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.
2011
Horabik, Joanna; Nahorski, Zbigniew
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/25366
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