CHARACTERIZING THE RANGE OF CHILDREN'S 

POLLUTANT EXPOSURE DURING SCHOOL BUS COMMUTES 

FINAL REPORT 

Prepared for the California Air Resources Board 

Contract No. 00-322 

Principal Investigator 

Dennis R. Fitz 

College of Engineering 

Center for Environmental Research and Technology 

University of California 

Riverside, CA 92521 

Co-Principal Investigators 

Arthur M. Winer, Ph.D. and Steven Colome, Sc.D. 

Department of Environmental Health Sciences 

School of Public Health 

University of California 

Los Angeles, CA 90095 

Participating Researchers 

Eduardo Behrentz, Lisa D. Sabin, Seong Jeong Lee, Kenneth Wong, Kathleen Kozawa 

Department of Environmental Health Sciences 

School of Public Health 

University of California 

Los Angeles, CA 90095 

David Pankratz, Kurt Bumiller, David Gemmill, Matt Smith 

College of Engineering 

Center for Environmental Research and Technology 

University of California 

Riverside, CA 92521 

October 10, 2003 
DISCLAIMER 

The statement and conclusions in the Report are those of the contractor and not

necessarily those of the California Air Resources Board. The mention of commercial products, 

their source, or their use in connection with material reported herein is not to be construed as 

actual or implied endorsement of such products. 

i 
ACKNOWLEDGEMENTS 

The contributions of the California Air Resources Board staff, particularly Scott Fruin, 

who made invaluable suggestions as Project Officer, and Bart Croes, Chief of Research, were 

greatly appreciated. 

We thank the staff of the Brentwood Science Magnet School, who provided important 

support in making this research possible, in particular, the school Principal, Sharon Katz, and the 

Science Director, Phyllis Rothman. We appreciate the cooperation of the Los Angeles Unified 

School District, specifically Angelo Bellomo, Director of the Environmental Health and Safety 

Branch, Antonio Rodriguez, Director of the Transportation Branch, and Bill Piazza, 

Environmental Assessment Coordinator in the Health and Safety Branch. 

We wish to acknowledge valuable contributions from David Cocker, who provided 

technical support at the Center of Environmental Research and Technology at University of

California, Riverside. We also thank Andrea Hricko, University of Southern California, who 

provided helpful information in selecting the bus route for the pilot study. 

We especially appreciated Shonna Pierce, of the UCLA Transportation Department, for

her outstanding professional driving of the school buses, and her enthusiastic support of the

project team. 

We thank Teri McLaughlin for production and formatting of this report. 

We gratefully acknowledge support for this research by the California Air Resources 

Board, the South Coast Air Quality Management District, and the U.S. Environmental Protection 

Agency. This report was submitted in partial fulfillment of ARB Contract No. 00-322, 

“Characterizing the Range of Children’s Pollutant Exposure During School Bus Commutes,” by 

the University of California, Riverside, College of Engineering Center for Environmental 

Research and Technology, and the University of California, Los Angeles, School of Public

Health, Environmental Health Sciences Department, under the sponsorship of the California Air 

Resources Board. Work was completed as of October 10, 2003. 

ii 
iii 
TABLE OF CONTENTS 

Page

Disclaimer ............................................................................................... i 

Acknowledgements..................................................................................... ii 

Table of Contents........................................................................................ iv 

List of Figures............................................................................................ x 

List of Tables............................................................................................. xvi 

List of Photographs..................................................................................... xx 

Abstract .................................................................................................. xxii 

1.0 EXECUTIVE SUMMARY 

Background........................................................................................... 1 

Methods.............................................................................................. 1 

Results................................................................................................. 1 

Conclusions.......................................................................................... 2 

2.0 INTRODUCTION AND BACKGROUND 

2.1 Introduction.................................................................................... 3 

2.2 Statement of the Problem.................................................................... 3 

2.3 Background.................................................................................... 3 

2.4 Previous In-Vehicle Studies................................................................. 4 

2.4.1 Related Traffic Exposure Studies.................................................. 7 

2.5 Objectives...................................................................................... 8 

2.5.1 Overall Objectives..................................................................... 8 

2.5.2 Specific Objectives................................................................... 8 

2.5.2.1 Pilot Study................................................................. 8 

2.5.2.2 Main Study................................................................ 8 

3.0 PILOT STUDY FINDINGS AND RECOMMENDATIONS 

3.1 Introduction.................................................................................... 9 

3.2 Summary of Pilot Study Findings........................................................... 9 

3.2.1 Exposure in Three School Bus Commute Microenvironments................ 9 

3.2.2 Conditions Leading to Highest Exposures Inside the Bus Cabin.............. 10 

3.3 Modifications of Experimental Design for Main Study................................. 12 

3.3.1 Relative Importance of School Bus Commute Microenvironments........... 12 

3.3.2 Precision Data........................................................................ 12 

3.3.3 Gradient Tests........................................................................ 13 

3.3.4 Conditions Leading to Highest Concentrations Inside the Bus Cabin........ 13 

3.3.5 Routes................................................................................. 13 

3.3.6 Summary of Pilot Study Findings.................................................. 13 

4.0 EXPERIMENTAL METHODS AND STUDY DESIGN 

4.1 Introduction.................................................................................... 15 

4.1.1 Characterization and Justification for the Selected Buses....................... 15 

iv 
TABLE OF CONTENTS (con’t) 

