RealReal--world Emission Characterizationworld Emission Characterization

Judith C. Chow (judith.chow@dri.edu)Judith C. Chow (judith.chow@dri.edu)

John G. Watson

Desert Research InstituteDesert Research InstituteNevada System of Higher Education

Reno, NV, USA

dPresented at:The Workshop on Vehicular Air Pollution and its Impact on Human Health

Indian Habitat Centre, New Delhi, India

September 2, 2011

ObjectivesObjectives

• Contrast real-world emissions measurements for emission rates and emission profiles to those made for other emission profiles to those made for other purposes

• Present emerging technologies for improving source test methods

• Illustrate variations in organic carbon (OC)

and elemental carbon abundances in and elemental carbon (EC) abundances in vehicle exhaust and biomass burning

filsource profiles

RealReal--world emissions need to be measured for emission world emissions need to be measured for emission inventories, source apportionment, and health assessmentinventories, source apportionment, and health assessment

Domestic cookingHome heatingShip emissionsStack emissions

Roadside vehicle exhaust monitoring

Diesel vehicle exhaust sampling

Cooking emission sampling atop laboratory cookstoves

Key Issues in Emission TestingKey Issues in Emission Testing

• Emissions measured for one purpose are ftypically inaccurate for other purposes

• Need to measure emission rates, particle size, chemical composition, and temporal variations

Roadside vehicle exhaust monitoring

Diesel vehicle exhaust sampling

Cooking emission sampling atop laboratory

cookstoves

RealReal--world emissions represent hardware, processes, world emissions represent hardware, processes, operating conditions, and fuels.operating conditions, and fuels.operating conditions, and fuels.operating conditions, and fuels.

(This contrasts with most emission tests that are made for certification and compliance)

C tifi ti V if th t d i i bl f • Certification: Verify that a process design is capable of achieving emissions below a regulated limit. (e.g., FTP engine tests)

• Compliance: Determine that in-use processes are within permitted values (e.g., vehicle smog tests, periodic stack tests, and opacity ptests)

• Emissions trading: Relate actual emissions to allowances (e.g., continuous SO2 monitors)

• Emission inventories: Real-world emissions for pollution planning

• Source apportionment: Speciated emissions for source • Source apportionment: Speciated emissions for source and receptor modeling

Emission CharacteristicsEmission Characteristics

• Emission Factor:Amount emitted per unit time or unit of activityAmount emitted per unit time or unit of activity.

• Particle Size:Determines transport and deposition properties.

• Chemical Composition:• Chemical Composition:Fractional abundance of gaseous and particulate chemical components in emissions. Used to speciate inventory and p p yto apportion ambient concentrations to sources.

• Temporal Variation:• Temporal Variation:Emissions change on daily, weekly, seasonal, and annual cycles. Timing of emissions affects atmospheric transport y g p pand dilution as well as human exposure to air pollution.

U.S. EPA’s stack emission certification and compliance U.S. EPA’s stack emission certification and compliance tests are taken with 50+ year old technologytests are taken with 50+ year old technologytests are taken with 50 year old technologytests are taken with 50 year old technology

(Ducted emissions: hot stack sampling with filters and impingers)

This requires hauling glass impingers, clean reagents and ice up and down

Stationary source certification collects PM on a hot filter and bubbles exhaust clean reagents, and ice up and down

dirty stacksPM on a hot filter and bubbles exhaust

through cold water impingers

U.S. EPA, 1996, 1997

Mobile source certification requires Mobile source certification requires dilutiondilution(St ti i h t filt /i i )

Dilution tunnel and sampling ports for Put generator on wheels and move it

(Stationary sources require hot filters/impingers)

vehicle exhaust and it is certified by dilution sampling

Install the generator permanentlyand it is certified by hot stack and it is certified by hot stack sampling and yields different emissions

Hot stack Hot stack (filter/(filter/impingerimpinger)) sampling measures too low for the sampling measures too low for the hot filter and too high for the impingershot filter and too high for the impingershot filter and too high for the impingershot filter and too high for the impingers

Impinger catch

Front filter catch

Sampling Date

The hot filter does not collect condensable material, The hot filter does not collect condensable material, while thewhile the impingersimpingers collect soluble gasescollect soluble gaseswhile the while the impingersimpingers collect soluble gasescollect soluble gases

Example of diesel exhaust with thermal denuders

(Preceding thermal denuders remove some ultrafine particles)(Preceding thermal denuders remove some ultrafine particles)

Example of diesel exhaust with thermal denuders

Burtscher, 2005, J. Aerosol Sci.

