Evidence map›Paper›PMID 35746112›Full record

ArticleSensors (Basel, Switzerland)2022

Direct Measuring Particulate Matters in Smoke Plumes from Chimneys in a Textile Dyeing Industrial Park by a Self-Developed PM Detector on an UAV in Yangtze River Delta of China.

Zhentao Wu, Xiaobing Pang, Zhangliang Han, Kaibin Yuan, Shang Dai, Jingjing Li, Jianmeng Chen, Bo Xing

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Trial
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.

Zhentao WuCollege of Environment, Zhejiang University of Technology, Hangzhou 310014, China.
Xiaobing PangCollege of Environment, Zhejiang University of Technology, Hangzhou 310014, China.
Zhangliang HanCollege of Environment, Zhejiang University of Technology, Hangzhou 310014, China.
Kaibin YuanCollege of Environment, Zhejiang University of Technology, Hangzhou 310014, China.
Shang DaiCollege of Environment, Zhejiang University of Technology, Hangzhou 310014, China.
Jingjing LiShaoxing Ecological and Environmental Monitoring Center of Zhejiang Province, Shaoxing 312000, China.
Jianmeng ChenCollege of Environment, Zhejiang University of Technology, Hangzhou 310014, China.
Bo XingShaoxing Ecological and Environmental Monitoring Center of Zhejiang Province, Shaoxing 312000, China.

Funding

Ministry of Science and Technology of the People's Republic of China 2020YFA0607502Science and Technology Bureau of Shaoxing 2020B33003Science and Technology Department of Zhejiang Province 2021C03165Science and Technology Department of Zhejiang Province 2022C03073Science and Technology Department of Zhejiang Province LZ20D050002
6 · The paper itself

Abstract

Directly measuring particulate matters (PM) from chimneys in an industrial park is difficult due to it being hard to reach the peak heights. A self-developed PM detector on an unmanned aerial vehicle (UAV) had been deployed to directly measure the PM emissions in smoke plumes from chimneys in a textile dyeing industrial park. Compared with a commercial PM device (LD-5R, SIBATA, Kyoto, Japan), the self-developed detector showed similar performance with a good correlation (R

Indexed as

Air PollutantsParticulate MatterCharcoalChinaEnvironmental MonitoringParticle SizeRiversSmokeTextilesAir PollutantsCharcoalParticulate MatterSmokeparticulate matterprinting and dyeing processsmoke plumetextile dyeing industryunmanned aerial vehicle

Identifiers

PMID35746112
PMCPMC9228992

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.