Evidence map›Paper›PMID 35574514›Full record

ArticleEnvironmetrics2022

A spatiotemporal analysis of NO

Guido Fioravanti, Michela Cameletti, Sara Martino, Giorgio Cattani, Enrico Pisoni

Open access · bronzeAbstract read
In one paragraph

Article in Environmetrics, 2022. 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
1.2field-weighted citation impact, top 23% 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

2 citing papers in PubMed, 11 citations in OpenAlex.

  1. Impact of particulate matter exposure on melanoma risk: A multicentre case-control study.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025
    Observational
  2. A spatiotemporal analysis of NOEnvironmetrics · 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

5 authors at 4 institutions in 2 countries.

Guido FioravantiIstituto Superiore per la Protezione e la Ricerca Ambientale Rome Italy.
Michela CamelettiDepartment of Economics University of Bergamo Bergamo Italy.
Sara MartinoNorwegian University of Science and Technology Trondheim Norway.ORCID https://orcid.org/0000-0003-4326-9029
Giorgio CattaniIstituto Superiore per la Protezione e la Ricerca Ambientale Rome Italy.ORCID https://orcid.org/0000-0001-9754-1935
Enrico PisoniEuropean Commission Joint Research Centre Ispra Italy.
Istituto Superiore per la Protezione e la Ricerca Ambientale · ITJoint Research Centre · ITNorwegian University of Science and Technology · NOUniversity of Bergamo · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify-in space and time-the effectiveness of the adopted strategy. The lockdown measures taken worldwide in 2020 to reduce the spread of the SARS-CoV-2 virus can be envisioned as a policy intervention with an indirect effect on air quality. In this paper we propose a statistical spatiotemporal model as a tool for intervention analysis, able to take into account the effect of weather and other confounding factor, as well as the spatial and temporal correlation existing in the data. In particular, we focus here on the 2019/2020 relative change in nitrogen dioxide (NO

Indexed as

COVID‐19 lockdownINLA‐SPDE approachintervention analysis/environmental policyNOspatiotemporal statistical model

Identifiers

PMID35574514
PMCPMC9087439
OpenAlexW3209263920

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.