THE IMPACT OF BUILDINGS AND ROADS ON DISPERSION OF
VEHICLE EMISSIONS: MODELS INFORMED BY EXPERIMENTAL
FINDINGS
Akula Venkatram
University of California, Riverside, CA
AcknowledgementsSponsors:South Coast Air Quality Management District, California Air Resources Board, California Energy Commission, USEPA
Students:Nico Schulte, Si Tan, Faraz Ahangar, SeyedmortezaAmini
Colleagues:Vlad Isakov, Richard Baldauf, David Heist, Steven Perry, Parikh Deshmukh, Sarav Arunachalam,
Overview
Approach to Modeling
Highway structures
Solid Noise Barriers
Depressed roadways
Building Effects
Conclusions
Semi-Empirical Modeling ApproachAnalyze data from field and wind tunnel studies
Idaho Falls
USEPA wind tunnel
Results from CFD models
Propose tentative mechanistic model that describes data
Use measurements to determine model parameters
Because such models are anchored to observations, they can form the basis for models such AERMOD and R-LINE (Snyder et al., 2013)
GOVERNING PROCESSES
0
min
2 1ln 1f w
w w
f
i
Te WC
W hU d
TeC
Uz
d
Boundary Layer
U𝜎𝑤Turbulence
h0
W
Concentration
iz
0
Traffic Flow Rate
Emission Factor
Wind Speed
Turbulence Level
Mixed Layer Height
Distance from Road Edge
Width of Road
Initial vertical plume spread
f
w
i
T
e
U
z
d
W
h
Maximum impact distance does not exist
Concentration does not fall off exponentially
Wind Tunnel Tests on Road Configurations (Heist et al, 2009)
USEPA Wind Tunnel Modeling of Road Configurations
Barrier EffectsWind Tunnel Results (Heist et al, AE, 43, 5101-5111)
CFD Simulation(Hagler et al. 2011)
Idaho Falls Tracer Study
SF6 simultaneously released from two sources
Concentrations measured at 56 receptors
Spanned neutral, unstable, and stable conditions
(Finn et al. 2010)
Idaho Falls(Finn et al. 2010)
Neutral Unstable
Slightly Stable Very Stable
- - With Barrier
- - Without Barrier
Reformulation of Plume Spreads for Flat Terrain(Venkatram et al., 2013)
z
u u xx
U U L
12/3
* *0.64 1 3
zvy zu L
*
1.6 1 1.5
Stable Conditions Unstable Conditions
z
u u xx
U U L*0.64 1 1.5
zvy zu L
1/2
*
1.6 1 0.5| |
Performance of RLINE –Idaho Falls
Barrier Model (Schulte et al, 2014)Concentration is well mixed over the height of the barrier, H
Vertical plume spread increased by a factor α
𝑈𝜎𝑧𝑏𝑎𝑟𝑟𝑖𝑒𝑟(𝑥) = 𝑈 𝑧𝑒𝑓𝑓 𝛼𝜎𝑧 𝑥 + 𝑈𝐻
2𝛾
𝜋
2𝐻
Comparison with Idaho Falls Data.Poor performance
during stable conditions
Sensitivity to Barrier Height
Unstable Stable
The model assumes that the emissions onthe highway that are covered by therecirculation zone originate from a linesource located on the upwind wall at halfthe height of the wall.
The sources outside the recirculationzone contribute directly to the downwindreceptors.
Based on the wind tunnel study.recirculation zone is assumed to beextended for 6 barrier heights behindthe upwind barrier (Heist et al. 2009).
Upwind Barrier Model (Ahangar et al., 2017)
Comparison with wind tunnel data (Ahangar et al., 2017) from Heist et al. (2009)
Model for Barrier Effects
𝜎𝑧 𝑥 = ℎ0 +𝜎𝑤𝑥
𝑈
𝐶 𝑥 =2
𝜋
𝑄
𝜎𝑤𝑤𝑙𝑛 1 +
𝑤
ℎ0𝑈𝜎𝑤
+ 𝑥
ℎ0 =2
𝜋𝐻
Atmospheric
turbulence
Barrier induced spread
Shifts source
upwind
ConclusionsBarrier causes:
Larger initial vertical plume spread
More rapid increase in the plume spread with distance from the source
Barrier effect persists farthest during stable conditions
The major effect of the barrier is to enhance initial plume spread by an amount proportional to the height of the barrier
Conclusion
Effect of Road Depression on DispersionLas Vegas Study (Baldauf et al., 2013)
Plume is assumed to be mixed through the depression before it affects receptors
Modeling Results for Las Vegas
(Venkatram et al., 2013)
Depressed Road in Wind Tunnel (Heist et al, 2009)
Inferred Model Parameters,𝒔 = 𝟏. 𝟑 (𝐀𝐦𝐢𝐧𝐢 𝐞𝐭 𝐚𝐥. , 2016)
Case 𝛽 ℎ0(𝑚) 𝛽/𝛽𝑓𝑙𝑎𝑡
FLAT 0.81 1.2 1
D690 0.91 4.8 1.12
D630 1.11 3.6 1.37
D990 1.06 5.9 1.31
𝐶 𝑥, 𝑧 =𝐴𝑞
ഥ𝑈 ҧ𝑧exp −
𝐵𝑧
ҧ𝑧
𝑠
𝜎𝑧 = 𝛽𝑢∗𝑈𝑟
𝑧𝑟𝑝𝑥 + ℎ0
𝑝+1
1𝑝+1
𝜎𝑧 increased by the factor 𝛽 and initial mixing specified by ℎ0
Model Performance,D990 (Amini et al, 2016)
Caltrans ProjectObjective: Collect data to evaluate AERMOD/R-LINE under real worldconditions
Tracer, SF6, released from several vehicles traveling in a loop around a 2 kmlength of road.
