Advancing PM2.5 Source Apportionment through Dispersion Normalized PMF: A Comprehensive Study across India

The recent IPCC report highlights PM_2.5 as one of the most important contributors to air pollution and its health impacts. Determining the sources of air pollution, quantifying their contributions to ambient pollutant levels, and characterizing their spatial and temporal patterns are all important steps in developing effective mitigation strategies (Hopke, 2016). Source apportionment studies, especially those using receptor models, are critical for this purpose. Of these, Positive Matrix Factorization (PMF) is a widely applied methodology. The current work evolves standard PMF (S-PMF) by Tapper and Paatero, 1994 based on the implementation of dispersion-normalized PMF (DN-PMF) suggested by Dai et al., 2020 that includes meteorologically-influenced enhancement towards increasing local source emissions resolution. DN-PMF preserves source information otherwise obscured by variable atmospheric conditions, yielding refined and distinct source profiles.
            This research investigates the spatial and seasonal variability of PM_2.5 sources across three Indian cities—Bhopal (MSL: 486 m), Mesra (MSL: 517 m), and Mysuru (MSL: 759 m)—as part of the COALESCE network in 2019. A Multiple Seasonal-Trend Decomposition using LOESS (MSTL) was applied to mixed layer height time series to decompose into their daily, weekly, and trend components. Stationarity tests were performed using the Augmented Dickey-Fuller with a p-value <0.05. Reconstructed mass (RCM) from PM_2.5 chemical constituent was validated against measured PM_2.5 gravimetric mass as suggested by Pullokaran et al., 2024. DN-PMF analysis was performed on chemically speciated datasets comprising organic and elemental carbon fractions (OC1, OC2 OC3,OC4, OP, EC1, EC2, EC3), water-soluble inorganic ions F^-, Cl^-, NO₃^- , SO₄^-2, Na⁺, NH₄⁺, K⁺), elements (Al, Mg, Ca, Si, P, K, V, Ti, Co, Ni, Cu, Cd, Fe, Ni, Zn, Se, Ba, Hg, Pb), and non-water-soluble potassium (K_nws).

A total of nine factors were resolved in Bhopal, the residential heating factor (23.1%) showed the highest contribution. A smelter source was also identified, due to the high explained variance in EC3 (40%), Zn (71%), Pb, Cu, and EC2. At Mysuru, seven factors were identified, with secondary sulfate (26.2%) identified as the dominant factor. Based on the high explained variance of F^- (57%), SO₄^-2 (39%), and minor loadings of NO₃^-, Mn, Fe, and Si a brick kiln source was identified in Mysuru. Mesra revealed eight factors, with secondary sulfate (22.8%) and secondary nitrate (19.1%) as major contributors. The biomass burning emissions peaked during India’s stubble burning period (pre-monsoon and post-monsoon) at all three sites. Potential regional and local sources of PM_2.5 sources were identified using the Potential Source Contribution Function (PSCF) analysis. The findings provide robust chemically speciated PM_2.5 data and improved source apportionment through DN-PMF. These results offer actionable insights for policymakers and environmental agencies, facilitating effective air quality management and targeted mitigation strategies.