Abstract:The performance of five linear models to predict the daily mean PM 10 concentrations was compared. The linear models proposed were: (i) multiple linear regression; (ii) principal component regression; (iii) independent component regression; (iv) quantile regression; and (v) partial least squares regression. The study was based on data from an urban site in Oporto Metropolitan Area and the analysed period was from January 2003 to December 2005. The linear models were evaluated with two datasets of different sizes belonging to the analysed period. Environmental data (SO 2 , CO, NO, NO 2 and PM 10 concentrations) and meteorological data (temperature, relative humidity and wind speed) were used as PM 10 predictors.During the training step, quantile regression presented the lowest residual errors for the two datasets. Independent component regression was the worst model using the larger dataset. Multiple linear regression, principal component regression and partial least squares regression presented similar results for both datasets. During the test step, independent component regression and quantile regression showed bad performance, while multiple linear regression, principal component regression and partial least squares regression presented similar results using the larger dataset. For the smaller dataset, the models that remove the correlation of the variables (principal component regression, independent component regression and partial least squares regression) presented better results than multiple linear regression and quantile regression. Independent component regression was the linear model with the lowest value of residual error. Concluding, the dataset size is also an important parameter for the evaluation of the models concerning the prediction of variables. The prediction of the daily mean PM 10 concentrations was more efficient when using independent component regression for the smaller dataset and partial least squares regression for the larger datasets.

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ABSTRACT:The utilization of activated carbons as catalyst supports for the treatment of gaseous effluents contaminated with volatile organic compounds allows advantage to be taken both of the adsorption of the pollutant on the welldeveloped porous texture of activated carbons and the possibility of an efficient catalytic conversion. The step of catalyst impregnation should be optimized to obtain a catalyst dispersion compatible with good conversions at sufficiently low temperatures.Textural evolution when impregnation is performed either on the raw material or after activation of the latter has already been well studied. The work described in this paper was directed towards an analysis of the textural evolution of impregnated active carbons when the impregnation step was performed between carbonization and activation. To date, such knowledge is quite scarce. To allow comparison, impregnation with CoO, Co 3 O 4 or CrO 3 was carried out both after activation and after carbonization of nutshells and pinewood sawdust.For both raw materials, impregnation after activation involved deposition of the impregnated oxides on the internal surface of the materials thereby blocking part of the initial micro-and meso-porous textures. When impregnation was conducted after carbonization, metal species acted as catalysts during the subsequent activation step (Co 3 O 4 being the most efficient and CrO 3 the least efficient) and allowed the better development of the porous texture. In nutshell carbons, the metal species remained dispersed in micropores with a smaller volume but a larger size and in the mesoporous texture with a larger volume. Sawdust carbons retained a microporous texture with narrow pores in which the deposition of catalysts did not occur.

and the impact of NHR on chronic rhinitis and rhinosinusitis, all underline the urgent need to invest into the unexplored role of NHR in the Rhinology field.

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