Evidence map›Paper›PMID 36402807›Full record

ArticleScientific reports2022

Identifying a suitable model for predicting hourly pollutant concentrations by using low-cost microstation data and machine learning.

Rongjin Yang, Lizeyan Yin, Xuejie Hao, Lu Liu, Chen Wang, Xiuhong Li, Qiang Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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. Review
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.

Rongjin Yang *Chinese Research Academy of Environmental Sciences, Beijing, China.
Lizeyan Yin *Higher Institute of Computer Modeling and Their Applications, Clermont Auvergne University, Clermont-Ferrand, France.
Xuejie HaoState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
Lu LiuState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
Chen WangState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
Xiuhong LiState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China. lixh@bnu.edu.cn.
Qiang LiuState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.

Funding

the National Key Research and Development Project 2016YFC0502106the Natural Science Foundation of China Research 41476161the Watershed Non-point Source Pollution Prevention and Control Technology and Application Demonstration Project 2021YFC3201505
6 · The paper itself

Abstract

Accurately predicting the concentration of PM

Indexed as

Air PollutantsEnvironmental PollutantsEnvironmental MonitoringMachine LearningParticulate MatterAir PollutantsEnvironmental PollutantsParticulate Matter

Identifiers

PMID36402807
PMCPMC9675857

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.