Multivariate receptor modeling for temporally correlated data by using MCMC Academic Article

abstract

• Multivariate receptor modeling aims to estimate pollution source profiles and the amounts of pollution based on a series of ambient concentrations of multiple chemical species over time. Air pollution data often show temporal dependence due to meteorology and/or background sources. Previous approaches to receptor modeling do not incorporate this dependence. We model dependence in the data using a time series approach so that we can incorporate extra sources of variability in parameter estimation and uncertainty estimation. We estimate parameters using the Markov chain Monte Carlo method, which makes simultaneous estimation of parameters and uncertainties possible. The methods are applied to simulated data and 1990 Atlanta air pollution data. The results show promise towards the goal of accounting for the dependence in the data. 2001, Taylor & Francis Group, LLC. All rights reserved.

authors

• Park, Eun

published proceedings

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

altmetric score

• 3

author list (cited authors)

• Park, E. S., Guttorp, P., & Henry, R. C.

citation count

• 44

complete list of authors

• Park, ES||Guttorp, P||Henry, RC

publication date

• December 2001

publisher

• Taylor & Francis  Publisher

published in

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION  Journal

keywords

• Air Pollution
• Chemical Species
• Compositions
• Dynamic Models
• Gibbs Sampler
• Kalman Filter
• Metropolis-hastings Algorithm
• Source Profile

Digital Object Identifier (DOI)

• 10.1198/016214501753381823

start page

• 1171

end page

• 1183

volume

• 96

issue

• 456

URL

• http://dx.doi.org/10.1198/016214501753381823