Bayesian Spatial Multivariate Receptor Modeling for Multisite Multipollutant Data-Reference-Cited by-同舟云学术

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Bayesian Spatial Multivariate Receptor Modeling for Multisite Multipollutant Data

Published:2018-05-14 Issue:3 Volume:60 Page:306-318
ISSN:0040-1706
Container-title:Technometrics
language:en
Short-container-title:Technometrics

Author:

Park Eun Sug¹^ORCID,Hopke Philip K.²^ORCID,Kim Inyoung³,Tan Shuman¹,Spiegelman Clifford H.⁴

Affiliation:

1. Texas A&M Transportation Institute, The Texas A&M University System, College Station, TX

2. Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY

3. Department of Statistics, Virginia Tech University, Blacksburg, VA

4. Department of Statistics, Texas A&M University, College Station, TX

Publisher

Informa UK Limited

Subject

Applied Mathematics,Modelling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00401706.2017.1366948

Reference41 articles.

1. Source apportionment of the ambient PM2.5 across St. Louis using constrained positive matrix factorization

2. Compositional receptor modeling

3. Source identification and apportionment of volatile organic compounds in Houston, TX

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1. Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model;Science of The Total Environment;2024-03

2. Bilinear model factor decomposition: A general mixture analysis tool;Chemometrics and Intelligent Laboratory Systems;2023-09

3. Predicting latent source-specific PM2.5 pollution from regional sources at unmonitored sites by Bayesian spatial multivariate receptor modeling;Environmental Pollution;2023-05

4. Health risk assessment and source apportionment of PM2.5-bound toxic elements in the industrial city of Siheung, Korea;Environmental Science and Pollution Research;2022-05-04

5. Bayesian multivariate receptor modeling software: BNFA and bayesMRM;Chemometrics and Intelligent Laboratory Systems;2021-04

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