Page

4.1.1.1 Fuel Used in the Test Buses............................................. 19 

4.1.2 Field Sampling Procedures.......................................................... 19 

4.1.2.1 Instrument Packaging and Power Supply.............................. 19 

4.1.2.2 Data Collection............................................................ 22 

4.1.2.2.1 Checkout and Installation of Instruments................ 22 

4.1.2.2.2 Sample Probe Positions.................................... 24 

4.1.2.3 Instrumentation............................................................ 27 

4.1.2.3.1 Integrated PM10 and PM2.5 Mass Concentration........ 27 

4.1.2.3.2 Real-Time PM10 and PM2.5 Mass Concentration........ 27 

4.1.2.3.3 Real-Time Particle Counts................................. 28 

4.1.2.3.3.1 Optical Particle Counter.................... 28 

 4.1.2.3.3.2 SEMS.......................................... 28 

4.1.2.3.4 Real-Time Black Carbon................................... 28 

4.1.2.3.5 Real-Time Particle Phase PAH............................ 29 

4.1.2.3.6 Gaseous Hydrocarbons....................................... 30 

 4.1.2.3.6.1 Tenax Cartridges............................. 30 

 4.1.2.3.6.2 Tedlar Bags................................... 30 

 4.1.2.3.6.3 Portable PID.................................. 30 

4.1.2.3.7 Gaseous Aldehydes and Ketones.......................... 30 

4.1.2.3.8 Real-Time Carbon Monoxide.............................. 31 

4.1.2.3.9 Real-Time Nitrogen Dioxide.............................. 31 

4.1.2.3.10 SF6 Tracer Gas............................................... 33 

4.1.2.3.11 Range Finder................................................. 33 

4.1.2.3.12 Bus Location................................................. 33 

4.1.2.3.13 Meteorological Data........................................ 33 

4.1.2.4 Use of Video Camera and Digital Camera............................ 34 

4.1.2.5 Documentation of Bus Commute, Traffic Conditions and 

Events During Each Run................................................. 34 

4.1.3 Bus Ventilation Rate Measurements............................................... 34 

4.1.4 Characterization and Justification for the Selected School...................... 38 

4.1.5 Characterization and Justification for Selected Bus Routes.................... 42 

4.1.5.1 Urban Route One........................................................... 42 

4.1.5.2 Urban Route Two.......................................................... 46 

4.1.5.3 Rural/Suburban Route..................................................... 48 

4.1.6 Characterization and Justification for the Selected Bus Stop................... 50 

4.1.7 Characterization of Window Position Test........................................ 51 

4.2 Data Analysis Methods........................................................................ 52 

4.2.1 Goals of Time Series Analysis...................................................... 52 

4.2.2 Data Analysis Techniques...........................................................52 

4.2.3 Video Record Analysis............................................................... 53 

4.2.3.1 Definitions...................................................................53 

v 
TABLE OF CONTENTS (con’t) 

Page

4.2.3.2 Data Generated from Videotapes........................................ 53 

4.2.3.3 Videotape Analysis Protocol............................................. 55 

4.2.3.4 Linking Events/Characteristics and Concentrations.................. 56 

5.0 RESULTS AND DISCUSSION 

5.1 Assessment of Measurement Methods...................................................... 57 

5.1.1 Overview............................................................................... 57 

5.1.2 Instrument Accuracy.................................................................. 57 

5.1.2.1 Audited Instruments...................................................... 59 

5.1.2.2 Unaudited Instruments.................................................... 60 

5.1.2.2.1 Particulate Matter Mass..................................... 60 

5.1.2.2.1.1 Integrated PM10 and PM2.5................. 60 

5.1.2.2.1.2 Continuous PM10 and PM2.5................ 60 

5.1.2.2.1.3 Comparison of Measurement Methods 

 for PM2.5 and PM10 .......................... 61 

5.1.2.2.1.3.1 Descriptive Statistics......... 62 

5.1.2.2.1.3.2 Assessment of Difference 

Between Samplers............ 65 

5.1.2.2.2 Real-Time Fine Particle Counts (0.3 to 10 μm)......... 66 

5.1.2.2.3 Real-Time Ultrafine Particle Counts (0.3 to 0.8μm).... 66 

5.1.2.2.4 Real-Time Black Carbon.................................... 66 

5.1.2.2.5 Real-Time Particle Phase PAH............................ 67 

5.1.2.2.6 Gaseous Analyses............................................ 67 

5.1.2.2.7 Portable Carbon Monoxide Analyzers.................... 67 

5.1.3 Evaluation of Precision between Paired Instruments............................ 68 

5.1.3.1 Precision Estimation Between Paired Instruments................... 72 

5.1.3.1.1 Absolute and Percent Differences......................... 73 

5.1.3.1.2 Coefficient of Variation..................................... 75 

5.1.3.2 Correlation Between Pairs of Instruments.............................. 75 

5.1.3.3 Overall Ranking............................................................ 78 

5.1.3.4 Use of Correlation Coefficients.......................................... 81 

5.1.4 Evaluation of Sampling Line Losses.............................................. 81 

5.1.5 Autocorrelation....................................................................... 84 

5.1.6 Normality.............................................................................. 87 

5.2 Relative Contribution of School Bus Related Microenvironments to 

 Children’s Exposure........................................................................... 89 

5.3 Important Variables Governing Exposure During Bus Commutes..................... 93 

5.3.1 Determination of Contribution of Bus’s Own Exhaust Inside the Cabin..... 94 

5.3.1.1 Analysis of SF6 Data...................................................... 95 

5.3.1.1.1 Measurement of SF6 Concentrations...................... 100 

5.3.1.2 Correlation Between SF6 versus Black Carbon and PM2.5.......... 102 

vi 
TABLE OF CONTENTS (con’t) 

