A Novel Combinatorial Approach of Volatile Organic Compound Tracers, Low-Cost Sensors, and Source-Receptor Modeling for Spatial Identification and Quantification of Natural and Anthropogenic Sources of Criteria Air Pollutants: Case Study from the Indo-Gangetic Plain

Attribution of air pollution is important to plan evidence-based mitigation. In this case study from Mohali, India, we show that a unique combination of volatile organic compound (VOC)-based source apportionment in combination with a dense network of monitoring grade air pollutant observations and a few low-cost sensors can collectively identify and locate the sources of criteria air pollutants impacting a receptor site. The study uses ambient chemical composition measurements from the COVID-19 pandemic-induced lockdown in 2020. The key drivers of pollutant reductions were the 74 ± 4% reduction in transport activity between prelockdown and postlockdown, accompanied by a 63 ± 5% drop in traffic VOC emission and a 96 (80–100)% drop in NO emissions. Reductions in industrial activity and power demand caused a 53 ± 3% drop in industrial VOC emissions and 67 ± 5% drop in SO₂ emissions, as well as a 59 ± 3% reduction in open waste burning during lockdown, which resulted in 43 ± 2% and 39 ± 1% reductions in PM_2.5 and PM₁₀ emissions, respectively. Our key finding that the strongest PM and SO₂ sources are located at the urban periphery and in the hinterland indicates that Indian clean air action plans need to shift their focus from urban noncompliance areas to a holistic air-shed approach.