Jethva, H; Chand, D; Torres, O; Gupta, P; Lyapustin, A; Patadia, F (2018). Agricultural Burning and Air Quality over Northern India: A Synergistic Analysis using NASA's A-train Satellite Data and Ground Measurements. AEROSOL AND AIR QUALITY RESEARCH, 18(7), 1756-1773.
In the recent years, New Delhi, the capital city of India, has ranked among the most polluted cities in the world regarding its air quality related to the submicron Particulate Matter (PM2.5). Using NASA's A-train satellite data (MODIS, OMI, and CALIOP), ground-level PM2.5 measured in New Delhi (2013-2016), and back-trajectory calculations, we show that the PM2.5 over New Delhi is strongly affected by the agricultural fires in the northwestern Indian states of Punjab and Haryana during the post-monsoon season (October and November). The mass concentration of PM2.5 escalates from similar to 50 mu g m(-3) measured prior to the onset of residue burning in early October to as high as 300 mu g m(-3) (24-hour averaged, 7day running mean) during the peak burning period in early November. A linear regression analysis reveals that the variations in PM2.5 over New Delhi can be attributed to the concurrent changes in the satellite retrievals of fire counts and aerosols over the crop burning area. The back-trajectory analysis shows that most clusters (> 80%) of the northwesterly flow near the ground intercepted the crop burning region before arriving at the receptor location in New Delhi; this further corroborates the transport patterns inferred from the satellite data. A 15-year long satellite record (2002-2016) reveals an increasing trend in agricultural fires (similar to 617 per year) and aerosol loading (0.031 and 0.04 per year in aerosol optical depth and UV aerosol index) in November. Increasing levels of crop residue burning and resulting particulate matter pollution at an alarming rate over northern India is a pressing concern demanding corrective measures to substantially reduce or completely diminish the crop burning through an effective residue management system.