Single Particle Mass Spectrometry
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Single Particle Mass Single Particle Mass Spectrometry Spectrometry Anthony S. Wexler, University of California, Davis Keith Bein, Yongjing Zhao, Mang Zhang, UC Davis Derek Lake, Mike Tolocka, Murray Johnston, University of Delaware
description
Single Particle Mass Spectrometry. Anthony S. Wexler, University of California, Davis Keith Bein, Yongjing Zhao, Mang Zhang, UC Davis Derek Lake, Mike Tolocka, Murray Johnston, University of Delaware. Sharp Orifice Particle Focusing. Assumption: inlet focuses only one stokes number. - PowerPoint PPT Presentation
Transcript of Single Particle Mass Spectrometry
Single Particle Mass Single Particle Mass SpectrometrySpectrometry
Anthony S. Wexler, University of California, Davis
Keith Bein, Yongjing Zhao, Mang Zhang, UC Davis
Derek Lake, Mike Tolocka, Murray Johnston, University of Delaware
Sharp Orifice Particle FocusingSharp Orifice Particle Focusing
f
pfStk
cpp
p CD
18
2
Udn
f
pc DC 266.11
218
266.112
n
ppp
f d
DUD
Stk
66.118
66.121
2
sonicp
fnp U
StkdD
Tp
T1101
10023.3 10
nd
opp 53.0
oTT 83.0
po, To
Usonic
Assumption: inlet focuses only one stokes number
Pittsburgh Supersite ExperimentPittsburgh Supersite ExperimentPurposePurpose
Assess single particle size and compositionAssess single particle size and composition
Source attributionSource attribution
Correlate co-incident measurementsCorrelate co-incident measurements– Ex. RSMS-3 and SMPSEx. RSMS-3 and SMPS
Sampling ProtocolSampling Protocol Sampling intervals start every three hours Sampling intervals start every three hours
Cycle through nine critically sized orifices Cycle through nine critically sized orifices – correspond to nine different particle sizes correspond to nine different particle sizes – about 30 nm to 1.1 micrometers about 30 nm to 1.1 micrometers
Operated at each orifice until either 10 Operated at each orifice until either 10 minutes expires or 30 particles are sampled, minutes expires or 30 particles are sampled, whichever comes first whichever comes first
Data ClusteringData ClusteringData reductionData reduction
Advancing technology = larger and larger Advancing technology = larger and larger data setsdata sets
Data classificationData classificationCluster data based upon some metric of Cluster data based upon some metric of
similaritysimilarityConstruct particle classes from data Construct particle classes from data
clustersclusters
What can we learn about PM problems using Single What can we learn about PM problems using Single Particle Analysis?Particle Analysis?
Instruments have high reliability –Instruments have high reliability –Operated for 9 months in Baltimore and 12 months in Operated for 9 months in Baltimore and 12 months in
Pittsburgh so significant statistics obtainable.Pittsburgh so significant statistics obtainable.
Metadata for PittsburghMetadata for Pittsburgh
0.01
0.03
0.05
0.07
0.09
0.11
0.13
0.15
0.17
0.19
0.21
Sampling month
Nor
mal
ized
frac
tion
of to
tal p
artic
le h
its
Positive & NegativePositive only
0
0.01
0.02
0.03
0.040
30
60
90
120
150
180
210
240
270
300
330
Sampling Sampling PeriodPeriod
Operation Operation DaysDays
Sampling Sampling DaysDays
Particle Particle HitsHits
09/20/01 – 09/20/01 – 10/01/0210/01/02 372372 306306 236,286236,286
PolarityPolarity Particle Particle HitsHits % of Total % of Total
Positive IonPositive Ion 185,244185,244 78.478.4
Positive & Positive & Negative IonNegative Ion 51,04251,042 21.621.6
0.02
0.04
0.06
0.08
0.10
0.12
0.14
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0.18
0.20
55 65 130 180 215 310 600 1030 1640Aerodynamic Diameter (nm)
Frac
tion
of to
tal p
artic
le h
its
Positive & NegativePositive only
What can we learn about PM problems using Single What can we learn about PM problems using Single Particle Analysis?Particle Analysis?
No pre-conceptions –No pre-conceptions –The instruments analyze everything.The instruments analyze everything.
