Variability of nitrogen oxide emission fluxes and lifetimes estimated from Sentinel-5P TROPOMI observations

Abstract

Abstract. Satellite observations of the high-resolution TROPOspheric Monitoring Instrument (TROPOMI) on Sentinel-5 Precursor can be used to observe nitrogen dioxide (NO2) at city scales to quantify short time variability of nitrogen oxide (NOx) emissions and lifetimes on a daily and seasonal basis. In this study, 2 years of TROPOMI tropospheric NO2 columns, having a spatial resolution of up to 3.5 km × 5.5 km, have been analyzed together with wind and ozone data. NOx lifetimes and emission fluxes are estimated for 50 different NOx sources comprising cities, isolated power plants, industrial regions, oil fields, and regions with a mix of sources distributed around the world. The retrieved NOx emissions are in agreement with other TROPOMI-based estimates and reproduce the variability seen in power plant stack measurements but are in general lower than the analyzed stack measurements and emission inventory results. Separation into seasons shows a clear seasonal dependence of NOx emissions with in general the highest emissions during winter, except for isolated power plants and especially sources in hot desert climates, where the opposite is found. The NOx lifetime shows a systematic latitudinal dependence with an increase in lifetime from 2 to 8 h with latitude but only a weak seasonal dependence. For most of the 50 sources including the city of Wuhan in China, a clear weekly pattern of NOx emissions is found, with weekend-to-weekday ratios of up to 0.5 but with a high variability for the different locations. During the Covid-19 lockdown period in 2020, strong reductions in the NOx emissions were observed for New Delhi, Buenos Aires, and Madrid.

Estimations of NOxemissions, NO2lifetime and their temporal variation over three British urbanised regions in 2019 using TROPOMI NO2observations

“…It is based on the NO x /NO 2 concentration ratio representing the “typical urban conditions and noontime sun” 1 and is commonly used for the calculation of the NO x estimates. 5,17,33 It is worth noting that Goldberg et al (2019) 34 used a ratio of 1.33 for their study in North America while Verstraeten et al (2018) 35 used a ratio calculated by a regional chemical-transport model and Lange et al (2022) 8 directly converted the NO 2 columns for each pixel into NO x columns, assuming that the Leighton photostationary state applies for the polluted air masses investigated. This conversion from NO 2 to NO x has not been investigated in this study.…”

“…The variability of this background has not been tested in this study and it is assumed to remain limited thanks to the estimation of this background during the fitting procedure, especially with the upwind data, as done by previous studies. 8…”

“…It has been recognized the method better suits sources with high contrast between source and background, 8,13 so the method has been tested over other locations. Regions less affected by clouds should also be preferred to maximize the number of satellite observations which can be used.…”

“…24,25 However, it is a fair assumption to state that the overpass time of the satellite captures the daily NO 2 tropospheric mean value, especially as the boundary layer is well mixed at this time. 8,15…”

Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX and in situ NO2 measurements over Antwerp, Belgium

“…The inverse modelling technique has been used to constrain NOx emissions based on TROPOMI NO2 data at various scales (e.g. Lorente et al, 2019;Ding et al, 2020;Souri et al, 2021;Zhu et al, 2021;Botero et al, 2021;Rey-Pommier et al, 2021;Lange et al, 2022;Fioletov et al, 2022). The characterization of potential biases in TROPOMI NO2 columns is therefore of crucial importance.…”