Particle characteristics* often vary during Particle characteristics* often vary during i i t ti i t temission testsemission tests

Mass concentration Number concentrationDp (um) Mass concentration Number concentrationDp (um)2.5

Loose sand mass

dp1.0 Nucleation/ reactions from bi d

Shakeout (15min)

binder

Pouring

0.1 Shakeout (15min)( )

(μg/m3) (#/cm3)

Chang et al., 2005, AST* ELPI and Grimm OPC Size Distributions from a casting foundry operation

(μg/m3) (#/cm3)

Many of the chemical components in a source profile are Many of the chemical components in a source profile are condensablecondensable (commonly(commonly measured elements, ions,measured elements, ions, carbon, and gases)carbon, and gases)condensable condensable (commonly (commonly measured elements, ions, measured elements, ions, carbon, and gases)carbon, and gases)

Watson and Chow, 2007

This is especially the case for organic compounds that are important source This is especially the case for organic compounds that are important source markersmarkers (e.g., lactones(e.g., lactones, , hopaneshopanes, , guaiacolsguaiacols, , syringolssyringols, , steranessteranes, and sterols, and sterols))

e) f)

g) h)

Dilution sampling provides a more realistic estimate of Dilution sampling provides a more realistic estimate of PMPM2 52 5 emission rates than hot stack samplingemission rates than hot stack samplingPMPM2.52.5 emission rates than hot stack samplingemission rates than hot stack sampling

Gas-Fired BoilerGas Fired Boiler

0.016

0.018

inorganic condensable (M202)

organic condensable (M202)

In-Stack

Methods

0.012

0.014organic condensable (M202)

Filterable PM (M201A)PM2.5 (dilution)

Methods

0.008

0.01

b/M

MBt

u

0.004

0.006

lb

0

0.002Dilution Method

Run 1 Run 2 Run 3 Run 1 Run 2 Run 3 AP42

England et al., 2007a; 2007b, JAWMA

Specific pollution sources need to be tested Specific pollution sources need to be tested with a dilution sampling systemwith a dilution sampling systemwith a dilution sampling systemwith a dilution sampling system

Casting foundry Dilution sampling system

Dilution sampling Dilution sampling ll tll t

Dilution Chamber

collects collects condensablescondensables and and

allows for allows for measurement ofmeasurement ofmeasurement of measurement of many chemical many chemical

ttSampling Manifold

componentscomponentsFilter Packs

Six two hour samples:Portable GC System

Six two-hour samples:

• Dilution ratio (22 – 45X)

• Residence time (28 2 sec)

Gas Monitor

• Residence time (28.2 sec)

• Stack and diluted temperatures (86-497 °F)

• Stack velocity (18.0-59 m/sec)

A stable ultrafine size distribution indicates a A stable ultrafine size distribution indicates a sufficient residence timesufficient residence timesufficient residence timesufficient residence time

Coal boiler Residual oil boiler

Organic carbon and sulfates form at lower temperatures Organic carbon and sulfates form at lower temperatures in diluted ship stack emissionsin diluted ship stack emissionsin diluted ship stack emissionsin diluted ship stack emissions

Diluted Samples

Hot Samples

Moldanova et al., 2009, Atmos. Environ.

