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Investigation of OM/OC Using Ambient Measurements CMAS 2009 Conference Heather Simon, Prakash Bhave, Jenise Swall, and Neil Frank Photo image area measures 2 H x 6.93 W and can be masked by a collage strip of one, two or three images.

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October 21, 2009

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division

Photo image area measures 2” H x 6.93” W and can be masked by a collage strip of one, two or three images. The photo image area is located 3.19” from left and 3.81” from top of page. Each image used in collage should be reduced or cropped to a maximum of 2” high, stroked with a 1.5 pt white frame and positioned edge-to-edge with accompanying images.

Heather Simon, Prakash Bhave, Jenise Swall, and Neil Frank

Investigation of OM/OC Using Ambient Measurements

CMAS 2009 Conference

Acknowledgements: Wyat Appel, Sergey Napelenok

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SLIDE 2

1

Organic Aerosol Components

sulfate

PM2.5

non-carbon organic matter (NCOM)

  • rganic

carbon (OC)

  • ther
  • rganic

matter (OM) soil nitrate

Organic Matter

  • A constant OM/OC is often used to convert

between OC and OM.

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SLIDE 3

2

Importance of OM/OC and NCOM

Bias in fine PM: CMAQv4.7 vs CSN data

  • 1.5
  • 1
  • 0.5

0.5 1 1.5 2 2.5

Jan 2006 Aug 2006

median bias (ug/m3) sulfate nitrate ammonium TC PM_OTHER

  • Foley et al (2009) found:

– Largest wintertime fine PM bias: PM_OTHER (includes NCOM) – Largest summertime fine PM bias: carbonaceous aerosol

  • NCOM is at the intersection of these two aerosol components
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SLIDE 4

3

Sources of NCOM

primary emissions

1.2 ? 1.8 VOC emissions SOA 1.4-2.7 POA 1.2-1.8 Oligomerization and gas-phase aging chemical aging (oxidation) aged SOA aged POA

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4

primary emissions

1.2 POC + PM_OTHER 1.2-1.8 chemical aging (oxidation) aged POA 1.4

CMAQ’s Treatment of OM and OC – Primary Organic Aerosols

  • POA is modeled as OC
  • NCOM is lumped with

PM_OTHER (becomes indistinguishable from soil, trace metals, etc.)

  • Although measurements

suggest different OM/OC values from different sources, we currently use the same OM/OC for all sources

  • Chemical aging is

accounted for in post- processing by adding 0.2*POC to PM_OTHER

1.2 1.2 1.8 1.2 ? 1.2

SMOKE and CMAQ Post- processing

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SLIDE 6

5

  • Secondary organic aerosols are

modeled as OM

  • To compare model predictions of SOA

(OM) to OC measurements, post- processing is needed

  • Traditionally OM/OC ratios used in post-

processing differ depending on the source VOC from which the SOA is formed. – Aromatic SOA: 2.0 – Isoprene SOA: 1.6-2.7 – Terpene SOA: 1.4 – Sesquiterpene SOA: 2.1 – Alkene SOA: 1.6 – Cloud SOA: 2.0 – Oligomerized SOA: 2.1 VOC emissions SOA 1.4-2.7 Oligomerization and gas-phase aging aged SOA

CMAQ’s Treatment of OM and OC – Secondary Organic Aerosols

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SLIDE 7

6

Driving Questions

  • How accurately does CMAQ simulate OM/OC and

NCOM?

  • How much do inaccurate NCOM predictions contribute

to bias in PM_OTHER?

First step: Estimate OM and NCOM from ambient data

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7

Current Measurement Techniques for OM/OC

  • GC/MS speciation of ambient OM (Turpin and Lim)
  • FTIR used to measure functional groups (several papers by Russell et al;

Kiss et al.)

  • Sequential extraction (El-Zanan et al.)
  • Coupled thermal gravimetric and chemical analyses (Chen et al.)
  • Mass closure using STN data (Frank)
  • IMPROVE network data analysis

– Mass closure

  • Assumptions include fully neutralized sulfate, no particle-bound water,

no nitrate volatilization – Regression – Hand and Malm

  • Does not rely on assumptions about 1) the presence of unmeasured

components (ammonium and water), 2) the amount of nitrate volatilization, or 3) the accuracy of the IMPROVE soil equation.

