Using TROPOspheric Monitoring Instrument (TROPOMI) measurements and Weather Research and Forecasting (WRF) CO modelling to understand the contribution of meteorology and emissions to an extreme air pollution event in India

Abstract

Abstract. Several ambient air quality records corroborate the severe and persistent degradation of air quality over northern India during the winter months, with evidence of a continued, increasing trend of pollution across the Indo-Gangetic Plain (IGP) over the past decade. A combination of atmospheric dynamics and uncertain emissions, including the post-monsoon agricultural stubble burning, make it challenging to resolve the role of each individual factor. Here we demonstrate the potential use of an atmospheric transport model, the Weather Research and Forecasting model coupled with chemistry (WRF–Chem) to identify and quantify the role of transport mechanisms and emissions on the occurrence of the pollution events. The investigation is based on the use of carbon monoxide (CO) observations from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite and the surface measurement network, as well as the WRF–Chem simulations, to investigate the factors contributing to CO enhancement over India during November 2018. We show that the simulated column-averaged dry air mole fraction (XCO) is largely consistent with TROPOMI observations, with a spatial correlation coefficient of 0.87. The surface-level CO concentrations show larger sensitivities to boundary layer dynamics, wind speed, and diverging source regions, leading to a complex concentration pattern and reducing the observation-model agreement with a correlation coefficient ranging from 0.41 to 0.60 for measurement locations across the IGP. We find that daily satellite observations can provide a first-order inference of the CO transport pathways during the enhanced burning period, and this transport pattern is reproduced well in the model. By using the observations and employing the model at a comparable resolution, we confirm the significant role of atmospheric dynamics and residential, industrial, and commercial emissions in the production of the exorbitant level of air pollutants in northern India. We find that biomass burning plays only a minimal role in both column and surface enhancements of CO, except for the state of Punjab during the high pollution episodes. While the model reproduces observations reasonably well, a better understanding of the factors controlling the model uncertainties is essential for relating the observed concentrations to the underlying emissions. Overall, our study emphasizes the importance of undertaking rigorous policy measures, mainly focusing on reducing residential, commercial, and industrial emissions in addition to actions already underway in the agricultural sectors.

Carbon Monoxide in Optically Thick Wildfire Smoke: Evaluating TROPOMI Using CU Airborne SOF Column Observations

“…Data from the ultraviolet–visible (UV–vis) detector on TROPOMI have been used to derive biomass burning trace gas emissions and lifetimes, , proxies for combustion efficiency, and to study direct emissions of CO (e.g., Borsdorff et al and Vellalassery et al). However, to the best of our knowledge, direct comparisons of TROPOMI CO columns with aircraft column measurements for biomass burning events have not yet been performed and hold the potential to improve the characterization of global wildfire emissions.…”

Impact of Lockdown on Air Quality During COVID-19 Pandemic: A Case Study of India

“…Among the satellite datasets, the Sentinel-5 satellite, which is operated by the European Space Agency (ESA), has been globally used to monitor air quality parameters, especially SO 2 , O 3 , NO 2 and CO (Veefkind et al, 2012 ). The TROPOspheric Monitoring Instrument (TROPOMI), which is onboard the Sentinel 5 precursor (S5P) and launched in October 2017, has a high spatial resolution (at a grid spacing of 3.5 × 7 km 2 for all aforementioned gases, except for CO and methane, which is at a grid spacing of 7 × 7 km 2 ) and temporal resolutions (24 h) and is free of cost and open source dataset (Vellalassery et al, 2021 ). The open source data were collected from the Sentinel 5P dataset using the Google Earth Engine interface.…”

Comparison of TROPOMI NO2, CO, HCHO, and SO2 data against ground‐level measurements in close proximity to large anthropogenic emission sources in the example of Ukraine

“…The most problematic are highly polluted areas where uncertainties increase (Liu et al, 2021; Verhoelst et al, 2021), especially for individual measurement stations and near large anthropogenic NO 2 emission sources (Potts et al, 2021; van Geffen et al, 2022). Carbon monoxide (CO) column data derived from TROPOMI also well reflects near‐surface CO content (Borsdorff et al, 2020; Borsdorff, de Brugh, et al, 2018; Sha et al, 2021; Vellalassery et al, 2021; Zong et al, 2019). Significant differences were observed at the sub‐city scale near CO emission sources and under cloudy conditions (Borsdorff et al, 2019; Martínez‐Alonso et al, 2020).…”

“…Huge advantages of TROPOMI data were presented for tracking and analyzing urban pollution (Potts et al, 2021; Saw et al, 2021; Su et al, 2020); biomass burning (Griffin et al, 2021; Savenets et al, 2020); shipping emissions (Pseftogkas et al, 2021); volcanic eruptions (Corradini et al, 2021; Queißer et al, 2019; Theys et al, 2021), and other emission types and sources. The ability of TROPOMI to represent relevant air quality was shown during the comparison against ground‐based measurements (Borsdorff, aan de Brugh, et al, 2018; Borsdorff et al, 2019; Ialongo et al, 2020; Sha et al, 2021; van Geffen et al, 2022; Verhoelst et al, 2021; Vigouroux et al, 2020); aircraft observations (Griffin et al, 2021; Tack et al, 2021; Zhao et al, 2021); emission inventories (Kim et al, 2020; Zong et al, 2019) and model results (Borsdorff, de Brugh, et al, 2018; Chi et al, 2022; Kim et al, 2020; Vellalassery et al, 2021).…”