Analysis of daily average PM10 predictions by generalized linear models in Brno, Czech Republic

Analysis of daily average PM10 predictions by generalized linear models in Brno, Czech Republic

WoS Article

Ambient air quality assessment and management plays an important role in the current European Union policy. Among others, air pollution by PM10 is being monitored. A previous analysis of PM10 (Hrdličková et. al, 2008) aimed at identifying factors affecting air pollution in stations in the City of Brno using available observed meteorological variables. However, the studied model cannot be used for predicting the level of PM10 pollution because the included meteorological variables are not available exactly at the time when the prediction is requested. In that case, we should base the predictions on available predicted variables, namely on temperature, wind direction, wind speed, and cloud cover. A comparison of obtained predictions with the observed values of PM10 during a testing period allows us to evaluate a loss of prediction quality when the predicted covariates are used instead of the observed ones. The presented analysis based on test of symmetry and test of homogeneity of the marginal distribution of two-way contingency tables shows that the loss of prediction quality by employing the predicted meteorological variables is non-significant in the studied case. This observation suggests that the model with predicted meteorological variables can be employed in environmental management processes.

Ambient air quality assessment and management plays an important role in the current European Union policy. Among others, air pollution by PM10 is being monitored. A previous analysis of PM10 (Hrdličková et. al, 2008) aimed at identifying factors affecting air pollution in stations in the City of Brno using available observed meteorological variables. However, the studied model cannot be used for predicting the level of PM10 pollution because the included meteorological variables are not available exactly at the time when the prediction is requested. In that case, we should base the predictions on available predicted variables, namely on temperature, wind direction, wind speed, and cloud cover. A comparison of obtained predictions with the observed values of PM10 during a testing period allows us to evaluate a loss of prediction quality when the predicted covariates are used instead of the observed ones. The presented analysis based on test of symmetry and test of homogeneity of the marginal distribution of two-way contingency tables shows that the loss of prediction quality by employing the predicted meteorological variables is non-significant in the studied case. This observation suggests that the model with predicted meteorological variables can be employed in environmental management processes.

PM10, Air pollution, Generalized generalized linear model, Gamma distribution, Prediction

PM10, Air pollution, Generalized generalized linear model, Gamma distribution, Prediction

HÜBNEROVÁ, Z.; MICHÁLEK, J.

2015

26.06.2014

Turkish National Committee for Air Pollution Research and Control

Dokuz Eylul University, Dpt of Environmental Engineering, Buca/Izmir, Turkey

1309-1042

Atmospheric Pollution Research

5

3

Republic of Turkey

471

476

6

http://www.atmospolres.com/articles/Volume5/issue3/APR-14-055.pdf