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FORECASTING OF HIGH-LEVEL AIR POLLUTION IN URBAN-INDUSTRIAL AGGLOMERATION BY MEANS OF NUMERICAL WEATHER FORECASTING Leszek Osródka∗, Marek Wojtylak**, Ewa Krajny* *Institute of Meteorology and Water Management, Katowice, Poland; **University of Silesia, Katowice, Poland Abstract The purpose of the work was performing Air Quality (AQ) forecast model. The idea has made a prediction of AQ in urban-industrial agglomeration basing on weather forecasts. Mezoscale meteorological forecasting UM model (Unified Model) developed by the UKMO, artificial neural networks and regression method was used. Two-step approaches were used to AQ forecasting. The first step was based on classification of meteorological conditions and air pollutants and the second step was based on training of the network in situations responsible for high concentrations. The obtained good fit between forecast air pollution levels (PM10, SO2) and real-time observed air quality. However, the significance advantage this works is enforcement results mezoscale numerical weather forecast to pollution prediction. Key words: urban air pollution, numerical weather forecast, forecasting air quality 1. INTRODUCTION Since 1995 within the Regional System of Air Quality Monitoring scientists have focused on research over employment artificial neural networks in weather forecasts with the inclusion of prediction of air pollution levels in Katowice region (Osródka, 1996, Wojtylak, 1998, Osródka et al., 2003). Forecast of air pollutant concentrations was performed with the employment of average regional values of air pollutant SO2, PM10 concentration. Average regional values provided 8 distant monitoring stations in the mode of average arithmetic values. Forecast is performed every 24 hours. Forecasting period is determined by qualitative availability of quantified weather forecast. During forecast formulation following input data were made available: Ø values of air pollutant concentrations from automatic monitoring stations from the last 24 hours until the forecast; Ø meteorological variables used were (air temperature, wind speed, wind direction) from the last 24 hours until the forecast; Ø values of other meteorological variables (air pressure, total rainfall, relative humidity) obtained from the last 24 hours until the forecast; Ø predicted values of selected meteorological variables for the next 24 hours with 3 hours intervals (air pressure, wind speed, wind direction, air temperature, total rainfall). The data are obtained as quantification of mezoscale weather forecasting model UMPL (Unified Model for Poland Area) developed by the UKMO (United Kingdom Meteorological Office) (Bell and Dickinson, 1987). 2. FORECAST MODEL Occurrence of high levels of air pollution concentration is dependable on well-defined meteorological aspects. Hence the idea of selection from all atmosphere states and meteorological variables group of situations which are responsible for similar pollutant concentrations. Such preliminary selection of the input data would facilitate the network learning process by restricting it to cases from one similarity group. Therefore forecasting here is reduced and takes two step approach (Figure 1): Ø Classification of meteorological conditions and pollutant concentration. In order to make classification of meteorological and air pollution phenomena MADALINE (Hertz, 1991; Pasini et al., 2001) network type has been applied. It learns itself (without external guidance by selection of the winner – competitive learning). Such network is called Kohonen layer. The design of such network is to make it recognise the groups of similar input vectors by one of the neurones. Ø Forecast of air pollutant concentrations. After addressing meteorological predictors and air pollutant concentrations into the respective group, forecast of air pollutant concentrations is developed with a feed-forward three layer network (Masters, 1993).
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Corresponding author address: Leszek Osródka, Department of Monitoring and Environment Research, Institute of Meteorology and Water Management, Bratków 10, 40-045 Katowice, Poland; e-mail:
[email protected]
Observed meteorological and air pollution conditions
Network classification MADALINE
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Fig. 1 Two step air pollution forecasting model in Katowice agglomeration. 3. FORECAST STAGES Exceedance of pollutant concentrations was defined determined in 24 hours period in which at least one of pollutants exceeds 75 percentile. In the first stage of forecast 3 types of air pollution concentrations were determined by means of one-layer network: Ø A - occurence of concentrations higher from mediana value carries load of probability lower than 0.01. Ø B - probability of concentration exceedance by 75 percentile (90 µg/m3 of SO2 and 95 µg/m3 of PM10) is less than 0.2. Ø C - probability of concentration exceedance by 75 percentile is greater than 0.5. The second stage of forecast focused on determination of momentary concentrations in Katowice agglomeration. Frequency of forecasting was every hour. Forecast was made using 24 hours pollutant concentrations. Such forecast sensitivity should be the compromise between accuracy of forecast and neural network. 4. RESULTS Training of network was performed on the data from the years 1996-1998 respectively but testing on a set of date covering years 1999-2000 (Figure 2). The ability of testing the method of air pollution forecasting by means of neural networks on a data set for the years 1999-2000 was limited due to relatively small number of episodes (2 with 114) where pollutant concentrations exceeded threshold values (SO2>90µg/m3 and PM10>95µg/m 3). In Katowice agglomeration high PM10 and SO2 concentrations occur simultaneously that is why both pollutants were forecasted together.