Page

5.3.1.3 Estimation of Bus Cabin Black Carbon Concentrations 

Using the SF6 Data........................................................ 104 

5.3.2 Effect of Window Position........................................................... 105 

5.3.2.1 Black Carbon.............................................................. 106 

5.3.2.2 Particle Counts in the Accumulation Mode............................ 106 

5.3.2.3 Effect of Window Position on Directly Emitted Pollutants 

versus Background Pollutants............................................ 109 

5.3.3 Effect of Bus Type................................................................... 110 

5.3.4 Effect of Route Type................................................................. 113 

5.3.5 Within Run Variables Investigated Using Real-Time Data and 

Videotape Analysis.................................................................. 115 

5.3.5.1 Selection of Runs for Videotape Analysis............................. 115 

5.3.5.2 Results of the Videotape Analysis...................................... 117 

5.3.5.2.1 The Effects of Following Diesel Vehicles With 

And Without Visible Exhaust.............................. 121 

5.3.5.2.2 The Effect of Bus Fuel Type or After-Treatment 

Technology................................................... 126 

5.3.5.2.2.1 Concentrations Inside Buses While 

Idling at Bus Stops........................... 132 

5.3.5.2.3 The Effect of Roadway Type: Freeway 

versus Surface Streets....................................... 134 

5.3.5.2.4 Effect of Route Type......................................... 138 

5.3.6 Variables Investigated Using Paired Instruments................................ 144 

5.3.6.1 Differences in Concentration Between the Front and the 

Rear of the Cabin......................................................... 144 

5.3.6.1.1 PAH........................................................... 144 

5.3.6.1.2 Particle Counts............................................... 147 

5.3.6.1.3 Correlation Between Front and Rear 

Concentrations................................................ 149 

5.3.6.2 Differences in Concentration Inside and Immediately 

Outside the Bus............................................................ 149 

5.3.6.2.1 PAH........................................................... 150 

5.3.6.2.2 Particle Counts............................................... 152 

5.3.6.2.3 Implications of the Results.................................. 153 

5.3.6.2.4 Correlation Between Inside and Outside 

 Concentrations............................................... 154 

5.4 Comparison of Pollutant Concentrations Measured Inside the Buses and at 

Nearby AQMD Monitoring Sites........................................................... 155 

5.4.1 Carbon Monoxide and Nitrogen Dioxide......................................... 155 

5.4.1.1 Carbon Monoxide......................................................... 155 

5.4.1.2 Nitrogen Dioxide.......................................................... 157 

5.4.2 Integrated Samples................................................................... 159 

5.4.2.1 Aromatic Hydrocarbons.................................................. 159 

vii 
TABLE OF CONTENTS (con’t) 

Page

5.4.2.2 1,3-Butadiene.............................................................. 160 

5.4.2.3 Gaseous Aldehydes and Ketones....................................... 161 

6.0 CONCLUSIONS AND RECOMMENDATIONS

6.1 Importance of the School Bus Microenvironment in Children’s 

Exposure During School Bus Commute-Related Activities........................... 165 

6.2 Contribution of a Bus’s Own Exhaust to Concentrations Inside the Cabin......... 166 

6.3 Variations in Exposure Across Bus Age, Fuel Type and After-Treatment 

Technology. ................................................................................. 167 

6.3.1 Windows Closed................................................................... 167 

6.3.2 Windows Partially Open.......................................................... 167 

6.3.3 Effect of Bus Type................................................................. 168 

6.4 Conditions Associated with Highest Concentrations Inside Bus Cabin............. 169 

6.4.1 Within-Bus Variables.............................................................. 169 

6.4.2 External Variables.................................................................. 169 

6.5 Comparison of Exposure to Air Pollutants During Bus Commutes with 

Ambient Air Concentrations Measured at Nearby AQMD Monitoring Sites....... 170 

6.6 Recommendations........................................................................... 171 

7.0 RECOMMENDATIONS FOR FUTURE RESEARCH...................................... 173 

8.0 REFERENCES.................................................................................... 175 

9.0 INVENTIONS REPORTED AND COPYRIGHTED MATERIALS PRODUCED..... 183 

10.0 GLOSSARY OF TERMS, ABBREVIATIONS, AND SYMBOLS........................ 185 

viii 
ix 
LIST OF FIGURES 

Figure No. Title Page 

4.1.1.1 California statewide distribution of school buses by model year.................. 16 

4.1.2.1 Plumbing schematic for school bus sampling instrumentation......................... 21 

4.1.2.2 Data logging design inside school bus platform...................................... 23 

4.1.2.3 Sampling system (including the isokinetic inlet) used at the front of 

the bus to sample inside and outside aerosol.......................................... 26 

4.1.4.1 Brentwood Science Magnet School bus route starting locations....................... 39 

4.1.4.2 Diagram of the Brentwood Science Magnet School..................................... 40 

4.1.5.1 Primary urban bus route no. 3215.......................................................... 45 

4.1.5.2 Second urban bus route no. 3235....................................................... 47 

4.1.5.3 Rural/Suburban bus route............................................................... 49 

4.2.3.1 Example of a peak, an outlier and the baseline........................................... 54 

4.2.3.2 PAH time series for Run 23. Lower panel shows circled peak expanded 

and resolved into two peaks ...................................................................... 54 