Tracer concentrations sampled downwind with a network of 36 bag samplers
Micrometeorological measurements made using 6 sonic anemometers
The field studies will be conducted along highways in Riverside/Los Angeles area.The three sites will be chosen to allow evaluation of AERMOD when applied to 1)road without barriers, 2) road with one barrier, and 3) road with two barriers, oneon each side of the road.
26
Three studies at three sites- no barrier, one barrier, two barriers
1. At least 36 samplers for collection of time-integrated carbon monoxide (CO), carbon dioxide (CO2) and tracer gas samples.
2. Three samplers for black carbon (BC) .3. Six sonic anemometers (3-axis
instruments). 4. A pair of video cameras will be located
at each site to monitor traffic flow.
How do buildings affect dispersion of vehicle emissions and near road concentrations?
?
28
Do transit oriented developments (TOD) with high building densities increase the impact of vehicle emissions?
Measurements in LA
Rural Roof Street
Site Streets Building Height (m)
Los Angeles 7th St. / Broadway 36
Temple City Temple City Blvd. / Las Tunas Dr.
6
Vertical Dispersion Model (Schulte et al., 2015)Q Street emission rate
Cs Surface concentration averaged over the street
Cr Roof concentration
W Street width
H Building height
ar Aspect Ratio (H/W)
σw Average standard deviation of vertical velocity fluctuations
β Empirical constant
h0 Initial vertical mixing
0
1
1 (1 )w
Q rs rW
r
aC C
ha
H
Roof concentration, 𝐶𝑟 ,corresponds to flat terrain conditions
Street averaged OSPM ? (Berkowicz, 2000)
Area Weighted Building Height
L Length of street
hi Height of building i
bi Length of building i along street
Area projected on vertical planeH
Length of block
Google earth view of 8th St LA field site.
1i i
i
H hbL
1i i
i
H hbL
Evaluation with LA county data Six field sites in LA county 2013 – 2014
Near road ultrafine particle number concentration measurements
Scatter plot (normalized by emission rate) 32
Evaluation with Riverside data
August/September 2015 Riverside Market Street
Carbon monoxide and UFP measurements
Top: Scatter plot, Bottom: QQ plot. 2 hour average CO concentrations
Estimation of VDM Inputs
How can VDM model inputs be estimated?Urban rooftop not routinely measured.Develop model to relate routine meteorology at an upwind reference location with the urban value.
34
Estimation of VDM Inputs
z Height from ground
u* Surface friction velocity
L Monin-Obukhov length
z0 Surface roughness length
U Wind speed
ψm Integrated form of non-dimensional wind speed gradient
Internal boundary layermodel describes evolutionof turbulence from ruralto urban area.
(Fisher et al. 2006)
𝑑ℎ
𝑑𝑧= 𝐴
𝜎𝑤
𝑈𝑢𝑟𝑏𝑎𝑛(ℎ)(Garratt 1990)
𝑈𝑢𝑟𝑏𝑎𝑛 ℎ = 𝑈𝑟𝑢𝑟𝑎𝑙(ℎ)=> 𝑢∗𝑢𝑟𝑏𝑎𝑛
𝑈 𝑧 =𝑢∗𝜅
ln𝑧
𝑧0+ 𝜓𝑚
𝑧0𝐿
− 𝜓𝑚𝑧
𝐿
Summary and ConclusionsSolid barrier always leads to reduction of near-road concentrations relative to those without barrier
The impact of solid barriers, upwind and downwind, as well as depressed roads can be incorporated into current flat terrain models: EPA’s RLINE model: RLIN_MODELDESCRIPTION_5-23-13.PDF, https://www.cmascenter.org/r-line/
Buildings enhance near surface concentrations relative to those in flat terrain
Ratio of area weighted building height to street width, the effective aspect ratio, governs enhancement of the impact of vehicle emissions
Models for building effects need further development
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