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10/14/2012
1
Organic aerosol factor analysis of long term ACSM data
Qi Zhang1, Caroline Parworth1, Jerome Fast2, Tim Shippert2, Chitra Sivaraman2, Fan Mei3, and Alison Tilp3
1University of California ‐ Davis2Pacific Northwest National Laboratory
3Brookhaven National Laboratory
Contact: dkwzhang@ucdavis.edu
1Funding: DE‐FG02‐11ER65293
Aerosol Chemical Speciation Monitor (ACSM) 3 . 0
2 . 5
2 . 0
1 . 5
1 . 0
0 . 5
0 . 0
Nitr
ate
Eq
uiva
lent
Mas
s C
onc
ent
ratio
n (µ
g m
-3)
1 41 2 01 0 08 06 04 02 0m /z ( D a lt o n s )
A m m o n iu m 4 .8 u g /m 3N it ra te 5 . 8S u lp h a te 9 . 4O rg a n ic s 1 3 . 4C h lo r i d e 0 . 1 5
M e x ic o C i ty 2 /2 0 0 2
• Long term, continuous measurements• Time resolution: 15 – 30 min• D. L. (g m‐3) for 30 min averaging: org = 0.15, sulfate = 0.024,
nitrate = 0.012, ammonium = 0.28, chloride = 0.011• Organic MS: unit mass resolution, 10 – 150 amu• 3 systems at DOE Atmospheric Radiation Measurement (ARM) sites
• The Southern Great Plains (SGP), Oklahoma• Tropical Western Pacific in Darwin, Australia• MAOS mobile facility
10/14/2012
2
0.00
0.05
0.10
0.15
0.20
0.25
0.30
050
100150
200250
23:20:00 23:30:00
23:40:00 23:50:00
00:00:00 00:10:00
00:20:00
Sig
na
l Int
ensi
ty
m/zSampling time
t0t1t2
t3......
All observations
ORGt×i
msmeasured= ca∙ msa + cb∙ msb + cc∙ msc + …
Multivariate
analysis
A limited number of OA Factors
Organic Factor Analysis of OA Spectra
Multivariate analysis methods:
• Positive Matrix Factorization (PMF)
• Multilinear Engine (ME‐2)
• Tracer‐based mutilinear decomposition
• Spectra‐based linear decomposition (CMB‐style) …
OA factor data derived from AMS and ACSM field data are useful for model validation.
Products:• Factors: HOA, OOA (SV‐, LV‐), BBOA, … • Time‐resolved concentration time series of OA factors (OAi); (OAi) ≈ Organics
• Mass spectra of OA factors that bear some information of their chemical properties, e.g., f44 O/C
Organic Factor Analysis of OA Spectra
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Ng, N. L. et al. (2011), An Aerosol Chemical Speciation Monitor (ACSM) for routine monitoring of the composition and mass concentrations of ambient aerosol, Aerosol Science and Technology, 45(7), 770‐784, 10.1080/02786826.2011.560211
ACSM Data: Intercomparisons (New York City)
6
50
40
30
20
10
0
AM
S S
peci
es (
µg/
m3 )
7/6/11 7/10/11 7/14/11 7/18/11 7/22/11 7/26/11 7/30/11 8/3/11 8/7/11 8/11/11 8/15/11
Date&Time (EDT)
2.0
1.5
1.0
0.5
0.0
4
3
2
1
0
12
8
4
0
30
20
10
0
AC
SM
Specie
s (µg/m
3)
2.0
1.5
1.0
0.5
0.0
4
3
2
1
0
12
8
4
0
30
20
10
0
20
15
10
5
0
Org SO42-
NH4+ NO3
- Total
(a)
(b)
(c)
(d)
(e)50403020100
Tot
al
35302520151050ACSM Species (µg/m
3)
2.0
1.5
1.0
0.5
0.0
NO
3
2.01.51.00.50.0
4
32
10
NH
4
43210
16
12
8
4
0
SO
4
121086420
30
20
10
0
20151050
7/21/2011
8/10/2011Date
AM
S S
peci
es (
µg/
m3 )
Org
anic
s
(a')
(b')
(c')
(d')
(e')r2 = 0.922
Slope = 1.507 ± 0.008
r2 = 0.860
Slope = 0.678 ± 0.006
r2 = 0.876
Slope = 1.11 ± 0.008
r2 = 0.930
Slope = 1.367 ± 0.01
r2 = 0.920
Slope = 1.701 ± 0.009
ACSM Data: Intercomparisons (Long Island, NY)