  • We expand upon Hand and Malm’s regression technique

]) [ ] [ ] [ ] [ ] ) ([( ] [

3 4 4 2 4 5 . 2

nts TraceEleme EC SOIL NO NH SO NH PM OM + + + + − = ] [ ] [ ] [ ] [ ] ) [( ] [ ] [

6 5 4 3 4 3 4 2 4 2 1 5 . 2

SeaSalt EC SOIL NO NH SO NH OC PM β β β β β β + + + + + =

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8

  • Use 2003-2008 data from IMPROVE network
  • Samples were split up by site and quarter
  • Sites that averaged less than 15 samples/quarter were not

analyzed : 154 sites * 4 quarters = 616 regression analyses

  • β1,β2, and β3 were allowed to vary by quarter. β4 was held

constant on an annual basis

  • No filtering of sampling data within a site/quarter grouping

Methods

] [ 8 . 1 ] [ 2 . 1 ] [ ] [ ] [ ] ) [( ] [ ] [

4 3 4 3 4 2 4 2 1 5 . 2 −

+ + + + + + = Cl K EC SOIL NO NH SO NH OC PM

non

β β β β

] [ 6 . ] [ Fe K Knon − = ] [ 94 . 1 ] [ 42 . 2 ] [ 63 . 1 ] [ 48 . 3 ] [ Ti Fe Ca Si SOIL + + + =

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SLIDE 10

9

  • Use 2003-2008 data from IMPROVE network
  • Samples were split up by site and quarter
  • Sites that averaged less than 15 samples/quarter were not

analyzed : 154 sites * 4 quarters = 616 regression analyses

  • 409/616 regressions had reasonable values for all 4

regression coefficients and reasonably low correlation between independent variables

Methods

] [ 8 . 1 ] [ 2 . 1 ] [ ] [ ] [ ] ) [( ] [ ] [

4 3 4 3 4 2 4 2 1 5 . 2 −

+ + + + + + = Cl K EC SOIL NO NH SO NH OC PM

non

β β β β

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SLIDE 11

10

Pitfalls of Multi-linear Regression Analysis

  • Model selection – Does the regression equation capture all

elements of the system?

  • Dataset selection – datasets should be selected such that β1, β2,

β3, and β4 are expected to be relatively constant

  • Colinearity of independent variables
  • Measurement uncertainty in independent variables

–An in depth analysis suggests that independent variable uncertainty may bias results as follows:

  • β1 is biased low by ~5% (10% in the winter)
  • β2 is biased high by ~2%
  • β3 is biased high by < 1%
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SLIDE 12

11

Goal of the Ambient Data Analysis

  • Identify key temporal and spatial trends in measured

OM/OC

  • Compare with CMAQv4.7
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12

  • Value are highest in the southeast (1.4-2.0 in SE, 1.0-1.6 in the rest
  • f the US)

– Due to biogenic SOA?

Spatial variation in OM/OC: Jan, Feb, Mar

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13

Spatial variation in OM/OC: Jan, Feb, Mar

  • Large number of sites with values less than 1 (+) in the west
  • Independent variable uncertainty correction might fix this
  • May be due to more OC volatilization from teflon than quartz
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14

How Do Wintertime Measurements Compare to Wintertime CMAQ Predictions?

CMAQv4.7 : 2002-2005 IMPROVE regression analysis : 2002-2008

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15

Spatial variation in OM/OC: Jul, Aug, Sep

  • Value are consistently higher than in the winter

– More oxidation occurs in the summer

  • Lowest values are in the Southwest

– Lower levels of biogenic SOA in this area

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SLIDE 17

16

Seasonal variation in β β β β1 (OM/OC)

More oxidation in the summer higher OM/OC ratios

Jan, Feb, Mar Jul, Aug, Sep

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17

How Do Summertime Measurements Compare to Summertime CMAQ Predictions?

