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Atmospheric Environment 97 (2014) 286e295

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Atmospheric Environment

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Effects of solid barriers on dispersion of roadway emissions

Nico Schulte a, Michelle Snyder b, Vlad Isakov b, David Heist b, Akula Venkatram a, *

a Department of Mechanical Engineering, University of California, Riverside, CA 92521, USAb U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Atmospheric Modeling and AnalysisDivision, Research Triangle Park, NC 27711, USA

h i g h l i g h t s

� Roadside barriers mitigate the impact of vehicular emissions on near road air quality.� The concentration reduction is largest during stable conditions.� The primary effect of barriers is to mix pollutants over the barrier height.� A simple model that incorporates enhanced mixing describes observations.

a r t i c l e i n f o

Article history:Received 1 February 2014Received in revised form10 August 2014Accepted 13 August 2014Available online 14 August 2014

Keywords:BarrierAir qualityDispersion modelingRoadwayLine source

* Corresponding author.E-mail addresses: venky@engr.ucr.edu,

(A. Venkatram).

http://dx.doi.org/10.1016/j.atmosenv.2014.08.0261352-2310/© 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

Several studies have found that exposure to traffic-generated air pollution is associated with severaladverse health effects. Field studies, laboratory experiments, and numerical simulations indicate thatroadside barriers represent a practical method of mitigating the impact of vehicle emissions becausenear road concentrations are significantly reduced downwind of a barrier relative to concentrations inthe absence of a barrier. These studies also show that the major effects of barriers on concentrations are:1) the concentration is well mixed over a height roughly proportional to the barrier height, and this effectpersists over several barrier heights downwind, 2) the turbulence that spreads the plume vertically isincreased downwind of the barrier, 3) the pollutant is lofted above the top of the barrier. This paper tiesthese effects together using two semi-empirical dispersion models. These models provide good de-scriptions of concentrations measured in a wind tunnel study and a tracer field study. Their performanceis best during neutral and stable conditions. The models overestimate concentrations near the barrierduring unstable conditions. We illustrate an application of these models by estimating the effect ofbarrier height on concentrations during neutral, stable, and unstable conditions.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

A comprehensive study conducted by the Health Effects Insti-tute (2010) concluded that living within about 300e500 m of amajor road is associated with several adverse health effects such asimpaired lung function and cardiovascular mortality (HEI, 2010).Air quality monitoring studies conducted near major roadwaysindicate that these health effects are associated with elevatedconcentrations, compared with overall urban background levels, ofmotor-vehicle-emitted compounds, which include carbon monox-ide (CO); nitrogen oxides (NOx); coarse (PM10-2.5), fine (PM2.5), and

venkatram@sbcglobal.net

ultrafine (PM0.1) particle mass; particle number; black carbon (BC),polycyclic aromatic hydrocarbons (PAHs), and benzene (Hitchinset al., 2000; Kim et al., 2002; Zhu et al., 2002; Kittelson et al., 2004).

Several approaches have been suggested to mitigate the nearroad impact of vehicle emissions, including optimized roadsidenoise barriers, roadside vegetation, elevated or depressed road-ways, road canopies in combination with methods to treat thepollutants trapped in the canopies (McCrae, 2010), catalytic coat-ings on barriers to convert NO2 to nitrate (McCrae, 2010), and dy-namic traffic management based on forecasts of conditions thatmight lead to poor air quality (McCrae, 2010). It turns out that oneof the most practical mitigation methods is the use of roadsidebarriers.

We summarize the effects of barriers on near road concentra-tions by reviewing results from field studies, laboratory experi-ments, and numerical simulations using Computational Fluid

N. Schulte et al. / Atmospheric Environment 97 (2014) 286e295 287

Dynamics (CFD). We then propose two semi-empirical models thatcapture the essential features of the results from these studies.These models are designed to provide guidance on the design ofbarriers to mitigate exposure to vehicle related pollutants. Weillustrate their application by estimating the impact of barrierheight on near road concentrations under neutral, unstable, andstable atmospheric conditions.

