Analysis of PM₁₀ air pollution in Brno based on generalized linear model with strongly rank‐deficient design matrix

Abstract

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.

Forecasting of Daily PM10 Concentrations in Brno and Graz by Different Regression Approaches

“…Note that all models are fitted on a training data set containing seven cold seasons 2002/2003-2008/2009at Graz-Mitte (six cold seasons 2003/2004-2008/2009 at GrazSüd) utilizing observed values of the covariates as in Stadlober et al (2008). These models were employed for predictions of the test data set (days ranging from November 3, 2009 to March 31, 2010).…”

“…The wind speed affects the pollution differently -higher emissions come up during stronger circulation (pollutant is brought from distant sources) as well as weaker circulation (the air is ventilated worse and polluting aerosol is accumulated). Unlike in Hrdličková et al (2008) and Veselý et al (2009), here the predicted wind direction D t and wind speed V t were considered in the model. Note that wind direction is measured as an oriented angle between the vector pointing north of the station and the observed wind direction vector pointing to the station.…”

“…In contrast to many other studies it was possible to show the performance of these models in operational mode based on a threeyear trial period. In Brno, generalized linear models have been used to analyze and predict PM10 levels by means of meteorological and seasonal variables Veselý, 2008, Veselý, Tonner, Hrdličková, Michálek, andKolář, 2009). Other typical approaches for PM prediction are neuronal networks (c.f.…”

Impacts of Built-Up Area Geometry on PM10 Levels: A Case Study in Brno, Czech Republic

“…This paper claimed that in urban areas with less intensive traffic, statistical predictions of air pollution using regression models are more accurate. A comparison of statistical prediction models describing air pollution in Brno was presented in [24]. The applicability of the regression model described in [23] was also evaluated using data obtained in Graz, Austria [25], and it was compared with the Austrian prediction model, which is used in Graz for official PM predictions.…”

Levels and Health Risk Assessment of PM₁₀ Aerosol in Brno, Czech Republic

“…Several studies have been carried out concerning PM air pollution in Brno (12)(13)(14)(15)(16)(17)(18)(19)(20). However, most of them are focused on PM-bound substances such as heavy metals or polycyclic aromatic hydrocarbons.…”