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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