2. Recent studies on barrier effects

Heist et al. (2009) studied dispersion of roadway emissions in a1:150 scale model of a 6 lane divided highway. 12 road configura-tions were simulated: one with flat terrainwith no barrier, six withflat terrain and upwind or downwind barriers, onewith an elevatedroadway, threewith depressed roadways, and onewith a depressedroadway with both upwind and downwind barriers.

Finn et al. (2010) examined the effect of a barrier on the disper-sion of SF6 tracer gas from a line source. The tracer was released fromtwo identical 54 m long line sources. One source was located 6 mupwind of a 90 m long, 6 m high solid barrier and the other had nostructures next to it. Tracer concentrations were measured simul-taneously on identical sampling grids downwind of the sources. Sixsonic anemometers measured turbulence around the barrier andatmospheric parameters were measured with other instruments.

Baldauf et al. (2008) conducted a field study in the vicinity ofinterstate I-440, Raleigh, North Carolina, to measure concentrationsof NOx, particulate matter, and air toxics behind a 1 km long noisebarrier. Concentrations were measured using fixed sampling in-struments and amobile laboratorymeasuring PM size distributionsat varying locations. This mobile laboratory was used to makemeasurements without a barrier, with a barrier, and with a barrierand vegetation. Ning et al. (2010) measured particulate and gasconcentrations near the I-710 and I-5 freeways. Two sites weremeasured near each freeway, one with a noise barrier present andone with no barrier. A mobile platform sampled PM size distribu-tions as well as black carbon, CO, and NO2, concentrations.

A study (Hooghwerff et al., 2010; McCrae, 2010) conducted inPutten, the Netherlands, between 2007 and 2009measured PM, NOx,and NO2 concentrations behind 9 different barriers next to a majorfreeway. Measurements were taken for 3 months for each barrier. A4 m tall barrier was chosen as a reference and a 7 m tall barrier andseven other “optimized” 4m tall barriers were tested. The optimizedbarriers included barriers with TiO2 coatings, vegetated barriers,porous barriers, and barriers with a T-shaped top.

In all these studies, the ground level concentration immediatelybehind a 6 m barrier was 15e50% lower than the concentrationwith no barrier when thewind directionwas close to perpendicularto the barrier, although the Idaho Falls study (Finn et al., 2010)found some concentration deficits greater than 50%. Concentra-tions were typically less than about 50% of the non-barrier con-centrations in the wake zone of the barrier, although in some casesconcentrations were as low as 20% of the non-barrier concentra-tions. This is similar to the concentration reduction found in thewind tunnel study by Heist et al. (2009). The Raleigh study (Baldaufet al., 2008) found that concentrations downwind of the barrierwere decreased by 15e50% when the wind blew from the road. PMconcentrations were reduced by up to 50%, with an averagereduction of 20%. The effect of the barrier persisted up to at least 20times the barrier height in these studies, after which the concen-tration approached the value that would occur without a barrier.

The wind tunnel study (Heist et al., 2009) found that the groundlevel concentrations beyond a distance of about 10 times the heightof the barrier could be modeled as a ground level source with twomodifications: 1) the source is shifted upwind, and 2) the effectiverate of vertical plume spread, the entrainment velocity, we, relative

to the friction velocity, u*, is increased in the presence of a barrier(Heist et al., 2009). The upwind shift in source location depends onthe particular geometry, with larger shifts necessary whenmultiplephysical effects are combined. The study also found that theentrainment velocity depends on the surface friction velocity andthe road geometry, with larger entrainment velocities occurring forcases with barriers rather than flat terrain and for rougherboundary layers (greater surface friction velocity).

Hagler et al. (2011) and Steffens et al. (2013) used CFD codes tostudy the effects of barriers on the flow field and the associatedconcentration distributions. Hagler et al. (2011) simulated disper-sion from a six lane divided highwaywith a 750m long barrier nextto the road. They found that a 3 m barrier reduced concentrationsby 20% immediately downwind of the barrier while an 18 m barrierreduced the concentrations by about 70%. The horizontal extent ofthe barrier effect was about 30 times the barrier height.

The simulated vertical concentration profiles (Hagler et al.,2011) show that the barriers and elevated roadways shift peakconcentrations vertically upward. This is consistent with the resultsfrom the wind tunnel (Heist et al., 2009), which are discussed inmore detail in a later section on model development. Steffens et al.(2013) show that the recirculating flow behind the barrier controlsthe concentrations close to the barrier.

