A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Contending with space–time interaction in the spatial prediction of pollution: Vancouver's hourly ambient PM10 field
10.1002/env.546.abs
In this article we describe an approach for predicting average hourly concentrations of ambient PM10 in Vancouver. We know our solution also applies to hourly ozone fields and believe it may be quite generally applicable. We use a hierarchical Bayesian approach. At the primary level we model the logarithmic field as a trend model plus Gaussian stochastic residual. That trend model depends on hourly meteorological predictors and is common to all sites. The stochastic component consists of a 24‐hour vector response that we model as a multivariate AR(3) temporal process with common spatial parameters. Removing the trend and AR structure leaves ‘whitened’ time series of vector series. With this approach (as opposed to using 24 separate univariate time series models), there is little loss of spatial correlation in these residuals compared with that in just the detrended residuals (prior to removing the AR component). Moreover our multivariate approach enables predictions for any given hour to ‘borrow strength’ through its correlation with adjoining hours. On this basis we develop a spatial predictive distribution for these residuals at unmonitored sites. By transforming the predicted residuals back to the original data scales we can impute Vancouver's hourly PM10 field. Copyright © 2002 John Wiley & Sons, Ltd.
Contending with space–time interaction in the spatial prediction of pollution: Vancouver's hourly ambient PM10 field
10.1002/env.546.abs
In this article we describe an approach for predicting average hourly concentrations of ambient PM10 in Vancouver. We know our solution also applies to hourly ozone fields and believe it may be quite generally applicable. We use a hierarchical Bayesian approach. At the primary level we model the logarithmic field as a trend model plus Gaussian stochastic residual. That trend model depends on hourly meteorological predictors and is common to all sites. The stochastic component consists of a 24‐hour vector response that we model as a multivariate AR(3) temporal process with common spatial parameters. Removing the trend and AR structure leaves ‘whitened’ time series of vector series. With this approach (as opposed to using 24 separate univariate time series models), there is little loss of spatial correlation in these residuals compared with that in just the detrended residuals (prior to removing the AR component). Moreover our multivariate approach enables predictions for any given hour to ‘borrow strength’ through its correlation with adjoining hours. On this basis we develop a spatial predictive distribution for these residuals at unmonitored sites. By transforming the predicted residuals back to the original data scales we can impute Vancouver's hourly PM10 field. Copyright © 2002 John Wiley & Sons, Ltd.
Contending with space–time interaction in the spatial prediction of pollution: Vancouver's hourly ambient PM10 field
Zidek, Jim (author) / Sun, Li (author) / Le, Nhu (author) / Özkaynak, Halûk (author)
Environmetrics ; 13 ; 595-613
2002-08-01
19 pages
Article (Journal)
Electronic Resource
English
The 1998 Hunter Lecture - Spatial-Temporal Models for Ambient Hourly PM10 in Vancouver
| Online Contents | 1999
Vancouver's Sea Island Airport
| Engineering Index Backfile | 1940
Vancouver's suburban involution
| Online Contents | 2014