Buttonhook nozzles in current stack tests do Buttonhook nozzles in current stack tests do not pass many large particlesnot pass many large particlesnot pass many large particlesnot pass many large particles

New nozzles are needed to measure large particle sizes New nozzles are needed to measure large particle sizes such as droplets from wet scrubberssuch as droplets from wet scrubberssuch as droplets from wet scrubberssuch as droplets from wet scrubbers

(Inertial Droplet Separator [IDS])

An IDS (Baldwin Environmental, Reno, NV, USA) has been designed to slow particles prior to extractionslow particles prior to extraction

IDS* is tested in a wetIDS* is tested in a wet--stack simulatorstack simulator

IDS

Filter inlet probe

Wet-stack simulator

*inertial droplet separator

Sampling effectiveness for IDS* using spherical Sampling effectiveness for IDS* using spherical glass beadsglass beadsglass beadsglass beads

100CDP: AvgCDP: Avg + Stdev

100CDP: AvgCDP: Avg + Stdev

80

90CDP: Avg + StdevCDP: Avg - StdevIDS/Iso: AvgIDS/Iso: Avg + StdevIDS/Iso: Avg - Stdev

y In

let

y In

let

80

90CDP: Avg + StdevCDP: Avg - StdevIDS/Iso: AvgIDS/Iso: Avg + StdevIDS/Iso: Avg - Stdev

y In

let

60

70

s Pe

rcen

tem

oved

bem

oved

b

60

70

s Pe

rcen

tem

oved

b

40

50

Cum

ulat

ive

Mas

srti

cles

Re

rticl

es R

e

40

50

Cum

ulat

ive

Mas

srti

cles

Re

20

30

C

IDS cut pointAvg: 19.8 µmLow: 17.7 µmHigh: 21 7 µmtio

n of

Pa

tion

of P

a

20

30

C

IDS cut pointAvg: 19.8 µmLow: 17.7 µmHigh: 21 7 µmtio

n of

Pa

0

10

0 10 20 30 40 50 60 70 80 90 100

High: 21.7 µm

Frac

tFr

act

0

10

0 10 20 30 40 50 60 70 80 90 100

High: 21.7 µm

Frac

t

*inertial droplet separator

0 10 20 30 40 50 60 70 80 90 100Aerodynamic Diameter (micrometers)Aerodynamic Diameter (micrometers)Aerodynamic Diameter (micrometers)

0 10 20 30 40 50 60 70 80 90 100Aerodynamic Diameter (micrometers)Aerodynamic Diameter (micrometers)

Testing such inlets requires an inTesting such inlets requires an in--stack stack measurement of droplet size distributionsmeasurement of droplet size distributionsmeasurement of droplet size distributionsmeasurement of droplet size distributions

(DMT forward scattering cloud droplet probe [CDP])probe [CDP])

www.dropletmeasurement.com

The CDP* needs to be reconfigured for wet stack The CDP* needs to be reconfigured for wet stack applicationsapplicationsapplicationsapplications

Wet stack simulator

*cloud droplet probe

The CDP* can be adapted to determine typical The CDP* can be adapted to determine typical droplet size distributions in a stack at thedroplet size distributions in a stack at thedroplet size distributions in a stack at the droplet size distributions in a stack at the

extraction pointextraction point

tbe

r cou

ntN

um

Channel

*cloud droplet probe

More complex detection systems are needed to obtain a full More complex detection systems are needed to obtain a full range of measurements with dilution sampling systemrange of measurements with dilution sampling system

CAT 797B HaulerExhaust Pipe

Flow diagram of DRI on-board li t

Muffler

Elbow

Connector EngineExhaust

DryerHEPA

Box1: Sample Conditioning Module

sampling system

ThermocoupleOmega TJ36-CASS-116U-6-SB

For Exhaust T URG-2000-30ENG

C l

0.8 L/min

ResidenceChamber

HEPAFilter

Activated CharcoalAir

Compressor Valve

32 L/minFlowmeter

Dilutor

Cyclone7.1 µm Cut

Filter

4.95 L/min26 L/min

2.17 L/min

33.12 L/min

PM2.5impactor

Filtersampler

Fl TSI DustTrak DRX

Grimm 1.108 OPC(Size distribution

0.3-25 µm)

Teflon + Citric acid (mass, babs, element,

isotope, NH3)

Pump

Quartz + AgNO

Nuclepore (Lichen study)

Flow meter

TSI DustTrak DRX(PM1, PM2.5, PM4,

PM10, PM15)Magee AE51

(BC)

TSI CPC 3007(Concentration 0.01-1 µm)

Testo 350(CO, CO2, NO, NO2,

SO2, O2,T, P)