Unidentified Organics 3.3%Unidentified Organics 3.3%
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Sampling month
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ized
frac
tion
of to
tal p
artic
le h
its
Positive & NegativePositive only
0.5
0.4
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0.0
Inte
grat
ed &
Nor
mal
ized
Ion
Cur
rent
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0.02
0.04
0.06
0.08
0.1
0.120
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120
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180
210
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270
300
330
27
29
39
41
57
59 9581109 123
135
149
69
Chambers Development Co.,
27 tons/yr. 108o
0.16
0.14
0.12
0.10
0.08
0.06
Frac
tion
of to
tal p
artic
le h
its
242220181614121086420Hour of the day
What can we learn about PM problems using Single What can we learn about PM problems using Single Particle Analysis?Particle Analysis?
High temporal resolution – High temporal resolution – Correlation to wind direction or other meteorological Correlation to wind direction or other meteorological
parameters.parameters.
Pittsburgh Supersite- PM2.5 Sources within 24 km of Site
Iron 1.2%Iron 1.2%
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Sampling month
Nor
mal
ized
frac
tion
of to
tal p
artic
le h
its Positive & NegativePositive only
0.8
0.6
0.4
0.2
0.0
Inte
grat
ed &
Nor
mal
ized
Ion
Cur
rent
100959085807570656055504540353025201510m/z
0.01
0.02
0.03
0.04
0.050
30
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Fe+
129o, USX Corp. –ET, 426 tons/yr
0.00
0.05
0.10
0.15
0.20
0.25
55 65 130 180 215 310 600 1030 1640Aerodynamic Diameter (nm)
Frac
tion
of to
tal p
artic
le h
its
Positive & NegativePositive only
Sodium, Potassium, Tin, Lead 0.8%Sodium, Potassium, Tin, Lead 0.8%
0.07
0.12
0.17
0.22
0.27
0.32
0.37
Sampling month
Nor
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ized
frac
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of to
tal p
artic
le h
its
Positive & NegativePositive only
0.5
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0.1
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Inte
grat
ed &
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ized
Ion
Cur
rent
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0
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Na+
K+
Sn+
Pb+
NaK+ 0.00
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Frac
tion
of to
tal p
artic
le h
its
Positive & NegativePositive only
What can we learn about PM problems using Single What can we learn about PM problems using Single Particle Analysis?Particle Analysis?
Source identification is robust –Source identification is robust –Source and ambient samples of the same emissions Source and ambient samples of the same emissions
give the same spectra.give the same spectra.
1.0
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Inte
grat
e an
d N
orm
aliz
ed Io
n C
urre
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e an
d N
orm
aliz
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urre
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Source Sampling: Clairton Coke PlantSource Sampling: Clairton Coke Plant
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urre
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n C
urre
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Alkyl AmineSite Sampling
0.6%
Alkyl AmineSource Sampling
81%
Li, Na, KSite Sampling
3.9%
Li, Na, KSource Sampling
5%
What can we learn about PM problems using Single What can we learn about PM problems using Single Particle Analysis?Particle Analysis?
Site-to-site comparisons can be made.Site-to-site comparisons can be made.
Carbonaceous Ammonium NitrateCarbonaceous Ammonium Nitrate
Fraction of particle hits vs particle diameter
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Diameter (nm)
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ized
frac
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1.0
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C1+
C2+
CO+
NO+
C3+C4+
NH4+
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0.4
0.2
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110105100959085807570656055504540353025201510m/z
C1+
NH4+
C2+
CO+ NO+
C3+
C4+
Atlanta58.0%
Pittsburgh54.4%
Fraction of particle hits vs particle diameter
0
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55 65 120 170 200 320 600 1040 1620
Diameter (nm)
Nor
mal
ized
Fra
ctio
n
Na & KNa & KFraction of particle hits vs particle diameter
0
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0.08
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0.2
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Diameter (nm)
Nor
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ized
frac
tion
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0.0
Inte
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ed Io
n C
urre
nt
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Li+
Na+
NO+
K+
NO2+ NaK+
Na+
NO+
K+
NaK+
Normalized fraction of particle hits vs. particle diameter Li, Na, K
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
55 65 120 170 200 320 600 1040 1620
Optimally focused particle diameter (nm)
Nor
mal
ized
frac
tion
Atlanta8.0%
Pittsburgh5.8%
What other new directions should there be?What other new directions should there be?
Reduced cost, size, weight, power consumption.Reduced cost, size, weight, power consumption.
Continuous Ion Mobility Continuous Ion Mobility SpectrometrySpectrometry
Needle – Sample In
Sheath Gas Wire to ElectrometerExhaust
Teflon Copper Net
Thanks to our sponsorsThanks to our sponsors
US EPA, US DOE, Dreyfus US EPA, US DOE, Dreyfus FoundationFoundation