CMAQv4.7 : 2002-2005 IMPROVE regression analysis : 2002-2008

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18

Conclusions

  • Developing a technique to calculate OM/OC from

IMPROVE data is important for creating a comprehensive dataset of values covering a large spatial and temporal extent

  • Regression analysis generally yielded realistic values
  • Key spatial and temporal trends have been identified
  • CMAQ tends to under-predict variability of OM/OC that

is seen in ambient data

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19

Next Steps

  • Finish refining and analyzing regression technique for

determining ambient OM/OC values

  • Modify CMAQ to explicitly model NCOM

–Add NCOM species to SMOKE and CMAQ –Process emissions to reflect different OM/OC values from different primary emission sources –Model an aging reaction for POA which leads to increased OM/OC and NCOM values

  • Compare modified CMAQ to ambient data to

determine if OM/OC and NCOM predictions are improved

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20

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21

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β1 (OC)

  • This represents OM/OC and by definition cannot be less

than 1

  • Use 2003-2008 data from IMPROVE network
  • Samples were split up by site and quarter
  • Sites that averaged less than 15 samples/quarter were not

analyzed : 154 sites * 4 quarters = 616 regression analyses

Physically Reasonable Coefficients Methods

] [ 8 . 1 ] [ 2 . 1 ] [ ] [ ] [ ] ) [( ] [ ] [

4 3 4 3 4 2 4 2 1 5 . 2 −

+ + + + + + = Cl K EC SOIL NO NH SO NH OC PM

non

β β β β

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SLIDE 24

23

β2 (ammonium sulfate)

  • Values less than 1 represent non-fully neutralized sulfate:

NH4HSO4 would be equivalent to a value of 0.87

  • Values greater than 1 represent hydrated aerosol. At high

RH, the value could be as high as 1.53.

  • Use 2003-2008 data from IMPROVE network
  • Samples were split up by site and quarter
  • Sites that averaged less than 15 samples/quarter were not

analyzed : 154 sites * 4 quarters = 616 regression analyses

Physically Reasonable Coefficients Methods

] [ 8 . 1 ] [ 2 . 1 ] [ ] [ ] [ ] ) [( ] [ ] [

4 3 4 3 4 2 4 2 1 5 . 2 −

+ + + + + + = Cl K EC SOIL NO NH SO NH OC PM

non

β β β β

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SLIDE 25

24

β3 (ammonium nitrate)

  • Values less than 1 represent partial or total nitrate
  • volatilization. Minimum value would be 0.
  • Values greater than 1 represent hydrated aerosol or NaNO3.

At high RH, the value could be as high as 1.35.

  • Use 2003-2008 data from IMPROVE network
  • Samples were split up by site and quarter
  • Sites that averaged less than 15 samples/quarter were not

analyzed : 154 sites * 4 quarters = 616 regression analyses

Physically Reasonable Coefficients Methods

] [ 8 . 1 ] [ 2 . 1 ] [ ] [ ] [ ] ) [( ] [ ] [

4 3 4 3 4 2 4 2 1 5 . 2 −

+ + + + + + = Cl K EC SOIL NO NH SO NH OC PM

non

β β β β

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SLIDE 26

25

β4 (soil)

  • Values other than one indicate that soil composition is different

from that used to create the IMPROVE soil equation

  • β4 values were calculated for a large variety of reported soil

compositions and ranged from 0.41 – 1.63

  • Use 2003-2008 data from IMPROVE network
  • Samples were split up by site and quarter
  • Sites that averaged less than 15 samples/quarter were not

analyzed : 154 sites * 4 quarters = 616 regression analyses

Physically Reasonable Coefficients Methods

] [ 8 . 1 ] [ 2 . 1 ] [ ] [ ] [ ] ) [( ] [ ] [

4 3 4 3 4 2 4 2 1 5 . 2 −

+ + + + + + = Cl K EC SOIL NO NH SO NH OC PM

non

β β β β

slide-27
SLIDE 27

26

Physically Reasonable Coefficients

  • β1 (OC)

– This represents OM/OC and by definition cannot be less than 1

  • β2 (ammonium sulfate)

– Values less than 1 represent non-fully neutralized sulfate: NH4HSO4 would be equivalent to a value of 0.87 – Values greater than 1 represent hydrated aerosol. At high RH, the value could be as high as 1.53.

  • β3 (ammonium nitrate)

– Values less than 1 represent partial or total nitrate volatilization. Minimum value would be 0. – Values greater than 1 represent hydrated aerosol or NaNO3. At high RH, the value could be as high as 1.35.

  • β4 (soil)

– Values other than one indicate that soil composition is different from that used to create the IMPROVE soil equation – β4 values were calculated for a large variety of reported soil compositions and ranged from 0.41 – 1.63