What caused the extreme CO concentrations during the 2017 high-pollution episode in India?

Abstract

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI), launched 13 October 2017, has been measuring carbon monoxide (CO) concentrations in the Earth's atmosphere since early November 2017. In the first measurements, TROPOMI was able to measure CO concentrations of the high-pollution event in India of November 2017. In this paper, we studied the extent of the pollution in India, comparing the TROPOMI CO with modeled data from the Weather Research and Forecasting model (WRF) to identify the most important sources contributing to the high pollution, both at ground level and in the total column. We investigated the period 11–19 November 2017. We found that residential and commercial combustion was a much more important source of CO pollution than the post-monsoon crop burning during this period, which is in contrast to what media suggested and some studies on aerosol emissions found. Also, the high pollution was not limited to Delhi and its direct neighborhood but the accumulation of pollution extended over the whole Indo-Gangetic Plain (IGP) due to the unfavorable weather conditions in combination with extensive emissions. From the TROPOMI data and WRF simulations, we observed a buildup of CO during 11–14 November and a decline in CO after 15 November. The meteorological conditions, characterized by low wind speeds and shallow atmospheric boundary layers, were most likely the primary explanation for the temporal accumulation and subsequent dispersion of regionally emitted CO in the atmosphere. This emphasizes the important role of atmospheric dynamics in determining the air quality conditions at ground level and in the total column. Due to its rapidly growing population and economy, India is expected to encounter similar pollution events more often in future post-monsoon and winter seasons unless significant policy measures are taken to reduce residential and commercial emissions.

Using satellite measurements and mesoscale modelling to understand the contribution to an extreme air pollution event in India

“…Unlike the surface CO mixing ratios, the majority of the column CO mixing ratio is contributed by the background signal. A recent study conducted by Dekker et al, (2019) concluded that there exists an underestimation in GFAS fire emission data over the Indian region, which is supported by our findings. However, over the Punjab region, biomass burning played a significant role in determining the ground level CO measurements, especially during 6-9 of November, during which enhanced fire activities occurred.…”

“…It achieves higher spatial and temporal (daily) resolution than almost any other inventory and can estimate near-real-time emissions. A number of studies have reported the underestimation of GFAS in fire emissions due to the limitations of the MODIS instruments, which do not capture all of the biomass burning emissions (Cusworth et al, 2018;Dekker et al, 2019;Huijnen et al, 2016;Kaiser et al, 2012;Mota and Wooster, 2018).…”

“…Episodes of pollution events are a major concern in the IGP region, which worsen during postmonsoon and winter seasons (Cusworth et al, 2018;Dekker et al, 2019;Girach and Nair, 2014). According to the World Air Quality Report 2019 based on ambient PM2.5 concentration, fourteen of the top twenty most polluted cities in the world are located in the IGP region, which also includes India's capital region, Delhi.…”

“…Kaiser et al, (2012) demonstrated an approach for calculating biomass-burning emissions by assimilating satellite-based fire radiative power (FRP) observations in which the combustion rate and trace gas emissions are subsequently derived with land/cover-specific conversion factors and emission factors compiled through literature surveys. While the FRP-based approach has clear advantage in enhancing accuracy compared to other inventory/based datasets such as the Global Fire Emission Database (GFED), several studies have indicated inaccuracies in the derived biomass burning products due to instrument limitations and usage of conversion factors (Cusworth et al, 2018;Dekker et al, 2019;Huijnen et al, 2016;Kaiser et al, 2012;Mota and Wooster, 2018). The recent availability of greenhouse gas satellite observations with unprecedented data density at high spatial and temporal resolution paves the more direct way for a detailed study on the origin, distribution and extent of trace gas levels over a vast domain on a monthly to daily basis.…”

“…and 5) What is the role of different emission sources in terms of their contribution to the enhanced concentration level during the high pollution episodes over India? An analysis focusing on identifying the sources contributing to the high pollution event in North India during November 2017 using WRF modelling and TROPOMI preliminary operational data was reported in Dekker et al, 2019, but here we present the analysis for the succeeding year, i.e. November 2018, which also differs from the previous study as follows: the present study (1) uses the retrievals from both WFM-DOAS (Schneising et al, 2019, see Sect.…”