ArticleAtmospheric environment (Oxford, England : 1994)2020
Application of land use regression to assess exposure and identify potential sources in PM
Article in Atmospheric environment (Oxford, England : 1994), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ambient air pollution and childhood obesity from infancy to late childhood: An individual participant data meta-analysis of 10 European birth cohorts.Environment international · 2025Pooled it
- Development of Life Course Exposure Estimates Using Geospatial Data and Residence History.International journal of environmental research and public health · 2025Article
- Xenobiotic Toxicants and Particulate Matter: Effects, Mechanisms, Impacts on Human Health, and Mitigation Strategies.Journal of xenobiotics · 2025Review
- High Temporal Resolution Land Use Regression Models with POI Characteristics of the PMInternational journal of environmental research and public health · 2021Article
- NOUrban climate · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundUnderstanding spatial variation of air pollution is critical for public health assessments. Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations. However, they have limited application in China due to the lack of spatially resolved data.
objectiveBased on purpose-designed monitoring networks, this study developed LUR models to predict fine particulate matter (PM
methodTwo one-week integrated samples were collected at 30 PM
resultsLUR explained 65% of the spatial variability in PM
conclusionWe concluded that based on a purpose-designed monitoring network, LUR model can be applied to predict PM
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Registered trials
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.