentration at thermal power plants fired by Donetsk coal. Power Technol Eng 48:218–221. https://doi.org/10.1007/s10749-014-0511-0

Wang Q, Huang XHH, Tam F.C. V, et al (2019a): Source apportionment of fine particulate matter in Macao, China with and without organic tracers/: A comparative study using positive matrix factorization. Atmos Environ 198:183–193. https://doi.org/doi.org/ 10.1016/j.atmosenv.2018.10.057

Wang S, Hu G, Yan Y, et al (2019b): Source apportionment of metal elements in PM2.5 in a coastal city in Southeast China/: Combined Pb-Sr-Nd isotopes with PMF method. Atmos Environ 198:302–312. https:/ /doi.org/doi.org/10.1016/j.atmosenv.2018.10.056

Yadav M, Soni K, Soni B.K., et al (2019): Source apportionment of particulate matter, gaseous pollutants, and volatile organic compounds in a future smart city of India. Urban Clim 28:100470. https:// doi.org/10.1016/j.uclim.2019.100470

Yu SY, Liu W, Xu Y, et al (2019): Characteristics and oxidative potential of atmospheric PM2.5 in Beijing/: Source apportionment and seasonal variation. Sci Total Environ 650:277–287. https://doi.org/doi.org/ 10.1016/j.scitotenv.2018.09.021

Zhou H, Hopke P.K., Zhou C, Holsen T.M. (2019): Ambient mercury source identi fi cation at a New York State urban site/: Rochester, NY. Sci Total Environ 650:1327–1337. https://doi.org/doi.org/10.1016/ j.scitotenv.2018.09.040

(2018): CPCB. http://cpcb.nic.in/. Accessed 26 Nov 2018.