PID 102+1 lit

Makeup FlowFor Balance

Quartz + K2CO3(Ions, WSOC, carbohydrates, organic acids, HULIS, SO2)

Quartz + AgNO3(EC/OC, markers, H2S)

Box 3: Integrated Sample Module

Box 4: Real Time PM Module

(VOC)

Box 2: Real Time Gas Module

1 liter Canister

(CH4, C2-C12)

Box5: Battery Module Chow et al., 2010

Sample Conditioning Module (#1) Real-time Gas Module (#2)

More compact and continuous in situ sensors are desiredMore compact and continuous in situ sensors are desiredCaterpillar 797B Heavy p g ( ) Real time Gas Module (#2) Caterpillar 797B Heavy Hauler (345 tons)

Battery MonitorIntegrated Sample Module (#3) Real-time PM Module (#4) Battery (#5)

Pumps

Filter PacksCanisterPump for Makeup Flow Flowmeters

Deep Cycle Marine Battery

Voltage Regulator

Each module measures = 80 cm L × 52 cm W × 32 cm H

RealReal--world sampling uses onworld sampling uses on--board instruments to sample board instruments to sample plumes and normalize concentrations to COplumes and normalize concentrations to CO22 and fuel carbon and fuel carbon

content to obtain content to obtain emission factor in gemission factor in g--pollutant/kgpollutant/kg--fuelfuel

Caterpillar 797B Heavy Hauler (345 tons)

•Battery powered– Particle light scattering

(bscat; normalized to filter mass)

P ti l i di t ib ti

Caterpillar 797B Heavy Hauler (345 tons)

– Particle size distribution– Black carbon (two

wavelengths)

– Volatile organic compounds (VOCs)

– Gases• O2

• CO• CO2

• CO• NO• NO2

• SO2

• H2S– Filter-based samples

Samples drawn from exhaust pipe. No interference with vehicle operations.

Chow et al., 2010; Watson et al., 2010

Sampling port is connected to the exhaust pipe Sampling port is connected to the exhaust pipe (muffler outlet)(muffler outlet)(muffler outlet)(muffler outlet)

AthabascaAthabasca Oil Sands Region

Sampling Modules

Emission concentrations varied by operating conditions Emission concentrations varied by operating conditions (time series)(time series)

40005000

Total VOCs(ppm)

0100200300400500600

(b) Emission analyzer

(a) PID analyzer

i. Idling: Concentration stable and low.

ii. Leaving parking lot: All concentrations increase.

CO2 (ppm)

020000400006000080000

CO(ppm)

01000200030004000

(c) CO2 analyzer

(b) Emission analyzer

iii. Top of uphill: Spikes of concentrations.

iv.Leaving with load: high concentration spikes when

0

NOx(ppm)

0

2000

4000

NumberConcentration

(cm-3) 2 83e+84e+85e+86e+8

(d) Emission analyzer

(e) CPC

concentration spikes when accelerating.

v. Leaving after dumping: concentration spikes when climbing phill

Black CarbonConcentration

(mg/m3)020406080

(cm )

01e+82e+8

PM2 5

2e+2

(f) micro-aethalometer

(g) DustTrak DRX climbing uphill.vi.Waiting for load: low

concentration except when moving forward in line.

PM2.5 Concentration

(mg/m3)0

1e+2

EngineSpeed(rpm)

600

9001200

1500(h) Truck

(g)

Ground Speed(mph)

0

10

20

600

Elevation(m) 260

280

300

(i) GPS

(j) GPS

Time (hh:mm)

12:35 12:40 12:45 12:50 12:55 13:00 13:05 13:10 13:15 13:20

(m)240

i. Idle ii. Leaving parking lot iv. Leaving

with load

v. Leaving after dumping

Leaving with load

iii. Top ofuphill road

vi. Waitingfor load

Distributions of VOC and Distributions of VOC and SVOCSVOC varied between varied between LDGVLDGVaa

andand HDDVHDDVbb ExhaustExhaust (Ft(Ft. McHenry. McHenry TunnelTunnel, Baltimore, MD), Baltimore, MD)and and HDDVHDDV Exhaust Exhaust (Ft(Ft. McHenry . McHenry TunnelTunnel, Baltimore, MD), Baltimore, MD)

