Crop residue burning in Punjab not major culprit behind Delhi’s air pollution: study

CHANDIGARH: In Punjab, it is not just about crop residue burning but also about industries, transportand domestic sector activity in Delhi and surrounding areas, leading to growth carbon monoxide levels in the national capital territory of Delhi and nearby cities, the study found. Stubble burning has been identified as a major contributor to air pollution in the NCT, with about 75% of the smoke coming from Punjab.
The study, titled “Attributing sources of carbon monoxide in northern India” during crop residue burning in Punjab,” was conducted by scientists from the Indian Institute of Remote Sensing, Dehradun, and the National Center for Atmospheric Research, Boulder, US.
Delhi and its surrounding areas—Meerut, Ghaziabad, Noida, Greater Noida, Faridabad, and Gurugram—suffer from poor air quality during the post-monsoon months of October to November. The study aimed to quantify the impact of various emission sources, including industry, residential construction, power generation, transportation, biomass combustion, photochemical production, and lateral transport, on the concentration of carbon monoxide, a major air pollutant, in northern India.
The results of the simulation of the contribution of the carbon monoxide sector in the national capital showed that crop residue burning contributed between 15.8% and 29.9% of the total emissions, with 11.7% to 23.3% attributed to farm fires in Punjab. Anthropogenic sources, which include human activities that affect the environment, contributed between 53.4% ​​and 77.2% of the total emissions, with residential, transport and industrial sectors being the largest donors.
The transport sector was the largest source of carbon monoxide emissions, accounting for 55.4% of the total. This is likely due to the presence of older emissions, more than a decade old. In contrast, background concentrations in Delhi contributed relatively little, ranging from 2.9% to 5.4%. Meanwhile, emissions from other sources varied, accounting for 4.2% to 17.1% of the total carbon monoxide emissions.
Analysis of contributing sectors in Punjab showed that widespread burning of paddy waste significantly contributed to carbon monoxide air pollution, contributing 56.2% to 76.5% of the total surface carbon monoxide concentration, with about 94% of these emissions coming locally from Punjab. In contrast, anthropogenic sector emissions account for 13.6% to 30%, driven mainly by the residential sector. Background concentration and other sectors contributed relatively little, ranging from 4.5% to 5.7% and 3.6% to 8.1%, respectively. However, it should be noted that satellite imagery may underestimate emissions from crop residue burning due to the inability to detect small fires in crop fields.
The study, which also examined the impact of critical meteorological parameters on carbon monoxide concentrations, found that the deteriorating air quality in the northern Indian region is due to a combination of unfavourable meteorological conditions, such as slow winds, shallow mixing layers and low temperatures, coupled with anthropogenic emissions. The study, conducted by Abhinav Sharma, Shuchita Srivastava, R. Kumar and D. Mitra, was published in the latest issue of the international journal Environmental Pollution.
Method used
Scientists used the Weather Research and Forecasting with Chemistry (WRF-Chem) model to simulate surface carbon monoxide concentrations over northern India during the peak rice residue burning period in Punjab from October 28 to November 16, 2021.
One of the researchers explained that the model included 12 additional carbon monoxide tracers to represent emissions from different sources and used six global anthropogenic emission inventories and three biomass burning emission inventories. The simulation results were validated by comparing them with surface carbon monoxide data from Measurements of Pollution in the Troposphere (MOPITT), demonstrating WRF-Chem’s ability to produce realistic carbon monoxide distributions with low bias.