Evidence map›Paper›PMID 38894239›Full record

ArticleSensors (Basel, Switzerland)2024

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods.

Hao Tang, Yunfei Cai, Song Gao, Jin Sun, Zhukai Ning, Zhenghao Yu, Jun Pan, Zhuohui Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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
–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

0 citing papers in PubMed.

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

8 authors.

Hao TangNHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China.
Yunfei CaiDepartment of General Management and Statistics, Shanghai Environment Monitoring Center, Shanghai 200235, China.
Song GaoSchool of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China.
Jin SunNHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China.ORCID 0009-0007-7437-0417
Zhukai NingSchool of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China.
Zhenghao YuNHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China.ORCID 0009-0007-9408-3650
Jun PanDepartment of General Management and Statistics, Shanghai Environment Monitoring Center, Shanghai 200235, China.
Zhuohui ZhaoNHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China.ORCID 0000-0001-8596-0278

Funding

Shanghai 3-year Public Health Action Plan GWVI-11.2-XD11Shanghai B&R Joint Laboratory Project 22230750300Shanghai International Science and Technology Partnership Project 21230780200
6 · The paper itself

Abstract

objectiveThe aim was to evaluate and optimize the performance of sensor monitors in measuring PM

methodParallel measurements and comparisons of PM

resultsIndoor particles were measured in a range of 0.8-370.7 μg/m

conclusionsSensor monitors could provide reliable measurements of PM

Indexed as

air pollutionenvironmental monitoringexposure assessmentlight-scattering method

Identifiers

PMID38894239
PMCPMC11174656

What OpenQuestion holds

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Registered trials

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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.