5.1.2.1 Comparison between DustTrak and HI data for PM2.5 (morning only)............ 64 

5.1.2.2 Comparison between DustTrak and HI data for PM2.5 (afternoon only).......... 64 

5.1.3.1 Variation in absolute differences (μg/m3

) between paired Aethalometers 

over the course of the main study......................................................70 

5.1.3.2 Variation in absolute differences (ppb) between paired NO2 instruments 

over the course of the main study....................................................... 70 

5.1.3.3 Variation in absolute differences (ng/m3

) between paired PAH 

instruments over the course of the main study........................................ 71 

5.1.3.4 Variation in absolute differences (#/cm3

) between paired fine particle 

count instruments over the course of the main study................................. 71 

x 
LIST OF FIGURES (con’t) 

Figure No. Title Page

5.1.3.5 Variation in absolute differences (μg/m3

) between the DustTrak 

 PM2.5 instruments over the course of the main study. .................................... 72 

5.1.5.1 Partial autocorrelation correlogram for black carbon during Run 6................ 85 

5.1.5.2 Partial autocorrelation correlogram for PAH during Run 6.......................... 85 

5.1.5.3 Partial autocorrelation correlogram for PM2.5 during Run 6 

(data collected with the DustTrak instruments)....................................... 86 

5.1.6.1. Histogram of PM2.5 during Run14......................................................... 88 

5.1.6.2 Normal Q-Q plots of PM2.5 during Run 14 (data collected with the 

 DustTrak instruments).................................................................... 88 

5.3.1.1 Schematic of the SF6 injection point................................................... 95 

5.3.1.2 Percentage of air inside the cabin originating from the bus’s 

own exhaust (%).......................................................................... 99 

5.3.1.3 Snap-and-idle test results expressed in terms of opacity (fraction)................ 100 

5.3.1.4 Schematic of school bus indicating the location of the SF6 sampling 

probes....................................................................................... 100 

5.3.1.5 Means and 95% confidence intervals of SF6 concentrations measured 

at front, rear, and outside locations during morning runs............................ 101 

5.3.1.6 Means and 95% confidence intervals of SF6 concentrations measured 

at front, rear, and outside locations during afternoon runs.......................... 102 

5.3.1.7 Correlation coefficients between SF6 versus black carbon and SF6 and PM2.5 

(data collected with the DustTrak instruments)....................................... 103 

5.3.2.1 Black carbon and particle counts (0.3 to 0.5 μm) concentrations during 

Run 32 on the particle-trap outfitted diesel bus....................................... 107 

5.3.2.2 Mean concentrations and 95% confidence intervals of black carbon (μg/m3

)... 107 

5.3.2.3. Cumulative frequency distributions for black carbon during Run 32.............. 108 

xi 
LIST OF FIGURES (con’t) 

Figure No. Title Page

5.3.2.4 Mean particle counts and 95% confidence intervals in the 

accumulation mode from 0.3 to 0.5μm (#/cm3

)........................................... 108 

5.3.2.5 Cumulative frequency distributions for particle counts during 

 Run 32...................................................................................... 109 

5.3.3.1 Mean concentrations and 95% confidence intervals for PAH and 

black carbon across six bus types...................................................... 110 

5.3.3.2 Mean concentrations and 95% confidence intervals for PM2.5 and PC............. 111 

5.3.3.3 Mean concentrations and 95% confidence intervals for NO2 and CO ............ 111 

5.3.3.4 Mean concentrations and 95% confidence intervals for VOC ......................112 

5.3.4.1 Mean concentrations and 95% confidence intervals for black carbon (μg/m3

) 

and PAH (ng/m3

).......................................................................... 114 

5.3.4.2 Mean concentrations and 95% confidence intervals for PC (#/cm3

) and PM2.5 

(μg/m3) by bus route and time of day .................................................. 114 

5.3.5.1 Run 31 (morning – windows closed) black carbon and PAH concentrations..... 116 

5.3.5.2 Run 33 (afternoon – windows open) black carbon and PAH concentrations .... 117 

5.3.5.3 Run 20 black carbon concentrations with associated events........................ 118 

5.3.5.4 Run 33 PAH concentrations with associated events................................. 119 

5.3.5.5 Events associated with increases/decreases in black carbon 

concentrations measured inside the CNG bus during Run 36...................... 120 

5.3.5.6 The effect of visible exhaust from a diesel vehicle in front of or adjacent 

to the test bus on concentrations of black carbon inside the bus cabin............ 122 

5.3.5.7 The effect of visible exhaust from a diesel vehicle in front of or adjacent 

to the test bus on concentrations of PAH inside the bus cabin..................... 123 

5.3.5.8 Concentrations of black carbon inside the test buses while following or 

adjacent to different types of vehicles................................................. 125 

xii 
LIST OF FIGURES (con’t) 

Figure No. Title Page

5.3.5.9 Concentrations of PAH inside the test buses while following or adjacent 

to different types of vehicles............................................................ 126 

5.3.5.10 Concentrations of black carbon inside the conventional diesel school buses 

while idling and/or following another diesel vehicle................................. 128 

5.3.5.11 Concentrations of black carbon inside the trap-outfitted diesel school 

bus while idling and/or following another diesel vehicle........................... 129 

5.3.5.12 Concentrations of black carbon inside the CNG school bus while idling 

and/or following another diesel vehicle................................................ 129 

5.3.5.13 Concentrations of PAH inside the conventional diesel school buses while 

idling and/or following another diesel vehicle........................................ 130 

