DISPERSION MODELLING: MOTOR VEHICLE EMISSIONS

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DISPERSION MODELLINGMOTOR VEHICLE EMISSIONS

• Line source in open terrain, CALINE and similar
  models.
• Line source in urban environment, CAR, OMG
• Intersections, CAL3QHC and similar
• Urban street canyons and more complex geometry,
  finite element modelling

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LINE SOURCES - Infinite line source

• Can be handled in principle as one dimensional
  dispersion from a point source.
• For wind perpendicular to line source
• q emission per unit time per unit distance

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• For a finite line source we must consider the end
  effects, the resulting concentration will be less
  than that for an infinite line source under the
  same conditions.
• p1 y1/sy p2 y2/sy y1 lt y2

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Oblique wind and finite line source

• For wind at an angle of ? with the line source,
  the strength is effectively increased by a
  factor of
• (sin ? )-1
• For a finite line source we must consider the end
  effects, the resulting concentration will be less
  than that for an infinite line source under the
  same conditions.
• Examples 4-9 and 4-10 (Wark, Warner Davis)
  demonstrate the application of the infinite line
  source case to CO concentrations near a highway.

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• Example 4-9 Wark, Warner Davis
• HC concentrations 300 m away from road,
  perpendicular wind
• Example 4-10 Wark, Warner Davis
• CO concentrations at roadside for perpendicular
  wind

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COMPLICATIONS

• For ? lt 45, the (sin ? )-1 correction becomes
  increasingly inaccurate.
• The dispersion due to vehicle induced turbulence
  and thermal buoyancy due to heat release from
  the vehicles are important factors
• The P-G-T dispersion coefficients were originally
  observed in flat grass terrain, most highways of
  interest have some roughness effects associated
  with them (bridge, below grade. above grade etc.)

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CALINE

• series of models developed to provide better
  estimations of motor vehicle pollutant
  concentrations near highways and arteries.
• Main features
• - Finite line segment approach
• - Mixing zone concept to incorporate traffic
  induced dispersion
• - New dispersion data near highways,
  adjustments for averaging time and surface
  roughness included for P-G-T coefficients

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Mixing Zone Model

• a zone of uniform emissions and turbulence the
  region over the traveled way (traffic lanes - not
  including shoulders) plus three meters on either
  side.
•  
• Experimental evidence indicates
• the mechanical turbulence created by moving
  vehicles
• the thermal turbulence created by hot vehicle
  exhaust
•  are the factors that predominate near the
  ground.
• This is valid for all but the most unstable
  atmospheric conditions.
•  

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Mixing Zone Model

• Traffic emissions are released near the ground
  level,
• Model accuracy is most important for neutral and
  stable atmospheric conditions,
• it is reasonable to model initial vertical
  dispersion (SGZ1) as a function of the turbulence
  within the mixing zone.

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• Mixing zone residence time
•  
• TR W2/U
•  
• Where, W2 Highway half-width
• U wind speed

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• Experimentally derived vertical dispersion due to
  mixing zone (averaging time of 30 min)
•  
• SGZ1 1.8 0.11 TR
• (m)
  (secs.)
•  
• SGZ1 accounts for all the enhanced dispersion due
  to traffic over and immediately downwind of the
  roadway.
•  
• SGZ1 is independent of surface roughness and
  atmospheric stability class

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• Adjustment for averaging times other than 30
  minutes
•  
• SGZ1ATIM SGZ130 (ATIM/30)0.2
•  
• Where, ATIM Averaging time (minutes)

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• Downwind of the mixing zone, additional
  dispersion is still governed by ambient stability
  conditions.
•  
• The variation of vertical dispersion coefficients
  with downwind distance are represented by
  fitting expressions to
•  
• I) the value of SGZ1 from the mixing zone model
• ii) the value of sz at 10 kilometers (SZ10) as
  defined by Pasquill(8).
•  
• In effect, the power curve approximation
  suggested by Pasquill is elevated near the
  highway by the intense mixing zone turbulence.

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• SZ10 is adjusted for Z0 and ATIM by
•  
• SZ10ATIM,Z0 SZ10(ATIM/3)0.2(Z0/10)
  0.07
•  
• Where, ATIM Averaging time (minutes)
• Z0 Surface roughness (cm)

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CALINE Treatment of Mixing Height

• Although mixing height limitations can be
  included (e.g. for multiple reflections) these
  are not significant unless mixing height is very
  low.
•  
• This happens mostly at night, when winds are
  typically low and it is difficult to make
  accurate estimates of mixing height.
•  
• Mixing height effects can be bypassed by
  specifying a mixing height of 1000 m that will
  not have appreciable effect on the contribution
  from the roadway.

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CALINE 3

• Cumulative impacts from 20 road links for 20
  receptors 
• Link types
• At-Grade
• Fill
• Bridge
• Depressed
•  Each link characterized by
• Traffic volume, vehicles/hour
• Emission factor, g/mile (per vehicle)
•  Settling and deposition for PM

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Roadway Intersections

•  Signalized road intersections present critical
  situations for ambient air pollution
•  
• - cumulative effects from two roads
• - vehicles travelling at low speed or idling,
  high emission factors (g/s, g/fuel consumed)
• - long queues of vehicles under congested
  conditions
•  

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CAL3QHC

• Includes the CALINE3 line source dispersion model
  and a traffic algorithm for estimating vehicular
  queue lengths at signalized intersections.
•  

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• CALINE-3 is designed to predict air pollutant
  concentrations near highways and arterial streets
  due to emissions from motor vehicles operating
  under free flow conditions. However, it does not
  permit the direct estimation of the contribution
  of emissions from idling vehicles.
• CAL3QHC enhances CALINE-3 by incorporating
  methods for estimating queue lengths and the
  contribution of emissions from idling vehicles.

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• CAL3QHC permits the estimation of total air
  pollution concentrations from both moving and
  idling vehicles. It is a reliable tool for
  predicting concentrations of inert air pollutants
  near signalized intersections. Because idle
  emissions account for a substantial portion of
  the total emissions at an intersection, the model
  is relatively insensitive to traffic speed, a
  parameter difficult to predict with a high degree
  of accuracy on congested urban roadways without a
  substantial data collection effort.

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9.13
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9.7
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9.7
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9.12
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9.1
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9.6
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9.9
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9.9
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9.9
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9.3
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9.8
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9.8