An important question is whether barriers can increase roadsideconcentrations. As far as we are aware, only one study, conducted byNing et al. (2010), showed that mass and number concentrations ofparticulate matter were small immediately behind the barrier,increased with distance from the barrier, reaching peaks at distancesof 80e100 m, and then decreased. The peak concentrations wereabout twice those observed at the samedistance in the absence of thebarrier. The occurrence of this peak concentration is attributed to theeffective elevation of the emissions by the barrier. However, the fieldand wind tunnel studies indicate that the recirculating flow down-wind of the barrier mixes the concentrations both in the horizontaland vertical directions, thus eliminating the peak.

In summary, the major effects of barriers on concentrationsare: 1) the concentration is well mixed in a zone extending fromthe ground to the barrier height, and several barrier heightsdownwind, 2) the turbulence spreading the plume is increaseddownwind of the barrier, 3) the pollutant is lofted above the top ofthe barrier, which increases the concentration near the top of thebarrier.

3. Framework for the barrier models

The physical features described earlier are the basis of thesource-shift and mixed-wake models proposed here. These modelsare based on the Gaussian plume formulation for a point source,which gives the concentration as:

C�x; y; z

� ¼ Qffiffiffiffiffiffi2p

psyðxÞ

exp

� y2

2syðxÞ2!Fzðx; zÞ (1)

where x, y, and z are the downwind distance from the source,crosswind distance, and height of the receptor, Q is the emissionrate, sy is the horizontal plume spread, and Fz is the vertical dis-tribution function. For the Gaussian formulation Fz is:

Fzðx; zÞ ¼ 1

U�z� ffiffiffiffiffiffi

2pp

sz

�x�"exp

� ðz� hÞ2

2szðxÞ2!

þ exp

� ðzþ hÞ2

2szðxÞ2!# (2)

N. Schulte et al. / Atmospheric Environment 97 (2014) 286e295288

where sz is the vertical plume spread, h is the source height, andUðzÞ is the wind speed evaluated at the effective plume centerlineheight, z, defined by:

z ¼

Z ∞

0zCyðx; zÞdzZ ∞

0Cyðx; zÞdz

(3)

where Cy(x,z) is the crosswind integrated concentration. Theexpression for z when the source height is zero is z ¼ ffiffiffiffiffiffiffiffiffi

2=pp

sz.We treat the roadways as line sources consisting of a set of point

sources. The concentration due to a line source is calculated byintegrating equation (1) along the source. The integral cannot beevaluated in closed form when the wind direction is not perpen-dicular to the source, but we use an analytical approximation to theintegral (Venkatram and Horst, 2006) which results in:

Cðx; y; zÞ ¼ qcosðqÞ Fz

�x

cosðqÞ; z�½erf ðt1Þ � erf ðt2Þ� (4)

ti ¼ðy� yiÞcosðqÞ � xsinðqÞffiffiffi

2p

sy

�x cosðqÞ þ ðy� yiÞsinðqÞ

�

where the subscripts refer to the two ends of the source, x is theperpendicular distance of the receptor from the source, y � yi is thedistance of the receptor from the two ends of the source along thedirection parallel to the source, q is the angle between the winddirection and the perpendicular to the source, and q is the linesource emission rate per unit length. This expression performs wellfor all wind directions, with errors less than 1% except whenq¼ ±90� or when the concentration is small (Venkatram and Horst,2006).

We limit our analysis to conditions where the wind direction isclose to perpendicular to the road because we expect the primaryeffects of the barrier, vertical mixing, increased turbulence thatspreads the plume vertically, and vertical lofting, to be largestduring perpendicular flow conditions, and because the two primaryexperimental data sets from the Idaho Falls (Finn et al., 2010) andwind tunnel (Heist et al., 2009) experiments focused on perpen-dicular wind conditions. Under parallel flow conditions the effect ofthe barrier on lateral plume spread could alter the near roadconcentration.