0 035

0.04

0.025

0.03

0.035

g/ve

h-m

i)

LD RATE HD RATE

0.015

0.02

mis

sion

Rat

e (g

0

0.005

0.01Em

0

ethe

ne

beab

yl

b1e3

me

t2pe

ne

cpen

ta

c6ol

e1

c2he

xe

cpen

e1

pen2

3m

pa22

4m

c8ol

e3

c8pa

2

c8pa

3

hex2

35

c9ol

e1

o_xy

l

m_e

tol

bz12

4m

inda

n

c4bz

_3

mei

nd

ind_

2m

c5bz

_3

c5bz

_4

c2in

d1

nap_

1m

c14p

_3

dmna

p1

acen

ap

c3na

p1

c3na

pa

n_he

pd

mea

nt2

CanisterCanister TenaxTenax

XADXADC1 to C4C1 to C4--

alkylbenzenesalkylbenzenes C5 to C6C5 to C6--

alkylbenzenesalkylbenzenes

Measured by combination of canisters and Tenax adsorbent cartridges (Sagebiel et al., 1996). Also shown is the analytical range for XAD cartridges used in the Kansas City Study to capture SVOCdownstream of the filter sample.

a LDGV: Light Duty Gasoline Vehicle b HDDV: Heavy Duty Diesel Vehicle

Large Particulate Large Particulate PAHPAH variations found in variations found in lubrication oillubrication oil ((Gasoline/Diesel PM Split Study)Gasoline/Diesel PM Split Study)lubrication oil lubrication oil ((Gasoline/Diesel PM Split Study)Gasoline/Diesel PM Split Study)

160160

120

corone

bghipe

incdpy120

corone

bghipe

incdpy

80tion

(ug/

g) bapyrn

bepyrn

bbjkfl80tio

n (u

g/g) bapyrn

bepyrn

bbjkfl80

Con

cent

rat

chrysn

baanth

80

Con

cent

rat

chrysn

baanth

4040

0

CI_

5 fu

el

CI_

II fu

el

I_IIb

fuel

_10

fuel

_11n

fuel

CI_

5 oi

l

CI_

II oi

l

CI_

IIb o

il

CI_

10 o

il

_11n

oil

_1w

1 oi

l

_2w

1 oi

l

_4w

1 oi

l

_5w

1 oi

l

_6w

2 oi

l

_6w

3 oi

l

_7w

1 oi

l

_7w

2 oi

l

_7w

3 oi

l

_8w

1 oi

l

_8w

2 oi

l

_9w

2 oi

l

10w

1 oi

l

10w

2 oi

l

10w

3 oi

l0

CI_

5 fu

el

CI_

II fu

el

I_IIb

fuel

_10

fuel

_11n

fuel

CI_

5 oi

l

CI_

II oi

l

CI_

IIb o

il

CI_

10 o

il

_11n

oil

_1w

1 oi

l

_2w

1 oi

l

_4w

1 oi

l

_5w

1 oi

l

_6w

2 oi

l

_6w

3 oi

l

_7w

1 oi

l

_7w

2 oi

l

_7w

3 oi

l

_8w

1 oi

l

_8w

2 oi

l

_9w

2 oi

l

10w

1 oi

l

10w

2 oi

l

10w

3 oi

l

Fujita et al., 2005

C C CI

CI

CI _ C C C

I

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_C C C

I

CI

CI _ C C C

I

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

SI_

Combustion sources can be characterized in the Combustion sources can be characterized in the laboratorylaboratorylaboratorylaboratory

Diesel Generator

Acetylene Flame in a hooda hood

• Electric Arc Generator

Wood Stove

(PALAS)

• Carbon Black and Graphite Powder

Many instruments are needed forMany instruments are needed forsource characterizationsource characterizationsource characterizationsource characterization

Dilution SystemDilution Sampling System

P ti l Si i I t t

Aethalometer

Sampling ConePAS-2000(PAH Monitor) Particle Sizing Instruments

(3 nm to 10 µm)

DustTrak

(PAH Monitor)

• TSI Nano-SMPSPhotoacoustic

• TSI Nano-SMPS(TSI, St. Paul, MN)

