University of California Transportation Center
UCTC- FR- 2010- 23
Investigation of Roadside Particulate Matter Concentration
Surrounding Major Arterials in Five Southern Californian Cities
Hansheng Pan, Christian Bartolome, and Marko Princevac
University of California, Riverside
Rufus Edwards and
Marlon Boarnet
University of California, Irvine
August 2010
Investigation of Roadside Particulate Matter Concentration
Surrounding Major Arterials in Five Southern Californian Cities
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Hansheng Pan, Christian Bartolome, Marko Princevac
Department of Mechanical Engineering, Bourns College of Engineering, University of California at
Riverside, Riverside, California, the United States
Rufus Edwards
Department of Epidemiology, School of Medicine, University of California at Irvine, Irvine,
California, the United States
Marlon Boarnet
Department of Planning, Policy, and Design, University of California at Irvine, Irvine, California,
the United States
ABSTRACT
Vehicular emissions from arterials may present a risk to public health considering the
type of surrounding built environments that can trap pollutants. In order to study the
influence of urban morphometry on flow and dispersion of vehicular emissions, field
measurements were performed in major arterials in 5 Southern Californian cities with
different building geometries. Local mean wind, turbulence, virtual temperature,
roadside fine particulate matter ( PM2.5) concentration, and traffic flow data were
collected in summer 2008. In each city, data were collected for three days, covering two
hours during the morning and evening commute and lighter mid- day traffic. First, the
observation shows the influence of building geometry on street level concentration of
particulates. Tall buildings cause a strong downdraft which upon impinging the street
level flushes street canyon from pollutants. Second, field experiments help us understand
the influence of local meteorological variables and their interaction with urban canopy to
particle concentration. Concentrations at the windward side of buildings within urban
canopy are extremely sensitive to wind direction. In addition to wind direction, turbulent
flux, sensible heat flux and turbulent velocity are also affecting concentrations by
enhancing vertical transport.
IMPLICATIONS
Transportation emissions in built environments surrounding major arterials can produce
high concentration spots and have potential adverse health impact. Dispersion of
pollutants within urban canopy is governed by flow and turbulence characteristics caused
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by building morphometry. Current dispersion models used for regulatory purpose have
difficulties in simulating the flow and dispersion for complex building cases, especially
when fine resolution is needed. Therefore, the investigation of roadside vehicular
emissions in different types of built environments is needed. This work presents field
experiments in 5 Southern Californian cities to investigate the influence of building
geometry, local meteorological conditions and traffic flow on roadside particulate
concentrations.
INTRODUCTION
In metropolitan cities, vehicular emissions are in close proximity to pedestrian,
residences and local business. Compared with emissions from a highway passing
through an open area, the study of local emissions from major arterials in urban area is
more challenging and need to consider more factors, such as variation of traffic activity,
local meteorological variables, built environments, urban heat island effect, etc. In street
scale or neighborhood scale, the dispersion of pollutants is heavily depending on the
mean flow and turbulence characteristics. 1- 2
The flow and dispersion through archetypal street canyons has been getting attentions for
decades. Field experiments found the relationship between roof wind direction and
canyon wind direction in street canyons, 3 and a clear pattern of vortex development and
circulation. 4 Laboratory experiments observed the deformation of the recirculating flow
with increasing canyon spacing with Particle Image Velocimetry ( PIV) measurements. 5
Numerical models, such as k- ε model6- 7 and large- eddy simulation, 8- 9 could also achieve
the reasonable mean flow and turbulence characteristics within street canyon. The
typical recirculating flow performing as a concentrated downdraft flow on the windward
side and as an extensive updraft flow on the leeward side causes a larger concentration at
the leeward side than at the windward side except for a step- down configuration, 1 which
was already proved by numerical methods8 and laboratory simulation. 10- 11 There are
several specific studies focusing on the dispersion of particles from vehicles within street
canyon. 12- 13
The understanding of flow and dispersion within street canyon was used to create
parameterized semi- empirical models, such as Operational Street Pollution Model
( OSPM), 14 which usually has practical applications in air pollution management, mobile
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source control strategies, etc. Zhou and Levy15 applied OSPM to study population
exposure to traffic related primary pollutants in densely populated street canyons in mid-town
Manhattan. Their findings indicated the street configuration ( e. g. street width- to-height
ratio) is a more sensitive factor in characterizing the intake fraction ( iF) than
traffic- related variables ( e. g. traffic volume, traffic speed, and percent of truck traffic).
