NORTRIP: Kjeller meeting NILU: Generalised road dust emissions model (GRD-2) Bruce Rolstad Denby and Ingrid Sundvor

Contents (1) Expectations from the meeting Model overview o Concepts o Road dust model (GRD-2) o Comparison between emission models (GRD-2/SMHI) o Surface moisture model (GRD-2) o Road dust and surface moisture linkages o Model documentation Some first results o Hornsgatan (2009) o Hornsgaten (2010) o RV4 (2005) o NB (2002)

Contents (2) Demonstrations o Suspension factor sensitivity (Hornsgatan) o Comparison of evaporation schemes (Hornsgatan) o Demonstration of cleaning (Hornsgatan) o Demonstration of salting (RV4) Datasets Plans

Expectations this meeting Demonstrate some aspects of the model Improve the parameterisations in regard to – wear parameters (VTI) – suspension parameters (DMU) – sanding and the sand paper effect (FMI) – impact of salting (NILU) – traffic induced turbulence parameterisation (DMU) – splash/spray parameterisation (SMHI) Plan and encourage – provision of data sets – inter-comparison (models and parameterisations) – develop application studies of the models

GRD-2 concept A structure within which road dust emission processes can be quantified and can interact Mass loading based on: – wear (road, tire and break) – addition of external mass Emissions based on: – direct wear – suspension Surface retention based on surface moisture process Described in documentation (v5)

Dust and salt surface mass balance Production through: – retained wear (road, tires, brakes) – ambient air deposition – salting and sanding – sand paper effect Removal through: – traffic induced suspension of surface mass – wind induced suspension of surface mass – drainage – spray/splash Distributed over road and shoulder surfaces

Emission through direct wear – inhibited by surface moisture Emission through suspension – traffic induced suspension – wind induced suspension – inhibited by surface moisture Size fraction: – fixed size distributions of emissions Emitted from road and shoulder surfaces Dust and salt emissions

GRD-2

Road dust (M) Wear W(N,V).f 0,direct. f q. f PM10 PM 10 1-f 0,direct. f q Direct emissions f suspend (V 2,3 ).f q.f PM10 Suspended emissions f 0,direct = Fraction of wear emitted directly(0.5) f 0,suspend = Fraction of mass suspended (Veh -1 hr -1 ) (1x10 -4 ) f q = Retention factor(0 – 1) f PM10 = PM fraction emitted as PM 10 (0.2) GRD-2 Removal

Road dust (l) Wear + sanding PM 10 a st.f q. e winter ref Suspended emissions a st = Fraction with studded tires(0.7) e winter ref = Reference emission factor(g/km)(1200 ) l = Normalised road dust loading(0 – 1) k decay = Decay rate for road dust (Veh -1 hr -1 ) (2x10 -6 ) Omstedt et al. f q. k decay Mass loading and sanding are normalised quantities Road dust removal not coupled to suspension

Surface moisture mass balance Production through: – precipitation (rain/snow) – snow melt – condensation (affected by salting) – wetting during salting/cleaning Removal through: – evaporation (affected by salting) – drainage – spray/splash Includes snow mass balance – removal by ploughing and by snow melt

Dust and moisture model linkages Surface moisture (and snow) retention: – retention of direct wear producing surface mass – retention of direct wear reducing emissions – retention of surface mass through inhibited suspension Drainage and spray of water, dust and salt – mixing of dust and salt in surface water – removal of mixed layer through drainage and spray Evaporation/condensation affected by salt – reduced vapour pressure Melting point affected by salt – reduced melting point

Retention based on surface moisture Surface moisture retention (f q ) is given by g thresh is the threshold surface moisture above which full retention occurs (f q = 0). – simple formulation – may be different for direct and suspended emissions – may be different for different wear sources – may be different for shoulder and road How wet is a dry road?

