Brno and Graz, the second largest cities of their countries, observe in each winter season PM10 concentrations of daily means which regularly exceed the limit value of 50 µg/m 3 . This is mainly caused by unfavorable dissemination conditions of the ambient air. Hence, partial regulation measures have to be taken in Brno and Graz where specific decisions for certain regulations may be based on the average PM10 concentration of the next day provided that reliable forecasts of these values are available. For several sites in the two cities we establish forecasts of daily PM10 concentrations based on multiple linear regression and generalized linear models utilizing both measured covariates of the present day and meteorological forecasts of the next day. The comparisons, based on different quality measures demonstrate the usefulness of both model approaches as they yield results of similar quality. Our prediction models may support future decisions concerning possible traffic restrictions or other regulations.
SUMMARYAn analysis of air pollution by suspended particulate matter (PM 10 ) in Brno, the second largest urban agglomeration of the Czech Republic, based on generalized linear model (GLM) is presented. Average daily concentrations coming from PM 10 monitoring for the period 1998-2005 have been processed. The measured meteorological factors: air temperature and humidity, direction and wind speed were considered as covariates along with some additional seasonal factors.Three standard and six GLMs with strongly rank-deficient design matrix have been applied. The rank deficiency is due to overparameterization which allows one more precise modeling involving, among others, identification of significant air pollution sources (PSs).From each of them the parameter estimates were obtained using both standard estimation procedure and a new sparse parameter estimation technique based on a four-step modification of the basis pursuit algorithm originally suggested for time-scale analysis of digital signals.As the standard estimation algorithms often fail due to numerical instability caused by strong overparameterization, we have applied this new computationally intensive approach allowing us to reliably identify nearly zero parameters in the model and thus to find numerically stable sparse solutions.The goal of the analysis was to identify the model and algorithm yielding most precise 1-day forecasts of the level of pollution by PM 10 with regard to the meteorological and seasonal covariates.
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