Estimation of NO₂ emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique

Abstract

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s−1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s−1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory. Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.