Volume
42
Year
2008
Page
8464–8469
Source
Atmospheric Environment
Format Published
PDF
Descriptors
Bayesian hierarchical model , Markov Chain Monte Carlo (MCMC) , Urban air quality , Multiple linear regression (MLR)
Abstract
Urban air quality is subject to the increasing pressure of urbanization, and, consequently, the
potential impact of air quality changes must be addressed. A Bayesian hierarchical modelwas
developed in this paper for urban air quality predication. Literature data on three pollutants
and four external driving factors in Xiamen City, China, were studied. The air quality model
structure and prior distributions of model parameters were determined by multivariate
statistical methods, including correlation analysis, classification and regression trees (CART),
hierarchical cluster analysis (CA), and discriminant analysis (DA). A multiple linear regression
(MLR) equation was proposed to measure the relationship between pollutant concentrations
and driving variables; and Bayesian hierarchical model was introduced for
parameters estimation and uncertainty analysis. Model fit between the observed data and
the modeled valueswas demonstrated, withmeanand median values and twocredible levels
(2.5% and 97.5%). The average relative errors between the observed data and the mean values
of SO2, NOx, and dust fall were 6.81%, 6.79%, and 3.52%, respectively.
Call. No.
EA 76
IndexDate
1397/10/19
Indexer
Dashagha
Title of Article
A Bayesian hierarchical model for urban air quality prediction under uncertainty
RecordNumber
77
Author/Authors