FT-IR OC & EC predictions in IMPROVE & CSN networks
across multiple years
Bruno Debus, Andy Weakley, Satoshi Takahama, Ann Dillner
Oct 22th, 2019Petaluma - California
1
Acknowledgements Funding for this project:
– EPA and IMPROVE (NPS Cooperative Agreement P11AC91045) – EPRI (Agreement 10003745 and 10005355)– Swiss Polytechnic University-Lausanne (EPFL)
Collaborators, post-docs and undergraduate / graduate students:
• CSN, FRM, IMPROVE, SEARCH programs and site/state personnel• Joann Rice, Mike Hays, Emily Li, EPA• Bret Schichtel and Scott Copeland, IMPROVE• Stephanie Shaw and Eric Edgerton, EPRI/ARA• Randall Martin and the SPARTAN personnel, SPARTAN and Washington University• Dave Diner and MAIA team members, MAIA and JPL2
Alexandra BorisKelsey SeibertTravis RuthenburgMohammed Kamruzzaman
Charlotte BurkiAmir YazdaniBrian TroutJenny Hand
Katie GeorgeCharity CourySean RaffuseTony Wexler
• Non-destructive
• Fast and low-cost– Analyzing all IMPROVE and CSN PTFE filters– 5 min/sample, 3 instruments, 400-700 filters/wk– 6 undergrads and 1 lab supervisor
• PM2.5 PTFE/Teflon filters– Routinely collected – No gas phase adsorption
• FT-IR spectra are information rich– TOR OC and EC– Organic functional groups, OM– Sources – Inorganics including SO4, NO3, SiO
3
FT-IR: Strengths & Limitations for network applications
• Calibration methods are complex
• PTFE filter manufacturer (Pall, MTL) dependent
• No directly comparable methods for functional groups and OM to validate data (no gold standard)
FT-IR in routine network measurements
FT-IR spectroscopy
Extract quantitative information about IR active
substances • Mass• Carbon• Ions• Elements• Functional groups
IMPROVE 75,505 Teflon filters
(2015 – 2018)
CSN26,936 Teflon filters
(2017 – 2018) 4
Teflon filters
Quantitative analysis of ambient samples using FT-IR
Basic considerations
5
Global model Single calibration using a sample subset from every site
Low prediction quality for samples collected during wildfires events
Smoke impacted sample detection?
Sites with unusual composition
Atypical / Typical partitioning?
IMPROVE
From “Global” to “Mutli-level” modeling
CSN
A.T. Weakley et al(2018) Ambient aerosol composition by infrared spectroscopy and partial least squares in the chemical speciation network: Multilevel modeling for elemental carbon, Aerosol Science and Technology, 52:6, 642-654
Requires collocated Teflon & Quartz modules at every location around
the network
Drawbacks & limitations:
1
2
3
0 10 20 30 40
TOR OC [ g/m 3]
0
10
20
30
40
FTIR
OC
[g/
m3
]
Wildfire detection – IMPROVE (2015)
Based on a simple OC/EC criterion
Seasonality & TOR OC concentrations are consistent with fire season / emissions
7
≈ 341 samples
0 10 20 30 40 50
TOR OC [ g/m 3]
0
10
20
30
40
50
60
70
Cou
nts
Biomass burning
1
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
50
100
150
200
Sam
ple
coun
t
Rural sites
Urban sites
Typical / Atypical sites – CSN (2017)
0 2 4 6 8 10 12
Mean OC/EC
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
Typical sites (n = 124 sites)
Atypical sites (n = 15 sites)
0 2 4 6 8 10 12
Mean OC/EC
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
Erro
r [g]
01-073-0023
01-073-200301-113-0003
02-090-003404-013-9997
04-019-102805-119-0007
06-019-0011
06-029-001406-037-110306-065-8001 06-067-0006
06-073-1022
06-085-000508-031-002610-003-2004
11-001-0043
12-011-0034
12-057-300212-073-0012
13-021-0007
13-069-0002
13-089-0002
13-215-001113-245-0091
13-295-0002
15-003-0010
