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

  • Ernst Stadlober Graz University of Technology, Austria
  • Zuzana Hübnerová Brno University of Technology, Czech Republic
  • Jaroslav Michálek Brno University of Technology, Czech Republic
  • Miroslav Kolář Masaryk University, Czech Republic

Abstract

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/m3. 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.

References

Cordeiro, G. M., and McCullagh, P. (1991). Bias correction in generalized linear models. Journal of the Royal Statistical Society, Series B, 53, 629-643.

Díaz-Robles, L. A., Ortega, J. C., Fu, J. S., Ree, G. D., Chow, J. C., Watson, J. G., et al. (2008). A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmospheric Environment, 42, 8331-8340.

EC Directive. (2008). Council Directive 2008/50/EC on ambient air quality and cleaner air for Europe. Official Journal of the European Communities, L 151, 1-44.

Fahrmeir, L., and Tutz, G. (1994). Multivariate Statistical Modelling Based on Generalized Linear Models. New York: Springer-Verlag.

Hauck, H., Berner, A., Frischer, T., Gomiscek, B., Kundi, M., Neuberger, M., et al. (2004). AUPHEP – Austrian project on health effects of particulates – general overview. Atmospheric Environment, 38, 3905-3915.

Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., and Brasseur, O. (2005). A neuronal network forecast for daily average PM10 concentrations in Belgium. Atmospheric Environment, 39, 3279-3289.

Hörmann, S., Pfeiler, B., and Stadlober, E. (2005). Analysis and prediction of particulate matter PM10 for the winter season in Graz. Austrian Journal of Statistics, 34, 307-326.

Hrdličková, Z., Michálek, J., Kolář, M., and Veselý, V. (2008). Identification of factors affecting air pollution by dust aerosol PM10 in Brno City, Czech Republic. Atmospheric Environment, 42, 8661-8673.

Papanastasiou, D. K., Melas, D., and Kioutsioukis, I. (2007). Development and assessment of neural network and multiple regression models in order to predict pm10 levels in a medium-sized mediterranean city. Water Air Soil Pollution, 182, 325-334.

Pérez, P., and Reyes, J. (2002). Prediction of maximum om 24-h average of PM10 concentrations 30h in advance in Santiago, Chile. Atmospheric Environment, 36, 4555-4561.

Pires, J. C. M., Alvim-Ferraz, M. C. M., Pereira, M. C., and Martins, F. G. (2010). Prediction of PM10 concentrations through multi-gene genetic programming. Atmospheric Pollution Research, 1, 305-310.

Pope III, C. A., and Dockery, D.W. (2006). Health effects of fine particulate air pollution: lines that connect. Journal Air Waste Management Association, 56, 709-742.

Schwarze, P. E., Øvrevik, J., Låg, M., Refsnes, M., Nafstad, P., Hetland, R. B., et al. (2006). Particulate matter properties and health effects: consistency of epidemiological and toxicological studies. Human & Experimental Toxicology, 25, 559-579.

Sfetsos, A., and Vlachogiannis, D. (2010). A new methodology development for the regulatory forecasting of PM10. Application in the Greater Athens Area, Greece. Atmospheric Environment, 44, 3159-3172.

Silva, C., Pérez, P., and Trier, A. (2001). Statistical modelling and prediction of athmospheric pollution by particulate material: two nonparametric approaches. Environmetrics, 12, 147-159.

Stadlober, E., Hörmann, S., and Pfeiler, B. (2008). Quality and performance of a PM10 daily forecasting model. Atmospheric Environment, 42, 1098-1109.

van der Wal, J. T., and Jansen, L. H. J. M. (1999). Analysis of spatial and temporal variations of PM10 concentrations in the Netherlands using Kalman filtering. Atmospheric Environment, 34, 3675-3687.

Veselý, V., Tonner, J., Hrdličková, Z., Michálek, J., and Kolář, M. (2009). Analysis of PM10 air pollution in Brno based on generalized linear model with strongly rank–deficient design matrix. Environmetrics, 20, 676-698.
Published
2016-02-24
How to Cite
Stadlober, E., Hübnerová, Z., Michálek, J., & Kolář, M. (2016). Forecasting of Daily PM10 Concentrations in Brno and Graz by Different Regression Approaches. Austrian Journal of Statistics, 41(4), 287–310. https://doi.org/https://doi.org/10.17713/ajs.v41i4.169
Section
Articles