Evidence map›Paper›PMID 32984572›Full record

ArticleHeliyon2020

Spatio-temporal air pollution modelling using a compositional approach.

Joseph Sánchez-Balseca, Agustí Pérez-Foguet

Abstract read
In one paragraph

Article in Heliyon, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Joseph Sánchez-BalsecaResearch Group on Engineering Sciences and Global Development (EScGD), Civil and Environmental Engineering Department, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Spain.
Agustí Pérez-FoguetResearch Group on Engineering Sciences and Global Development (EScGD), Civil and Environmental Engineering Department, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Air pollutant data are compositional in character because they describe quantitatively the parts of a whole (atmospheric composition). However, it is common to use air pollutant concentrations in statistical models without considering this characteristic of the data and, therefore, without control of common statistical problems, such as spurious correlations and subcompositional incoherence. This paper now proposes a daily multivariate spatio-temporal model with a compositional approach. The air pollution spatio-temporal model is based on a dynamic linear modelling framework with Bayesian inference. The novel modelling methodology was applied in an urban area for carbon monoxide (CO, mg·m

Indexed as

Air qualityAtmospheric scienceCoDaCompositional dataEngineeringEnvironmental analysisEnvironmental chemical engineeringEnvironmental impact assessmentEnvironmental statisticsModellingStatistics

Identifiers

PMID32984572
PMCPMC7495062

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.