• Grimm SMPS+C(Grimm, Ainring, Germany)(Grimm, Ainring, Germany)

• MSP Wide Range Spectrometer(MSP; St. Paul, MN)

Abundances of different carbon fractions vary by source Abundances of different carbon fractions vary by source (reproducibility is (reproducibility is ++ 15% for all except for wood burning)15% for all except for wood burning)

100%

70%

80%

90%

%) OC1

OC2

50%

60%

70%

erce

ntag

e ( OC2

OC3OC4OPR

20%

30%

40%

Mas

s P

e

EC1-OPREC2EC3

OC1 – OC4 at 140, 280, 480, and

0%

10%

kW , 20 -

me

6.5

950

ent

50

580 °C in a 100% He atmosphere

EC1 EC3

Die

sel @

4k

D.R

. ~40

Woo

d sm

oke

Dilu

tion

Rat

io 2

120

Ace

tyle

ne F

lam

(2")

; D.R

. ~16

PA

LAS

@ 9

stro

m c

urre

lect

ric A

rc @

9EC1 – EC3 at 580, 740, and 840 °C in a 98% He/2% O D EHe/2% O2atmosphere

Chow et al., 2006, Report to ARB

Characterizing biomass burning is most Characterizing biomass burning is most h ll ih ll ichallengingchallenging

Flaming Phase: hot and dark; high combustion efficiency

Smoldering Phase: not-so-hot and white; low combustion efficiency(W t f ll Fi C Cit NV 14 J l 2004)(Waterfall Fire near Carson City, NV; 14 July 2004)

• Optical Properties }p p• Size Distribution• Emission Factors } Phase Specific?

Experimental setExperimental set--up for vegetative burningup for vegetative burning(U.S. Forest Service Fire Science Laboratory; Missoula, MT, U.S.A)(U.S. Forest Service Fire Science Laboratory; Missoula, MT, U.S.A)( y; , , )( y; , , )

Roof

Wildland Fuel

CO2/H2O

CO

CRD/CED

TEOM/DustTrak

Tandem Neph

Controlled vegetative burning; Smoke Jumper Base

CO

THC

NO/NOx

Gas Sampling

Photoacoustic

(2 – 5 wavelength)

P-Trak/ELPI/SEM filterGas Sampling Pack

17 m

P Trak/ELPI/SEM filter

Mixing ChamberSampling system

DustTrak

Sequential Filter PackBalance

Video/Camera (IR)

Carbon fractions in biomass burning varied by fuel and Carbon fractions in biomass burning varied by fuel and combustion conditionscombustion conditions (EC varied from 3% to 80% in(EC varied from 3% to 80% in PMPM2 52 5))combustion conditions combustion conditions (EC varied from 3% to 80% in (EC varied from 3% to 80% in PMPM2.52.5))

120

100

in P

M

OC1OC2

80

57

79

60

80

tage

(%) OC2

OC3OC4

5751

363640

60

s P

erce

nt EC1EC2EC3

176 30

20Mas

s

EC

30

dero

saW

ood

.

dero

saN

eedl

es

hite

Pin

eee

dles

gebr

ush

Exc

elsi

or

bo G

rass

Mon

tana

Gra

ss

dra

Cor

e

ene

Soo

t

Chen et al., 2007, Environ. Sci. Technol.

Pon

Pin

e

Pon

dP

ine

N

Wh N Sag E

Dam

b M

Tund

Ker

ose

T i iT i iTransmission Transmission Electron Electron

MiMiAcetylene flame Flaming rice straw Diesel exhaust

Microscope Microscope (TEM) shows (TEM) shows

degree of degree of carbonization carbonization Flaming white oak Kerosene flame

varies by varies by sourcesource

Smoldering pine bark Burned plastic

Tumolva et al., 2010, Aerosol Sci.Tech.

