STATUS OF GRD-2: Model development within NORTRIP Bruce Rolstad Denby Ingrid Sundvor Linkoping ( ) The Norwegian Institute for Air Research (NILU). PO BOX 100, Kjeller 2027, Norway.

Presentation Aim Major changes Overview of concept Some details on the road dust model Some details on surface moisture model Some examples Demonstration Way forward

Aim of NORTRIP “The ultimate aim is to develop a process based emission model, that can be applied in any city without site specific empirical factors, for management and evaluation of abatement strategies and that is able to describe the (non- exhaust) emissions on an hourly or at least daily basis with satisfactory accuracy.”

GRD-2 major changes since last meeting Removal of ‘shoulder’ concept Not found to be beneficial and doubled the complexity Introduction of ‘non-suspendable’ dust loading Typically the major part of sanding Impacts on abrasion (sandpaper effect) Can be crushed to form ‘suspendable’ dust at a defined (unknown) rate Impact of salting on surface humidity and melt temperature Reduces the surface humidity and tends to keep the surface wetter Has been shown to improve some datasets but not all New datasets A number of new datasets are available from Copenhagen, Helsinki and many from Hornsgatan User interface executable version of the model available

NORTRIP model concept and processes Emissions Direct emissions of wear (road/tyre/brake) Suspended emissions (traffic /wind blown) Both inhibited by surface moisture Dust mass balance model Production through wear, abrasion, deposition, salting and sanding Removal through suspension, cleaning, ploughing, spraying and drainage Moisture balance model for water and ice Production through precipitation, condensation, wetting Removal through drainage, evaporation, spraying, ploughing Assessment Comparison with observations using NO X as a dispersion indicator Comparison with observed road moisture and temperature

NORTRIP model concept and processes Surface moisture conditions Salting Wear road, brake and tyre Sanding Cleaning Melting and freezing Meteorological conditions Particles TSP PM 10 PM 2.5 Deposition Drainage Sandpaper Retained wear Road dust and salt load Drainage Spray Ploughing Precipitation Impact of salting Salting Crushing Wetting Evaporation condensation Wind suspension Direct emissions Traffic suspension Moisture retention Temperature, humidity and radiation

Emissions are the sum of direct wear emissions and suspended emissions in the size fraction x Suspended emissions are the sum of traffic induced and windblown emissions Emissions Dust and salt loading Suspension rate Proportion in size fraction x

Suspension rate for both dust and salt depends on suspension factor and moisture retention Suspension factor per vehicle is dependent on speed Suspension rate Traffic volume Suspension factor per vehicle Moisture retention factor Tabulated suspension factor Vehicle speed

Tabulated suspension factors 1. Suspension factors are based on model fitting to data. Could be calculated independently if dust loading was measured. Relative rates for different tyre types could be based on SNIFFER or road simulator data 2. Removal rates of non-suspendable dust can be based on SNIFFER or other measurement campaigns where dust is applied. 3. Is quadratic speed dependence correct? Road suspension f 0,suspension (veh -1 )Studded tyres (st)Winter tyres (wi)Summer tyres (su) Non- suspendable removal Heavy (he)4.00E E E E-04 Light (li)1.00E E E-04

Tabulated size fractions (direct and sus) 4. Size fractions for road wear and suspension are based on VTI information. Will direct emitted and suspended size fractions be different? 5. Size fractions for tyre and brake wear are literature based. Are these appropriate? Fractional size distribution emissions Wear parameterPM TSP PM 10 PM 2.5 f PM,dir-roadwear f PM,dir-tirewear f PM,dir-brakewear f PM,sus-road

Mass balance on the road surface for suspendable and non-suspendable dust (j) and salt Mass balance of dust and salt

Production terms for suspendable dust Production terms for non-suspendable dust Production of road dust Retained wear Deposition from ambient air Suspendable fraction of applied sand Road wear due to non-suspendable dust Crushing of non-suspendable dust Non-suspendable fraction of sanding Other fugitive sources

Production through the retention of road wear Wear rates Road wear: equations Road wetness Wear rates Traffic volume Fraction not directly emitted Linear dependence on speed Dependence on pavement type Dependence on snow depth Tabulated wear rates

Road wear W 0,roadwear (g km -1 veh -1 )Studded tyres (st)Winter tyres (wi)Summer tyres (su) Heavy (he)2422 Light (li)60.5 Tire wear W 0,tirewear (g km -1 veh -1 )Studded tyres (st)Winter tyres (wi)Summer tyres (su) Heavy (he) Light (li)0.1 Brake wear W 0,brakewear (g km -1 veh -1 )Studded tyres (st)Winter tyres (wi)Summer tyres (su) Heavy (he)0.04 Light (li)0.01 Tabulated wear rates 6. Need update of tyre and brake wear rates. 7. Summer tyre road wear needs to be set very high to simulate summer concentrations. Why? What is the summer wear source? Tyre, gravel abrasion? True for both Stockholm and Copenhagen data

Crushing of the non-suspendable dust (sand) Abrasion (sandpaper effect) almost exactly the same Crushing and abrasion of sand Snow depth dependence Tabulated crushing factor Traffic volume Speed dependence Non- suspendable dust loading 8. In the model formulation there is little difference between crushing and abrasion. Chemical analysis of emissions or dust loaded needed 9. Should we include enhanced abrasion of tyres with sanding?