S. Zhou, Q. Zhang, M. Fan et al.
10/14/2012
4
7
1.0
0.8
0.6
0.4
0.2
0.0
7/6/11 7/10/11 7/14/11 7/18/11 7/22/11 7/26/11 7/30/11 8/3/11 8/7/11 8/11/11 8/15/11
Date&Time (EDT)
10
8
6
4
2
0
2.5
2.0
1.5
1.0
0.5
0.0
20
10
0
-10
-20
x10-3
1.2
0.8
0.4
0.0
12
8
4
0
4
3
2
1
0
0.4
0.2
0.0
-0.2
AM
S S
peci
es (
µg
/m3 ) P
ILS
Sp
ecies (µ
g/m
3)
(a) Chloride
(b) SO4
(c) NH4
(d) NO30.5
0.4
0.3
0.2
0.1
0.0
NO
3
1.20.80.40.0
PILS Species (µg/m3)
3.0
2.0
1.0
0.0
NH
4
43210
1086420
SO
4
121086420
20
10
0
-10
x10-3
0.50.40.30.20.10.0
AM
S S
peci
es (
µg/
m3)
7/21/2011
8/10/2011Date
Chl
orid
e
(a')
(b')
(c')
(d')r2 = 0.467
Slope = 0.401 ± 0.014
r2 = 0.911
Slope = 0.955 ± 0.009
r2 = 0.800
Slope = 0.743 ± 0.009
r2 = 0.162
Slope = 0.05 ± 0.006
HR‐TOF‐AMS vs. PILS, Long Island, NY
S. Zhou, Q. Zhang, M. Fan et al.
Oklahoma Gas and Electric Company‐Coal‐fired power station
ARM SGP Site
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5
9
ARM ACSM Data Processing Flow Chart
1 year SGP ACSM Data
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SGP ACSM Data Sanity Check
SGP ACSM Data Sanity Check
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SGP ACSM Data Sanity Check
SGP ACSM Data Sanity Check
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SGP ACSM Data Sanity Check
Basic PMF Reminder
Assumption: the ACMS organic aerosol data matrix represents the linearcombination of OA factors with constant profiles that havevarying contributions across the dataset.
• One may choose # of factors (P) base on evaluating Q vs. P• # of factors extractable determined by chemical and temporal
resolution.
Ulbrich et al., 2009
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Proposed Outline for PMF Analysis
• Obtain background information about site and surrounding areas– Emission sources– Wind and meteorological patterns
• Characterize existing data– Determine uncertainty associated with data– Average MS– Time series of species– Contributions of each species to entire mass loading
• Prep data for PMF analysis– Determine best pretreatment steps
• Perform PMF on prepared data
Error Pretreatment
18
• Goal 1: Compare removal of noisy m/z with down‐weighing of m/z– Determined that down‐weighing noisy m/z by factor of 10 is more preferable than deleting these m/z
• Goal 2: What S/N criteria should be used when down‐weighing m/z– Eg: Down‐weigh m/z that have S/N < 0.1 by factor of 10 vs down‐weigh m/z with S/N < 0.2 by factor of 10
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Error Pretreatment: Objective 1
Removal of noisy m/z
Down‐weighing of noisy m/z
vs
S/N<0.1 S/N<0.2 S/N<0.3 S/N<0.4 S/N<0.5
vs
Down‐weigh m/z with S/N<0.2 by factor 10
&Down‐weigh m/z with
0.2<S/N<2.0 by factor of 2
Criteria:• S/N < 0.2 down‐weighted by
factor of 10 (bad m/z)
• 0.2 < S/N < 2 down‐weighted by factor of 2 (weak m/z)
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Evaluation and selection of PMF solutions
21
Zhang, Q., J. L. Jimenez, M. Canagaratna, N. L. Ng, I. Ulbrich, D. Worsnop, and Y. L. Sun (2011), Understanding Organic Aerosols via Factor Analysis of Aerosol Mass Spectrometry: a Review, Analytical and BioanalyticalChemistry, 401, 3045‐3067, DOI 3010.1007/s00216‐00011‐05355‐y,
22
1. Apply minimum error of 5E‐3 to ORGerr
2. Remove spikes in ORG and ORGerr, spike threshold set by v_spike_thr
3. Down‐weigh m/z associated with m/z 44 by a factor of 2, includes m/z 16, 17, 18 and 44
4.Down‐weigh m/z in ListofBadAmus (mean S/N < 0.2) by factor of 10
5.Down‐weigh m/z in ListofWeakAmus (mean 0.2<S/N<2) by factor of 2
6.Down‐weigh m/z in ListofSmallError (10th percentile S/N < ‐2) by factor of 5
7.Remove all NaNs in data and error matrices
Data Pretreatment Steps
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23
Rolling Window Analysis
24
Calculation of Q/Qexp and Residuals
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