Statistical and optimized results concerning the mobile measurements of fine and coarse emission factors by the Sniffer-system Jari Härkönen Senior Researcher Finnish Meteorological Institute, Air Quality Research, Dispersion modelling NORTRIP meeting (Linköping)

Background of the study Data: Measured Sniffer PM10 concentrations divided into coarse and fine fractions during in Helsinki (Pirjola et al., 2009, Kupiainen et al., 2011). Hourly meteorological data was monitored at the Testbed weather station about km from the study site. Data analysis: Statistical analysis of the data was performed (Härkönen et al., 2011a). The results suggested the relations between the emission factors and the meteorological parameters. Optimization (Härkönen et al., 2011b) was based on the robust loess smoothed emissions factors against velocity speed. The results proposed the regimes for the influences of the background, turbulence and tire-road surface interaction as a function of vehicle speed.

Sniffer data Sniffer route Streets Helsinki Measuring sites

Summary of the results Conversion from concentrations to emission factors: The constant volume rate is and v is the vehicle speed. Statistical analysis of the data was performed (Härkönen et al., 2011a). The results suggested the relations between the emission factors and the meteorological parameters. The referred work suggests also the running mean influence of the vehicle velocity on the emission factors. Optimization procedure (Härkönen et al., 2011b) solves the regimes for the influence of the background, turbulence and tire-road surface interaction as a function of vehicle speed.

The principal statistical results

Interaction between tires and road surface A shematic figure illustrates the exchange of the momentum of the tire into normal and shear stress (force/area) within the tire-road interface. The shear stress is assumed to be responsible for the emissions and the normal stress mostly for the heat.

The relations between emissions and the shear stress When the total shear stress (2) during the averaging time is proportional to the observed PM concentration (3), the corresponding emission factor is proportional to the squared vehicle speed (4). The function f(met) is constant in case of the meteorologically fitted data.

The terms used in the equations: The total shear stress is  tot T ave is the avaraging time (10 s) m r is the mass on the rear tire w is the width of the tire s is the contact length (10 cm assumed) of the tire and road surface, which depends on the radius of the tire The contact length s reduces emissions clearly more effectively than the width of the tire All parameters in the equations 3 and 4 are known or measurable

The optimization procedure The emission factors of the smoothed loess curves increase at a low vehicle speed. It is interpreted as a methodological artefact AF(v) inversely proportional to the vehicle speed. The first guess correction (right panel)

The mathematical model The RHS emission factor curves include three regimes: 1. Approximately constant region < 20 km/h is interpreted as the influence of PM background concentrations on the measurements. 2. S-type increase between 20 – 40 km/h, which is typical for the cumulative probability distribution of random processes as turbulence. 3. Continuous increase of emission factors for vehicle speed > 40 km/h associated with equation (4). The mathematical form of the unconstrained nonlinear objective function for optimization is

Numerical values of the optimized parameters

Graphical comparison of the optimized coarse and fine emission factors

Conclusions The stochastic hypothesis for turbulence and shear stress for tire-road interaction are theoretically consistent with the statistical data based on running mean values. Background and turbulence influences mainly on the emission factors under normal urban conditions, while shear stress dominates on the main roads. The equations 3 and 4 can be tested experimentally in laboratory: The relative changes of the ratio m r /w s 3 and PM concentrations and emission factors must be equal at a constant vehicle velocity e.g. 60 km/h. SEM-study could be applied to samples of velocity regimes e.g km/h, km/h and km/h.

References: Härkönen, J., Pirjola, L., Kupiainen, K., Kauhaniemi, M., Kangas, L., and Karppinen, A., 2011a. Influence of vehicle dynamics on the non-exhaust PM- emission process. In Proceedings of 14 th Conference on Harmonization within Atmospheric Dispersion Modelling for Regulatory Purposes – 2-6 October 2011, Kos, Greece, p Härkönen, J., Pirjola, L., Kupiainen, K., Kauhaniemi, M., Kangas, L., and Karppinen, A., 2011b. Mean dependence of non-exhaust PM-emissions on vehicle speed. NOSA 2011 Aerosol Symposium – November 2011, Tampere, Finland. Kupiainen, K., Pirjola, L., Ritola, R., Väkevä, O., Viinanen, J., Stojiljkovic, A., and Malinen, A., Street dust emissions in Finnish cities – summary of results from Publications by City of Helsinki Environment Centre 5/2011. Pirjola, L., Kupiainen, K.J., Perhoniemi, P., Tervahattu, H., and Vesala, H., Nonexhaust emission measuremet system of the mobile laboratory SNIFFER. Atmos. Environ, vol. 43, p