16-001-0010
17-031-0057
17-031-007617-031-420117-043-4002
17-119-0024
18-019-000618-037-2001
18-065-000318-089-0022
18-163-002119-163-001520-173-0010
20-209-0021
21-111-0067
22-033-0009
24-005-300124-033-0030
25-013-000825-025-0042
26-081-002026-091-000726-163-0001
26-163-001526-163-0033
27-003-100227-053-0963
28-049-002029-099-0019
29-510-008530-049-000430-093-000531-055-0019
32-003-0540
32-031-001634-007-000234-013-0003
34-023-001134-027-3001
35-001-002336-001-0005
36-005-011036-029-0005 36-031-000336-055-100736-081-0124 36-101-000337-067-0022
37-119-0041
37-183-001438-015-000339-035-003839-035-0060
39-061-0040
39-081-001739-093-300239-113-003839-135-100139-151-0017
40-109-103740-143-1127
41-051-0080
42-001-0001
42-003-000842-003-0064
42-021-0011
42-029-010042-045-000242-071-000742-071-001242-101-005542-125-5001
42-129-000844-007-1010
45-025-0001
45-079-000746-099-0008
47-093-1020
47-157-007548-113-0069
48-141-0044
48-201-1039 48-203-000249-011-000449-035-3006
49-049-400150-007-0012
51-087-0014
53-033-008053-053-0029
53-077-000954-051-1002
55-009-000555-027-000155-079-0026
55-119-800156-021-0100
08-001-0008
08-123-0008
09-009-0027
13-115-0003
18-097-007834-039-0004
36-061-0134
39-035-0065
39-035-0076
39-153-0023
42-045-0109
42-101-0048
53-033-0030
54-039-0020
72-021-0010
o Cluster 1: 124 Typical sites
o Cluster 2: 15 Atypical sites
Site name StateAdams County CO
Platteville COCriscuolo Park CT
Rome - Elementary School GAIndianapolis - Washington Park IN
Elizabeth Lab NJNew York - Division Street NYHarvard Yard (Cleveland) OH
Southerly WTP OHAkron - 5 Points OH
Marcus Hook PANE Wastewater Treatment Plant PA
Jail at Bayamon PRSeattle 10th Ave WA
Charleston NCore WV
Atypical sites ( n = 15)
Each site is represented as function of it mean OC/EC ratio and prediction error (Global model)
Clustering is used to partition Atypical sites from Typical sites
8
2
Site selection - Flowchart
9
Typical
Atypical
Non Fire
Site number
Site combination
Optimization
3
0.75
0.8
0.85
0.9
0.95
R2
2 4 5 7 9 11 14 17 20 25 32 40 50 61 71 86 100
Percentage of the network (%)
-10
-5
0
5
10
Bias
(%)
4 6 8 11
15
17
22
28
32
40
52
64
81
98
114
138
161
Site # (Cal)
0
10
20
30
Erro
r (%
)
2015 2016 2017 2018
Site number - Optimization (EC)
IMPROVE CSN
0.75
0.8
0.85
R2
3 5 7 10 13 16 19 23 30 40 52 62 71 85 100
Percentage of the network (%)
0
5
10
15
Bias
(%)
4 6 9 12
16
20
24
28
37
50
65
77
88
106
124
Site # (Cal)
0
10
20
30
Erro
r (%
)
Site combination - Optimization (EC)
11
-5 0 5 10
Bias (%)
0
50
100
150
200
250
300
Site
com
bina
tion
#
0.2 0.4 0.6 0.8
R 2
0
100
200
300
400
500
600
700
Site
com
bina
tion
#
Identify site combination with optimal predictions:
Examine 3,000 potential site list candidates using a Monte Carlo method
• High R2 & near zero bias• Reliable predictions for both OC & EC• Consistent predictions across multiple years
Optimal IMPROVE site list
12
14 % of the network
Optimal CSN site lists
13
≈ 14 % of the network
Calibration / FT-IR predictions Results
14
Results – OC & EC prediction (IMPROVE)
0 20 40 60 80
TOR OC [ g/m 3]
0
20
40
60
80
FTIR
OC
[g/
m3
]
IMPROVE 2015 - 2018
0 0.5 1 1.5 2 2.5 3 3.5 4
TOR EC [ g/m 3]
0
1
2
3
4
FTIR
EC
[g/
m3
]
IMPROVE 2015 - 2018
R2 Bias (%)
Error (%)
< MDL (%)
OC 0.98 -0.3 12.9 4.3
EC 0.92 0.2 25.7 32.7
2015 – 2016 – 2017 – 2018 Satisfactory prediction metrics across a 4 year period
Predictions from both Fire & non fire impacted samples are reported together 15
Results – OC & EC prediction (CSN)