Chemical Analysesa

Complete characterization of filter samples allows markers to be identifiedComplete characterization of filter samples allows markers to be identified

Nuclepore polycarbonate-membrane

Silver nitrate-impregnated cellulose-fiber

K2O3-impregnated cellulose-fiber

Citric acid-impregnated cellulose-fiber

Quartz-fiber filter

Quartz-fiber filter

Teflon-membrane filter

Teflon-membrane

Quartz-fiber

Quartz-fiber

Citric acid-impregnated

K2O3-impregnated

Silver nitrate-impregnated cellulose fiber

Nucleporepolycarbonate-

bmembrane filter

cellulose fiber filter

cellulose fiber filter

cellulose fiber filter

filter

~1-2 cm2

0.5 cm2 punch

½ filter extracted in

XRF for 51 elementsb

½ filter extracted in

Whole filter without

Elemental analysis or

½ filter extracted in

filter filter filter cellulose-fiber filter

cellulose-fiber filter

cellulose-fiber filter

membrane filter

punch

20 ml distilled-deionized water (DDW)

Acid OC EC carbon Organic Ammonia by

10 ml 1:11 hydrogen peroxide: DDW dilution

extraction morphological analysis for lichen studies

Sulfur dioxide by Hydrogen

10 ml DDW

a Analytical Instruments: Acid Digestion

ICP-MS for rare-earth elements and

OC, EC, carbon fractions, carbonate by thermal/optical carbon

Organic Markers by TD-GC/MSc

Ammonia by AC

Sulfur dioxide by IC

Hydrogen sulfide by XRF as sulfur

y AAS: Atomic absorption spectroscopy AC: Automated colorimetry ELSD: Evaporative light scattering

detector HPLC-IEC: High performance liquid

chromatography with an ion exchange column

IC: Ion chromatography b Al – U (see Table 7-1) elements and

isotopesd

10 ml for anions and cationse by IC, AC, and AAS, acidified to pH 2 with

1 ml for total WSOC by thermal/optical

Filtration of 5 ml through 0.2 µm PTFE syringe filter

IC: Ion chromatography IC-PAD: IC with pulsed amperometric

detector ICP-MS: Inductively coupled plasma –

mass spectrometry PTFE: Polytetrafluoroethylene SEC: Size-exclusion chromatography TD-GC/MS: Thermal desorption-gas

c 124 organic marker species (see Table 7-1)

d Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Pb204, 205, 206, 207, 208

e Cl-, NO2, NO3-, PO4

=, SO4= (by

IC); NH4+ (by AC); Na+, Mg++, K+,

and Ca++ (by AAS) AAS, acidified to pH 2 with thermal/optical carbon

1 ml speciated WSOC 1 ml for NC 1 ml for MDA 1 ml for PA

chromatography/mass spectrometry UV/VIS: Ultraviolet detector XRF: X-ray fluorescence Observables OC: Organic carbon EC: Elemental carbon HULIS: Humic-like substances

and Ca (by AAS)

1 ml speciated WSOC separated into three classes: NC, MDA, and PA by HPLC-IEC and UV/Vis detection at 254 nm

1 ml for NC speciation (e.g., carbohydrates) by IC-PAD

1 ml for MDA speciation (e.g., organic acids) by IC with conductivity detector

1 ml for PA speciation (e.g., HULIS) by HPLC–SEC–ELSD–UV/VIS

HULIS: Humic like substances MDA: Mono/dicarboxylic acids NC: Neutral/basic compounds PA: Polycarboxylic acids

Organic speciation allows better quantification of combustion sourcesOrganic speciation allows better quantification of combustion sources(Lactones(Lactones, , hopaneshopanes, , guaiacolsguaiacols, , syringolssyringols, , steranessteranes, and , and sterols)sterols)

L tHopanes

Lactones Cholesterol

Guaiacols SyringolsSyringols

Global carbon inventories are uncertainGlobal carbon inventories are uncertain

Anthropogenic Fossil Fuels Biomass Burning

Sum of fossil fuel and biomass burningBC: 8 – 14 Tg/yr

Tg/yr = teragrams/year

BC: 8 14 Tg/yrOC: 33.4 – 62.2 Tg/yr

PM emission models for onPM emission models for on--road engines are improving, but road engines are improving, but these become less important emitters with engine improvementsthese become less important emitters with engine improvements

5

mile

)

MOBILE 6.2 Max MOBILE 6.2 Min

3

4

acto

r (g/

m EMFAC2007 Average Gas/Diesel Split Measurement

Heavy-Duty DieselLight-/Medium-Duty DieselUrban

Bus

2

3

issi

on F

a

0

1

PM2.

