Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India

• Published in: Environmental research letters 2018-04, Vol.13 (4), p.44018
• Main Authors: Cusworth, Daniel H, Mickley, Loretta J, Sulprizio, Melissa P, Liu, Tianjia, Marlier, Miriam E, DeFries, Ruth S, Guttikunda, Sarath K, Gupta, Pawan
• Format: Article
• Language: English
• Subjects: Agricultural practices; agriculture; Air pollution; Air pollution measurements; Air quality; Anthropogenic factors; Burning; Crop residues; Emission inventories; Emissions; Farmers; Fires; Harvesting; Human influences; India; Outdoor air quality; Particulate matter; Smoke; Stochasticity
• ISSN: 1748-9326; 1748-9326
• DOI: 10.1088/1748-9326/aab303

Since at least the 1980s, many farmers in northwest India have switched to mechanized combine harvesting to boost efficiency. This harvesting technique leaves abundant crop residue on the fields, which farmers typically burn to prepare their fields for subsequent planting. A key question is to what extent the large quantity of smoke emitted by these fires contributes to the already severe pollution in Delhi and across other parts of the heavily populated Indo-Gangetic Plain located downwind of the fires. Using a combination of observed and modeled variables, including surface measurements of PM2.5, we quantify the magnitude of the influence of agricultural fire emissions on surface air pollution in Delhi. With surface measurements, we first derive the signal of regional PM2.5 enhancements (i.e. the pollution above an anthropogenic baseline) during each post-monsoon burning season for 2012-2016. We next use the Stochastic Time-Inverted Lagrangian Transport model (STILT) to simulate surface PM2.5 using five fire emission inventories. We reproduce up to 25% of the weekly variability in total observed PM2.5 using STILT. Depending on year and emission inventory, our method attributes 7.0%-78% of the maximum observed PM2.5 enhancements in Delhi to fires. The large range in these attribution estimates points to the uncertainties in fire emission parameterizations, especially in regions where thick smoke may interfere with hotspots of fire radiative power. Although our model can generally reproduce the largest PM2.5 enhancements in Delhi air quality for 1-3 consecutive days each fire season, it fails to capture many smaller daily enhancements, which we attribute to the challenge of detecting small fires in the satellite retrieval. By quantifying the influence of upwind agricultural fire emissions on Delhi air pollution, our work underscores the potential health benefits of changes in farming practices to reduce fires.