Evidence map›Paper›PMID 36245640›Full record

ArticleJournal of geophysical research. Atmospheres : JGR2022

Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NO

Sanbao Zhang, Shanshan Wang, Ruibin Xue, Jian Zhu, Aimon Tanvir, Danran Li, Bin Zhou

Open access · bronzeAbstract read
In one paragraph

Article in Journal of geophysical research. Atmospheres : JGR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.4field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 12 citations in OpenAlex.

  1. Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NOJournal of geophysical research. Atmospheres : JGR · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Sanbao ZhangShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) Department of Environmental Science and Engineering Fudan University Shanghai China.ORCID https://orcid.org/0000-0003-0761-6687
Shanshan WangShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) Department of Environmental Science and Engineering Fudan University Shanghai China.ORCID https://orcid.org/0000-0003-2590-9563
Ruibin XueShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) Department of Environmental Science and Engineering Fudan University Shanghai China.ORCID https://orcid.org/0000-0002-7153-1939
Jian ZhuShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) Department of Environmental Science and Engineering Fudan University Shanghai China.ORCID https://orcid.org/0000-0003-1926-3364
Aimon TanvirShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) Department of Environmental Science and Engineering Fudan University Shanghai China.
Danran LiShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) Department of Environmental Science and Engineering Fudan University Shanghai China.
Bin ZhouShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) Department of Environmental Science and Engineering Fudan University Shanghai China.ORCID https://orcid.org/0000-0003-1781-138X
Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Responses to the COVID-19 pandemic led to major reductions on air pollutant emissions in modern history. To date, there has been no comprehensive assessment for the impact of lockdowns on the vertical distributions of nitrogen dioxide (NO

Identifiers

PMID36245640
PMCPMC9538289
OpenAlexW4290005855

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.