identifying sources contributing to poor air quality using ...ams overview - quantitative,...
TRANSCRIPT
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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29
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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ss f
ractio
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Date
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0.8
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0.4
0.2
0.0
ATOFMS-derived bulk mass fractions
AMS/MAAP bulk mass fractions
OA NH4
NO3
SO4
BC
33
ATOFMS reconstructed mass vs AMS
(size resolved)