Doutorado em Engenharia Ambiental

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http://repositorio.ufes.br/handle/10/17611

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Navegando Doutorado em Engenharia Ambiental por Autor "Andrade Filho, Marinho Gomes de"

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    Modelo ARFIMA espaço-temporal em estudos de poluição do ar
    (Universidade Federal do Espírito Santo, 2013-08-28) Monroy, Nátaly Adriana Jiménez; Subba Rao, Tata; Reisen, Valdério Anselmo; Santos, Jane Meri; Vasconcellos, Klaus Leite Pinto; Andrade Filho, Marinho Gomes de; Albuquerque, Taciana Toledo de Almeida
    In air pollution studies is frequent to observe data measured on time over several spatial locations. This is the case of measures of air pollutant concentrations obtained from monitoring networks. The dynamics of these kind of observations can be represented by statistical models, which consider the dependence between observations at each location or region and their neighbor locations, as well as the dependence between the observations sequentially measured. In this context, the class of the Space-Time Autoregressive Moving Average (STARMA) models is very useful since it explains the underlying uncertainty in systems with a complex variability on time and space scales. The process with STARMA representation is an extension of the univariate ARMA time series. In this case, besides the modeling of the single series on time, their evolution over a spatial grid is also considered. The application of the STARMA models in air pollution studies is not much explored. This thesis proposes a class of space-time models which consider the long memory dependence usually observed in time series of air pollutant concentrations. This model is applied to real series of daily average concentrations of PM10 and SO2 at Greater Vit´oria Region, ES, Brazil. The results obtained showed that the dispersion dynamics of the studied pollutants can be well described using the STARMA and STARFIMA models, here proposed. These class of models allowed to estimate the influence of the pollutants on the pollution levels over the neighbor regions. The STARFIMA process showed to be appropriate for the series under study since they have long memory characteristics. Taking into account the long memory properties lead to a significant improvement of the forecasts, both on time and space.