Particulate matter (PM) is one of the most critical air pollutants because of its effects on the human health and the environment. It is well known that covariates, such as meteorological and geographical variables, have a significative influence on PM concentration. In this work we model PM concentration, measured by the monitoring network in Piemonte, taking into account the uncertainty of covariates that are output of a deterministic model chain, by means of a spatio-temporal error-in- variables model. The aim is to map the PM concentration random field all over Piemonte region considering all the uncertainty sources, i.e. the error related to the PM measurements and the covariate simulation as well as the error coming from the spatial prediction procedure.
(2011). Error-in-variables spatio-temporal model for PM10 mapping [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/28532
Error-in-variables spatio-temporal model for PM10 mapping
CAMELETTI, Michela;
2011-01-01
Abstract
Particulate matter (PM) is one of the most critical air pollutants because of its effects on the human health and the environment. It is well known that covariates, such as meteorological and geographical variables, have a significative influence on PM concentration. In this work we model PM concentration, measured by the monitoring network in Piemonte, taking into account the uncertainty of covariates that are output of a deterministic model chain, by means of a spatio-temporal error-in- variables model. The aim is to map the PM concentration random field all over Piemonte region considering all the uncertainty sources, i.e. the error related to the PM measurements and the covariate simulation as well as the error coming from the spatial prediction procedure.Pubblicazioni consigliate
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