5.3.5.14 Concentrations of PAH inside the trap-outfitted diesel school bus while 

idling and/or following another diesel vehicle........................................ 130 

5.3.5.15 Concentrations of PAH inside the CNG school bus while idling and/or 

following another diesel vehicle........................................................ 131 

5.3.5.16 Concentrations of black carbon inside the cabin for conventional diesel, trapoutfitted diesel, and CNG school buses while idling at bus stops.................. 133 

5.3.5.17 Concentrations of PAH inside the cabin for conventional diesel, trap-outfitted 

diesel, and CNG school buses while idling at bus stops............................ 134 

5.3.5.18 Mean and 95% confidence intervals for black carbon inside diesel school 

buses on different roadway types with or without other diesel vehicles 

 present...................................................................................... 136 

5.3.5.19 Mean and 95% confidence intervals for black carbon inside the trap-outfitted 

diesel school bus for different roadway types with or without other diesel 

 vehicles present........................................................................... 137 

5.3.5.20 Mean and 95% confidence intervals for black carbon inside the CNG 

school bus for different roadway types with or without other diesel vehicles 

 present...................................................................................... 137 

5.3.5.21 Means and 95% confidence intervals for black carbon (a) and PAH (b) 

concentrations measured inside school bus RE2 while traveling on two 

different urban routes..................................................................... 141 

xiii 
LIST OF FIGURES (con’t) 

Figure No. Title Page

5.3.5.22 Boxplots of black carbon (a) and PAH (b) concentrations measured inside 

school bus RE2 while traveling on two different urban routes..................... 142 

5.3.5.23 Means and 95% confidence intervals for black carbon (a) and PAH (b) 

concentrations measured inside school bus RE2 for two urban bus routes, 

divided by roadway type................................................................. 143 

5.3.6.1 PAH concentrations (ng/m3

) inside the cabin during Run 12....................... 145 

5.3.6.2 PAH concentrations (ng/m3

) inside the cabin during Run 13....................... 145 

5.3.6.3 Means and 95% confidence intervals of the average PAH concentrations 

during morning runs (ng/m3

)............................................................ 146 

5.3.6.4 Means and 95% confidence intervals of the average PAH concentrations 

during afternoon runs (ng/m3

)........................................................... 146 

5.3.6.5 Concentrations (#/cm3

) of PC (0.3 to 0.5 μm) during Run 5........................ 147 

5.3.6.6. Concentrations (#/cm3

) of PC (0.3 to 0.5 μm) during Run 13...................... 147 

5.3.6.7 Means and 95% confidence intervals of the average PC concentrations 

(0.3 to 0.5 μm) at front and rear of cabin during morning runs (#/cm3)............ 148 

5.3.6.8 Means and 95% confidence intervals of the average PC concentrations 

(0.3 to 0.5 μm) at front and rear of cabin during afternoon runs (#/cm3

)......... 149 

5.3.6.9 Inside (rear) and outside concentrations (ng/m3

) of PAH during Run 28 

(urban, morning, windows closed)......................................................151 

5.3.6.10 Means and 95% confidence intervals of the average PAH concentrations 

 (ng/m3

)..................................................................................... 151 

5.3.6.11 Concentrations (#/cm3

) of PC (0.3 to 0.5 μm) during Run 33...................... 152 

5.3.6.12 Mean and confidence intervals for PC (#/cm3

)....................................... 153 

5.4.1.1 CO concentrations (ppm) inside buses and at the selected AQMD 

stations..................................................................................... 157 

5.4.1.2 NO2 concentrations (ppb) inside buses and at the selected AQMD 

stations..................................................................................... 159 

xiv 
xv 
LIST OF TABLES 

Table No. Title Page

1.1 Average exposure factors (air concentration * time) in three 

 microenvironments...................................................................... 1 

1.2 Mean concentrations and 95% confidence intervals during commutes by 

 bus type...................................................................................... 2 

3.2.1 Calculated exposure factors for three school bus commute-related 

microenvironments........................................................................... 11 

4.1.1.1 Distribution of buses in southern California by manufacturer...................... 16 

4.1.1.2 School buses selected for testing....................................................... 18 

4.1.2.1 Measurement parameters and methods used in the main study.................... 20 

4.1.2.2 Length of sampling lines................................................................ 27 

4.1.3.1 Ventilation time constants measured inside buses at various speeds and for 

open and closed windows............................................................... 37 

4.1.5.1 Comparison of the distribution of roadway types on selected BSMS 

 bus routes.................................................................................. 43 

4.1.5.2 Distribution of roadway types on BSMS bus route no. 3215....................... 44 

5.1 Summary of main study exposure runs................................................ 58 

5.1.2.1 Summary of previous studies comparing samplers employed for 

measurements of PM2.5 and PM10 mass concentrations............................. 62 

5.1.2.2 Descriptive statistics for PM2.5 (μg/m3

) measured by HI and DustTrak.......... 63 

5.1.2.3 Descriptive statistics for PM10 (μg/m3

) measured by HI and DustTrak.......... 64 

5.1.3.1 General characteristics of the paired instrument precision experiments.......... 69 

5.1.3.2 Paired instrument precision (average absolute and percent differences).......... 74 

5.1.3.3 Estimation of precision between paired instruments using the Coefficient 

 of Variation (%)........................................................................... 76 

5.1.3.4 Correlation coefficients for paired instrument precision experiments ............ 77 

xvi 
LIST OF TABLES (con’t) 