Documentation and model code All model processes are described in the documentation (v5.0) – still in development (v5.1) Model is coded in MatLab – easy visualisation and development possibilities – will be transferred to FORTRAN when complete – will be available to NORTRIP Input data and model input parameters – model parameter excel sheets (dust model only) – model input data excel sheets (RV4, NS, Hornsgaten) All code/data uploaded to the NORTRIP ftp site

Example of model output for Hornsgatan: (Daily means for winter 2009) Traffic data Meteorological data Mass balance and emissions Surface wetness Other factors Energy balance Concentrations 60% studded tires, veh/day at 45 km/hr

Example of model output for Hornsgatan: (Daily means for 2010) Meteorological data Mass balance and emissions Surface wetness Concentrations 40% studded tires in winter, 2% in summer veh/day at 45 km/hr

Example of model output for RV4 (2004) Meteorological data missing – wind, radiation, cloud cover 27% studded tires veh/day 80 km/hr Known to have significant contributions from salt, around 30% for the whole period.

Example of model output for NB (2002) Meteorological data missing – radiation, cloud cover No PM2.5 data Only daily mean average background PM10 32% studded tires veh/day at 94 km/hr

Suspension concepts/questions Suspension is the combined effect of grinding and emission. – creates ‘suspendable’ particle sizes from larger particles (  V?) – ejects these particles mechanically or through turbulence (  V?) Is it necessary to divide the surface mass into >TSP and <TSP size distributions? – better suspension description – better road cleaning description – better sanding and sand paper effect description How to determine suspension parameters from measurements? – road dust loading under dry conditions – fitting the model to observed concentrations

f 0,direct = 1.0 f 0,suspend = 1x10 -4 f 0,direct = 0.0 f 0,suspend = 1x10 -4 f 0,direct = 0.5 f 0,suspend = 1x10 -5 f 0,direct = 0.5 f 0,suspend = 1x10 -4 (default) Suspension factor sensitivity

Stop

Surface moisture mass balance Production through: – precipitation (rain/snow) – snow melt – condensation (affected by salting) – wetting during salting/cleaning Removal through: – evaporation (affected by salting) – drainage – spray/splash Includes snow mass balance – removal by ploughing and by snow melt

Basis for Penman and energy balance equations Penman does not know the surface flux but requiresas input. Also uses parameterisation of net long wave radiation. Energy balance solves for surface temperature and outgoing radiation using Surface energy balance

Penman modified Energy balance

Comparison of evaporation/condensation 1.Fixed evaporation time scale (48 hours) 2.Penman-Omstedt (surface RH = 100%) – potential evaporation from a wet surface – evaporation reduced with a fixed factor (f evap = 0.075, 0.3) – no information on surface heat flux 3.Penman modified (surface RH = g surf /g thresh ) – no information on surface heat flux – reduces surface relative humidity for g surf < 0.2 mm 4.Bulk energy balance method (surface RH) – prognoses surface temperature and surface heat flux – reduces surface relative humidity for g surf < 0.2 mm

1. Constant time scale (48 hr) 2. Penman - Omstedt (f=0.075) 4. Penman – modified (G=0.2*net rad) 5. Energy balance 3. Penman - Omstedt optimised (f=0.3) Evaporation demonstration

Surface moisture mass balance Drainage – Drainage time scale (4 hours) – Drainage stops at a threshold value (1 mm) – Also removes mass, assuming a well mixed layer Splash and spray – not implemented – should remove significant amounts, down to a threshold of X mm? Snow mass balance – degree hour model for snow melt (can also use EB model) – removal by ploughing

1. Without road cleaning 2. With road cleaning Road cleaning demonstration

Road salting demonstration 1. Without salting 2. With dry salting (3 days, -10 < T < 0)

Road surface moisture – Meteo data, surface temperature, surface moisture, salt content, cleaning and salting activities, splash and spray data Concentration data – Street level and background for PM and NOx Activity data – Salting activities – Cleaning activities – Ploughing activities – Sanding activities Mass balance data – Road dust and salt loading – Concentrations in surface water and drainage water Data sets required

Model development – Implementation of new parameterisations – Develop code and interface using excel sheets (executable) – Division of road dust into > TSP and < TSP size categories? Database – Add Kirkeveien and RV4 2005, 2006 to database (Other?) – Supplement with other meteorological data Apply the models – Use a range of input data sources for model inter-comparisons – Speed reduction studies – Salting impact on surface moisture – Contribution of salting and sanding to PM concentrations Plans for 2011 (1)

Reports – Complete and QC model documentation – Model users guide Possible publications – A review of road dust emission data – GRD-2 description and validation – Inter-comparison of road dust emission models – Impact of speed reduction, salting on road dust emissions – Model applications to road surface management Plans 2011 (2)