5 Em

0

985)

CI-4

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1988

) CI-5

990)

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995)

CI-9

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999)

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000)

CI-I

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1999

) CI-I

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000)

CI-I

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985)

CI-1

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993)

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95) C

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997)

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CI-9

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2) C

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2

P

(19 (1

(198

9 - 1

9

(19

(199

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(199

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2

Vehicle Sample Composite (Model Year)

Hot City-Suburban route (HCS) driving cycle during the Gas/Diesel Split Study with MOBILE 6.2 and EMFAC 2007 model estimates for the Federal Test Procedure (FTP) cycle.

OC and EC abundances in PMOC and EC abundances in PM2.5 2.5 are highest are highest in diesel exhaust from older enginesin diesel exhaust from older enginesin diesel exhaust from older enginesin diesel exhaust from older engines

Chow et al., 2011, Atmos. Environ.OC abundances: 20–83% of PM2.5EC abundances: 15–74% of PM2.5

in PM2.5

OC and EC abundance in PMOC and EC abundance in PM2.52.5 are viable for biomass are viable for biomass burningburningburningburning

in PM2 5

IWC: Industrial Wood Combustion; RWC: Residential Wood Combustion Chow et al., 2011, Atmos. Environ.

OC abundances: 22–67% of PM2.5EC abundances: 3–33% of PM2.5

in PM2.5

OC and EC abundances in OC and EC abundances in PMPM2.5 2.5 are generally low in industrial are generally low in industrial stack emissions with efficient control measuresstack emissions with efficient control measures

in PM2 5

OC abundances: 2–82% of PM2.5EC abundances: below minimum detectable limit–0-5% of PM2.5 Chow et al., 2011, Atmos. Environ.

2.5

Improvements are neededImprovements are neededfor realfor real--world emission source testingworld emission source testingfor realfor real--world emission source testingworld emission source testing

• Replace hot filter/impingerp / p gstack testing method with dilution sampling

• Reconcile test methods among stationary and g ymobile sources

• Ensure comparability between emission testing and • Ensure comparability between emission testing and ambient sampling methods

• Integrate multiple gas/particle measurements with a single source testwith a single source test

ConclusionsConclusions• U.S. EPA certification methods are costly to apply

and do not represent real-world emissionsp• Resources used for certification and compliance

tests would yield more useful results if they were tests would yield more useful results if they were directed toward more real-world emission testing

• A variety of modern emission characterization • A variety of modern emission characterization methods exist that can practically obtain real-world emission factors profiles and activity world emission factors, profiles, and activity levels for global emission inventories

• Black Carbon (BC) or elemental carbon (EC) • Black Carbon (BC) or elemental carbon (EC)

abundances vary by an order of magnitude among pollution sources especially for vehicle among pollution sources, especially for vehicle exhaust and biomass burning

ReferencesReferences• Burtscher, H. (2005). Physical characterization of particulate emissions from diesel engines: A review. J. Aerosol Sci.,

36(7): 896-932.

• Chang, M.-C.O.; Chow, J.C.; Watson, J.G.; Glowacki, C.; Sheya, S.A.; Prabhu, A. (2005). Characterization of fine particulate emissions from casting processes. Aerosol Sci. Technol., 39(10): 947-959.

• Chen, L.-W.A.; Moosmüller, H.; Arnott, W.P.; Chow, J.C.; Watson, J.G.; Susott, R.A.; Babbitt, R.E.; Wold, C.E.; Lincoln, E.N.; Hao, W.M. (2007). Emissions from laboratory combustion of wildland fuels: Emission factors and source profiles. Environ. Sci. Technol., 41(12): 4317-4325.

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• England, G.C.; Watson, J.G.; Chow, J.C.; Zielinska, B.; Chang, M.-C.O.; Loos, K.R.; Hidy, G.M. (2007b). Dilution-based emissions sampling from stationary sources: Part 2. Gas-fired combustors compared with other fuel-fired systems. J. Air Waste Manage. Assoc., 57(1): 79-93.J. Air Waste Manage. Assoc., 57(1): 79 93.

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