The plume spreads are calculated using new plume spread for-mulations (Venkatram et al., 2013) derived from the concentrationsmeasured at the open terrain site during the Idaho Falls study (Finnet al., 2010). They are given by equations (5) and (6):

sz ¼ a*0:57u*UðzÞ x

1

1þ 3 u*

UðzÞ�xL

�2=3; L>0 (5a)

sz ¼ a*0:57u*UðzÞ x

�1þ 2

u*UðzÞ

xjLj�; L<0 (5b)

sy ¼ 1:6sv

u*sz

�1þ 1:5

sz

L

�; L>0 (6a)

sy ¼ 1:6sv

u*sz

�1þ 0:5

sz

jLj��1=3

; L<0 (6b)

where sv is the standard deviation of horizontal velocity fluctua-tions, L is the MonineObukhov length, and we include the factor a,which accounts for the increased rate of plume spread in the barrier

models. The plume spreads andwind speed at z are interdependentand must be evaluated simultaneously within this formulation.

3.1. Source-shift model

We extend the source-shift model, proposed by Heist et al.(2009), by including a formulation for the source shift distancebased on the barrier height. The source-shift model estimatesconcentrations using the Gaussian plume formulation (Equation(4)), where the source is moved upwind by some distance, s, toaccount for the barrier.

One way to parameterize the source shift distance is to enforcethe condition that the concentration downwind of the barrier iswell mixed over the height of the barrier. Then, the vertical plumespread at the location of the barrier is proportional to the barrierheight. Based on this assumptionwe canwrite the shift distance as:

sz

�sþ xbb cosðqÞ

�¼

ffiffiffi2p

rH (7)

where H is the barrier height, xb is the distance from the physicalsource to the barrier, q is the angle between the mean wind di-rection and the normal to the barrier, and b is an empiricalcorrection factor, whichwe include to calibrate themodel. Equation(7) is only valid if the source is near the barrier, where s is positive.

3.2. Mixed-wake model

The mixed-wake model assumes that the concentration is wellmixed between the surface and the barrier height to mimic theeffect the effect of the recirculating zone behind the barrier. Theconcentration above the barrier follows a Gaussian distribution.Then:

Fz ¼ Cs=q; z<H (8a)

Fz ¼ Csqexp

� ðz� HÞ2

2szðxÞ2!; z>H (8b)

where the surface concentration Cs is given by:

Csq

¼ 1

U�H2

�H þ UðzÞ ffiffiffi

p2

psz�x� (9)

The physical source height does not enter into the mixed-wakemodel equations if it is smaller than the barrier height. The equa-tion would not be valid if the source was elevated far above thebarrier height, but in this situation the barrier would have littleeffect on the concentration.

There are two important wind speeds in this formulation(which are included in equation (9)): the wind speed at half thebarrier height and the wind speed at the mean plume height. Thepollutant mass that is mixed below the barrier height is advectedwith the wind speed at half the barrier height, and the rest of theplume is advected with the wind speed at the effective plumeheight.

4. Interpretation of the vertical plume spread measured inIdaho Falls within the model framework

One way to understand the effect of the barrier on the con-centrations measured in the Idaho Falls study is to plot the dilution,Usz, against the distance from the source. The dilution is related to

Fig. 1. Usz calculated from the Idaho Falls data for cases with and without barriers during neutral e top left, unstable e top right, stable e bottom left, and very stable e bottomright, atmospheric conditions. Best fit curves are included. Error bars are one standard deviation. The barrier causes an initial vertical plume spread during all stabilities and anincreased rate of growth of the plume spread for neutral and stable conditions. Barrier e , No Barrier e .

N. Schulte et al. / Atmospheric Environment 97 (2014) 286e295 289

the entrainment velocity, used by Heist et al. (2009) to describe thewind tunnel data, by we ¼ d/dx(Usz). Fig. 1 shows Usz for neutral,unstable, stable, and very stable conditions, along with best fitfunctions of the form Usz ¼ a þ bx for neutral conditions,Usz ¼ a þ bx þ cx2 for unstable conditions, and Usz ¼ a þ bx þ cx1/3

for stable conditions, where a, b, and c are best fit constants. Theseforms are chosen to be consistent with the vertical plume spreadfunctions, equation (5).