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In recent years, there are series of field experiments conducted to study the flow and
dispersion in urban area. URBAN 200016 was an urban tracer and meteorological field
campaign conducted in Salt Lake City, Utah. This study was designed to investigate the
urban nocturnal boundary layer ( stable to neutral atmospheric condition). The strength of
this study is that it provides a dataset that resolves interacting scales of motion from the
individual building up through the regional scale under the same meteorological
condition. Joint Urban 2003 ( JU2003) field campaign which was designed to investigate the
daytime boundary layer ( neutral to unstable) was performed in Oklahoma City. 17 Velocity
data obtained within a street canyon were used to explore the directional dependence of the
mean flow and turbulence within a real- world street canyon. 18- 19 The Madison Square
Garden July 2004 ( MSG04) field experiment was carried out in the deep urban canyons. 20- 21
This experiment allowed continued improvement of the understanding of the atmospheric
circulations and rapid vertical dispersion in the deep canyons of very large cities such as New
York City. Other field experiments include: Basel Urban Boundary- Layer Experiment
( BUBBLE) in Basel, Switzerland, 22 Dispersion of Air Pollution and its Penetration into
the Local Environment ( DAPPLE) in London, UK, 23- 25 and Canyon Particle Experiment
( CAPAREX) in Essen, Germany. 26
The studies on the dispersion of vehicular emissions were also focusing on urban street
canyon. 26- 28 There is a major limitation on past field experiments: most field experiments
have often focused on a single street canyon, and the vertical profile of velocity and
turbulent flux within and above the street canyon. The variation of building geometry is
hardly addressed. However, in build- up urban area, urban morphometry plays an
important role on flow and dispersion, where most building geometries do not have the
same features as street canyon. Understanding of flow and dispersion within street
canyon or simple arrays is obtained under ideal situations. Application of these results in
realistic case is difficult. Near source studies on dispersion of vehicle exhaust pollutants
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in built environments are still limited. The understanding of vehicular emissions in built
environments surrounding major arterials has benefit on urban planning strategies, such
as pedestrian- friendly community design, transportation planning, etc. Thus, the
objectives of this study are to investigate a wider range of urban morphometry and more
urban- like rough surface, and to study the influence of built environments on near source
PM
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2.5 concentration.
FIELD MEASUREMENTS
Site Description
The classification of building arrangements has no uniform standard. Theurer29
suggested a classification scheme for wider urban areas in German towns. The building
arrangements are divided into 9 types according to the function of buildings for urban air
pollution modeling. Stewart and Oke30 suggested 9 thermal climate zones in the city
series for urban heat island study. In this study, the classification of building
arrangements ( shown in table 1) is due to development patterns and the proximity of
buildings to the arterial. 5 typical building arrangements are selected from 5 southern
Californian cities: 1) low density settlement, 1- 2 stories; 2) low- rise settlement, 3- 4
stories; 3) mid- rise settlement, 10- 20 stories; 4) high- rise settlement, more than twenty
stories and 5) a strip mall with surface parking separating the building and the arterial.
Sampling Description
The field measurements were conducted during the weekdays from June 19 2008 to
August 1 2008 at five cities. Each city was equipped with a 3- D sonic anemometer
( CSAT3, Campbell Sci.), measuring mean wind speed, turbulence and virtual air
temperature, six DustTraks ( TSI Inc.), measuring PM2.5 concentration, and three digital
cameras ( JVC), recording traffic flow. For each city, parallel experiments were
conducted for three days, covering the morning ( 7: 00 a. m. ~ 9: 00 a. m. local time) and
evening ( 5: 00 p. m. ~ 7: 00 p. m. local time) commute and lighter mid- day ( 11: 00 a. m. ~
1: 00 p. m. local time) traffic. Sonic anemometer collected 10 Hz data for 12 hours ( 7: 00
a. m. ~ 7: 00 p. m.) and DustTrak collected 1 Hz data for 6 hours. Table 2 described the
sites in detail. All sites except P6 and LB6 are near ground level. For the sites near
ground level, the height of DustTrak inlet is 2 m above the ground and the sonic
anemometer was mounted at the height of 1.4 m at site 6, together with a DustTrak. Both
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P6 and LB6 are at the roof of parking garage. P6 is 16 m above the ground and LB 6 is 24
m above the ground. Hence, for meteorological data, three sites ( Huntington Beach,
Anaheim and Los Angeles) are on the street level and the other two ( Long Beach and
Pasadena) are on the roof level. The locations of sonic anemometers for all 5 cities are
chosen to be far away from arterials to avoid being affected by traffic induced turbulence.
A quality assurance procedure was performed during each measurement period. Prior to
measurements, zero calibration and synchronization of DustTraks were performed. In
addition, in order to minimize the error made by difference of each DustTrak readings, all
six DustTraks were sampling for 10 minutes at the same time and place to get the correct
factor which was applied for accurate PM2.5 concentration calibration.
RESULTS AND DISCUSSION
Mean Wind and Turbulent Characteristics in Observation
Table 3 shows summary of mean wind and turbulent characteristics for each city for 12
hours average data. U is mean wind speed, WD is wind direction, σ is standard
deviation of wind component fluctuations. Subscripts u, v and w correspond to three
wind components, south- north, east- west and vertical, respectively. Horizontal wind
fluctuation is , is friction velocity, and turbulent kinetic energy is
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h u v σ = σ + σ
( )
)∗
u
2 2 2 2 u v w TKE= σ + σ + σ . Comparing three ground level sites, LA6, HB6 and A6, we
can see that mean wind speed in Huntington Beach and Anaheim was about 3- 4 times
mean wind speed in Los Angeles and even a little higher than roof level measurements in
Long Beach and Pasadena. Overall
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w σ values on the roof level were higher than ground
level and
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∗ u w σ / values in our measurement were greater than the results reported by
Britter and Hanna
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148 1. They reported 1.1 in urban canopy and 1.3 near and above average
building height H. Our data is more close to JU2003 data and MSG05 data21. ∗ u w σ /
values were in the range from 1.44~ 1.66 for JU2003 and 1.18~ 2.17 for MSG05.
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Comparison of High- Rise Settlement and Strip Mall Case
The meteorological data collected by 3- D sonic anemometers were averaged each 30
153 minutes. Figure 1 shows averaged mean wind speed, U , and turbulent intensities,
U w 154 σ / , for 2 cities, Los Angeles ( high- rise settlement) and Huntington Beach ( strip mall
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case). The maximum mean wind speed in Los Angeles was 0.7 m/ sec, while in
Huntington Beach the maximum mean wind speed was 1.7 m/ sec. However, vertical
velocity fluctuation in Los Angeles was comparable with Huntington Beach. Therefore,
turbulent intensity,
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σ w / U , in Los Angeles was much higher than that in Huntington
Beach. This is reasonable since the building arrangement in Los Angeles is classified as
high- rise settlement, with much rougher surface. Thus, although mean wind speed is low,
high turbulence is easy to attain.
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Figure 2 shows turbulent flux and sensible heat flux for two cities. For both cities, we
can see the continuous increasing of turbulent flux and sensible heat flux during the
morning and relatively high values during the afternoon and dropping down in the late
afternoon. Although building arrangements in Los Angeles and Huntington Beach have
huge difference, turbulent flux were not very different and the mean values for Los
Angeles and Huntington Beach were 0.06 m2/ sec2 and 0.07 m2/ sec2, respectively.
Sensible heat flux in Huntington Beach was a little higher than Los Angeles. The
average values were 212 W/ m2 for the former and 124 W/ m2 for the latter. The lower
sensible heat flux in Los Angeles is again caused by its building arrangements. High- rise
settlement and more dense buildings create more shades of buildings on the ground,
hence, less heating by sun.
The relationships between roadside PM2.5 concentration and traffic count in Los Angeles
and Huntington Beach are shown in figure 3. Traffic composition includes passenger car,
bus and truck. The traffic data are collected from site LA2, LA3 and LA4 in Los Angeles
and HB2, HB3 and HB4 in Huntington Beach. Although traffic flow in Los Angeles is
about 3000 to 6000 vehicles per 20 minutes, that is much heavier than Huntington Beach,
the concentration in Los Angeles is not higher than that in Huntington Beach. The
average concentrations are 43± 17 μg/ m3 in Los Angeles and 43± 14 μg/ m3 in Huntington
Beach. As we discussed before, turbulent intensity in Los Angeles is much higher than
Huntington Beach. High turbulent level dilutes the pollutants concentration. This is
agreement with what Britter and Hanna1 discussed that the increased turbulence levels
within the urban canopy result in larger dispersion coefficients and canopy ventilation.
Our results demonstrate that shear produced turbulence caused by building roughness
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dominates the dispersion compared with buoyancy produced turbulence since sensible
heat flux in Los Angeles is lower than in Huntington Beach.
PM2.5 Concentration on Leeward Side and Windward Side
Figure 4 shows time series of PM2.5 concentration with 1 Hz sampling frequency
measured in Los Angeles from two opposite sites. Black lines represent site LA1 which
is located at the windward side on 6th street and red lines represent site LA5 which is
located at the leeward side on the same street facing LA1. PM2.5 concentration peaks
always appeared at leeward side. The performance of concentrations at windward side
during the morning, noon and afternoon periods was different. In the morning ( Figure
4a), concentration valley values appeared at windward side corresponding to the arising
of concentration peaks at leeward side. At noon and in the afternoon, the fluctuations of
concentration at windward side were not as obvious as that in the morning. At this
location, buildings height at windward side ( the highest one is 188 m) is much higher
than that at leeward side ( the highest one is 54 m).
Relation between PM2.5 Concentration and Meteorological Variables
Figure 5 shows meteorological variables at site LB4, which was collected in Long Beach
on July 2, 2008. The dominant wind direction measured by sonic anemometer on the
roof of the building on that day is around 270° ( westerly), almost perpendicular to the
arterial. Under this wind condition, site S4 is located at the windward side of building
and arterial is just at the upwind direction of DustTrak sampling. Figure 6 shows relation
between PM2.5 concentrations and meteorological variables. The plot of wind direction-
PM2.5 concentration relationship shows that all concentrations greater than 70 μg/ m3
appeared under the condition of wind direction around 270°. The plot of turbulent flux-
PM2.5 concentration relationship ( Figure 6) shows high concentration appeared when
wind speed, w σ and turbulent flux was small. When wind speed, w σ and turbulent flux
became large, concentrations stayed at low level. These relationships were not found at
other sites located in streets parallel to the dominant wind direction in which
concentration stayed constant with changes in turbulence and fluxes.
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SUMMARY
This study is a part of the University of California Transportation Center sponsored
project ‘ Near source modeling of transportation emission in built environments
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surrounding major arterials’. The results presented here are based on analysis of field
experiments conducted in 5 Southern Californian cities. Main highlights of the study are:
1) Mean wind speed measured on ground level in relatively open Huntington Beach and
Anaheim are 3- 4 times the mean wind speed in Los Angeles and even a little higher
than roof level measurements in Long Beach and Pasadena. The average σ w / u ∗ from
our observation is 1.5, similar to MSG05 data reported by Hanna
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21.
2) The comparison of Huntington Beach case and Los Angeles case indicated significant
influence of building arrangement on the local meteorological condition and pollutant
concentration. Although mean wind speed in Los Angeles is very low, much higher
turbulent intensity is obtained caused by complex building geometry. Hence, the
roadside PM2.5 concentration in Los Angeles is not higher than Huntington Beach
although the traffic flow in Los Angeles is 3- 4 times heavier than Huntington Beach.
3) Particulate concentration data in Los Angeles shows leeward side of lower building
could trap pollutants and produce high concentration while windward side with higher
building has low concentration caused by clean air flushing.
4) Long Beach case helps us understand the influence of local meteorological variables
on pollutants concentration and the role of receptor position within urban canopy.
When monitor site is located at the windward side of building within urban canopy,
wind direction has a significant influence on pollutions concentrations. In addition to
wind direction, turbulent flux, sensible heat flux and turbulent velocity w σ , can also
affect concentrations, especially on producing extremely high concentration peaks.
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ACKNOWLEDGMENTS
This research has been sponsored by the University of California Transportation Center.
The authors appreciate the assistance provide by Anahita Sfazl, Uzair Ahmed, Anh
Nguyen, Eric Wittenmeier, Chai Yang, Xiangyi Li, Yanyan Zhang and Shiyan Chen, who
have participated in the field experiments.
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About the Authors
Hansheng Pan is a doctoral candidate and Christian Bartolome is doctoral student in the
Department of Mechanical Engineering, University of California at Riverside, Riverside,
CA. Marko Princevac is an assistant professor in the same department. Rufus Edwards is
an assistant professor in the Department of Epidemiology, School of Medicine,
University of California at Irvine, Irvine, CA. Marlon Boarnet is a professor in the
Department of Planning, Policy & Design, University of California at Irvine, Irvine, CA.
Please address correspondence to: Marko Princevac, Department of Mechanical
Engineering, University of California, Riverside, Bourns Hall A315, Riverside, CA
92521, the United States; phone: + 1- 951- 8272445; fax: + 1- 951- 8272899; email:
marko@ engr. ucr. edu.
Table 1. The classification of building arrangements.
Low density
settlement
Low rise
settlement
Mid- rise
settlement
High- rise
settlement A strip mall
Stories 1 to 2 stories 3 to 5 stories 10 to 20 stories > 20 stories 1 to 2 stories
City Anaheim Pasadena Long Beach Los Angeles Huntington Beach
Arterials Harbor Blvd. East Colorado Blvd. East Ocean Blvd. 6th Ave. Beach Blvd.
Typical
buildings
351 Table 2. Specification of each site.
City Site Instrument Arterials Distance to arterials ( m)
Anaheim A1 1DustTrak
1 Camera Harbor Blvd. 3
A2 1DustTrak
1 Camera Harbor Blvd. 5
A3 1DustTrak
1 Camera Lampson Ave. 1
A4 1DustTrak Lampson Ave. 1
A5 1DustTrak Citruswood Ave. 1
A6 1DustTrak
1 Sonic Anemometer Harbor Blvd. 24
Pasadena P1 1DustTrak El Molino Ave. 1
P2 1DustTrak Colorado Blvd. 1
12
1 Camera
P3 1DustTrak
1 Camera Colorado Blvd. 1
P4 1DustTrak
1 Camera Colorado Blvd. 1
P5 1DustTrak El Molino Ave. 1
P6 ( Roof) 1DustTrak
1 Sonic Anemometer Green St 34
Long Beach LB1 1DustTrak Ocean Blvd. 2
LB2 1DustTrak
1 Camera Ocean Blvd. 2
LB3 1DustTrak Broadway 1
LB4 1DustTrak
1 Camera Pine Ave. 1
LB5 1DustTrak
1 Camera Broadway 1
LB6 ( Roof) 1DustTrak
1 Sonic Anemometer Pine Ave. 60
Los Angeles LA1 1DustTrak
1 Camera 6th St. 1
LA2 1DustTrak
1 Camera Grand Ave. 1
LA3 1DustTrak
1 Camera Grand Ave. 1
LA4 1DustTrak
1 Camera 6th St. 1
LA5 1DustTrak 6th St. 1
LA6 1DustTrak
1 Sonic Anemometer Olive St. 50
Huntington Beach HB1 1DustTrak Garfield Ave. 1
HB2 1DustTrak
1 Camera Garfield Ave. 1
HB3 1DustTrak
1 Camera Beach Blvd. 1
HB4 1DustTrak
1 Camera Beach Blvd. 1
HB5 1DustTrak Beach Blvd. 22
HB6 1DustTrak
1 Sonic Anemometer Beach Blvd. 12
352 Table 3. Summary of mean wind and turbulent characteristics.
date site U WD u σ σ v σ h σ w TKE u∗ σ h / u ∗ ∗ u w σ /
m/ sec degree m/ sec m/ sec m/ sec m/ sec m2/ sec2 m/ sec
6/ 19/ 2008 LA6 0.38 228.17 0.70 0.74 1.03 0.34 0.59 0.22 4.93 1.60
6/ 23/ 2008 LA6 0.31 239.39 0.75 0.85 1.14 0.34 0.72 0.26 4.64 1.36
6/ 30/ 2008 LA6 0.47 179.57 0.72 0.74 1.03 0.34 0.61 0.27 3.88 1.25
7/ 2/ 2008 LB6 1.00 260.08 0.78 0.64 1.01 0.54 0.70 0.34 3.09 1.67
7/ 7/ 2008 LB6 0.67 213.28 0.80 0.74 1.09 0.54 0.76 0.35 3.24 1.60
7/ 9/ 2008 LB6 0.92 216.12 0.81 0.71 1.08 0.56 0.80 0.37 3.09 1.61
7/ 16/ 2008 HB6 1.07 213.94 0.59 0.70 0.92 0.37 0.53 0.29 3.51 1.38
7/ 18/ 2008 HB6 1.02 229.31 0.68 0.60 0.91 0.36 0.48 0.25 3.69 1.48
7/ 21/ 2008 HB6 1.19 258.42 0.82 0.71 1.09 0.40 0.71 0.23 4.85 1.78
7/ 23/ 2008 P6 0.87 156.92 0.88 0.76 1.16 0.47 0.90 0.40 2.90 1.24
13
14
7/ 25/ 2008 P6 0.62 159.06 0.68 0.65 0.94 0.44 0.58 0.34 2.73 1.31
7/ 29/ 2008 P6 0.74 180.40 0.86 0.75 1.14 0.49 0.90 0.42 2.64 1.21
7/ 30/ 2008 A6 1.04 220.70 0.92 0.53 1.01 0.38 0.68 0.18 6.93 2.49
7/ 31/ 2008 A6 1.38 213.55 0.73 0.89 1.15 0.39 0.75 0.28 4.35 1.45
8/ 1/ 2008 A6 1.33 211.26 0.63 0.82 1.03 0.35 0.63 0.23 4.50 1.54
353
6: 00 9: 00 12: 00 15: 00 18: 00
0
0.5
1
1.5
2
2.5
Time of Day
wind Speed [ m/ sec]
6: 00 9: 00 12: 00 15: 00 18: 00
0
1
2
3
Time of Day
σw/ U
( a)
6: 00 9: 00 12: 00 15: 00 18: 00
0
0.5
1
1.5
2
2.5
Time of Day
wind Speed [ m/ sec]
6: 00 9: 00 12: 00 15: 00 18: 00
0
1
2
3
Time of Day
σw/ U
( b)
Figure 1. Mean wind speed and vertical velocity fluctuations in ( a) Huntington Beach and ( b) Los Angeles.
( Note: black, red and blue indicate three different days.)
6: 00 9: 00 12: 00 15: 00 18: 00
0
0.05
0.1
0.15
0.2
Time of Day
Turbulent Flux [ m 2/ sec 2]
6: 00 9: 00 12: 00 15: 00 18: 00
0
100
200
300
400
500
Time of Day
Sensible Heat Flux [ W/ m 2]
( a)
6: 00 9: 00 12: 00 15: 00 18: 00
0
0.05
0.1
0.15
0.2
Time of Day
Turbulent Flux [ m 2/ sec 2]
6: 00 9: 00 12: 00 15: 00 18: 00
0
100
200
300
400
500
Time of Day
Sensible Heat Flux [ W/ m 2]
( b)
Figure 2. Turbulent flux and sensible heat flux in ( a) Huntington Beach and ( b) Los Angeles. ( Note: black,
red and blue indicate three different days.)
0 1000 2000 3000 4000 5000 6000 7000
0
20
40
60
80
100
Traffic Count [ vehicles/ 20 minutes]
PM2.5 Concentration [ μg/ m3]
Los Angeles
Huntington Beach
Figure 3. Relationship between traffic flow and PM2.5 concentration in Los Angeles ( dot) and Huntington
Beach ( circle)
9.08 9.1 9.12 9.14 9.16 9.18 9.2 9.22 9.24
40
50
60
70
80
Decimal Time of Day
PM2.5 Concentration [ μg/ m3]
S1
S5
( a)
11.54 11.56 11.58 11.6 11.62 11.64 11.66 11.68
50
60
70
80
Decimal Time of Day
PM2.5 Concentration [ μg/ m3]
S1
S5
( b)
17.02 17.04 17.06 17.08 17.1 17.12 17.14 17.16 17.18
10
20
30
40
50
Decimal Time of Day
PM2.5 Concentration [ μg/ m3]
S1
S5
( c)
Figure 4. Time series (~ 10 minutes) of PM2.5 concentration at site1 ( windward side) and site 5 ( leeward
side) in Los Angeles during ( a) morning, ( b) noon and ( c) afternoon. ( Note: data are collected on 06/ 19/ 2008
in ( a) and ( b), 06/ 30/ 2008 in ( c).)
30
210
60
240
270 90
120
300
150
330
180
0
0 0.5 1 1.5 2 2.5
0
0.2
0.4
0.6
0.8
1
Wind Speed [ m/ sec]
σw [ m/ sec]
( a)
( b)
Figure 5. ( a) Wind rose and ( b) relation between wind speed and wrms on 07/ 02/ 2008 at Long Beach.
0 60 120 180 240 300 360
50
60
70
80
90
wind direction [ degree]
PM2.5 concentration [ μg/ m3]
0 0.5 1 1.5 2 2.5
50
60
70
80
90
Wind Speed [ m/ sec]
PM2.5 Concentration [ μg/ m3]
0 0.2 0.4 0.6 0.8 1
50
60
70
80
90
σ w [ m/ sec]
PM2.5 Concentration [ μg/ m3]
0 0.1 0.2 0.3 0.4
50
60
70
80
90
Turbulent Flux [ m 2/ sec2]
PM2.5 Concentration [ μg/ m3]
Figure 6. Relation between PM2.5 concentrations and meteorological variables.