Modeling and forecasting daily maximum hourly ozone concentrations using the RegAR model with skewed and heavy-tailed innovations

This paper considers the modeling and forecasting of daily maximum hourly ozone concentrations in Laranjeiras, Serra, Brazil, through dynamic regression models. In order to take into account the natural skewness and heavy-tailness of the data, a linear regression model with autoregressive errors and innovations following a member of the family of scale mixture of skew-normal distributions was considered. Pollutants and meteorological variables were considered as predictors, along with some deterministic factors, namely week-days and seasons. The Oceanic Niño Index was also considered as a predictor. The estimated model was able to explain satisfactorily well the correlation structure of the ozone time series. An out-of-sample forecast study was also performed. The skew-normal and skew-t models displayed quite competitive point forecasts compared to the similar model with gaussian innovations. On the other hand, in terms of forecast intervals, the skewed models presented much better performance with more accurate prediction intervals. These findings were empirically corroborated by a forecast Monte Carlo experiment.     

Single adsorption of diclofenac and ronidazole from aqueous solution on commercial activated carbons: effect of chemical and textural properties

Environmental Science and Pollution Research - The importance of the textural and physicochemical characteristics upon the adsorption capacity of the commercial activated carbons (ACs) Coconut,...     

The challenges in the identification of Escherichia coli from environmental samples and their genetic characterization

Environmental Science and Pollution Research - Escherichia coli bacteria are an essential indicator in evaluations of environmental pollution, which is why they must be correctly identified. This...