Evidence map›Paper›PMID 36228798›Full record

ArticleThe Science of the total environment2023

Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning.

Yunqian Lv, Hezhong Tian, Lining Luo, Shuhan Liu, Xiaoxuan Bai, Hongyan Zhao, Kai Zhang, Shumin Lin, Shuang Zhao, Zhihui Guo and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Science of the total environment, 2023. 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
–field-weighted citation impact
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.

  1. 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

12 authors.

Yunqian LvState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
Hezhong TianState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China. Electronic address: hztian@bnu.edu.cn.
Lining LuoState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
Shuhan LiuState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
Xiaoxuan BaiState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
Hongyan ZhaoState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
Kai ZhangDepartment of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Albany, NY, USA.
Shumin LinState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
Shuang ZhaoState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
Zhihui GuoState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
Yifei XiaoState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
Junqi YangState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil-Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015-2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM

Indexed as

Air PollutantsAir PollutionCOVID-19ChinaCitiesCommunicable Disease ControlEnvironmental MonitoringHumansMachine LearningParticulate MatterPublic HealthAir PollutantsParticulate MatterAir qualityCOVID-19Disease burdenLong-term trendsMeteorological parametersRandom forest

Identifiers

PMID36228798
PMCPMC9550286

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.