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Identifying meteorological drivers of PM2.5 levels via a Bayesian spatial quantile regression

Environmetrics(2021)10.1002/env.2669Source: DataRank Database

Identifying meteorological drivers of PM2.5 levels via a Bayesian spatial quantile regression is a research paper published in Environmetrics (2021). On theSindex it has a DataRank of 0. It has been cited 4 times.

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0DataRank · unranked

Scored on demand from live citation data

4 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 0.41)OA: closedTopics (20)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Christopher S. McMahan ORCID,Brook T. Russell ORCID,Stella Self ORCID

Live enrichment

PubMed:20725694 OpenAlex:https://openalex.org/W3130794297

National Institutes of Health

Grant: R01‐AI121351

National Science Foundation

Grant: OIA‐1826715

Office of Naval Research

Grant: N00014‐19‐1‐2295

U.S. Army Corps of Engineers

Grant: IPA000‐19‐0‐0003

FWCI

0.41

Citation Percentile

0.6%

Citation Trend

2022

2023

closedLicense: http://onlinelibrary.wiley.com/termsAndConditions#am

publisherhttps://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2669

publisherhttps://onlinelibrary.wiley.com/doi/full-xml/10.1002/env.2669

publisherhttps://onlinelibrary.wiley.com/doi/am-pdf/10.1002/env.2669

journalhttps://doi.org/10.1002/env.2669

Fields of Study

Air Quality and Health ImpactsEconomic and Environmental ValuationAir Quality Monitoring and Forecasting

Keywords

Quantile regressionEnvironmental scienceQuantileAir pollutionBayesian probabilityRegressionRegression analysisParticulatesMeteorologyHuman healthAtmospheric sciencesEconometricsStatisticsClimatologyGeographyMathematicsEnvironmental health

Sustainable Development Goals

SDG 0: Sustainable cities and communities