Atmospheric Photochemistry Studies of 

Pollutant Emissions from Transportation 

Vehicles Operating on Alternative Fuels 

H. Jeffries, K. Sexton, and J. Yu 

University of North Carolina 

Alternative Fuels Hotline: 1-800-423-1 DOE 

Alternative Fuels Data Center World Wide Web Site: http://www.afdc.doe.gov 

National Renewable Energy Laboratory 

161 7 Cole Boulevard 

Golden, Colorado 80401 -3393 

A national laboratory of the 

U.S. Department of Energy 

Managed by the Midwest Research Institute 

For the U.S. Department of Energy 

Under Contract No. DE-AC36-83CH10093 
Atmospheric Photochemistry Studies of 

Pollutant Emissions from Transportation 

Vehicles Operating on Alternative Fuels 

H. Jeffries, K. Sexton, and J. Yu 

University of North Carolina 

NREL Technical Monitors: Brent Bailey, Michelle Bergin, 

and Paul Bergeron 

Alternative Fuels Hotline: 1-800-423-1 DOE 

Alternative Fuels Data Center World Wide Web Site: http://www.afdc.doe.gov 

National Renewable Energy Laboratory 

161 7 Cole Boulevard 

Golden, Colorado 80401 -3393 

A national laboratory of the 

U.S. Department of Energy 

Managed by the Midwest Research Institute 

For the U.S. Department of Energy 

Under Contract No. DE-AC36-83CH10093 

Prepared under Subcontract Number XAT-3-11153-01 

July 1998 
NOTICE 

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Neither the United States government nor any agency thereof, nor any of their employees, makes any 

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and opinions of authors expressed herein do not necessarily state or reflect those of the United States 

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9-3 Printed on paper containing at least 50% wastepaper, including 20% postconsurner waste 
Contents 

1 Executive Summary 1 

1.1 Purpose .................................. 1 

1.2 Findings .................................. 2 

1.3 Background ................................ 4 

1.3.1 Summary of Background ..................... 7 

1.4 Experimental Design ............................ 8 

1.4.1 VOC.Mixtures ........................... 8 

1.4.2 Experimental Design ........................ 8 

1.5 Experimental Results ............................ 9 

1.5.1 Block 6: Chamber Characterization Tests ............. 9 

1 .5.2 Block 1 : Base CNG Tests ..................... 11 

1.5.3 Block 5: Base LPG Tests ..................... 11 

1 S.4 Block 2: Base E85 Tests ...................... 12 

1.5.5 Block 3: Single Mixture E85 Tests ................ 12 

1 S.6 Block 4: E85 Mixture Variations Tests ............... 12 

1.6 Simulation Results ............................. 16 

1.6.1 Temperature Effects ........................ 16 

1.6.2 Block 6: Chamber Characterization Tests ............. 18 

1.6.3 Alternative Fuel Simulations .................... 18 

1.7 New Analytical Methods .......................... 23 

1.8 Aromatics Mechanism ........................... 24 

1.9 UAM Simulations and Analysis ...................... 27 

1.9.1 Emissions Inventory Improvements ................ 27 

1.9.2 Process Analysis .......................... 29 

1.9.3 UAM Simulation Results ..................... 30 

2 Experimental Design 31 

2.1 VOC Mixture Composition ........................ 31 

2.2 Chamber Experimental Conditions ..................... 38 
CONTENTS CONTENTS 

3 Experimental Results 40 

3.1 Outdoor Smog Chamber Results ...................... 40 

3.1.1 Block 1: Urb/IAG versus UrbICNG ................ 42 

3.1.2 Block5: Urb/IAGversusUrb/LPG ................ 51 

3.1.3 Block 2 - Urb/IAG versus Urb/E85 ................ 56 

3.1.4 Block 3: Single Mixture Experiments ............... 76 

3.1.5 Block 4: Mixture Variations .................... 83 

3.1.6 Block 6: Characterization ..................... 95 

3.2 Relevant CRC Methanol Study Results .................. 106 

4 Modeling Results 115 

4.1 The Simulation Process .......................... 115 

4.2 TemperatureEffects ............................ 117 

4.2.1 Effect of Temperature on Ozone .................. 117 

4.2.2 Modeled Temperature Effect .................... 127 

4.2.3 Temperature Effect Conclusions .................. 136 

4.3 Characterization Run Simulations ..................... 137 

4.3.1 Block6 .............................. 137 

4.4 Alternative Fuels Runs Simulations .................... 141 

4.4.1 Block1 .............................. 141 

4.4.2 Block5 .............................. 149 

4.4.3 Block2 .............................. 151 

4.4.4 Block3 .............................. 159 

4.4.5 Block4 .............................. 161 

5 New Analytical Methods 169 

5.1 Detection and Identification of Carbonyls ................. 169 

5.1.1 The Derivatization Procedure ................... 169 

5.1.2 Ion Trap Mass Spectra ....................... 170 

6 Aromatic Reaction Mechanisms 175 

6.1 Experimental Results ............................ 175 

6.1.1 Aromatic Aldehydes ........................ 209 

6.1.2 Quinones .............................. 209 

6.1.3 Di-unsaturated I, 6-Dicarbonyls .................. 212 

6.1.4 Unsaturated 1, 4-Dicarbonyls .................... 213 

6.1.5 Unsaturated 1, 4.dicarbonyls .................... 214 

6.1.6 Saturated Dicarbonyls ....................... 223 

. . Photochemical Studies of Alternative Fuels 11 ESENNC-CH 
CONTENTS CONTENTS 

6.1.7 Hydroxy Dicarbonyls ....................... 223 

6.1.8 Triones .............................. 228 

6.1.9 Glycolaldehyde and Hydroxyacetone ............... 229 

6.1.10 Unknown Carbonyls ........................ 232 

6.2 Epoxy Carbonyls .............................. 233 

6.2.1 Epoxide Toxicity .......................... 233 

6.2.2 Theoretical Considerations ..................... 234 

6.2.3 Model Epoxides .......................... 235 

6.2.4 Epoxy Carbonyls in Experiments ................. 237 

6.2.5 Conclusions ............................ 241 

7 Airshed Modeling 250 

7.1 Emissions Inventory Improvements .................... 250 

7.1.1 Area Source Emissions ...................... 250 

7.1.2 Mobile Source Emissions ..................... 251 

7.2 Process Analysis .............................. 252 

7.2.1 The Model's Processes ....................... 253 

7.2.2 The Code Changes ......................... 254 

7.2.3 The Process Analyses ....................... 257 

7.2.4 Example Analyses Results ..................... 258 

A Chamber Facilities 277 

A . 1 The UNC Outdoor Chamber ........................ 277 

A.2 The Teflon Bag Reactor Chamber ..................... 278 

B UAM Ozone Sensitivities 279 

... Photochemical Studies of Alternative Fuels 111 ESEIUNC-CH 
List of Figures 

1.1 Schematic of the many steps needed to make inferences about the effect of 

alternative fuels on ozone production in urban areas ............. 

1.2 A legend for the experimental plan ..................... 

1.3 The original DOE experimental plan .................... 

1.4 A chamber characterization test example showing matched chamber performance .................................. 

1.5 Base CNG and LPG test examples ..................... 

1.6 Base E85 test examples ........................... 

1.7 Single mixture E85 test examples ...................... 

1.8 Comparison of SynURB/SynlAG experiments from 1991 and 1994 ..... 

1.9 Simulation results with the CB4 mechanism for JL0992 using different year's 

temperatures ................................. 

1.10 Run 3-9: 1 SynUrban/SynIAG.NO, versus 9: 1 SynUrbanISynCNG: NOx; 

Carbon Bond Four Simulation ....................... 

1.1 1 Run 10-6: 1 SynUrbanIS ynIAG: NOx versus 6: 1 SynUrban/SynE85E: NOx . 

1.12 Run 12-6: 1 SynUrbanISynIAG: NOx versus 6: 1 SynUrban/SynE85B : NOx; 

Carbon Bond Four Simulation ....................... 

1.13 The 0-(2,3,4,5,6 )-pentafluorobenzyl oxime of of acetaldehyde ....... 

1.14 ~econstructed m/z 18 1 ion chromatogram of the batch sample collected 

from an outdoor smog chamber toluene/NOx daytime experiment ...... 

1.15 Ion trap methane CI mass of the PFBHA derivative of 2,3-epoxy-2, 4- 

dimethyl-6-0x0-4-heptenal ......................... 

2.1 Summary composition of the CRC VOC mixtures . The NMOC bars are the 

distribution of the non-methanol fraction of the mixtures .......... 

2.2 Summary composition of the compressed natural gas VOC mixtures .... 

2.3 Summary composition of the ethanol VOC mixtures ............ 

2.4 DOE Experimental Plan .......................... 

3.1 Run 2-6: 1 S ynUrban/SynIAG:NOx versus 6: 1 SynUrbanISynCNGNO, . . 

3 -2 Run 3-9: 1 S ynUrban/SynIAG: NOx versus 9: 1 S ynUrbanIS ynCNG: NOx . 
LIST OF FIGURES LIST OF FIGURES 

3.3 Run 5-6: 1 SynUrban/SynIAG:NO, versus 6: 1 SynUrban/SynCNG:NO, . . 48 

3.4 Run 6-9: 1 SynUrban/SynIAG:NO, versus 9: 1 S ynUrban/SynCNG:NO,. . 50 

3.5 Run 7-6: 1 S ynUrban/S ynIAG: NOx versus 6: 1 S ynUrban/S ynLPG: NOx . . 5 3 

3.6 Run 7-6: 1 SynUrbadSynIAG: NO, versus 6: 1 SynUrbanISynLPG: NOx . . 55 

3.7 Run 94.5: 1 SynUrban/SynIAG:NO, versus 4.5: 1 SynUrban/SynE85E:NOx 59 

3.8 Run 94.5: 1 SynUrban/SynIAG:NO, versus 4.5: 1 SynUrban/SynE85E: NOx 6 1 

3.9 Run 10-6: 1 SynUrban/SynIAG:NO, versus 6: 1 SynUrban/SynE85E:NOx. 63 

3.10 Run 10-6: 1 SynUrban/SynIAG:NO, versus 6: 1 SynUrban/SynE85E:NOx. 65 

3.1 1 Run 10-6: 1 SynUrban/SynIAG:NO, versus 6: 1 SynUrban/SynE85E:NOx. 67 

3.12 Run 1 1-9: 1 SynUrban/SynIAG:NO, versus 9: 1 SynUrban/SynE85E:NOx . 69 

3.13 Run 1 1-9: 1 SynUrban/SynIAG:NO, versus 9: 1 SynUrban/SynE85E:NOx . 7 1 

3.14 Run 12-6: 1 SynUrban/SynIAG:NO, versus 6: 1 SynUrban/SynE85B:NOx 73 

3.15 Run 12-6: 1 SynUrban/SynIAG:NO, versus 6: 1 SynUrban/SynEISB: NOx 75 

3.16 Run 14--6: 1 SynUrban:NO, versus 6: 1 SynE85E:NOx . . . . . . . . . . . 78 

3.17 Run 14-6: 1 SynUrban:NO, versus 6: 1 SynE85E:NOx . . . . . . . . . . . 80 

3.18 Run 15-6: 1 SynIAG:NO, versus 6: 1 SynE85E:NOx . . . . . . . . . . . . 82 

3.19 Run 17-6: 1 SynUrban:NOx versus 6: 1 SynUrban/SynE85E:NOX . . . . . 86 

3.20 Run 18-6: 1 S ynUrban: NOx versus 6: 1 SynUrban/SynE85E(no ETOH): NOx 88 

3.2 1 Run 19-6: 1 S ynUrban/SynIAG:NO, versus 6: 1 SynUrbanlSynE85E (xt 

EtOH):NOx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 

3.22 Run 20-6: 1 SynUrban/S ynIAG: NOx versus 6: 1 S ynUrbadSynE85E (no 

aldehyde) :NO,. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 

3.23 Run 2 1-6: 1 S ynUrban/S ynIAG: NOx versus SynUrbanlSynE85E (equal 

mass):NOx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 

3.24 Run C2-100 pprn CO:NOx versus 250 pprn CO:NOx . . . . . . . . . . . . 97 

3.25 Run C2-100 pprn CO:NOx versus 250 pprn CO:NOx . . . . . . . . . . . . 99 

3.26 Run C3-250 pprn CO:NOx versus 250 pprn CO:NOx . . . . . . . . . . . . 101 

3.27 Run C4-6: 1 propene:NOx versus 6: 1 propene:NOx . . . . . . . . . . . . . 103 

3.28 Run C5-6: 1 SynUrban:NO, versus 6: 1 SynUrban:NO, . . . . . . . . . . . 105 

3.29 Run CRC-2-6: 1 S ynUrban/SynIAG:NO, versus 6: 1 SynUrban/SynM85:NOx 108 

3.30 Run CRC- 1 1-6: 1 SynUrban/SynIAG:NO, versus 6: 1 SynUrban/SynM85/NOxl10 

3.3 1 Run C5-6: 1 SynUrban:NOx versus 6: 1 SynUrban:NO, . . . . . . . . . . . 1 12 

3.32 RunCRC-C1-250ppmCH4:NOxversus500ppmCH4:NOx . . . . . . . . 114 

4.1 The Chamber simulation process. . . . . . . . . . . . . . . . . . . . . . 1 16 

4.2 Comparison of SynURB-only experiments from 1991 and 1994. . . . . . . 1 19 

4.3 Comparison of SynURB/SynlAG experiments from 1991 and 1994. . . . . . 12 1 

4.4 Comparison of SynURBISynlAG experiments from 1992 and 1994. . . . . . 122 

Photochemical Studies of Alternative Fuels v ESEIUNC-CH 
LIST OF FIGURES LIST OF FIGURES 

4.5 Comparison of SynURB/SynlAG experiments from 1992 and 1994 123 ...... 

4.6 Comparison of SynURB/SynlAG experiments from 1992 and 1994 ...... 125 

4.7 Comparison of SynURB/Syn IAG experiments from 1992 and 1994 ...... 126 

4.8 Solar radiation simulation for JL3092 .................... 127 

4.9 Carbon Bond Four simulation of methaneIN0, experiment ......... 128 

4.10 Carbon Bond Four simulation of full urban and alkane/alkene-only experiment 130 

4.1 1 Comparison of in chamber temperature in 1992 and 1994 .......... 131 

4.12 Solar radiation simulation for JL0992 .................... 131 

4.13 Carbon Bond Four simulation of JL0992 at different temperatures ..... 132 

4.14 Solar radiation simulation for JL1092 .................... 133 

4.15 Solar radiation simulation for JL1592 .................... 133 

4.16 Carbon Bond Four simulation of JL 1092 at different temperatures . 134 

4.17 Carbon Bond Four simulation of JL 1592 at different temperatures ... 135 

4.18 Run C2-100 ppm CO:NOx verses 250 ppm CO:NOx; Carbon Bond Four 

simulation ................................. 138 

4.19 Run C2-Solar radiation simulation .................... 139 

4.20 Run CS-Solar radiation simulation .................... 139 

4.2 1 Run C5-6: 1 SynUrban: NOx verses 6: 1 SynUrban: NOx; Carbon Bond Four 

Simulation ................................. 140 

4.22 Run 2-6: 1 S ynUrban/SynIAG: NO, verses 6: 1 S ynUrbadS ynCNGN0, . 

Carbon Bond Four simulation ....................... 143 

4.23 Run 2Solar radiation simulation ..................... 144 

4.24 Run 3Solar radiation simulation ..................... 144 

4.25 Run 3-9: 1 S ynUrban1SynIAG: NOx verses 9: 1 S ynUrban1S ynCNG: NOx; 

Carbon Bond Four simulation ....................... 145 

4.26 Run 5-6: 1 S ynUrban/S ynIAG: NOx verses 6: 1 SynUrban/S ynCNG: NOx; Carbon Bond Four simulation ......................... 146 

4.27 Run 5Solar radiation simulation ..................... 147 

4.28 Run &Solar radiation simulation ..................... 147 

4.29 Run 6-9: 1 S ynUrban/S ynIAG: NOx verses 9: 1 SynUrbanIS ynCNG: NO, . . 148 

4.30 Run 7Solar radiation simulation ..................... 149 

4.3 1 Run 7-9: 1 SynUrban/SynIAG.NO, verses 9: 1 SynUrbanISynLPG: NOx; 

Carbon Bond Four simulation ....................... 150 

4.32 Run 10-6: 1 SynUrbadSynIAG: NOx verses 6: 1 SynUrban/SynEevE.NOx . 153 

4.33 Run 10Solar radiation simulation .................... 154 

4.34 Run 10-Solar radiation simulation ..................... 154 

4.35 Run 10-6: 1 SynUrbadSynIAG: NOx verses 6: 1 SynUrbadSynEevE.NO, . 155 

Photochemical Studies of Alternative Fuels vi ESEIUNC-CH 
LIST OF FIGURES LIST OF FIGURES 

4.36 Run 1 1-9: 1 S ynUrban1S ynIAG: NOx verses 9: 1 SynUrbanlSynE85E: NOx; 

Carbon Bond Four simulation ....................... 

4.37 Run 1 1Solar radiation simulation .................... 

4.38 Run 12Solar radiation simulation .................... 

4.39 Run 12-6: 1 S ynUrban1SynIAG: NOx verses 6: 1 SynUrbanlSynE85B: NOx; 

Carbon Bond Four simulation ....................... 

4.40 Run 14Solar radiation simulation .................... 

4.41 Run 14-- 6. 1 SynUrban:NOx verses 6: 1 SynE85E:NOx; Carbon Bond Four 

simulation ................................. 

4.42 Run 17-6: 1 SynUrban: NOx verses 6: 1 SynUrbanlSynE85E: NOx; Carbon 

Bond Four simulation ........................... 

4.43 Run 17Solar radiation simulation .................... 

4.44 Run 18Solar radiation simulation .................... 

4.45 Run 18-6: 1 SynUrban:NOx verses 6: 1 SynUrban/SynE85E(no ETOH):NOx; 

Carbon Bond Four simulation ....................... 

4.46 Run 19-6: 1 SynUrbanISynIAG: NOx verses 6: 1 SynUrban/SynESSE (xt 

EtOH) : NOx; Carbon Bond Four simulation ................ 

4.47 Run 19Solar radiation simulation .................... 

4.48 Run 20Solar radiation simulation .................... 

4.49 Run 20-6: 1 SynUrbanI SynIAG: NOx verses 6: 1 SynUrbanI SynE85E (no 

aldehyde) :NOx; Carbon Bond Four simulation ............... 

5.1 The reaction of 0.-(2.3.4.5.6 ).pentafluorobenzyl) hydroxylarnine with a carbony1 to produce two isomers for non-symmetrical carbonyls ........ 

5.2 A schematic of the Saturn I1 capillary column gas chromatograph with ion 

trap mass spectrometer detector ....................... 

5.3 Cross section of the Saturn I1 ion trap mass spectrometer detector ...... 

6.1 Run Arom- 1 Toluene: NOx verses m-Xylene: NOx .............. 

6.2 Run Arom-2 1.3.5.Trimethy l.benzene.NO, versus p.Xylene.NO, ...... 

6.3 Run Arom-3 1.2.4.Trimethyl.benzene.NOx versus o.Xylene.NOx ...... 

6.4 Reconstructed dz 181 ion chromatogram of the batch sample collected 

from an outdoor smog chamber tolueneIN0, daytime experiment ...... 

6.5 Detailed Reconstructed m/z 181 ion chromatogram of the batch sample 

collected from an outdoor smog chamber toluene/NOx daytime experiment . 

6.6 Reconstructed rnlz 181 ion chromatogram of the batch sample collected 

from an outdoor smog chamber p-xylene/NOx daytime experiment ..... 

6.7 Reconstructed dz 181 ion chromatogram of the batch sample collected 

from an outdoor smog chamber m-xylene/NOx daytime experiment ..... 

Photochemical Studies of Alternative Fuels vii ESEIUNC-CH 
LIST OF FIGURES LIST OF FIGURES 

6.8 Detailed Reconstructed m/z 181 ion chromatogram of the batch sample 

collected from an outdoor smog chamber m-xyleneIN0, daytime experiment. 19 1 

6.9 Reconstructed mlz 181 ion chromatogram of the batch sample collected 

from an outdoor smog chamber o-xyleneIN0, daytime experiment. . . . . 

6.10 Reconstructed m/z 18 1 ion chromatogram of 1,3,5-trimethylbenzene/NO, 

in an outdoor smog chamber daytime experiment. . . . . . . . . . . . , . 

6.1 1 Detailed Reconstructed m/z 18 1 ion chromatogram of the batch sample collected from an outdoor smog chamber 1,3,5-trimethylbenzenelN0, daytime 

experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

6.12 Reconstructed rn/z 18 1 ion chromatogram of the batch sample collected 

from an outdoor smog chamber 1,2,4-trimethylbenzene/NO, daytime experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . 

6.13 The MS of normal acetone and 13C3-acetone. . . . . . . . . . . . . . . . 

6.14 Run Arom- 1 Toluene:NO, versus Xylene:NO,; VOC series. . . . . . . . . . 

6.15 Run Arom-1 Toluene:NO, versus Xylene:NO,; VOC profiles. . . . . . . . . 

6.16 Ion trap methane CI spectra of the PFBHA derivatives of butendial (bottom), 

4-0x0-2-pentenal (middle), and 3-hexene-2,5 -dione (top). . . . . . . . . . 

6.17 Run Arom-1 Toluene:NO, versus Xylene:NO,; VOC series. . . . . . . . . . 

6.18 Run Arom-2 1 ,3,5-Trimethyl-benzene:Ox versus Xylene:NO,; VOC series. . 

6.19 Run Arom- 1 Toluene:NOx versus Xylene:NO,; VOC series. . . . , . . . . . 

6.20 Run Arorn-3 1,2,4-Trimethyl-benzene:O, versus o-Xylene:NO,; VOC series. 

6.2 1 Run Arom-3 1 ,2,4-Trimethyl-benzene:O, versus o-Xylene:NO,; VOC series. 

6.22 Run Arom-2 1 ,3,5-Trimethyl-benzene;O, versus Xylene:NO,; VOC series. . 

6.23 Ion trap CI mass of the PFBHA derivatives of two C5 hydroxy dicarbonyls.Top: 

peak at 27.82 rnin, Bottom: peak at 28.78 min. . . . . . . . . . . . . . . 

6.24 Time series of C5 hydroxy dicarbonyl peaks in the 1,3,5 TMB and 1,2,4 

TMB experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

6.25 Time series of a C4 hydroxy dicarbonyl, C5 triones (or C5 epoxy carbonyls), 

and a C4 trione (or a C4 epoxy dicarbonyl) peaks in the 1,3,5 TMB and 1,2,4 

TMB experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

6.26 Run Arom-2 1,3,5-Trimethyl-benzene:O, versus p-Xylene: NOx; VOC series. 

6.27 Ion trap methane CI mass of the PFBHA derivative of glycidaldehyde. . . . 6.28 Ion trap methane CI mass of the PFBHA derivative of Epoxide A. . . . . . 

6.29 Run Arom-2 1,3,5-Trimethyl-benzene:O, versus Xylene: NOx; VOC series. . 

6.30 The formation of Epoxide A and its subsequent reactions. . . . . . . . . . 

7.1 Air quality models must be modified to include new code to output the 

process rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 

7.2 Schematic of a 'complete' process analysis for a model scenario run. . . . 255 

. . . Photochemical Studies of Alternative Fuels vlll ESEAJNC-CH 
LIST OF FIGURES LIST OF FIGURES 

7.3 One frame from Day Two of a UAM movie of ozone mixing ratios for a 

1988 Charlotte, North Carolina episode. The darkness is not proportional 

to O3 in this figure and deep blue (for no 03) and bright red (for O3 equal to 

or exceeding 120 ppb) both show as dark colors. .............. 

7.4 Process time series for Charlotte 1987 episode. .............. 

7.5 Model Predicted and Observed VOC composition in 6 by 6 5-km cells near 

downtown Charlotte North Carolina in 1987 UAM scenario. ........ 

7.6 UAM predicted and observed O3 mixing ratio and the change caused by 

each process in the UAM. ......................... 

7.7 Process composition times series for O3 in Episode 287FC Charlotte, North 

Carolina UAM simulation ......................... 

7.8 Process composition spatial contribution to a single cell's O3 in Episode 

287FC Charlotte, NC UAM simulation. .................. 

7.9 The 'OH and NOx Cycles from Integrated Reaction Rates for 6x6 Cell Area 

in UAM Scenario 287FC for Charlotte, NC. ................ 

7.10 The NO Cycle and Mass Balance from Integrated Reaction Rates for 6x6 

Cell Area in UAM Scenario 287FC for Charlotte, NC. ........... 

7.11 The distribution of reacted VOC and ozone produced in an Urban Airshed 

Model simulation of Charlotte, NC in 1987. ................ 

- 

Photochemical Studies of Alternative Fuels ix ESE/UNC-CH 
List of Tables 

2.1 The VOC Mixtures used in the CRC Methanol-85 Chamber Study-Part 1 . 33 

2.2 The VOC Mixtures used in the CRC Methanol-85 Chamber Study-Part 2 . 34 

3.1 Initial Conditions for ST1093 ....................... 43 

3.2 Initial Conditions for ST2393 ....................... 45 

3.3 Initial Conditions for JL2594 ......................... 47 

3.4 Initial Conditions for AU2694 ....................... 49 

3.5 Initial Conditions for ST2494 ....................... 52 

3.6 Initial Conditions for ST2794 ....................... 54 

3.7 Initial Conditions for AU 1294 ....................... 58 

3.8 Initial Conditions for AU2394 ....................... 60 

3.9 Initial Conditions for AUO494 ....................... 62 

3.10 Initial Conditions for AU0794 ....................... 64 

3.11 Initial Conditions for AU1194 ....................... 66 

3.12 Initial Conditions for AU2494 ....................... 68 

3.13 Initial Conditions for AU2594 ....................... 70 

3.14 Initial Conditions for AU0394 ....................... 72 

3.15 Initial Conditions for AUO894 ....................... 74 

3.16 Initial Conditions for ST0994 ....................... 77 

3.17 Initial Conditions for ST1 194 ....................... 79 

3.18 Initial Conditions for ST0894 ....................... 81 

3.19 Initial Conditions for ST1494 ....................... 85 

3.20 Initial Conditions for ST 1594 ....................... 87 

3.2 1 Initial Conditions for ST2894 ....................... 89 

3.22 Initial Conditions for AU0994 ....................... 91 

3.23 Initial Conditions for OC0994 ....................... 93 

3.24 Initial Conditions for AU3093 ....................... 96 

3.25 Initial Conditions for AU3 194 ....................... 98 

3.26 Initial Conditions for AU2994 ....................... 100 
LIST OF TABLES LIST OF TABLES 

3.27 Initial Conditions for JL0194 ........................ 102 

3.28 Initial Conditions for ST 1294 ....................... 104 

3.29 Initial Conditions for JL 1092 ........................ 107 

3.30 Initial Conditions for JL1592 ........................ 109 

3.3 1 Initial Conditions for JL0992 ........................ 111 

3.32 Initial Conditions for JL3092 ........................ 113 

6.1 Initial Conditions for AU3095 ....................... 176 

6.2 Initial Conditions for AU0395 ....................... 178 

6.3 Initial Conditions for ST0195 ....................... 180 

6.4 Carbonyl Products Detected in a Sample Collected from a Toluene/NOx 

Daytime Smog Chamber Experiment ................... 185 

6.5 (Continued) Carbonyl Products Detected in a Sample Collected from a 

Toluene/NOx Daytime Smog Chamber Experiment ............. 186 

6.6 Carbonyl Products Detected in a Sample Collected from a p-XyleneINO, 

Daytime Smog Chamber Experiment ................... 188 

6.7 (Continued) Carbonyl Products Detected in a Sample Collected from a pXyleneINO, Daytime Smog Chamber Experiment ............. 189 

6.8 Carbonyl Products Detected in a Sample Collected from a m.xyleneIN0, 

Daytime Smog Chamber Experiment ................... 192 

6.9 (Continued) Carbonyl Products Detected in a Sample Collected from a mxyleneIN0, Daytime Smog Chamber Experiment ............. 193 

6.10 (Continued) Carbonyl Products Detected in a Sample Collected from a mxylenelNOx Daytime Smog Chamber Experiment ............. 194 

6.11 (Continued) Carbonyl Products Detected in a Sample Collected from a mxyleneIN0, Daytime Smog Chamber Experiment ............. 195 

6.12 Carbonyl Products Detected in a Sample Collected from a o.xylene/NOx 

Daytime Smog Chamber Experiment ................... 197 

6.13 (Continued) Carbonyl Products Detected in a Sample Collected from a oxylene/NOx Daytime Smog Chamber Experiment ............. 198 

6.14 (Continued) Carbonyl Products Detected in a Sample Collected from a oxyleneIN0, Daytime Smog Chamber Experiment ............. 199 

6.15 Carbonyl Products Detected in a Sample Collected from a 1.3.5.trimethylbenzene/NOX 

Daytime Smog Chamber Experiment ................... 202 

6.16 (Continued) Carbonyl Products Detected in a Sample Collected from a 

1.3.5.trimethylbenzenelN0, Daytime Smog Chamber Experiment ..... 203 

6.17 Carbonyl Products Detected in a Sample Collected from a 1.2.4.trimethylbenzene/NOX 

Daytime Smog Chamber Experiment ................... 205 

6.18 (Continued) Carbonyl Products Detected in a Sample Collected from a 

1.2.4.trimethylbenzene/NOx Daytime Smog Chamber Experiment ..... 206 

Photochemical Studies of Alternative Fuels xi ESEIUNC-CH 
LIST OF TABLES LIST OF TABLES 

6.19 List of Unknown Carbonyl Products .................... 232 

6.20 Structure I1 epoxy carbonyls formed from six alkyl benzenes ........ 243 

6.21 Structure III epoxy carbonyls fon-ned from six alkyl benzenes ....... 244 

6.22 Structure IV epoxy carbonyls formed from six alkyl benzenes ....... 245 

6.23 Structure V epoxy carbonyls formed from six alkyl benzenes ........ 246 

6.24 Structure VI epoxy carbonyls formed from six alkyl benzenes ....... 247 

6.25 Structure VIII epoxy carbonyls formed from six alkyl benzenes ....... 248 

6.26 Structure X epoxy carbonyls formed from six alkyl benzenes ........ 249 

7.1 The First Part of the Integrated Process Rate Output ............ 256 

7.2 The Second Part of the Integrated Process Rate Output .......... 256 

7.3 NOy Mass Balance fromIntegrated Reaction Rates for UAM Scenario 287FC 

for Charlotte ................................ 271 

Photochemical Studies of Alternative Fuels xii ESEAJNC-CH 
Chapter 1 

Executive Summary 

1.1 Purpose 

The goal of this project was to improve our ability to predict the changes in urban ozone 

resulting from the widespread use of alternative fuels in automobiles. 

The purposes of the work reported here were: 

To conduct side-by-side outdoor chamber experiments using highly realistic, yet 

carefully formulated, synthetic mixtures of volatile organic compounds (VOCs) that 

represent the best estimate of the total emissions (tail pipe, evaporative, and running 

losses) from vehicles operated on alternative fuels. This work especially focused 

on compressed natural gas and ethanol blended with gasoline fuels. These experiments were designed to demonstrate directly the relative effectiveness of alternative 

fuels under realistic conditions and served to test photochemical reaction models in 

a stressful manner. 

To use mathematical models to simulate these experiments using the most commonly 

photochemical reaction mechanism now used in Eulerian airshed modeling and that 

are used in the U.S. Environmental Protection Agency's (EPA) regulatory models 

as a means of demonstrating the adequacy of these mechanisms to make accurate 

predictions of the large scale use of alternative fuels in an urban environment. 

To investigate the detailed reaction mechanisms for alkyl-substituted benzenes, which 

make up approximately 30% of urban atmospheric non-methane carbon. Despite 

significant effort in the last 15 years, atmospheric chemists have been unable to 

describe such reactions because of a lack of analytical ability to detect the form of 

the intermediate products subsequent to initial attack by hydroxyl radical, the major 

loss process for these type of compounds. 

To perform urban air shed model simulations and to investigate the role that alternative 

fuels may have on the ozone produced in these simulations. 
1.2 FINDINGS CHAPTER 1. EXECUTIVE SUMMARY 

1.2 Findings 

A summary of our findings and conclusions follows. 

Compressed natural gas (CNG) used as an alternative fuel produced signijicantly less 

ozone than did industry averaged gasoline (IAG), even at hydrocarbon to nitrogen oxides ratios (HC:NOx) of 9: 1. This difference was reproducible by the Carbon Bond Four 

photochemical reaction mechanism (CB4) used in a chamber mathematical simulation 

model, but there are caveats (see below) regarding the mechanism's formulation. 

When liquefied propane gas (LPG) was used as an alternative fuel, it also produced 

signijicantly less ozone than did I AG at a HC: NOx of 6: 1. This difference was also 

reproducible by the CB4 mechanism in a chamber mathematical simulation model, 

but there are caveats. 

When ethanol blended with 15% gasoline (E85) was used as an alternative fuel, it produced signifcantly more ozone than did IAG at HC: NOx of 4.5 : 1 and 6: 1, and produced 

the same ozone at 9: 1. This difference was reproducible by the CB4 mechanism in a 

mathematical chamber simulation model, but there are caveats. 

The principal reason that blended ethanol mixtures produced more ozone than did 

IAG was demonstrated in experiments in which the ethanol mixture composition was 

varied. These showed that the significant fraction of acetaldehyde present in the 

exhaust was the cause. The reactions of acetaldehyde produce peroxyacyl nitrate 

(PAN) that, at the high concentrations of acetaldehyde in the ethanol blends, stores a 

significant amount of NOx. PAN, however, exhibits a strong temperature dependence 

on its decomposition rate, and as afternoon chamber temperatures increased, the 

PAN releases the NO, back into the system where it can produce ozone. In the IAG 

systems, much less PAN is produced and the NOx is removed to more permanent stable 

products; thus, less is available in the afternoon to produce ozone. These effects were 

reproducible by the CB4 mechanism in a chamber mathematical simulation model, 

but there are caveats. 

Experiments conducted at two different air temperatures, but otherwise nearly identical conditions, show almost no temperature efect on ozone production and at best 

suggest a small negative effect. These experiments do suggest a negative effect on 

afternoon nitrogen-containing compounds that are measurable by the NO,-channel of 

a standard analyzer (this includes nitrogen dioxide, PAN and PAN-like compounds, 

organic nitrates and nitrites, but little response to nitric acid). It appears that the 

strong positive temperature effect exhibited by alkane VOCs such as n-butane are 

nearly perfectly offset by the temperature labile organic nitrogen products produced 

in our much more complex VOC mixtures that contain a variety of aromatic species. 

The CB4 mechanism does not reproduce the observed temperature effect. Instead, 

it is much too reactive at chamber temperatures above 90°F, and at temperatures 

Photochemical Studies of Alternative Fuels 2 ESEAJNC-CH 
CHAPTER 1. EXECUTIVE SUMMARY 1.2 FINDINGS 

above 100°F it tends to over-predict observed ozone by as much as 200%. At peak 

afternoon temperatures near 80°F, the CB4 mechanism produces predictions that agree 

accurately with the chamber observations. 

The use of 0-(2,3,4,5,6-pentafluorobenzy1)-hydroxylamine (PFBHA) to form oximes 

derivatives of polyfunctional carbonyls, which are then separated in a gas chromatograph and detected by ion trap mass spectrometry, is a very specific and sensitive new 

analytical method developed as part of this project. This method is capable of providing the molecular weight and some structural information about unknown carbonyls 

without the need for a standard. 

In indoor chamber and outdoor chamber experiments using single aromatic VOC 

species with NOx, the new PFBHA method identified more than 40 new carbonyl products formed in the photooxidation of these important urban atmospheric compounds. 

These included not only the previously detected aromatic aldheydes and the saturated 

dicarbonyls (e.g., glyoxal and methyl glyoxal), but also hydroxy carbonyls (e.g., 

glycolaldehyde, hydroxy acetone); hydroxy dicarbonyls; a variety of di-unsaturated 

1,6-dicarbonyls; and unsaturated l,4-dicarbonyls, quiones, and possiablly triones. 

The most significant products detected in these aromatic systems were a variety of 

epoxy carbonyls and epoxy dicarbonyls. These species, which may account for as 

much as as one-third of the aromatic oxidation pathway, are possibly mutagens. An 

epoxy carbonyl similar in structure to several of the intermediates we detected, 2,3- 

epoxypropanal has been reported to be a potent mutagen in a variety of in vitro test 

systems and has been shown to induce sarcomas in rats by subcutaneous injection. 

As a bifuctional agent, 2,3-epoxy propanal is capable of forming cyclic adducts 

and crosslinks with DNA bases. Hence, the possible formation of epoxides from 

atmospheric oxidation of aromatic compounds is a significant public health concern, 

deserving additional laboratory and field work. If it becomes necessary to reduce or 

remove aromatic species from gasoline fuels because of their potential health effects, 

the use of alternative fuels such as CNG becomes even more compelling. 

Between this project and another UNC project, we developed a new method for 

improving the computed emissions from mobile sources in urban airshed models. This 

method combines the outputs of travel demand models and mobile source emissions 

models to more correctly temporally and spatially allocate the various components 

of mobile emissions in the gridded domain. These treatments resulted in up to 25% 

increases in the VOC emissions in downtown cells in the model, while reducing the 

concentrations along the Interstate highways. 

New methods for performing analysis of urban airshed model predictions were developed jointly between this project and yet another UNC project. 

More details are given in the following sections. 

Photochemical Studies of Alternative Fuels 3 ESEIUNC-CH 
1.3 BACKGROUND CHAPTER 1. EXECUTIVE SUMMARY 

1.3 Background 

In 1988, Congress passed the Alternative Motor Fuels Act (AMFA) with the intent to reduce: 

ambient air levels of ozone (03) and carbon monoxide (CO); American dependence on 

foreign oil; and gases contributing to global warming. 

The AM FA encourages the development and widespread use of alternative transportation 

fuels by providing for changes in corporate average fuel economy (CAFE) regulations and 

purchases of vehicles for the Federal fleet. 

Although it is clear that alternative fuels can lead to less dependence on foreign oil and 

can perhaps result in reduced CO emissions, doubt remains about the first goal of AMFA. 

That is, methanol can be made from domestic coal and ethanol from corn, and both tail pipe 

and on-road measurements have shown about a 16% reduction in direct CO emissions when 

oxygenated fuels are used, but urban O3 is a secondary pollutant resulting from non-linear 

chemistry of dispersed reactants. This means that the case of large benefits arising from the 

use of alternative fue1s.i~ not so easily made. 

Organic emissions from the exhaust of alternative fuel vehicles contain substantial 

fractions of the unburned fuel, as well as the aldehyde derivatives. For example, exhaust 

from vehicles running on methanollgasoline blends contains large quantities of methanol and 

acetaldheyde(CCHO), while exhust from vehicle running on ethanol fuels contains ethanol 

and formaldehyde (HCHO). Although methanol or ethanol, by themselves, are generally 

thought of as less photochemically reactive than most of the hydrocarbons emitted from 

vehicles runing on gasoline, HCHO and CCHO are much more reactive. Furthermore, in M85 

or E85 fuel for which gasoline comprises 15% of the fuel, gasoline-related emissions make 

up almost 40% of the exhaust carbon. In newer gasoline vehicles, exhaust is becoming 

a smaller fraction of the total vehicle emissions; other important sources are evaporative 

losses and running losses. The compositions of these emissions are dominated by the "light" 

species in the fuels, such as n-butane and isopentane. To estimate the benefits of alternative 

fuels to improve air quality, we first must understand how the fuel composition affects the 

vehicle exhaust, evaporative, and running loss compositions. Then we must make inferences 

about the effect of changes in these VOC compositions on subsequent O3 formation in an 

already complex urban chemical environment. 

Figure 1.1 shows the many steps necessary to support the assertion that a particular 

alternative fuel might lead to the reduction of ozone in an urban area. We performed research 

in essentially all areas shown in this figure. 

The mechanism whereby alternative fuels would have an impact on urban O3 is the 

supposedly lower "reactivity" of the alternative fuel exhausts and fuel losses. "Reactivity" 

refers to the extent to which a compound or a mixture of compounds contributes to atmospheric oxidation of VOC, oxidation of NO to NOz, and subsequent O3 production in the 

ambient atmosphere. Unfortunately, reactivity is not an immutable characteristic of a VOC 

molecule, but rather it arises as a synergistic interaction among all reacting species. The 

reactivity of a VOC is as much a property of the chemical and physical environment in which 

it is reacting as it is of the VOC molecule itsev It has been shown both experimentally and 

. 

Photochemical Studies of Alternative Fuels 4 ESEIUNC-CH 

1.3 BACKGROUND CHAPTER 1. EXECUTIVE SUMMARY 

theoretically that if the conditions in which the reactions are occurring changes, then so do 

the reactivities of the compounds. Some compounds (e.g., toluene and longer paraffins) 

even change the direction of their reactivity effect, making a positive contribution under 

one set of conditions and a negative contribution under other conditions. Thus, one cannot 

talk about the reactivity of one compound relative to another without first making clear the 

specific environment in which the reactivity is occurring. 

Thus, the most important information needed to understand how a compound will contribute to mixture reactivity is to know exactly which products are produced after hydroxyl 

radical (.OH) attack and to know the reactions subsequent to reaction of peroxy-radicals 

(RO,) with NO. Current knowledge of the products formed after NO oxidation is quite 

incomplete, especially for aromatics, and the field is ripe for further investigation. 

In urban atmospheric environments, aromatic hydrocarbons (e.g., toluene, the xylenes, 

and the trimethyl-benzenes) make up as much as 30% of the carbon [I]. It is understood 

that these species react with hydroxyl radicals, but the identity of many of the products 

formed and their subsequent reactions are unknown. These species have been the subject 

of many laboratory investigations over the last 20 years, but the products and mechanisms 

have yet to be completely explained [2,3]. The major reasons for this failure has been the 

lack of appropriate analytical methods to detect and identify the intermediates and the large 

number of potential reaction pathways. 

Although it is necessary to have this detailed mechanistic understanding of how a 

particular VOC can react, it is the surrounding environment that determines the conditions 

under which it will react. Thus, results from smog chambers, which are useful for the 

former, do not tell us about the latter. For this, an urban airshed model is needed that can 

simulate the combined effects of emissions, meteorology, and chemistry. Such a model 

requires significant inputs including 

Spatial and temporally resolved emissions inventories of mobile, area, and point 

sources 

Spatial and temporally resolved wind fields, radiation fields, and temperature fields 

Temporally resolved boundary conditions at the sides and top of the model domain 

A relatively complete photochemical reaction mechanism. 