For all stabilities, the barrier induces initial vertical plumespread, indicated by the y-intercept of the plots, which is largerthan the plume spread when the barrier is absent. This initialspread is larger in unstable and neutral conditions than in stableconditions. During neutral and stable conditions, the plume spreadalso increases more rapidly with downwind distance when a bar-rier is present. The effect of the barrier on plume spread is notevident during unstable conditions when atmospheric turbulencelevels are high. These results are supported by the wind tunnel data(Heist et al., 2009), which show the increased rate of vertical plumespread in terms of an increased relative entrainment velocity, and alarger initial plume spread obtained by shifting the source upwind.

We parameterize a, the factor that accounts for increased rate ofplume spread, by fitting the observed Usz with that implied byequation (9). We assume that this factor is related to the relativemagnitude of the production rates of turbulent kinetic energy dueto the barrier and production of turbulent kinetic energy in theabsence of the barrier by surface shear. Assuming that the pro-duction rate of turbulent kinetic energy due to the barrier, Pbarrier, isproportional to the drag force on the barrier, we writePbarrier¼½CdU(H)3/H, where Cd is the barrier drag coefficient, takento be a constant. The shear production rate in the surface layer isu2*dU=dz � u2

*UðHÞ=H. Taking the ratio of the production rates re-sults in:

afðUðHÞ=u*Þ2 (10)

where U(H) is the wind speed at the top of the barrier. As U(H)/u*approaches zero, a must approach one for the model to be correctin the limit of no barrier. It should also approach unity at largedistances when the barrier induced turbulence decays to smallvalues. The turbulence in the wake of an infinitely long obstacledecays as sw~x�1/2 (Tennekes and Lumley, 1972). Based on theseconstraints, a function for a that fits the Idaho Falls andwind tunneldata is: a ¼ 1 þ 0.005(U(H)/u*)2/(1 þ (x/20H)1/2).

5. Comparison with Idaho Falls and wind tunnelmeasurements

In this section we evaluate the performance of the source-shiftand mixed-wake models in describing near ground level concen-trations measured during Idaho Falls. The model performance isexpressed quantitatively by the geometric mean and standard de-viation of the residuals between the observations and predictions,by the fraction of data points that are within a factor of two of theobservations, and by the correlation coefficient between the data.The geometric mean,mg, and standard deviation, sg, are defined as:

lnmg ¼Xi

εiN (11)

ln sg ¼"X

i

�εi � lnmg

�2ðN � 1Þ#12

(12)

where ε ¼ ln(Co)�ln(Cp) is the residual between the observedconcentration Co and the predicted concentration, Cp, and N is thenumber of data points. A perfect correspondence between obser-vations and predictions will produce mg and sg equal to 1. If mg isless than 1 the observations are on average smaller than the modelpredictions.

N. Schulte et al. / Atmospheric Environment 97 (2014) 286e295290

5.1. Idaho Falls

The scatterplots in Fig. 2 show the performance of the source-shift model at explaining Idaho Falls crosswind maximum con-centrations when the wind direction is within 40 degrees ofperpendicular to the line source (75% of the data). The meteoro-logical inputs required for the models were derived from themeasurements made by the sonic anemometer placed 9.6 m up-wind of the source. The source is positioned 6 m upwind of thebarrier. The source shift calibration constant, b, was 0.4.

The 4 days of the Idaho Falls study represent measurementsduring atmospheric stability conditions that are: neutral e day 1,unstablee day 2, stablee day 3, and very stablee day 5. Themodelperforms best during neutral and slightly stable conditions andworst during very stable conditions. During day 1 and day 3, sg isless than 1.25 and there is good correlation between model andobservations.

Themodel is unbiased during day 1, withmg equal to 0.9. Duringday 2, the model overestimates near the barrier and sg is large. Day2 corresponds with very unstable, light wind conditions, where theaverage wind speed at 3 m height is 2 m/s, compared with 7.7 m/sof day 1 and 3.5 m/s of day 3. The concentration directly behind thebarrier is inversely proportional to the wind speed, resulting inlarge predicted concentrations during day 2. The model could beoverestimating concentrations near the barrier during unstableconditions because the effective advection velocity downwind ofthe barrier is likely to be larger than the assumed velocity at half ofthe barrier height, in the absence of the barrier. This could under-estimate the effective velocity, which is likely to be well mixed andtherefore higher in the presence of the barrier. The model could beimproved by including a more realistic model of the velocity profiledownwind of the barrier.

