Crop Residue Burning in North-Western India: Emission Estimation and Uncertainty Quantification

被引:0
|
作者
Ambulkar, Rupal [1 ,2 ]
Govardhan, Gaurav [1 ,3 ]
Gavhale, Srujan [4 ]
Kalita, Gayatry [5 ]
Pande, Chaitanya [1 ]
Jat, Rajmal [1 ]
Kulkarni, Santosh [4 ]
Khare, Manoj [4 ]
Attri, S. D. [6 ]
Ghude, Sachin D. [1 ]
机构
[1] Minist Earth Sci, Indian Inst Trop Meteorol, Pune, India
[2] Savitribai Phule Pune Univ, Dept Environm Sci, Pune, India
[3] Minist Earth Sci, Natl Ctr Medium Range Weather Forecasting, Noida, India
[4] Ctr Dev Adv Comp, Pune, India
[5] Reg Meteorol Ctr Guwahati, India Meteorol Dept, Gauhati, India
[6] Minist Environm Forest & Climate Change, Commiss Air Qual Management Natl Capital Reg & Adj, New Delhi, India
关键词
crop residue burning; satellite imagery; emissions estimation; uncertainties; Sentinel-2; VIIRS; DECISION-SUPPORT-SYSTEM; AIR-QUALITY MANAGEMENT; BURNED AREA; FIRE; PRODUCT; MODIS; ALGORITHM; CHINA; DELHI;
D O I
10.1029/2024JD042198
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Air quality in India faces significant risk from agricultural residue burning, especially in Punjab and Haryana, which are pivotal to the world's second-largest agrarian economy. This study quantifies emissions from post-monsoon biomass burning (10 October-30 November 2022) in these states using VIIRS fire detection data and Sentinel-2-derived burnt areas. Ground validation via district-level surveys aligns with the findings of our study. Results show 51% of the total crop area was burned (14,700 km2 in Punjab; 8,300 km2 in Haryana), leading to substantial emissions of PM2.5 (54.28 Gg; 7.94 Gg), CH4 (25.63 Gg; 3.75 Gg), CO2 (1,100.3 Gg; 195.7 Gg), NH3 (0.83 Gg; 0.15 Gg), SO2 (0.68 Gg; 0.12 Gg), and CO (62.1 Gg; 11.04 Gg). Emissions in Punjab are about 6.5 times higher than in Haryana attributable to greater burnt area (similar to 14,700 km2), higher crop yield, and elevated residue-to-crop ratios. Compared to VIIRS, Sentinel-2 data provides approximately 3.6 times higher emission estimates, reflecting improved burnt area detection. District-level emission variations underscore the influence of diverse farming practices, weather, and residue management. An uncertainty analysis, derived from multiple emissions estimates and methodologies, highlights regional disparities: SO2 exhibits the highest uncertainty in both states with PM2.5 and CO, respectively, showing the least. Understanding these emissions and uncertainties is vital for forecasting air pollution in downwind cities such as New Delhi and for formulating targeted mitigation strategies.
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页数:16
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