Table No. Title Page

5.1.3.5 Regression results for the paired instrument precision data ........................ 79 

5.1.3.6 Bias between the instruments during instrument precision experiments.......... 80 

5.1.3.7 Overall ranking of the paired instrument precision data quality ................... 78 

5.1.3.8 Correlation between PAH and other pollutants during exposure runs............. 82 

5.1.3.9 Correlation between PC and other pollutants during exposure runs............... 82 

5.1.4.1 Mean DustTrak PM10 response on ambient air with and without 

 Bev-A-Line tubing........................................................................ 83 

5.2.1 Mean concentrations in three microenvironments in pilot and main studies .... 90 

5.2.2 Ratios of mean concentrations inside the bus and at bus stops relative to 

the loading/unloading zone ............................................................. 91 

5.2.3 Estimated time spent in each microenvironment..................................... 91 

5.2.4 Mean exposure factor calculations ..................................................... 92 

5.2.5 Exposure ratios for the three microenvironments.................................... 93 

5.3.1.1 SF6 measurement results................................................................. 98 

5.3.2.1 Window position runs.................................................................... 105 

5.3.3.1 Summary of mean concentrations and 95% confidence intervals inside the 

cabin for commutes with windows closed (morning) by bus type ................ 112 

5.3.5.1 Bus runs selected for videotape analysis.............................................. 118 

5.3.5.2 The effect of visible exhaust from a diesel vehicle in front of or adjacent 

to the test bus on concentrations of black carbon and PAH inside the 

 bus cabin................................................................................... 122 

5.3.5.3 Concentrations measured inside the bus cabin while traveling behind 

different types of vehicles............................................................... 124 

xvii 
LIST OF TABLES (con’t) 

Table No. Title Page

5.3.5.4 Mean black carbon and NO2 concentrations inside the test buses 

under selected conditions................................................................ 128 

5.3.5.5 Mean black carbon, PAH and NO2 concentrations measured inside the test 

buses while idling at bus stops.......................................................... 132 

5.3.5.6 Mean black carbon concentrations inside the test buses on different 

roadway types, stratified by the presence of a diesel vehicle in front 

of the bus................................................................................... 135 

5.3.5.7 Comparison between urban route one and urban route two........................ 139 

5.3.5.8 Mean black carbon concentrations measured inside the bus on urban route 

one and urban route two (Runs 20 and 26). Mean black carbon (μg/m3

)......... 140 

5.3.5.9 Mean PAH concentrations inside the bus on urban route one 

and urban route two (Runs 20 and 26). PAH (ng/m3

)................................140 

5.3.6.1 Front versus rear comparison runs...................................................... 144 

5.3.6.2 Observed rear-to-front ratios............................................................. 149 

5.3.6.3 Inside/outside comparison runs......................................................... 150 

5.3.6.4 Summary of major findings of inside versus outside comparisons................ 153 

5.3.6.5 Observed outside-to-inside concentration ratios for PAH and PC for CNG 

and trap-outfitted buses................................................................... 154 

5.4.1.1 Mean CO concentrations (ppm) inside buses and at two nearby AQMD 

stations for the morning runs (windows closed) ..................................... 156 

5.4.1.2 Mean CO concentrations (ppm) inside buses and at two nearby AQMD 

stations for the afternoon runs (windows open) on urban route one ............... 156 

5.4.1.3 Mean NO2 concentrations (ppb) inside buses and at two nearby AQMD 

stations for the morning runs with windows closed on urban route one.......... 158 

5.4.1.4 Mean NO2 concentrations (ppb) inside buses and at two nearby AQMD 

stations for the afternoon runs with windows open on urban route one .......... 158 

xviii 
LIST OF TABLES (con’t) 

Table No. Title Page

5.4.2.1 Mean concentrations (ppb) of aromatic hydrocarbons during morning runs 

(windows closed).......................................................................... 160 

5.4.2.2 Mean concentrations (ppb) of aromatic hydrocarbons during afternoon runs 

(windows open)........................................................................... 160 

5.4.2.3 Concentrations of 1,3-butadiene (ppb) during the main study...................... 161 

5.4.2.4 Studies measuring 1,3-butadiene (ppb) inside vehicles.............................. 161 

5.4.2.5 Mean concentrations (μg/m3

) of carbonyls during morning runs (windows 

closed)...................................................................................... 162 

5.4.2.6 Mean concentrations (μg/m3

) of carbonyls during afternoon runs (windows 

open)........................................................................................ 162 

6.1 Mean concentrations measured in two microenvironments and in 

 background air............................................................................ 165 

6.2 Exposure factors in three school bus commute-related 

microenvironments....................................................................... 166 

6.3 Mean concentrations and 95% confidence intervals during commutes by 

bus type .................................................................................... 168 

6.4 Morning mean concentrations: buses versus ambient air at West Los Angeles 

monitoring site............................................................................ 171 

xix 
LIST OF PHOTOGRAPHS 

Photo No. Title Page

1 a) High emitter conventional diesel test bus parked at UCLA 

b) CNG Bus parked in front of a bus stop during an afternoon run................ 19 

2 a) Array of instruments inside a test bus 

b) Preparing the instruments before an afternoon run............................... 24 

3 Inlet of the sampling line for outside measurements of gaseous species.......... 26 

4 a) Data and power cables and sampling lines 

b) Aethalometer, PC analyzer, PAH analyzer, and Harvard Impactors.......... 29 

5 (a) Power and data cables, sampling lines, CO monitors, and VOCs 

 analyzers; 

