Nitrogen oxides emissions from selected cities in North America, Europe, and East Asia observed by the TROPOspheric Monitoring Instrument (TROPOMI) before and after the COVID-19 pandemic

被引:2
|
作者
Lonsdale, Chantelle R. [1 ]
Sun, Kang [1 ,2 ]
机构
[1] SUNY Buffalo, Dept Civil Struct & Environm Engn, Buffalo, NY 14260 USA
[2] SUNY Buffalo, Res & Educ Energy Environm & Water Inst, Buffalo, NY 14260 USA
关键词
CHANGING NOX LIFETIME; AIR-QUALITY; POLLUTANTS; MEGACITY;
D O I
10.5194/acp-23-8727-2023
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Nitrogen oxides (NOx = NO + NO2) emissions are estimated in three regions in the Northern Hemisphere, generally located in North America, Europe, and East Asia, by calculating the directional derivatives of NO2 column amounts observed by the TROPOspheric Monitoring Instrument (TROPOMI) with respect to the horizontal wind vectors. We present monthly averaged emissions from 1 May 2018 to 31 January 2023 to capture variations before and after the COVID-19 pandemic. We focus on a diverse collection of 54 cities, 18 in each region. A spatial resolution of 0.04 degrees resolves intracity emission variations and reveals NOx emission hotspots at city cores, industrial areas, and sea ports. For each selected city, post-COVID-19 changes in NOx emissions are estimated by comparing monthly and annually averaged values to the pre-COVID-19 year of 2019. While emission reductions are initially found during the first outbreak of COVID-19 in early 2020 in most cities, the cities' paths diverge afterwards. We group the selected cities into four clusters according to their normalized annual NOx emissions in 2019-2022 using an unsupervised learning algorithm. All but one of the selected North American cities fall into cluster 1 characterized by weak emission reduction in 2020 (-7% relative to 2019) and an increase in 2022 by +5 %. Cluster 2 contains mostly European cities and is characterized by the largest reduction in 2020 (-31 %), whereas the selected East Asian cities generally fall into clusters 3 and 4, with the largest impacts in 2022 (-25% and -37 %). This directional derivative approach has been implemented in object-oriented, open-source Python and is available publicly for high-resolution and low-latency emission estimation for different regions, atmospheric species, and satellite instruments.
引用
收藏
页码:8727 / 8748
页数:22
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