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Record W3194131981 · doi:10.1016/j.envint.2021.106818

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions

2021· article· en· W3194131981 on OpenAlexaff
Ranjeet S. Sokhi, Vikas Singh, Xavier Querol, Sandro Finardi, Admir Créso Targino, María de Fátima Andrade, Radenko Pavlovic, Rebecca M. Garland, Jordi Massagué, Shaofei Kong, Alexander Baklanov, Lu Ren, Oksana Tarasova, Greg Carmichael, Vincent‐Henri Peuch, Vrinda Anand, Graciela Arbilla, Kaitlin Badali, Gufran Beig, Luis Carlos Belalcázar, Andrea Bolignano, Peter Brimblecombe, Patricia Camacho, Alejandro Casallas, Jean-Pierre Charland, Jason Choi, Eleftherios Chourdakis, Isabelle Coll, Marty Collins, Josef Cyrys, Cleyton Martins da Silva, Alessandro Domenico Di Giosa, Anna Di Leo, Camilo Ferro, Mario E. Gavidia-Calderón, Amiya Gayen, А. С. Гинзбург, Fabrice Godefroy, Yuri González, Marco Andrés Guevara-Luna, Sk. Mafizul Haque, Henno Havenga, Dennis Herod, U. Hõrrak, Tareq Hussein, Sérgio Ibarra-Espinosa, Mónica Jaimes, Marko Kaasik, Jhoon Kim, Anu Kousa, Jaakko Kukkonen, Markku Kulmala, Joel Kuula, Nathalie La Violette, Guido Lanzani, Xi Liu, Stephanie MacDougall, Patrick M. Manseau, Giada Marchegiani, Brian McDonald, Swasti Vardhan Mishra, L. T. Molina, Dennis Mooibroek, Suman Mor, Ν. Moussiopoulos, Fabio Murena, Jarkko V. Niemi, Steffen M. Noe, Thiago Nogueira, Michael Norman, Juan Luis Pérez-Camaño, Tuukka Petäjä, Stuart Piketh, Ken Reid, Armando Retama, Olivia Rivera, Néstor Y. Rojas, Jhojan Pool Rojas Quincho, Roberto San José, Odón R. Sánchez-Ccoyllo, Rodrigo Seguel, Salla Sillanpää, Yushan Su, Nigel Tapper, Antonio Terrazas, Hilkka Timonen, Domenico Toscano, George Tsegas, Guus J. M. Velders, Christos Vlachokostas, Erika von Schneidemesser, Rajasree VPM, Ravi Yadav, Rasa Žalakevičiūtė, M. Zavala

Bibliographic record

VenueEnvironment International · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsAlberta Biodiversity Monitoring InstituteMinistry of the Environment, Conservation and ParksCapital Regional DistrictThe Audio Recording AcademyAlberta Environment and Protected AreasMinistère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des ParcsEnvironment and Climate Change CanadaMinistère des Ressources naturelles et des ForêtsGDG Environnement
FundersEuropean Regional Development FundNatural Environment Research CouncilMinistry of Science and Higher Education of the Russian FederationEesti TeadusagentuurSight Research UKMinistry of Earth SciencesEuropean Commission
KeywordsAir quality indexEnvironmental sciencePollutantAir pollutantsNOxClimatologyAtmospheric sciencesMeteorologyAir pollutionGlobeGeographyChemistryPhysics

Abstract

fetched live from OpenAlex

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.058
GPT teacher head0.366
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations231
Published2021
Admission routes1
Has abstractyes

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