(b) Cables, standard gas tanks, rotometers and sampling lines..................... 32 

6 School bus pulling up at BSMS, at the end of a morning run, with 

children in the unloading zone.......................................................... 41 

7 Line of buses in front of BSMS before an afternoon run........................... 41 

8 a) Departing from BSMS during an afternoon run, school bus ahead 

b) Driving behind a school bus on an arterial street................................. 42 

9 Another school bus passing our idling bus at a bus stop during a morning run.. 46 

10 Rural (a) and suburban (b) segments of the rural/suburban route.................. 49 

11 Caravanning after departing from BSMS during an afternoon run 

(line of 5 buses)............................................................................ 94 

12 School bus ahead three-car lengths (maximum distance used for 

 videotape analysis)....................................................................... 120 

13 a) Smoky diesel school bus ahead on an arterial street. 

b) Driving behind a heavy-duty diesel with high exhaust emitting 

 visible smoke.............................................................................. 121 

14 Medium duty diesel vehicle ahead on the freeway.................................. 124 

15 Typical congestion on the freeway during an afternoon run......................... 127 

xx 
LIST OF PHOTOGRAPHS (con’t) 

Photo No. Title Page

16 View out the front window of the test bus while idling at a bus stop 

(at Vermont Avenue School) during an afternoon run.............................. 132 

17 West L.A. SCAQMD Station........................................................... 155 

xxi 
ABSTRACT 

 

To determine the range of children’s exposures during their bus commutes, especially 

those conditions leading to high exposures, real-time and integrated measurements of pollutant 

concentrations were conducted inside five conventional diesel school buses, as well as a diesel 

bus outfitted with a particulate trap and a bus powered by natural gas. Measurements were made

during 20 bus commutes on a Los Angeles Unified School District bus route from South Central 

Los Angeles to the west side of LA, with additional runs on a second urban route, a 

rural/suburban route, and to test the effect of window position. 

Children’s school bus commutes in Los Angeles appear to expose them to significantly higher 

concentrations of vehicle-related pollutants than ambient air concentrations and frequently higher 

concentrations than those measured on roadways. Concentrations of diesel vehicle-related 

pollutants such as black carbon and particle-bound PAHs were significantly higher on board 

conventional diesel buses when windows were closed. This was due to the intrusion of the bus’s 

own exhaust, as demonstrated through the use of a tracer gas added to each bus’s exhaust. When 

windows were open, increased ventilation rates markedly reduced this effect, although high peak 

concentrations were then observed when following other diesel vehicles. On-board 

concentrations of vehicle-related pollutants were also significantly higher on the urban routes

compared to the rural/suburban route, indicating the importance of surrounding traffic density. 

Other related exposure scenarios such as bus loading and unloading, and time spent

waiting at bus stops, were shown to make relatively insignificant contributions to children’s 

exposure, due to the generally lower concentrations and the short times spent at those activities 

compared to bus commutes. Results from this study show that minimizing commute times, using 

the cleanest buses for the longest routes, and reducing bus caravanning and idling time will 

reduce children’s exposure to bus-related pollutants. 

xxii 
xxiii 
1.0 EXECUTIVE SUMMARY 

Background. Because children’s lungs are still developing and children are more susceptible to adverse 

health effects from air pollution, potentially high pollutant exposures during school bus commutes are of 

concern. Studies of pollutant concentrations inside vehicles show high exposures are typical, but few

studies have attempted to characterize concentrations on-board and near school buses. This study’s 

main purpose was to determine the range of children’s exposures during their bus commutes, with an 

emphasis on determining the specific factors and conditions leading to high exposures and comparing 

the effects of different bus and traffic characteristics and bus operating conditions. 

Methods Real-time and integrated measurements of pollutant concentrations were conducted in the 

spring of 2002 while driving school bus routes in Los Angeles (LA) with five conventional diesel school 

buses, manufactured from 1975 to 1998, a 1998 diesel bus outfitted with a particulate trap, and a 2002 

bus powered by natural gas. All diesel buses used low sulfur “green” diesel fuel. Runs included ten 

morning and ten afternoon bus commutes over an LA Unified School District (LAUSD) bus route from 

South Central LA to the west side of LA, with four additional runs on a second LAUSD urban route, 

seven additional runs on a rural/suburban route, and to three additional runs to test the effect of window 

position. 

Results: Bus stop and bus loading/unloading activities were shown to make small contributions to 

overall commute-related exposures due to the low concentrations found there and the short lengths of 

time involved in those activities. Exposure factors calculated were as much as two orders of magnitude 

higher for bus commutes on urban routes than for the bus stop or loading/unloading microenvironments 

(Table 1.1). 

Table 1.1 Average exposure factors (air concentration * time) in three microenvironments. 

 Loading/Unloading1 Bus Stops2 Urban Commutes2

Black Carbon (μg/m3

* min) 5 20 600 

Particle-Bound PAH (ng/m3

* min) 45 230 10000 

NO2 (ppb * min) 105 270 5500

Particle Counts (#/cm3 * min) 25 310 10000 

PM2.5 (μg/m3

* min) N/A 130 3500 1

Based on measurements taken during pilot study. 2Based on five min. spent at bus stop and a 76 min. commute. 

