1. J. K. Gietl * and O. Klemm Institute of Landscape Ecology, University of Münster, Germany Corresponding author: johanna.gietl@uni-muenster.de, http://kli.uni-muenster.de T06A236P, European Aerosol Conference - August 2008 - Thessaloniki, Greece WeekdayWeekend PCHDVRiPCHDVRi 6 nm - 100 nm Number Conc. 0.740.96-0.810.870.57-0.12 100 nm - 225 nm Number Conc. 0.790.92-0.760.540.32 PM 10 Mass Conc. 0.790.96-0.87-0.41-0.016-0.063 ParameterRankMLP 10-13-1 Temperature 12.42 Time of Day 22.38 Relative Humidity 32.08 Wind Direction 41.27 Pressure 51.26 Wind Speed 61.16 HDV 71.15 Precipitation of Last 24 Hours 81.11 PC 91.09 Day of Week 101.05 Acknowledgement This study was financially and logistically supported by the municipality of Münster. Tab.1: Spearman rank correlation coefficients for mean diurnal particle concentrations (1-h values), Richardson number (Ri), and vehicle numbers. To gain knowledge of the variables determining PM 10 a pre- diction model was calculated by artificial neural networks. The predicted concentration by the final multilayer perceptron model correlated well with the observed data (Spearman rank correlation = 0.72, p< 0.05, RMSE for the validation data = 9.0). Input variables which did not have an effect on the prediction quality (sensitivity analysis result ≤ 0) have been excluded: Richardson number and precipitation. The rank of importance for the used input variables is found in Tab 2. Temperature, time of day and relative humidity have the greatest influence on the variability of PM 10, whereas traffic numbers play subordinate parts. Spearman rank correlations between meteorological data and particle concentrations showed different behaviour for mass and number concentrations. Particle number concentrations of diameter 100 nm) with wind speed (-0.4). The PM 10 mass con-centrations showed highest correlations with precipitation (-0.4) and wind speed (-0.4). Fig. 1: Mean variability of PM 10 concentrantion, vehicle numbers and Richardson niumber at weekdays Tab. 2: Rank of importance of the input variables for predicting PM 10 During the measurement period the rise in atmospheric turbulence coincided on working days with the increase of traffic numbers and vice versa. This simultaneous change makes it difficult to state the effect of the atmospheric stability on the PM 10 concentration. On weekends the correlation is weaker due to decreased traffic intensity. 1 Motivation The aim of this study was to qualify the influence of traffic and meteorological parameters on the PM 10 mass concentration in Münster, NW Germany. The long term objectives were to prove whether the exceedances of the European PM 10 limit values can be predicted and whether PM10 can be reduced by street traffic measures. 2 Method Between March 2006 and September 2007, the PM 10 mass concentration was measured with a TEOM / FDMS and the fine particle number concentration (6 to 225 nm) with a SMPS at a 4 to 6-lane road in Münster. Traffic intensity was counted, separated in passenger cars (PC) and heavy-duty vehicles / busses (HDV). The meteorological data were measured at two sites nearby the traffic site, each about one km away. For statistical analysis Spearman rank correlation and artificial neural networks were applied. 3 Results The traffic numbers, especially of HDVs, correlated well with the particle mass and number concentrations on weekdays, whereas on weekends the lower traffic numbers seem to be insufficient to clearly affect the PM 10 concentration (Fig. 1, Tab.1). Instead PM 10 seemed suppressed by other sources and meteorology. 4 Conclusion Both traffic and meteorological parameters influence the particle concentrations but in different manner. The meteo- rorological parameters contribute highly to the variability of PM 10, whereas the effect of traffic with its diurnal and weekly cycle was of more regular nature and provided a more or less constant PM 10 pattern, of which the absolute magnitude is governed by the meteorology.