R2 Bias (%)
Error (%)
< MDL (%)
OC 0.93 -0.8 13.6 0.6
EC 0.75 0.5 25.5 9.9
2017 – 2018 Satisfactory prediction metrics across a 2 year period
Predictions from both Typical & Atypical sites are reported together
16
0 5 10 15 20 25
TOR OC [ g/m 3]
0
5
10
15
20
25
FTIR
OC
[g/
m3
]
0 1 2 3 4 5 6
TOR EC [ g/m 3]
0
1
2
3
4
5
6
FTIR
EC
[g/
m3
]
Ions & Elements FT-IR predictions
IMPROVE CSN
R2 Bias (%)
Error (%)
< MDL (%)
S 0.98 0.2 5.6 1.4PM2.5 0.98 0.5 5.7 0.2
SO4 0.98 0.1 5.8 1.1NH4 0.95 1.2 8.8 1.2Soil 0.98 1.5 9.5 8.9
Si 0.98 2.0 11.2 14.7Ca 0.97 1.0 11.3 6.9Al 0.98 0.6 12.3 8.8OC 0.98 0.8 12.7 0.4
HIPS 0.88 -3.0 22.1 17.7Fe 0.93 3.1 23.9 16.8Ti 0.92 0.6 24.8 19.4EC 0.91 1.6 25.9 15.8
NO3(winter north)
0.93 10.7 48.6 25.1
R2 Bias (%)
Error (%)
< MDL (%)
S 0.94 0.1 9.6 2.7OC 0.94 -0.7 13.3 0.7SO4 0.83 -0.3 14.8 4.2EC 0.79 0.2 25.2 12.6Ca 0.82 -0.9 31.1 16.7Si 0.86 -2.9 41.3 34.6
NO3 0.88 13.3 45.7 15.2Ti 0.68 -9.6 59.9 27.1
NH4 0.84 3.0 66.8 47.7Al 0.73 -41.7 102.6 68.6
Similar (%) error compared to OC
Similar (%) error compared to EC
Besides carbon, additional IR active materials can be predicted from Teflon filters (XRF, IC)
This data can be used for QC and the calibrations developed for CSN could be extended to FRM (as previously shown for OC & EC)
17
Conclusions
18
Multi-levels models accommodate unique variations in aerosols composition across the networks and improve predictions IMPROVE Fire / Non fire models CSN Atypical / Typical sites
The number of sites retained in the calibration to maintain accurate predictions and the corresponding site selection was optimized via a Monte Carlo method IMPROVE 22 sites retained (14 % of the network) CSN 20 sites retained (14 % of the network)
The multi-level modeling provides reliable TOR-equivalent OC & EC concentrations across a 4 years of IMPROVE and 2 years for CSN.
In addition to carbon, IR active materials sulfate and silicate can be predicted from IR spectra of Teflon filters Useful for QC for IMPROVE and CSN CSN calibrations can be used for the FRM network
Thank you for your attention
Please send request for additional plots / analysis to [email protected]
20
Supporting Materials
21
FT-IR Lab – UC Davis
Automatic LN2 refilling system
Purge system
Purge system
IR1IR2IR3
Wildfire detection – IMPROVE (2016)
≈ 180 samples
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
10
20
30
40
50
Sam
ple
coun
tRural sitesUrban sites
0 10 20 30 40 50
TOR OC [ g/m 3]
0
10
20
30
40
50
60
70
Cou
nts
Wildfire detection – IMPROVE (2017)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
50
100
150
200
250
Sam
ple
coun
tRural sites
Urban sites
0 10 20 30 40 50
TOR OC [ g/m 3]
0
20
40
60
80
100
120
140
Cou
nts
≈ 620 samples
Wildfire detection – IMPROVE (2018)
≈ 560 samples
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
100
200
300
400
Sam
ple
coun
tRural sites
Urban sites
0 20 40 60 80 100
TOR OC [ g/m 3]
0
50
100
150
200
250
Cou
nts
Initial site selection strategy
Each site is summarized by it median TOR EC & NH4 concentrations
Site close to the bin center is selected for calibration (representative)
The optimal number of site is assessed by varying the number of bins
Example of bin segmentation
(IMPROVE 2015)
26
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
14 %
Global
0.75
0.8
0.85
0.9
0.95
R2
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
14 %
Global
-0.01
0
0.01
0.02
Bias
[g/
m3
]
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
14 %
Global
-2
0
