NORTRIP Different measures to reduce PM10 concentrations –a model sensitivity analysis Gunnar Omstedt, SMHI Introduction model description model sensitivity analysis -reducing studded tyres, sanding, street sweeping conclusions

NORTRIP Model description see earlier description for more details

NORTRIP Sensitivity analysis

NORTRIP Sensitivity analysis The effect of not using sand in the model for this case decrease the emission factor with about 45 mg/vkm and the PM10 concentration (90-p daily mean) with about 14 µg/m 3 baseline: Hornsgatan 2000

NORTRIP Sensitivity analysis daily mean PM10 concentrations µg/m 3 Case 3: keeping the road surface wet for 1 week

NORTRIP Street sweeping Why is it so difficult to clean streets? PM10 Jagtvej year 2003 Ketzel, M., et al., 2007: Estimation and validation of PM2.5/PM10 exhaust and non-exhaust emissionfactors for practical street pollution modelling. Atmospheric Environment 41, Production and removal of road dust particles are in balance related directly to the traffic. After street sweeping a new balance will arise.

NORTRIP Street sweeping but how about Nordic winter conditions with strong seasonal variations in concentrations? l is the baseline case 4: street sweeping 1 March

NORTRIP Street sweeping so the timing is important! case 5: street sweeping 15 April

NORTRIP Street sweeping Case 6: street sweeping 1 March and no sanding after that studded tyres is more important than sand

NORTRIP Studded tyres Norman, M., Johansson, C., 2006 Atmospheric Environment 40,

NORTRIP Studded tyres Case 7: max stud=50% with sand Cases: 7 and 12

NORTRIP Summary of results

NORTRIP Summary of results

NORTRIP Conclusions  The model seems to response qualitatively rather realistic to different measures for reducing PM10 concentrations  Studded tyres is the most important parameter  Sanding can increase the emissions  Street sweeping can decrease emissions but the effectiveness is strongly dependent of timing  Street sweeping, doing it at the right time, can be as effective as not using sand  A combination of different measures will probably be the best solution This is only a model sensitivity study so the conclusions are first of all related to the model!

NORTRIP Applications of the models: discussion on the suitability of the models for assessment/ planning/ management

NORTRIP PM10 90-percentil > 50 µg/m µg/m µg/m µg/m 3 <35 µg/m 3 Norrköpings kommun Miljö- och hälsoskyddskontoret Robert Sandsveden Åtgärdsprogram-PM10 Norrköping nuläge Example

NORTRIP Norrköping scenario 2015 med ökad trafik genomförda åtgärder PM10 90-percentil > 50 µg/m µg/m µg/m µg/m 3 <35 µg/m 3 Norrköpings kommun Miljö- och hälsoskyddskontoret Robert Sandsveden Trafikuppräkning Dammbindnig med CMA, tidigare samt upprepad vårrengörning, Fysiska åtgärder hamnbron, Sjötullsgatan Söderleden+ ny Norrled Beteendeändringar: minskad dubbdäck, mer kollektiv trafik

NORTRIP Type of stations  19 streets and close to roads stations  21 urban background stations Andersson, S. och Omstedt, G., 2009: Validering av SIMAIR mot mätningar av PM10, NO2 och bensen. Utvärdering för svenska tätorter och trafikmiljöer avseende år 2004 och SMHI Meteorologi, Nr Model validation of SIMAIRroad

NORTRIP Results- streets and roads The uncertainty of modelling estimation is defined as the maximum deviation between the measured and calculated concentration levels for 90 % of individual monitoring points, without taking into account the timing of the events. The average annual modelling uncertainty for PM10 is defined as ±50% Comparison with EU Air Quality Directive targets MRPE annual mean=0.38 ; MRDE annual mean=0.24 MRPE 90-percentile =0.42 Fairmode

NORTRIP Uncertainties in models SIMAIR: MRPE annual mean=0.38 and MRPE 90-percentile =0.42 this means that the model is OK ?, but how should we communicate such results? Example: calculated yearly mean PM10 concentration is 25 µg/m 3 and calculated 90-percentil is 40 µg/m 3 then the uncertainties is: Yearly mean: 25 +/-9.5 µg/m 3 i.e. between µg/m 3 90-percentil(daily mean): 40 +/ µg/m 3 i.e. between µg/m 3 Fairmode

NORTRIP Improvements/ decreasing uncertainties  inputdata traffic, geometric configurations, uncertain information about studded tyres, sanding/salting, cleaning etc., background concentrations, meteorological data  model description simple dispersion concept such as street canyon and open roads  meteorological uncertainties  measurements: representatives, errors