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Fig. 2 Daily mean concentrations of air pollutants SO2 and PM10 in winter season in the period 1996-1998 and 1999-2000. In the period under study identification of 24 cases was made where the levels of air pollution were exceeded. Forecast of air pollution concentration proved to be correct for type C. In case of type A exceeded levels of pollutants were connected with unfavourable speed of wind predicted by UKMO model. In this case forecast showed higher values from observed ones. In order to evaluate forecast quality based on weather forecast UKMO model, simulation was made which replaced data from UKMO forecast in training sequence and learning by real
data. Change of forecasted meteorological variables for real values did not influence in any way forecast of pollutants. According to the authors this change justifies the proper selection of forecasted meteorological factors. Figure 3 presents the types of forecasted air pollution episodes in respective classification groups for predicted air pollution concentrations.
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Fig. 3 Types of forecasted air pollution episodes in 1999-2000 years.
Forecasted particulate matter concentrations [ g m -3]
Forecast of daily average concentration of particulate matter generally corresponds better to measure values than sulphur dioxide. Correlation coefficients 0.660 and 0.620 respective for PM10 and SO2 were statistically significant at the level of α=0.05 (Figure 4).
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Fig. 4 Example of forecasted and observed daily average PM10 concentrations in the years 1999-2000. During forecast training period covering 24 hour cycle, one of the most undesirable situations took place on the 21st and 22nd of January 1999. Comparative analysis revealed that during this period forecast for SO2 proved to be better than for PM10. Figures 4 demonstrate findings of hourly concentrations observed and calculated for PM10. As in case of SO2 24-hour forecast is generally compatible with measured values, in case of PM10 the forecast is definitely underestimated. Probably one of the reasons of this underestimation is missing description of atmosphere's physical condition. Perhaps the accuracy of pollutants concentration forecast would be better if the input data was supplemented by observatory elements such as: mixing layer and inversion heights or vertical thermodynamic stratification of boundary layer. Inserting into the model real meteorological data does not improve the forecast quality AQ.
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Fig. 4 Comparison between forecasted and observed hourly PM10 concentrations for the period 20-21 January 1999. 5. DISCUSSION Ø
Ø
Seven years of studies on the development of modelling techniques employed for forecasting of 24-hour pollutant concentrations in large urban-industrial region enabled to formulate a prediction based on two-step model. Because of strong link between meteorological case and air pollution levels and because of the need to forecast highest values of air pollutant concentrations it seems desirable to further develop the research in this direction. Search for the best study area for this model should be narrowed to the methods, which would successfully, forecast the situations recognised as responsible for high concentration levels. Simultaneously verification of meteorological input data should be performed and also replacement of momentary values by 24-hour pollutant input should be evaluated. At this stage of research for the first time for construction of pollutant forecast projected meteorological data were obtained from the UKMO. Under the same meteorological conditions very different concentrations of air pollutants were sometimes observed. Forecast of SO2 and PM10 pollutant concentration is comparable. However, SO2 concentrations forecast data are closer to the real values than PM10. The next step will be verification of these meteorological forecasts and analysis of possibilities how data from other modelling weather centre can be used. The continuation of the work has made a prediction of AQ basin on two weather forecasts: UMPL model and DM model. The numerical meteorological forecasting DM model developed by the German Meteorological Service DWD (Deutscher Wetterdienst). Two-step approach use to AQ forecasting can be change. The first step will be prediction elements of concentration pollution distribution. Model UMPL and neural network will be used. The prediction using UMPL model will determined element distribution for selected air pollutants and its probability (diurnal maximum concentration, average diurnal concentration and time occurrence value). The second step will be prediction of hourly concentrations of ambient AQ. The DM model and regression method and simple neural network will be used.
References Bell, R.S., Dickinson, A., 1987, The Meteorological Office operational numerical weather prediction system. Met. Office Sci. Paper No. 41, United Kingdom Meteorological Office, Bracknell, UK. Hertz, J., Krogh, A., Palmer, R.G., 1991, Introduction to the theory of neural computation. Addison-Wesley. Masters, T., 1993, Practical neural network recipes in C++. Academic Press, Inc. Osródka L., Wojtylak M., Krajny E., Blazek Z., Cernikovsky L., 2003, Comparison of selected concentrations of pollutants in air in Katowice and Ostrava-Karvina agglomerations in years 1997-2001 by EU standards. Proc.of th the 4 Int. Conf. On Urban Air Quality, eds. R.S. Sokhi & J. Brechler, University of Hertfordshire, UK, 250-253. Osródka, L., 1996, Development of smog warning system in large industrial metropolises based on the example of Upper Silesian Industrial Region. In: Allegrini, I., de Santis, F. (Eds.), Urban Air Pollution. NATO ASI Series, Springer-Verlag, 367-375. Pasini, A., Pelino, V., Potesta, S., 2001, A neural network model for visibility nowcasting from surface observations: Results and sensitivity to physical input variables, Journal of Geophysical Research, 106 (D14), 14,951-14,959. Wojtylak, M., Osródka, L., Osródka, K., 1998, The development of artificial neural networks in the forecasting of air pollution (in Polish). Reports of Institute of Meteorology and Water Management, 21, 117-130.
