Résumé
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.
Résumé
Lockdown measures to control the spread of the novel coronavirus disease (COVID-19) sharply limited energy consumption and carbon emissions. The lockdown effect on carbon emissions has been studied by many researchers using statistical approaches. However, the lockdown effect on atmospheric carbon dioxide (CO2) on an urban scale remains unclear. Here we present CO2 concentration and carbon isotopic (δ13C) measurements to assess the impact of COVID-19 control measures on atmospheric CO2 in Xi'an, China. We find that CO2 concentrations during the lockdown period were 7.5% lower than during the normal period (prior to the Spring Festival, Jan 25 to Feb 4, 2020). The observed CO2excess (total CO2 minus background CO2) during the lockdown period was 52.3% lower than that during the normal period, and 35.7% lower than the estimated CO2excess with the effect of weather removed. A Keeling plot shows that in contrast CO2 concentrations and δ13C were weakly correlated (R2 = 0.18) during the lockdown period, reflecting a change in CO2 sources imposed by the curtailment of traffic and industrial emissions. Our study also show that the sharp reduction in atmospheric CO2 during lockdown were short-lived, and returned to normal levels within months after lockdown measures were lifted.