Predicting TOR OC and EC from FT-IR Spectra of IMPROVE samples Ann M. Dillner Assoc. Research Scientist University of California, Davis Satoshi Takahama Assistant Professor Ecole Polytechnique Federale de Lausanne, Switzerland IMPROVE Steering Committee Cape Romain, South Carolina October 15, 2014

OC and EC in IMPROVE  IMPROVE measures organic carbon (OC) and elemental carbon (EC) using the thermal optical reflectance (TOR) method  Quartz filters (C module)  Use punch from filter (3 punches per filter)  Punch is heated and evolved carbon is measured 1

An Alternative Approach for OC and EC is FT-IR  Samples collected on Teflon filters (A modules)  Non-destructive – samples can be used for other analyses (weight, XRF, laser) and archived  Obtain OM/OC and functional groups from same sample FT-IR spectrum 2

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IMPROVE sites analyzed by FT-IR in samples

Partial Least Squares (PLS) Regression 5  Input  FT-IR spectra (2784 points) from ambient samples  TOR OC and EC data (from FED website)  artifact corrected OC  flow corrected to Teflon  ~2/3 of samples (529 samples) for calibration  Cross validation and RMSE to select number of components in calibration

PLSR calibration, b j  Calculating OC and EC:  Use remaining ~1/3 of samples (264, test set) to evaluate calibration 6

Input: Three spectral types 7

Performance Metrics for OC and EC  difference = FT-IR OC – TOR OC  Error = |difference|  Normalized error = Error/TOR OC, %  Bias = Median difference  Error bars = 25 th to 75 th percentile  R 2 8

Base Case Calibration  Order filters by site and date  Every third filter for each site goes into the test set to evaluate calibration  All other filters go into calibration set 9 Mesa Verde 01/03/ /06/ /09/ /12/ /15/ /18/2011 ….. Phoenix 01/03/ /06/ /09/ /12/ /15/ /18/2011 ….. repeat for all sites

Organic Carbon 10

FT-IR OC - Base Case 11 Units are mass divided by the IMPROVE volume of 32.8 m 3 1:1 line

FT-IR OC based on different spectra types 12

MDL and precision for FT-IR-OC and TOR-OC 13 *Concentration units of  g/m 3 for MDL and precision are based on the IMPROVE volume of 32.8 m 3. **Reported network MDL in concentration units. FT-IR-OC full spectraTOR-OC FT-IR-OC baseline corrected spectra FT-IR-OC truncated spectra MDL (  g/m 3 )* ** % below MDL precision (  g/m 3 )*

Causes of Bias and Error -OC 14 Low values have higher measurement errors and artifact correction errors.

Composition - OM/OC 15 Error increases when organic composition is different in test and calibration sets Similar results for OC/EC

Ammonium/OC 16 Error and bias increase when calibration trained without ammonium interference.

Can we decrease error in low OC samples?  Localize calibration with respect to mass  Calibration set - lowest 1/3 of OC range  Test set - lowest 1/3 of OC range  Compare predictions of same test set:  Uniform OC (full range of OC) calibration  Low OC calibration  Compare to collocated TOR in same mass range 17

Low OC 18 Error at low OC is irreducible and due to TOR analytical, artifact correction errors

Predictions for sites  Calibrations  All filters from one site in test set  All other filters in calibration  Can we determine which sites will be well predicted based on our understanding of what causes error and bias? 19

Low OC sites 20

Well predicted sites 21

Phoenix and Sac and Fox 22 Using low OC to predict high OC produces small bias

Phoenix and Sac and Fox 23 Low Amm/OC to predict high increases error (Sac and Fox) High OM/OC to predict low increases error (Phoenix and Sac and Fox)

Conclusions - OC  Accurate predictions of TOR OC (~10% error)  Same order as TOR precision  Select calibration set by site, OC or OM/OC  All three spectral types  Low OC error is irreducible due to artifact and TOR analytical error (17 – 25% error for 3 sites)  Differences in organic composition increases error (12-14%)  Not training calibration with ammonium (12%)  Best to include range of OC, OM/OC and ammonium/OC in calibration but only small increase in error induced when not included 24

Elemental Carbon 25

EC – base case 26

Spectra Types 27

MDL and precision for FT-IR-EC and TOR-EC 28 *Concentration units of  g/m 3 for MDL and precision are based on the IMPROVE volume of 32.8 m 3. **Value reported for network (0.44  g) in concentration units. FT-IR-EC full spectraTOR-EC FT-IR-EC baseline corrected spectra FT-IR-EC truncated spectra MDL (  g/m 3 )* ** % below MDL precision (  g/m 3 )*

Causes of bias and error - EC 29 Bias for low to predict high, high errors for low EC

OC/EC 30 Different OC/EC in calibration and test increases error

OM/OC 31 Different aerosol composition in calibration and test increases error

Ammonium/EC 32 Different Ammonium/EC in calibration and test increases error

Can we decrease MDL and error in low EC samples?  Localize calibration with respect to mass (using lowest 1/3 of samples, EC < 2.4  g)  Calibration set, EC < 2.4  g  Test set, EC < 2.4  g  Compare to predictions on same test set  Uniform EC, full range of EC samples  Low Uniform EC, EC < 2.4  g  Compare to collocated TOR, EC < 2.4  g 33

Low EC samples 34

Hybrid calibration 35

MDL and precision for Hybrid FT-IR-EC model 36 *Concentration units of  g/m 3 for MDL and precision are based on the IMPROVE volume of 32.8 m 3. **Value reported for network (0.44  g) in concentration units. Hybrid FT-IR-EC full spectraTOR-EC Hybrid FT-IR-EC baseline corrected spectra Hybrid FT-IR-EC truncated spectra MDL (  g/m 3 )* ** % below MDL 1323 precision (  g/m 3 )*

Site Predictions 37

Site predictions 38 PMRF predicted well 3 sites increased error

Phoenix OM/OC and OC/EC 39 Different composition, higher error in Phoenix

Ammonium/EC 40 Different Amm/EC produces higher error Phoenix – all 3 causes Sac & Fox and St. Marks – EC and Amm/EC

EC Summary  Prediction errors same order as TOR EC precision  MDL on same order as TOR EC  Sensitive to selection of calibration standards  Bias - low to predict high,  Low EC calibration reduces  error in low EC  MDL  Calibration with different distributions of EC, OM/OC, ammonium/EC increases error  Best to use hybrid model with distributions of EC, OM/OC and ammonium/EC, similar to test set 41

Next Steps  Develop calibration models to predict OC and EC for samples collected in different years and sites  Analyze samples from 2013 (complete)  Evaluate and revise calibration  Scale up to develop a calibration model for the whole network  Analyze one year of IMPROVE samples  Begin analysis early

Total Carbon 43 We acknowledge funding for this project from the IMPROVE program and EPA (National Park Service Cooperative Agreement P11AC91045)

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