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 Ind

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 PM2.5 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.