ArticleEnvironmetrics2014
A Bayesian Multivariate Receptor Model for Estimating Source Contributions to Particulate Matter Pollution using National Databases.
Article in Environmetrics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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Who cites it
13 citing papers in PubMed.
- A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology.Biostatistics (Oxford, England) · 2025Article
- Elevated exposure to air pollutants accelerates primary glomerular disease progression.Clinical kidney journal · 2025Review
- Elevated Exposure to Air Pollutants Accelerates Primary Glomerular Disease Progression.Kidney international reports · 2024Article
- A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution.Environmetrics · 2023Article
- Forecasting PMEnvironmental science and pollution research international · 2020Article
- An assessment of important SPECIATE profiles in the EPA emissions modeling platform and current data gaps.Atmospheric environment (Oxford, England : 1994) · 2019Article
- Source-specific pollution exposure and associations with pulmonary response in the Atlanta Commuters Exposure Studies.Journal of exposure science & environmental epidemiology · 2018Article
- A hierarchical modeling approach to estimate regional acute health effects of particulate matter sources.Statistics in medicine · 2017Article
- Recent Approaches to Estimate Associations Between Source-Specific Air Pollution and Health.Current environmental health reports · 2017Review
- A review of AirQ Models and their applications for forecasting the air pollution health outcomes.Environmental science and pollution research international · 2017Review
- Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities.Environmental health perspectives · 2017Article
- Trends in PMAtmospheric environment (Oxford, England : 1994) · 2016Article
- A model-based approach for imputing censored data in source apportionment studies.Environmental and ecological statistics · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Time series studies have suggested that air pollution can negatively impact health. These studies have typically focused on the total mass of fine particulate matter air pollution or the individual chemical constituents that contribute to it, and not source-specific contributions to air pollution. Source-specific contribution estimates are useful from a regulatory standpoint by allowing regulators to focus limited resources on reducing emissions from sources that are major contributors to air pollution and are also desired when estimating source-specific health effects. However, researchers often lack direct observations of the emissions at the source level. We propose a Bayesian multivariate receptor model to infer information about source contributions from ambient air pollution measurements. The proposed model incorporates information from national databases containing data on both the composition of source emissions and the amount of emissions from known sources of air pollution. The proposed model is used to perform source apportionment analyses for two distinct locations in the United States (Boston, Massachusetts and Phoenix, Arizona). Our results mirror previous source apportionment analyses that did not utilize the information from national databases and provide additional information about uncertainty that is relevant to the estimation of health effects.
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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.