Evidence map›Paper›PMID 37299787›Full record

ArticleSensors (Basel, Switzerland)2023

A Distributed IoT Air Quality Measurement System for High-Risk Workplace Safety Enhancement.

Lorenzo Parri, Marco Tani, David Baldo, Stefano Parrino, Elia Landi, Marco Mugnaini, Ada Fort

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.6field-weighted citation impact, top 19% 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

0 citing papers in PubMed, 13 citations in OpenAlex.

No citing paper in PubMed yet.

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.

Lorenzo ParriDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.ORCID 0000-0001-8580-3862
Marco TaniDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
David BaldoDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
Stefano ParrinoDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
Elia LandiDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
Marco MugnainiDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
Ada FortDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.ORCID 0000-0003-0916-1516
University of Siena · IT

Funding

Regione Toscana POR FESR TOSCANA 2014 - 2020 3553.04032020.158000224
6 · The paper itself

Abstract

The safety of an operator working in a hazardous environment is a recurring topic in the technical literature of recent years, especially for high-risk environments such as oil and gas plants, refineries, gas depots, or chemical industries. One of the highest risk factors is constituted by the presence of gaseous substances such as toxic compounds such as carbon monoxide and nitric oxides, particulate matter or indoors, in closed spaces, low oxygen concentration atmospheres, and high concentrations of CO

Indexed as

Air PollutantsAir PollutionCarbon MonoxideEnvironmental MonitoringGasesHumansParticulate MatterWorkplaceAir PollutantsCarbon MonoxideGasesParticulate Matterair qualitydistributed measurement systemgas sensorssensor networkworkplace safety

Identifiers

PMID37299787
PMCPMC10255443
OpenAlexW4378227952

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.