During day 5, the model underestimates concentrations, espe-cially near the barrier where the spread of the data is large. Thecomparison during day 5 may be misleading, because during very

Fig. 2. Scatterplots comparing the source-shift with crosswind maximum concentrations obwithin outer two lines are within a factor of two of model estimates. The source is positio

stable atmospheric conditions, tracer moved around the edges ofthe barrier rather than over the top of the barrier and mixed backinto the middle, causing large observed concentrations.

Fig. 3 compares the downwind variation of the source-shiftmodel predictions with observations from Idaho Falls. Themodeled and observed variations are similar during neutral andslightly stable conditions. During unstable conditions, the resultsimply that the modeled plume spread increases more rapidly thanobserved, and the initial plume spread is too small.

Fig. 4 shows scatterplots comparing the mixed-wake modelwith crosswind maximum concentrations measured at Idaho Falls.The model performance is similar to that of the source shift, exceptthat during very stable conditions (day 5) the model does not un-derestimate as much as the source-shift model. The spread of thedata is smaller than that of the source shift during all stabilities. Themixed-wakemodel performs best during neutral and slightly stableatmospheric conditions, and slightly overestimates concentrationsnear the barrier during unstable conditions.

The downwind variations of the mixed-wake model predictionsand observed concentrations are shown in Fig. 5. The results aresimilar to that of the source shift model.

5.2. Wind tunnel measurements

The emission source in the 1:150 scale model in the wind tunnelwas 48 cm long (72 m full scale), but the measured concentrationscan be adjusted to represent the concentrations that would bemeasured if the source was infinitely long (Heist et al., 2009). Twowind tunnel simulations were conducted with a barrier downwindof the road, one with a smooth approach flow and one with a roughapproach flow. The smooth approach flow has a boundary layerwith parameters z0 ¼ 0.18 cm (0.27 m full scale) and u* ¼ 0.25 m/s,while the rough approach flow has a boundary layer with adisplacement height of 5.4 cm (8.1 m full scale) and z0 ¼ 0.52 cm(0.78 m full scale) and u* ¼ 0.3 m/s. The comparisons between

served during Idaho Falls. Center black lines correspond to 1 to 1 lines and observationsned 6 m upwind of the barrier. The model was run with b ¼ 0.4.

Fig. 3. Downwind crosswind maximum concentration variation of source-shift model and Idaho Falls observations. The source is positioned 6 m upwind of the barrier. The modelwas run with b ¼ 0.4. Error bars show standard deviation of observations.

N. Schulte et al. / Atmospheric Environment 97 (2014) 286e295 291

model estimates and observations for the smooth and rough casesare shown on the left and right, respectively of Fig. 6.

The receptors where the observed concentrations are nearlyconstant correspond to the near wake of the barrier, where theconcentration is well mixed. The model overestimates concentra-tions in this region. Outside the near wake, the source-shift model

Fig. 4. Scatterplots comparing the mixed-wake model with crosswind maximum concentraCenter black lines correspond to 1 to 1 lines and observations within outer two lines are w

explains the data very well for the rough approach flow: the modelis well correlated with the data but overestimates by about 50%. Forthe smooth approach flow the model is also well correlated withobservations and is unbiased outside the near wake.

Fig. 7 shows a comparison of the mixed-wakemodel predictionswith the wind tunnel concentrations. The comparisons for the

tions observed during Idaho Falls. The source is positioned 6 m upwind of the barrier.ithin a factor of two of model estimates.

Fig. 5. Downwind crosswind maximum concentration variation of mixed-wake model and Idaho Falls observations. The source is positioned 6 m upwind of the barrier. Error barsshow standard deviation of observations.