2
4
Bias
(%)
2015 2016 2017 2018
Inter-year comparison of the top 30 sites list candidates (IMPROVE – OC)
Optimum
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
14 %
Global
0.7
0.8
0.9
R2
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
14 %
Global
-2
0
2
4
6
Bias
[g/
m3
]
10 -3
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
14 %
Global
-2
0
2
4
Bias
(%)
2015 2016 2017 2018
Inter-year comparison of the top 30 sites list candidates (IMPROVE – EC)
Optimum
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
Global
0.92
0.93
0.94
0.95
Rob
ust R
2
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
Global
-0.5
0
0.5
1
Bias
[g]
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
Global
-4
-2
0
2
4
6
8
Bias
(%)
2017 2018
Inter-year comparison of the top 30 Atypical sites list candidates (CSN – OC)
Optimum
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
Global
0.7
0.75
0.8
Rob
ust R
2
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
Global
-0.5
0
0.5
Bias
[g]
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
Global
-10
-5
0
5
10
Bias
(%)
2017 2018
Inter-year comparison of the top 30 Atypical sites list candidates (CSN – EC)
Optimum
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
13 %
Global
0.956
0.958
0.96
0.962
0.964
0.966
0.968
Rob
ust R
2
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
13 %
Global
-0.2
0
0.2
0.4
Bias
[g]
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
13 %
Global
-2
0
2
Bias
(%)
2017 2018
Inter-year comparison of the top 30 Typical sites list candidates (CSN – OC)
Optimum
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
13 %
Global
0.82
0.84
0.86
Rob
ust R
2
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
13 %
Global
-0.1
0
0.1
0.2
0.3
Bias
[g]
L1 L2 L3 L4 L5 L6 L7 L8 L9 L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
L21
L22
L23
L24
L25
L26
L27
L28
L29
L30
13 %
Global
0
5
10
Bias
(%)
2017 2018
Inter-year comparison of the top 30 Typical sites list candidates (CSN – EC)
Optimum
Site index Site name State02-090-0034 Alaska NCore AK06-067-0006 Sacramento - Del Paso Manor CA
11-001-0043 Washington DC - McMillan Reservoir DC
18-163-0021 Evansville - Buena Vista Road IN29-510-0085 St. Louis - Blair Street MO34-013-0003 Newark Firehouse NJ36-031-0003 Whiteface NY37-067-0022 Winston-Salem - Hattie Ave NC37-119-0041 Garinger High School NC39-113-0038 Sinclair Community College OH40-109-1037 OCUSA Campus OK42-071-0012 Lancaster Downwind PA42-125-5001 East of Pittsburgh- Florence PA48-201-1039 Deer Park TX53-033-0080 Seattle - Beacon Hill WA55-119-8001 Perkinstown CASTNET WI
Typical sites
Atypical sites
Site index Site name State08-123-0008 Platteville CO09-009-0027 Criscuolo Park CT18-097-0078 Indianapolis - Washington Park IN34-039-0004 Elizabeth Lab NJ
IMPROVE CSN
Optimal site lists – Details
Site index Site name State AffiliationBAND1 Bandelier NM NPSBIBE1 Big Bend National Park TX NPSCABA1 Casco Bay ME STATECORI1 Columbia River Gorge OR FSFLTO1 Flat Tops Wilderness CO FSGLAC1 Glacier MT NPSHAVO1 Hawaii Volcanoes HI NPSJARB1 Jarbidge NV FSLASU2 Lake Sugema IA STATELIGO1 Linville Gorge NC FS
LTCC1 Lake Tahoe Community College CA STATE
MAVI1 Martha's Vineyard MA TRIBEMONT1 Monture MT FSMOOS1 Moosehorn ME FWSMORA1 Mount Rainier WA NPSOLYM1 Olympic WA NPSRAFA1 San Rafael CA FSSHRO1 Shining Rock NC FSTALL1 Tallgrass KS STATETHSI1 Three Sisters OR FSVIIS1 Virgin Islands VI NPS
WHIT1 White Mountain NM FS
IMPROVE 2015 – OC & EC prediction
