Identifying sources contributing to poor air quality

using aerosol mass spectrometry techniques

1 April 2014

Robert Healy

2

Overview

- Background

- Aerosol Mass Spectrometer (AMS) and source apportionment

- Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) case studies

- Conclusions and future directions

3

Background: Aerosol and air quality

- Poor air quality events in urban environments result in human exposure to elevated

aerosol mass concentrations

- Knowledge of aerosol chemical composition helps to identify aerosol sources

- Potentially toxic aerosol constituents include transition metals, certain organic

compounds and black carbon

- Single particle mass spectrometers help to identify which chemical species are

present in which particles

- Aerosol ‘mixing state’ can then be investigated

4

Background - aerosol mixing state

Fully externally mixed particles

Fully internally mixed particles

Organic aerosol

Black carbon

Sulphate

OR

5

- Filter sampling often involves low time resolution (24 h)

- Bulk composition is obtained but single particle information is lost

- Can be difficult to identify sources or investigate processing using bulk results

Filter

Particulate Matter Bulk Composition

extraction & analysis

Aerosol bulk sampling- offline

6

Aerosol bulk sampling- online:

Aerosol Mass Spectrometer (AMS)

*Aerodyne Research Inc.

*

- Quantitative determination of organic aerosol and inorganics

- Refractory black carbon (rBC) now also measured using SP module

7

Aerosol bulk sampling- online:

AMS

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Aerosol bulk sampling- online:

AMS

- High time resolution (1 s – 1 min), size resolved measurements (<1000 nm)

- Can identify and apportion aerosol sources

- Single particle information is lost

Data output:Bulk composition

9

AMS data treatment: PMF

- Organic aerosol is routinely ‘apportioned’ to different sources using positive matrix

factorization (PMF)*

- This approach is based on the similar temporal variability observed for organic ions in

the mass spectral data that are associated with the same source

Nitrate

Sulphate Organic aerosol

HOA

BBOA

COA

OOA

*Ulbrich et al. Atmos. Chem. Phys. 2009

PM1 speciation

Organic aerosol source contributions

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AMS data treatment: PMF

- Recent efforts apply PMF analysis to the full AMS mass spectral dataset, including both

inorganic and organic aerosol ions*

*e.g. McGuire et al. Atmos. Chem. Phys. Disc. 2014

Traffic source

OOA-rich source

Nitrate/OAsource

Sulphate/OAsource

Nitrate

Sulphate

Organic aerosol

PM1 source contributions

PM1 speciation

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AMS overview

- Quantitative, size-resolved, high temporal resolution speciation of PM1

- Very useful for source apportionment of organic aerosol and PM1

- Bulk composition information is obtained but single particle information and thus mixing

state information is lost

12

Single particle sampling- online:

Aerosol Time-of-Flight Mass Spectrometer (ATOFMS)

- Qualitative determination of organic aerosol, inorganics, metals and rBC

- High time resolution (1 s)

- Size resolved data (150-3000 nm)

*

*TSI Inc. (Model 3800)

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Single particle sampling- online:

ATOFMS

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Single particle sampling- online:

ATOFMS

- Single particle information retained

- Enables source identification and investigation of chemical processing

- Data typically qualitative only

Data output:Single particlemixing state(qualitative)

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ATOFMS Case study 1: Cork Harbour

Shipping source

XB

BB

0.05 0.1 0.15 0.2

0

45

90

135

180

225

270

315

0 - 2 2 - 4 4 - 6 6 - 8 8+

(m s-1

)

Vehicle sourceHome heating source

Cork

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Cork Harbour shipping source

*Healy et al. Atmos. Environ. 2009

*

Average dual ion mass spectrum of ship exhaust particles

Re

lative

in

ten

sity

+

-

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Cork Harbour shipping source

0

50

100

150

200

250

300A

TO

FM

S c

ou

nts

Date

ATOFMS "Shipping" class

- High temporal resolution of ATOFMS very useful for short-lived events

wind from docks wind from docks

wind from north

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Cork Harbour Source Apportionment

