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
Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from three-year (April 2018 - March 2021) TROPOMI observations by combining a wind-assigned anomaly approach and a Machine Learning (ML) method, the so-called Gradient Descent. This combined approach is firstly applied to the Saudi Arabian capital city Riyadh, as a test site, and yields a total emission rate of 1.04×1026 molec./s. 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 where the cement plant and power plants are located and over areas along the highways. Using the same approach, an emission rate of 1.80×1025 molec./s is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable to the CAMS inventory. Weekly variations of NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 24% at weekends in Riyadh, and high reductions are found near the city center and the areas along the highway. An average weekend reduction estimate of 30% in Madrid is found. The regions with dominant sources are located in the east of Madrid, where the residential areas and the Madrid-Barajas airport are located. Additionally, the NO2 emissions decreased by 21% in March-June 2020 compared to the same period in 2019 induced by the COVID-19 lockdowns in Riyadh. A much higher reduction (60%) 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 cover smaller areas during weekdays than at weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Though our analysis is limited to two cities as testing examples, the method has proved to provide reliable and consistent results. Therefore, it is expected to be suitable for other trace gases and other target regions. [ FROM AUTHOR] Copyright of Atmospheric Measurement Techniques Discussions is the property of Copernicus Gesellschaft mbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
Résumé
The anthropogenic impact is a major factor of climate change, which is highest in industrial regions and modern megacities. Megacities are a significant source of emissions of various substances into the atmosphere, including CO2 which is the most important anthropogenic greenhouse gas. In 2019 and 2020, the mobile experiment EMME (Emission Monitoring Mobile Experiment) was carried out on the territory of St Petersburg which is the second-largest industrial city in Russia with a population of more than 5 million people. In 2020, several measurement data sets were obtained during the lockdown period caused by the COVID-19 (COronaVIrus Disease of 2019) pandemic. One of the goals of EMME was to evaluate the CO2 emission from the St Petersburg agglomeration. Previously, the CO2 area flux has been obtained from the data of the EMME-2019 experiment using the mass balance approach. The value of the CO2 area flux for St Petersburg has been estimated as being 89±28 kt km-2 yr-1, which is 3 times higher than the corresponding value reported in the official municipal inventory. The present study is focused on the derivation of the integral CO2 emission from St Petersburg by coupling the results of the EMME observational campaigns of 2019 and 2020 and the HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectories) model. The ODIAC (Open-Data Inventory for Anthropogenic CO2) database is used as the source of the a priori information on the CO2 emissions for the territory of St Petersburg. The most important finding of the present study, based on the analysis of two observational campaigns, is a significantly higher CO2 emission from the megacity of St Petersburg compared to the data of municipal inventory, i.e. ∼75800±5400 kt yr-1 for 2019 and ∼68400±7100 kt yr-1 for 2020 versus∼30000 kt yr-1 reported by official inventory. The comparison of the CO2 emissions obtained during the COVID-19 lockdown period in 2020 to the results obtained during the same period of 2019 demonstrated the decrease in emissions of 10 % or 7400 kt yr-1.