Removal of road dust and salt Sink terms for suspendable dust and salt Traffic induced suspension of dust and salt Wind blown suspension of dust and salt Removal through drainage Removal by cleaning Removal through snow ploughing Removal through splash and spray All sink terms (except cleaning) are dependent on the surface moisture mass balance

Suspension sink uses the same suspension rate as for suspended emissions Suspension sinks Traffic volume Suspension factor per vehicle Moisture retention factor Wind blown dust sink

Based on moisture removal rates ( R g from moisture model) and an efficiency factor h eff Drainage and spray Efficiency factors for dust and salt Efficiency parameterDust(suspendable)Dust (non-suspendable)Salt h ploughing-eff h cleaning-eff h drainage-eff h spraying-eff Any information available to define these efficiency parameters?

Suspendable dust loading Example of mass balance Non- suspendable dust loading Studded tyre season Daily mean emission and dust loading example Hornsgatan Measurement of dust and salt loading (size specific) needed to validate the model!

Moisture balance for water (g) and ice/snow (s) on the road surface Production terms Moisture model Sink terms Surface energy balance model

Energy balance model to determine latent heat flux ( L s ) Energy balance model LsLs R short-net R long-net HsHs GsGs Energy balance: average daily cycle H traffic

Energy balance model predicts the surface temperature Energy balance model Modelled Observed Predicts the surface moisture Observed Modelled Drainage problem

Drainage rate given as constant time scale Drainage and spray removal processes Spray dependent on traffic volume and spray factor 12. Drainage may not be fast enough. Needs adjustment based on available moisture data from HBAC and Hornsgatan. Update numerics 13. Spray factor is lower than indicated by VTI. Needs an expert assessment!

Moisture retention Observed and modelled moisture Too dry Just right Too wet Dry Wet Moisture retention factor ( f q ) Dry Wet No dust retention Dust retention

Impact of salt surface on moisture Reduces the surface vapour pressure and as a result the freezing point. Use Antoine equation to determine the saturated vapour pressure Saturated NaCl solution reduces RH by 25% Reduces freezing point to -21 C

Impact of salt on moisture: Example No impact of salt on vapour pressure Impact of salt on vapour pressure included Model too dry

Impact of salt on moisture 14. Need observational data concerning freezing point and salt concentrations on the road. HCAB data and Hornsgatan data? Other data that is available? 15. Need improved activity data. Quantity and timing of salting, salt solution. Any cleaning associated with salting. 16. In regard to salt also need measured ambient air concentrations of salt. This is available for Oslo but not assessed yet.

Example applications: Oslo RV4 PM 10 PM 2.5 Cold wet periodDry period Salt Close to exhaust emissions Net daily mean concentration including salting Studded tyre season Salting

Example applications: Hornsgatan Using modelled moisture r 2 = 0.47 Summer Cold and wet Using observed moisture r 2 = 0.83 Much better Studded tyre season Net daily mean concentration without salting Dry spring

Example applications: Hornsgatan Using modelled moisture r 2 = 0.47 Using observed moisture r 2 = 0.83 Net daily mean concentration Model too dry

Example applications: Hornsgatan Using modelled moisture r 2 = 0.35 Using observed moisture r 2 = 0.71 Net hourly mean concentration Too dry Too wet

A brief summary Dust loading and surface moisture approach can provide very good results (Omstedt et al., 2005) Road surface moisture impacts on both the timing and the total emissions Modelling both dust loading and moisture is essential Salt can make a significant contribution to the emissions (observed in Oslo) Shoulder contribution not found to be significant

NORTRIP future model development Go through the list presented today Improve vehicle speed dependence of suspension, based on experimental data Improve data concerning road maintenance activities Direct comparison of the road surface moisture model with surface moisture measurements Apply to non-Nordic countries to assess its applicability in other environments (interested?) Apply to road and air quality management applications

Development needs Summer time emissions To reproduce the summer time emissions a significantly higher wear rate has been employed than is expected from laboratory studies of summer tyres. Why? Many processes, too many parameters, too many unknowns A number of the parameters used in the model are currently poorly defined. Further measurements of processes, such as road dust/salt loading and suspension rates, are required for improving these. Activity data is required but is seldom available

Thank you Bruce Rolstad Denby

Input data requirements Metadata Road configuration, pavement type, surface albedo, etc. Traffic data Volume, tyre type, vehicle type, vehicle speed Meteorological data Wind, temperature, humidity, global radiation (cloud cover) Activity data Salting, sanding, cleaning, ploughing, wetting Model parameters Wear rates, suspension rates, etc.

NORTRIP future model applications Implementation in AQ modelling systems Urban scale models Regional scale models Management applications Impact of salting and dust binding Quantification of salt contribution to PM Quantification of sanding contribution to PM Impact of studded tyres Impact of cleaning Impact of traffic speed Impact of meteorological conditions