Evidence map›Paper›PMID 35151743›Full record

ArticleThe Science of the total environment2022

Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain.

Jaime González-Pardo, Sandra Ceballos-Santos, Rodrigo Manzanas, Miguel Santibáñez, Ignacio Fernández-Olmo

Abstract read
In one paragraph

Article in The Science of the total environment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
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  4. A hybrid model for daily air quality index prediction and its performance in the face of impact effect of COVID-19 lockdown.Process safety and environmental protection : transactions of the Institution of Chemical Engineers, Part B · 2023
    Article
  5. Article
  6. Article
  7. Article
  8. Impact of the 2020 COVID-19 lockdown on NOAtmospheric environment (Oxford, England : 1994) · 2022
    Article
  9. 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

5 authors.

Jaime González-PardoDepartment of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain. Electronic address: jaime.diez@unican.es.
Sandra Ceballos-SantosDepartment of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain. Electronic address: sandra.ceballos@unican.es.
Rodrigo ManzanasSantander Meteorology Group, Dpto. De Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Santander 39005, Spain. Electronic address: rodrigo.manzanas@unican.es.
Miguel SantibáñezDepartment of Nursing, Global Health Research Group, Universidad de Cantabria, Avda. Valdecilla s/n, 39008 Santander, Cantabria, Spain; Research Nursing Group, IDIVAL, Calle Cardenal Herrera Oria s/n, 39011 Santander, Cantabria, Spain. Electronic address: miguel.santibanez@unican.es.
Ignacio Fernández-OlmoDepartment of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain. Electronic address: ignacio.fernandez@unican.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In response to the COVID-19 pandemic, governments declared severe restrictions throughout 2020, presenting an unprecedented scenario of reduced anthropogenic emissions of air pollutants derived mainly from traffic sources. To analyze the effect of these restrictions derived from COVID-19 pandemic on air quality levels, relative changes in NO, NO

Indexed as

Air PollutantsAir PollutionCOVID-19CitiesCommunicable Disease ControlData MiningEnvironmental MonitoringHumansPandemicsParticulate MatterReproducibility of ResultsSpainAir PollutantsParticulate MatterAir qualityCOVID-19K-Nearest NeighborsMultiple Linear Regressionq-q mappingRandom Forest

Identifiers

PMID35151743
PMCPMC8828445

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.