Evidence map›Paper›PMID 33668193›Full record

ArticleInternational journal of environmental research and public health2021

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt.

Jin-Wei Yan, Fei Tao, Shuai-Qian Zhang, Shuang Lin, Tong Zhou

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Spatiotemporal Evolution and Driving Forces of PMInternational journal of environmental research and public health · 2023
    Article
  5. The Cause of China's Haze Pollution: City Level Evidence Based on the Extended STIRPAT Model.International journal of environmental research and public health · 2022
    Article
  6. Article
  7. Article
  8. Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PMInternational journal of environmental research and public health · 2021
    Article
  9. 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

5 authors.

Jin-Wei YanSchool of Geographical Sciences, Nantong University, Nantong 226007, China.ORCID 0000-0002-3777-6916
Fei TaoSchool of Geographical Sciences, Nantong University, Nantong 226007, China.ORCID 0000-0002-7317-5974
Shuai-Qian ZhangSchool of Geographical Sciences, Nantong University, Nantong 226007, China.ORCID 0000-0001-6224-1991
Shuang LinSchool of Geographical Sciences, Nantong University, Nantong 226007, China.
Tong ZhouSchool of Geographical Sciences, Nantong University, Nantong 226007, China.ORCID 0000-0002-3041-6264

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China's urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

Indexed as

Air PollutantsAir PollutionChinaCitiesEnvironmental MonitoringParticulate MatterRiversAir PollutantsParticulate MatterGeodetectorgeographically weighted regressionPM2.5 concentrationsspatiotemporal distributionurban agglomeration

Identifiers

PMID33668193
PMCPMC7967664

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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.