Evidence map›Paper›PMID 32075222›Full record

ArticleSensors (Basel, Switzerland)2020

An Unmanned Aerial Vehicle-Based Gas Sampling System for Analyzing CO

Chaoqun Li, Wenting Han, Manman Peng, Mengfei Zhang, Xiaomin Yao, Wenshuai Liu, Tonghua Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

7 authors.

Chaoqun LiCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
Wenting HanCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
Manman PengCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
Mengfei ZhangCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
Xiaomin YaoCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
Wenshuai LiuCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
Tonghua WangCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.ORCID 0000-0002-1621-2986

Funding

Major Project of Industry-Education-Research Cooperative Innovation in Yangling Demonstration Zone in China 2018CXY-23National Natural Science Foundation of China 51979233the 111 Project No. B12007the 13th Five-Year Plan for Chinese National Key R&D Project 2017YFC0403203
6 · The paper itself

Abstract

We developed and tested an unmanned aerial vehicle-based gas sampling system (UGSS) for collecting gases and atmospheric particulate matter (PM). The system applies an alternative way of collecting both vertical and horizontal transects of trace gases in order to analyze them in the laboratory. To identify the best position of the UGSS intake port, aerodynamic flow simulations and experimental verifications of propeller airflow were conducted with an unmanned aerial vehicle (UAV) in hover mode. The UGSS will automatically replace the original gas in the system with gas from a target location to avoid the original gas being stored in the air bags. Experimental results show that the UGSS needs 5 s to replace the system's own original gas using its pump. CO

Indexed as

gas collectiongas monitoringparticulate matterpropeller airflowUAV-based gas sampling system

Identifiers

PMID32075222
PMCPMC7070813

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.