PM2.5 pollution from household solid fuel burning practices in Central India: 2. Application of receptor models for source apportionment

Abstract

USEPA's UNMIX, positive matrix factorization (PMF) and effective variance-chemical mass balance (EV-CMB) receptor models were applied to chemically speciated profiles of 125 indoor PM measurements, sampled longitudinally during 2012-2013 in low-income group households of Central India which uses solid fuels for cooking practices. Three step source apportionment studies were carried out to generate more confident source characterization. Firstly, UNMIX6.0 extracted initial number of source factors, which were used to execute PMF5.0 to extract source-factor profiles in second step. Finally, factor analog locally derived source profiles were supplemented to EV-CMB8.2 with indoor receptor PM chemical profile to evaluate source contribution estimates (SCEs). The results of combined use of three receptor models clearly describe that UNMIX and PMF are useful tool to extract types of source categories within small receptor dataset and EV-CMB can pick those locally derived source profiles for source apportionment which are analog to PMF-extracted source categories. The source apportionment results have also shown three fold higher relative contribution of solid fuel burning emissions to indoor PM compared to those measurements reported for normal households with LPG stoves. The previously reported influential source marker species were found to be comparatively similar to those extracted from PMF fingerprint plots. The comparison between PMF and CMB SCEs results were also found to be qualitatively similar. The performance fit measures of all three receptor models were cross-verified and validated and support each other to gain confidence in source apportionment results.

Influence of fireworks emission on aerosol aging process at lower troposphere and associated health risks in an urban region of eastern central India

“…Apart from that the study site has been also used as a receptor site of pollutants by many researchers (Matawle et al, 2018;Sahu et al, 2018). The source receptor based studies suggested the greater importance of the study site.…”

Comparison of five methodologies to apportion organic aerosol sources during a PM pollution event

“…chemical mass balance (CMB) and positive matrix factorization (PMF)) (Watson et al, 1990;Paatero, 1997;Paatero and Tapper, 1994), SOA-tracer method (Kleindienst et al, 2007) and radiocarbon ( 14 C) measurements (Szidat et al, 2009;Gelencsér et al, 2007). These methodologies have been successfully applied worldwide and, in general, good agreements have been reported when compared by twos directly (Song et al, 2006;Kim et al, 2004;Kleindienst et al, 2010;Pachon et al, 2010;Heo et al, 2013;Al-Naiema et al, 2018;Srivastava et al, 2018b;Bove et al, 2018;Bae et al, 2019;Antony Chen and Cao, 2018;Hettiyadura et al, 2018;Jiang et al, 2018;Keerthi et al, 2018;Matawle et al, 2018;Shi et al, 2018;Shirmohammadi et al, 2016;Lanzafame et al, 2020). However, large discrepancies have been also observed when apportioning the SOA fraction, depending on the methods compared, the period of the year considered or atmospheric conditions observed (Srivastava et al, 2018b).…”

Particulate Matter Source Contributions for Raipur-Durg-Bhilai Region of Chhattisgarh, India

“…Using data on water-soluble ions in PM 2.5 and PM 1 from Raipur and Durg, Deshmukh et al (2010) and Deshmukh et al (2011) reported contributions from two major sources -soil/crustal dust and combustion emissions including biomass and fossil fuel burning. In a more recent study in slum communities, Matawle et al (2018) apportioned 40% of the total indoor PM to solid fuel combustion, 30% of traffic emissions, 26% coal-ash based construction materials and 4.3% to iron-processing industries in the area, using Positive Matrix Factorization (PMF). These results, however, are difficult to interpret with respect to ambient air since the results are based on indoor measurements.…”