Photochemical Studies of Alternative Fuels 6 ESEIUNC-CH 
CHAPTER 1. EXECUTIVE SUMMARY I .3 BACKGROUND 

1.3.1 Summary of Background 

One of the reasons for switching to alternative fuels is to reduce urban 03. 

Urban o3 is a secondary pollutant resulting from non-linear chemistry of many dispersed reactants, and the case of benefits arising from the use of alternative fuels is 

not easily made. 

The industry-sponsored Auto/Oil vehicle emissions research program and programs 

sponsored by the U.S. Department of Energy (DOE) have made good progress in 

establishing the exhaust, evaporative, and running loss emissions compositions arising 

from the use of industry standard gasoline, various reformulated gasolines, methanol, 

and other alternative fuels in a fleet of new and older cars and light-duty trucks. 

Reactivity is the mechanism where by switching to alternative fuels is supposed to 

lead to changes in urban O3 formation. 

The reactivity of a given VOC is a not an immutable characteristic of a VOC molecule, 

but rather arises as a synergistic interaction among all reacting species, and can be 

positive in one set of circumstances and negative in others. In short, a VOC's reactivity 

is strongly dependent on the environment in which it is reacting. 

Most of the ozone in urban air is formed by the least reactive compounds in the VOC 

mix. 

The most reactive compounds in urban VOC mixtures are prolific sources of "new 

radicals.'' These new radicals arise for the most part via photolysis of the carbonyl 

products produced in the rapid oxidation of some of the VOCs. 

Current knowledge of these potentially photolyzable products is quite incomplete, 

especially for aromatics and biogenic-type VOCs, and the field is ripe for further 

investigation. 

Current air quality reaction mechanisms are only well-formulated for a few small 

VOCs (e.g., methane, formaldelhyde, ethene, acetaldehyde, and PAN); a few other VOC 

mechanisms are based on a limited set of chamber data. The vast majority of VOC 

species observed in urban air have never been tested in chambers or reactor systems, 

and the reactions mechanisms for these compounds are the result of speculation. 

The urban airshed model has limitations arising from its highly generalized chemical 

reaction mechanism, and from the quality of the input data used to drive simulations. 

It is clear from these points that no single approach is adequate for determining the 

potential atmospheric benefits of alternative fuel use-both experimental and modeling 

studies are needed. Further, fundamental advances are needed to improve scientific understanding of reactivity. Practical studies are needed to bound modeling studies and to 

Photochemical Studies of Alternative Fuels 7 ESENNC-CH 
1.4 EXPERIMENTAL DESIGN CHAPTER 1. EXECUTIVE SUMMARY 

provide conclusive observational knowledge about reactivity effects likely to arise from 

fuel switching. 

1.4 Experimental Design 

14.1 VOC Mixtures 

This study design was similar to that of our Coordinating Research Council (CRC) Methanol 

Fuels Reactivity Project [4] and relied on two of the mixtures produced in that study. These 

were: 

SynURB, a 54-component synthetic urban VOC mixture, and 

SynlAG, a 49-component synthetic mixture based on the weighted tail pipe, evaporative, and running loss emissions from a fleet of vehicles running on IAG. 

These mixtures are the most complex yet high-precision composition mixtures ever used 

in smog chamber studies. About 65% of the carbon was explicitly represented in these 

mixtures (i.e., the exact compound is included in the mixture) and the rest of the carbon was 

represented by a similar species with similar structure. Some attention was paid to ease of 

analysis and to producing a precise composition from experiment to experiment. 

For the current study we used new data supplied by EPA and by NIPER for the composition of emissions of CNG vehicles and for E85 vehicles to produce new synthetic mixtures. 

There were two sets of data for E85 vehicles with different exhaust composition and so we 

blended two slightly different E85 mixtures. These three mixtures were called SynCNG, 

SynE85E, and SynE85B. 

The compositional details of all these mixtures is given in Chapter 2. 

1.4.2 Experimental Design 

Figure 1.2 shows a legend and Figure 1.3 shows the originally planned experimental design. 

This too was similar to the plan used in the CRC project. 

The two sides of the chamber were used to compare the effects of changing the VOC 

composition directly. Each comparison would take place at three HC:NOx ratios: 4.5: 1, 6: 1, 

and 9: 1. Most urban centers are near 6: 1 and for most mixtures reactivity effects disappear 

(because the ozone formation is NOx-limited) under 9: 1 conditions. 

Because reactivity is as much a function of the chemical environment in which a VOC 

reacts as it is a function of the compound itself [I], to evaluate the relative reactivity of each 

alternative fuel we compared them in a background of SynURB, with 50% of the carbon 

being due to the alternative fuel. On the other side of the dual chamber we always used 50% 

SynURBwith 50% SynlAG. That is, we compared 5050 mixtures of SynURB:SynlAGwith 

Photochemical Studies of Alternative Fuels 8 ESEIUNC-CH 
CHAPTER 1. EXECUTIVE SUMMARY 1.5 EXPERIMENTAL RESULTS 

one chamber half 

SynU rb/Syn IAG 

type of HC mixture run number 

in each half 

other chamber half 

Figure 1 -2: A legend for the experimental plan. 

5050 mixtures of SynURB:SynCNG, or with 50:50 mixtures of SynURB:SynE85E, or with 

5050 mixtures of SynU RB:SynE85B. 

The experiments were further organized into "Blocks" with a different objective for 

each block. As designed, the block objectives were: 

1. The base CNG program which included a repeat of each of the three HC:NO, conditions 

2. The base E85 program which included all three HC:NOx ratios 

3. The single mixture comparison block at 6: 1 HC:NOx ratios 

4. The special E-85 program block to demonstrate potential effects of various changes 

in the emissions 

5. The liquefied propane gas (LPG) program block. 

6. The chamber characterization run program block. 

1.5 Experimental Results 

The experimental results are described in a logical analysis order (6, 1, 5, 2, 3, 4) rather 

than the numerical order described above. 

1.5.1 Block 6: Chamber Characterization Tests 

As part of this project, we conducted five chamber characterization tests; other chamber 

characterization tests not shown here were conducted under other funding and are a part of 

the UNC Standard Chamber Database. 

Photochemical Studies of Alternative Fuels 9 ESEIUNC-CH 

CHAPTER 1. EXECUTIVE SUMMARY 1.5 EXPERIMENTAL RESULTS 

5 6 7 8 9 10 It 12 13 14 15 16 17 18 19 

HOURS, EDT 

0.50 

0.40 

E 

Q 

Q 

Figure 1.4: A chamber characterization test example showing matched chamber perforrnance. 

To demonstrate that the two sides of the dual chamber were well matched during the 

alternative fuel tests, we show one of the chamber characterization tests in Figure 1.4 in 

which both sides of the chamber had the same gases injected. Assuming that we achieved 

our target injection conditions in the other block runs, differences between the two sides 

can be taken to reflect differences in the chemistry of the two alternative fuels that were 

compared, and were not caused by different characteristics of the two sides of the chamber. 

1 , I I I I I I I I I I I a.50 

1.5.2 Block 1: Base CNG Tests 

- 2.0 ppmc SynURB 12-Sep-94 - , , , , , , , , , 2.0 ppmC SynURB 

- 

- 03 - 

We conducted four experiments with a 5050 mixture of SynURB/SynCNG compared to 

SynURBISynlAG at two HC:NO, ratios, 6: 1 and 9: 1. See the 23-Sep-93 experiment in Figure 1.5 for an example of these experiments. 

-3.40 

CNG Finding: In all cases, SynCNG showed an ozone production of 4040% that of 

Synl AG, even at 9:l ratios. 

1.5.3 Block 5: Base LPG Tests 

We conducted only two experiments with a 5050 mixture of SynURB/SynLPG compared 

to SynURB/SynlAG at a HC:NOx ratio of 6:l; only one experiment took place under good 

- - - - - - - - - - 

Photochemical Studies of Alternative Fuels 11 ESEIUNC-CH 
1.5 EXPERIMENTAL RESULTS CHAPTER 1. EXECUTIVE SUMMARY 

meteorological conditions. See the 27-Sep-94 experiment in Figure 1.5 for an example of 

these experiments. 

LPG Finding: In the.one fall experiment, SynLPG showed an ozone production of 4060% 

that of SynlAG, and was very similar to CNG. 

1.5.4 Block 2: Base E85 Tests 

Because there were two sources of of E85 composition (the EPA E85 composition had less 

acetaldehyde than the NIPER E85 composition), there were two different test mixtures used 

for conducting experiments. 

We conducted seven experiments with a 5050 mixture of SynURB/SynE85E compared 

to SynURB/SynlAG at three HC:NOx ratios, 4.5: 1,6: 1, and 9: 1. See the 7-Aug-94 experiment 

in Figure 1.6 for a good example of these experiments. 

Two experiments were conducted with a 5050 mixture of SynU RB/Syn E85B compared 

to SynURB/SynlAG at a HC:NOx ratio of 6: 1. See the 3-Aug-94 experiment in Figure 1.6 for 

an example of these experiments. 

E85 Finding: At 4.5: 1 and 6: 1 HC:NOx ratios, the SynE85E mixture produced 15% to 25% 

more ozone than did the SynlAG mixture when blended 50:50 with SynURB; the SynE85B 

mixture produced 60%.more ozone than did the SynlAG mixture under the same conditions. 

At the 9: 1 HC:NOx ratio, the SynE85E mixture produced exactly the same ozone as did the 

SynlAG mixture. 

1.5.5 Block 3: Single Mixture E85 Tests 

Three experiments were conducted with SynE85E. It was compared with SynURB at 6: 1 and 

was compared with SynlAG also at 6: 1. See Figure 1.7 for examples of these experiments. 

E85 Finding: At a 6: 1 HC:NOx ratio, the SynE85E mixture produced 75% less ozone than 

the SynURB rnixture, but produced nearly 100% more ozone than the SynlAG mixture. 

1.5.6 Block 4: E85 Mixture Variations Tests 

We conducted five experiments with a 5050 rnixture of SynURB/SynE85E. It was compared 

with only SynURB at 6:l and was compared with 100% SynURB at 6:1, but without any 

ethanol in the SynE85E rnixture. In another experiment, the amount of ethanol was increased. 

Finally, an "equal mass" experiment was conducted in which 0.84 ppmC of SynE85E was 

compared to 1.0 ppmC of SynlAG both in a blend with 1.0 ppmC of SynURB. 

Photochemical Studies of Alternative Fuels 12 ESEIUNC-CH 



1.6 SIMULATION RESULTS CHAPTER 1. EXECUTIVE SUMMARY 

E85 Finding: At a 6: 1 HC:NOx ratio, removing the ethanol from the SynE85E mixture 

decreased the ozone produced from 80% of the ozone produced by the SynURB-only mixture 

to 60% of the ozone produced by the SynURB-only mixture. Increasing the ethanol resulted 

in the SynURB/SynE85E mixture producing more ozone than did the SynURB-only mixture. 

Without the acetaldehyde the SynURBSynE85E mixture reactivity approached that of the 

SynURBSynlAG mixture. Because of the lower molar amount of carbon in the "equal mass" 

experiment, the SynE85E mixture and the the SynlAG mixture produced the same amount of 

ozone. 

1.6 Simulation Results 

1.6.1 Temperature Effects 

Because new Teflon film was installed on the chamber in 1994, replacing the film that had 

been used for 15 years, there was a large decrease in in-chamber air temperature because 

the new film exhibits much less of a greenhouse effect than did the old film. The old film 

had become covered in fine layer of particulate matter that was absorbing infrared radiation. 

This resulted in two very interesting sets of experimental data: 

A 199 1-92 SynURB and SynlAG data set from the CRC sponsored work with maximum 

chamber temperatures often exceeding 100°F 

A 1994 SynURB and SynlAG data set from the current work with maximum chamber 

temperatures near 80°F. 

Several replicate experiments between these two data sets afforded the opportunity to examine how well CB4 could simulate temperature effects. 

Comparisons of replicate runs with significantly different afternoon in-chamber temperatures showed that there was little effect of temperature on ozone formation (see, for 

example, Figure 1.8). This result appeared coorelated with an inverse relationship for afternoon response on the NOx analyzer, that is, higher temperatures give lower afternoon NOx 

analyzer readings. Our particular analyzer responds to NO2, PAN, organic nitrates, but does 

not respond to HN03. Because PAN-like compounds would be expected to be temperaturelabile, they can store NO2 for subsequent release later in the run and thus maintain a higher 

NOx concentration in the chamber during the afternoon. Because more PAN-like compounds 

would be available in cooler runs, more ozone could be produced, thus compensating for 

other processes that would be expected to lead to a decrease in ozone production. Because 

our VOC mixtures are so complex, we have a better chance to represent such complex internal 

compensation processes than have other chamber studies. 

In the CRC report [4], we showed that the simulations of the complex VOC mixture 

experiments performed with the CB4 mechanism were very reactive relative to the chamber 

observations, on average predicting ozone about 150% too high. But in the simulations 

Photochemical Studies of Alternative Fuels 16 ESE/UNC-CH 

1.6 SIMULATION RESULTS CHAPTER 1. EXECUTIVE SUMMARY 

performed here for the 1994 DOE experiments the CB4 simulations are in much better 

agreement with the chamber observations. This led us to conduct a series of comparisons of 

replicate experiments to investigate the observed temperature dependence of ozone production on temperature and to compare this with the CB4's temperature dependence. Further, 

we substituted the 1994 temperatures for the observed values in the 1992 simulations and 

showed that it was indeed the high in-chamber air temperatures that caused CB4's predictions 

to be so much higher than the chamber observations in the CRC study (see, for example, 

Figure 1.9). 

Temperature Sensitivity Findings: Conclusions from these intercomparisons were: 

a The outdoor smog chamber experimental results suggest that when using complex 

mixtures with the realistic compositions of synthetic urban and IAG, there is at best 

a small temperature effect. Also, some evidence exists to suggest that there may 

be a small inverse relationship between temperature and ozone production for these 

mixtures. 

The experimental evidence refutes the large positive effect of temperature on ozone 

production that is demonstrated by the CB4 mechanism. 

These imply that while the CB4 simulations reported later in this document show 

excellent agreement with the chamber observations, this accuracy may be merely the result of 

having tuned the somewhat speculative organic chemistry portion of the CB4 using chamber 

experiments from the 1980's in which the chamber temperatures were more like those in 

the 1994 data set. In comparisons with hotter chamber data from 1992, the CB4 exhibits too 

much increase in ozone with increasing temperature and will not be a reliable predictor of 

ozone for conditions warmer than 80°F. 

1.6.2 Block 6: Chamber Characterization Tests 

CB4 showed excellent agreement with all chamber characterization runs that did not involve 

complex mixtures; i.e., CB4 gave nearly perfect simulations for methanelN0, experiments 

and for CO/NOx experiments. 

1.6.3 Alternative Fuel Simulations 

The CB4 mechanism did reproduce the reactivity difference between 5050 mixtures of 

SynURB/SynCNG and 5050 mixtures of SynURBISynlAGat 6: 1 and at 9: 1 HC:NOx ratios (see, 

for example, Figure 1.10). CB4 performed similarly for the SynLPG experiments. 

The CB4 mechanism did reproduce the reactivity difference between SO:50 mixtures 

of SynURB/SynESSE and 50:50 mixtures of SynURB/SynlAGat 6: 1 and at 9: 1 HC:NOx ratios 

Photochemical Studies of Alternative Fuels 18 ESEIUNC-CH 




CHAPTER 1. EXECUTIVE SUMMARY 1.7 NEW ANALYTICAL METHODS 

(see, for example, Figure 1.1 1). CB4 also reproduced the reactivity difference between the 

EPA E85 composition and the NIPER E85 composition (see Figure 1.12). 

On the other hand, CB4 did not perform as well when simulating just SynE85E (being 

somewhat over reactive), or simulating SynU RBlSynE85E vs. just SynU RB mixtures (again 

too over reactive for the E85 side). 

CB4 Tests Conclusion 

Given the various responses of CB4 reported above, it is clear that the processes in CB4 

were carefully balanced under a particular set of chamber test conditions. Given that the 

temperature dependence for PAN formation and decay were changed in CB4 after it was 

formulated and there was no reevaluation of CB4 against a large set of chamber data, it is 

not surprising that CB4 exhibits an incorrect and overly sensitive temperature dependence 

for ozone formation. 

CB4 Finding: On the other hand, when conditions are near those used to formulate CB4, 

which include air temperatures below 80°F and urban-like VOC mixtures, CB4 can accurately 

simulate the chamber results with a high level of accuracy. It must be remembered, however, 

that such accuracy is achieved by means of internal compensation among some correctly 

specified and some incorrectly specified processes in the model. 

1.7 New Analytical Methods 

One of the most successful parts of this project has been the development and refinement of 

a new analytical method for detecting and identifying unknown multi-functional carbonyls. 

Carbonyls are the ubiquitous intermediate products of all atmospheric organic oxidation 

reactions. Identifying and quantifying these carbonyls are critical to developing a scientific 

understanding of the reaction mechanisms and thus formulating an accurate reaction model 

for use in regulatory decision making. 

This method has been published [6], and was in part responsible for Ms. Yu receiving 

the American Chemical Society's Best Graduate Student Paper Award in 1995. 

The new method uses 0-(2,3,4,5,6-pentafluorobenzy1)-hydroxylane (PFBHA) to form 

oximes derivates of polyfunctional carbonyls. Figure 1.13 shows the PFBHA oxime of acetaldehyde. The presence of the five fluorine atoms not only raises the vapor pressure of the 

oxime permitting gas chromatography to be used for the separation, but also results in ready 

detection by electron capture (ECD). Furthermore, in a mass spectrometer, the stability of 

the CBFSCHP moieties produced by fragmentation gives a very strong m/z 181 peak that 

uniquely identifies the compound as being a carbonyl derivative. 

The chemical ionization mass spectra using an ion trap produce direct molecular weight 

information and some of the fragmentation patterns assist in determining the structure of the 

Photochemical Studies of Alternative Fuels 23 ESEIUNC-CH 
1.8 AROMATICS MECHANISM CHAPTER 1. EXECUTIVE SUMMARY 

Figure 1 .I 3: The 0-(2,3,4,5,6)-pentafluorobenzyl oxime of of acetaldehyde. 

carbonyls. An example mass spectrum will be shown in the following section. The ability 

to determine the molecular weight is the key to the power of this method, as many of the 

intermediate carbonyls are not available commercially for use as standards. 

The method has been shown to work for all standard ketones and aldehydes and dicarbonyls from HCHO to C9 and for all hydroxy carbonyls for which we could obtain a 

standard. In addition, it has been show to work for epoxy carbonyls and dicarbonyls. It will 

not detect carboxylic acids (but a modification to use pentafluorobenzyl bromide (PFBBr) 

as the derivatizing agent has been shown in our laboratories to detect these acids). 

1.8 Aromatics Mechanism 

More than 20 indoor Teflon Bag Reactor (TBR) experiments with alkyl substituted benzene 

compounds and NOx were conducted as part of this work. These were used to develop the 

sampling and identification techniques. Although these are useful in determining the types 

of products that were produced in these systems, the photolytic spectrum for the TBR differs 

significantly from sunlight. Because many of the carbonyl products photolyze, outdoor 

experiments would more realistically represent the behavior of this complex system. 

Three dual outdoor chamber experiments were performed for this project: 

toluene versus m-xylene 

p-xylene versus 1,3,5-trimethyl benzene 

o-xylene versus 1,2,4-trimethyl benzene. 

Batch and time-resolved samples were collected each side of each of these experiments 

and were analyzed using the new carbonyl method described above. The presence of carbony1 products in each batch sample was readily determined by examining the reconstructed 

Photochemical Studies of Alternative Fuels 24 ESEIUNC-CH 
CHAPTER 1. EXECUTIVE SUMMARY 1.8 AROMATICS MECHANISM 

ion chromatogram for the m/z=18 1 ion because the electron ionization spectra of PFBHA 

oximes always have a strong m/z=181 peak. The molecular weights of these carbonyl 

products are then determined from their methane chemical ionization mass spectra. 

After methane, toluene is the most ubiquitous urban hydrocarbon, appearing among 

the top 25 most concentrated HC species in all the EPA canister samples. Aromatic species 

make up about 30% of all urban carbon. The reconstructed m/z 181 ion chromatogram 

from the toluene/NOx experiment is shown in Figure 1.14. Every peak in this figure is a 

carbonyl compound and it reveals the enormous diversity of the reaction products from 

toluene photooxidation. In the body of the report, we identify all the number peaks and 

present reaction paths leading to the formation of each carbonyl. 

Although we have proposed a series of at least seven major reaction pathways leading to 

a large variety of intermediate and final products, the conversion of these reaction schemes 

into photochemical reaction mechanisms for use in models will require significant new 

work. Some of this work is currently underway in our laboratories under EPA funding 

and will require more than three years to complete. Other work, such as determining the 

absorption cross sections and quantum yields for the photolysis of the carbonyls, cannot be 

done in our laboratories and we need to establish collaborations and find funding for other 

investigators who can make these measurements. We have established a collaboration with 

organic synthesis chemists in the UNC Department of Chemistry in an effort to synthesize 

usable quantities of key intermediate multi-functional carbonyls and may be able to furnish 

samples to other investigators. 

One of our most important discoveries in working with the aromatics photooxidation 

is that one of the important reaction pathways is via epoxy carbonyl production. This 

is consistent with a theoretical prediction of the formation of epoxide intermediates in the 

toluene oxidation that was made by Bartolotti and Edney [13] nearly simultaneously with our 

measurements. They used a series of density functional quantum mechanical calculations 

to identify potential intermediates following the OH addition to toluene. In working closely 

with Bartolotti, we produced the following reaction scheme: 0 'OH- byTo@-o*, \ ,om 02 

(1) (11) "H ' 0 (111) 

Bartolotti and Edney's calculations indicate that the epoxide structure I1 is the most stable 

Photochemical Studies of Alternative Fuels 25 ESEIUNC-CH 

CHAPTER 1. EXECUTIVE SUMMARY 1.9 UAM SIMULATIONS AND ANALYSIS 

intermediate among the likely intermediates. We have detected the reaction products labeled 

111, IV, and V. 

One of the most impressive mass spectra we have produced was from a 1,3,5-trimethyl 

benzene1NQ experiment in the outdoor chamber (see Figure 1.15). The existence of the first 

ring-opened epoxide, 2,3-epoxy-2,4-dimethyl-6-oxo-4-heptenal (called Epoxide A hereafter) is clearly revealed. 

The detection of epoxide intermediates in the -OH oxidation of aromatic compounds 

raises a concern over their possible toxicity. An epoxy carbonyl similar in structure to several 

intermediates we detected, 2,3-epoxypropanal (glycidaldehyde), has been reported to be a 

potent mutagen in a variety of in vitro test systems [14,15] and has been shown to induce 

sarcomas in rats by subcutaneous injection [16]. As a bifunctional agent, 2,3-epoxy propanal 

is capable of forming cyclic adducts and crosslinks with DNA bases [17]. In general, the 

epoxy functional group can serve as electrophile to attack DNA, nucleosides [18-201. It 

seems possible that the mutagenic activity of irradiated toluene/NO, mixtures reported by 

Shepson et al. [21] and by Shiraishi et al. [22] may have been due to the production of epoxide 

compounds. Hence, the possible formation of epoxides from atmospheric oxidation of 

aromatic compounds is a significant public health concern, deserving additional laboratory 

and field work. 

UAM Simulations and Analysis 

Chemistry is only one component in the predictions made by an airshed model. Meteorology and emissions are extremely important parameters in these models. Because of 

our involvement with the State of North Carolina in performing State Implementation Plan 

calculations for Charlotte, North Carolina, an ozone non-attainment area, we were able to 

investigate and improve a number of elements of the EPA's Urban Airshed Model (UAM) 

that would be used to investigate the effects of alternative fuels use. 

The first of our efforts relates to improving the inventory, both area inventory and the 

mobile source inventory. Other efforts were directed at explaining the role of meteorology 

and its interaction with emissions and chemistry using a new technique developed in part 

on this project. 

1.9.1 Emissions Inventory Improvements 

In our UAM studies, we developed relational database management techniques and applied 

them to emissions inventory and spatial surrogate files for the North Carolina Modeling 

Domain [23]. Application of this code to quality assurance work on emissions inventory 

files resulted in many refinements of the spatial allocation of emissions. 

The total gridded emissions may be correct, but may be in the wrong grid cells. The 

gridded emissions may be in the correct grid cells, but the total gridded emissions may be 

incorrect if the total fractional surrogates do not correctly sum to 1. 

Photochemical Studies of Alternative Fuels 27 ESEIUNC-CH 

CHAPTER 1. EXECUTIVE SUMMARY 1.9 UAM SIMULATIONS AND ANALYSIS 

Both types of errors were found in the Noah Carolina surrogate file. When allocating 

emissions using the original urban and water surrogates, the surrogates were zero for some 

counties, resulting in lost emissions during the gridding process of the Emisions Processing System codes (EPS). Also, the surrogates apparently originated from older land-use 

databases using a very coarse grid size, resulting in over-smoothing of the emissions field 

and the artificial spreading of emissions beyond the emissions sources. 

To improve the mobile source inventory, we developed a new mobile inventory submodel [24] that: 

Implemented the current emission estimation methodology in a relational database 

management system to produce inventories in a more flexible and efficient manner 

Incorporated information from a UTPS-type transportation demand model to improve 

the spatial and temporal resolution of vehicle activity in the emission calculations. 

1.9.2 Process Analysis 

As part of this project (and others) we have developed a method called integrated process 

rate analysis, (IPRA), or just "process analysis" that allows us to fully explain a model's 

prediction. In process analysis, we focus on the change caused by each major physical 

and chemical process in the model, rather than just examining the concentration field for 

a pollutant. We thus output additional data for each cell in the model; these are data for 

the change over the model's time step caused by chemistry, horizontal transport, vertical 

transport, emissions, and deposition. In post processing programs, we subject these data to 

additional analysis by four major techniques: 

1. Animation movies of major processes such as total odd oxygen production per cell 

per hour 

2. Process time series plots 

3. Process composition plots 

4. Integrated reaction rate and mass balance system analysis. 

These have proved to be invaluable in performing diagnostic analysis of particular episodes 

in which the simulation results were strange. 

Some of the UAM simulation problems revealed by process analysis were: 

Because of the wind flow model's inability to treat correctly light and highly variable 

wind fields, there were major horizontal convergence problems that caused ozone 

to accumulate more than 50 ppb above the observed peak and that resulted in the 

simulation having to be abandoned 

Incorrect shifts in vertical transport when there was a large change in horizontal wind 

direction 

Photochemical Studies of Alternative Fuels 29 ESE/UNC-CH 
1.9 UAM SIMULATIONS AND ANALYSIS CHAPTER I. EXECUTIVE SUMMARY 

Incorrect ratio of toluene to xylene in the model's emissions inventory compared to 

ambient observations 

A 10% NOx mass balance error. 

Examples of these are given in Chapter 7. 

1.9.3 UAM Simulation Results 

In this study we tested the sensitivity of the UAM's ozone predictions to three assumptions 

in the motor vehicle emission calculations: trip average emission factor, average daily temperature, and uniform speed by road classification. These were all preliminary to evaluating 

the effects of changing the mobile emissions composition to reflect the use of an alternative 

fuel. 

The assumptions were tested by preparing a series of motor vehicle modeling inventories using conventional methodology (C), disaggregated emission factors (D), hourly temperature profiles (T), and speeds by road (S). These inventories were then merged with the 

area, point, biogenic, and nonroad mobile inventory for a 1988 ozone episode in Charlotte, 

North Carolina, and used in UAM simulations. 

Spatially and temporally allocating the disaggregated emissions to trip starts, trip ends, 

and trip mileage increased both the maximum gridded hourly emissions and the variability of 

gridded hourly emissions. VOC and CO showed a larger change than NOx. This reallocation 

moved emissions from major highway corridors to the central business district. 

Varying the ambient temperature had little impact on either emission estimates or ozone 

predictions. Disaggregating the emission factor and varying speeds on individual links had 

significant impacts on the distribution and mass of emissions, particularly for CO and VOCs. 

The spatial redistribution caused by disaggregation was even more significant at higher grid 

resolutions. However, the impact of these changes on ozone predictions was small-peak 

ozone changed by less than 3 ppb-but spatial ozone changes were more significant. This 

ozone result was primarily due to the NO,-limited nature of the Charlotte area in 1988. 

Effects on ozone concentrations in scenarios with different VOC to NO, ratios may be more 

significant. Furthermore, disaggregation may have a significant impact on predictions of 

CO concentrations. 

Although the conventional methods for estimating motor vehicle emissions have been 

criticized as being unrealistic and unrepresentative, this work demonstrates that, under 

some conditions, major efforts to refine the emission estimates will have little impact on air 

quality predictions, at least at the current resolution of air quality models. Under different 

conditions, however, these changes in the emissions estimates may produce very significant 

air quality impacts. 

The impact of a large scale use of natural gas vehicles in the Charlotte UAM scenario 

is currently being investigated under separate funding. 

Photochemical Studies of Alternative Fuels 30 ESE/LTNC-CH 
Chapter 2 

Experimental Design 

2.1 VOC Mixture Composition 

This study design was similar to that of our CRC Methanol Fuels Reactivity Project and 

relied on the SynURB and SynlAG complex VOC mixtures produced in that study. Detailed 

description of the analysis and creation of these mixtures is given in the CRC report [4]. 

These mixtures, each comprising about 55 compounds, are the most complex, yet 

high-precision, composition mixtures ever used in smog chamber studies (see Table 2.1, 

2.2, and Figure 2.1). They were based on extensive gas chromatographic analysis of early 

morning cannister samples collected by the EPA in 30 cites over a period of about 6 years. 

We did statistical analysis on more than 2000 samples with up to 300 HC species to produce an average mixture of about 120 species. From these we selected the 55 species for 

inclusion in the synthetic mixtures based on the prevalence and concentration fraction of 

the species. About 65% of the carbon was explicitly represented in these mixtures (i.e., the 

exact compound is included in the mixture) and the rest of the carbon is represented by a 

similar species with similar structure. Some attention was given to ease of analysis and to 

producing a precise composition from experiment to experiment. 

To meet the latter requirement, we divided the synthethic mixtures into two classes: 

those compounds that could be purchased in a high-pressure gas cylinder in nitrogen (14 

components) and those species that were liquids (38 components). We purchased the former 

from a gas supply manufactor at 2% component accuracy and a total carbon concentration 

of about 10,000 ppmC. Injections of 15 liters of this tank into the chamber would produce 

1.0 ppmC of the mixture. We blended the liquid mixture using pure liquids and careful 

measurement of individual HC volumes to create a single blend liquid with the proper 

composition. Injection of a few 100 microliters of this liquid, along with evaporation with 

a hot air gun, was used to introduce these components into the chamber. Finally, the higher 

aldehydes were blended into another liquid separately (up to 5 aldehydes) and injected in a 

manner similar to the HC liquid. Formaldehyde (HCHO) was injected using sublimation of 

a weighed amount (about 120 mg) of solid paraformaldehyde. 
2.1 VOC MIXTURE COMPOSITION CHAPTER 2. EXPERIMENTAL DESIGN 

For this study, we added to the SynURB and SynlAG mixtures a new mixture based on 

emissions from a compressed natural gas fueled vehicle called SynCNG (see Figure 2.2). 

We added two new mixtures mixtures based on composite emissions from ethanol fueled 

vehicles, one determined by EPA (called SynE85E) and one determined by NIPER (called 

SynE85B). The latter are described in Figure 2.3. 

Photochemical Studies of Alternative Fuels 32 ESEAJNC-CH 
CHAPTER 2. EXPERIMENTAL DESIGN 2.1 VOC MIXTURE COMPOSITION 

Table 2.1 : The VOC Mixtures used in the CRC Methanol-85 Chamber Study-Part 1 

ppbC in 1 ppmC injection 

Component Urban IAG M85 UrbIlAG UrblM85 

1 ethane 

2 propane 

3 n-butane 

4 isobutane 

5 n-pentane 

6 isopentane 

7 2,2-dimethylbutane 

8 2,3-dimethylbutane 

9 2-methylpentane 

10 3-methylpentane 

11 n-hexane 

12 2,3-dimethylpentane 

13 2-methylhexane 

14 3-methylhexane 

15 n-heptane 

16 2,2,4-trimethylpentane 

17 2,5-dimethylhexane 

18 2,3,4-trimethylpentane 

19 n-octane 

20 nonane 

21 n-decane 

22 4-methylnonane 

23 n-dodecane 

24 methylcyclopentane 

2 5 cyclohexape 

26 methylcyclohexane 

27 ethene 

28 propene 

29 2-methylpropene 

30 1-butene 

31 c-2-butene 

32 t-2-butene 

33 1,3-butadiene 

34 1-pentene 

35 t-2-pentene 

.. .- 

Photochemical Studies of Alternative Fuels 33 ESEIUNC-CH 
2.1 VOC MIXTURE COMPOSITION CHAPTER 2. EXPERIMENTAL DESIGN 

Table 2.2: The VOC Mixtures used in the CRC Methanol-85 Chamber Study-Part 2 

ppbC in 1 ppmC injection 

Component Urban IAG M85 UrbIlAG UrblM85 

3 6 c-2-pentene 13.5 3.0 4.3 8.2 8.9 

37 2-methyl-1-butene 

38 2-methyl-2-butene 

39 2-methyl-1-pentene 

40 2-methyl-2-pentene 

41 2,3,3-trimethyl-1-butene 

42 1-nonene 

43 1-octene 

44 cyclohexene 

45 isoprene 

46 cyclopentene 

61 a-pinene 

4 7 benzene 

48 toluene 

49 ethylbenzene 

50 n-propylbenzene 

51 m-xylene - 

52 o-xylene 

53 1,2,4-trimethylbenzene 

54 1,3,5-trimethylbenzene 

55 m-ethyltoluene 

56 p-ethyltoluene 

57 sec-butylbenzene 

58 1,3-diethylbenzene 

59 1,2,3,5-tetramethylbenze 

60 a-methylstyrene 

62 styrene 

63 naphthalene 

64 formaldehyde 

65 acetaldehyde 

66 propionaldehyde 

67 p-tolualdehyde 

68 crotonaldehyde 

69 acetone 

70 methanol 414.3 207.1 

Total 1,000 1,000 1,000 1,000 1,000 

Photochemical Studies of Alternative Fuels 



2.2 CHAMBER EXPERIMENTAL CONDITIONS CHAPTER 2. EXPERIMENTAL DESIGN 

2.2 Chamber Experimental Conditions 

Figure 2.4 shows the revised planned experimental design (the legend is the same as that 

shown in Figure 1.2). Revisions to the original plan shown in Figure 1.3 were made to 

accommodate further investigation of unexpected results obtained in early experiments. For 

example, we found that blends of SynE85E were more reactive than was SynlAG. Because 

this was an unexpected result, we devoted more chamber time to repeating this experiment 

and to creating new exprimental conditions to support these results. 

The two sides of the chamber were used to compare the effects of changing the VOC 

composition directly. Each comparison would take place at three HC:NOx ratios: 4.5: 1,6: 1, 

and 9: 1. Most urban centers are near 6: 1 and for most mixtures, reactivity effects disappear 

(because the ozone formation is NO,-limited) under 9: 1 conditions. 

Photochemical Studies of Alternative Fuels 38 ESEIUNC-CH 

Chapter 3 

Experimental Results 

3.1 Outdoor Smog Chamber Results 

A total of 29 alternative fuel outdoor smog chamber dual experiments were performed for 

this program. As indicated in Chapter 2, during the course of the work, we changed the 

experimental plan to explore more fully some of the surprising results obtained in our early 

experiments. This resulted in a greater number of replicate experiments for some conditions 

and a lower priority for other experimental conditions. Ultimately we did not perform four 

of the originally planned conditions. This is not a significant loss to the overall program, 

however, as the probable outcome of these conditions can be fairly well guessed, and it was 

more important to confirm the results for other conditions. 

As usual, some experiments took place under unpredicted poor meteorological conditions and thus are not very useful for model testing. (See, for example, our first attempt 

at Run 7. It was conducted on September 24, 1994 and was nearly totally devoid of sunlight because of heavy overcast all day.) These had to be repeated under better weather 

conditions. 

In other situations, significant modifications to the alternative-fuel-derived VOC cornpositions were used to demonstrate an hypothesis and to provide a full range of sensitive 

conditions. These modifications allowed us to challenge the photochemical models' ability 

to make accurate predictions for these unusual and stressful conditions. 

The experimental results will be presented in the order of the experimental plan and 

block by block. Within each block, the relevant portion of the experimental plan plot will 

be shown, followed by a brief discussion of the experiments. Each experiment's results are 

shown here as an initial condition table and a SegFile standard plot. In the initial condition 

table, there are two main columns, one for the RED side of the chamber and one for the 

BLUE side of the chamber. The table has six sections, 

The total nitrogen oxides concentration (in bold), followed by the NO concentration 

and the NO2 concentration in each side at sunrise 
CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

The total HC mixture concentraton (in bold), followed by the total mixture component concentrations, and below a line, the total VOC mixture concentration (in bold) 

followed by the CO concentration (in bold) in each side at sunrise 

The total of the other measured organic or background VOCs in each side at sunrise 

The estimated concentration of species known to be present, but not measured in each 

side at sunrise 

The estimated values of the chamber wall model parameters needed to simulate the 

experiment 

The three previous chamber events, i.e., the time and type of "runs" that proceeded 

the current experiment. 

The SegFile standard plot shows in the top plot the concentration time series of NO, NO2, and 

O3 on the two chamber sides and in the bottom plot shows time series for the meteorological 

data for the experiment (total solar radiation [TSR], ultraviolet radiation [UV], and the 

chamber air temperature and chamber dew point temperature [a form of absolute humidity]). 

In these plots, 

RED chamber data are always shown in SOLID lines. 

BLUE chamber data are always shown in DASHED lines. 

A legend identifies the contents of the RED and BLUE chamber sides. 

NO2 is (NOx-NO) and includes PAN and organic nitrates (RONO*), but not the HN03. 

TSR (broadband 295-2800 nm radiation measured outside the chamber on a 10 m 

tower) is always shown as a SOLID line. 

UV (broadband 300-385 nm radiation measured outside the chamber on a 10 m tower) 

is always shown as a DASHED line. 

Air temperature inside one of the chamber-halves is always shown as a SOLID line. 

Dew point temperature inside each chamber is shown as a SOLID line for the RED side 

and a DASHED line for the BLUE side of the chamber. 

The initial conditions shown here are within &5% of the actual values. The ozone, 

nitrogen oxides, temperature, dew point, and solar radiation have been processed with final 

calibration factors and are accurate within &2%, 

Photochemical Studies of Alternative Fuels 41 ESEIUNC-CH 
3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

3.1.1 Block 1: UrbDAG versus UrblCNG 

Block 1 experiments were designed to compare the reactivity of SynCNG with SynlAG 

with each mixture blended 5050 with SynUrb. In an earlier chapter, we discussed the reasons 

for the experimental design of blending the different alternative fuel emissions test mixtures 

with SynUrb. In brief, this provides reasonably realistic test scenarios for direct observational 

comparisons to be made of substitution effects of these mixtures into urban atmospheres. 

In addition, these experiments provide good tests for the chemical mechanisms that will be 

used to model different urban control scenarios. 

Run 26:l UrbDAG versus UrblCNG 

ST1093 This run (Table 3.1 and Figure 3. I), shows that SynCNG has a significant benefit 

for reducing ozone compared to SynlAG even under cooler (fall) conditions. 

Run 3,9:1 UrblIAG versus UrbICNG 

ST2393 Usually at 9:l VOC:NOx, reactivity makes almost no difference. This run (Table 3.2 and Figure 3.2), however, shows that for CNG, the benefits extend to well beyond 

9: 1. 

Run 5,6:1 UrbnAG versus UrbICNG 

JL2594 This run (Table 3.3 and Figure 3.3), is a repeat of ST1093, conducted in warm 

weather. This run shows that the benefits of CNG are sustained under warm, high intensity 

(summer) sun conditions. 

Run 6,9:1 UrbDAG versus UrblCNG 

AU2694 This is a repeat of Run 3 (ST2393) under warm, high sun (summer) conditions. 

This run (Table 3.4 and Figure 3.4), shows that the benefits of CNG are sustained under 

warm, high sun conditions even for high HC:NOx ratios, unlike M85 and E85 results. 

Photochemical Studies of Alternative Fuels 42 ESE/UNC-CH 
CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.1 : Initial Conditions for ST1093 

(Units are ppm or ppmC) 

RlED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3387 NO, 0.3405 

NO 0.2940 NO 0.2899 

NO2 0.0447 NO2 0.0506 

Initial Volatile Organic Compounds 

SynUrban 

SYNURBGAS91 

SYNURBLlQ91 

CCHO 

HCHO 

CCC 

HCHO 0.00 10 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

Syn IAG 

CH4 

SYNIAGGAS91 

SYNIAGLP092 

SYNlAGARO92 

SY NtAGALD91 

HCHO 

Total VOCs 1.9974 Total VOCs 

CO 1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 -- 

Estimated Initial Compounds 

HONO 0.007 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NOn --+ HONO 1.8 x NO2 --+ HONO 1.8 x 

wall.HN03 + NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

930830.SEG Delta CO run 

930824.SEG Propylbenzene vs Ethylbenzene run 

930823.SEG Delta Ethene run 

Photochemical Studies of Alternative Fuels 43 ESEIUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.2: Initial Conditions for ST2393 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3342 NO, 0.3349 

NO 0.2612 NO 0.2636 

NO2 0.0730 NO2 0.07 13 

Initial Volatile Organic Compounds 

SynUrban 1.4999 

SYNURBGAS91 0.587 1 

SYNURBLIQ91 0,8865 

CCHO 0.0 137 

HCHO 0.0 126 

Synl AG 1.2894 

CH4 0.21 15 

SYNIAGGAS91 0.728 1 

SYNIAGLP092 0.2362 

SYNIAGARO92 0.298 1 

SYNIAGALD91 0.02 19 

HCHO 0.005 1 

SynUrban 

SYNURBGAS91 

SYNURBLIQSI 

CCHO 

HCHO 

SynCNG 

CCC 

HCHO 

Total VOCs 

CO 

2.7893 Total VOCs 

1.3000 CO - - 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.008 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 1.8 lou3 NO2 + HONO 1.8 lou3 

wall.HN03 + NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

930920.SEG Delta methane run 

9309 16.SEG Night Isoprene run 

9309 15 .SEG Night Isoprene run 

Photochemical Studies of Alternative Fuels 45 ESE/UNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.3: Initial Conditions for JL2594 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3360 NO, 0.3347 

NO 0.285 1 NO 0.2874 

NO2 0.0509 No2 0.0473 

Initial Volatile Organic Compounds 

SynUrban 

CCHO 

HCHO 

SynCNG 

CCC 

HCHO 0.00 10 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

Synl AG 

CH4 

SYNIAGGAS91 

SYNIAGLP092 

SYNlAGAR092 

SYNIAGAtD91 

HCHO 

Total VOCs 

CO 

1.9974 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.003 HONO 0.003 

CH4 1.790 CH4 1.790 

H2 0.580 H2 0.580 

Chamber Model Parameters 

NO2 HONO 2.5 x NO2 + HONO 2.5 

wall.HN03 + NO2 2.0 x lo-' wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

940720.SEG Delta Toluene run 

9407 15.SEG Delta Toluene run 

940708.SEG m-Xylene vs Toluene run 

Photochemical Studies of Alternative Fuels 47 ESE/UNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.4: Initial Conditions for AU2694 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3208 NO, 0.3213 

NO 0.26 19 NO 0.2635 

NO2 0.0589 NO2 0.0578 

Initial Volatile Organic Compounds 

SynUrban 1.4999 SynUrban 1.4999 

SYNURBGAS91 0.587 1 SYNURBGAS91 0.587 1 

SYNURBLIQ91 0.8865 SYNURBLIQ91 0.8865 

CCHO 0.0 137 CCHO 0.01 37 

HCHO 0.0126 HCHO 0.0 126 

SynCNG 1.4965 SynlAG 1.2894 

CH4 1.4304 CH4 0.21 15 

CC 0.0474 SYNIAGGAS91 0.728 1 

C:C 0.01 10 SYNIAGLP092 0.2362 

CCC 0.0063 SYNIAGAR092 0.298 1 

SY NIAGALD91 0.02 19 

HCHO 0.00 14 HCHO 0.005 1 

Total VOCs 2.9964 Total VOCs 2.7893 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.005 HONO 0.005 

CH4 1.790 CH4 1.790 

H2 0.580 H2 0.580 

Chamber Model Parameters 

NO2 ---+ HONO 3.0 x NO2 HONO 3.0 x 

wall.HNOs + NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

940825.SEG SynE85E vs SynIAG run 

940824.SEG SynE85E vs SynIAG run 

940823.SEG SynE85E vs SynIAG run 

Photochemical Studies of Alternative Fuels 49 ESE/UNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

3.1.2 Block 5: UrbDAG versus UrbLPG 

Static, 10-hour experiment 

6: 1 9: 1 

SynU rbanISynlAG 

ST2494 8 

SynUrbanISynLPG 

Block 5 experiments were designed to compare the reactivity of SynLPG with SynlAG, 

with each mixture blended 50:50 with SynUrb. 

Run 7,6:1 Urb/IAG versus UrbLPG 

ST2494 LPG shows a benefit very similar to CNG. This particular experiment, (Table 3.5 

and Figure 3.9, had very cloudy conditions. The experiment was repeated. 

ST2794 This experiment (Table 3.6 and Figure 3.6), is a repeat of the September 24 

experiment with much better sun. 

Photochemical Studies of Alternative Fuels 5 1 ESEIUNC-CH 
3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.5: Initial Conditions for ST2494 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO 0.2792 NO 0.276 1 

NO2 0.057 1 NO2 0.0564 

NO, 0.3363 NO, 0.3325 

Initial Volatile Organic Compounds 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

SynLPG 

CCC 

CH4 

CC 

CC:C 

C:C 

HCHO 0.0020 

SynUrban 

SYNURBGAS91 

SYNURBLIQSI 

CCHO 

HCHO 

Synl AG 

SYNIAGGASSI 

SYNIAGLP092 

SY N IAGAR092 

SYNIAGALD91 

HCHO 

Total VOCs 1.9509 Total VOCs 

CO 1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.005 HONO 

CH4 1.790 CH4 

Hz 0.580 H2 

Chamber Model Parameters 

NO2 -+ HONO 2.5 x NO2 --+ HONO 2.5 x 

wall.HN03 + NOp 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

940920.SEG Night Isoprene run 

9409 19.SEG Night Isoprene run 

annw 5 = S-F-- 

Photochemical Studies of Alternative Fuels 52 ESEIUNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.6: Initial Conditions for ST2794 

(Units are ppm or ppmC) 

IUCD SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3254 NO, 0.3253 

NO 0.27 18 NO 0.272 1 

NO2 0.0536 NO2 0.0532 

Initial Volatile Organic Compounds 

SynUrban 0.9997 

SYNURBGAS91 0.3953 

SYNURBLIQSI 0.5909 

CCHO 0.0070 

HCHO 0.0065 

SynLPG 0.9512 

CCC 0.7360 

CH4 0.1285 

CC 0.0593 

CC : C 0.0200 

C:C 0.0054 

HCHO 0.0020 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

Synl AG 

SYNIAGGAS91 

SYNIAGLP092 

SYNIAGAR092 

SYNIAGALD91 

HCHO 

Total VOCs 1.9509 Total VOCs 1.9997 

CO 1.3000 CO 1.3000 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.005 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 1.3 x NO2 -+ HONO 1.3 x 

wall.HN03 - NO2 2.0 x lou3 wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

YYMM24.SEG SynLPG vs SynIAG run 

YYMM20.SEG Night Isoprene run 

YY-G N-n~ p~fi 

Photochemical Studies of Alternative Fuels 54 ESE/UNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

3.1.3 Block 2 - UrbIIAG versus UrblE85 

Static, 10 hour experiments Static, 10 hour experiments I 

AUf 1 94 

Block 2 experiments qwere designed to compare the reactivity of SynE85E with SynlAG, 

with each mixture blended 5050 with SynURB. Two sources of E85 composition data 

resulted in two different test mixtures being used to conduct experiments. 

Run 9,4.5:1 UrbnAG versus UrbLESSE 

AU1294 In this low HC concentration experiment (Table 3.7 and Figure 3.7), the EPA 

version of E85, which has less acetaldehyde (CCHO) than the NIPER version of the exhaust, 

is seen to be worse than IAG (is more reactive and produces more ozone) when blended 

5050 with the SynUrb mixture. 

AU2394 This experiment, (Table 3.8 and Figure 3.8), is a repeat of AU1294 under clearer 

skies. The results, however, still indicate that E85E is worse than IAG when blended 5050 

with the SynUrb mixture. 

Run 10,6:1 UrbIIAG versus UrbLESSE 

AU0494 In this intermediate HC concentration experiment (Table 3.9 and Figure 3.9) the 

sky was overcast all day. SynE85E is also worse than IAG under these conditions. 

AU0794 This experiment was conducted with more sun than AU0494 (Table 3.10 and 

Figure 3.10). The results are the same as the previous experiments of this type. 

AU1194 This experiment had more sun than either AU0494 or AU0794 (Table 3.11 and 

Figure 3.11). The results are the same as the previous experiments of this type. 

Run 11,9:1 UrbDAG versus UrbIESSE 

AU2494 Unlike the case with CNG, this 9: 1 IAG versus E85 experiment (Table 3.12 and 

Figure 3.12) shows essentially no difference between the two sides. This is the normal 

experience for 9: 1 runs, and helps show the unusually low reactivity of the CNG. 

Photochemical Studies of Alternative Fuels 56 ESEIUNC-CH 
CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

AU2594 This experiment (Table 3.13 and Figure 3.13), duplicates the results of the 

AU2494 experiment. 

Run 12,6:1 UrblIAG versus UrblESSB 

AU0394 This experiment used the NIPER E85 composition (Table 3.14 and Figure 3.14). 

It shows an even larger difference between IAG and E85 than does the EPA composition. 

This is because E85B has a larger fraction of acetaldehyde than does E85E. 

AUO894 This experiment (Table 3.15 and Figure 3. IS), duplicates the results of the 

AU0394 experiment. 

Photochemical Studies of Alternative Fuels 57 ESE/UNC-CH 
3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.7: Initial Conditions for AU1294 

(Units are ppm or pprnC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3230 NO, 0.3242 

NO 0.2630 NO 0.2629 

NO2 0.0600 NO2 0.06 13 

Initial Volatile Organic Compounds 

SynU rban 0.7498 SynUrban 0.7498 

SYNURBGASSI 0.2980 SYNURBGAS91 0.2980 

SYNURBLIQ91 0.4432 SYNURBLIQ91 0.4432 

CCHO 0.0045 CCHO 0.0045 

HCHO 0.0041 HCHO 0.004 1 

SynE85E 0.7497 Syn l AG 0.7494 

SYNEPAE85GAS 0.1401 SYNIAGGAS91 0.4237 

SYNEPAE85LIQ 0.1201 SY NIAGLP092 0.1370 

SYN IAGAR092 0.1730 

CCHO 0.0456 SYNIAGALD91 0.0127 

HCHO 0,0045 HCHO 0.0030 

ETOH 0.4394 

Total VOCs 

CO 

1.4995 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.003 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 2.5 x NO2 HONO 2.5 x 

wall.HNOs + NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

9408 1 1 .SEG SynE85E vs SynIAG run 

940809.SEG SynE85E-RCHO vs SynIAG run 

940808.SEG SynE85B vs SynE85 run 

. . -. . . 

Photochemical Studies of Alternative Fuels 58 ESE/UNC-CH 
CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

0.50 - I I I I I 111 IIIIIIIIIII~.~~ 

- 0.75 ppmC SynE85E + 0.75 ppmC SynURB 12-Aug-94 - , , , , , , , , , 0.75 ppmC SynlAG + 0.75 ppmC SynURB 

0.40 - -3.40 

5 6 7 -8 9 10 11 12 13 14 15 16 17 18 19 

HOURS, EDT 

10 11 12 13 14 

HOURS, EDT 

Figure 3.7: Run 94.5: 1 SynUrbanIS ynIAG: NO, versus 4.5: 1 SynUrbdSynE85E: NO,. 

Photochemical Studies of Alternative Fuels 59 ESEIUNC-CH 
3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.8: Initial Conditions for AU2394 

(Units are ppn or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3476 NO, 0.3453 

NO 0.2832 NO 0.2833 

No2 0.0644 NO2 0.0620 

Initial Volatile Organic Compounds 

SynUrban 0.7498 

SYNURBGAS91 0.2980 

SYNURBLIQ91 0.4432 

CCHO 0.0045 

HCHO 0.0041 

Syn IAG 0.7494 

SYNIAGGAS91 0.4237 

SYNIAGLP092 0.1370 

SYNIAGAR092 0.1730 

SYNIAGALDSI 0.0 127 

HCHO 0.0030 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

SynE85E 

SYNEPAE85GAS 

SYNEPAE85LIQ 

CCHO 

HCHO 

ETOH 

Total VOCs 

co 

1.4992 Total VOCs 1.4995 

1.3000 CO 1.3000 

Other ~easured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.005 HONO 

CH4 1.790 CH4 

H2 0.580 Hz - - 

Chamber Model Parameters 

NO2 + HONO 1.2 x lo-3 NO2 + HONO 1.2 x lo-3 

wall.HN03 + NOn 2.0 x wall.HN03 -+ NOn 2.0 x 

Three Previous Chamber Events 

940812.SEG SynE85E vs SynIAG run 

9408 1 1 .SEG SynE85E vs SynIAG run 

940809.SEG SynE85E-RCHO vs SynIAG run 

Photochemical Studies of Alternative Fuels 60 ESEIUNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.9: Initial Conditions for AU0494 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3463 NO, 0.3451 

NO 0.2806 NO 0.2790 

No2 0.0657 No2 0.066 1 

Initial Volatile Organic Compounds 

SynUrban 0.9997 

SYNURBGAS91 0.3953 

SYNURBLIQ91 0.5909 

CCHO 0.0070 

HCHO 0.0065 

CCHO 

HCHO 

ETOH 

Total VOCs 

CO 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

Syn IAG 

SYNIAGGAS91 

SYNIAGLP092 

SYNIAGAR092 

SYNIAGALD91 

HCHO 

1.9995 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.002 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 2.5 x NO2 + HONO 2.5 x 

wall.HN03+N02 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

940803.SEG SynE85B vs SynIAG run 

940801 .SEG Delta Toluene run 

940725.SEG SynCNG vs SynIAG run 

Photochemical Studies of Alternative Fuels 62 ESE/UNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.1 0: Initial Conditions for AU0794 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3200 NO, 0.3195 

NO 0.2547 NO 0.2568 

No2 0.0653 NO2 0.0627 

Initial Volatile Organic Compounds 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

SynE85E 

SYNEPAE85GAS 

SYNEPAE85LIQ 

CCHO 

HCHO 

ETOH 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

Synl AG 

SYNIAGGAS91 

SYNIAGLP092 

SYNIAGAR092 

SYNIAGALD91 

HCHO 

Total VOCs 

CO 

1.9995 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.002 HONO 0.002 

CH4 1.790 CH4 1.790 

Hz 0.580 H2 0.580 

Chamber Model Parameters 

NO2 --+ HONO 1.2 lo-3 NO2 + HONO 1.2 x lo-3 

wall.HN03 + NO2 2.0 x wall.HN03 ---+ NO2 2.0 x lo-' - 

Three Previous Chamber Events 

940804.SEG SynE85E vs SynIAG run 

940803.SEG SynEISB vs SynIAG run 

94080 1 .SEG Delta Toluene run 

Photochemical Studies of Alternative Fuels 64 ESE/UNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.1 1 : Initial Conditions for AU1194 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3286 NO, 0.3287 

NO 0.2719 NO 0.2744 

NO2 0.0567 NO2 0.0543 

Initial Volatile Organic Compounds 

SynUrban 0.9997 

SYNURBGAS91 0.3953 

SYNURBLIQ91 - 0.5909 

CCHO 0.0070 

HCHO 0.0065 

CCHO 

HCHO 

ETOH 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

Synl AG 

SYNIAGGAS91 

SYNIAGLP092 

SYNIAGAR092 

SYNIAGALD91 

HCHO 

Total VOCs 

CO 

1.9995 Total VOCs 1.9997 

1.3000 CO 1.3000 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.004 HONO 

CH4 1.790 CH4 

Hz 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 2.1 x lo-3 NO2 + HONO 2.1 x lo-3 

wall.HN03 + NOn 2.0 x wall.HN03 + NOn 2.0 x 

Three Previous Chamber Events 

940809.SEG SynE85E-RCHO vs SynIAG run 

949808.SEG SynE85B vs SynIAG run 

940807.SEG SynE85E vs SynIAG run 

Photochemical Studies of Alternative Fuels 66 ESEIUNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.1 2: Initial Conditions for AU2494 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3227 NO, 0.3200 

NO 0.2633 NO 0.2659 

NO2 0.0594 NO2 0.054 1 

Initial Volatile Organic Compounds 

SynUrban 1.4999 SynUrban 1.4999 

SYNURBGAS91 0.587 1 SYNURBGAS91 0.587 1 

SYNURBLIQ91 0.8865 SYNURBLIQ91 0.8865 

CCHO 0.0 137 CCHO 0.0137 

HCHO 0.0 126 HCHO 0.0 126 

Synl AG 1.5010 SynE85E 1.4995 

SYNIAGGAS91 0.8475 SYNEPAE85GAS 0.2802 

SYNIAGLP092 0.2750 SYNEPAE85LIQ 0.2402 

SYNlAGAR092 0.3470 

SYNIAGALD91 0.0255 CCHO 0.09 12 

HCHO 0.0060 HCHO 0.0090 

ETOH 0.8788 

Total VOCs 

CO 

3.0009 Total VOCs 

1.3000 CO --- - - - 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.005 HONO 

CH4 1.790 CH4 

Hz 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 2.5 x NO2 --+ HONO 2.5 x 

wall.HN03 ---+ NO2 2.0 x wall.HN03 + NOn 2.0 x 

Three Previous Chamber Events 

940823.SEG SynE85E vs SynIAG run 

9408 12.SEG SynE85E vs SynIAG run 

9408 1 1 .SEG SynE85E vs SynIAG run 

Photochemical Studies of Alternative Fuels 68 ESEIUNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.13: Initial Conditions for AU2594 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3289 NO, 0.3267 

NO 0.268 1 NO 0.2674 

NO2 0.0608 NO2 0.0593 

Initial Volatile Organic Compounds 

SynUrban 1.4999 

SYNURBGASSI 0.587 1 

SYNURBLIQSI 0.8865 

CCHO 0.0 137 

HCHO 0.0 126 

Synl AG 1.5010 

SYNIAGGAS91 0.8475 

SYNIAGLP092 0.2750 

SYNIAGAR092 0.3470 

SYNIAGALD91 0.0255 

HCHO 0.0060 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

SynE85E 

SY NEPAE85GAS 

SY NEPAE85LIQ 

CCHO 

HCHO 

ETOH 

Total VOCs 

CO 

3.0009 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.001 HONO 

CH4 1.790 CH4 

H2 0.580 HP 

Chamber Model Parameters 

NO2 + HONO 2.9 x NO2 + HONO 2.9 x 

wall.HN03+N02 2.0 x wall.HN03 -+ NO2 2.0 x 

Three Previous Chamber Events 

940824.SEG SynE85E vs SynIAG run 

940823.SEG SynE85E vs SynIAG run 

940812.SEG SynE85E vs SynIAG run 

Photochemical Studies of Alternative Fuels 70 ESEIUNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.1 4: Initial Conditions for AU0394 

(Units are ppm or pprnC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NOx 0.3332 NOx 

NO 0.2786 NO 

NO2 0.0546 NO2 

Initial Volatile Organic Compounds 

SynUrban 0.9997 

SYNURBGASSI 0.3953 

SYNURBLIQSI 0.5909 

CCHO 0.0070 

HCHO 0.0065 

SynE85E 1.0134 

SYNE85BARTGAS 0.24 17 

SYNE85BARTLIQ 0.0955 

CCHO 

HCHO 

ETOH 

SynUrban 

SYNURBGASSI 

SYNURBLIQSI 

CCHO 

HCHO 

SynlAG 

SYNIAGGASSI 

SY N IAG LP092 

SYNIAGARO92 

SYNIAGALD91 

HCHO 

Total VOCs 

CO 

2.0131 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.002 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 HONO 2.5 x NO2 + HONO 2.5 x 

wall.HN03 + NO2 2.0 x wall.HN03 ---+ NO2 2.0 x 

Three Previous Chamber Events 

94080 1 .SEG Delta Toluene run 

940725.SEG SynCNG vs SynIAG run 

940720.SEG Delta Toluene run 

Photochemical Studies of Alternative Fuels 72 ESE/UNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.1 5: Initial Conditions for AU0894 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.2613 NO, 0.3229 

NO 0.3262 NO 0.2642 

No2 0.0649 NO2 0.0587 

Initial Volatile Organic Compounds 

SynUrban 0.9997 SynUrban 0.9997 

SYNURBGAS91 0.3953 SYNURBGAS91 0.3953 

SYNURBLIQ91 0.5909 SYNURBLIQ91 0.5909 

CCHO 0.0070 CCHO 0.0070 

HCHO 0.0065 HCHO 0.0065 

SynE85B 1.0134 Synl AG 1.0000 

SYNE85BARTGAS 0.24 17 SYNIAGGASSI 0.5650 

SYNE85BARTLIQ 0.0955 SYNIAGLP092 0.1830 

SYNIAGAR092 0.23 10 

CCHO 0.1 148 SYNIAGALD91 0.0 170 

HCHO 0.01 36 HCHO 0.0040 

ETOH 0.5478 

Total VOCs 

CO 

2.0131 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0,004 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 1.3 x NO2 + HONO 1.3 

wall.HNOs + NO2 2.0 x wall.HN03 --+ NO2 2.0 x 

Three Previous Chamber Events 

940807.SEG SynE85E vs SynIAG run 

940804.SEG SynE85E vs SynIAG run 

940803.SEG SynE85B vs SynIAG run 

. 

Photochemical Studies of Alternative Fuels 74 ESEAJNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

3.1.4 Block 3: Single Mixture Experiments 

Static, 10 hour experiments 

runs exist 

Syn lAG SynEBSE 

Block 3 experiments were conducted with different test mixtures, but without blending 

with SynUrb. These are part of the experimental design to provide experiments for modeling 

testing and to provide information to help understand the chemical processes in these pure 

mixtures. 

Run 14,6:1 Urb vs.ESSE 

ST0994 In this experiment E85 is compared directly against SynUrb (Table 3.16 and Figure 3.16). The E85 side resulted in an ozone maximum concentration 40% less than the 

SynUrb side. This experiment was repeated because of the cloudy conditions. 

ST1194 This is the repeat of the September 9 experiment (Table 3.17 and Figure 3.17). 

The E85 side resulted in an ozone maximum concentration 25% less than the SynUrb side, 

under more sunny conditions. 

Run 15,6:1 IAG vs. ESSE 

ST0894 This dual experiment compares E85 against IAG (Table 3.18 and Figure 3.18). 

The E85 side produced an ozone maximum 80% higher than the IAG side by the end of the 

experiment. 

Photochemical Studies of Alternative Fuels 76 ESEIUNC-CH 
CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.16: Initial Conditions for ST0994 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3350 NO, 

NO 0.2750 NO 

NO:! 0.0600 NO2 

Initial Volatile Organic Compounds 

SynE85E 1.9996 

SYNEPAE85GAS 0.3738 

SYNEPAE85LIQ 0.3202 

CCHO 0.1216 

HCHO 0.0 122 

ETOH 1.1718 

Total VOCs 1.9996 

CO 1.3000 

SynUrban 1.9994 

SYNURBGASSI 0.7906 

SYNURBLIQ91 1.1818 

CCHO 0.0 140 

HCHO 0.0 130 

sn 

Total VOCs 1.9994 

CO 1.3000 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 -. 

Estimated Initial Compounds 

HONO 0.003 HONO 

CH4 1,790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 --+ HONO 2.5 x NO2 --+ HONO 2.5 x 

wall.HN03 + NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

940908.SEG SynE85E vs SynIAG run 

94083 1 .SEG Delta CO run 

940829.SEG Delta CO run 

Photochemical Studies of Alternative Fuels 77 ESEIUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.1 7: Initial Conditions for ST1 194 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3464 NO, 0.3448 

NO 0.2868 NO 0.2879 

NO2 0.0596 No2 0.0569 

Initial Volatile Organic Compounds 

SynUrban 1.9994 SynE85E 1.9996 

SYNURBGASSI 0.7906 SY NEPAE85GAS 0.3738 

SYNURBLlQ91 1.1818 SYNEPAE85LIQ 0.3202 

CCHO 0.0 140 CCHO 0.1216 

HCHO 0.0 130 HCHO 0.0 122 

ETOH 1.1718 

Total VOCs 

CO 

1.9994 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.005 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 --+ HONO 2.2 lom3 NO2 + HONO 2.2 x lo-3 

WUZZ.HNO~ + NO2 2.0 x wdZ.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

940909.SEG SynE85E vs SynURE3 run 

940908.SEG SynE85E vs SynIAG run 

94083 1 .SEG Delta CO run 

Photochemical Studies of Alternative Fuels 79 ESE/UNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.1 8: Initial Conditions for ST0894 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3380 NO, 

NO 0.2727 NO 

NO2 0.0653 NO2 

Initial Volatile Organic Compounds 

Synl AG 2.0000 SynE85E 1.9996 

SYNIAGGASSI 1.1300 SYNEPAE85GAS 0.3738 

SYNIAGLP092 0.3660 SYNEPAE85LIQ 0.3202 

SYNIAGAR092 0.4620 

SYNIAGALDSI 0.0340 CCHO 0.1216 

HCHO 0.0080 HCHO 0.0 122 

ETOH 1.1718 

Total VOCs 

CO 

2.0000 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.003 HONO 0.0 10 

CH4 1.790 CH4 1.790 

Hz 0.580 H2 0.580 

Chamber Model Parameters 

NO2 + HONO 2.5 x NO2 + HONO 2.5 x 

wall.HN03 -+ NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

94083 1 .SEG Delta CO run 

940829.SEG Delta CO run 

940826.SEG SynCNG vs SynIAG run 

Photochemical Studies of Alternative Fuels 8 1 ESEIUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

3.1.5 Block 4: Mixture Variations 

Static, 10 hour experiments 

SynUrban Hc'Nox 2 SynUrban j18 SynUrbarVSynIAG 29 2 21 

ST1494 " ST1 594 ST2894 AU0994 0C0994 

SynUrbanI SynUrbanI SynUrbaniSynE85 

SynE8SE SynE85E no ETOH xt ETOH no acetaldehyde 

Equal Mass 

Block 4 experiments were conducted with test mixtures that were variations of the 

standard mixtures and our standard experiments. These were added as the results of other 

experiments were obtained and designed to help answer questions. Some of these were 

designed to demonstrate, by direct observation, the effects of modifying the composition, 

including injecting more or less of certain mixture components. One was designed to test 

the effect of comparing equal mass experiments rather than the standard matched carbon 

experiments. Although all of these are useful for chemical mechanism testing, a few were 

part of the experimental design more to provide experiments for modeling stress testing and 

to help understand the chemical processes in these pure, but modified, mixtures. 

Run 17,6:1 Urb versus Urb/ESSE 

ST1494 This experiment tests the effect of substituting 50% of the SynUrb with the E85 

mixture (Table 3.19 and Figure 3.19) by comparing directly against unsubstituted SynUrb. 

The E85 side resulted in a 20% reduction in ozone maximum concentration. 

Run 18,6:1 Urb versus Urb/E85E (no ETOH) 

ST1594 This experiment tests the ozone contribution of the EtOH by removing the ethanol 

from the E85 mixture, in a experiment designed to compare with the September 14 substi- 

. tution experiment (Table 3.20 and Figure 3.20). 

Run 19,6:1 UrbnAG versus UrblE85 (xt EtOH) 

ST2894 This experiment was conducted with extra ethanol, to provide for a wider range 

of mechanism testing conditions (Table 3.2 1 and Figure 3.2 1). 

Run 20,6:1 Urb/IAG versus UrblES5 (no aldehyde) 

AU0994 This experiment was designed to show the effect of removing the acetaldehyde 

from the E85 mixture (Table 3.22 and Figure 3.22), to help understand the reactivity of the 

Photochemical Studies of Alternative Fuels 83 ESEfUNC-CH 
3.1 OUTDOOR SMOG CHAMBER RESLETS CHAPTER 3. EXPERIMENTAL RESULTS 

E85 mixture, and to provide a wider range of chemical mechanism tests. Even with no initial 

acetaldehyde, the E85 mixture was more reactive than IAG with aldehyde. 

Run 21,6:1 Urb/IAG versus UrbIE85 (equal mass) 

OC0994 This experiment was designed to show the comparison of a matched-mass (including the mass of oxygen) version of E85 vs IAG when blended with the SynUrb (Table 3.23 

and Figure 3.23). This experiment resulted in equal ozone production. 

Photochemical Studies of Alternative Fuels 84 ESE/TJNC-CH 
CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.1 9: Initial Conditions for ST1494 

(Units are ppm or ppmC) 

Rl3D SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3469 NO, 0.3440 

NO 0.2865 NO 0.2864 

No2 0.0604 NO2 0.0576 

Initial Volatile Organic Compounds 

SynUrban 0.9997 SynUrban 1.9994 

SYNURBGASSI 0.3953 SYNURBGAS91 0.7906 

SYNURBLIQ91 0.5909 SYNURBLIQ91 1.1818 

CCHO 0.0070 CCHO 0.0140 

HCHO 0.0065 HCHO 0.0130 

SynE85E 0.9998 

SYNEPAE85GAS 0.1869 

SYNEPAE85LIQ 0.1601 

CCHO 0.0608 

HCHO 0.006 1 

ETOH 0.5859 

Total VOCs 1.9995 Total VOCs 1.9994 

CO 1.3000 CO 1.3000 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.003 HONO 0.003 

CH4 1.790 CH4 1.790 

H2 0.580 H2 0.580 

Chamber Model Parameters 

NO2 + HONO 2.9 x NO2 + HONO 2.9 

wall.HN03 + NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

9409 12.SEG Matched SynURB run 

9409 1 1 .SEG SynE84E vs SynURB run 

940909.SEG SynE85E vs SynURB run 

Photochemical Studies of dlternative Fuels 85 ESEIUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.20: Initial Conditions for ST1594 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3433 NO, 0.3431 

NO 0.2794 NO 0.2799 

NO2 0.0639 NO2 0.0637 

Initial Volatile Organic Compounds 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

SynE85E 

SYNEPAE85GAS 

SYNEPAE85LIQ 

CCHO 

HCHO 

0.9997 SynUrban 1.9994 

0.3953 SYNURBGAS91 0.7906 

0.5909 SY NURBLIQ91 1.1818 

0.0070 CCHO 0.01 40 

0.0065 HCHO 0.01 30 

0.4139 

0.1869 

0.1601 

0.0608 

0.006 1 

Total VOCs 

CO 

1.4136 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.004 HONO 0.004 

CH4 1.790 CH4 1.790 

H2 0.580 H2 0.580 

Chamber Model Parameters 

NO2 + HONO 2.9 x NO2 + HONO 2.9 x 

wall.HNOs + NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

940914.SEG SynE85E vs SynUFU3 run 

9409 12.SEG Matched SynURB run 

94091 1 .SEG SynE85E vs SynURB run 

Photochemical Studies of Alternative Fuels 87 ESEIUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.21 : Initial Conditions for ST2894 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3257 NO, 0.3277 

NO 0.2600 NO 0.264 1 

NO2 0.0657 NO2 0.0636 

Initial Volatile Organic Compounds 

SynUrban 0.9997 SynUrban 0.9997 

SYNURBGAS91 0.3953 SYNURBGASSI 0.3953 

SYNURBLlQ91 0.5909 SYNURBLIQ91 0.5909 

CCHO 0.0070 CCHO 0.0070 

HCHO 0.0065 HCHO 0.0065 

SynE85E 0.9998 Syn IAG 1.~00 

SYNEPAE85GAS 0.1869 SYN IAGGAS91 0.5650 

SYNEPAE85LIQ 0.1601 SY NIAGLP092 0.1830 

SYNIAGAR092 0.23 10 

CCHO 0.0608 SYNIAGALDSI 0.0170 

HCHO 0.006 1 HCHO 0.0040 

ETOH 0.7000 

Total VOCs 

CO 

1.9995 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.006 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 1.6 x NOn + HONO 1.6 x 

wall.HN03 + NO2 2.0 x lo-' wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

940927.SEG SynLPG vs SynIAG run 

940924.SEG SynLPG vs SynIAG run 

940920.SEG Night Isoprene run 

Photochemical Studies of Alternative Fuels 89 ESEtUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.22: Initial Conditions for AU0994 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

Initial Volatile Organic Compounds 

SynUrban 0.9997 

SYNURBGAS91 0.3953 

SYNURBLIQ91 0.5909 

CCHO 0.0070 

HCHO 0.0065 

CCHO 

HCHO 

ETOH 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

Syn l AG 

SYNIAGGAS91 

SYNIAGLP092 

SY NIAGARO92 

SY N IAGALD91 

HCHO 

Total VOCs 

CO 

1.9387 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.005 HONO 

CH4 1.790 (3-44 

H2 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 2.3 x lom3 NO2 + HONO 2.3 x 

wall.HN03 -+ NOn 2.0 x wall.HNOs+N02 2.0 x 

Three Previous Chamber Events 

940808.SEG SynE85B vs SynIAG run 

940807.SEG SynE85E vs SynIAG run 

940804.SEG SynE85E vs SynLAG run 

Photochemical Studies of Alternative Fuels 91 ESEIUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

Table 3.23: Initial Conditions for OC0994 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3191 NO, 0.3176 

NO 0.2683 NO 0.2650 

NO2 0.0508 NO2 0.0526 

Initial Volatile Organic Compounds 

SynUrban 

SYNURBGASSISYNURBLIQ91 

CCHO 

HCHO 

SynE85E 

SYNEPAE85GAS 

SYNEPAE85LIQ 

CCHO 

HCHO 

ETOH 

SynUrban 

SYNURBGAS91 

SYNURBLIQ91 

CCHO 

HCHO 

Synl AG 

SYNIAGGAS91 

SYNIAGLP092 

SYNIAGAR092 

SYNIAGALD91 

HCHO 

Total VOCs 

co 

1.8460 Total VOCs 

1.3000 CO 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.003 HONO 

(3-44 1.790 CH4 

H2 0.580 HP 

Chamber Model Parameters 

NOa + HONO 2.5 x NO2 --+ HONO 2.5 x 

wall.HN03 + NO2 2.0 x wall.HN03--+NO2 2.0 x 

Three Previous Chamber Events 

940928.SEG SynE85E + EtOH vs SynIAG run 

940927.SEG SynLPG vs SynIAG run 

940924.SEG SynLPG vs SynIAG run 

Photochemical Studies of Alternative Fuels 93 ESEIUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.1 OUTDOOR SMOG CHAMBER RESULTS 

3.1.6 Block 6: Characterization 

500 100 250 6: 1 6: 1 Met:2cl :2 ~~3194 :i23 prOpen;L$ synu

Methane Propene SynUrban 

250 250 250 

Block 6 experiments were used to deterrnine the performance of the chamber and to 

identify its contribution to the results of the smog chamber experiments. Matched injection 

experiments demonstrated that the chambers were matched and there were no problems 

in injecting and replicating test mixtures into the chambers. Chamber matching assures 

that there is minimal dilution from sampling, no major leaks in chamber, no leaks in the 

sample lines, matched wall reactivity, and matched sun (no major shadows effecting one side 

of the chamber). Chamber characterization experiments are conducted with either simple 

compounds with better-understood chemistry or with a basic test mixture such as SynUrb. 

Run C2,100 pprn CO versus 250 pprn CO 

AU3093 This experiment compares 100 pprn of CO with 250 pprn CO (Table 3.24 and 

Figure 3.24). It was designed to show the wall radical flux of the chambers. 

AU3194 This experiment compares 100 pprn of CO with 250 pprn CO (Table 3.25 and 

Figure 3.25). It was designed to show the wall radical flux of the chambers. 

Run C3,250 pprn CO versus 250 pprn CO 

AU2994 This experiment compares 250 pprn of CO with 250 pprn CO at different ratios 

of initial NOn(Table 3.26 and Figure 3.26). It was designed to show the wall radical flux of 

the chambers. 

Run C4, Propene vs. Propene 

JL0194 This experiment compares 100% propene with 100% propene at 6: 1 (Table 3.27 

and Figure 3.27). It was designed to show that the two chamber halves are matched. 

Run C5,6:1 Urb versus Urb 

ST1294 This experiment compares 100% SynURB with 100% SynURBat 6: 1 (Table 3.28 

and Figure 3.28). It was designed to show that the two chamber halves are matched and 

that we could replicate complex mixtures. 

Photochemical Studies of Alternative Fuels 95 ESENNC-CH 
3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.24: Initial Conditions for AU3093 

(Units are ppm or ppmC) 

-. . .. 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3198 NO, 

NO 0.2757 NO 

NO2 0.044 1 NO2 

Initial Volatile Organic Compounds 

Total VOCs 0.0000 Total VOCs 0.0000 

CO 250.0000 CO 100.0000 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.007 HONO 

CH4 1.790 CH4 

H2 0.580 )-f2 

Chamber Model Parameters 

NO2 + HONO 0.37 x NO2 + HONO 0.7 x 

wall.HN03 + NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

Photochemical Studies of Alternative Fuels 96 ESEIUNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.25: Initial Conditions for AU3194 

(Units are ppm or ppmC) - 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3594 NO, 0.3614 

NO 0.2733 NO 0.27 13 

NO2 0.086 1 NO2 0.090 1 

Initial Volatile Organic Compounds 

Total VOCs 0.0000 Total VOCs 0.0000 

CO 250.0000 CO 1 00.0000 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.007 HONO 0.007 

CH4 1.790 CH4 1.790 

H2 0.580 H2 0.580 

Chamber Model Parameters 

NO2 --+ HONO 0.37 x NO2 + HONO 0.7 x 

wall.HN03 NO2 2.0 x 1W3 wall.HN03+N02 2.0 x 

Three Previous Chamber Events 

940829.SEG Delta CO run 

940826.SEG SynCNG vs SynIAG run 

940825.SEG SynE85E vs SynIAG run 

Photochemical Studies of Alternative Fuels 98 ESE/UNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.26: Initial Conditions for AU2994 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3413 NO, 0.3415 

NO 0.2864 NO 0.2597 

NO2 0.0549 NO2 0.08 18 

Initial Volatile Organic Compounds 

CO 250.0000 CO 25 0.0000 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.003 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 --+ HONO 2.5 x NO2 --+ HONO 2.5 x 

wall.HNOs * NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

940826.SEG SynCNG vs SynIAG run 

940825.SEG SynE85E vs SynIAG run 

940824.SEG SynE85E vs SynIAG run 

Photochemical Studies of Alternative Fuels 100 ESE/UNC-CH 

3.1 OUTDOOR SMOG CHAMBER RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.27: Initial Conditions for JL0194 

(Units are ppm or pprnC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3021 NO, 0.3057 

NO 0.2047 NO 0.2095 

NO2 0.0974 No2 0.0962 

Initial Volatile Organic Compounds 

CC : C 1 .SO00 CC:C 1.5000 

Total VOCs 1.5000 Total VOCs 1.5000 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

Estimated Initial Compounds 

HONO 0.003 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 --+ HONO 2.5 x NO2 --+ HONO 2.5 x 

wall.HN03 --+ NO2 2.0 x lov3 wall.HN03+N02 2.0 x 

Three Previous Chamber Events 

93 1006.SEG previous season run 

930930.SEG previous season run 

930929.SEG previous season run 

Photochemical Studies of Alternative Fuels 102 ESEIUNC-CH 

3.1 OUTDOOR SMOG CHAMBER REStTLTS CHAPTER 3. EXPERIMENTAL RESULTS 

Table 3.28: Initial Conditions for ST1294 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3229 NO, 0.3196 

NO 0.2666 NO 0.2662 

No2 0.0563 NO2 0.0534 

Initial Volatile Organic Compounds 

SynUrban 1.9994 SynUrban 1.9994 

SYNURBGAS91 0.7906 SYNURBGAS91 0.7906 

SYNURBLIQSI 1.1818 SYNURBLIQ91 1.1818 

CCHO 0.0140 CCHO 0.0 140 

HCHO 0.01 30 HCHO 0.0 130 

Total VOCs 

CO 

1.9994 Total VOCs 

1.3000 co 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

Estimated Initial Compounds 

HONO 0.003 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NOe --+ HONO 2.1 x lo-3 NO2 + HONO 2.1 x 

wall.HN03 -+ NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

94091 1 .SEG SynE85E vs SynURB run 

940909.SEG SynE85E vs SynURB run 

940908.SEG SynE85E vs SynIAG run 

Photochemical Studies of Alternative Fuels 1 04 ESE/UNC-CH 

3.2 RELEVANT CRC METHANOL STUDY RESULTS CHAPTER 3. EXPERIMENTAL RESULTS 

3.2 Relevant CRC Methanol Study Results 

Because the chamber was re-built and the Teflon film was replaced with new film in early 

1994, we found it useful to compare the results from the 1994 experiments with those for 

the same SynURB and SynlAG mixtures from the CRC study performed in 1991 and 1992. 

These older CRC experiments will be re-simulated in the next chapter in comparison to the 

new DOE experiments. To introduce these results in the same way as the DOE runs the 

initial conditions tables and standard plots are shown here. 

Table 3.29 and Figure 3.29 show, on one side, a 6: 1 HC-toNO, experiment using 5050 

SynURB/SynlAG; we are not interested in the SynM85 parts of this experiment. Similarly, 

Table 3.30 and Figure 3.30 show, on one side, a 6:l HC-toNO, experiment using 5050 

SynURBISynlAG; we are also not interested in the SynM85 parts of this experiment. Table 3.3 1 and Figure 3.3 1 show a dual 6: 1 HC-toNOx experiment using only SynURB. Finally, 

Table 3.32 and Figure 3.32 show a chamber characterization experiment performed with 

500 ppm and 250 ppm methane. In addition to these, we will use an older experiment performed with just the paraffinlolefin portion of SynURB and an experiment performed with 

just the aromatic portion of SynURB (these will be shown in the next chapter). 

Photochemical Studies of Alternative Fuels 106 ESEIUNC-CH 
CHAPTER 3. EXPERIMENTAL RESULTS 3.2 RELEVANT CRC METHANOL STUDY RESULTS 

Table 3.29: Initial Conditions for JL1092 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3517 NO, 0.3553 

NO 0.2676 NO 0.269 1 

NO2 0.084 1 NO2 0.0862 

Initial Volatile Organic Compounds 

SynUrban 1.0341 SynUrban 1.0457 

SYNURBGAS91 0.4029 SYNURBGAS91 0.4143 

SYNURBLIQSI 0.6177 SYNURBLIQ91 0.6179 

CCHO 0.0070 CCHO 0.0070 

HCHO 0.0065 HCHO 0.0065 

Syn IAG 1.0413 SynM85 1.0107 

SYNlAGGAS91 0.5876 SYNM85GAS91 0.3592 

SYNIAGLIQ91 0.4327 SYNM85LIQ91 0.2339 

SYN IAGALD91 0.0170 SYNM85ALD91 0.0033 

HCHO 0.0040 HCHO 0.0227 

MEOH 0.4143 

Total VOCs 

CO 

2.0754 Total VOCs 

1.3200 CO 

Other Measured Initial Compounds 

BVOC9109 0.150 BVOC9109 

Estimated Initial Compounds 

HONO 0.008 HONO 

CH4 1.790 CH4 

H2 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 0.1 lo-3 NO2 + HONO 0.1 x lo-3 

wall.HNO3 + NO2 2.0 x lov3 wall.HN03 -+ NO2 2.0 x 

Three Previous Chamber Events 

JL0992.SEG SynUrban vs. SynUrban 

JL0892.SEG SynUrban vs. SynUrban 

JL0792.SEG CO vs. CO 

Photochemical Studies of Alternative Fuels 107 ESEIUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULITS 3.2 RELEVANT CRC METHANOL STUDY RESULTS 

Table 3.30: Initial Conditions for JL1592 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3393 NO, 0.3365 

NO 0.2760 NO 0.2750 

No2 0.0633 NO2 0.0614 

Initial Volatile Organic Compounds 

SynUrban 1.1223 SynUrban 1.0561 

SYNURBGAS91 0.4276 SYNURBGAS91 0.4 164 

SYNURBLIQ91 0.6819 SYNURBLIQQI 0.6235 

CCHO 0.0070 CCHO 0.0070 

HCHO 0.0058 HCHO 0.0093 

SynM85 1.1210 Syn l AG 1.0668 

SYNM85GAS91 0.3708 SYNIAGGAS91 0.6072 

SYNM85LIQ91 0.2582 SYNIAGLIQ91 0.4368 

SYNM85ALD91 0.0033 SYNIAGALD91 0.0170 

HCHO 0.0612 HCHO 0.0058 

MEOH 0.4275 -- - 

Total VOCs 2.2433 Total VOCs 2.1229 

CO 1.3200 CO 1.2900 - - --- 

Other Measured Initial Compounds 

BVOC9109 0.136 BVOC9109 -- -- - - 

Estimated Initial Compounds 

HONO 0.009 HONO 0.009 

CH4 1.790 CH4 1.790 

H2 0.580 H2 0.580 

Chamber Model Parameters 

NO2 + HONO 1.8 x NO2 + HONO 1.4 x 

wall.HN03 + NO2 2.0 x wall .HN03 * NO2 2.0 x 

Three Previous Chamber Events 

JL1492.SEG SynUrban/lAG vs. SynUrban/M85 

JL 1392.SEG CarterMixISynUrban vs. SynUrban 

JL1092.SEG SynUrbanIlAG vs. SynUrbanlM85 

Photochemical Studies of Alternative Fuels 109 . ESEIUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.2 RELEVANT CRC METHANOL STUDY RESULTS 

Table 3.31 : Initial Conditions for JL0992 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3800 NO, 0.3770 

NO 0.3 170 NO 0.3 160 

NO2 0.0640 NO2 0.0610 

Initial Volatile Organic Compounds 

SynUrban 2.4494 SynUrban 2.5804 

SYNURBGAS91 0.88 11 SYNURBGAS91 0.8606 

SYNURBLIQSI 1.5459 SYNURBLIQ91 1.6974 

CCHO 0.0224 CCHO 0.0224 

Total VOCs 

CO 

2.4494 Total VOCs 

1.450 CO 

Other Measured Initial Compounds 

BVOC9109 0.150 BVOC9109 

Estimated Initial Compounds 

HONO 0.008 HONO 0.008 

CH4 1.790 CH4 1.790 

H2 0.580 H2 0.580 

Chamber Model Parameters 

NO2 -+ HONO 2.5 x NO2 --+ HONO 2.5 x 

wall.HN03 ---+ NO2 2.0 x wall.HN03 + NO2 2.0 x 

Three Previous Chamber Events 

JL0892.SEG SynUrban vs. SynUrban 

JL0792.SEG CO run 

JN2892.SEG methacrolein run 

Photochemical Studies of Alternative Fuels 111 ESEIUNC-CH 

CHAPTER 3. EXPERIMENTAL RESULTS 3.2 RELEVANT CRC METHANOL STUDY RESULTS 

Table 3.32: Initial Conditions for JL3092 

(Units are ppm or ppmC) -- 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.3544 NO, 0.3544 

NO 0.3 100 NO 0.3 109 

NO2 0.0445 NO2 0.0439 

Initial Volatile Organic Compounds 

CH4 500.0000 CH4 

Other Measured Initial Compounds 

CO 0.3 10 CO 

BVOC9109 0.150 BVOC9109 . . 

Estimated Initial Compounds 

HONO 0.008 HONO 0.0 12 

CH4 1.790 CH4 1.790 

H2 0.580 H2 0.580 

Chamber Model Parameters 

NO2 + HONO 1.0 NO2 + HONO 1.4 lo13 

wall.HNOs ---t NO2 2.0 x lob3 wall.HN03 - NO2 2.0 x lom3 

Three Previous Chamber Events 

JL2992 aborted run 

JL1592.SEG Urb:IAG vs. Urb:M85 

JL1492 aborted run 

Photochemical Studies of Alternative Fuels 113 ESEIUNC-CH 

Chapter 4 

Modeling Results 

4.1 The Simulation Process 

Figure 4.1, taken from the EPA's oxidant mechanism evaluation protocol guidance document 

[5], is a schematic representation of the chamber simulation process. In this figure it was 

assumed that in a full evaluation of a mechanism, chamber data from at least four different 

chambers would be used. In our evaluation below, we used only data produced in this 

DOE funded project and some data produced in the previous CRC methanol fuels project 

in the UNC dual chamber. Thus, we only have one Chamber Auxiliary Mechanism (i.e., 

UNCCham-rxn that contains chamber specific reactions for the UNC chamber) that was ' combined with only one Principal Reaction Mechanisms (i.e., the CB4) to produce one 

Combined Mechanism (CB4+UNCCham.rxn) that was the one actual tested. 

For the UNC chamber, the vast majority of the operations in Figure 4.1 are automated, 

meaning that computer programs (with supporting input data bases) were used to perform 

the operations. Thus, documentation of the detail procedures exists in the form of the source 

codes for these programs. All of the processing programs (and documentation) needed for 

MS-DOS personal computers to carry out the operations shown in Figure 4.1, as well as 

all of the chamber data files, the simulation data files, and the summary measures files, are 

available from UNC. All can be obtained from the Internet in various sub-directories at URL 

ftp://airsite.unc.edu/pub/pgm~/ese~unc/je££ries/. 

Detail information on the simulation process, on the UNC Quality Assurance activities 

and descriptions of standard modeling procedures are available in UNC's CRC final report 

[4]. This report is also available on the World Wide Web at URL 

CHAPTER 4. MODELING RESULTS 4.2 TEMPERATURE EFFECTS 

4.2 Temperature Effects 

After the outdoor smog chamber was rebuilt in 1994, we observed much lower afternoon 

temperature maximums compared to previous recent seasons. In checking over the the entire 

20-year data set, we determined that in experiments conducted in the late 1970s and early 

1980s the reported afternoon chamber temperatures were also lower than those measured 

in the early 1990's. 

Fortunately, as part of our standard QA/QC efforts, we had installed a backup temperature measurement system to allow for frequent checking of the chamber's standard chamber 

temperature measurement system. This comparison record is extensive, spanning five years, 

and it includes frequent comparison of morning and afternoon temperature measurements 

by the two systems. In addition, on noticing the lower temperature measurements in 1994, 

we checked the temperature measurement system calibration with a reference mercury thermometer used in place with the other two systems. These efforts have assured us that the 

reported temperature data are accurate. 

It is clear that changing the Teflon film after more than 15 years of use resulted in 

a large chamber temperature difference before and after the change. From direct analysis 

of the film surface, we know that the old Teflon film walls were coated with very fine 

particulate matter. We actually determined, by secondary ion emission time-of-flight mass 

spectrometry (SIMSD'OF) on samples of the aged film, that it contained significant sulfate 

on the surface. Because we have never injected sulfur-containing compounds into the 

chamber, we surmised that the sulfate came from atmospheric deposition of airborne sulfate 

particles introduced during chamber venting with ambient air. The charge on the Teflon 

walls attracts and holds such fine particulates. In addition, in the late 1980s and early 1990s 

we conducted inside and outside spectral radiation measurements in the chamber as part of 

the actinic flux mathematical model development. These spectral data show very elevated 

near-infrared radiation inside the chamber. Because Teflon film itself has only a few bands 

in the infrared, the increase in chamber heat is most likely due to an enhanced greenhouse 

effect caused by the particles absorbing outgoing radiation and reradiating it back into the 

chamber. The new Teflon film is much clearer than the old film, has not acquired much of a 

particle layer, and thus exhibits a significantly smaller greenhouse effect than did the older 

film. 

4.2.1 Effect of Temperature on Ozone 

We compared all replicate complex mixture smog chamber experiments conducted between 

1990 and 1994 to determine the effect of the different temperature conditions on the experiments (we excluded low HC:NOx ratio experiments and October experiments). Two types 

of replicate experiments of interest have been found: SynURB and the 5050 blend of the 

IAG with the SynURB at two different levels of HC. Experiments used for comparison were 

similar in terms of initial conditions and sunlight quality. The following figures present the 

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4.2 TEMPERATURE EFFECTS CHAPTER 4. MODELING RESULTS 

nitrogen oxides, ozone, temperature, dew point data, and the UV time series (TSR is omitted 

for clarity). 

Syn URB-only experiments 

Figure 4.2 compares experiments conducted in the fall on three different days, September 12, 

and 15 of 1994 and September 12 199 1. The initial conditions were similar. The 199 1 day 

was hotter than the 1994 days. Little difference can be observed in the experimental results. 

The 1991 day did, however, produce less ozone even though it was the hotter day. Notice 

that the hottest day does have the lowest afternoon "NO2". (Remember this is not really NO2 

but any nitrogen species that can be converted to NO by the instrument's converter. This 

includes PAN, organic nitrates, and some nitric acid.) The hotter conditions should result in 

less PAN, which is very temperature-labile; thus, more NO2 should be converted to organic 

nitrates and nitric acid. 

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4.2 TEMPERATURE EFFECTS CHAPTER 4. MODELING RESULTS 

SynU RB/Syn l AG blend experiments 

Figure 4.3 compares two blended IAG and SynURB experiments conducted in the fall. The 

initial conditions were within 5%. The 1991 experiment was much hotter than the 1994 

experiment, yet little difference is observed in the experimental results, and the differences 

that were observed might be explainable in terms of the "holes" in the afternoon solar 

radiation (caused by clouds) in the 1994 experiment. Notice again the difference in afternoon 

"NO2", 

Figure 4.4 compares two blended IAG and SynURB experiments conducted in the summer. The initial conditions are similar except that the 1992 day contained approximately 

15% more NO,. The 1992 experiment was much hotter than the 1994 experiment. Again 

very little difference was observed between these two runs. We were quite surprised by this 

outcome. One normally assumes that additional NOx should have resulted in more ozone 

production, but the fact that the extra NOx was mostly NO2 may have led to increased .OH 

radical termination. 

Figure 4.5 compares two blended IAG and SynURB experiments conducted in the summer. The initial conditions are similar except that the 1992 experiment contained approximately 10% more NO,. The 1992 experiment was much hotter than the 1994 experiment. 

Again, little difference was observed in the ozone experimental results. 

. - 

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4.2 TEMPERATURE EFFECTS CHAPTER 4. MODELING RESULTS 

Two other experiments with similar initial conditions may be compared with the August 

1 1, 1994 experiment. These were conducted on August 7, 1994, August 16, 199 1 and 

August 25, 1992 (see Figure 4.6). Solar radiation was similar on these days (except that it 

was slightly more cloudy on August 25). The two pre-1994 days were not as hot as were the 

July 10 and 15, 1992 days, being only 10 to 15OF warmer. This comparison shows much 

less ozone production in the 1991 and 1992 experiments even though these had slightly 

more initial NOx. It appears that if the sun had been more clear on the August 25, 1992 day, 

it might have matched the ozone production better with the August 16, 199 1 day. 

Comparison can also be made at a higher HC:NOx ratio (see Figure 4.7). Again, 

the August 26, 1994 experiment produced more ozone than the closely matching replicate 

experiments conducted on August 2 1,199 1 and August 3 1,1992. The two older experiments 

shown here were not as hot as the July 10 and 15, 1992 experiments, being only 10 to 15OF 

hotter. 

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CHAPTER 4. MODELING RESULTS 4.2 TEMPERATURE EFFECTS 

4.2.2 Modeled Temperature Effect 

Methane 

As a standard chamber characterization experiment, we performed high concentration 

methaneIN0, experiments. Because the chemistries of methane and its products are believed to be fully understood, these methane experiments served to verify that we have the 

physical parameters of the chamber well characterized. This means that any difficulties 

in simulating the CH4/N0, experiments implies that something is wrong with the specified 

chamber conditions, not that anything is wrong with the principal methane chemical reaction 

mechanisms. In the CRC study performed in 1991 and 1992, we ran several CH4 experiments. One that is relevant to this discussion is shown in Figure 4.8 and Figure 4.9. The 

figures include the predictions of the CB4 mechanism using the photolytic and temperature 

conditions measured on this day. Clearly the CB4 mechanism (with an update to the CH4 + 

OH rate constant in the published CB4 mechanism) simulates methaneIN0, conditions well, 

and this supports the conclusion that the physical conditions specified for the chamber and 

the inorganic and basic organic radical chemistries in the CB4 mechanism are reasonably 

accurate representations of the real-world conditions. 

modlobs: TSR UV 

0 0.0 

5 6 7 -8 9 10 11 12 13 14 15 16 17 18 19 

HOURS, EDT 

Figure 4.8: Solar radiation simulation for JL3092. 

Photochemical Studies of Alternative Fuels 127 ESE/UNC-CH 

CHAPTER 4. MODELING RESULTS 4.2 TEMPERATURE EFFECTS 

SynURB Mixtures 

Figure 4.10 shows simulations of two special experiments we performed in the CRC project 

to investigate the difficulties simulations with the CB4 had in fitting the SynURB experiments. 

In the top plot, the only species included in the VOCs injected into the chamber were alkanes 

and alkenes. For this condition, the simulations agreed quite well with the observations. In 

the bottom plot of the figure, the only species included in the VOCs injected into the chamber 

were aromatic species. The models performed very poorly for this condition, grossly overpredicting the observations. These results are consistent with the methane simulation shown 

just above. 

The CB4 mechanism exhibited poor performance in simulating the aromatic VOC components. The reactions of aromatic species that are used in CB4 were formulated without 

knowledge of the details of the true reaction mechanisms or even pathways for aromatic 

oxidation. The properties of the model species were adjusted to permit the gross features 

(i.e., O3 time-series and VOC decay) of a very limited number (ca. 6) of single aromatic 

compounds experiments to be simulated. As will be shown below, however, CB4 does fit the 

cooler chamber experiments for SynURB mixtures. This suggests that most of the temperature problem in the mechanism is located in the aromatics portion. Only new fundamental 

knowledge of aromatic oxidation (see Chapter 7) can solve these mechanism formulation 

problems. 

In the CRC report, we showed that the simulations of the complex VOC mixture experiments performed with the CB4 mechanism were very reactive relative to the chamber 

observations, on average predicting ozone about 150% too high. As will be shown later 

in this chapter, however, the CB4 simulations of the 1994 experiments are in much better 

agreement with the chamber observations. This led us to conduct a series of modeling 

experiments in which we substituted the 1994 temperatures for the 1992 temperatures in 

the 1992 simulations (see Figure 4.1 1) to see if this was the cause of the model's large 

over-prediction in the CRC study. These results are shown in Figure 4.10 to 4.17. 

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4.2 TEMPERATURE EFFECTS CHAPTER 4. MODELING RESULTS 

These simulation results show that the CB4 mechanism exhibits a much larger temperature sensitivity for complex mixtures than is seen in the chamber observations. This 

explains why the CB4 performed so poorly for the CRC experiments, which were all "hot" 

relative to the DOE experiments reported here. 

4.2.3 Temperature Effect Conclusions 

The conclusions from these intercomparisons follow. 

The outdoor smog chamber experimental results suggest that when using complex 

mixtures with the realistic compositions of synthetic urban and IAG, there is at best 

a small temperature effect. In addition, there is some evidence to suggest that an 

inverse relationship may exist between temperature and ozone production for these 

mixtures. There does appear to be a difference in the afternoon NO, such that 

higher temperatures give lower NOy measurements suggesting that there may be a 

compensating effect between the amount of PAN-like compounds produced and the 

ozone produced. 

The experimental evidence refutes the large positive effect of temperature on ozone 

production that is demonstrated by the CB4 mechanism. 

These imply that although the CB4 simulations reported later in this report show excellent agreement with the chamber observations, this accuracy may merely result from having 

tuned the more complex organic chemistry in the CB4 using chamber experiments from the 

1980s in which the chamber temperatures were more like those in the 1994 data set. In 

comparisons with hotter chamber data from 1992, the CB4 exhibits too much increase of 

ozone with increasing temperature. 

Fortunately, the main application region for C84 in the Urban Airshed Model is more 

like the temperatures observed in the outdoor chamber in the 1994 experiments. Nevertheless, the results of such simulations must be taken with a "grain of salt." 

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CHAPTER 4. MODELING RESULTS 4.3 CHARACTERIZATION RUN SIMULATIONS 

4.3 Characterization Run Simulations 

4.3.1 Block 6 

500 100 250 6: 1 6: 1 

ST1 294 

Methane CO Propene SynUrban 

250 250 250 

Run C2,100 ppm CO versus 250 ppm CO 

Earlier chapters discussed the importance of the smog chamber characterization experiments. Briefly, they 

Test and demonstrate that the chambers and associated sampling apparatus are operating properly 

Demonstrate the reproducability of establishing test conditions and experimental results 

Help determine the chamber background reactivity 

Allow demonstration that our measurement of light and calculation of basic photolytic 

rates is sufficient for modeling experiments with compounds with well understood 

chemistry. 

AU3194 This experiment compares 100 ppm of CO with 250 ppm CO (see Figures 4.18 

and 4.19). It is designed to show, and estimate by modeling, the wall radical flux of the 

chambers. 

Run C5,6:1 Urb versus Urb 

* 

ST1294 This experiment compares 100% SynURB with 100% SynURBat 6: 1 (see Figures 

4.20 and 4.21). It is designed to show that the two chamber halves and the sample systems 

are matched, as well as the ability to replicate complex mixtures. 

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CHAPTER 4. MODELING RESULTS 4.4 ALTERNATIVE FUELS RUNS SIMULATIONS 

4.4 Alternative Fuels Runs Simulations 

The initial conditions tables in the experimental results chapter show the estimated initial 

conditions of HONO and the chamber model parameters used to model these experiments. 

These values were estimated by repeated simulations after adjusting the values to obtain the 

best simulation agreements with the experimental results, while keeping within the bounds 

for these values (as determined from simulations of the chamber characterization experiments, consideration of the chamber history before each experiment, and from measurements 

of HONO made in other experiments). The same or similar estimates were usually used for 

each chamber side for each experiment unless recent chamber history of experiments with 

widely different reactivity justified different chamber background estimates. 

4.4.1 Block 1 

Sy nUrbadSynlAG SynUrbadSynlAG 

SynUrbadSynCNG SynUrbanlSynCNG 

Run 2,6:1 UrbIIAG versus UrbICNG 

ST1093 This run shows that SynCNG has a significant benefit for reducing ozone compared 

to SynlAG even under cooler, shorter-day, fall conditions (see Figures 4.22 and 4.23). The 

CB4 mechanism was successfully in simulating this experiment. Given that 10 days of 

chamber venting preceded this experiment (the chamber was not used), the same chamber 

background estimates were used to model each side. The IAG side was over-predicted 

somewhat compared to the relatively good simulation of the SynCNG side. 

Run 3,9:1 UrbnAG versus UrbICNG 

ST2393 Usually at 9:l VOC:NO,, reactivity makes almost no difference. This run (Figure 4.25), however, shows that for CNG, the benefits extend to well beyond 9: 1. The solar 

radiation simulation is shown in Figure 4.24. The CB4 mechanism was used to successfully simulate this experiment. The IAG side was slightly over-predicted compared to the 

relatively good simulation of the SynCNG side. Given that two days of chamber venting 

preceded this experiment (the chamber was not used), and that the only previous experiment 

for six days prior was a methane run, the same chamber background estimates were used 

to model each side. It is gratifying that given that both this experiment and the September 10 experiment were preceded by minimal chamber use, the same chamber background 

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4.4 ALTERNATIVE FWELS RUNS SIMULATIONS CHAPTER 4. MODELING RESULTS 

estimates were used to successfully model these two experiments, which were of widely 

different chemical reactivity. Also, while the temperature maximum was the same for these 

two days (94*F), the morning temperatures were significantly different (69 OF on September 

10 and 56OF for September 23). 

Run 5,6:1 UrbflAG versus UrbICNG 

JL2594 This run (Figures 4.26 and 4.23 was a repeat of ST1093 in warm weather. This 

run shows that the benefits of CNG are sustained under warm, high sun conditions. The solar 

radiation simulation is shown in Figure 4.27. The CB4 mechanism was used to successfully 

simulate the IAG side (slightly early in ozone production). The SynCNG side was not 

simulated well. Standard chamber background estimates were used in the simulations. 

This chamber-side experiment is the only non-ethanol experiment that was poorly modeled 

(not even extreme chamber background esimates yielded a successful simulation). Given 

that the model simulation of this SynCNG experiment is consistent with other experimental 

results, we suspect that there is some undiscovered (at this time) problem with this particular 

experiment. The IAG side, however, is an excellent clear-sun, midsummer, dry experiment. 

Run 6,9:1 UrbflAG versus Urb/CNG 

AU2694 This was a repeat of Run 3 (ST2393) under warm, high-sun conditions. This run 

showed that the benefits of CNG are sustained under warm, high-sun conditions even for high 

HC:NO, ratios. The solar radiation simulation is shown in Figure 4.28. The CB4 mechanism 

was used to successfully simulate (very well) both sides of this experiment (Figure 4.29). 

Equal and relatively higher chamber background estimates were used to simulate both sides 

of this experiment, consistent with the chamber history. 

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CHAPTER 4. MODELING RESULTS 4.4 ALTERNATIVE FUELS RUNS SIMULATIONS 

4.4.2 Block 5 

Static, 10-hour experiment 

6: 1 9: 1 

SynUrban/SynlAG 

8 

SynUrbanISynLPG 

Run 7,6:1 UrbIIAG versus UrbLPG 

ST2794 The LPG experiments show a benefit in ozone production similar to CNG. The 

solar radiation simulation is shown in Figure 4.30. The CB4 mechanism was used to 

successfully simulate (very well) both sides of this experiment (Figure 4.31). Equal and 

low chamber background estimates were used to simulate both sides of this experiment 

consistent with the chamber history. 

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 

HOURS, EDT 

Figure 4.30: Run 7-Solar radiation simulation. 

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CHAPTER 4. MODELING RESULTS 4.4 ALTERNATIVE FUELS RUNS SIMULATIONS 

Block 2 

Static, 10 hour experiments Static, 10 hour experiments 

HC:NOX 4.5:1 6: 1 9: 1 HC:NOx 6: 1 

SynUrbanISynlAG SynUrban/SynlAG 

12 

SynUrbanlSynE85B 

Run 10,6:1 UrbLIAG versus UrbIESSE 

AU0794 This experiment compareed the reactivity of the EPA E85 and IAG compositions 

with blending 50:50 with SynURB. The E85 side yielded an ozone maximum concentration 

approximately 15 % higher than the IAG side. The CB4 mechanism was used to successfully 

simulate (very well) both sides of this experiment (Figures 4.32 and 4.33). Equal and 

low chamber background estimates were used to simulate both sides of this experiment, 

consistent with the chamber history. 

AU1194 This experiment was a repeat of AU0794 with better sun. The results are consistent. The solar radiation simulation is shown in Figure 4.34. The CB4 mechanism was used 

to successfully simulate (very well) both sides of this experiment (Figure 4.35). Equal and 

medium chamber background estimates were used to simulate both sides of this experiment, 

consistent with the chamber history. 

Run 11,9:1 UrbnAG versus Urb/ESSE 

AU2594 This experiment duplicated the results of the AU2494 experiment, showing 

matched reactivity at higher HC:NOx ratios. The CB4 mechanism was used to successfully simulate both sides of this experiment (Figure 4.36 and 4.37). Both sides were slightly 

under-predicted after a cloudy period which occured before the separation between the data 

and model results. Equal and relatively higher chamber background estimates were used to 

simulate both sides of this experiment, consistent with the chamber history. 

Run 12,6:1 Urb/IAG versus Urb/E85B 

AUOS94 This experiment compared the reactivity of the NIPER E85 and IAG compositions 

with blending 50:50 with SynUrb. The E85 side yielded an ozone maximum concentration 

approximately 35 percent higher than the IAG side. The solar radiation simulation is shown 

in Figure 4.38. The CB4 mechanism was used to successfully simulate (very well) both 

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4.4 ALTERNATIVE FUELS RUNS SIMULATIONS CHAPTER 4. MODELING RESULTS 

sides of this experiment (Figure 4.39). Equal and medium chamber background estimates 

were used to simulate both sides of this experiment consistent with the chamber history. 

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CHAPTER 4. MODELING RESULTS 4.4 ALTERNATIVE FUELS RUNS SIMULATIONS 

4.4.4 Block 3 

- 

Static, 10 hour experiments 

HC:NOX 6:l 6: 1 6: 1 

runs exist ST11 94 

SynlAG SynE85E SynE85E 

Run 14,6:1 Urb versus ESSE 

ST1194 This experiment compared the reactivity of the EPA E85 and SynURB compositions directly without blending with other mixtures. The E85 side yielded an ozone 

maximum concentration approximately 23 % lower than did the SynURB side. The solar 

radiation simulation is shown in Figure 4.40. The CB4 mechanism successfully simulated 

the SynURB side of this experiment (Figure 4.41), but over-predicted the E85 side. Equal and 

medium chamber background estimates were used to simulate both sides of this experiment, 

consistent with the chamber history. 

modlobs: TSR UV 

0 0.0 

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 

HOURS, EDT 

Figure 4.40: Run 14-Solar radiation simulation. 

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CHAPTER 4. MODELING RESULTS 4.4 ALTERNATIVE FUELS RUNS SIMULATlONS 

4.4.5 Block 4 

Static, 10 hour experiments I 

HC:NOx 6:1 6: 1 

SynUrban SynUrban SynUrbanISynl AG 

18 

SynUrbanI SynUrbani 

SynE85E SynE85E no ETOH xt ETOH no acetaldehyde 

Eaual Mass 

Run 17,6:1 Urb versus Urb/ESSE 

ST1494 This experiment compared the reactivity of the EPA E85 blended 5050 with 

SynURB (50 % substitution) against pure SynURB compositions. The E85 side yielded 

an ozone maximum concentration approximately 20 % lower than did the SynURB-only 

side. The CB4 mechanism successfully simulated the SynURBside of this experiment, but 

over-predicted the E85 side (Figures 4.42 and 4.43). Equal and medium-high chamber 

background estimates were used to simulate both sides of this experiment consistent with 

the chamber history. 

Run 18,6:1 Urb versus UrbIE85E (no ETOH) 

ST1594 This experiment repeated the September 14 experiment minus the ethanol (EtOH). 

The E85-minus-EtOH side yielded an ozone maximum concentration approximately 40 % 

lower than did the SynURB-only side, indicating the significant role in reactivity when 

compared to the September 14 experiment. The solar radiation simulation is shown in 

Figure 4.44. The CB4 mechanism successfully simulated the SynURB side of this experiment, 

but over-predicted the E85 side by approximately 15 % (Figure 4.45). Equal and mediumhigh chamber background estimates were used to simulate both sides of this experiment, 

consistent with the chamber history. Some lesser amounts of the chamber background 

estimates used in the modeling may have resulted in a slight under-prediction of the SynURB 

side and a lesser over-prediction of the E85 side. The values used were selected on the basis 

of chamber history. 

Run 19,6:1 UrbDAG versus UrblE85 (extra EtOH) 

ST2894 This experiment repeated experiment 10 with additional EtOH. Experiment 10 

compareed the reactivity of the EPA E85 and IAG compositions with blending 5050 with 

SynURB. This experiment was, however, conducted in September. The E85 side yielded 

an ozone maximum concentration approximately 20 % higher than the IAG side. The CB4 

mechanism was used to successfully simulate (very well) the IAG side of this experiment, 

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4.4 ALTERNATIVE FUELS RUNS SIMULATIONS CHAPTER 4. MODELING RESULTS 

over-predicting the E85 side (Figures 4.46 and 4.47). Equal and medium chamber background estimates were used to simulate both sides of this experiment consistent with the 

chamber history. 

Run 20,6:1 UrbDAG versus UrblE85 (no aldehyde) 

AU0994 This experiment was designed to show the effect of removing the acetaldehyde 

from the E85 mixture. It repeated experiment 10, the comparison of the reactivity of the 

EPA E85 (but without the acetaldehyde) and IAG compositions with blending each 5050 

with SynURB. This experiment was conducted in September, however. Even with no initial 

acetaldehyde, the E85 mixture was more reactive than IAG with aldehyde. The E85 side 

yielded an ozone maximum concentration approximately 20 % higher than did the IAG side. 

The solar radiation simulation is shown in Figure 4.48. The CB4 mechanism was used to 

successfully simulate both sides of this experiment with some small underprediction of the 

IAG side near the end of this experiment (Figure 4.49). Equal and medium-high chamber 

background estimates were used to simulate both sides of this experiment, consistent with 

the chamber history. 

-- 

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Chapter 5 

New Analytical Methods 

5.1 Detection and Identification of Carbonyls 

0-(2,3,4,5,6-pentafluorobenzy1)-hydroxylane or PFBHA has been shown to be an excellent derivatizing agent for detecting polyfunctional carbonyls [6-101. The corresponding 

aldehyde or ketone oximes are easily resolved by gas chromatography (GC) and, because 

of the five fluorine atoms in the molecule, are amenable to detection by electron capture 

detection (ECD). GC coupled to mass spectrometery (MS) offers a powerful tool to identify 

unknown polyfunctional carbonyls as well as simple carbonyls. 

PFBHA reacts with a carbonyl to produce both the E and Z geometric isomers of the 

corresponding oxime (see Figure 5.1). The reaction is generally conducted in aqueous 

solution with subsequent extraction of the oximes into an organic phase. 

5.1 The Derivatization Procedure 

We used the following derivatization procedure. It applies to either standards made from 

pure carbonyl compounds or to gas-phase samples collected in water by use of impingers. 

To the sample, an excess amount of PFBHA aqueous solution (0.5 ml of 5 mg/ml solution) is 

added to 5 rnl of the carbonyl solution. The mixture is allowed to stand at room temperature 

for 24 h. Two drops of 37% HCI are added to acidify the mixture just before 2 ml of hexane 

are added to extract the PFBHA derivatives. Most of the excess PFBHA remains in the acidic 

aqueous phase, thus avoiding the deterioration of the column and detector by excess PFBHA. 

About 50 mg of anhydrous Na2S04 is added to the hexane extract to adsorb trace amounts 

of water remaining after separation. One microliter of the hexane extract is injected into 

a Varian Saturn I1 Gas Chromatograph and Ion Trap Mass Spectrometer system operated 

with an MS-grade 60 m x 0.325 rnrn J&W DB-5 chemically bonded, fused-silica capillary 

column that is directly connected to the ion trap. The temperature program used is: initial 

temperature 60°C, hold for 1 min, 60°C to 100°C at S°C Imin, 100°C to 280°C at 10°C 

Imin, hold at 280°C for 10 min. The injector has an initial temperature of 60°C and is kept 
5.1 DETECTION AND IDENTIFICATION OF CARBOICEWFIER 5. NEW ANALYTICAL METHODS 

Figure 5.1 :. The reaction of 0-(2,3,4,5,6)-pentafluorobenzyl) hydroxylamine with a carbonyl to 

produce two isomers for non-symmetrical carbonyls. 

at this temperature for 0.80 minutes. Then the injector temperature is raised to 250°C at a 

rate of 180°C/min and is held at this temperature until the analysis is finished. The transfer 

line between the column and the ion trap is kept at 280°C. The ITMS can be readily operated 

in either EI (electron ionization) or CI (chemical ionization) mode, allowing back-to-back 

analysis of the same extract using different ionization modes. When CI mode was selected, 

the CI reagent gas used was methane. A mass scan range between 50 and 650 is usually 

chosen. 

5.1.2 Ion map Mass Spectra 

The Chromatography Systems Division of Varian Associates, Inc., Walnut Creek, California, 

has made a "permanent loan" of a Saturn I1 Gas Chromatography and Ion Trap Mass 

Spectrometer to our research group. This instrument is dedicated to our PFBHA work and 

is supported by a field service contract. 

Figure 5.2 is a schematic diagram of the Varian Saturn gas chromatograph and ion trap 

mass spectrometer. Figure 5.3 is a detailled cross section of the ion trap itself. The GC uses 

a capillary column with helium carrier gas to separate species in the injected sample. The 

end of the column passes through a heated transfer line and exits into the cavity of an ion 

trap mass spectrometer (ITMS). The ion trap is small, only about 2 inches in diameter, and 

it sits atop a turbomolecular pump that is connected to an external roughing pump. The ion 

trap actually consists of a ring electrode and top and bottom end cap electrodes that form a 

particularly shaped cavity. Holes in the top and bottom end cap electrodes provide openings 

to the electron charging source above the top end cap and to the electron multiplier detector 

beneath the bottom end cap electrode. When sample flows into the ion trap cavity, the ion 

source is allowed to impart a charge to the sample molecules. The ITMS can be readily 

operated in either EI or CI modes (more on this below). After the sample molecules are 

charged, voltages on the electrodes cause these ions to be trapped in orbits inside the cavity. 

By manipulating the electrode voltages, ions of a given mass-to-charge ratio can be made 

to exit the cavity. The electron multiplier detects positive ions as they are ejected through 

the holes in the bottom end cap electrode. The electron multiplier has a gain of about lo5. 

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CHAPTER 5. NEW ANALYTICAL ME~~ETECTION AD IDENTIFICATION OF CARBONYLS 

Rotary Vane Pump 

Figure 5.2:. A schematic of the Saturn I1 capillary column gas chromatograph with ion trap mass 

spectrometer detector. 

A signal line connects the output of the electron multiplier to the processing electronics in 

the MS interface located in an MS-DOS 386 computer. 

An AC voltage of constant fequency (1.05 MHz) and variable amplitude (0 to 7500 

volts zero to peak) is applied to the ring electrodes of the ion trap. Because of the frequency 

of this voltage, it is called the radio frequency or RF voltage. When the proper RF voltage is 

applied, the ion trap electrodes create a three-dimensional, hyperbolic electric field. In this 

field, ions can be trapped in stable, aperiodic orbits. Prior to ionization of sample molecules, 

the RF voltage applied to the ring electrode of the ion trap assembly is set to near 0 volts 

for 2 ms. Above the ion trap is a heated filament that emits thermal electrons capapble 

of ionizing sample moleucles. These electons are allowed to enter the trap under selected 

conditions. During the ionization of sample molecules, the RF voltage is set to a specific 

voltage, called the storage voltage, which is appropiate to store or trap all ions of interest 

in the ion trap cavity. After the ionization period, the RF voltage is ramped quickly to the 

voltage apppropriate for the beginning of the mass analysis process (usually above 20 amu 

to avoid H20). During mass analysis, the RF voltage is ramped from a low voltage to a 

higher voltage. As the RF voltage is increased, the trajectories of the ions become unstable 

in order of increasing mass per charge. Ions with unstable orbits are ejected from the ion 

trap and are detected by the electron multiplier located below the ion trap. The RF voltage 

is ramped at a constant rate approximately 5600 amu/s. One complete scan at this fixed 

rate produces a rnicroscan. Depending on the mass range and scan time, more than one 

microscan may be obtained during each scan. If two or more microscans are obtained, they 

are averaged by the data processing system, which improves the spectral quality. 

Photochemical Studies of Alternative Fuels 171 ESEIUNC-CH 
5.1 DETECTION AND IDENTIFICATION OF CARBONXMPTER 5. NEW ANALYTICAL METHODS 

Focus 

I n n I Filiment for thermal electrons 

and charging gates 

1-1 Top End Cap 

I \ / -1 Central Ring 

Bottom End Cap 

Electorde 

Electron ~rnplirll nlnrrtm~ 

Multiplier Anodi ,meter circuit 

e 

Figure 5.3: Cross section of the Saturn I1 ion trap mass spectrometer detector. 

The flow of sample and helium carrier is controlled by the head pressure on the GC 

column and by the the Teflon ring spacers in the bottom of the ion trap exiting to the 

turbomolecular pump. The partial pressure is maintained at approximately 1 x torr 

(1.33 x 10-I Pa) by the electronic flow controllers connected to the computer. Sample is 

present at about 5 x Ton: CI reagent gas is present at a partial pressure of Torr 

if operating in the CI mode. The presence of helium in the ion trap cavity is important 

because it significantly enhances sensitivity and mass spectral resolution. Prior to their 

ejection from the ion trap cavity, sample ions collide with helium atoms. These collisions 

reduce the kinetic energy of the ions, thereby dampening the amplitude of their oscillations. 

As a result, the ions are focused into the center of the cavity rather than spread throughout 

the cavity. 

The number of ions stored in the ion trap cavity is proportional to the ionization time 

and the filament emission current. Although the maximum ion-storage capacity of the ion 

trap is approximately lo6 to 10' ions, space-charge repulsion occurs when the number of 

ions exceeds lo4 to lo5. Space-charge interactions causes a loss in unit mass resolution. 

To overcome this limitation, a small AC voltage, the axial modulation voltage, is applied 

to the end cap electrodes. This voltage reduces the effects of the space-charge and mass 

Photochemical Studies of Alternative Fuels 172 ESEIUNC-CH 
CHAPTER 5. NEW ANALYTICAL METZ8)DBETECTION AND IDENTIFICATION OF CARBONYLS 

resolution is significantly improved. 

CI is a "softer" ionization than is EI, meaning that CI imparts less energy to the sample 

molecule being ionized than does EI. Thus the ionized sample molecule undergoes less 

fragmentation, and an ion indicative of the molecular weight is more likely to be observed. 

In addition to molecular weight confirmation, CI mass spectra often provide other significant 

structural information that may not be available for EI mass spectra. 

In CI, ionization of sample molecules is a two-step process. First, reagent gas ions are 

formed by interaction with electrons emitted by the filament to form reagent gas ions. In 

the second step, the reagent gas ions react with sample molecules in the ion trap to form 

sample ions. There are four principal types of reactions, described below, between reagent 

gas ions and sample molecules. 

For the compounds examined here, the derivative molecules and the stable fragment 

ions C6F5CH2+ are abundant in the ion trap chamber and they can form ion adducts. It is 

noted that the concentration used was high, so there were plenty of derivative molecules in 

the chamber. In a case where the analyte concentration is low, it is not surprising to find 

that the M+18 1 ions are not always present. Actually this is confirmed by the isoprene run 

analysis, in which the mass spectra of weak unknown carbonyls didn't give the M+ 18 1 ion. 

In the ion trap, a molecular ion in the EI mass spectra is present for all standard compounds examined. Because the presence of a benzene ring attributes to the stabilization of 

the molecular ion, aromatic aldehydes give strong molecular ions (RI>39%). The protonated molecular ion ((MH)+) has a comparable intensity to the molecular ion. An adductive 

ion (M + 181)+ is also present because of a special principle of the ITMS. The ITMS holds 

ions in orbits in a chamber and uses a radio frequency (RF) field to separate and analyze 

ions. As the RF voltage is scanned upwards, ions of increasing mass-to-charge ratio become 

unstable and are ejected out the chamber. Before ions are ejected, however, secondary ion 

reactions are possible. For the compounds examined here, the derivative molecules and 

the stable fragment ions C6F5CH2+ are abundant in the ion trap and they have time to form 

ion adducts before the dz 181 ions are ejected out the trap. We have observed, however, 

that when the analyte concentration is very low, the (M + l8l)+ ions are often below the 

detection limit. Other fragment ions characteristic of the molecular weight of the analytes 

also appear at (M - 17)+, (M - 30)+, (M - 181)+ and (M - 197)+, which are postulated to 

result from loss of -OH, NO, C6F5CH2, and C6F5CH20 moieties from the molecules. The 

intensity of the M - 30 ion for aromatic carbonyls is higher than that for aliphatic carbonyls. 

The M - 17 ion is considerably more stable for the hydroxyl carbonyls and ketones. Dicarbonyls show a strong ion at m/z M - 197 (RI>9%) and the relative intensity increases 

with increasing molecular weight. All of these ions can be useful in confirming a proposed 

molecular weight. Based on its EI mass spectrum, the molecular weight can be determined 

for an aromatic carbonyl because ions containing molecular weight information are present 

with reasonably strong intensity. 

The EI molecular ion is not always strong enough, however, for an unambiguous 

molecular weight determination for aliphatic carbonyls. The chemical ionization mass 

spectra (CIMS) of all PFBHA derivatives show a strong protonated molecular ion MH+ 

Photochemical Studies of Alternative Fuels 173 ESEIUNC-CH 
5.1 DETECTION AND IDENTIFICATION OF CARBOPUHAPTER 5. NEW ANALYTICAL METHODS 

with relative intensity ranging from 7% to 100%. The intensity of MH+ ion for hydroxy 

carbonyls are considerably lower than other carbonyls. The MH+ ion is formed through 

proton transfer with the major ions formed from methane on electron impact such as CH5+, 

C2H5+, and C3H5+, e.g. An ion with dz=M - 197, loss of C6F5CH20 moiety from the 

neutral molecule, is consistently present with reasonably strong intensity (RI>3%, except 

for hydroxy carbonyls). Some of the isomers of hydroxyl carbonyl PFBHA derivatives 

have a relatively lower intensity ion at m/z=M - 197. Ions with dz=M + 29 and M + 41, 

arising from the reaction of neutral molecule with the major reagent ions C2H5+ and C3H5+, 

are observed for all carbonyls. A peak at dz=M + 181 is also present in CIMS to help 

confirm molecular weight diagnosis. These ions aid the determination of molecular weight 

of unknown carbony 1s. 

The PFBHA derivatives of hydroxy carbonyls in CIMS have a strong characteristic 

peak at dz=M - 17 (RI>23%), corresponding to loss of -OH from the molecular ion or 

loss of H20 from MH+ ions. Other carbonyl derivatives also show a peak at M - 17, but 

not so strong as hydroxy carbonyls (R1<12%). Therefore the presence of a strong ion of 

M - 17 is a useful but not conclusive indicator of the presence of a hydroxy group in addition 

to the carbonyl functional group. Further confirmation of the presence of hydroxy group 

can be accomplished by a second derivatization step, e.g., derivatizing the hydroxyl group 

using silylation reagents or methylation reagents. The disappeance of the original PFBHA 

derivative peak and the emergence of a new peak serve as evidence of the presence of an 

hydroxyl group [ 101. We are developing other confirmation methods to identify unsaturated 

carbonyls and carbonyls with adjacent hydroxy groups. These methods, which we have 

demonstrated to work on laboratory test samples, carry out additional reactions upon the 

extracted derivatives using selective oxidative reagents. In the first case, after performing 

the first injection into the GCIMS system, an aliquot of the extract is reacted with bromine 

in CCL. Carbonyl compounds with unsaturated bonds will react with the bromine and be 

destroyed and new peaks of the brominated products will appear. In the second case, a 

similar process is carried out, but the reagent added to an aliquot of the extract is periodic 

acid (H1O4), which will attack hydroxyl groups on adjacent carbons, cleave the carboncarbon bond, and produce aldehyde and ketone groups where there were hydroxyl groups. 

The latter method has been used for some time in carbohydrate chemistry to detect sugars. 

Photochemical Studies of Alternative Fuels 174 ESEIUNC-CH 
Chapter 6 

Aromatic Reaction Mechanisms 

6.1 Experimental Results 

We conducted more than 20 indoor Teflon Bag Reactor (TBR) experiments with alkyl subsitututed benzene compounds and NO,. These were used to develop the sampling and 

indentification techniques. Although these are useful in determining the types of products 

that were produced in these systems, the photolytic spectrum for the TBR differs significantly 

from sunlight. Because many of the carbonyl products produced also photolyze, outdoor 

experiments more realistically represent the behavior of this complex system. 

Three dual outdoor chamber experiments were performed for this project: 

Toluene verses m-xylene 

p-xylene verses 1,3,5-trimethyl benzene 

o-xylene verses 1,2,4-trimethyl benzene. 

Detailed analysis of these experiments will be presented here. 

The outdoor chamber initial conditions are given in Tables 6.1 to 6.3. Standard SegFile 

data plots of NO, NO2 and O3 time series and the light conditions, temperature, dew point, 

TSR and UV intensity are given in Figures 6.1 to 6.3. For each of the aromatic compounds 

examined, a series of carbonyl products was detected using the PFBHA-GCIMS technique. 

These will be described in detail in subsequent figures. Analysis of the batch samples 

collected in the indoor TBR experiments showed similar carbonyl products to those detected 

in the outdoor smog chamber experiments. Therefore, only the results from the outdoor 

chamber experiments will be described in this report. 
6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Table 6.1 : Initial Conditions for AU3095 

(Units are ppm or ppmQ 

FWD SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.6218 NO, 0.6171 

NO 0.5027 NO 0.5230 

No2 0.1 191 No2 0.094 1 

Initial Volatile Organic Compounds 

m - XVI 8.0200 Toluene 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 0.1440 

CO 0.250 CO 0.250 

Estimated Initial Compounds 

HONO 0.003 HONO 

CH4 1.790 (3-44 

H2 0.5 80 H2 

Chamber Model Parameters 

NO2 + HONO 2.5 x NO2 ---+ HONO 2.5 x 

wall.HN03 + NO2 2.0 x wall.HN03+N02 2.0 x 

Three Previous Chamber Events 

950803.SEG Aromatics run 

95080 1 .SEG Delta Ethylene run 

950726.SEG Matched Propylene run 

Photochemical Studies of Alternative Fuels 176 ESEIUNC-CH 

6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Table 6.2: Initial Conditions for AU0395 

(Units are ppm or ppmC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.6407 NO, 0.6209 

NO 0.5323 NO 0.5368 

NO2 0.0984 NO2 0.0841 

Initial Volatile Organic Compounds 

P - Xyl 8.0300 C-I(C-3)Bz5-C 9.0000 

Other Measured Initial Compounds 

BVOC9109 0.1440 BVOC9109 

CO 0.250 CO 

Estimated Initial Compounds 

HONO 0.003 HONO 0.003 

CH4 1.790 CH4 1.790 

H2 0.580 H2 0.580 

Chamber Model Parameters 

NO2 + HONO 2.5 x NOe HONO 2.5 x 

wall.HN03+N02 2.0~ low3 wall.HN03 ---+ NO2 2.0 x 

Three Previous Chamber Events 

95080 1 .SEG Delta Ethylene run 

950726.SEG Matched Propylene run 

950722.SEG CO run 

Photochemical Studies of Alternative Fuels 178 ESEIUNC-CH 

6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Table 6.3: Initial Conditions for ST0195 

(Units are ppm or pprnC) 

RED SIDE BLUE SIDE 

Compound Amount Compound Amount 

Initial Nitrogen Oxides 

NO, 0.6916 NO, 0.6502 

NO 0.5582 NO 0.5404 

NO2 0.1334 NO2 0.1098 

Initial Volatile Organic Compounds 

C-I(C-2)Bz4-C 9.1300 o - Xyl 8.0800 

Other Measured Initial Compounds 

BVOC9t 09 0.1440 BVOC9109 

CO 0.250 CO 

Estimated Initial Compounds 

HONO 0.003 HONO 

CH4 1.790 CH4 

Hz 0.580 H2 

Chamber Model Parameters 

NO2 + HONO 2.5 x NO2 + HONO 2.5 

wall.HN03 --+ NO2 2.0 x wall.HN03-NO2 2.0 x 

Three Previous Chamber Events 

950830.SEG Aromatics run 

950803.SEG Aromatics run 

95080 1 .SEG Delta Ethylene run 

Photochemical Studies of Alternative Fuels 180 ESEIUNC-CH 

6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

The presence of carbonyl products in each batch sample was readily determined by 

examining the reconstructed ion chromatogram for the m/z= 18 1 ion because the EI spectra 

of PFBHA oximes always have a strong rn/z=181 peak. The molecular weights of these 

carbonyl products are then determined from their methane CI mass spectra. 

A sample was taken from the background chamber air before every experiment and 

treated in the same way as the samples collected after the injection of the HC and NO,. 

Carbonyl peaks detected in the background sample included small amounts of formaldehyde, acetaldehyde, acetone, pentafluorobenzaldehyde, glyoxal and methylglyoxal, as well 

as several very small unknown peaks. The possible sources for these background contaminants include the chamber air, laboratory air, distilled water, and the derivatizing reagent. 

Pentafluorobenzaldehyde was a by-product arising from the derivatization reaction. 

The reconstructed rnlz=18 1 ion chromatograms for the batch sample collected in the 

outdoor smog chamber experiments are shown in Figures 6.4 to 6.12 for the six aromatic 

compounds - toluene, p-xylene, m-xylene, o-xylene, 1 ,3,5TMB, and 1 ,2,4TMB. Tables 6.4 

to 6.18 list the carbonyl products corresponding to the experiments, along with the molecular 

weigth information and their identities (when identification was possible). Carbonyl peaks 

eluting before glycolaldehyde-formaldehyde, acetaldehyde and acetone-are not shown 

here due to the limited information they provide. Trace amounts of these carbonyls are 

present in the laboratory air and/or the distilled water used in the derivatization process. 

Although more than 45 carbonyl peaks were detected in each experiment, the number 

of carbonyl peaks detected does not reflect the actual number of carbonyl products. Except 

for symmetric carbonyls such as formaldehyde and acetone, PFBHA forms two or more geometric isomers depending on the structure of carbonyls. Non-symmetric mono-carbonyls, 

such as acetaldehyde, have two isomers for their PFBHA derivatives. Non-symmetric dicarbonyls, such as methylglyoxal, can forrn as many as four geometric isomers with PFBHA. 

When carbonyls have a C=C in addition to two carbonyl function groups on each side of 

the C=C bond, such as 4-0x0-2-pentenal, they can produce a maximum of eight geometric 

isomers because the carbonyls have cis- and trans- isomers and each of which could form 

four isomers of PFBHA derivatives. The GC conditions tested and used for this work were 

able to distinguish some, but not all, of these isomeric forms. 

Photochemical Studies of Alternative Fuels 182 ESE/UNC-CH 


CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

Table 6.4: 

Carbonyl Products Detected in a Sample Collected from a Toluene/NOx 

Daytime Smog Chamber Experiment 

Peak # I RT (min) I Identity I MWa I MW' 

I I I I 

to15 17.93 C, hydroxy carbonyl 297 102 I 

toll ,to12 16.42/16.81 glycolaldehyde 

to13 17.28 hydroxyacetone 

to14 17.51 hydroxyacetone or C, hydroxy carbonyl 

I I I I 

to18 20.56 C,F,CHO (by product of derivatizing reaction) 1 391 196 I 

255 

269 

2691283 

C,F,CHO (by product of derivatizing reaction) 

mono-deri. of C, hydroxy dicarbonyl 

60 

74 

74188 

391 

297 

to19 21.03 mono-deri. of MW 504 carbonyl 

to1 10 21.25 C, di-unsaturated carbonyl or fururall methyl 

I I 

methyl furural or acetyl furan 1 305 1 110 

196 

102 

furural or acetyl furan 

mono-deri. of MW 488 carbonyl 

benzaldehyde 

309 

2911305 

114 

9611 10 

293 

301 

C, unsaturated hydroxy epoxy cyclic carbonyl, see 

I I 

mono-deri. of MW 5 1 8 carbonyl 1 323 I 128 

98 

106 

I I 

mono-deri. of methyl benzoquinone 1 317 1 22 

321 

108 

1 22 

Table 6.6 I 

unknown, containing -OH group 1 337 1 142 

1 26 

mono-deri, of benzoquinone 

mono-deri. of methyl benzoquinone 

I I 

methylglyoxal 1 462 1 72 

303 

317 

I I 

K\ molecular weight of PFBHA-carbonyl derivative 

Nb: molecular weight of carbonyl 

see table 6.1 

gl yoxal 

glyoxallmethylglyoxa1 

Photochemical Studies of Alternative Fuels 185 ESEIUNC-CH 

335 

448 

4481462 

140 

58 

58/72 
6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Table 6.5: 

(Continued) Carbonyl Products Detected in a Sample Collected from a 

Toluene/NOx Daytime Smog Chamber Experiment 

Photochemical Studies of Alternative Fuels 186 ESEIUNC-CH 

Identity 

C, unsaturated epoxy dicarbonyl/methylglyoxal 

methylglyoxal 

propandial 

C4 saturated dicarbonyl or C, trione 

C, unsaturated epoxy dicarbonyl 

C, saturated dicarbonyl or Cg trione 

C, hydroxy dicarbonyl 

C4 epoxy dicarbonyllc, trione 

C4 hydroxy dicarbonyl 

C4 epoxy dicarbonyllc, trione 

C, epoxy dicarbonyl/C, trione 

C4 hydroxy dicarbonyl 

Peak # 

to127 

to128 

to129 

to130 

to13 1,t0132 

to133 

to134 

to135 

to136 

to137 

to138 

to139 

MWa 

3331462 

462 

462 

476 

333 

476 

492 

490 

492 

490 

504 

492 

RT (min) 

25.86 

26.06 

26.41 

26.5 1 

26.58126.74 

27.08 

27.43 

27.76 

28.09 

28.19 

28.34 

28.53 

to140 

to141 

MWb 

128172 

n 

72 

86 

128 

86 

102 

100 

102 

100 

114 

102 

28.66 4-0x0-2-pentenal 1 488 

28.84 butendial 474 

5% 

84 

to142 29.04 C4 hydroxy dicarbonyl 492 102 

to143 29.18 4-0x0-2-pentenal 488 98 

to144,to145 

to146 

to147 

to148,to149 

to150 

to15 1 

to152 

to153 

to154 

1 

29.24129.33 

29.43 

29.60 

29.78130.23 

30.86 

33.14 

34.09 

34.24 

34.67 

butendial 

C5 epoxy dicarbonyllc, trione 

4-0x0-2-pentenall butendial 

474 

504 

4881474 

84 

114 

98/84 

98 

128 

108 

122 

1 24 

138 

4-0x0-2-pentenal 

C, unsaturated hydroxy dicarbonyl 

benzoquinone 

methyl-p-benzoquinone 

C, di-unsaturated dicarbonyl 

C, unsaturated epoxy dicarbonyl 

488 

518 

498 

512 

514 

528 

6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Carbonyl Product! 

Dr 

Peak RT (min) 

PI 16.45 

@ 16.55 ' 

@ 16.83 

~47 P5 17.29117.56 

Table 6.6: 

Detected in a Sample Collected from a p-XyleneNOx 

ytime Smog Chamber Experiment 

Identity I MWa I MW~ 

I I 

glycolaldehyde 1 255 I 60 

glycolaldehydel2-hydroxy propanal 

2-hydroxy propanal 

I 1 

hydroxyacetone I 269 1 74 I I 

C4 epoxylC5 saturated carbonyl I 28 1 I 86 

2551269 

269 

I I 

C,F,CHO (derivatizing reaction byproduct) I 391 I 196 

60174 

74 

I 1 

mono-deri. of 3-hexene-2,5-dione I 307 I 112 

mono-deri. of C4 hydroxy dicarbonyl 

1 I 

mono-deri. of 3-hexene-2,5-dionelC,F,CHO 1 3071391 1 1121196 t I 

mono-deri, of 3-hexene-2,5-dione 1 307 I 112 

297 

I 1 

C, unsaturated carbonyl 

1 02 

I I 

C7 hydroxy carbonyl I 325 I 130 I 1 

C, epoxylc, saturated carbonyl I 28 1 I 86 

mono-deri. of 3-hexene-2,5-dione 

benzoquinone 

p-tolualdehyde 

epoxy carbonyll mono-deri. of C, di-unsaturated 

307 

303 

315 

dicarbonyl 

C7 unsaturated epoxy carbonyl 

hydroxy unsaturated epoxy cyclic carbonyl I I 

112 

108 

1 20 

3351333 

methylglyoxal 

mono-deri. of dimethyl-p-benzoquinonel C, 

1401138 

321 

1 I 

methylglyoxal 1 462 1 72 

1 26 

462 

3311349 

glyoxal 

glyoxaVmethylglyoxal 

C7 unsaturated epoxy carbonyllmethylglyoxal 

Photochemical Studies of Alternative Fuels 188 ESEAJNC-CH 

72 

1361154 

448 

4481462 

3211462 

58 

58172 

126/72 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESUL.TS 

Table 6.7: 

Carbonyl Products Detected in a Sample Collected from a 

p-Xylene/NOx Daytime Smog Chamber Experiment (Continued) 

-- - - 

Photochemical Studies of Alternative Fuels 189 ESEIUNC-CH 

MWh 

1 141100 

86 

100186 

861102 

102 

100 

112 

102 

1 1 U102 

112 

% 

112 

98 

112/98 

98 

98 

Peak 

P27 

P28 

P29 

P30 

p3 1, p32 

p33, p34 

~35 

I 

p36, p37 

~38 

~39 

p40, p41 

P42 

P43 

p44 

P45 

7 

RT (min) 

26.18 

26.53 

26.74 

27.08 

27.30,27.43 

27.63127.80 

28.01 

28.13128.36 

28.55 

29.10 

29.88 

29.90/30.15 

30.34 

30.7 1 

34.63 

36.30 

Identity 

C, epoxy carbonyY C, epoxy carbonyl 

C4 saturated dicarbonyllc, trione 

C4 epoxy carbonyl or C4 trionel C4 saturated 

dicarbonyl or Cg trione 

C4 saturated dicarbonyl or Cg trionelc, hydroxy 

dicarbonyl 

C, hydroxy dicarbonyl 

C, epoxy carbonyl or C, trione 

3-hexene-2,5-dione 

C4 hydroxy dicarbonyl 

3-hexene-2,5-dionelc, hydroxy dicarbonyl 

3-hexene-2,5-dione 

C, unsaturated dicarbonyl 

3-hexene-25-dione 

C, unsaturated dicarbonyl 

3-hexene-2,5-dionelC5 unsaturated dicarbonyl 

2-methyl-butendial 

2-methyl-butendial 

MWa 

5041490 

476 

4901476 

4761492 

492 

490 

502 

492 

5021492 

502 

488 

502 

488 

5OU488 

488 

488 


6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Table 6.8: 

Carbonyl Products Detected in a Sample Collected from an 

m-~ylene/N~x Daytime Smog chamber Experiment 

Peak I RT (min) I Identity I I 

ml 1 16.43116.81 1 glycolaldehydel2-hydroxy propanal I I 

m2 I 16.55 I 2-hydroxy propanal 1 269 1 74 I I I I 

m3 1 16.81 I glycolaldehyde 1 255 1 60 I I I I 

m4 1 17.28117.53 1 hydroxy acetone 1 269 1 74 I I I I 

I I 

m7 I 17.94 mono-deri. of C4 hydroxy dicarbonyl 297 102 

rn5 

m6 

I I 

m8 I 18.13 I C4 epoxylc, saturated carbonyl 28 1 85 

17.53 I hydroxy acetone1 C4 hydroxy carbonyl 

I I I 

m9 I 18.59 I C, epoxylC, saturated carbonyl 295 100 

17.63 

I I 

ml 1 I 18.98 I C, di-unsaturated carbonyl 1 279 1 84 

C4 hydroxy carbonyl 

m10 

I I I I 

m12 I 19.11 I C,F,CHOl C, hydroxy carbonyl 1 3911269 1 196174 

19.1 1 1 mono-deri. of C, hydroxy carbonyl 1 311 

283 

18.81 

I I 

19.74 I mono-deri. of C, hydroxy carbonyl 311 

88 

C, hydroxy carbonyl 

20.09 I C4 hydroxy carbonyl 283 

19.86 

I I 

21.08 I mono-deri. of C, unsaturated dicarbonyl 307 

283 

I I 

21.25 ( C, di-unsaturated carbonyl 305 

88 

C, unsaturated carbonyl 

I I 

21.51 I C, unsaturated carbonyll C7 di-unsaturated 2931305 

293 

21.8 1,22.26 

22.39 

22.58 

I I 

23.44 I C, hydroxy unsaturated epoxy cyclic carbonyl 335 

22.94 

carbonyl 

C, di-unsaturated carbonyl 

C,/ C, di-unsaturated carbonyl 

C,/ C, di-unsaturated carbonyl 

I m-tolualdehyde I 

3 19 

3 191305 

3 191305 

C, unsaturated epoxy carbonyl 

23.59 

Photochemical Studies of Alternative Fuels 192 ESEIUNC-CH 

321 

C, hydroxy unsaturated epoxy cyclic carbonyl I 3351315 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

Table 6.9: 

Carbonyl Products Detected in a Sample Collected from an 

m-xylene/NOx Daytime Smog Chamber Experiment (Continued) 

Photochemical Studies of Alternative Fuels 193 ESE/UNC-CH 

Peak 

m27 

m28 

m29 

m30 

m3 1 

m32 

m33 

m34 

m35 

m36 

m37 

m3 8 

m39 

m40 

m4 1 

m42 

m43 

m44 

m45 

rn45 

MWa 

321/317 

321/317 

335 

317/321 

335 

33 1 

4621335 

321 

365 

448 

4481462 

462135 1 

462 

504 

349 

4761365 

365 

4761462 

462 

4901347 

MWb 

1261122 

126/122 

140 

122J126 

140 

136 

721140 

126 

170 

58 

58/72 

72/156 

72 

114 

154 

86/170 

1 70 

86/72 

72 

1001152 

RT (min) 

24.06 

24.19 

24.59 

24.64 

24.69 

24.99 

25.12 

25.23 

25.35 

25.40 

25.60 

25.91 

26.06 

26.16 

26.41 

26.53 

26.66 

26.73 

26.85 

26.85 

Identity 

C, unsaturated epoxy carbonyV mono-deri. of 

methylbenzoquinone 

C, unsaturated epoxy carbony11 mono-deri. of 

methylbenzoquinone 

C, hydroxy unsaturated epoxy cyclic carbonyl 

mono-deri. of methylbenzoquinone/ C7 

unsaturated epoxy carbonyl 

C, hydroxy unsaturated epoxy cyclic carbonyl 

mono-deri. of dimethylbenzoquinone 

methylglyoxaVC, hydroxy unsaturated epoxy 

cyclic carbonyl 

C, unsaturated epoxy carbonyl 

unknown 

glyoxal 

glyoxaI1methylglyoxal 

methylglyoxallunknown, containing -OH group 

methylglyoxal 

C, epoxy dicarbonyl or C, trione 

C, hydroxy unsaturated epoxy cyclic carbonyl 

C4 saturated dicarbonyl or Cg trione/unknown 

unknown, containing -OH 

C, saturated dicarbonyl or C3 trionel C, 

dicarbonyl 

C3 dicarbonyl 

C4 epoxy dicarbonyl or C4 trionel C, saturated or 

C, epoxy carbonyl 
6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Table 6.10: 

Carbonyl Products Detected in a Sample Collected from an 

m-xylene/NOx Daytime Smog Chamber Experiment (Continued) 

Photochemical Studies of Alternative Fuels 194 ESE/UNC-CH 

MWa 

4761492 

504 

492 

506 

4901347 

506 

4921488 

504 

5041492 

506 

4921502 

518 

488 

506 

492 

488 

502 

504 

488 

488 

488 

502 

488 

516/500 

4881502 

Identity 

C, saturated dicarbonyl or C, trioneIC, hydroxy 

dicarbonyl 

C5 epoxy dicarbonyl or C, trione 

C, hydroxy dicarbonyl 

C, hydroxy dicarbonyl 

Peak 

m46 

m47 

m48, m49 

m50 

MWb 

861102 

114 

102 

116 

100/152 

116 

10U98 

114 

1 1411 02 

116 

10211 12 

128 

98 

116 

102 

98 

112 

114 

% 

98 

98 

112 

98 

12611 10 

9811 12 

RT (rnin) 

27.07 

27.15 

27.28127.43 

27.63 

m5 1 

m5 1 

m52 

m53 

m54 

m55 

m55 

m56 

m56 

m57 

m58 

m59 

m60 

m6 1 

m62, m63 

m63 

m64 

m65 

m66, m67 

m68 

m69 

27.78 

27.8 1 

28.10 

28.10 

28.34 

28.42 

28.53 

28.53 

28.66 

28.76 

29.03 

29.19 

29.30 

29.43 

29.59 

29.64 

29.83 

30.09 

30.24130.35 

30.46 

30.71 

C, epoxy dicarbonyl or C4 trionel C6 saturated or 

C5 epoxy carbonyl 

C, hydroxy dicarbonyl 

C4 hydroxy dicarbonyll C5 unsaturated dicarbonyl 

C5 epoxy carbonyl or C, trione 

C, epoxy carbonyl or C5 triond C4 hydroxy 

dicarbonyl 

C5 hydroxy dicarbonyl 

C4 hydroxy dicarbonyy C, unsaturated dicarbonyl 

C6 unsaturated hydroxy dicarbonyl 

4-0x0-2-pentenal 

C, hydroxy dicarbonyl 

C4 hydroxy dicarbonyl 

4-0x0-2-pentenal 

2-methyl-4-0x0-2-pentenal 

C, epoxy carbonyl or C5 trione 

4-0x0-2-pentenal 

4-0x0-2-pentenal and 2-methyl butendial 

4-0x0-2-pentenal 

2-methyl-4-0x0-2-pentenal 

4-0~0-2-pentenal/2-methyI-butendial 

C, unsaturated dicarbonyl/unknown 

2-methyl-butendial 12-methyl-4-0x0-2-pentenal 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

Table 6.1 1 : 

Carbonyl Products Detected in a Sample Collected from an 

m-xylene/NOx Daytime Smog Chamber Experiment (Continued) 

Photochemical Studies of Alternative Fuels 195 ESE/UNC-CH 

Peak 

rn70 

m7 1 ,m72 

m73 

m74 

m75 

m76 

MW" 

516 

500 

558 

542 

502 

5 16/542 

488 

502 

488 

528 

488 

MWb 

126 

110 

168 

152 

112 

1261152 

98 

112 

98 

138 

% 

RT (rnin) 

31.04 

3 1.29/32.33 

32.68 

32.86 

33.18 

33.73 

Identity 

C, unsaturated dicarbonyl 

unknown 

unknown 

unknown 

2-methyl-4-0x0-2-pentenal 

C, unsaturated dicarbonyVunknown 

m77 

m78 

m79 

m80 

m8 1 

34.58 

34.75 

36.27 

36.63 

37.% 

2-methyl-butendial 

2-methyl-4-0x0-2-pentend 

2-methyl-butendial 

C, di-saturated dicarbonyl 

C, unsaturated carbonyl 

CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESILTS 

Table 6.1 2: 

Carbonyl Products Detected in a Sample Collected from an 

o-XyleneINOx Daytime Smog Chamber Experiment 

Peak I RT (min) I Identity I MW~ 1 MW~ 1 

16.41116.79 1 glycolaldehyde 255 60 1 

17.25 I hydroxyacetone 1 269 1 74 

I 

17.61 I C4 hydroxy carbonyl or mono-deri. of C, hydroxy 1 283 1 88 

17.51 

dicarbonyl 

18.03 C4 unsaturated carbonyl 265 70 

18.11 I C4 epoxyICr saturated carbonyl 28 1 86 

hydroxyacetone/C4 hydroxy carbonyl 

n I 

18.38 I C4 hydroxy carbonyVC, epoxy carbonyl n 1 I 

18.54 I CS epoxylC, saturated carbonyl 295 100 

283 

I 

C,F,CHO/ C3 hydroxy carbonyi 3911269 

88 

I 

furural or C, di-unsaturated carbonyl 29 1 

C5 unsaturated carbonyl 293 

mono-deri. of C4 hydroxy dicarbonyl 297 

C6 epoxy carbonyVC4 hydroxy dicarbonyl 1 3091283 

I 

C, di-unsaturated carbonyl 305 

C7 di-unsaturated carbonyl 305 

C, di-unsaturated carbonyl 305 

C5 unsaturated carbonyl 293 

benzaldehyde 301 

C, unsaturated epoxy carbonyl 321 

C, di-unsaturated carbonyl 305 

benzoquinone 303 

Photochemical Studies of Alternative Fuels 197 ESEIUNC-CH 
6.1 EXPERIMENTAL RESmTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Table 6.13: 

Carbonyl Products Detected in a Sample Collected from an 

o-XylenelNOx Daytime Smog Chamber Experiment (Continued) 

Photochemical Studies of Alternative Fuels 198 ESENNC-CH 

Peak 

026 

027 

028 

029 

030 

03 1 

032 

033 

034 

035 

036 

037 

038 

039 

040 

040 

041 

042 

043 

044 

045 

046 

047 

048 

Identity 

C, hydroxy unsaturated epoxy cyclic carbonyl 

o-tolualdehyde 

Cg di-unsaturated carbonyl 

mono-deri. of C, di-unsaturated dicarbonyl 

mono-deri. of methyl benzoquinone /C7 

unsaturated epoxy carbonyl 

mono-deri. of methyl benzoquinone 1C7 

unsaturated epoxy carbonyl 

2,3-butanedione 

mono-deri. of 2-methyl-4-0x0-2-pentenal 

mono-deri. of methyl benzoquinone 

C, hydroxy unsaturated epoxy cyclic carbonyl 

methylglyoxal 

2,3-butanedione 

glyoxd 

glyoxallrnethyIglyoxa1 

methylglyoxal 

mono-deri. of dimethylbenzoquinone 

methylglyoxal 

C, epoxy dicarbonyl or C, trione 

C3 dicarbonyl 

2,3-butandione 

C, dicarbonyl 

C, dicarbonyl 

C4 dicarbonyl or C3 trionel C4 hydroxy dicarbonyl 

unknown 

RT (rnin) 

23.43 

23.68 

23.83 

23.95 

24.04 

24.16 

24.30 

24.43 

24.63 

24.95 

25.09 - 

25.25 

25.39 

25.58 

25.89 

25.89 

26.04 

26.15 

26.41 

26.53 

26.74 

26.81 

27.06 

27.1 3 

MWa 

335 

3 15 

319 

333 

3171321 

3171321 

476 

307 

317 

349 

MW~ 

140 

1 20 

124 

138 

122126 

122126 

86 

112 

122 

154 

462 72 

476 

448 

4481462 

462 

3311347 

86 

58 

58172 

72 

1361152 

462 

504 

462 

476 

462 

4621347 

4761492 

343 

72 

114 

72 

86 

72 

7211 52 

861102 

148 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

Table 6.1 4: 

Photochemical Studies of Alternative Fuels 199 ESEIUNC-CH 

Carbonyl Products Detected in a Sample Collected from an 

o-Xylene/NOx Daytime Smog Chamber Experiment (Continued) 

Peak 

049 

050 

05 1 

052 

053 

054 

055 

056 

057 

057 

058 

059 

060 

061 

062 

MWa 

492 

506 

506 

492 

478 

488 

474 

492 

4881492 

474 

474 

474/488 

488 

488 

524 

MWb 

102 

116 

116 

102 

88 

98 

84 

102 

981102 

84 

84 

84198 

98 

98 

134 

RT (min) 

27.41 

27.61 

27.81 

28.08 

28.33 

28.76 

28.86 

29.01 

29.16 

29.24 

29.3 1 

29.58 

29.76 

30.23 

35.48 

Identity 

C4 hydroxy dicarbonyl 

C, hydroxy dicarbonyl 

C, hydroxy dicarbonyl 

C, hydroxy dicarbonyl 

C, hydroxy dicarbonyl 

4-0x0-2-pentenal 

butendial 

C4 hydroxy dicarbonyl 

4-0x0-2-pentenal /C, hydroxy dicarbonyl 

butendial 

butendial 

butendiaV4-0x0-2-pentenal 

4-0x0-2-pentenal 

4-0x0-2-pentenal 

o-phathalicdicarboxyaldehyde 


6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Table 6.1 5: 

Carbonyl Products Detected in a Sample Collected from an 

tll 1 20.56 1 C,FsCHO 

1,3,5=Trimethylbenzene/NOx Daytime Smog Chamber : 

t16 1 22.83 I mono-deri. of C, di-unsaturated dicarbonyl 

Identity 

glycolaldehyde 

hydroxyacetone 

mono deri. of m504 carbonyl 

mono-deri. of C5 hydroxy carbonyl /C,F,CHO 

mono-deri. of C, hydroxy carbonyl 

mono-deri, of C, hydroxy carbonyl 

Peak 

tl, t2 

t3, t4 

t5 

t6 

t7, t8 

t9,tlO 

t12 

t13 

t14 

t15 

t17 I 23.56 C, unsaturated epoxy carbonyl 

RT (min) 

16.46116.79 

17.30117.54 

18.64 

19.09 

19.40119.58 

19.66,19.75 

I 

t18 I 23.98 C, hydroxy unsaturated epoxy cyclic carbonyl 

21.44 

21.53 

21.84 

22.34 

I 

t19 I 24.46 I 3,Sdimethyl benzaldehyde 

mono-deri. of C6 unsaturated dicarbonyl 

C, di-unsaturated carbonyl 

mono-deri. of C, unsaturated dicarbonyl 

C, epoxylc, saturated carbonyl 

I I unsaturated epoxy cyclic carbonyl 

t20 

I I 

t2 1 I 24.90 I 3,5-dimethyl benzaldehyde 

24.60 C, unsaturated epoxy carbonyll C, hydroxy 

I I 

I 25.01 I dimethylbenzoquinone 

t22 I 25.13 . I methylglyoxal 

ixperiml 

MW" 

255 

t23 

t24 

t25 

t26 

Photochemical Studies of Alternative Fuels 202 ESEIUNC-CH 

25.43 

25.61 

25.91 

26.08 

glyoxal 

glyoxallmethylglyoxa1 

methylglyoxal 

methylglyoxall C, epoxy dicarbonyllC4 trione 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

Table 6.16: 

Carbonyl Products Detected in a Sample Collected from an 

1.3.5-Trimethylbenzene/NOx Daytime Smog Chamber Experiment (Continued) , , - - 

Peak I RT (rnin) I Identity I MW~ 1 MW~ 

I I 

t28, t29 1 26.59126.76 1 C, saturated dicarbonyl or C, trione 476 86 

t27 

I I I 

t30 I 26.86 I C, epoxy carbonyl or C, trione 1490 1 100 

I I I I 

I 27.06 I trirnethyl-benzoquinone 1 345 1 150 

26.15 

I 

C, epoxy dicarbonyl or C, trione 504 

t3 1 

I I 

t33 I 27.61 C5 hydroxy dicarbonyl 506 116 

114 

t32 

I I 

t34 1 27.82 I C, hydroxy dicarbonyY C, saturated carbonyl 1 5061476 1 116186 

27.16 

27.29 

C5 epoxy carbonyl or C5 trioneIC, epoxy carbonyl 

t35 

I 

5041490 

or C, trione 

C, hydroxy dicarbonyl 

t36 

I I I I 

t38, t39 1 28.49,28.78 1 C5 hydroxy dicarbonyl 506 116 

27.98 

t37 

492 

28.13 . 

102 

C, unsaturated dicarbonyVC, unsaturated 

28.35 

t40 

dicarbonyl 

C, unsaturated dicarbonyllc, epoxy dicarbonyl or 

t41, t42 

Photochemical Studies of Alternative Fuels 203 ESEIUNC-CH 

5161502 

C, trione 

C, hydroxy dicarbonyl 

29.04 

t43 

t44, t45 

1261112 

4741504 

I 

2-methyl-4-0x0-2-pentenal lC5 unsaturated 

29.33130.73 

8611 14 

492 

33.23 

33.8313413 

102 

dicarbonyl 

2-methyl-4-0x0-2-pentenal 

2-methyl-4-0x0-2-pentenal 

C, unsaturated epoxy dicarbonyl 

502 112 

502 

558 

112 

168 

CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

Table 6.17: 

Carbonyl Products Detected in a Sample Collected from an 

1,2,4-Trimethylbenzene/NOx Daytime Smog Experiment 

Peak RT (min) Identity I MWa I I I 

fl 16.33 I C, saturated/C, epoxy carbonyl I 28 1 

C, hydroxy carbonyl/C,F,CHO 269139 1 

f2 

f3 

f4 

f5 

f6 

I I 

B 19.73 I mono deri. of C, hydroxy dicarbonyl I 311 

I I I 

flO 1 22.31 I mono-deri. of C, unsaturated dicarbonyl I 321 

16.44 

16.56 

16.81 

17.28 

17.53 

C, unsaturated epoxy carbonyl 

C, hydroxy unsahlrated epoxy cyclic carbonyl I 

glycolaldehydel2-hydroxy propanal 

2-hydroxy propanal 

glycolaldehyde 

hydroxyacetone 

hydroxyacetonelC, hydroxy carbonyl 

unknown 

C, hydroxy unsaturated epoxy cyclic carbonyl 

2,3-butandione 

2551269 

269 

255 

269 

2691283 

C8 unsaturated epoxy carbonyl 

C, unsaturated epoxy carbonyl 

methylglyoxall C, unsaturated epoxy carbonyl 

2,3-butandionel dimethyl benzaldehyde 

dimethylbenzoquinonel glyoxal 

glyoxal/methylglyoxa1 

dimethyl benzaldehyde 

methylglyoxal 

C, epoxy dicarbonyl or C, trione 

2,3-butandione 

C, hydroxy dicarbonyl 

Photochemical Studies of Alternative Fuels 205 ESE/UNC-CH 
6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Table 6,18: 

Carbonyl Products Detected in a Sample Collected from an 

1,2,4=Trimethylbenzene/NOx Daytime Smog Experiment (Continued) 

Photochemical Studies of Alternative Fuels 206 ESE/UNC-CH 

Peak 

f26 

f27 

f28 

f28 

f29, f30 

f31, f32 

f33 

f34, f35 

f36 

f37 

f38 

i39 

f40 

f4 1 

f42 

f43 

f44 

f45 

f46 

f47, f48 

f49 

f50 

f51 

MWa 

490 

492 

504 

490 

492 

506 

502 

MWb 

100 

102 

114 

100 

102 

116 

112 

RT (min) 

26.86 

27.08 

27.14 

27.19 

27.29127.43 

27.63127.81 

28.02 

28.09128.36 

28.43 

28.53 

28.78 

29.05129.10 

29.33 

29.44 

29.66 

29.91 

30.36 

30.7 1 

30.9 1 

33.19 

33.83133.41 

34.59 

34.76 

36.28 

Identity 

C4 epoxy dicarbonyl or C4 trione 

C4 hydroxy dicarbonyl 

C5 epoxy dicarbonyl or C, trione 

C4 epoxy dicarbonyl or C4 trione 

C4 hydroxy dicarbonyl 

C, hydroxy dicarbonyl 

3-hexene-2,5-dione 

C, hydroxy dicarbonyl 

C5 hydroxy dicarbonyl 

3- hexene-2,5-dione 

C5 hydroxy dicarbonyl 

C4 hydroxy dicarbonyY3-hexene-2,5-dione 

2-methyl-4-0x0-2-pentenal 

C, unsaturated dicarbonyl 

2-methyl-butendial 

3-hexene-2,s-dione 

2-methyl-butendial 

3-hexene-2,5-dione or 2-methyl-4-0x0-2-pentend 

C, unsaturated dicarbonyi 

2-methyl-4-0x0-2-pentenal 

C, unsaturated epoxy dicarbonyl 

2-methyl-butendial 

2-methyl-4-0x0-2-pentenal 

2-methyl-butendial 

492 102 

506 116 

502 

506 

492/502 

502 

516 

488 

502 

488 

502 

516 

502 

558 

488 

502 

488 

112 

116 

1021 12 

112 

126 

98 

112 ! 

98 

112 

1 26 

112 

168 

98 

112 

98 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

Some of the carbonyl products coeluted with other peaks because of the limited separation power of the GC column and the high number of carbonyl products present. For 

example, the second peak of glyoxal-PFBHA derivative (to126 in Figure 6.5) coelutes with 

the second peak of methylglyoxal. The mass spectrum of this peak contains unique ions 

of each derivative, making it possible to determine the molecule weight of the individual 

component. This peak, however, can not be used to quantify either glyoxal or methylglyoxal 

when both are present, because the m/z 18 1 ion used for quantification comes from both 

derivatives. Fortunately, the first peak of the glyoxal derivative (to125) and the fourth peak 

(to128) of methylglyoxal are free from interference from other carbonyls present. Therefore, these two peaks are used for the quantification of glyoxal and methylglyoxal. To 

generalize: whenever some isomer peaks of a carbonyl coelute with another peak, the peaks 

without a coelution problem are used for quantification; when all the isomer peaks are free 

of coelution, the sum of area of all the isomer peaks are used. 

The concentrations of glyoxal, methylglyoxal, 2,3-butandione, glycolaldehyde, hydroxyacetone, and methyl-p-benzoquinone (MBQ) were computed using the calibration 

factors established in the TBR. Because benzaldehyde did not have a calibration factor 

from the TBR, and because p-tolaldehyde showed similar calibration factors in the TBR 

and the outdoor chamber, the concentrations of benzaldehyde and p-tolualdehyde were 

computed using calibration factors established in the outdoor chamber. The calibration 

factor for p-tolualdehyde was also used to estimate the concentrations of m-tolualdehyde, 

o-tolualdehyde, and dimethylbenzaldehyde. The calibration factor for MBQ was used to 

compute the concentrations for benzoquinone and dimethylbenzoquinone. 

No estimation of calibration factors was made for other carbonyl products, and the 

relative peak areas-defined as the peak area ratio between m/z=18 1 ion of a target carbonyl 

and m/z=59 ion of 13C3-acetone-added as an internal standard-were used to represent the 

concentration of any particular carbonyl product in the chamber. The ability to detect the 

13C3-acetone relative to normal acetone is shown in Figure 6.13. 

The estimation of the yields for carbonyl products is difficult for reaction systems 

in experimental setting such as the UNC outdoor smog chamber. As mentioned above, 

carbonyls can undergo photolysis and reaction with 'OH radicals in the atmosphere, so 

calculation of their yields must take into account the loss that results from both processes. 

The use of sunlight as the photolysis light source and a lack of wavelength-dependent 

quantum yields for the compounds of interest makes it difficult to calculate the loss of 

carbonyls resulting from photolysis. For these reasons, no carbonyl yields were estimated 

for the work reported here. 

Each of the aromatic compounds tested here can have produced a number of different 

carbonyl forms. For ease of presentation, the results shown here have been organized by 

carbonyl type in the following discussion of possible reaction pathways. Each type is 

described in a separate section, with the chemical structures for the relevant carbonyl forms 

shown at the top of that section. 

Photochemical Studies of Alternative Fuels 207 ESEIUNC-CH 

CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

6.1.1 Aromatic Aldehydes 

Benzaldehyde (to1 12 and to1 13), p-tolualdehyde (4-methyl-benzaldehyde) (p 17 and p 18), 

m-tolualdehyde (m26) and o-tolualdehyde (027), are positively identified in the toluene, 

p-xylene, m-xylene, and o-xylene experiments by matching the retention times and mass 

spectra of the carbonyls with those of the appropriate standards. 

benzaldehy de tolualdehyde dimeth ylbenzaldehy de 

The molecular weight of the benzaldehyde-PFBHA derivative, 301, is clearly indicated 

by five strong ions characteristic of molecular weight: ions at m/z 104 (M-197), resulting 

from loss of C6F5CH20 moiety; m/z 271 (M-30), resulting from loss of NO; rnlz 302 (M+l), 

resulting from protonation of neutral molecule; m/z 330 (M+29), resulting from addition of 

the reagent ion C2H5+; and dz 482 (M+18 I), resulting from addition of C6F5CH2+ ion to 

the neutral molecule. 

Peaks t 19 and t2 1 (Figure 6.10) from the 1,3,5TMB experiment were tentatively identified as 3,5-dimethylbenzaldehyde and peaks f 18 and f20 (Figure 6.12) in the 1,2,4-TMB 

experiment were tentatively identified as a dimethylbenzaldehyde because the molecular 

weight data obtained from the CI mass spectra match that of a dimethylbenzaldehyde. In the 

1,3,5TMB system, 3,5-dimethylbenzaldehyde is the only dimethylbenzaldehyde expected 

because of the symmetric structure of 1,3,5TMB. 1,2,4-TMB, however, could produce three 

possible dimethylbenzaldehydes: 2,4-; 2,5-; and 3,4-dimethylbenzaldehyde. Additional 

differentiation among the three possible isomers cannot made based on the CI spectrum. 

Benzaldehyde reacts with .OH radicals at a rate two times faster than its parent compound toluene; tolualdehydes react with .OH at a rate comparable to their parent compounds xylenes. The time profiles of benzaldehyde and m-tolualdehyde are shown in 

Figure 6.15, along with their parent compounds-toluene and m-xylene. p-Tolualdehyde, 

o-tolualdehyde, dimethylbenzaldehydes have similar time series to those of benzaldehyde 

and m-tolualdehyde. Degradation of the aromatic aldehydes (as shown in Figure 6.15) is 

because of reaction with OH and photolysis. 

6.1.2 Quinones 

Methyl-p-benzoquinone (MBQ) was positively identified in the samples from toluene (to1 18, 

to119, to121, and to152), m-xylene (m27, m28, and m30), and o-xylene (030,031, and 033) 

photooxidation, but no MBQ was observed in the p-xylene experiment. 

Photochemical Studies of Alternative Fuels 209 ESEIUNC-CH 
6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

benzoquinone methy 1-p-benzoquinone dimethyl-p-benzoquinone 

The PFBHA derivatives of MBQ could result in three peaks from the mono-derivative 

and one peak from the di-derivative, however, the di-derivative is formed with a lower yield 

than the mono-derivative and could be below the detection limit when the concentration is 

low. This possibility may explain why the di-derivative was not detected in the m-xylene 

and o-xylene experiments. 

Benzoquinone (BQ) was identified as a product from photooxidation of toluene (to1 17 

and to15 I), p-xylene, and o-xylene (025). Additional outdoor smog chamber experiments 

conducted using benzene and ethylbenzene as the parent hydrocarbons showed benzoquinone is also a product of their photooxidation. Like MBQ, BQ could form both monoand di-derivatives with PFBHA and the di-derivative formed again at a lower yield than the 

mono-derivative. 

1~1~1~l~l~l~l~l~I~

- 

- toluene, (L) 

- 30-Aug-95 - --*--*- - -- 

- -. methyl-p-benzoquinone, (R) 

- 

- 

- \ 

\ - 

- \ 

\ 

- \ 

\ - \ 

- 

- 5- 

3 - benzoquinone, (R) - - 

- 73 

- rn 

- 

- 

- 

- 

e 

- I 

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 

HOURS, EDT 

Figure 6.14:. Time series of benzoquinone and methyl-p-benzoquinone in the toluene outdoor 

chamber experiment. 

Photochemical Studies of Alternative Fuels 210 ESEIUNC-CH 

6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

Dimethylbenzoquinones (DMBQ) were present in the three xylenes (p22, m32 and 040), 

1 ,2,4TMB (f 19) and 1 ,3,5TMB systems. The retention times showed that the DMBQ formed 

from 1,2,4TMB is the same as the one from p-xylene and that the DMBQ from 1,3,5TMB the 

same as the one from m-xylene. Trace amounts of trimethylbenzoquinones were observed 

in 1 ,3,5TMB and 1 ,2,4TM B experiments. 

The time series of benzoquinone and methyl-p-benzoquinone in the toluene outdoor 

chamber experiment are shown in Figure 6.14. They are typical of the quinone products. 

From the time series, it appears that benzoquinone, methylbenzoquinone and dirnethylbenzoquinone are stable once they are formed, and likely are secondary products. Gery 

also detected trace amount of p-benzoquinone from photooxidation of benzaldehyde and 

methyl-p-benzoquinone from o-tolualdehyde. In addition, they showed that 2,6-dimethylp-benzoquinone was a product of 2,6-dimethylphenol photooxidation. Thus, two possible 

precursors of quinone products are aromatic aldehydes and phenols. 

Using the calibration factors determined for MBQ in the TBR, maximum concentrations 

of quinone up to 2 ppbV in the toluene, m-xylene and o-xylene systems and 8 ppbV in the 

p-xylene system, and even lower in the two TMB systems were detected. Considering the 

low concentrations detected, quinone formation probably does not constitute an important 

reaction pathway for degradation of aromatic compounds. 

Trace amounts of C7 and Cg di-unsaturated 1,6-dicarbonyls were tentatively identified in 

both the indoor TBR and the outdoor chamber experiments of toluene and xylenes. 

0 0 

I I I I 

C, di-unsaturated- l,6-dicarbonyl H- C-C = CH -CH =CH - C- H 

I 

0 0 

I I I I 

C, di-unsaturated- l,6-dicarbonyl CH3-C -CH = CH-C = CH- C-H 

I 

Although C9 di-unsaturated 1,6-dicarbonyls were not detected in the outdoor experiments of TBRs, they were observed in the TBR experiments. This discrepancy is most likely 

explained by the fact that much higher initial concentrations of the TBRs used in the TBR, 

producing the intermediates in a quantity sufficient to be observed. 

Klotz et a1 [ll] studied the photooxidation chemistry of a C6 di-unsaturated 1,6- 

Photochemical Studies of Alternative Fuels 212 ESE/UNC-CH 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

dicarbonyls, 2,4-hexadienedial, and a C7 2-methyl-2,4-hexadienedial. Their results indicated that the di-unsaturated dicarbonyls are extremely reactive with -OH radicals, having a 

reaction rate of about 1 x lo-'' cm3 molecule^-^ -s-', a rate similar to that of isoprene 

with OH. Thus, the low concentrations of di-unsaturated dicarbonyls likely are the results 

of their strong reactivities. 

6.1.4 Unsaturated 1,4-Dicarbonyls 

Three 1,4-unsaturated dicarbonyls-butendial, 4-0x0-2-pentenal, and 3-hexene-2,5-dionewere synthesized by oxidizing furan, 2-methyl furan, and 2,s-dimethyl furan through reaction with .OH radicals in the TBR [3]. The formation of butendial from the reaction of .OH 

radicals with furan is proposed as follows: 

H H -QHoHd

- L - 

cis-butendial 

H H 

0 

0 

H trans-butendial 

In a similar way, 4-0x0-2-pentenal and 3-hexene-2,5-dione are formed from 2-methyl 

furan and 2,5-dimethylfuran: 

2-methyl furan cis-4-0x0-2-pentenal trans-4-0x0-2-pen tenal 

CH? 

2,5-dimethyl furan cis -3-hexen-2,5-dione trans-3- hexene-2,5-dione 

In the TBR experiments, UV light was present to provide -OH radicals from photolysis 

oftipop. Studies by Bierbach et al. [3] and Tuazon et al. [12] showed that unsaturated 

1,4-dicarbonyls undergo photoisomerization between cis- and trans-isomers; therefore, the 

Photochemical Studies of Alternative Fuels 213 ESEfUNC-CH 
6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

resulting products are a mixture of cis- and trans-isomers. In this research, no attempt was 

made to differentiate among these. The PFBHA derivatives of butendial isomers show four 

peaks with retention times of 28.86, 29.24, 29.33, and 29.61 min. The PFBHA oximes of 

4-0x0-2-pentenal isomers show five peaks at retention times 28.68,29.18,29.61,29.80, and 

30.23 min. Five peaks were observed to represent the PFBHA derivatives of 3-hexene-2,5- 

dione isomers under the analytical conditions used for this work. The mass spectra of the 

isomers for each compound are similar, and hence only one spectrum for each carbonyl is 

shown in Figure 6.16. 

6.1.5 Unsaturated 1,4-dicarbonyls 

By matching retention times and mass spectra with the synthesized standard, butendial was 

positively identified as a product in the toluene and o-xylene photooxidation experiments, 

as was 4-0x0-Zpentenal in the toluene, o-xylene and m-xylene experiments, and as was 3- 

hexene-2,5-dione in the p-xylene and 1,2,4TMB experiment (see Figure 6.16). The common 

product butendial from toluene and o-xylene reflects the common structure moiety among the 

three compounds, i.e., four adjacent unsubstituted sites on the benzene ring. Similarly, one 

methyl group and three adjacent unsubstituted positions common to toluene, m-xylene, and 

o-xylene, leads to the formation of 4-0x0-2-pentenal in those experiments. The formation 

of 3-hexene-2,5-dione in p-xylene and 1,2,4TMB experiments results from another common 

moiety: two methyl groups in para-position with two unsubstituted positions between. 

The time series of butendial, 4-0x0-2-pentenal and 3-hexene-2,5-dione indicate that 

they quickly react after their formation and likely are primary products since their concentration maxima were early in the morning (Figures 6.17 to 6.22). The reaction with 

.OH radicals of these unsaturated dicarbonyls are about one half the rate of .OH-isoprene 

reaction [3], which explains their rapid loss. 

In the 1,2,4TMB photooxidation, two peaks (f40 and f45), with a MW of 5 16 matching 

the PFBHA derivatives of C7 unsaturated dicarbonyls, were detected. These are most probably 

3-methyl-3-hexene-2,5-dione. 

Six C6 unsaturated dicarbonyl peaks at retention times of 27.98, 29.05, 29.33, 30.73, 

33.23, and 34.76 min (t35, t40, t41, t42, t43, and t47) were observed in the 1,3,5TMB 

experiments. The six carbonyl peaks exhibit three types of time series pattern (Figure 6.22). 

Peaks at 27.98,29.33, and 30.73 min (t35, t41, and t42) have an early maximum near 0830 

EDT, with the peak at 30.73 min being the strongest and the peak at 27.98 min the weakest 

among the three. Carbonyl peaks eluting at 33.23 and 34.78 min (t43 and t47) have a later 

maximum near 1 130 EDT and do not drop as precipitously as the previous three. The peak 

at 29.05 rnin (t40) continues to accumulate over time and no decline is seen during the entire 

experimental duration. Of these, the peak at 34.78 min is more than five times stronger than 

the others. 

Four of the above six C6 unsaturated carbonyls (excluding the two weak peaks at 

27.98 and 29.05 rnin) were also observed in the m-xylene (m60, m69, m75, and m78) and 

Photochemical Studies of Alternative Fuels 214 ESEIUNC-CH 

6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

1,2,4TMB (f44, f46, and f50) experiments. This observation is in agreement with the fact 

that these three aromatics species possess a common structure, two methyl groups in the 

meta-position. In the m-xylene and 1,2,4TM6 experiments, the absence of the two peaks is 

likely caused by lower concentrations resulting from the lower yields of this carbonyl. The 

similar time series patterns were observed (see Figures 6.19 and 6.21). The time series of 

two peaks at 29.33 and 30.73 rnin (f44) in the 1,2,4TMB experiment are not shown because 

the 30.73 min peak coeluted with 3-hexene-2,5-dione, a major product from the 1,2,4TMB 

system and the 29.33 min peak is very small. 

Considering the common moiety in m-xylene, 1,2,4TMB, and 1,3,5TMB, the six Cs 

unsaturated dicarbonyl peaks most likely represent 2-methyl-4-0x0-2-pentenal. 

A common set of five C5 unsaturated dicarbonyl peaks at retention times of 29.66, 

30.36, 30.7 1,34.59, and 36.30 min were observed in the p-xylene (p42, p43, p44 and p45), 

m-xylene (m63, m67, m69, m77, and m79), and 1,2,4TMB (f41, f43, f44, f49, and f51) 

experiments. These peaks probably represent 2-methyl-butendial, formed as a result of the 

common structure: a methyl group with three adjacent unsubstituted sites on the benzene 

ring, two on one side of methyl group and one on another side. Two types of time series 

were observed, peaks at 29.66, 30.36 and 30.71 rnin peaking in the morning and peaks at 

34.58 and 36.36 min peaking in the afternoon (see Figures 6.18 and 6.2 1, labeled as m488- 

ul). The 29.66 min peak coeluted with one of the 4-0x0-2-pentenal peaks in the m-xylene 

experiment; the 30.7 1 min peak coeluted with one of the 3-hexene-2,5-dione peaks. 

Photochemical Studies of Alternative Fuels 216 ESE/UNC-CH 






CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

6.1.6 Saturated Dicarbonyls 

Glyoxal and methylglyoxal were observed as products in every aromatic system. 

0 0 

gly oxal II II 

H-C-C-H 

methylglyoxal 

I, 3,5TMB produced the largest concentration of methylglyoxal(678 ppbV) and toluene 

produced the least (27.3 ppbV). The largest concentration of glyoxal(108 ppbV) was seen 

in the toluene system; the lowest (7.5 ppbV) was observed in the 1,3,5TMB system. The 

time series of glyoxal and methylglyoxal are shown in Figures 6.17 to 6.22. In the toluene 

experiment, double maxima were observed for glyoxal and methylglyoxal, indicating they 

are both primarily and secondary products or that they are secondary products for both fast 

and slow reaction intermediates. 

Biacetyl or 2,3-butanedione was identified as a product from the o-xylene and 1,2,4TMB 

systems, as expected because of the presence of two adjacent methyl groups. A maximum 

concentration of 18 ppbV was detected in both systems. The time series of 2,3-butandione 

showed similar pattern to those of methylglyoxal in the o-xylene and 1,2,4TMB systems 

(Figures 6.20 and 6.21). 

6.1.7 Hydroxy Dicarbonyls 

Three different hydroxy dicarbonyls were observed: 

C3 hydroxy dicarbonyl H- C- CHN C- H 

I 

0 0 

I I I I 

C4 hydroxy dicarbonyl CH 3-C - cH-C - H 

I 

OH 

0 0 

I I I I 

C5 hydroxy dicarbonyl CH 3-~ - CH -C - CH~ 

I 

A common set of four peaks at 27.61, 27.82, 28.44, and 28.78 min with MW 506 

Photochemical Studies of Alternative Fuels 223 ESE/UNC-CH 

CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

experiment. These C4 hydroxy dicarbonyls were often weak and some of them coelute with 

another carbonyl products. The typical time series for C4 hydroxy dicarbonyls showed a 

gradual increase followed by leveling off (Figure 6.24). 

Two C3 hydroxy dicarbonyl peaks at 28.33 (053) and 28.61 min were observed in the 

o-xylene and toluene experiment. because of their low intensity, not all the above peaks 

were labeled. 

. -- 

Photochemical Studies of Alternative Fuels 225 ESE/UNC-CH 


6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

6.1.8 Triones 

There is evidence for the detection of a series of triones: 

000 

C3 trione 

II II II 

H-C-C-CH 

0 0 0 

C4 trione 

I1 II II 

CH3-CCCH 

C5 trione CH3-CCCCH3 

Three carbonyl peaks with a MW of 504 at 26.15,27.16, and 28.13 min were detected 

in the m-xylene (m40, m47, and m53); 1 ,3,5TMB (t27, t3 1 and t36); and 1,2,4TMB (f23, 

f28 and f34) photooxidation experiments. In these, the 26.15 min peak was the strongest, 

and the 28.13 min peak the weakest. This common set of carbonyl peaks for three aromatic 

compounds coincides with the occurrence of a common set of C5 hydroxy dicarbonyl peaks 

and C5 unsaturated dicarbonyl peaks, which tends to suggest a link between the three types of 

carbonyls. The time series of these three peaks in the 1,3, STMB and 1,2,4TMB experiments 

showed a pattern of accumulation throughout the entire experimental period, indicating that 

the MW 504 carbonyls are not reactive (Figure 6.25). 

There are three types of carbonyl compounds matching the MW of 504: Cs saturated 

dicarbonyls, C5 epoxy dicarbonyls, and C5 triones. 

Carbonyl peaks with a MW of 490 were also detected in every aromatic system. Two 

common MW 490 peaks at 26.86 and 27.19 min were observed in the m-xylene (m45 and 

m46); 1,3,5TMB (t30 and t3 1); and 1,2,4TMB (f26 and f28) experiments. One common 

MW 490 peak at 27.76 rnin was present in toluene (to135) and every xylene experiment. 

Again, there are three types of carbonyl compounds matching the MW of 490: C5 saturated 

dicarbonyls, C4 epoxy dicarbonyls, and C4 triones. 

Two common peaks with a MW of 476 at 26.51 and 27.08 min were present in each 

aromatic experiment. The PFBHA derivative of a C4 saturated dicarbonyl, or the di-derivative 

of a C3 trione, would each have a MW of 476. The possibility of 2,3-butandione was ruled 

out because 2,3-butandione shows three peaks at 24.30,25.25, and 26.54 rnin, and the first 

two peaks were not observed in the toluene, p-xylene, m-xylene, and 1,3,5TMB experiments. 

In the o-xylene and the 1,2,4TMB experiments, 2,3-butandione was a product, and all three 

peaks were observed. Another C4 saturated dicarbonyl, 2-0x0-butanal which elutes at 26.04, 

26.15, and 26.28 min, can also be ruled out based on a retention time mismatch. Therefore, 

the candidates for these two MW 476 peaks are reduced to the following: 

Photochemical Studies of Alternative Fuels 228 ESETCTNC-CH 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.1 EXPERIMENTAL RESULTS 

The time series of these two peaks in the toluene experiment are shown in Figure 6.17, 

indicating a slow reactivity in the smog chamber. 

6.1.9 Glycolaldehyde and Hydroxyacetone 

Glycolaldehyde and hydroxyacetone were observed in every aromatic system examined. 

0 

II 

Glycolaldehyde HOCH r C- H 

0 

II 

Hydroxyacetone - HOCH 2- C- CH3 

The 1,3,5TMB system produced the largest concentration of glycolaldehyde (up to 18 

ppbV) and p-xylene system produced the most hydroxyacetone (up to 46 ppbV). The time 

series indicate that they are secondary products (see Figure 6.26). 

Photochemical Studies of Alternative Fuels 229 ESEIUNC-CH 


6.1 EXPERIMENTAL RESULTS CHAPTER 6. AROMATIC REACTION MECHANISMS 

6.1.10 Unknown Carbonyls 

Despite the long list of carbonyls described in this chapter, there are still some carbonyls 

that can not fit into the above categories. Table 6.19 lists several detected but unknown 

carbonyl products in the six alkylbenzene systems. 

Table 6.1 9: 

a molecular weight of PFBHA-carbonyl derivative 

hmolecular weight of carbonyl 

'coelute with another peak 

List of Unknown Carbonyl Products 

Photochemical Studies of Alternative Fuels 232 ESEIUNC-CH 

Aromatics 

toluene 

m-xylene 

m-xylene 

m-x ylene 

m-xylene 

m-xylene 

m-x ylene 

o-xylene 

1,3,5-TMB 

1,2,4-TMB 

peak 

to122 

m35 

m68" 

m7 1 

m72 

m74 

m76' 

048 

t46 

f12* 

RT (min) 

24.95 

25.35 

30.36 

31.29 

32.33 

32.86 

33.73 

27.13 

34.33 

23.94 

MWa 

337 

365 

500 

500 

500 

542 

542 

343 

572 

365 

MWh 

142 

170 

110 

110 

110 

152 

152 

148 

182 

170 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.2 EPOXY CARBONYLS 

6.2 Epoxy Carbonyls 

Bartolotti and Edney [13] performed a series of density functional quantum mechanical 

calculations to identify potential intermediates following the .OH addition to toluene. The 

calculations indicate that the epoxide structure (I) is the most stable intermediate among the 

likely intermediates. 

CH3 I 

The subsequent reactions of I leads to the formation of three epoxy carbonyls (structures 

11, 111, and IV); the abstraction of H-atom by O2 yields a C7 unsaturated hydroxy epoxy 

cyclic carbonyl (11); the decomposition of I results in a C7 unsaturated epoxy dicarbonyl 

(IV); and, intermediate I may also undergo loss of Hp to form a C7 unsaturated epoxy cyclic 

dicarbonyl(II1). 

6.2.1 Epoxide Toxicity 

If epoxide intermediates play a role in the .OH oxidation of aromatic compounds, their 

possible toxicity will be a matter of some concern. An epoxy carbonyl similar in structure to 

structure -N, 2,3-epoxypropanal (glycidaldehyde) has been reported to be a potent mutagen 

Photochemical Studies of Alternative Fuels 233 ESE/UNC-CH 
6.2 EPOXY CARBONYLS CHAPTER 6. AROMATIC REACTION MECHANISMS 

in a variety of in vitro test system [14,15] and has been shown to induce sarcomas in rats 

by subcutaneous injection. [16] As a bifunctional agent, 2,3-epoxy propanal is capable of 

forming cyclic adducts and crosslinks with DNA bases. [17] In general, the epoxy functional 

group can serve as electrophile to attack DNA and nucleosides [18-201. It seems possible 

that the mutagenic activity of irradiated toluene/NOx mixtures reported by Shepson et 

al. [21] and by Shiraishi et al. [22] may have been because of the production of epoxide 

compounds. Hence, the possible formation of epoxides from atmospheric oxidation of 

aromatic compounds is a significant public health concern, deserving additional laboratory 

and field work. 

6.2.2 Theoretical Considerations 

Structure IV is expected to further react with OH radicals or ozone to form one epoxy 

dicarbonyl (V) and one saturated dicarbonyl by the pathways shown in the reaction scheme 

above. In addition, IV can undergoes photolysis, one possible pathway being a Norrish 

Type I reaction, i.e., loss of CO to form an unsaturated epoxy carbonyl with one lower 

carbon number (Structure VI). The reactivity of the saturated epoxy dicarbonyl is at this 

point unknown. 

0 0 OH 0 0 

CH-CH-C-C-H - - I I I /\ II 

I H-C-CH-CH-CH-C-CI IV CH3 

000 0 0 \ 

CH-CH-CH-CH3 + CO I1 / \ II I1 II 

H-C-CH-C-C-H + H-C-CI 

According to Bartolotti and Edney's calculations, structure VII has the second lowest 

energy of all the possible epoxide intermediates. When the energy barriers to the corresponding transition states are comparable, the formation of intermediate VII can be competitive 

with that of I. The subsequent reactions of structure VII will lead to carbonyl products 

(VIII, IX and X) different from those from Structure I. 

Photochemical Studies of Alternative Fuels 234 ESEIUNC-CH 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.2 EPOXY CARBONYLS 

"?q, OH '*UH 

- 4 0 VII 

VIII 

Similar epoxy carbonyls are also expected to form from .OH oxidation of other aromatic 

compounds. Evidence supporting the formation of the intermediates I and VII will be 

discussed, based on the carbonyl products detected in the toluene, xylenes, and 1,3,5TMB 

experiments. 

First, however, we must show that the PFBHA method is capable of detecting and 

identifying these epoxy carbonyls. Because none of the epoxy carbonyls described above 

are available commercially, we must conduct our demonstration using the few commercially 

available epoxy carbonyls and a few simple epoxy carbonyls that we can synthesize as model 

compounds. 

6.2.3 Model Epoxides 

Epoxides are known to hydrolyze slowly in water [20]. A commercially available epoxy 

carbonyl- 1,2-epoxy-3-cyclohexanone-and three synthesized epoxy carbonyls-glycidaldehyde, 

2-epoxy-3-butanone (methyl vinyl ketone epoxide), and 4,5-epoxy-pentanal-were employed to demonstrate that an epoxy carbonyl could survive the PFBHA derivatization 

procedure conducted in aqueous solution and be detected by GC/MS. The model epoxy 

carbonyls were successfully derivatized by PFBHA in water solution without loss of the 

epoxide functionality and all were detected by GC/MS. They generally elute later than the 

simple carbonyls, but iarlier than the hydroxy carbonyls with the same carbon number. For 

example, glycidaldehyde eluted after the C3 simple carbonyls such as acetone, propanal 

and acrolein, but earlier than hydroxyacetone. Figure 6.27 illustrates the full spectrum of 

the PFBHA derivative of glycidaldehyde. The CI mass spectra patterns are similar to those 

simple carbonyls described in the paper by Yu et al. [6], showing strong M + 1 and M - 197 

ions. The M + 181 ion is often present to confirm the determination of molecular weight; 

however, there is not a particular ion characteristic of the epoxy functional group. 

Photochemical Studies of Alternative Fuels 235 ESEIUNC-CH 

CHAPTER 6. AROMATIC REACTION MECHANISMS 6.2 EPOXY CARBONYLS 

A literature search confinned that no study exists on the atmospheric chemistry of 

unsaturated epoxides having structures IV and VI. Hence, two simple unsaturated epoxides, 

5,6-epoxide- 1 -hexene and 3,4-epoxide butene (butadiene monooxide) were chosen as model 

compounds for additional experimental investigation. A simple epoxide, propene oxide, 

was also examined. TBR experiments were conducted using 10 ppm epoxide with 10 ppm 

NO, and samples were collected and analyzed in the same manner as earlier TBR samples. 

Carbonyl products resulting from the 40H addition reactions with the carbon-carbon 

unsaturated bond were found to be the major products. A strong C4 hydroxy carbonyl is also 

observed among the carbonyl products from the .OH reaction with butadiene monooxide, 

likely resulting from the following pathway: 

In the .OH oxidation of propene oxide, a trace amount of hydroxyacetone was positively 

identified as a product by comparison with an authentic standard. This observation indicates 

the OH attacking the C-atom adjacent to the epoxide 0-atom pathway is operative. This 

pathway could be facilitated by either an adjacent M or H group stabilizing the alkoxy 

radical. 

6.2.4 Epoxy Carbonyls in Experiments 

Tables 6.20 to 6.26 list the primary epoxy carbonyl products formed from the epoxide 

intermediates via the pathways depicted above. Carbonyl peaks with MW matching the 

predicted products having the form of structures 11, 111, IV, and VII types have been 

observed for all aromatic compounds examined. These peaks are all weak, indicating that 

these carbonyls are either very reactive, or are not formed with significant yields. 

It is difficult to assign a definite structure to every observed carbonyl peak based on the 

MW information because often a given MW corresponds to a number of possible structures 

with different functionality. For example, the mono-derivative of structure IV type carbonyls 

have the same MW as structure I1 type carbonyls. A carbonyl peak with a MW of 140 in 

the toluene experiment, for example, can have either one of the structures: 

The limited information from mass spectra does not permit distinguishing between the 

two possibilities, but the presence of either of the carbonyls supports the supposition of an 

epoxide intermediate precursor. 

Photochemical Studies of Alternative Fuels 237 ESEIUNC-CH 
6.2 EPOXY CARBONYLS CHAPTER 6. AROMATIC REACTION MECHANISMS 

In other cases, however, only one particular structure can fit the observed molecular 

weight and structural information data obtained from the mass spectrum. For example, two 

peaks with a MW of 558 were observed in the sample collected from the 1,3,5TMB outdoor 

chamber experiment. The CI spectrum of one peak is shown in Figure 6.28, along with 

the fragmentation diagram. This compound is very likely 2,3-epoxy-2,4-dimethyl-6-0x0-4- 

heptenal (Epoxide A hereafter) (see Table 6.22), a product logically expected to result from 

the decomposition pathway of the epoxide intermediate for 1,3,5TMB following the same 

pattern as was shown for toluene. Few other structures could match the combination of 

functionality identification, the observed molecular weight data, and the CI fragmentation 

pattern. Therefore, the 2,3-epoxy-2,4-dimethyl-6-oxo-4-heptenal assignment seems to be 

the only logical fit for this compound. In addition, two peaks with MW of 558 were also 

detected in the 1,2,4TMB/NO, experiment, which tends to confirrn the proposed formation 

pathway for structure IV type epoxy carbonyls. The di-derivatives of structure IV type 

epoxy dicarbonyls were not detected in the toluene and xylenes experiments, but carbonyl 

peaks with MW matching those of the mono-derivatives were detected. 

The time series of Epoxide A in Figure 6.29 shows a peak concentration near 1130 

EDT, followed by decay to one-third of its peak concentration within 2.5 h. Epoxide A is 

expected to have two possible loss pathways: .OH attacking the unsaturated bond to form 

methylglyoxal and a C6 epoxy dicarbonyl, 2,3-epoxy-2-methyl-2-one-pentanal; and loss of 

CO via photolysis to form a Cg unsaturated epoxy carbonyl, 5,6-epoxy-4-methyl-2-one-3- 

heptene. Indeed, two carbonyl peaks with a MW matching Cs unsaturated epoxy carbonyls 

(i.e., 335) were present. The time series of the sum of the two MW 335 peaks shown in 

Figure 6.29 indicates a concurrent increase in concentrations of MW 335 and Epoxide A 

peaks, which tends to support the proposed mechanism. 

Products resulting from the photolysis pathway of structure IV type epoxy carbonyls for 

other aromatic compounds were also observed and are listed in Table 6.24. The expected C6 

epoxy dicarbonyl from Epoxide A (MW 5 18) was not detected in the outdoor experiments, 

but was detected in the TBR experiments for p-xylene, and for 1,3,5TMB. A C5 epoxy 

dicarbonyl with a MW of 504 was also detected in the toluene TBR experiments. Because 

the radiation field in the TBR is signficantly weaker (and differs spectrally), these epoxy 

diacarbonyls may have a much longer lifetime in the TBR compared to that in the outdoor 

chamber under intense sunlight. Unfortunately, no available standards exist to test this 

compound further. In the toluene and xylenes systems, carbonyl peaks with MW matching 

the corresponding epoxy dicarbonyls were observed and are listed in Table 6.23. However, 

as discussed above, a trione-a likely product from hydroxy dicarbonyl-has the same 

MW as an epoxy dicarbonyl of the same carbon number. For example, the following two 

carbonyls have the same MW of 114 and their PFBHA derivatives have the same MW of 

504. 

Photochemical Studies of Alternative Fuels 238 ESEIUNC-CH 

6.2 EPOXY CARBONYLS CHAPTER 6. AROMATIC REACTION MECHANISMS 

... 

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 

HOURS, EDT 

1~1~1~1~1~1~1~1~1~1~

- 

- O3-A~g-95~~~~ 

- I ,3,5-trimethylbenzene, (L) - R - 8 p..,: P*. 

- 8 '.-.. m335-u2. (R) \ 

- 

- 8 * j - - 

- \ 

t ,i - 

- \ 

\ ,i - 

- \ 

- t ,I' - 

- \ - \ ,I' - - 

- - - 

- - 

- - 

- 

- - 

- - - 

- - - I I I 

Figure 6.29:. Time series of unsaturated dicarbonyls and saturated dicarbonyls in the 1,3,5 TMB 

outdoor chamber experiment. 

3.07 

3.06 

3.05 

3.04 

3.03 

3.02 

-3.01 

0.00 

One interesting observation is that two strong C5 hydroxy carbonyls were present early 

in the outdoor smog chamber experiment (Figure 6.24). The .OH addition reaction to a 

C6 epoxy dicarbonyl followed by unimolecular decomposition could lead to a C5 hydroxy 

carbonyl (Figure 6.30), a similar reaction pathway to the one in our model epoxide studies 

described above. 

As discussed previously, however, unsaturated 1,4-dicarbonyls also serve as a possible 

source for hydroxy dicarbonyls; thus, the observation of hydroxy dicarbonyls is not proof 

of the reaction scheme shown in Figure 6.30. 

Other Epoxides 

Peaks with MW of 321, 335 and 349 matching Cs, C7, and C8 hydroxy unsaturated epoxy 

cyclic carbonyl, were detected from the toluene, xylenes, and trimethylbenzenes experiments, respectively. They would be epoxide carbonyls similar in structure to that of Structure 11, but having one less carbon (Table 6.25). This observation can be explained by 

invoking structure VII, a second-lowest intermediate. This results in such epoxy carbonyls 

by loss of a methyl group. As an example, Figure 6.29 shows the time series of MW 349 

peaks in the 1,3,5TMB experiments: the pattern of gradually accumulating concentrations 

over time suggests that these types of carbonyls are not reactive in the outdoor chamber. 

Photochemical Studies of Alternative Fuels 240 ESEIUNC-CH 
CHAPTER 6. AROMATIC REACTION MECHANISMS 6.2 EPOXY CARBONYLS 

6.2.5 Conclusions 

The observation of a series of carbonyl peaks with molecular weights of PFBHA derivatives 

matching the proposed epoxy carbonyls strongly supports the supposition of the formation 

of epoxide intermediates as suggested by theoretical calculations. Confirmation of the 

observation of these in actual experimental conditions, however, still requires standards. 

These epoxide compounds may play a role in the -OH oxidation of aromatic compounds, 

and their potential toxicity and mutagenicity raise public health concerns. For these reasons, 

the atmospheric chemistry of these epoxide compounds should be studied in greater detail 

with indoor and outdoor chamber and field experiments in order to assess their role in the 

urban atmosphere. 

Photochemical Studies of Alternative Fuels 24 1 ESEIUNC-CH 








Chapter 7 

Airshed Modeling 

7.1 Emissions Inventory Improvements 

7.11 Area Source Emissions 

The Urban Airshed Model and its preprocessors use data originating in many different 

places. North Carolina Modeling Domain data included emissions data from industrial 

point sources, traffic data from transportation demand models, vehicle emissions data from 

an emission factor model such as EPA's MOBILE model, road locations from Geographic 

Information Systems (GIs) software, nonroad mobile calculations, area source calculations, 

and temperature and wind field data from National Weather Service and private weather 

stations. 

The Emissions Preprocessor System within the UAM is written in FORTRAN and is 

responsible for converting the county-based emissions inventory from a base year to a gridbased, hourly emissions inventory for the days being modeled. Coordinates of point sources 

and link-based mobile sources are specified in the inventory. However, exact locations of 

other emissions sources are not known. These sources are spatially allocated to grid cells 

using surrogates such as population and water. It is frequently true that these surrogates 

are incomplete or wrongly specified for many domains. For example, the "water" surrogate 

(representing a river) has often been found to suddenly stop at a county line, yet continue 

in the next county. 

In our UAM studies, we developed a relational database management techniques applied them to emissions inventory and spatial surrogate files for the North Carolina Modeling 

Domain [23]. Application of this code to QA work on emissions inventory files resulted in 

many refinements of the spatial allocation of emissions. 

Working with emissions files in a relational database management system (RDBMS) 

has many advantages over continuing to work in FORTRAN. When writing programs in 

FORTRAN and other third-generation computer languages, the file structure, method of 

data access, and input and output functions must be explicitly written in each program. 
CHAPTER 7. AIRSHED MODELING 7.1 EMISSIONS INVENTORY IMPROVEMENTS 

Although the main processing portion of any individual program may be only a few lines 

long, the entire program may be many pages long. All this code must be debugged and 

tested for accuracy. However, when writing programs using an RDBMS, the data structure 

is defined once in a centralized data dictionary, and the RDBMS itself handles the method of 

data access and input and output processing. Some RDBMS languages even include "high 

level functions" such as TOTAL and COUNT functions, eliminating the need to explicitly 

write such code every time it is needed. Also, an RDBMS inherently has the ability to relate 

data in different files in the database, simplifying lookup functions and file comparisons. 

Ad hoc reports may be quickly generated using an English-like query language, allowing 

people other than computer programmers access to data for analysis. 

To investigate the quality of emissions data and spatial surrogates, these data files 

were loaded into a PROGRESS database. Query programs then searched the emissions 

inventory files for missing and duplicate data. Quality assurance was also performed on the 

spatial surrogate file provided by EPA. Query programs operated on the emissions and the 

spatial surrogate file in the database, performing many of the tasks of UAM's Emissions 

Preprocessor System 2.0 7 (EPS). Then, using the spatial surrogates, emissions using each 

spatial surrogate were allocated to the appropriate grid cells in the modeling domain. These 

gridded emission files for each surrogate were then exported to SASIGRAPH for spatial 

analysis. 

The total gridded emissions may be correct, but may be in the wrong grid cells. The 

gridded emissions may be in the correct grid cells, but the total gridded emissions may be 

incorrect if the total fractional surrogates do not correctly sum to 1. 

Both types of errors were found in the North Carolina surrogate file. When allocating 

emissions using the original urban and water surrogates, the surrogates were zero for some 

counties, resulting in lost emissions during the gridding process of EPS. Also, the surrogates 

apparently originated from older land-use data bases using a very coarse grid size, resulting 

in over-smoothing of the emisions field and artifically spreading of emissions beyond the 

emissions sources. 

7.1.2 Mobile Source Emissions 

Preparing motor vehicle emission inventories for use in photochemical modeling can be 

time-consuming and cumbersome. To simplify the calculations, current methods often 

ignore the variability of traffic flow characteristics and environmental conditions over space 

and time. The manpower and resources necessary to represent this variability using current 

methods is prohibitive, and therefore, its effect on predicted emissions and air quality has 

not been adequately explored. 

As part of this project and in cooperation with another project at UNC, we have 

addressed these problems by [24] : 

Implementing the current emission estimation methodology in a relational database 

management system to produce inventories in a more flexible and efficient manner 

Photochemical Studies of Alternative Fuels 25 1 ESEIUNC-CH 
7.2 PROCESS ANALYSIS CHAPTER 7. AIRSHED MODELING 

Incorporating information from a UTPS-type transportation demand model to improve 

the spatial and temporal resolution of vehicle activity in the emission calculations. 

The first task has resulted in a SAS-based package known as ProcMobl 1 .O, which: 

Predicts future traffic volumes based on linear regression of historical data 

Applies trip-averaged emission factors from EPA's MOBILE 5a model 

Chemically speciates the emissions for representation in UAM 

Temporally allocates the emissions by hour 

spatially allocates the emissions to UAM grid cells. 

ProcMobl 1.0 calculates emissions for individual roadway links or for entire counties. 

The system includes specific quality control checks and facilitates flexible reporting of 

calculation results. 

The current EPA methodology implemented in ProcMobl 1.0 estimates vehicle emissions based on an average emission per vehicle mile traveled. This approach ignores the 

fact that emissions vary according to whether the vehicle is starting a trip, is in the middle of 

a trip, has just ended a trip, or is parked. These modes of vehicle operation are distributed 

differently in space and time. For example, in the morning, a residential suburb may experience many trip starts while a central business district may experience many trip ends. This 

sort of spatial and temporal variability may be important to the formation of urban ozone, 

but it is not captured by the current emission calculation methodology. 

Building on ProcMobl 1.0, we developed ProcMobl 2.0 to represent the spatial and 

temporal variability of emissions based on four operating modes (starts, stabilized, ends, and 

parked). ProcMobl2.0 uses linear regression models to represent the relationships between 

speed and ambient temperature and the emissions for each operating mode as predicted by 

MOBILE 5a. The emissions are spatially and temporally allocated using the information 

generated by an UTPS-type transportation demand model. 

To evaluate the effects of increased resolution, motor vehicle emission inventories were 

developed for the Charlotte, NC, metropolitan area. 

In a subsequent paper, we used these different inventories in UAM simulations of a 

Charlotte, North Carolina ozone episode. These results were reported at an International 

Specialty Conference [25]. This paper is attached as an appendix. 

7.2 Process Analysis 

As part of this project (and others) we have developed a method called integrated process 

rate analysis, (IPRA), or just "process analysis," which allows us to fully explain a model's 

prediction. We have applied this method to static smog chamber simulations [26], to 

Photochemical Studies of Alternative Fuels 252 ESEIUNC-CH 
CHAPTER 7. AIRSHED MODELING 7.2 PROCESS ANALYSIS 

trajectory models [l, 27-29], to regional Eulerian models [3 1,321, and most recently to the 

Urban Airshed Model [33]. 

7.2.1 The Model's Processes 

Most air quality models create output files of just species concentrations over space and time. 

In process analysis we focus on the change caused by each major physical and chemical 

process in the model, and thus our method requires additional model outputs. The processes 

that we need separately quantified at each time step are: 

Chemistry: To perform a complete system analysis of the chemical transformations, the 

integrated reaction rates, or IRRs, of each chemical reaction in the model are needed. The 

reaction rate and IRR for a second order reaction is defined as 

where Mi and Mj are the mixing ratios of reactants in reaction g, and kg is the reaction 

rate constant. In addition, for other types of process analysis, we need the net change in 

each species' mixing ratio caused just by the chemistry, A Mc. This value and other similar 

values are obtained by integrating the rate of change of each process separately rather than 

summing all rates of change and then integrating once, that is, 

and p indicates the process. 

Horizontal transport: The change in each species' mixing ratio caused by advection, 

A MH, from cell to cell by winds must be quantified and 

where the subsubscripts indicate the direction of transport from the north, east, south, or 

west into the cell. 

Vertical transport: The change in each species caused by advection and diffusion of 

material, A MV, from above and below each cell must be quantified. 

Emissions: The change in each species caused by emissions, A ME, into each cell must 

be quantified. It is also useful to distinguish low level emissions from elevated emissions. 

-. . 

Photochemical Studies of Alternative Fuels 253 ESE/UNC-CH 
7.2 PROCESS ANALYSIS CHAPTER 7. AIRSHED MODELING 

IPR 1 :gn 

Figure 7.1 : Air quality models must be modified to include new code to output the process rates. 

Deposition: The change in each species caused by deposition of material on surfaces, 

A MD, must be quantified. 

Initial: The concentration of each species at the beginning of the time step, Mi, is also 

needed, i.e., the initial species concentrations for the time step. 

It is clear that 

where M,f is the final concentration for each time step. Obviously, some of the terms 

on the right-hand-side will be negative and some will be positive, so at any given time 

different processes are driving the species' mixing ratios up and some are driving it down. 

In an analysis of mixing ratio predictions, it is not possible to explain the mixing ratio 

time series by just examining the mixing ratios. Therefore, in the IPRA method, all of the 

additional output described above is subjected to further processing by several auxiliary or 

post-simulation programs. The code changes needed and the auxiliary programs will be 

briefly described next. 

7.2.2 The Code Changes 

To perform process analysis, minor additions (less than 500 lines) must be made to the solver 

codes in each air quality model to be analyzed and a new executable version of the model 

must be produced (see Figure 7.1). These changes have already been made to the regulatory 

version and to our CRAY vectorized version of UAM-IV, to the High Resolution RADM, 

and are currently being made to the UAM-V. In making these changes, a small amount of 

additional code is added at the end of each of the process routines in the model [34]. The 

purpose of this new code is to perform a simple integration of the rate of each process that 

effects species concentrations over each model solution time step and, depending upon a 

user command, to either accumulate these integrated rates for an hour before writing them 

- - - - -- 

Photochemical Studies of Alternative Fuels 254 ESENNC-CH 
CHAPTER 7. AIRSHED MODELING 7.2 PROCESS ANALYSIS 

---------I------------- 

mperations on CRAY I 

ASCII cell by cell 11_1 

, or a!! 

; IPR Data 

I 

........ by species 

I I 

I I ! 1 

pijipq ra hics 

IPR TS Plols IPR PI Comp 

2-~1/1 QTime ~IProass 20 

3-D & ~ovies ##& Eldian QD 

Plot Plot 

IRR Pr Chem 

Figure 7.2: Schematic of a 'complete' process analysis for a model scenario run. 

I 

I---$--- I I / \ I 

out, or to write them out each time step. Thus, the model's output will consist of both the 

usual species concentration data and the new IPR data (see Figure 7.2). These additional 

model calculations use the process rates of change that the model already computes, and 

thus, place only a very small additional computational burden on the system. An abbreviated 

example of the extract of one time-step output from an IPR file is shown in Tables 7.1 and 

7.2. 

For an environmental chamber analysis or for a Lagrangian air quality model analysis, 

the total output file consists of a set of values like those in Tables 7.1 and 7.2, usually one 

set for each hour. Note that some of the items in the IPR file are not needed for these simple 

types of analysis (e.g, the horizontal transport terms) and in these cases these items are 

written out as a field of zeros. In other cases, some species are computed by a steady-state 

approximation and are not transported from cell to cell. These species therefore appear 

with small initial values and have zero rates for many physical processes. For an Eulerian 

model, such a set as in the tables would be written for each cell of the model, resulting in a 

large amount of additional output-nearly a doubling the output of the model if all cells are 

written out. For this reason, the changes made to the UAM allow rectangular "regions" to be 

selected for process analysis output. Furthermore, some types of process analysis (e.g, the 

process compositional analyses) require output data at every advective time step (6 minfor 

-- 

Photochemical Studies of Alternative Fuels 255 ESENNC-CH 
7.2 PROCESS ANALYSIS CHAPTER 7. AIRSHED MODELING 

Table 7.1 :. The First Part of the Integrated Process Rate Output: The Integrated Reaction Rates of 

Each Chemical Reaction in the Model for One Time Step. 'IR/MB FOR UAM-IV* 

Time = 11. Dark = F 

I 'RM no Int rate' 

( 1) 1.285OZE-01 

( 2) 3,087293-01 

( 3) 1.0634lE-01 

( 4) 3.617503-06 

( 5) 6.07917E-07 

( 6) 1.33169E-07 

( 7) 9.16786E-04 

( 8) 1.71110E-01 

{ 9) 1.027863-02 

{ 10) 8.842783-03 

..a. 

{ 24) 1.606283-05 

{ 25) 1.615433-12 

.. m. 

{ 81) 4.347453-04 

{ 82) O.OOOOOE+00 

( 83) O.OOOOOE+00 

{ 84) 2.741003-06 

( 85) 9.534713-05 

( 86) 0.00000E+00 

Table 7.2:. The Second Part of the Integrated Process Rate Output: The Integrated Process Rates 

of Each Physical and Chemical Process in the Model for One Time Step. ! species Init conc Gas Chem West Trans East Trans South Trans North Trans 

'0 0.10OOOOE-19 O.OOOOOOE+OO 0.000000E+00 O.OOOOOOE+00 0.000000E+00 0.000000E+00 

'OlD 0.10000OE-19 0.00OOOOE+00 0.OOOOOOE+00 O.OOOOOOE+00 O.OOOOOOE+00 0.000000E+00 

. 

"PAR . 0.60487BE-01 -0.561872E-02 0.4456778-01 -0.473587E-01 0.487423E-03 0.636204E-02 

'TOL 0.127839E-02 -0,3871303-03 0.839104E-03 -0.936106E-03 0.102265B-04 0.103055E-03 . 'ISOP . 0.176950E-04 -0.242456E-03 0.3408368-04 -0.370586E-04 0.930089E-07 0.382198E-05 

.CLBR . 0.993197E+00 0.000000E+00 0.823980E+00 -0.8297843+00 0.868804E-02 0.115545E+00 

I Species 

-0 

'01D ' 

.P&R . 'TOL ' 

. "ISOP ' 

'CLBR ' 

Titrate 

0.000000E+00 

0.000000E+00 

. 0.000000E+00 

0.000000E+00 

0.000000E+OO 

0.000000E+00 

Ver Trans 

0.000000E+00 

O.000OOOE+00 

-0.150652E-01 

-0.409817E-03 

-0,5437363-04 

-0.113493E+00 

Elev PT emis 

0.000000E+OO 

0.000000E+00 

. 0.101726E-04 

0.000000E+OO 

0.0000OOE+OO 

0.0000OOE+00 

Deposition 

O.OOO000E+00 

O.O00000E+00 

. 0.290276E-14 

0.551037E-16 

0.147928E-17 

0.515579E-13 

Final conc 

0.100000E-19 

0.100000E-19 

. 0.508696E-01 

0.822499E-03 

0.2648463-04 

0.998133E+00 

the UAM), while the rest use values accumulated for 1 h. 

For efficiency in storage and for speed during the simulation, the IPR data are written 

as binary data in a model-specific format. At the present time, a post-simulation program, 

EXTRACT, is used to select cells and times from the binary file and to produce ASCII 

files that are portable to other computing platforms such as PCs. Although the hourly 

output for 0500-1 900 LDT for a single cell in the ASCI 1 format is only 152 kilobytes, the 

"every-time-step" output needed for the process compositional analysis is 1.8 megabytes 

per cell for the same time period. Typically a 6 by 6 cell region is analyzed which results in 

having to transfer and store 65 megabytes of data. Therefore, we are considering changing 

the EXTRACT program to output HDF data sets as an alternative to the ASCII format. 

HDF, or hierarchal data format, was invented at the National Center for Supercomputing 

Applications for exchanging data among different applications. It can also exchange binary 

data among different computer platforms, e.g., CRAY's and PC's. 

Photochemical Studies of Alternative Fuels 256 ESE/UNC-CH 
CHAPTER 7. AIRSFED MODELING 7.2 PROCESS ANALYSIS 

7.2.3 The Process Analyses 

As shown in Figure 7.2, there are currently four basic types of process analyses: 

1. Movies of lumped process magnitude fields, which will be described in more detail 

below, show by color in each model cell, for each hour, the total new radical production, and in another movie the odd-oxygen production, and in a third movie the total 

termination product formation (i.e., the beginning, middle, and end of the chemical 

cycles). Movies of species concentrationfields, show 03, Ox, NO, NOn, CO, and VOC 

mixing ratios in each cell each hour. These movies are useful combined in a spatial 

and temporal analysis. These will be described in more detail below. These movies 

were produced by data visualization software on Macintosh computers in combination with Apple's QuickTime movie extensions for both Macintosh and MS-Windows 

on DOS PCs. Each movie is small-about 350 kilobytes-and can be easily played 

by anyone with Microsoft Windows. 

2. IPRA time series plots that show a time series of the mixing ratio of selected species 

(e.g., NO, NO2, OBI CO, and total VOCs) along with the time series of the change produced each hour by chemistry, net horizontal transport, net vertical transport, elevated 

and low level emissions, and deposition. These plots have been typically produced 

by common PC spreadsheet programs using ASCII text files that are imported into 

the spreadsheet program. 

3. IPRA process composition plots and movies. In this analysis, the composition of 

a selected species in a given cell at a particular time is expressed in terms of the 

following sources: 

(a) Initial-here-the fraction or mixing ratio that was initially present in the target 

cell (and still remains) 

(b) Initial-othe-r-the fraction or mixing ratio that was initially present in some 

other cell and was subsequently transported to the target cell 

(c) Emitted-here-the fraction or mixing ratio that was emitted into the target cell 

(and still remains) 

(d) Emitted-other-the fraction or mixing ratio that was emitted into some other 

cell and was subsequently transported to the target cell 

(e) Chemistry-here-the fraction or mixing ratio that was chemically formed in the 

target cell (and still remains) 

(0 Chemistry-other-the fraction or mixing ratio that was chemically formed in 

some other cell and was subsequently transported to the target cell 

(g) Boundary-horizontal-the fraction or mixing ratio that was transported through 

a N, S, E, or W domain boundary face and was subsequently transported to the 

target cell 

Photochemical Studies of Alternative Fuels 257 ESEIUNC-CH 
7.2 PROCESS ANALYSIS CHAPTER 7. AIRSHED MODELING 

(h) Boundary-vertical-the fraction or mixing ratio that was transported through a 

top or bottom boundary face and was subsequently transported to the target cell. 

The simplest form of this output is an area chart for the composition in the target 

cell over time. Movies, one for each process (initial, emission, chemistry, vertical 

boundary, and horizontal boundary), which show the spatial and temporal distribution 

of the process contribution to a selected target cell are the most complex form of 

output for this analysis. Note that it is possible to reverse this analysis and report 

the contribution of a particular source to all affected cells. The data processing for 

this analysis has typically occurred on large memory PCs with visualization via PC 

spreadsheet tools. 

4. Integrated Reaction Rate or lRWMB process analysis is applied to just the chemical 

transformation change. It is usually presented in the form of time series, piecharts, 

flowcharts, and systems diagrams. These outputs include: 

(a) Time series of new radical strength and pie charts of source composition 

(b) Time series and average hydroxyl radical chain length, or number of times each 

hydroxyl radical is used before being lost; time series and average NO chain 

length, or number of times each NO is oxidized to NO2 before being lost as a 

nitrogen product 

(c) The amount of VOC consumed by hydoxyl radicals, by 03, or by photolysis 

reactions, and the average number of NO-to-NO2 conversions per VOC reacted 

(d) The amount of O3 produced by each VOC reacted 

(e) The total odd-oxygen production, the average O3 yield per NO2 photolysis, and 

the total amount of O3 produced 

(f) The VOC and NOx propagation factors and the distribution of termination products 

(g) The amount of 0, and o3 fonned by each VOC in the system 

(h) Detailed VOC and nitrogen mass balances. 

PCs or workstations were used to do the data processing for this analysis, which has 

resulted in a collection of 12 ASCII tables being produced. Selected data from these 

tables are manually entered into spreadsheets or onto plots produced via PCIMac 

Drawing programs. 

Example output from some of these analyses will be briefly described below. 

7.2.4 Example Analyses Results 

The purpose of this section is to demonstrate our recent success in unraveling problems and 

finding errors in applications of air quality models. We have conducted this work as part 

Photochemical Studies of Alternative Fuels 258 ESEIUNC-CH 
CHAPTER 7. AIRSHED MODELING 7.2 PROCESS ANALYSIS 

Figure 7.3:. One frame from Day Two of a UAM movie of ozone mixing ratios for a 1988 Charlotte, 

North Carolina episode. The darkness is not proportional to 03 in this figure and deep 

blue (for no 03) and bright red (for o3 equal to or exceeding 120 ppb) both show as 

dark colors. 

of our work on a EPA cooperative agreement; work with the North Carolina Department 

of Environment, Health, and Natural Resources on the state's request for redesignation for 

Charlotte North Carolina; and work for this DOE project. 

Movies of Concentrations 

We cannot show you movies here, but they are available for Macintosh and MS Windows 

3.1 computers with color monitors having at least VGA resolution and 256 colors. The 

QuickTime Movie viewer software is available free from Apple Computer's WWW Site 

and example UAM movies of concentration fields and selected process magnitude fields are 

available free from Jeffries' WWW and ftp site at f tp : / / airsi te . unc . edu. 

The movies have one frame for each hour of a 24-h simulation and play in 24 s. Often 

of more value is the ability to stop the movie play and "rock back and forth" over a series 

of frames to gain insight into what changed over the time interval. Figure 7.3 is a grayscale 

rendition of one frame from the o3 mixing ratio movie for day two of a 1988 Charlotte, North 

Carolina episode basecase simulation. Although the color transformations to grayscale are 

not linear, this frame still shows the essential features of the UAM movies: 

Photochemical Studies of Alternative Fuels 259 ESEIUNC-CH 
7.2 PROCESS ANALYSIS CHAPTER 7. AIRSHED MODELING 

The total view is 25 by 25 cells centered on downtown Charlotte; 

The cell size is 5 by 5 kilometers and is clearly visible in the figure 

County lines are marked in black and the NC-SC state line is marked in bold black 

a the dotted lines are the approximate locations of two Interstate highways; the one that 

runs diagonal is I85 and the one that runs N-S is I77 

the legend for map symbols is: 

+ The three +-marks are the sites of the monitoring stations in Charlotte (S to N, 

Arrowwood, Plaza, and County line) 

+ The four X-marks are the sites of Duke Power's fossil fuel power plants 

+ The O-mark in Lincoln county is the future site of Duke Power's gas turbine 

station for meeting peak power demand (this was not present in the 1988 base 

case, but was in the future 1999 and 2005 cases) 

+ The O-mark in Iredell county is a natural gas pumping station 

+ The O-marks are smaller low level NOx sources. 

The movies are 385 by 385 pixels and two movies can be played side by side on a 

standard VGA monitor. On a computer with large monitors, as many as four movies can 

be played at the same time. By comparing the movie frames of species mixing ratios (e.g., 

03) with movie frames of lumped processes, such as total odd oxygen production per hour 

per cell, one can see spatial and temporal relationships that would be difficult to achieve 

any other way. For example, by showing total odd oxygen production locations a few 

hours before the peak O3 time, we can determine the most important sources that should be 

controlled to reduce the 03 maximum. 

IPRA Time Series 

Figure 7.4 shows process analysis time series plots for NO, NO2, VOC, and O3 for a Charlotte 

UAM scenario in 1987. These are probably the outputs that are the most useful to nonmodelers. In this example, the IPR data values were aggregated for a 6 x 6 cell region 

that included both the Arrowwood and Plaza monitors for this analysis. The 1987 episode 

was very stagnant and the UAM was having problems predicting accurate O3 mixing ratios 

in areas south of the center city; e.g., see the large late increase in O3 in Figure 7.4. The 

measured peak maximum for this day was 13 1 ppb (see Fig. 7.6) 

Nitric Oxide: In the NO processes plot, we see that emissions in the 6 x 6 area increased 

the NO mixing ratio by 57 ppb over the time period 500-1900 LDT and only 4.6 ppb came 

from elevated sources. Chemistry processes were the major consumer of NO, removing 

45 ppb NO over the day. Vertical transport was an important process for NO in the morning 

hours, but decreased to small values during the afternoon when the NO mixing ratio was 

being driven to low values by the chemistry processes. 

Photochemical Studies of Alternative Fuels 260 ESE/UNC-CH 
CHAPTER 7. AIRSHED MODELING 7.2 PROCESS ANALYSIS 

14 

-- NO Processes 

-7 Total 

Emissions 

(57.0 ppb) 

i Elevated emissions 1 

Mixing ratio, I PP~ L-7 

---- cia A r-----------* ---- ---- ---. p--* --- 

lIIII1~~~I~1 

6 8 10 12 14 16 18 20 Hours 

VOC Processes 

2 160 

130 

PAd /-?i Emissions I Oral (37.8 ppbc) Emissions Em~s~s ~--1;r- ----------. 4 -_-____-, --4'-- 

(203.7 ppbC)- i- -?&--.--- .-----.-.-. r---=.-- --- .-------,,,,----------l----cl- .------- 

NO, Processes 

I' Total 

€mi--'--- ' (6.3 - ..-..- ;-.. -.-.. d.-..d 1.-..a---0-- 

7-Hd"zontal Trans. Vertizal Tra~ ns. 

.p -8 - I 1 

.: -10 - j rz-tertical Trans. I I (-34.4 ppb) -12 - I---: -14 tll""lll"l'l 

10 12 14 16 18 20 Hours 6 8 10 12 14 16 18 20 Hours 

Figure 7.4: Process time series for Charlotte 1987 episode. 

Nitrogen Dioxide: In the NO2 processes plot, we see that NOp emissions were a much 

smaller contributor to NOx, only 6.3 ppb. Chemistry contributed the large majority of the 

NOa, 29.6 ppb, but note that the increase in NO2 was less than the NO decrease because of 

chemistry. Vertical transport of NO2 removed 34 ppb of NO2 from layer one. In keeping 

with the view that this was a relatively stagnant episode, horizontal transport of NO2 was 

very low, only - 1.6 ppb. 

VOCs: In the VOC process plot in Figure 7.4, we see that total emissions were 344.6 ppbC. 

Combined with the NO, emissions, this gives a morning peak emissions VOC-to-NOx ratio 

of 5.4: 1. The model VOC-to-NO, ratio based on mixing ratios, however, was 7.7: 1. The 

difference was due to carryover VOC from the day and night before. Note that the isoprene 

emissions into the 6 x 6 grid were 37.8 ppbC, yet its contribution to the VOC mixing ratio 

was almost undetectable. Another time series process analysis not shown here revealed 

that chemical loss accounted for 70% and that vertical transport accounted for 29% of the 

isoprene emissions. Note that in the middle of the day, isoprene emissions were about 

Photochemical Studies of Alternative Fuels 26 ‘1 ESEIUNC-CH 
7.2 PROCESS ANALYSIS- CHAPTER 7. AIRSMED MODELING 

Emissions VOC Composition 1 

Figure 7.5:. Model Predicted and Observed VOC composition in 6 by 6 5-km cells near downtown 

Charlotte North Carolina in 1987 UAM scenario. The bars labeled with hours are 

the model's composition, the last bar on right is the average from EPA ambient VOC 

canister sampling program. Note the difference in toluene and xylene. 

one-fourth of the total VOC emissions. If isoprene is removed from the total VOC emissions, 

the anthropogenic-VOC-to-NOx ratio would be 4.7: 1, which is a factor of 1.6 lower than that 

based on the mixing ratios. This shows some of the difficulty in attempting to compare 

model predictions with ambient observations and blaming the differences on the emissions 

inventory. 

Before we leave the issue of VOC emissions, let us illustrate another problem that process analysis found in the the UAM inputs. Figure 7.5 shows a bar chart of VOC composition 

for each hour in the 1987 Charlotte UAM scenario. At the right end of the chart, we show 

the average composition that was determined from a 6-9 a.m. canister sampling program 

(ambient observations) that was conducted in Charlotte in 1984 as part of a larger EPA 

program. Although there is remarkably good agreement for the classes paraffins, olefins, 

and aromatics, within the aromatics class there is a large difference: the TOL to XYL ratio 

in the model is backwards compared to the observations. In other words, the model's TOL 

was about twice the model's XYL. This disagreement prompted us to conduct a detailed 

examination of TOL-to-XYL ratios in the emissions input files, which led to the discovery 

that the speciation for all the area surface coatings and solvents were computed with a single species pru$le by EPS 2.0, the EPA-approved UAM emissions processor. Subsequent 

investigation of this profile revealed that it was based on only four paint samples from Los 

Angeles that were taken in the late 1970s and these samples were very high in xylene and 

very low in toluene. EPA's Emissions Inventory Group has been informed of this error and 

Photochemical Studies of Alternative Fuels 262 ESEIUNC-CH 
CHAPTER 7. AIRSHED MODELING 7.2 PROCESS ANALYSIS 

Figure 7.6:. UAM predicted and observed 0, mixing ratio and the change caused by each process 

in the UAM. 

they are now creating new speciation profiles based on newer data. 

Ozone: In the O3 process plot in Figure 7.4, we see that the early morning source of 03 

was vertical transport from aloft. Chemistry was a negative contributor to O3 during this 

time period as the freshly emitted NO titrated the downward mixed 03. After 0900 LDT, 

chemistry became a producer of 03, and remained positive until sunset, producing a total of 

113.9 ppb of 03. After chemistry started to produce 03, vertical transport out of the layer 

one cell became a loss process. Deposition of O3 was a significant loss process, consuming 

about 38 ppb of 03. Until 1600 LDT, horizontal transport of O3 was negligible, but at 

1600 LDT in this scenario, horizontal transport brought 29 ppb of 03 into this 6 x 6 cell area 

that already had the highest O3 mixing ratio in the model. This additional O3 increased the 

6 x 6 cell's O3 mixing ratio from 105 ppb to 135 ppb. This was in dramatic contrast to the 

observed values reported by the two monitors located within the 6 x 6 cells, which showed 

decreases of a during this time period (see Figure 7.6). Although horizontal transport was 

increasing the cell's mixing ratio, vertical transport was removing less than half the increase 

and chemical production was still increasing the mixing ratio by about 20 ppb. Individual 

cells within the 6 x 6 region showed even more dramatic effects-as much as an 80 ppb 

increase in O3 from 1600 to 1700 LDT, producing a peak O3 mixing ratio of 170 ppb (see 

Figure 7.7). 

Because process analysis plots made it clear that horizontal transport was responsible 

for this large increase, the incorrect behavior of the model was traced to problems in the 

Photochemical Studies of Alternative Fuels 263 ESE/UNC-CH 
7.2 PROCESS ANALYSIS CHAPTER 7. AIRSHED MODELING 

Diagnostic Windfield Model (DWM). Inappropriate interpolation of the hourly monitored 

wind data was producing artificial convergence. Numerous experiments with the DWM 

were unable to resolve this problem. Eventually, it was necessary to use manual "objective 

analysis" of the wind data to obtain any reasonable predictions. 

Other Wind Problems Figure 7.6 shows a process time series plot for O3 for a single 

surface cell (the one containing the Plaza O3 monitor-the middle cross in Figure 7.3) and it 

reveals another problem we have repeatedly observed in UAM simulations-a switch in the 

direction of vertical transport when there is a major change in wind direction in the UAM. 

In this plot, observe that vertical transport was first positive into the lower layer during the 

morning hours as the mixing height was rising and O3 from aloft was being brought down. 

Once the cell began to produce O3 chemically (after 1000 LDT) vertical transport became 

strongly negative, carrying the higher concentration O3 in the lowest cell upward; i.e., until 

the hour 1500-1600 LDT in which suddenly vertical transport was a source of 03 in the 

lowest cell. At 1600 LDT, the vertical transport once again returned to its former nature. 

This one hour of positive vertical O3 transport caused a significant "glitch" in the O3 mixing 

ratio time series, resulting in the over-prediction and a predicted excedance of the NAAQS 

for this cell. What happened during the hour starting at 1500? The wind, which in the UAM 

is held constant for each hour and is only changed on the hour, made a major change in 

direction at 1500 LDT. Every UAM scenario we have run in which the wind made a large 

shift in direction in the afternoon has exhibited this vertical transport transient behavior. 

Because the winds in each layer of UAM are independent of each other, and the model 

attempts to conserve mass by means of a calibration gas tracer species, the rapid shift in 

winds from one time step of the model to the next results in an artifact in vertical transport 

to compensate for the dynamic imbalance in the model. 

IPRA Process Composition 

Figure 7.7 shows a "process composition time series" plot for O3 in a single cell (the one 

containing the Arrowwood O3 monitor) in the 287FC UAM simulation for Charlotte, North 

Carolina. By "process composition" we mean that we have effectively "colored" the O3 by 

the type of process that gave rise to the O3 that is still in the cell at a particular time. At the 

beginning of the analysis time, all the O3 in the cell would be from the "initial process". As 

time proceeds and the meteorological processes cause vertical cell growth and horizontal 

mass flow, this "initial 03" decreases and O3 with other process origins are brought into the 

cell. For example, in the top plot of Fig. 7.7 we see that vertical transport from layer two 

into the layer one cell began to increase the cell's O3 at 0600 LST (at the same time, this 

decreased the mixing ratio of the "initial 03" because of dilution). Also at the same time, 

O3 that originated somewhere to the west of the Arrowwood cell was being transported into 

the Arrowwood cell. At 0700 LST, transport also began to contribute O3 from the north of 

the Arrowwood cell. We also see from the plot that after 1500 LST, there was a very large 

Photochemical Studies of Alternative Fuels 264 ESEIUNC-CH 
CHAPTER 7. AIRSHED MODELING 7.2 PROCESS ANALYSIS 

5 6 7 8 9 10 11 12 13 14 15 16 17 18 

Hours, EST 

5 6 7 8 9 10 11 12 13 14 15 16 17 18 

Hours, LST 

Figure 7.7:. Process composition times series for o3 in Episode 287FC Charlotte, North Carolina 

UAM simulation. TOP: Sources of o3 by process in Arrowwood Cell; BOTTOM: 

Sources of 0, by process in Arrowwood Cell with transport sources replaced by original 

sources. 

Photochemical Studies of Alternative Fuels 265 ESEIUNC-CH 
7.2 PROCESS ANALYSIS CHAPTER 7. AIRSHED MODELING 

amount of 03 transported from the north which was responsible for the large and rapid rise 

in 03 from about 100 ppb to over 170 ppb. 

These process composition plots are possible because each cell in an Eulerian model 

is well-mixed. Therefore, they can be treated as a continuous stirred tank reactor (CSTR), 

which is a chemical engineering term for a common type of flow-through reactor. In a 

CSTR, just as in an Eulerian model cell, mass flows into the reactor volume, mass flows out 

of the reactor volume, and reactions occur in the volume. By performing a mass balance on 

the cell and using the mass changes by process from the IPR file for the cell as a function of 

time, we can develop the fraction of mass in a cell that was accumulated from each source 

(details of how to carry out these calculations are available in [35]). By keeping such a 

process composition on every cell and by replacing the transported-in mass with the process 

composition of the up-flow cell, that is, resolve the mass of material transported in a cell 

into its process composition by applying to it the composition of the material in the cell 

from which the transport is occurring, thus, eliminating all transport terms. When such 

calculations are carried out at each timestep, we can obtain a new process composition time 

series plot that looks like the bottom plot in Figure 7.7. 

In the bottom plot, there is no mass labeled with transport directions. Instead the 

original process origins of the transported masses have been identified and these have been 

collected into groups of origin. For example, "In it-Othe r" in the plot represents o3 that was 

initial 03 in one of the other 6x6 cells at the start of the analysis. "Bdry-TOP" represents 03 

that came through the layer one top boundary in any of the 6x6 cells and "Bdry-Horizontal" 

represents O3 that came from outside the 6x6 cells. 

This latter plot shows that until after 1200 a large amount of O3 in this cell came from 

O3 that was initially present in the 6x6 region at the start of the day or from O3 that was aloft 

("old 03.") 

In Figure 7.8 we take a different view of this same 03. These "bar" plots show the 

spatial distribution of the origins of the O3 that was in the Arrowwood cell at the indicated 

times. For this plot, the identity of the the original process that created the O3 in the other 

cells has been neglected. Here, we are only interested in the cell location where the O3 

originated, not the process that created it. Thus, at 0700,55% of the O3 in the Arrowwood 

cell originated in the Arrowwood cell (and from Figure 7.7 we know that this would have 

been from initial O3 and from O3 transported vertically down into the Arrowwood cell. The 

rest of the 03 in the Arrowwood cell had come from the north and west as shown by the 

height of the bars to the west and north of the Arrowwood cell. By 0830, the spatial origins 

of the O3 in the Arrowwood cell included most of the 6x6 cell region to the west and north, 

and significant amounts of 03 had started to come from regions outside the 6x6 cells (e.g., 

there was 7.2% of the Arrowwood cell's 03 from the west and 4.4% from the north of the 

6x6 cell area). 

Photochemical Studies of Alternative Fuels 266 ESEIUNC-CH 
CHAPTER 7. AIRSHED MODELING 7.2 PROCESS ANALYSIS 

O3 Arrowwood, Charlotte, NC 

0700 EST Day 2,287FC 0830 EST 

1200 EST 1800 EST 

Figure 7.8:. Process composition spatial contribution to a single cell's 03 in Episode 287FC Charlotte, NC UAM simulation. Each 3-D plot shows the contribution of all processes in 

each cell of a 6x6 cell area and from outside the 6x6 cell area to Arrowwood cell's 0, 

at the hour indicated. At hour 1200 LST, for example, 49% of the 03 in the Arrowwood 

cell was from processes that occurred in one of the 6x6 cells and the rest was from 

transport from outside the 6x6 cell region. For the latter, 40.5% was transported from 

the east and 7.4% was transported from the south. 

IRR Chemical Process Analysis 

This type of analysis is applied to the chemical transformations within model cells or 

aggregated model cells using the IRR data from the chemical reaction mechanism included 

in the air quality model. Details on how this analysis works are in the literature [I, 26-28]. 

Basically we have adopted an abstract or systems approach, recognizing two inter-acting 

cycles (the .OH radical cycle and the NO oxidation cycle (see Figure 7.9), within a positive 

feedback loop (the photolysis of O3 provides about one-half of the new radicals in the system). 

Radicals are accounted for via initiation, propagation, and termination processes. Oxides of 

Photochemical Studies of Alternative Fuels 267 ESE/UNC-CH 
7.2 PROCESS ANALYSIS CHAPTER 7. AIRSHED MODELING 

Charlotte 6x6 5-km surface cells, Day 2 287fc 6-- 19 Hours 

13.9 ppb from 

aldehydes + hv 

4.1 1 OH Cycles II 

propagation and termination 

A VOC= 128.1 ppbV 

1 14.9 ppb OH 87.0 ppb OH 

I R02 + NO+N02 + RO; r dt = 160.5 ppb I 

I 

Figure 7.9:. The -OH and NO, Cycles from Integrated Reaction Rates for 6x6 Cell Area in UAM 

Scenario 287FC for Charlotte, NC. 

40, + NO-+ NO, + 4; 1 r dt = 18.4 ppb I 

18.4 ppb old o3 (aloft) 

nitrogen are accounted for via emissions, oxidation, and terminal product production. The 

two cycles are coupled via odd-production by reactions such as 

which is necessary for both the radical and nitrogen cycles to continue. 

This systems process view introduces several important new parameters that can be 

used to classify and compare the chemistries of different scenarios for the same model, the 

same scenario for different models, or different locations, scenarios, and models. Some of 

these new parameters are: 

total of 

178.9 ppb 

NO+N02 

new radical source strength-new radicals are formed by photolysis of organic 

(mostly aldheydes) and inorganic (mostly 03) species that are both present initially 

and are formed via the oxidation cycles. We have found that for urban simulations, 

about half the radicals come from organic and about half come from inoganic sources. 

That is, o3 is very important in producing more O3 (see details in [27]). In Figure 7.9 

there New York City in a RADM simulation, there was 34.1 ppb new .OH produced. 

+H,O 

6.9 ppb o,+~v 

181.9 PP~ NO - NO:= , ppbV 1 32.94 ppb 

.OH propagation factor and chain length--each new radical created above is used, 

recreated, and used again in a chain oxidation process. We measure the fraction of 

Photochemical Studies of Alternative Fuels 268 ESEIUNC-CH 

No re-created 4 [Od,=124.5 ppb( 56.1 ppb new NO reacted 

PN0=0.731 

propagation and ternunatlon [0Jd[fd2]e 0.937 

I 

3.24 NO Cycles 6.6 ppb new NO 

from new NO- 46.3 ppb 

12.8 ppb NO P3Iinit - 

balance 28.9 ppb 

57.0 ppb NO emissions. [031Htrans-, 

17.1 ppb ' [03],Bacl - 6.6 ppb 

[O~I~trans 

38.1 ppb 

w [03ldepo 

1 [O3l1 =131.0 PPbl 
CHAPTER 7. AIRSHED MODELING 7.2 PROCESS ANALYSIS 

Charlotte 6x6 5-km surface cells, Day 2,287fc, 6--19 Hours 

[NO2],,, 4.6 PP~ 

re-created [NO 

181.9 ppb 49.5 ppb 

93.0 ppb j. 1 liol 

[OH1 [ALDI + [HOPI 

produced [N?]iniI 1.2 PP~ 

r [No2' new [NO~P-, [NO~lernltted 1 epb 7.5 PP~ 6.3 ppb 

36.0 ppb 1 133.8 ppb 1 11.6 ppb 1 0.5 ppb 1 1.5 ppb 

[OH1 [RCO,] [ROI C C 

[NO] + [O] -$.+t2 wn8~~tpanics 

132.9 ppb 1 4 new [031 

Figure 7.1 0:. The No Cycle and Mass Balance from Integrated Reaction Rates for 6x6 Cell Area 

in UAM Scenario 287FC for Charlotte, NC. 

.OH radicals that make it all the way through the chain each cycle; this is called the 

.OH-propagation factor. We have found that this is usually about 0.75 regardless of 

chemical mechanism; e.g., in the New York City RADM simulation P, was 0.747 

giving 3.96 cycles per -OH. The throughput for the CB4 mechanism in Charlotte 

translates into 4.11 cycles for each .OH before being lost in termination. The reacted 

VOC in Charlotte for a 3fi2 km area was 128.1 ppbV, where as for New York City for 

RADM for a 202 km area this was 112.9 ppb. 

NO propagation factor and chain length-similar to .OH-propagation factor, each 

NO is oxidized to NO2, and re-created via photolysis to be reoxidized again. Some 

reactions remove NO and NO2, leading to less than 100% throughput. We have found 

that in most models this factor is about 0.73-0.78, or each NO is used about 3.2 to 4.4 

times before being lost. New NO (or NO emissions) in the 3fi2 km area in Charlotte 

were 56 ppb for the day, whereas in New York City for a 202 km area they were 

47.9 ppb. 

NO oxidations per VOC consumed-this is the total number of No's oxidized divided 

by the total amount of primary VOC reacted and is a measure of the "reactivity" of the 

VOCs in the model. This value was 1.84 for the RADM mechanism in New York City 

for fresh VOC emissions, but as shown in Figure 7.9 the value was only 1.26 for the 

CB4 mechanism in Charlotte. This was traced to the fact that on this second day of 

simulation in a stagnant episode, about one-fourth of the reacted VOC was older, aged 

VOC, which had a much shorter oxidation chain length and therefore fewer NO-to-NO2 

Photochemical Studies of Alternative Fuels 269 ESE/UNC-CH 
7.2 PROCESS ANALYSIS CHAPTER 7. AIRSHED MODELING 

conversions per chain. 

O3 producedper NO2 photolysis-not all of the atomic oxygen that is produced when 

NO2 photolyzes is converted into 03. In the New York City RADM simulation this 

fraction was 0.909, compared to 0.937 in the UAM Charlotte simulation. 

Except for methane, the products of the low-reactivity-compounds' reactions are not 

strong sources of new radicals; for example, OH + CO produces Cop and the alkanes produce 

longer chain aldehydes that have low photolysis rates. This is in contrast to products of 

the other high-reactivity-compounds in the model mixture--olefins, xylene, and emitted 

aldehydes-which are prolific sources of new radicals. So, in Figure 7.1 1, the low-reactive, 

higher-concentration species were the source of most of the 03, while the high-reactive, 

lower-concentration species were the source of most of the new radicals. This is why 

when the so-called low reactivity compounds are irradiated by themselves, the produce 

little 0,. The alkanes (e.g., PARS), for example, produce aldehydes that are very slow at 

photolyzing; thus, alone with NOx these do not produce much ozone. In the presence of 

another species' HCHO, however, the alkanes can produce significant 03. Thus, there is 

a synergistic interaction among the different VOC species in a mixture. Because in urban 

areas there is always a large amount of CO and methane that can easily utilize any .OH 

radicals introduced, the source of new radicals becomes the most important factor for the 

production of ozone. At present, the aldehydes and dialdehydes either emitted, or produced 

when emitted VOCs are reacted-and maybe even left over from the day before-are the 

most important factors in explaining the origins of urban ozone. 

From this brief comparison of IRR process parameters we can see that the downtown 

area of Charlotte, North Carolina has chemistry just as intense as the center of New York 

City. A major difference, however, is that Charlotte only has only one area with this intensity, 

whereas the next area downwind in New York City was similar to the one described here and 

the one after that too. Thus, in Charlotte chemical processes decrease rapidly with distance 

from the center city, whereas in New York City these same processes are sustained at high 

levels over a much larger distance. 

Figure 7.11 shows another type of output that can be produced by IRR/MB analysis. We 

have developed a set of history lists that are maintained on each reaction. Using these, we can 

"replace" intermediates that are formed as products in the reaction of the primary VOCs with 

the products of the reactions of the intermediates, thus eliminating the intermediate species. 

In this way we can obtain an accurate assessment of the contribution of each primary VOC 

to the total O3 production as is shown in the figure. In every case where we have performed 

this analysis, CO, methane, and the paraffins-the least reactive species-have accounted 

for more than half the O3 produced. 

For example, on the second day of an UAM simulation of a 1987 episode in Charlotte, 

one-fourth of the O3 was formed by reactions of CO and methane (CH4), and another onefourth by reactions of the alkanes (PAR) in the mixture (see Figure 7.11, bottom). Methane 

has been considered essentially "unreactive" and CO is rarely discussed as a major source 

of urban ozone. Another one-forth of the ozone on Day 2 of this simulation was formed 

Photochemical Studies of Alternative Fuels 270 ESEAJNC-CH 
CHAPTER 7. AIRSHED MODELING 7.2 PROCESS ANALYSIS 

Table 7.3:. NO, Mass Balance from Integrated Reaction Rates for 6x6 Cell Area in UAM Scenario 

287FC for Charlotte. Units are parts per billion (ppbN). These results show a 4.8 ppb 

gain in NO, mass over the course of the simulation. The error does not arise in the 

chemistry solver, but must be due to errors in the transport solver. 

Process Sources Sinks 

Initial Conc. I Emissions 1 57.01 

Net Horizontal Trans -0.63 

Net Vertical Trans -12.17 

Chemistry 132.94 -1 82.48 

Deposition -0.06 

Final Conc -0.1 1 

NO2 

Sources Sinks 

8.97 

6.28 

NOy - NOx 

Sources Sinks 

7.18 

0.00 

1.44 

2.86 

19.21 -7.44 

-13.59 

-9.31 

30.68 -30.34 

NOy 

Sources Sinks 

17.08 0.00 

63.29 0.00 

1.44 -2.28 

2.86 -46.53 

337.23 -337.23 

0.00 -16.77 

0.00 -14.69 

421.90 -417.51 

Sum 

17.08 

63.29 

-0.84 

-43.68 

0.00 

-1 6.77 

-14.69 

4.39 4.39 

from oxygenated compounds (HCHO, RCHO, MGLY, and PAN) left over from the oxidation 

of VOC emissions from the day before (this was a very stagnant case). Thus, the compounds 

that are commonly described as "reactive" (i.e., OLE, TOL, XYL, and the "natural" hydrocarbons, including isoprene) actually account for only about one-fourth of the chemical 

ozone production. This apparent contradiction-least reactive, most ozone produced--can 

be explained by recognizing that hydroxyl (.OH) radicals will react with any VOC proportional to its rate constant times its concentration. Most people have focused on the species 

rate constant and subsequent chemistry when declaring a species to be "reactive." In the 

scenario discussed here (see top pie chart in Figure 7.1 l), nearly two-thirds of the reacted 

carbon was CO, methane, and alkanes; this was because their higher concentrations relative 

to the concentrations of the so-called "reactive" VOCs in the urban environment compensates 

for their lower reaction rate constants for reaction with .OH. Thus, the "less reactive" but 

much higher concentration VOCs compete very effectively for the available -OH radicals and 

contribute significantly to urban ozone production. 

This is due to large mass in the urban area, whereas the so-called most reactive species 

have small emitted masses and are rapidly consumed before producing much 03. The 

radicals produced by these so-called reactive species are what really do the work using the 

large mass of CO, methane, and paraffins. 

Finally, Table 7.3 shows a disturbing result of a full mass balance for NOy for this case. 

The total NO, sources plus initial NOy exceed the total NOy sinks plus final NOy by 4.39 ppb. 

We had expected the chemistry solver in UAM, which uses steady-state approximations, 

to be at fault, but to our surprise the chemistry process was balanced within 0.005 ppbN. 

Instead, the problem lies somewhere within the transport processes. A similar problem 

showed up in a St. Louis UAM simulation. EPA has given SAI, the UAM authors, a task 

order to investigate this problem. 

Photochemical Studies of Alternative Fuels 27 1 ESEIUNC-CH 
7.2 PROCESS ANALYSIS CHAPTER 7. AIRSHED MODELING 

Total VOC Reacted = 128.1 ppbV 

1 % MGLY, 0,- 

- 

PAR 28% 

Chemical Ozone produced= 124 ppb 

3% 

PAN Others 1 % 1% MGLY \ I 

Figure 7.11 :. The distribution of reacted voc and ozone produced in an Urban Airshed Model 

simulation of Charlotte, NC in 1987. The so-called "least reactive" vocs create more 

than half of the chemically produced ozone, because most of the reacted voc are these 

compounds due to their larger concentrations in urban areas. 

Photochemical Studies of Alternative Fuels 272 ESEIUNC-CH 
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[35] Jeffries, H.E., "How to Track Origins of NO, Mass in Eulerian Models", UNC Environmental 

Modeling Laboratory Technical Report, 1993 (available at £ tp : / / airsi te . unc . edu. 

[36] Jeffries, H.E., Sexton, K.G., Kamens, R.M., Hollernan, M.S., "Outdoor Smog Chamber Experiments To Test Photochemical Models: Phase 11." Environmental Protection Agency, EPA600/3-85/029, March 1985. 

1371 Jeffries, H.E.,Sexton, K.G., Holleman, M.S. "Outdoor Smog Chamber Experiments: Reactivity 

of Methanol Exhaust Part 11: Quality Assurance and Data Processing System Description," 

Environmental Protection Agency Report, EPA-46013 -85/009b, September 1985. 

Photochemical Studies of Alternative Fuels 275 ESEIUNC-CH 
BIBLIOGRAPHY BIBLIOGRAPHY 

[38] Sexton, K.G., Jeffries, H.E., Arnold, J.R., Kale, T.L., Kamens, R.M., "Validation Data For 

Photochemical Mechanisms," Environmental Protection Agency Report, EPA-600/3-87/003, 

January 1987. 

[39j Murdoch, R.W., Shores, R.C., Wright, R.S., Yeager, W.M., "Technical Systems 

Audit of University of North Carolina Smog Chamber Laboratory," bound report 

prepared for Coordinating Research Council, Research Triangle Institute, Research 

Triangle Park, NC, RTI/5462/001-OlF, 70 pages, April, 1993. Also availalble at 

£tp://airsite,unc.edu/pdfs/ese_unc/jeffries/reports/qa/. 

. . . - 

Photochemical Studies of Alternative Fuels 276 ESEfUNC-CH 
Appendix A 

Chamber Facilities 

A.l The UNC Outdoor Chamber 

Location. The UNC Ambient Air Research Facility is off-campus, approximately 32 

kilometers from the university of North Carolina at Chapel Hill. It is in the adjacent county, 

Chatham County, and is approximately 10 kilometers from the small town of Pittsboro in 

an isolated rural area. Chatham County is one of the most rural, least industralized counties 

in North Carolina and is heavily wooded. These conditions contribute to low background 

in the air used to fill the chambers. The background concentrations of NOx and nonmethane 

hydrocarbons at this site are usually less than 5 ppb and less than 80 ppbC. More importantly, 

the air exhibits very low reactivity in the chamber. 

Materials. The chamber surfaces are Fluorinated Ethylene Propylene (FEP) Teflon. The 

film's transmission in the UV and visible regions of the solar spectrum is excellent, and it 

has only a few absorption bands in the IR, a property necessary to reduce the "greenhouse 

effect". It has a very low permeability for most chemical species and can be heat-sealed 

to form large durable panels. For this application its worst property is its ability to hold a 

static charge for long periods of time. Type A film, 0.13 mm thick is used. 

Physical Design. Inlet and outlet doors, stirring fans, manifolds, and other fittings come 

in through a solid floor. The sides are free for light entry. The floor of the chamber is 

elevated approximately 1.2 m to allow for easy access under the chamber. 

The design is an A-frame 9.14 m wide, 12.10 m long and 6.10 m high at the peak on a 

plywood floor 1.22 m above the ground. Wooden beams, 5.08 cm by 20.32 crn, located on 

edge at 99.1 cm centers form an exterior framework on top of the plywood floor. Continuous 

16.46 m lengths of film are attached to the inside by aluminum u-channels, screwed firmly 

to the wooden beams, thus compressing the film against the external support. 
A.2 THE TEFLON BAG REACTOR CHAMBER APPENDIX A. CHAMBER FACILITIES 

A.2 The Teflon Bag Reactor Chamber 

UNC has an indoor chamber that uses 500-liter Teflon bags exposed at constant temperature 

(25' C) to constant irradiation from a mixture of black lamps and sunlamps placed on both 

sides of the bag in a aluminum foil-lined box. We call this system the Teflon Bag Reactor 

(TBR). The TBR is flushed and filled with clean air from a 250 literslmin Addco Clean Air 

Generator. In a typical TBR experiment, 10 ppmC of organic compound, 10 ppm of NO, 

and about 10 ppm of hydrogen peroxide are added to the filling stream. After the initial 

conditions has been established, the lights are turned on for up to 3 h of irradiation. 

Photochemical Studies of Alternative Fuels 278 ESE/UNC-CH 
Appendix B 

UAM Ozone Sensitivities 
The Sensitivity of Urban Ozone Predictions 

to Assumptions in the Motor Vehicle Emission Inventory 

Terry J. Keating, Jo Ellen Brandmeyer-Cawlfield, John Voyzey, 

Ashraf Chaudhary, and Harvey Jeffries 

Department of Environmental Sciences and Engineering 

CB 7400, Rosenau Hall 

University of North Carolina 

Chapel Hill, NC 27599-7400 

terry-keating @ unc.edu 

Presented at 

The Emission Inventory: Programs & Progress 

An AWMA/EPA Specialty Conference 

October 11-13, 1995 

Research Triangle Park, NC 

ABSTRACT 

This study tested the sensitivity of the Urban Airshed Model's (UAM) ozone predictions to three 

assumptions in the motor vehicle emission calculations: trip average emission factor, average daily 

temperature, and uniform speed by road classification. These assumptions were tested by 

preparing a series of motor vehicle modeling inventories using conventional methodology, 

disaggregated emission factors, hourly temperature profiles, and speeds by road. These inventories 

were then merged with the area, point, biogenic, and nonroad mobile inventory for a 1988 ozone 

episode in Charlotte, North Carolina, and used in UAM simulations. 

Spatially and temporally allocating the disaggregated emissions to trip starts, trip ends, and trip 

mileage increased both the maximum gridded hourly emissions and the variability of gridded 

hourly emissions. VOC and CO showed a larger change than N%. This reallocation moved 

emissions from major highway corridors to the central business district. Varying the temperature 

by hour had little effect on emissions. Assigning speeds to individual roads using output from a 

travel demand model decreased NOx and significantly increased VOC emissions. For this ozone 

episode, peak ozone changed by less than 3 ppb, but spatial ozone changes were more significant. 

Effect of this disaggregation methodology may be greater in other urban areas or future year 

scenarios with different VOC to NOx ratios. Also, the disaggregation methodology may be more 

important for CO calculations than for ozone. 

INTRODUCTION 

The conventional method for estimating motor vehicle emissions embodied in EPA's MOBILE 

emission factor model [1,2] and inventory guidelines [3] has been widely criticized as being 

inaccurate and simplistic [4,5,6]. Many of the criticisms arise from the conventional method's 
10/13/95 Keating, et al. 2 

reliance on highly-aggregated data and unrepresentative assumptions which fail to accurately 

characterize the spatial and temporal variability of vehicle emissions. Current research efforts are 

attempting to develop more realistic characterizations of driver behavior, vehicle performance, 

traffic flow characteristics, and environmental conditions [7]. While these efforts will improve 

our ability to estimate motor vehicle emissions in the future, it is not clear what effect these efforts 

will have on our ability to predict ambient concentrations of secondary pollutants, such as urban 

ozone. 

In this paper, we evaluate the sensitivity of urban ozone concentrations predicted by the Urban 

Airshed Model (UAM) [8] to changes in the assumptions in the motor vehicle emissions 

estimates. We focus on three assumptions of the conventional methodology: 

1) the use of a trip-averaged emission factor 

2) the use of a daily average temperature 

3) the use of a uniform speed for all roadways of a particular functional class 

These assumptions are challenged by using data from a travel demand model to: 

1) separate the emissions components associated with different segments of a vehicle trip 

2) vary ambient temperature by hour of the day 

3) vary vehicle speeds by individual roadway link according to the traffic volume and 

road capacity 

The impact of these assumptions is described in terms of changes in the spatial and temporal 

distribution of emissions of nitrogen oxides (NO*) and volatile organic compounds (VOC) and 

UAM-predicted ozone concentrations for one meteorological episode in Charlotte, North Carolina. 

METHODS 

Automation of the Inventory Development 

Preparing motor vehicle emission inventories for use in photochemical modeling can be timeconsuming and cumbersome. Before exploring the impact of different assumptions in the motor 

vehicle emissions inventory, we needed an efficient system for developing the motor vehicle 

emission inputs. To meet this need, we developed ProcMobl, a flexible computer package for 

calculating mobile source emissions and preparing UAM inputs. Using the relational database 

management features of SAS, ProcMobl 

1) predicts future traffic volumes based on linear regression of historical data 

2) applies trip-averaged or component-specific emission factors from EPA's MOBILE 

5a model 

3) chemically speciates NOx and VOC for representation in UAM 

4) temporally allocates daily average emissions by hour 

5) spatially allocates emissions from counties or roadway links to grid cells 

ProcMobl automates calculation and data manipulation steps and replaces UAM's Emission 

Preprocessor System [9] for on-road mobile emissions. ProcMobl also facilitates quality 

assurance checks and report generation. 

Conventional Emission Estimation Method 

Using the conventional method, emissions may be estimated on the basis of a link (road segment) 

or geographical area (county). Typically, the link approach represents travel along major 
10/13/95 Keating, et al. 3 

thoroughfares. The area, or nonlink, approach represents travel between major thoroughfares 

along local streets. Both the link and nonlink calculations are based on the following simple 

equation: 

Emissions = Vehicle * Emission * Speciation * Temporal * Gridding (1) 

Activity Factor Factor Factor Factor 

The terms of equation (1) are defined below. 

Vehicle Activity. Vehicle activity is conventionally defined in units of average daily vehicle miles 

traveled (VMT), which is the product of average daily traffic volume and roadway length. In the 

link calculation, the VMT for each link is used. In the nonlink calculation, the VMT for the entire 

area is distributed over each functional road class. In both calculations, the VMT is distributed 

over a characteristic vehicle type mix and assigned a characteristic speed according to road class. 

Emission Factor. The emission factors, in units of grams of pollutant per VMT, are generated 

using the MOBILE 5a model. A separate emission factor is generated for each road class, vehicle 

type, pollutant (CO, NOx, VOC), and control program (e.g., inspection and maintenance program, 

gasoline volatility controls) combination. In our evaluation, a constant daily average ambient 

temperature was assumed in the conventional method calculations. 

Speciation Factor. Speciation factors are used to convert the emissions from grams of pollutant 

to moles of the chemical species represented in the photochemical model. Thus, grams of NOx 

are converted into moles of NO and NO, and grams of VOC are converted into moles of UAM 

Carbon Bond species. Separate sets of speciation factors are applied to the exhaust and 

evaporative emissions from each vehicle type. 

Temporal Factor. Temporal factors are used to allocate the average daily VMT to each hour of 

the day. Different hourly distributions are assigned according to road class. 

Gridding Factor. Finally, gridding factors are used to assign emissions from the link or area to 

the appropriate grid cells used by the photochemical model. For links, the gridding factor is 

defined as the fraction of the link length within each grid cell. In the nonlink calculation, the 

gridding factor is based on some surrogate for the spatial distribution of traffic. In our evaluation, 

population was the spatial surrogate. The nonlink gridding factors, therefore, represented the 

fraction of the total county population that fell within each grid cell. 

Disaggregated Estimation Method 

Vehicle emissions vary according to whether the vehicle is starting a trip, is in the middle of a trip, 

has just ended a trip, or is parked. Figure 1 presents a hypothetical time-series profile of VOC 

emissions associated with a single vehicle trip. Before the trip, low levels of emissions are 

generated by the vehicle. These emissions are known as diurnal emissions, caused by the 

evaporation of fuel due to ambient temperature increases, and resting losses, caused by the 

evaporation of fuel seeping through fuel lines and gaskets. When the vehicle is started, it produces 

a large spike of emissions which quickly tapers off as the vehicle warms up. The size and shape 

of the spike depends on whether the vehicle is cold upon ignition or is already hot. During the 

trip, the mass of emissions varies as the vehicle accelerates and decelerates. After the vehicle 
10/13/95 Keating, et al. 4 

Time 

Figure 1. The conventional representation of emissions from a vehicle trip. 

stops, fuel in the hot engine evaporates producing hot soak emissions which decrease as the engine 

cools down. 

The conventional emissions estimation methodology described above does not attempt to represent 

this variability of emissions between vehicle operating modes. Instead, the sum of the emissions 

which occur over the entire trip (and a weighted fraction of diurnal and resting emissions) are 

spread out over the length of the trip by the grams per mile emission factor, also shown in Figure 

1. This uniform rate approach may approximate the overall mass of emissions well (i.e., equal 

areas under the two curves), but it ignores the fact that the various modes of vehicle operation are 

distributed differently in space and time. For example, during a morning commute, a residential 

suburb may experience many trip starts while a central business district may experience many trip 

ends. Using the conventional link calculation approach, the start and end emissions would not be 

assigned to either the suburb or the central business district. Instead, they would be spread out 

along the roadways between the two. While this dislocation of emissions may not be important 

for calculating the total mass of emissions from an urban area, it may be important to the 

formation of urban ozone. 

To represent the variability of vehicle emissions due to operating mode, we divided the vehicle 

emission profile into four segments: start, stabilized, end, and nontrip. These segments are shown 

in relation to our hypothetical VOC emissions profile in Figure 2. The terms in Equation (I) for 

each of these segments are defined below. 

Vehicle Activity. To disaggregate the various segments of the vehicle trip, we used information 

from a UTPS-type four-step travel demand model [lo] which is widely used in transportation 

planning. 
Keating, et al. 

I Start 

(Hot, Cold) 

Figure 2. The disaggregated representation of emissions from a vehicle trip. 

For the stabilized emissions, the calculation is almost identical to the link approach described 

above. We defined vehicle activity as the average daily VMT for each roadway link as predicted 

by the travel demand model. Characterizing vehicle activity for the other trip segments was not as 

straightforward. 

To calculate the emissions associated with vehicle starts and ends, we used the travel demand 

model to predict where vehicle trips begin and end. In the travel demand modeling process, the 

modeling domain is divided into traffic analysis zones. Using census data to extrapolate from the 

information collected in a home survey, the travel demand model estimates the number of trips 

which originate in one zone and end in another. The result of this process, known as a trip table, is 

a matrix with the origination zone on one axis and the destination zone on the other. For our 

purposes, we only needed to know the row and column totals of the matrix indicating the total 

number of starts and ends in each zone. The travel demand model may generate a separate trip 

table for different types of trips. In our evaluation, six types of trips were identified: home-based 

work trips, home-based other trips, non-home-based trips, airport trips, truck and taxi trips, and 

externally-based trips (with respect to the study area). Thus, for start and end emissions, we 

defined vehicle activity as the number of starts or ends of trips of a particular type within each 

traffic analysis zone. 

Diurnal and resting loss evaporative emissions comprise the nontrip related emissions associated 

with parked vehicles. It is not possible to estimate the total population of vehicles from traffic 

observations or the standard travel demand model. Therefore, we estimated the total mass of 

nontrip related emissions using total county VMT and the grams per mile emission factors 

generated by the MOBILE model. We then used the population and employment in each traffic 

analysis zone and the trip table information to spatially distribute the emissions over the county, 
101 13/95 Keating, et al. 6 

using population and home-based trips as a surrogate for cars parked at home, and employment 

and work-based trips as a surrogate for cars parked at work. 

Emission Factor. Instead of using the default trip-averaged emission factor, we used the 

component emission factors generated by the MOBILE model to calculate emissions from each of 

the four trip segments. Instead of fixing vehicle speeds by road class and using a single daily 

average temperature, we wanted to allow the speed to vary by link and the temperature to vary by 

hour. Therefore, we ran the MOBILE model with a range of critical speed and temperature inputs. 

We fit these model outputs with separate regression models for each emission component and 

vehicle type. These regression models were then used to generate emission factors for speed and 

temperature conditions in our simulations. 

The stabilized segment emission factors consist of the stabilized CO, NOx, and VOC exhaust 

emissions and crankcase and running loss VOC evaporative emissions. To represent the effect of 

speed and temperature on the stabilized emission factors, we fit the product of emission factor 

times speed with a polynomial involving both speed and temperature. 

Emission factors for the incremental contribution of cold and hot starts to CO, NO,, and VOC 

exhaust were calculated by subtracting the cold and hot start components from the stabilized 

component as predicted by the MOBILE model for a vehicle speed of 19.5 mph and a temperature 

of 75T, which are the conditions of the Federal Test Procedure (FTP). The resulting grams per 

mile emission factor was converted to grams per start by multiplying by 3.59 miles, the distance 

covered in the start portion of the FTP [l 11. 

For trip ends, the MOBILE model generates a separate hot soak emission factor for VOC in 

grams per trip end. 

As discussed above, nontrip emissions were estimated using the grams per mile emission factors 

for the diumal and resting loss VOC evaporative components. The MOBILE model generates a 

separate grams per mile emission factor for resting losses. For the diurnal emissions, however, 

the MOBILE model output contains a mileage-weighted diurnal emission factor in grams per day. 

Therefore, we modified the MOBILE model to output the grams per mile diumal factor before the 

mileage weighting was applied. 

Speciation Factor. Speciation for the individual trip segments was performed the same as in the 

conventional method with separate speciation profiles applied to the evaporative and the exhaust 

emissions of each vehicle type. 

Temporal Factor. Instead of using hourly profiles associated with different road classes, the 

daily emissions for each of the trip segments were distributed over the day using hourly profiles of 

traffic observed in different regions of the county. An hourly profile of average ambient 

temperature across the study area was also used to calculate the emissions in each hour. 

For start emissions, a third temporal profile was applied enabling the ratio of cold to hot starts to 

vary over the day [12]. Separate cold to hot profiles were applied to each trip type. 

Separate hourly profiles were applied to the diurnal and resting loss components of the non-trip 

emissions. The resting loss component was assumed to be distributed uniformly over the entire 

day. The diurnal emissions were assigned to each hour of the day according to the fraction of the 
Keating, et al. 

daily temperature rise occurring in that hour. Thus, diurnal emissions were zero in hours during 

which the ambient temperature remained constant or declined. 

Gridding Factor. The four segments of vehicle emissions were spatially allocated to the UAM 

grid. Stabilized emissions were assigned in the same manner as in the conventional link 

calculation: A factor was calculated representing the fraction of each in each grid cell. 

For the start, end, and non-trip emissions, gridding was performed by assigning all of the 

emissions associated with a particular zone to the cell in which the zone centroid was located. This 

crude assumption was adequate because the traffic analysis zones were much smaller than the 5 x 

5 km grid cells used in our UAM simulations. 

The Comparison 

To evaluate the sensitivity of urban ozone production to the different assumptions and methods 

described above, we conducted four simulations of a particular meteorological episode which 

occurred on June 16-18, 1988, in Charlotte, North Carolina. This episode is one of three episodes 

modeled by the State of North Carolina for purposes of their ozone State Implementation Plan 

(SIP) [13]. 

Four sets of motor vehicle emissions inputs were developed for a 25 x 25-5 krn grid cell domain 

centered over charlotte.' - 

1) Scenario C, the conventional method 

2) Scenario D, the disaggregated method 

3) Scenario T, the disaggregated method with hourly temperatures 

4) Scenario S, the disaggregated method with hourly temperatures and link-specific 

speeds 

Scenario C is an implementation of the conventional methodology using the same assumptions as 

the State of North Carolina in their SIP modeling. In this scenario, the emissions associated with 

traffic on major thoroughfares were calculated on a link basis. All other emissions were calculated 

on a county basis. 

In Scenario D, the link calculations for Mecklenburg County, the county in which Charlotte is 

located, were replaced by the four disaggregated trip segments described above. However, the 

same vehicle speed and ambient temperature assumptions were used as in Scenario C. The 

emissions in the other counties in the domain were calculated using the conventional methods. 

Scenario T is identical to Scenario D except that the ambient temperature was allowed to vary by 

hour. 

Scenario S is identical to Scenario T except that the speeds on the links were assigned by the travel 

demand model as opposed to uniformly set by road class. 

A fifth scenario (H) was performed using the conventional method and the VMT predicted from HPMS 

observations, as opposed to VMT predicted by a travel demand model used in Scenario C. For Mecklenburg 

County in 1988, HPMS predicted a total VMT of 9,366,090 [13]. The VMT predicted by the travel demand 

model was 14,663,589 [lo]. Scenario H also used a less dense link network than in Scenario C. 
1011 3/95 Keating, et al. 8 

Each of the four mobile input files were substituted into the UAM inputs used by the State of 

North Carolina and executed. 

In addition, the impact of grid size on the effect of the disaggregation of emissions was tested by 

preparing inputs for Scenarios C and D for a domain of 125 x 125 - 1 km grid cells, referred to as 

Scenarios C 1 and D 1. 

RESULTS 

Emission Impacts 

The overall impact on the emission inventory of changing the assumptions between the four 

scenarios is summarized in Table 1. Comparing Scenarios C and D, the disaggregation of the link 

emissions did not significantly change the total mass of emissions of NOx and slightly increased 

the total mass of VOC. For both pollutants, disaggregation increased the maximum emissions in 

any one cell in any one hour, and the effect was much stronger for VOC than for NO,. 

Disaggregation also increased the variability of VOC emissions as measured by the variance of the 

hourly emissions in each cell. 

Varying the ambient temperature by hour in Scenario T had little effect on the total mass or 

distribution of NOx emissions. For VOC, the temperature variations had a small impact: 

decreasing the total mass and increasing the maximum cell. 

Changing the speed assumptions in Scenario S had opposite impacts on VOC and NOx. For 

NO,, the total mass and maximum cell decreased by a small amount. For VOC, the total mass 

and maximum cell increased significantly. 

The spatial redistribution of emissions due to the disaggregation is shown in Figure 3. This figure 

shows the difference between Scenarios D and C in terms of the mass of VOC assigned to each 

cell over the entire day. Negative values indicate areas from which the disaggregation removed 

emissions, and positive values indicate areas to which the disaggregation added emissions. There 

is a detectable movement of emissions away from the major highway corridors and into the central 

business district. The spatial redistribution due to disaggregation was even more dramatic when 

the grid size was reduced to 1 km grid cells in scenarios C 1 and D 1 .2 

Ozone Impacts 

The impact of the various assumptions on predicted ozone concentrations is summarized in Table 

2. In all four scenarios, the location and timing of the peak ozone concentration did not change on 

either day of interest due to the changes in the motor vehicle emissions. The height and breadth of 

the peak, however, did change somewhat. Moving from Scenario C to D, the disaggregation did 

increase the peak concentrations on both days. Allowing the temperature to vary by hour had 

practically no impact on either day. Although changing the speed assumptions significantly 

The total mass predicted for Scenario H was 14% and 25% lower for NO, and VOC, respectively, as 

compared to Scenario C, corresponding to the 36% decrease in VMT. The maximum grid cell decreased by 

33% and 61% for NO, and VOC, respectively, due to the decrease in the density of the link network. 
1011 3/95 Keating, et al. 9 

Table 1. Impact of Disaggregation, Temperature, Speed, and Grid Resolution on Emissions. 

Scenario 

T S 

Units 

C1 Dl 

Grid Cell Size 

Total Mass 

CO Max Cell 

Variance 

580 626 tons 

536 848 lbs/hr/cell 

0 0 (lo3 lb~/hr/cell)~ 

Total Mass 

NOx Max Cell 

Variance 

83 83 tons 

100 101 lbs/hr/cell 

3 1 3 1 (~bs/hr/cell)~ 

77 81 tons 

70 148 lbs/hr/cell 

16 22 (lb~lhr/cell)~ 

Total Mass 

VOC Max Cell 

Variance 

Table 2. Impact of Disaggregation, Temperature, and Speed on Ozone Concentrations. 

Scenario 

D T 

Max Cell 

June 16, Grid Cell Hours 

1988 Grid Cell Hours 

Grid Cell Hours 

(PPb) 

> 120 ppb 

> 100 ppb 

s 80 ppb 

Max Cell 

June 17, Grid Cell Hours 

1988 Grid Cell Hours 

Grid Cell Hours 

(PP~) 

> 120 ppb 

> 100 ppb 

> 80 ppb 
Keating, et al. 

Change ir 

ue to Disag 

l VOC 

;gregation 

~ylgridcell) 

500 

400 

Figure 3. The change in daily VOC emissions due to disaggre 

trip. Note the decrease in emissions along the Interstatein emissions in the central business district. 

[sting portions 

'7 corridor and 

of th e vehicle 

the increase 
101 13/95 Keating, et al. 11 

increased emissions of VOC, the peak ozone concentration decreased. This effect is attributable to 

the slight decrease in NOX emissions and the NOX-limited nature of the Charlotte area [131.~ 

CONCLUSIONS 

We tested the impact of three assumptions inherent in the conventional motor vehicle emission 

estimation method: the trip averaged emission factor, the daily average temperature, and uniform 

speeds by road class. Varying the ambient temperature had little impact on either emission 

estimates or ozone predictions. Disaggregating the emission factor and varying speeds on 

individual links had significant impacts on the distribution and mass of emissions, particularly for 

CO and VOC. The spatial redistribution due to disaggregation was even more significant at higher 

grid resolutions. However, the impact of these changes on ozone predictions was small, primarily 

due to the NO,-limited nature of the Charlotte area in 1988. Effects on ozone concentrations in 

scenarios with different VOC to NOx ratios may be more significant. Furthermore, disaggregation 

may have a significant impact on predictions of CO concentrations. 

While the conventional methods for estimating motor vehicle emissions have been criticized as 

being unrealistic and unrepresentative, this work demonstrates that, under some conditions, major 

efforts to refine the emission estimates will have little impact on air quality predictions, at least at 

the current resolution of air quality models. Under different conditions, however, these changes in 

the emissions estimates may produce very significant air quality impacts. 

ACKNOWLEDGMENTS 

The authors would like to acknowledge the assistance of Joseph McLelland, Charlotte Department 

of Transportation; Behshad Norowzi and Laura Boothe, NCDEHNR Air Quality Section; Jeff 

Vukovich and Don Olerud, MCNC Environmental Programs; and GeGe Coleman, ITRE GIs 

group. Support was provided by the Center for Transportation and the Environment in 

cooperation with the North Carolina Department of Transportation and the University of North 

Carolina Institute for Transportation Research and Education. CTE is an activity of North Carolina 

State University. Additional support was provided by the National Renewable Energy Laboratory 

under contract to the U.S. Department of Energy. 

REFERENCES 

1. Office of Mobile Sources; Programmer's Guide to MOBILE4, EPA-AA- TEB-89-02; 

U.S. Environmental Protection Agency: Ann Arbor, 1989. 

2. Office of Mobile Sources; Mobile 5a Update to Chapter 2; U.S. Environmental Protection 

Agency: Ann Arbor, March 26, 1993. 

3. Office of Mobile Sources; Procedures for Emission Inventory Preparation, Volume IV: 

Mobile Sources, EPAl45014-8 11026D-REV; U.S. Environmental Protection Agency, Ann 

Arbor, 1992. 

The peak ozone concentration predicted for Scenario H was 124.6 ppb on both days. 
10113195 Keating, et al. 

U.S . General Accounting Office; Air Pollution: Reliability of EPA 's Mobile Source 

Emission Model Could Be Improved, GAOIRCED-90- 1 38. 8; Washington, DC, 1990. 

Fujita, E.M., et al.; JAWMA 1992 42,264-276 

Beaton, S .P., Bishop, G.A., S tedman, D.H.; Science 1995 m,99 1. 

Cadle, S.H., Carlock, M., Cullen, K., Gorse, R.A., Knapp, K.T., Lawson, D.R.; JAWMA 

1994 44, 1 180-1 187. 

Office of Air Quality Planning and Standards; User's Guide for the Urban Airshed Model, 

EPN45014-90-007; U.S. Environmental Protection Agency, Research Triangle Park, 1990. 

Office of Air Quality Planning and Standards; User's Guide for the Urban Airshed Model, 

Volume IV: User's Manual for the Emissions Preprocessor System 2.0, EPAl45014-90- 

007D(R); U.S. Environmental Protection Agency, Research Triangle Park, 1992. 

Charlotte Department of Transportation; 2005 Transportation Plan Technical Report; 

Transportation Planning Division; Charlotte, NC, 1992. 

Office of Mobile Sources; FTP Test Program Driving Cycles; U.S. Environmental 

Protection Agency: Ann Arbor, November 9, 1993. 

Venigalla, M.M. A Network Assignment Based Approach to Modeling Mobile Source 

Emissions, Ph.D. Dissertation; University of Tennessee, Knoxville, 1994. 

North Carolina Department of Environment, Health, and Natural Resources; Supplement to 

the Redesignation Demonstration and Maintenance Plan for The CharlotteIGastonia Ozone 

Nonattainment Area; Division of Environmental Management, Raleigh, NC, December 16, 

1994. 
UNCfs Response to Comments on DOE Report 

Review 1 

How appropriate is the 54-component SynURB smog chamber mixture for representing 

today? composition of urban VOC? The actual conditions today are quite likely 

different. 

The UNC SynURB mixture is based on the most comprehensive and best 

QA'ed urban VOC data available. More than 4 years of data from 41 cities over 

the period 1984 to 1988 were analyzed for up to 300 species by a single 

investigator at EPA using a single instrument and calibration set. UNC 

analyzed and further QA'ed more than 1000 canister samples from this set. To 

quote from the CRC Final Report (copies obtainable from 

ftp: / /airsite.unc .edu/-pdfs/ese-uric! jef fries/reports/crcmel/pdfs/*), which 

described the creation of this mixture: 

In determining the VOC speciation for the average urban mixture, we 

examined several VOC speciation data bases: i ) the US EPA National 

Ambient Volatile Organic Compounds Data Base, i i ) the 1990 Southern 

Oxidant Study Atlanta VOC and aldehyde data base, i i i ) the Southern 

California Air Quality Study (SCAQS) VOC and aldehyde data base, i V) the 

US EPA Five-Cities Aldehyde Study, and V) the US EPA Ambient 6-9 AM 

Urban Hydrocarbon Study. In a previous project, UNC had extensively 

quality assured the data reported in the last study named above. These data 

were produced by Bill Lonnemann using canisters collected in 29 cities over 

the period 1984-1986. For this study, we obtained from Lonnemann 

additional ambient data VOC data for the years 1987-1988 and integrated this 

with the data from the previous study resulting in a data set that spanned 

1984-1988 and included 4 1 cities. The 1987-1988 data included extensive 

multiple analyses of canisters and multiple measurements of certain 

compounds with different analytical instruments. These permitted us to 

conduct a series of quality assurance analysis and to request Lonnemann to 

provide additional scrutiny for any questions that were raised. More than 

1000 canisters were analyzed by us to produce an average urban composition 

having 200 different organic species. 

We believe that this Lonnemann data set is the best urban VOC composition 

data base available. This set was compared with the SCAQS data base, for 

example, and it was determined that, while there was good agreement for the 

largest species present (e.g., propane, iso-pentane, and toluene) there was 

significant disagreement (up to a factor of two) for most species. This was 

explained by the fact that many fewer species were measured in the SCAQS 

data, e.g., only 27 species account for 80% of the carbon reported in the 

SCAQS data, while 48 species are needed in the EPA data set. After we 
determined this, we chose not to do any further analysis with the SCAQS 

data. 

For part of the Lunnemann data set, additional data were obtained in an 

EPA 5-site ambient aldehyde study. The 240 measured three-hour aldehyde 

concentrations in the five cities were carefully matched with the VOC 

canisters which were taken at the same time at the same location so that 

reliable carbon fractions for the aldehydes could be obtained. In addition, we 

examined real-time FTIR data from the 1990 Atlanta field program, which 

confirmed the magnitudes of formaldehyde in the morning before the mixing 

height starts to rise. While the SCAQS data showed similar levels of 

formaldehyde and acetaldehyde as the values used here, these data reported 

very high fractions (e.g., up to 8% for C6 ketones) of some compounds. As 

these data were quite uncertain at the time we were formulating our mixtures, 

and because the SCAQS VOC composition data were highly suspect, and 

because the EPA 5-cities study was carefully matched with the VOC canister 

data, we elected to use only the EPA data for the aldehyde fractions to be 

included in the urban mixture. 

The purpose of producing the SynURB mixture was to move from using 

three species (as Carter has done) or even 20 species VOC mixtures in smog 

chamber studies to a mixture that 1) was much more representative, 2) was much 

more complex, 3) was very easy to analyze, and 4) was very reproducible. In 

constructing the mixture, we did cluster analysis on the different city 

compositions and we did find that different cities have slightly different 

compositional signatures. For example, some cities were clearly "oil and gas" 

producing cities while others were clearly "carw-only cities. The biggest variable 

among the different cities was the aromatic/olefin fraction. SynURB was based 

on the median composition of all cities. 

We must stress, however, that the purpose of the smog chamber runs is 

not to "simulate" a city, but to provide an extremely well characterized and 

reproducible chemical and physical (e.g., radiation and temperature) 

environment in which to generate quality species concentration observations to 

challenge predicted concentrations from photochemical reaction mechanisms. 

This role of the smog chamber data was clearly illustrated in Figure 1.1 on page 5 

of this final report. One first must, as a necessary but not sufficient condition, 

obtain agreement between the theoretical predictions of the photochemical 

reaction mechanisms and chamber observations, where only chemistry is responsible 

for the concentration changes, before one can claim that an urban air quality model 

using the mechanism would be capable of predicting the effects of fuel changes 

on ozone in future scenarios with different urban compositions. To further 

create as stressful a test as possible, we use the side-by-side experiment to create 

two different conditions under the same physical environment, measured with 

the same instruments, and require that an acceptable model must agree with both 

sides at the same time. 
It is not as important that the mixture composition match closely the 

current ambient conditions of a particular city or national average. What is most 

important is that the range of composition variation in the experiments match or 

exceed the expected variation in the atmosphere. 

We believe that there is no better high quality ambient HC dataset from a 

large number of cities representing current conditions than the one we used. 

In addition, we wanted to be able to compare the results of this work with 

the previous work conducted with methanol/gasoline fuels which also used the 

UNC SynURB mixture. Also, the SynURB mixture has been used to generate 

test data in other EPA sponsored work including TVA and CSIRO in Sydney, 

Australia, permitting a comparison of performance across different chambers, 

operated by different groups. 

The SynURB mixture appears to be photochemically "hot;" it is more reactive than any of 

the vehicle emissions mixtures that were investigated. 

The Syn U RB mix reflects ambient measurements containing emissions 

from area and industry emission sources, as well as automobile emissions from 

"real" vehicles. The emissions from different fuels and automobiles on 

dynamometers represents a more controlled source of data as would be expected 

from a new technology. The modem fuels (especially the alcohol based fuels) 

are designed to be unreactive. Compared to SynlAG, the SynURB mix has a 

larger fraction of aromatics reflecting solvent use, and the SynlAG has larger 

fraction of benzene and toluene reflecting the industry average composition of 

gasoline. The SynlAG has a larger alkane fraction with a larger fraction of nbutane reflecting the evaporative contribution. The alkene fraction of SynlAG 

has a larger ethylene fraction. Similar statements can be said about the methanol 

fuel; it has a large alkane fraction with a large butane fraction. The ethanol fuel 

emissions have large fractions of relatively unreactive components such as 

ethane, butane, pentane, ethene, benzene and toluene. All of these 

characteristics would tend to make the "pure" fuel compositions somewhat less 

reactive than the SynURB mixture. 

When adding synthetic emissions mixtures to SynLIRB, a major consequence is that the 

highly reactive Syn LIXB mixture is diluted. Is this really an accurate representation of 

the processes believed to occur when using conventional and a1 ternative-fueled vehicles 7 

The experimental design is to consider the reactivity of the SynURB 

mixture as an atmospheric benchmark (i.e., a reasonable estimate of national 

urban reactivity, a reasonable base composition containing all of the basic HC 

classes in a reasonable distribution, certainly a reasonable challenge for a 

chemical mechanism to simulate). The design then compares the reactivity of 
50% substitution of the SynURB with either the SynlAG or alternative fuels. The 

50% substitution is not an expected estimate of actual regulatory policy or actual 

usage but a level that shows what changes in reactivity one can expect from a 

more than reasonable level of substitution and change in atmospheric 

composition, and provides the chemical mechanisms with a wider range of 

composition change than is actually expected anytime soon. This level of 

composition change provides for an excellent test for the chemical mechanisms. 

The experimental design then allows for comparisons of different substitution 

mixtures such as URBIIAG versus URBlE85. 

What is the basis for the composition ofthe SynCNG mixture? Does it comefvom testing 

just three vehicles, as is implied by Fig 2.2 ? What was the composition of the CNG fuel 

used in these emissions tests? 

The CNG emissions composition data is from the Federal Test Procedure 

(FTP) results from three OEM 1992 Sierra pickup trucks (low mileage 4000-6000, 

PAS conversion, tests numbers 780,291, and 259, conducted between March and 

May of 1993). These data were obtained by Southwest Research Institute under 

DOE/NREL funding and were supplied to us for producing the test mixture for 

the chamber experiments. The fuel was identified only as CNG. 

Why are the two SynE85 compositions shown in Fig 2.3 so differentfuom each other? Is 

this a refection of different vehicle technologies between the two data sets? This large 

variation in emissions composition suggests that caution must be exercised when 

drawing conclusions about ozone impacts from use of alternative fuels. 

We do know that there is some difference in technologies between the two 

data sets. Both sources of the emissions data are considered reliable and were 

specified for the program. We decided to treat the two sets as the basis for the 

range of variation in the ethanol fuel emissions and test with both compositions. 

The usefulness of the two compositions is to show that the chemical mechanisms 

are able to simulate the reactivity of both mixtures, and that there is no concern 

about "tuning" to a particular mixture reactivity. 

It is somewhat confusing to show the original smog chamber experimental plan in the 

Executive Summary page 22 but not the final plan that was actually executed. Perhaps 

both plans could be included. 

In the first case we were discussing the conceptual design of the program and in 

the other case we wish to show what we achieved. To show both would be too 

much detail in the Executive Summary. 
The HCmx ratios used in each experiment should be indicated on the smog chamber 

plots shown in the Exec Summary. 

The NOx levels are always the same (0.33), so the VOC is a direct indicator of the 

HC/NOx ratio. Thus, 4.5,6.0, and 9.0 HC/NOx are merely 1.5,2.0, and 3.0 

ppmC, which are already labeled on the plots. 

What is the basis for the SynLPG composition used in this study? 

The basis for the SynLPG composition is FTP dynamometer data from the 

EPA, Research Triangle Park, NC and Southwest Research Institute, San 

Antonio, Texas. The EPA vehicle was stated to be more representative of 

standard LPG vehicles, and was specified to be the direct source of the 

composition data for the design of our SynLPG (J.A.W.M.A. V45,770-777,1995). 

The composition data is from three replicate FTP tests for a dedicated LPG, 1993 

Ford F150 Pick-up with a 5.0 L V-8, with about 60,000 miles, using a fuel 

specified as LPG1 (94.5% propane, 3.8% ethane, 0.97% isobutane, 0.6% propene, 

0.13% other). The SRI data was compared and used as supporting data. The SRI 

vehicle was a converted1993 Chevy Lumina with a 3.1 L V-6 and about 5700 

miles, equipped with a catalyst (not the same as used for gasoline), selected 

because of its effectiveness on methane. The conversion kit was a Mogas ECOLO 

with Autotronics oxygen-sensor feedback, very basic with no adaptive learn. 

The test fuel was commercially available LPG, composition : propane 91.4%, 

propylene 6.1%, ethane 2.0%, butane and higher 0.4%, methane 0.1%. 

It is implied on page 23 that two E85 compositions were used because of the large 

difference in acetaldehyde levels between the two. To explore this issue, it would seem 

preferable to use a single base composition, and to va y acetaldehyde concentration in a 

series of sensitivity tests. 

Because there are several other important differences between the two 

mixtures besides the difference in acetaldehyde, both were used in the program. 

The acetaldehyde fraction appeared to be the most obvious difference in the two 

compositions. The NIPER data had only a 2% aromatic fraction compared to the 

12.7O/0 fraction in the EPA data. Also the NIPER had an alkane fraction 

approximately twice that of the EPA data. 

The experimentalfinding that there was ve y little effect of temperature upon ozone 

formation in these smog chamber experiments seems very surprising. Isn't it well 
established that temperature has a strong positive effect on ozone formation in actual 

urban atmospheres? Could this finding be a "smog chamber artifact" which results fvom 

the static conditions or some other factor related to the manner in which these 

experiments were conducted? Similarly, could the conclusion on page 31 that the CB4 

mechanism "exhibits an incorrect and overly sensitive temperature dependence for ozone 

formation " be limited to these experimental smog chamber conditions? 

Ambient ozone levels do show a general trend with temperature such that 

higher temperatures tend to be associated with higher ozone levels, but a wide 

range of specific temperatures can be associated with a single ozone level. This 

is not a cause and effect relationship, but one in which temperature is a surrogate 

for other meteorological factors. For specific individual compounds like nbutane, temperature does have a strong positive effect on ozone formation. This 

is due to the large temperature effect on the radical unimolecular decomposition 

rate. This is already included in the CB4 mechanism. Our obsewations are 

definitely not due to "smog-chamber-artifacts." We have QA/QC data that 

shows that the temperature data are reliable. The only chamber effect which can 

account for the higher temperatures before 1994 is the wall particulate discussed 

in the report. 

Remember that the methane/NOx experiments were easily simulated 

with CB4 regardless of the chamber temperature. "Chamber artifacts," if they 

exist, should show up in these experiments, but they are easily simulated with 

out any special assumptions. N-butane experiments, not shown in the report, do 

show a strong temperature effect in the UNC chamber. It is only the complex 

SynURB mixture that does not show the strong temperature effect. CB4 does not 

reproduce the observed results and therefore CB4 is clearly incorrect in this 

temperature range for the complex SynURB mixture. Note that this mixture was 

not used in the formulation and testing of CB4, only much simpler mixtures and 

virtually no mixtures with aromatics. Clearly it is CB4 that is at fault here and 

not the "real" observations. 

Thefigure of the 0-Opentaflurobenzyl oxime of acetaldehyde shown on page 36 is 

somewhat difficult to understand-at least when presented in shades of gray. I would 

prefer a depiction of the chemical equation, showing reactants and products, as on page 

183. 

This was due to the poor reproduction of the copies sent out to reviews. The 

better copies show excellent gray scale shading. 

In the many tables of initial smog chamber conditions that are shown in Chapter 3, it 

would be helpful to include an indication of the HC/NOx ratios. For instance, this would 

show clearly that different HC/NOx ratios were used on the two sides of the chamber on 

15-Sep-94 (see Table 3.20 on page 100). 
See comment above. 

It is mentioned on page 97 that "Even with no initial acetaldehyde, the E85 mixture was 

more reactive than the IAG with aldehyde." What is the explanation for this? 

Ethanol reacts with OH to produce acetaldehyde. 

The smog chamber experiment on 28-Sep-94 was meant to investigate the impact of a 

large ethanol fraction in the SynE85 mixture. Yet, it appears fiom the initial conditions 

shown in Table 3.21 (page 102) that ve y little additional ethanol was included-maybe a 

15% increase. Am I missing something here? 

The ethanol fraction is already large in the original composition. The 

ethanol was increased 19% (to us this seems large) without adjusting the 

remaining initial conditions, resulting in an additional 11% in carbon compared 

to the other side. We were interested in determining how sensitive the 

experimental results were to reasonable, but non-trivial changes in composition 

such as this increase in ethanol. 

The issues of reactivity and reactivity scales are mentioned fvequently in this report. It 

may be useful to show the specific reactivity of all the synthetic VOC mixtures used in 

this study, as computed with the most common reactivity scale, the Carter MIR scale. 

Comparing these computed reactivity figures with the experimental results observed 

under a variety of smog chamber conditions would emphasize the severe limitations of a 

fked reactivity scale. 

We don't see any value in applying scales derived from trajectory modeling to 

static chamber conditions. 

The identification of numerous new carbonyl products fiom the photochemical oxidation 

of aromatics is ve y encouraging! Could the authors speculate as to how much of the 

"missing carbon" that is usually associated with reaction of toluene and xylenes can 

now be accounted for? Have any of the triones or epoxycarbonyls which are identified in 

this work ever been observed before in photochemical experiments with these aromatics? 

Two new papers are now in print in Atmos. Environment (1997) that describe 

time series for many of these species and provide an estimate of the relative 

importance of the various pathways. The epoxide and trione observations are 

totally new with this project. 
REVIEW 2 

A major finding of this work is that temperature variations have only a small effect on the 

ozone formation. I wonder why other groups such as UCR, GM and Johnson have seen a 

rather large effect of temperature. What this means in my opinion is that we still do not 

understand the size of or the reason for the temperature effect on urban ozone formation. 

The statement above is not a correct representation of what we reported. 

We reported that for the SynURB mixture, at temperatures between 80 and 105 

degrees Fahrenheit there appeared to be a compensating mechanism between the 

nitrogen products and the ozone production such that little dependence of ozone 

on temperature was observed. We certainly have observed strong ozone 

dependence on temperature for n-butane and for other simple mixtures. Also 

note that CB4 did correctly predict the methane/NOx experiments in the UNC 

Chamber. We certainly do not understand the nitrogen products that are 

produced in this system and we believe based on the time series of the NOxanalyzer that it is these compounds that are causing this temperature response. 

I was surprised that only five characterization experiments were performed, especially 

when the Tejlon film was renewed and some chamber parameters were changed. Also, it 

would be useful to include a Table of all runs, the average temperature, and some 

measure of the light intensity such as the NO2 photolysis or the integrated Eppleymeasured UVjlux. 

Five characterization experiments were performed as part of this project. 

More characterization experiments were performed but they were for other 

projects and were not reported in the DOE report. These are available as part of 

the entire UNC Chamber Data Base. Further, the chamber characterization 

experiments did not produce any surprising results and there was a great desire 

to complete as much of the planned experimental program as possible, which we 

were not able to do due to the weather. 

REVIEW 3 

In many of the NOx-VOC-air irradiations, the "N02" has not been consumed 

(admittedly, this may not really N02) and the 03 has not reached a maximum by the 

time the experiment is terminated. Therefore, many of the conclusions are drawn from the 

relatively initial stages of the photoxidations where the rate of formation of ozone is 

dominant, and not from the maximum amounts of ozone formed. 

These assertions are wrong. The "N02" data after the NO2 peak is 

definitely not NO2 and except by modeling one has no idea how much NO2 
remains. The smog chamber experiments are full day experiments. All 

experiments "reach their" maximum ozone under these conditions because the 

sun sets. This is quite different than constant light indoor chambers where 

letting the experiment run longer produces more ozone. 

How can the reviewer say that ozone formed 14 hours into to an experiment is 

"relatively initial stage of the photoxidations"? 

The conclusions stated on page 148 concerning the temperature eflect predicted by the 

Carbon Bond 4 model are extremely important and surely have important implications 

for airshed modeling. I urge the authors to publish their research and conclusions 

concerning the efects of temperature on ozone formation in irradiated NOx-VOC-air 

mixtures, and comparison of these resu 1 ts with the CB4 chemical mechanism. 

Considered. 

As noted above, the derivatization method used is providing important information as to 

the reaction products formed. At present, this method appears to be largely qualitative; 

its extension to obtain quantitative concentration data is important and should be 

continued. 

See comments above. 
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3. REPORT TYPE AND DATES COVERED 

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1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 

July 1998 

4. TITLE AND SUBTITLE 

Atmospheric Photochemistry Studies of Pollutant Emissions from Transportation Vehicles Operating on Alternative Fuels 

6, AUTHOR(S) 

H. Jeffries, K. Sexton, and J. Yu 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 

Department of Environmental Sciences and Engineering 

School of Public Health 

University of North Carolina 

Chapel Hill, NC 27599-7400 

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National Renewable Energy Laboratory 

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13. ABSTRACT (Maximum 200 words) 

This project was undertaken with the goal of improving our ability to predict the changes in urban ozone resulting from the widespread use of alternative fuels in 

automobiles. This report presents the results in detail. It is clear that no single approach is adequate for determining the potential atmospheric benefits of alternative fuel 

use: both experimental and modeling studies are needed. In addition, fundamental advances are needed to improve scientific understanding of reactivity. 

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290 

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14. SUBJECT TERMS 

Alternat~ve fuels, transportation fuels, air quality, emissions, urban airshed modeling 

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