0 5 10 15 20 25 30 35 40
TOR OC [ g/m 3]
0
5
10
15
20
25
30
35
40
FTIR
OC
[g/
m3
]
R2 Bias (%)
Error (%)
< MDL (%)
OC 0.98 0.8 12.7 0.4
EC 0.91 1.6 25.9 15.8
0 0.5 1 1.5 2 2.5 3
TOR EC [ g/m 3]
-0.5
0
0.5
1
1.5
2
2.5
3
FTIR
EC
[g/
m3
]
IMPROVE 2016 – OC & EC prediction
R2 Bias (%)
Error (%)
< MDL (%)
OC 0.97 -1.7 14.5 1.5
EC 0.91 0.4 29.0 18.8
0 10 20 30 40
TOR OC [ g/m 3]
0
10
20
30
40
FTIR
OC
[g/
m3
]
0 0.5 1 1.5 2 2.5 3
TOR EC [ g/m 3]
-0.5
0
0.5
1
1.5
2
2.5
3
FTIR
EC
[g/
m3
]
IMPROVE 2017 – OC & EC prediction
R2 Bias (%)
Error (%)
< MDL (%)
OC 0.98 -0.8 12.1 1.5
EC 0.91 -0.2 23.8 4.8
0 10 20 30 40
TOR OC [ g/m 3]
0
10
20
30
40
FTIR
OC
[g/
m3
]
0 0.5 1 1.5 2 2.5 3 3.5
TOR EC [ g/m 3]
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
FTIR
EC
[g/
m3
]
IMPROVE 2018 – OC & EC prediction
R2 Bias (%)
Error (%)
< MDL (%)
OC 0.99 0.6 12.4 0.4
EC 0.92 -0.9 24.0 7.1
0 20 40 60 80
TOR OC [ g/m 3]
0
20
40
60
80
FTIR
OC
[g/
m3
]
0 0.5 1 1.5 2 2.5 3 3.5 4
TOR EC [ g/m 3]
0
0.5
1
1.5
2
2.5
3
3.5
4
FTIR
EC
[g/
m3
]
CSN 2017 – OC & EC prediction
0 5 10 15 20
TOR OC [ g/m 3]
0
5
10
15
20
FTIR
OC
[g/
m3
]
R2 Bias (%)
Error (%)
< MDL (%)
OC 0.94 -0.7 13.3 0.7
EC 0.79 0.2 25.2 12.9
0 1 2 3 4 5 6
TOR EC [ g/m 3]
0
1
2
3
4
5
6
FTIR
EC
[g/
m3
]
CSN 2018 – OC & EC prediction
R2 Bias (%)
Error (%)
< MDL (%)
OC 0.92 -1.0 14.1 0.4
EC 0.69 1.3 26.0 7.1
0 5 10 15 20 25
TOR OC [ g/m 3]
0
5
10
15
20
25
FTIR
OC
[g/
m3
]
0 1 2 3 4
TOR EC [ g/m 3]
0
1
2
3
4
FTIR
EC
[g/
m3
]
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Percentile FTIR OC (%)
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Med
ian
bias
[g/
m3
]
Median
Theoritical trend (zero line)
TOR Uncertainty ( )
0.00
0.14
0.20
0.24
0.29
0.34
0.39
0.44
0.50
0.56
0.63
0.71
0.79
0.89
1.02
1.16
1.35
1.60
2.01
2.88
99.70
FTIR OC [ g/m 3]
OC bias distribution – IMPROVE (2015 – 2018)
Percentile bias located within TOR uncertainty boundaries
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Percentile FTIR EC (%)
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
Med
ian
bias
[g/
m3
]
Median
Theoritical trend (zero line)
TOR Uncertainty ( )
0.00
0.02
0.03
0.04
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.11
0.12
0.14
0.16
0.19
0.22
0.27
0.34
0.50
4.38
FTIR EC [ g/m 3]
EC bias distribution – IMPROVE (2015 – 2018)
Percentile bias located within TOR uncertainty boundaries
OC bias distribution – CSN (2017 – 2018)
Percentile bias located within TOR uncertainty boundaries
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Percentile FTIR OC (%)
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Med
ian
bias
[g/
m3
]
Median
Theoritical trend (zero line)
TOR Uncertainty ( )
0.02
0.53
0.68
0.82
0.93
1.04
1.15
1.26
1.37
1.49
1.61
1.74
1.89
2.05
2.24
2.46
2.72
3.06
3.57
4.48
26.74
FTIR OC [ g/m 3]
EC bias distribution – CSN (2017 – 2018)
Percentile bias located within TOR uncertainty boundaries
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Percentile FTIR EC (%)
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
Med
ian
bias
[g/
m3
]
Median
Theoritical trend (zero line)
TOR Uncertainty ( )
0.00
0.14
0.19
0.23
0.27
0.30
0.34
0.37
0.40
0.43
0.46
0.50
0.54
0.59
0.64
0.70
0.78
0.87
1.01
1.27
4.05
FTIR EC [ g/m 3]
IMPROVE – Prediction of IR active ions & elements from Teflon filter
Winter North sample only
Spatial distribution of the 79 sites considered for developing a Winter North Nitrate calibration (IMPROVE)
CSN – Prediction of IR active ions & elements from Teflon filter