The purpose of this study is to develop a method for allocating pollutant concentrations to finer spatial scales conditional on covariate information observable in a fine grid. Spatial dependence is modeled with the conditional autoregressive structure. The maximum likelihood approach to inference is employed, and the optimal predictors are developed to assess missing concentrations in a fine grid. The method is developed for a practical application of an output from the dispersion model CALPUFF run for Warsaw agglomeration.
(2011). Spatial disaggregation of pollutant concentration data [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/25366
Spatial disaggregation of pollutant concentration data
2011-01-01
Abstract
The purpose of this study is to develop a method for allocating pollutant concentrations to finer spatial scales conditional on covariate information observable in a fine grid. Spatial dependence is modeled with the conditional autoregressive structure. The maximum likelihood approach to inference is employed, and the optimal predictors are developed to assess missing concentrations in a fine grid. The method is developed for a practical application of an output from the dispersion model CALPUFF run for Warsaw agglomeration.File | Dimensione del file | Formato | |
---|---|---|---|
59.pdf
accesso aperto
Descrizione: publisher's version - versione dell'editore
Dimensione del file
149.31 kB
Formato
Adobe PDF
|
149.31 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo