Spatial Heterogeneity of Air Quality in Los Angeles

Wonsik Choi,1 Shishan Hu,

Hwajin Kim, Ying Wang, Xiaobi

Kuang, Chuautemoc Arellanes,

Meilu He,1 Kathleen Kozawa,2

Steve Mara,2 Arthur Winer1 and

Suzanne Paulson1 1UCLA

2California Air Resources Board

Supported by the California Air Resources Board, the Department of

Energy, and the National Science Foundation

Measurements

Instrument Measurement Parameter

CPC (TSI, Model 3007) UFP number concentration (10 nm ~ 1mm)

FMPS (TSI, Model 3091) Particle size distribution (5.6~560 nm)

DustTrak (TSI, Model 8520)

PM2.5 and PM10 mass

EcoChem PAS 2000 Particle bound PAHs

LI-COR, Model LI-820 CO2

Teledyne API Model 300E

CO

Teledyne-API Model 200E

NO

Sonic Anemometer (Vaisala)

Temperature, Relative humidity, Wind speed/direction

Garmin GPSMAP 76CS GPS

SmartTetherTM Vertical profiles of temperature, RH, wind speed/direction

KciVacs video Video record

ARB’s Toyota RAV4 electric vehicle

SmartTetherTM

Ultrafine

Fine Coarse

Very fine dust from

mechanical

processes

Mostly formed in

the atmosphere Directly emitted or

formed in the

atmosphere

Size Distribution of Atmospheric Particles

Mostly from vehicular emissions highly concentrated on UFP region: ~80% of the total number conc. but negligible in mass conc. [Kumar et al., 2010]

Formed generally by condensation in the diluting exhaust plume (semi-volatile hydrocarbons and hydrated sulfuric acid) [Shi et al., 2000]

Plot Source: Wilson et al. (1977)

1mm 0.1mm (and smaller)

E.R. Weibel, University of Bern

TRANSLOCATION FROM AIR TO BLOOD

Courtesy of Peter Gehr, U. Bern

Increased Morbidity and Mortality Associated with Exposure to Roadway Pollutants

Acute respiratory diseases;

acute asthma, chronic obstructive pulmonary disease, pneumonia, lung cancer

Cardiovascular disease;

Heart attacks and stroke

Many other diseases

Air pollution degrades overall health, beginning prior to birth. This results in higher incidences of many diseases and conditions.

5

Residential Proximity to Freeway Truck Traffic Increases

Chances of Pre-Term and Low Birth Weight Babies Number of freeway

trucks passing

within 750 feet of a home

per day

Odds Ratio (95% Cl)

(n=4,346; 26,606)

≥ 13,290 heavy-duty

diesel vehicles

1.23 (1.06-1.43)

≥ 8,684 heavy-duty

diesel vehicles

1.18 (1.02-1.37)

Model adjusted for all maternal risk factors as covariates, background air pollution concentrations and census block-group level socio-economic status Ritz et al. UCLA

Freeway plumes in the early morning

The Freeway Imprint is Many Times Larger

Before and Just After Sunrise (normalized data)

0

0.2

0.4

0.6

0.8

1

-1500 -1000 -500 0 500 1000 1500 2000 2500 3000

Distance from Freeway (m)

Rela

tive

UF

P C

on

cen

tratio

n

Pre-Sunrise: Winter

Pre-Sunrise: Summer

Daytime (Zhu et al 2002b)

DownwindUpwind

Freeway

Pico

Pearl

Olympic

Ocean Park

Donald Douglas

Palms

Kansas

Hu et al., 2009

The Atmosphere Strongly Traps

Pollution Near the Surface in the Early

Morning

Red line indicates temperature profile

Santa Monica: Summer is Cleaner; why?

0.0E+00

2.0E+04

4.0E+04

6.0E+04

8.0E+04

1.0E+05

-1500 -1000 -500 0 500 1000 1500 2000 2500 3000

Distance from Freeway (m)

UF

P C

oncentr

ation (

#/c

m^3

)

Pre-Sunrise: Winter

Pre-Sunrise: Summer

DownwindUpwind

Freeway

Pico

Pearl

Olympic

Ocean Park

Donald Douglas

Palms

Kansas

Hu et al., 2009

Traffic Counts

Increase Rapidly

in the Early AM

0.0

0.2

0.4

0.6

0.8

1.0

0:0

0

1:0

0

2:0

0

3:0

0

4:0

0

5:0

0

6:0

0

7:0

0

8:0

0

9:0

0

10:0

0

11:0

0

12:0

0

13:0

0

14:0

0

15:0

0

16:0

0

17:0

0

18:0

0

19:0

0

20:0

0

21:0

0

22:0

0

23:0

0

0:0

0

Time

Ra

tio to P

eak T

raffic

Co

unt .

Winter

Summer

Night

Pre-

Sunrise Day Night

0

200

400

600

800

1000

1200

3:3

0

4:0

0

4:3

0

5:0

0

5:3

0

6:0

0

6:3

0

7:0

0

7:3

0

8:0

0

Time

Tre

affic

Counts

on F

reew

ay

(#/5

min

ute

s)

March 7March 12

March 18WinterJune 30

July 2Summer

Winter measurement

period: 6:00-7:30

Summer measurement period:

4:15-6:35

Summer sunrise time

Winter sunrise time

Summer is cleaner because there is less traffic during the pre-sunrise period

Hu et al., 2009

Sampling Area and Transects

101

91

I-110

I-210 DoLA

(overpass FWY)

Paramount (overpass)

Carson

(underpass)

Claremont

(underpass FWY)

Hu et al., 2008

Zhu et al., 2001, 2005

-500 0 500 1000 1500 2000 2500

0

0.2

0.4

0.6

0.8

1

No

rmalized

[U

FP

]

Distance from freeway (m)

SMwinter

(Hu, 2009)

SMsummer

(Hu, 2009)

WLAday

(Zhu, 2002)

DoLA (this study)

Paramount (this study)

Carson (this study)

Claremont (this study)

Wide Impact Area Downwind of Freeways

[Choi et al., Atmos. Environ., 62, 318-327, 2012]

bkgndUFPUFPUFP ][][][ peakUFP

xUFPxUFP

][

)]([)]([ Normalized

Upwind Downwind

Freeway-Transect Geometry

Winds

Source height = 0 m

Sampling height = 1.5 m

Freeway

Transect

Underpass Freeways Carson Claremont

Sampling height = 1.5 m

Source height = 8 m

Winds

Freeway

Transect

Overpass Freeways DoLA Paramount

2

2

2

2

2

5.1exp

2

5.1exp)5.1,(

zzz

c HmHmQmxC

Gaussian Plume Dispersion model

References Equation

form Land use Stability Class

Dispersion

coefficients

Briggs (1973)

Rural

Ea (slightly stable) a = 0.03 b = 0.3×10-3

Fa (moderately

stable) a = 0.016 b = 0.3×10-3

Urban E Fa (stable) a = 0.08

b = 1.5×10-3

x

xz

b

a

1

Fits Model to Observed Profiles to Extract Emission

Factor and Dispersion Coefficients

Qc = Emission rate corrected with wind speeds H = Source height 1.5m = Measurement height z = Dispersion parameter x = Horizontal distance from the source

Dispersion Parameter

distance

[Choi et al., submitted ]

Estimating the Particle Number Emission Factor

min5/ 80.26

min530010)/ 2.0/ 64.0(1012.82

flow traffic

2

3

364

vehicles

sm

cmsmsm

UQq ec

veh

e

vehc

U

qQ

22

flow Traffic

= 7×1013 particlesmi-1vehicle-1

4.9×1014 particlesmi-1vehicle-1 in 2001 [Zhu and Hinds, AE, 2005]

[Chock, AE, 1978]

Qc = Wind speed-corrected Emission rate (# m cm-3)

qveh = Particle number emission factor (PNEF)

(# mile-1 vehicle-1)

Traffic flow = vehicles s-1

Ue = Effective wind speeds

(wind speed + speed correction factor due to traffic wake)

This is 15% of the Particle Emission Factor measured in West LA in 2001

with the mean values obtained from observations

200m

1,500 m

200 m

91 Freeway

Paramount

Daytime

Night

Winds

As much as 50% of population lives within 1.5 km of freeways in California South Coast Air Basin [Polidori et al., 2009] About 11% of US households are located within 100 m of 4-lane highways [Brugge et al., 2007] Extension of pre-sunrise freeway plume up to 2 km has potentially significant implication for human exposure to UFP as well as other pollutants

Night and Day

Air Quality in Several Los Angeles Neighborhoods

Temporal trends are quite different in different areas. Data are for residential areas only.

SUMMER_PM

SUMMER_AM

SPRING_PM

SPRING_AM

0.0E+00

1.0E+04

2.0E+04

3.0E+04

4.0E+04

5.0E+04

6.0E+04

7.0E+04

UF

P C

oncentr

ation in D

OLA

Resid

ential A

rea

SPRING_AM

SPRING_PM

SUMMER_AM

SUMMER_PM

0.0E+00

1.0E+04

2.0E+04

3.0E+04

4.0E+04

5.0E+04

6.0E+04

UF

P C

on

ce

ntr

atio

ns in

WL

A (

cm

^(-3

))

West Los

Angeles

measurement

areas in 2008

and 2011

2 m/s

A

B

C

SMA

Ultrafine Particle Concentrations Vary

Substantially between Neighborhoods

UF

P c

on

c.

(# c

m-3

)

0.0

5.0e+3

1.0e+4

1.5e+4

2.0e+4

2.5e+4

2.0e+5

4.0e+5

6.0e+5

2008 observations

Friday 2011

Saturday 2011

405 Closure day

SMA SMA SMA SMA

ABC ABC ABC ABC

Summary

1. Early morning extension of freeway plumes far downwind (> 2 km) is a general phenomenon in Southern California, and presumably most locations around the globe.

2. Data indicate a strong drop in emissions of ultrafine particles over the past decade.

3. Plume intensity as well as met. Parameters control pollutant plume lengths downwind of freeways.

4. Plume shapes and areal impact can be predicted from routinely measurable parameters.

5. Behavior of UFP concentrations in neighborhoods is sufficiently complex as to be easy to explain but somewhat difficult to predict.

Other Topics of Active Research in My Lab

Meteorological variables Importance on primary pollutant level

Upper-air

(NCEP model)

Geopotential heights (F) at 1000/925/850/500

mbar

Indicator of synoptic-scale weather

pattern or vertical mixing height

Mean temperature (T) at 1000/925/850 mbar A measure of the strength and height of

the subsidence inversion

Stability (T1000mbar – T925mbar, T1000mbar – T850mbar) Indicator of atmospheric stability

Thickness (F925mbar – F1000mbar) Related to the mean temperature in the

layer

Relative humidity at 1000 mbar (RH1000mbar) Indirect effect

Pressure gradient at 1000 mbar level (Fnorth –

Fsouth, Feast –Fwest)

Related to wind fields and ventilation

strength

Surface

observations

(LAX)

mean/min./max. temperature (Tmean, Tmin, Tmax) Indirect effects on air stability and

emission rates from the engine

mean/max. wind speed (Umean, Umax) Related to dispersion/ventilation

strength

Relative humidity (RH) Indirect effect

Mean surface pressure Indicator of synoptic-scale weather

2. Meteorological variables and their effects on atmospheric primary pollutant concentrations

2. How to Compare Disparate Data Sets? --Develop Regression Trees to Classify Days to determine their meteorological comparability

Daily [CO]max (ppm)

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Daily [

NO

] max

(p

pb

)

0

100

200

300

400

Daily [

NO

] mean

(p

pb

)

0

50

100

150

200

250

300

[NO]max

linear fit for [NO]max

[NO]mean

linear fit for [NO]mean

Different pollutants at the same site are

closely correlated.

3. Reactive Oxygen Species: What is it about particles that make people sick?

ROS have been implicated in asthma, pulmonary and circulatory morbidity and mortality and in carcinogenesis.

ROS are generated by lung tissues in response to foreign material, but sometimes this process gets out of control, resulting in a state of oxidative stress and inflammation.

______________________________________________

• Measure:

• OH production

• H2O2 production

• soluble metals

• Fe(II) and Fe(III)

• quinones

• DTT response

• PM Mass

On-Going studies of ROS Production and Mechanisms by Ambient Aerosols • Extract particles in

physiologically relevant solutions

______________________________________________

4. Aerosol Optical Properties

Supported by the Department of Energy

Aerosols are the most uncertain part of radiative forcing leading to climate change.

IPCC, 2007

Particle Diameter (nm)

200 250 300 350 400 450 500

Re

fra

cti

ve

in

de

x (

mr)

1.2

1.3

1.4

1.5

1.6

1.7

Toluene/NOx(32)_Aug_24_11

Toluene/NOx(32)_Aug_24_11_TD

Toluene/NOx(15)_Aug_29_11

Toluene/NOx(15)_Aug_29_11_TD

66

66

66

66

6666

65 67727575 75

9598

9590

65 6565

66

6677 9595

96

Toluene Secondary Organic Aerosol Refractive Indices

Wide range of refractive indices between 1.35-1.62.

Types of SOA

Refr

active index (

mr)

1.2

1.3

1.4

1.5

1.6

1.7

low NOx

High NO

x

Photooxidation Thermodenuded

ULAQ/Italy (Kinne et al., 2003)

PartMC-MOSAIC,CHEMERE (Kinne et al., 2003; Zaveri et al., 2010)

ECHAM4/ Max-planck-Inst., GOCART (Geogia tech., NASA Goddard), GISS/NASA-GISS, CCSR/Japan, Grantour/(U. of Michigan) (Kinne et al., 2003)

MIRAGE/PNNL (Kinne et al., 2003; Pere et al., 2011)

Barkey et al., 2007

Schnaiter et al., 2005

Photooxidation of VOC from Holm Oak (Lang-Yona et al, 2010)

Nakayama et al., 2010

Nakayama et al., 2010; 2013

High NO

x

low NOx

IntermediateNO

x

SOA generated from Phenol (unpublished data)

SOA generated from b-pinene (Kim et al., 2010)

Ozonolysis

SOA generated from a-pinene (Kim et al., 2010;Kim et al., 2012 and current results)

SOA generated from limonene (Kim et al., 2012 and current results)

SOA generated from Toluene (Kim et al., 2010 and current results)

IntermediateNO

x

Ozonolysis

High NO

x

IntermediateNO

xlow NO

x

532 nm 670 nm

Yu et al., 2008 (measured at 550 nm(at 532 nm section) and 700 (at 670nm section), respectively)

Real refractive indices span a surprisingly large range, with significant implications for climate calculations

The Credit is Really Due to:

Wonsik Choi

Shishan Hu

Hwajin Kim

Ying Wang

Michelle Kuang

Chuautemoc Arellanes

David Gonzalez-Martinez

Meilu He

Kathleen Kozawa*

Steve Mara*

Arthur Winer

Dilhara Ranashinghe

Karen Bunavage

*California Air Resources Board

Supported by the California Air Resources Board,

the Department of Energy, and the National Science

Foundation

Thank you for your attention

Prediction of Dispersion Coefficients

),...,3,2,1( or 543,2,1

543,21,

kjCRHcWSRcTcWDcQc

CRHcWSRcTcWDcTrafficcQ

jjjjreljcjj

jjjjreljjc

ba

Multivariate Regression Model

R2 = 0.88 R2 = 0.86

Qc : emission rate factor WDrel: relative wind direction to freeway T : temperature WSR : vector mean resultant wind speed RH : relative humidity C : correction factor

0 0.02 0.04 0.06 0.08 0.1 0.120

0.02

0.04

0.06

0.08

0.1

0.12

Pre

dic

ted

a

Observed a

Overpass freeways

Underpass freeways

-2 0 2 4 6 8 -2

0

2

4

6

8

Pre

dic

ted

b (

1

0-3

)

Observed b(10-3

)

Overpass freeways

Underpass freeways

R2 = 0.95

0 0.5 1 1.5 2 2.5

x 105

0

0.5

1

1.5

2

2.5x 10

5

Pre

dic

ted

Qc (

1

05)

Observed Qc (10

5)

Paramount, March 18th, 2011

-400 -200 0 200 400 600 800 1000 1200 1400 1600 1800

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

Nu

mb

er

co

nc

. (

10

4 #

c

m-3

)

Distance from freeway (m)

1 range of observations

median PNC

FitGaussian

background conc.

Predicted profile

-400 -200 0 200 400 600 800 1000 1200 1400 1600 1800 2000

2

3

4

5

6

7

8

9

Nu

mb

er

co

nc

. (

10

4 #

c

m-3

)

Distance from freeway (m)

1 range of observations

median PNC

FitGaussian

background conc.

Predicted profile

Carson, February 2nd, 2011

Predicted Profiles Match the Data Well