Marian Mazur

Application of the Statistical Error and Quantitative Performance Measures in the Evaluation Process of Short-Term Air Quality Forecasts for Krakow (Poland).: Zastosowanie statystycznych miar błędów i wskaźników wydajności modelu w procesie oceny krótkoterminowych prognoz jakości powietrza w Krakowie (Polska).

Currently, the results of mathematical air quality modelling serve many purposes (e.g. global and local pollution prevention strategies, industrial emissions reduction, actions of local governments). Additionally, in the past few years, due to continuous digital technology development, prognostic models have been utilised for drawing up publicly available short-term forecasts of the upcoming air quality. This information is of particular importance in the areas of densely populated cities, which are frequently struggling with poor air quality. Issuing alerts about possible exceedances of the air quality standards [8] helps to mitigate adverse eff ects on the health of residents [6]. Krakow, located in the south-eastern Poland and with more than 750 000 inhabitants [3], has been recently qualifi ed as one of the most polluted European cities due to excessive concentrations of PM2.5 [11]. In 2011, on behalf of the Marshal Offi ce of Małopolska Voivodeship, an air quality forecasting system based on the results of global multiscale chemical weather modelling system GEM-AQ was developed and fully operational since then [23, 24]. The air quality forecasts for the area of Małopolska Voivodeship and major cities within are prepared on the basis of a modelling system created by the EkoForecast foundation, which utilises a global multiscale chemical weather model – GEM-AQ [9]. In order to ensure the correct functioning of the short-term air pollution forecasting system, an appropriate assessment at every stage of this application is essential. Basically, the validation process of a mathematical model consists of scientifi c, operational and statistical evaluation. Scientifi c evaluation requires a thorough knowledge of the model’s basis. It examines the accuracy of described physical and chemical processes, as well as other assumptions in the model. Operational