N. Schulte et al. / Atmospheric Environment 97 (2014) 286e295292

smooth and rough wind tunnel cases are on the left and right,respectively. The model tends to underestimate concentrationsnear the barrier in the smooth boundary layer case and

Fig. 6. Comparison of source-shift model with wind tunnel infinite source concentrationobservations within outer two lines are within a factor of two of model estimates. The smorough approach flow wind tunnel case (z0 ¼ 0.78 m, u* ¼ 0.3 m/s, displacement height ¼

underestimates by about 50% far from the barrier. The correlationwith the rough wind tunnel data is very good, but the modeloverestimates by about 50% far from the barrier.

for receptors below a height of 1H. Center black lines correspond to 1 to 1 lines andoth approach flow wind tunnel case (z0 ¼ 0.27 m, u* ¼ 0.25 m/s) is on the left and the8.1 m) is on the right. The model was run with b ¼ 0.4.

Table 1Meteorology used in the barrier height sensitivity model runs. This meteorology isderived from measurements made during Idaho Falls.

Modeled stability U (m/s) (at 3 m height) u* (m/s) L (m)

Neutral 6.7 0.66 �260Unstable 1.4 0.3 �20Stable 3.1 0.3 40Very stable 1.8 0.1 13

N. Schulte et al. / Atmospheric Environment 97 (2014) 286e295 293

Both the source shift and mixed-wake models predict largerconcentrations in the rough approach flow case than in the smoothcase because the wind speed is smaller. However, the observationsdo not show a large difference in concentration between the twocases. The models are more sensitive to changes in surface rough-ness length than the observations suggest.

6. Impact of barrier height on near road concentrations

We now present results from the source shift and mixed-wakemodels when the barrier height is varied for a range of meteoro-logical conditions. The models were runwith a single source placed6 m upwind of the barrier at ground level. Concentrations werecalculated at ground level. We use average meteorology deter-mined from the meteorology measured during Idaho Falls as theinput to the models because the measurements span a wide rangeof atmospheric stabilities and are representative of real meteoro-logical conditions.

For each model, we plot the ratio of the concentrations to thosewith no barrier against downwind distance from the source for 5barrier heights: 1 m, 2 m, 3 m, 6 m, and 12 m. The model pre-dictions for the meteorological conditions measured during each ofthe four days of Idaho Falls are shown on separate plots, with nearneutral stability on the upper left, very unstable on the upper right,

Fig. 7. Comparison of mixed-wake model concentration with wind tunnel infinite source colines and observations within outer two lines are within a factor of two of model estimates.and the rough approach flow wind tunnel case (z0 ¼ 0.78 m, u* ¼ 0.3 m/s, displacement h

slightly stable on the lower left, and very stable on the lower right.Table 1 is a summary of the meteorological inputs to the models.The surface roughness length was 5.09 cm. Themodels are runwiththe increased entrainment velocity, a, given by the empiricalfunction in Section 4.

Fig. 8 shows the sensitivity of the source-shift model predictionsto variations in the barrier height, plotted as a function of non-dimensional distance x/H, where H is the barrier height and x isthe distance from the barrier. As expected, the largest impact of thechange in barrier height occurs close to the barrier. This impactdecreases with distance as vertical mixing by atmospheric turbu-lence becomes more dominant relative to that induced by thebarrier. As the barrier height decreases, and for large x/H, theconcentration ratio approaches1.

Atmospheric stability affects how far downwind the effects ofthe initial vertical mixing due to the barrier persist. During unstableconditions (upper right) the concentration ratio is greater than 0.8at a downwind distance of about 100H, while during very stableconditions (bottom right) the concentration ratios for all but the1 m barrier are still less than 0.8 beyond x ¼ 100H.

Fig. 9 shows the sensitivity of the mixed-wake model pre-dictions to variations in the barrier height, versus downwind dis-tance. The plots are similar to those for the source shift model. Theconcentration ratio near the barrier ranges from 0.4 to nearly 0,depending on atmospheric stability and barrier height. For smallbarriers, when the barrier height is doubled the concentration ratiois reduced by about 0.1e0.2 at receptors near the barrier, but theeffect of increasing the barrier height on the initial concentrationratio downwind of the barrier becomes smaller for larger barriers.

Both the source-shift and mixed-wake models show that thechange in barrier height has the greatest impact on the concen-tration ratio far from the barrier during stable conditions. This issignificant because the largest concentrations occur during stableconditions corresponding to early morning, late evening, and

ncentration for receptors below a height of 1H. Center black lines correspond to 1 to 1The smooth approach flow wind tunnel case (z0 ¼ 0.27 m, u* ¼ 0.25 m/s) is on the lefteight ¼ 8.1 m) is on the right.

