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 bdebus@ucdavis.edu

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