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

Abstract

The TROPOspheric Monitoring Instrument (TROPOMI), launched 13 October 2017, measures 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 modelled data from the Weather Research and Forecast model (WRF) to identify the most important sources contributing to the high pollution, both at ground-level and in the total column. We 5 investigated the period between 11 and 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 neighbourhood but the accumulation of pollution extended over the whole Indo-Gangetic Plain (IGP) due to the unfavourable weather conditions in combination with extensive emissions. From the TROPOMI data and WRF simulations, we observed 10 a build-up of CO during 11-14 November and a decline in CO after the 15 th of November. The meteorological situation, characterized by low wind speeds and shallow atmospheric boundary layers, was most likely the primary explanation for the temporal accumulation and subsequent dispersion of regionally emitted CO in the atmosphere, emphasizing the important role of atmospheric dynamics. 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 15 and commercial emissions.

Crop residue burning practices across north India inferred from household survey data: bridging gaps in satellite observations

“…In 2017, an almost 3-week cloudy/hazy period persisted from October 30 to November 17, with minimal fire activity detected during the second and third weeks of November. Using a model combined with satellite data, Dekker et al (2019) suggested that residential and commercial combustion was the most important driver of extreme pollution over the IGP from November 11-19, 2017. However, we argue that agricultural fire activity during this period is grossly underestimated and likely also a key emissions source.…”

“…The small size and duration of the fires, as well as increasing haziness from the smoke itself, also complicate interpretation of satellite observations (Thumaty et al 2015, Liu et al 2019a. These challenges may lead to gross underestimation of fire emissions driving atmospheric models (Cusworth et al 2018, Dekker et al 2019, Lasko et al 2017. To date, models have relied on global fire emissions inventories due to the lack of inventories specific to India, but these emissions estimates, including those for aerosols, in the global inventories can differ by an order of magnitude (Liu et al 2019a).…”

Improving regional air quality predictions in the Indo-Gangetic Plain-Case study of an intensive pollution episode in November 2017

“…Negatively biased wind speed with ME of 1.1 m/s and RMSE of 1.28 m/s shows the model generally underestimated wind speed and it was most predominant between Nov. 17 th and Nov. 25 th . Figure 2c shows the model did not accurately capture nighttime 2m temperature minima but captured the maximum values with overall overestimated ME of 3.52° C and RMSE of 4.01° C. The wind speed satisfied the benchmark RMSE value of 2.0 m/s, while temperature was higher than the targeted ME goal of 2.0° C (Emery et al, 2001). The representation error plays an 235 important role in evaluating results due to different horizontal resolutions in the model and MERRA-2 dataset (~0.15*0.15 vs 0.625*0.5 degree), specifically in urban areas.…”

“…Normalized Mean Bias (NMB), Normalized Mean Error (NME) and correlation coefficient (R) as defined in supporting information (Emery et al, 2017;Emery et al, 2001). Since low values can have significant impacts on normalized values, which are used in mean normalized metrics, normalized mean values are better metrics and used in this study (Emery et al, 2017).…”

“…Figure 1 WRF-Chem modeling domain, ground measurement stations, and observed air quality: a) modeling domain and location of Delhi (⁎) and AERONET stations at Jaipur and Kanpur (▲), and underlying VIIRS AOD (550nm) averaged over November 2017, the black line also shows the path that was used for vertical cross section analysis. b) location of CPCB stations (black stars) and US Embassy station (red star), c) Calendar map of averaged daily PM2.5 concentration measured at US Embassy, d) Calendar map of averaged daily ozone concentration measured 75 at North Campus, DU Models usually underestimate the concentrations during extreme pollution periods unless they apply chemical data assimilation (Dekker et al, 2019;Kulkarni et al, 2020;Kumar et al, 2015b). Moreover, there are different input data in terms of chemical boundary conditions and fire emissions that can affect air quality modeling results (He et al, 2019).The main objective of this study is to investigate the impacts of different global input data sets on improving modeled PM2.5 concentrations over the IGP 80 during Nov. 2017.…”