Fig. 8. Sensitivity of source shift model predicted concentrations to changes in barrier height vs. downwind distance for meteorology representing neutral (top left), unstable (topright), slightly stable (bottom left), and strongly stable (bottom right) atmospheric conditions. The barrier height, H, takes values of 1 m, 2 m, 3 m, 6 m, and 12 m.

N. Schulte et al. / Atmospheric Environment 97 (2014) 286e295294

nighttime periods. So a barrier will be most effective at reducingconcentrations when the reduction is needed the most. The effectof changing the barrier height also has a big impact on the distancefrom the barrier at which the concentrations are significantlyreduced. Note that the horizontal scale in the figures is x/H. So, the

Fig. 9. Sensitivity of mixed-wake model predicted concentrations to changes in barrier heigright), slightly stable (bottom left), and strongly stable (bottom right) atmospheric conditio

actual distance at which a specified concentration ratio is reached islarger for a taller barrier.

The modeling results from this study show that the primaryeffect of the roadside barrier is to enhance the initial spread of theplume and the rate of plume spread by an amount that depends on

ht vs. downwind distance for meteorology representing neutral (top left), unstable (topns. The barrier height, H, takes values of 1 m, 2 m, 3 m, 6 m, and 12 m.

N. Schulte et al. / Atmospheric Environment 97 (2014) 286e295 295

the height of the barrier. The impact of the barrier as a function ofdistance from the barrier also depends on atmospheric turbulenceand distance of the line source from the barrier. The interaction ofthese factors can be illustrated by formulating a simple model(Venkatram et al., 2007) for dispersion of emissions from a roadthat has a width w. Equation (9) for the mixed-wake model sug-gests the following expression for the vertical spread of the plume:

szðxÞ ¼ h0 þswxU

(13)

where sw includes the effects of the barrier on atmospheric tur-bulence, and h0 ¼ ffiffiffiffiffiffiffiffiffi

2=pp

H. Then, the concentration C(x), at a dis-tance x from the barrier, when the wind direction is perpendicularto the road, is given by (Venkatram et al., 2007),

CðxÞ ¼ffiffiffi2p

rq

swwln

0BB@1þ w

h0Usw

þ x

1CCA (14)

We see that the presence of the barrier is equivalent to shiftingthe road upwind by the distance

ffiffiffiffiffiffiffiffiffi2=p

pHU/sw.

7. Conclusions

Roadside barriers affect dispersion of vehicle related emissionsin three ways: 1) they increase vertical dispersion through addi-tional turbulence generated in the wake of the barrier, 2) theyinduce vertical mixing behind the barrier in the cavity region, and3) they loft the emissions above the barrier. We have presented twomodels that account for these physical effects of barriers indifferent ways. The source-shift model, previously proposed byHeist et al. (2009) to model barriers, shifts the source upwind toaccount for increased vertical turbulent mixing at a given distancebut does not account for vertical lofting or recirculation. This modeltends to overestimate concentrations within the recirculation zone.The mixed-wake model simulates increased turbulent mixingbelow the height of the barrier and effectively lofts the plumeabove the barrier. Themixed-wakemodel captures the essentials ofthe effects of barriers, and its predictions compare well with datafrom Idaho Falls and the wind tunnel.

Both models do not capture the concentrations within the bar-rier's near wake region. The models overestimate concentrationsduring unstable conditions and show a greater sensitivity to thesurface roughness length than wind tunnel observations, possiblybecause the models are too sensitive to the surface wind speed.Improved models of the wind speed profile downwind of the bar-riers may improve the dispersion models.

A sensitivity study of the effect of barrier height indicates thatthe effect of changes in barrier height have the greatest influence

on concentrations at receptors near the barrier. Atmospheric sta-bility influences how far downwind the concentrations are affectedby a barrier. During very stable conditions the barrier effect persiststo larger downwind distances than during unstable or neutralconditions.

Acknowledgments

We thank the South Coast Air Quality Management District forfunding this research and the U.S. EPA for providing the windtunnel and Idaho Falls data.

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