- Observed and apportioned 3 different particle types associated with domestic

coal, peat and wood combustion

- Also detected and apportioned ship exhaust, sea salt, road dust and vehicle

exhaust particles

Coal

Peat

Wood

Traffic

Road dust

Sea salt

Shipping

relative number contribution

*Healy et al. Atmos. Chem. Phys. 2010

*

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ATOFMS Case study 2: Paris

PARIS

SIRTA

LHVP

20km

GOLF

Livry

- EU project ‘MEGAPOLI’ winter campaign

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Paris: Quantitative approach

- Single particle qualitative mixing state information is very useful

- But can we be quantitative?

- Wealth of support instrumentation co-located on site for the MEGAPOLI project

- Number-size distribution data, size resolved non-refractory aerosol data and BC

data available

- Combination of ATOFMS and AMS data highly complementary

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Paris: Quantitative approach

1: Derive ATOFMS mass spectral relative sensitivity factors (RSF) for OA, BC,

NO3, SO4, NH4, and K

2: Calculate quantitative chemical composition estimates for each single particle

*Healy et al. Atmos. Chem. Phys. 2013

RSF

*

quantitative

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Paris: Quantitative approach

- Chemical composition of each particle in the population can also be summed to

produce size-resolved bulk composition information

quantitative

30

25

20

15

10

5

0

dM

/dlo

gD

p (

µg m

-3)

900

800

700

600

500

400

300

200

Aerodynamic diameter (nm)

K NH4

NO3

SO4

OA BC

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Paris: Quantitative approach- classes

- Single particles can also be classified into discrete “classes”

- Chemical composition and dependence upon time of day and air mass origin

used to differentiate local and transported particles

BC

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Paris: Source apportionment

- Quantitative ATOFMS data for particle classes enables an assessment of local

vs transported contributions to air quality in Paris

100

80

60

40

20

0

Rela

tive

Ma

ss C

ontr

ibu

tion

(%

)

BC OA NH4 SO4 NO3 PM0.15-1

Local Transported

59%

41%

24%

76%

5%

95%

16%

84%

8%

92%

22%

78%

- Poor air quality events in Paris were associated with continental transport

events during MEGAPOLI 2010. Quite a different story to March 2014!

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Conclusions and future directions

• AMS and ATOFMS offer different but complementary perspectives to help

understand the sources of aerosol during poor air quality events

• AMS provides quantitative source apportionment of organic aerosol and

more recently PM1

• ATOFMS provides single particle information

• Most recent efforts aim to provide quantitative estimates for single particle

composition and mixing state

26

Thanks to…

• John Wenger, UCC

• Greg Evans, UofT

• Michael Murphy UofT

• Laurent Poulain, IfT

• Jean Sciare, LSCE

• Andreas Stohl, NILU

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Questions?

*

*Kovarik, Agence France-Presse (Getty Images)

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102

103

104

105

Sca

ling

fa

cto

r

150-

191

nm

191-

244

nm

244-

312

nm

312-

399

nm

399-

511

nm

511-

653

nm

653-

835

nm

835-

1067

nm

Box-plot of hourly size-dependent scaling factors for the entire measurement period (n = 624). Median, 75th percentile and 90th percentile are denoted by the solid line, box and whisker respectively.

Size-dependent number scaling factors

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Relative sensitivity factors by species

3

4

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1

2

3

4

56

10

2

3

4R

ela

tive

sen

sitiv

ity facto

r (a

rbitra

ry u

nits)

SO4

OANH4

NO3

BC

Box-plot of hourly mass spectral relative sensitivity factors (n = 610). Median, 75th percentile and 90th percentile are denoted by the solid line, box and whisker respectively.

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ATOFMS reconstructed mass vs AMS/MAAP

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ATOFMS reconstructed mass vs AMS/MAAP

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ATOFMS-derived bulk mass fractions

AMS/MAAP bulk mass fractions

OA NH4

NO3

SO4

BC

33

ATOFMS reconstructed mass vs AMS

(size resolved)