NOx emissions reduction and rebound in China due to the COVID-19 crisis

Ding, Jieying; A, Ronald van der; Eskes, Henk; Mijling, Bas; Stavrakou, Trissevgeni; Geffen, Jos van; Veefkind, Pepijn

doi:10.1002/essoar.10503971.1

Cited by 36 publications

(45 citation statements)

References 25 publications

(36 reference statements)

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“…Guevara et al (2020), or Barré et al (2020) report European emissions reductions up to 60% for NOx, and up to 15% for Non-Methane Volatile Organic Compounds (NMVOC) in March/April 2020. Based on satellite observations of NO2 columns (Bouwens et al, 2020), comparable NOx emissions reductions are reported for Chinese cities in February 2020 (Ding et al, 2020;. Globally averaged CO2 emissions decreased by 8.8% during the first half of 2020 (Liu et al, 2020), consistent in timing and magnitude with the aforementioned NO2 emission reductions.…”

Section: Introductionsupporting

confidence: 63%

COVID‐19 Crisis Reduces Free Tropospheric Ozone Across the Northern Hemisphere

Steinbrecht

Kubistin

Plaß-Dülmer

et al. 2021

Geophysical Research Letters

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Key Points (shortened to less than 140 characters each, and changed as suggested by Reviewer #2): • In spring and summer 2020, stations in the northern extratropics report on average 7% (4 nmol/mol) less tropospheric ozone than normal. • Such low tropospheric ozone, over several months, and at so many sites, has not been observed in any previous year since at least 2000. • Most of the reduction in tropospheric ozone in 2020 is likely due to emissions reductions related to the COVID-19 pandemic.

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Section: Introductionsupporting

confidence: 63%

COVID‐19 Crisis Reduces Free Tropospheric Ozone Across the Northern Hemisphere

Steinbrecht

Kubistin

Plaß-Dülmer

et al. 2021

Geophysical Research Letters

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“…While emissions can be estimated from satellite observations using data-driven methods (Beirle et al, 2019, Beirle et al, 2021Fioletov et al, 2016;Goldberg et al, 2019) or using complex inverse modelling techniques (e.g. Millet et al, 2008;Stavrakou et al, 2009;Bauwens et al, 2016;Ding et al, 2020;Miyazaki et al, 2020;Borsdorff et al, 2019;Borsdorff et al, 2020), here we use a more qualitative approach to probe emission changes. First we compare the concentrations in 2020 with those from the same period from earlier years and then carry out additional analysis to separate the lockdown-driven variability from seasonal and meteorological variability taking in account emission changes driven by mechanisms.…”

Section: Understanding and Interpreting Tropomi Trace Gas Retrievalsmentioning

confidence: 99%

Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI

Levelt

Zweers

Aben

et al. 2021

Preprint

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Abstract. The aim of this paper is two-fold: to provide guidance on how to best interpret TROPOMI trace gas retrievals and to highlight how TROPOMI trace gas data can be used to understand event-based impacts on air quality from regional to city-scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO and CHOCHO) detected by the Sentinel-5P TROPOMI instrument, driven by reductions of anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in NO2 concentrations on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in two-week averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short time scale are detectable from space, is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China which is in concert with the other trace gas reductions observed during lockdown, however large, interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19 lockdown-driven reductions in satellite observed trace gas column amounts, using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction of anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future, the combined use of inverse modelling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here, will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission.

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“…Undoubtedly, nearly globally implemented social distancing and isolation measures have resulted in a significant reduction of air pollution (Bauwens et al., 2020; Ding et al., 2020; European Space Agency, 2020; NASA, 2020; Navinya et al., 2020; Nikkei Asian Review, 2020; Querol et al., 2007; Shi & Brasseur, 2020; P. Wang et al., 2020). These statistics of air quality improvements are commonly reported by comparing the restricted human activity periods with the “normal conditions,” months or years before the sanitary emergency with no way to estimate the reliability of these results.…”

Section: Introductionmentioning

confidence: 99%

Assessing the COVID‐19 Impact on Air Quality: A Machine Learning Approach

Rybarczyk

Žalakevičiūtė

2021

Geophysical Research Letters

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The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross‐validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by −53 ± 2%, −45 ± 11%, −30 ± 13%, and −15 ± 9% for NO2, SO2, CO, and PM2.5, respectively. The traffic‐busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence.

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NOx emissions reduction and rebound in China due to the COVID-19 crisis

Cited by 36 publications

References 25 publications

COVID‐19 Crisis Reduces Free Tropospheric Ozone Across the Northern Hemisphere

COVID‐19 Crisis Reduces Free Tropospheric Ozone Across the Northern Hemisphere

Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI

Assessing the COVID‐19 Impact on Air Quality: A Machine Learning Approach

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