Overall, children’s school bus commutes in Los Angeles appear to expose them to significantly higher 

concentrations of vehicle-related pollutants than ambient air concentrations and frequently higher 

concentrations than those measured on roadways. Self-pollution from the bus’s own exhaust was found 

to play a significant role in on-board bus concentrations, especially when windows were closed, as was 

demonstrated by on-board measurements of an inert tracer gas, SF6, added to each bus’s exhaust. Older

buses showed higher rates of exhaust intrusion, but intrusion was detected in all buses. With closed 

windows, mean concentrations of diesel vehicle-related pollutants such as black carbon and particlebound PAHs on board conventional diesel buses were more than double the mean concentrations with 

windows open. Under closed window conditions, diesel vehicle-related pollutants were also

significantly higher on-board the conventional diesel buses as compared to the single CNG-powered 

bus, while the trap-equipped bus had concentrations between the two (although diesel-related pollutant

concentrations on board this specific trap-outfitted bus appeared to be higher than expected based on 

1 
emissions data reported for other trap-equipped diesel vehicles). In contrast, natural gas-related 

pollutants such as formaldehyde were higher aboard the CNG bus. With closed windows, 

concentrations were also somewhat higher in the rear of the bus, but front/rear differences were 

generally smaller than the bus-to-bus concentration differences. When windows were open, the

resulting high ventilation rates appeared to strongly reduce the amount of self-pollution, while the 

influence of following individual vehicles became more pronounced, and high transient concentrations 

of diesel vehicle-related pollutants were associated with proximity to other diesel vehicles. 

On-board concentrations were also strongly influenced by other traffic sources. Table 1.2 

presents the mean pollutant concentrations by run type for the urban and rural/suburban routes, 

under open and closed window conditions. Both window position and surrounding traffic 

density can be seen to have strongly affected on-board concentrations for vehicle-related 

pollutants, but for pollutants with a strong background component, such as PM2.5 and fine 

particle counts (0.3-0.5 μm), differences between routes and differences due to window position 

were less distinct. 

Table 1.2. Concentrations means and range of means by route and by window position1. 

Urban Route

1 Rural/Suburban Route

Windows Closed (a.m.)2 Windows Open (p.m.)2 Windows Open (p.m.)2 Pollutant 

Mean Range of Means Mean Range of Means Mean Range of Means 

Black Carbon (μg/m3) 10 2.5 – 19 5.2 2.9 – 9.1 2.7 0.9 – 4.8 

PM-bound PAH (ng/m3) 198 64 – 400 96 33 – 147 36 14 – 66 

NO2 (ppb) 64 34 – 110 73 39 – 120 45 23 – 68 

Formaldehyde (μg/m3) 2.1 0.89 – 4.8 1.1 0.55 – 2.1 0.93 0.34 – 2.0 

Particle Counts (#/cm3) 113 51 – 235 96 19 – 276 159 29 – 253 

Carbon Monoxide (ppm) 5.1 3.7 – 6.4 2.4 1.9 – 2.9 na3 na3 

1

Includes commutes on urban route one only. 2Windows closed in morning and open in afternoon, similar to conditions 

uniformly observed in other buses in Los Angeles for April through June. 3No measurements conducted. 

Conclusions: Measurements made on-board school buses in Los Angeles indicated higher exposures are 

occurring during children’s commutes than ambient air concentrations would indicate. These exposures 

resulted primarily from the commute itself, and not from loading, unloading, or waiting at bus stops. 

High commute exposures had several causes: the high concentrations of pollutants already present on 

roadways, especially if traffic was heavy; the direct influence of other vehicles being followed; and the 

contribution of the bus’s own emissions. The extent of a bus’s own contribution to these high 

concentrations appeared to be higher when windows were closed and for older buses. However bus-tobus variability was relatively high and therefore with the small number of buses studied, and the limited 

number of routes covered, the findings of this study should not be viewed as inherently typical for all 

buses under all conditions. However, minimizing commute times, using the cleanest buses for the 

longest bus routes, and reducing bus “caravanning” and unnecessary idling time would reduce children’s 

exposures to bus-related pollutants. 

2 
2.0 INTRODUCTION AND BACKGROUND 

2.1 Introduction

The health of children in California is the focus of intense interest by the Governor, the 

State Legislature, and local, state and federal health agencies, as well as by academic researchers. 

Growing evidence indicates children’s health may be affected by environmental influences, 

including air pollutants. Although ambient (outdoor) air pollution may contribute to adverse 

health effects, the highest exposures to a range of air contaminants may occur in other 

microenvironments, especially in vehicles. Of particular concern to the California Air Resources 

Board (ARB) is the time spent by children during school bus commutes. Out of the six million 

school children in California, one million are transported by public school buses (California 

Department of Education, 2002). About 70% of the 26,000 school buses operating in California 

are powered by diesel engines (Horie et al., 1994; Long, 2000), and the ARB recently declared 

particulate matter in diesel exhaust to be a toxic air contaminant. 

A recent study of in-vehicle concentrations, conducted in Sacramento and Los Angeles 

using a passenger vehicle as a chase car (Rodes et al., 1998), found that proximity to diesel 

vehicles caused high concentrations of in-vehicle fine particles and black carbon. Moreover, 

children may be exposed to high concentrations of diesel particulate matter (DPM) and other 

associated vehicle emissions (e.g. nitrogen dioxide and carbon monoxide) while waiting at 

school bus stops, riding on buses (particularly where buses are “caravanning”), or during the 

time they are assembled at school for loading or unloading near buses that are idling. 

2.2 Statement of the Problem

Because children’s lungs are still developing and because they are more susceptible to the 

health effects of certain pollutants, there is concern regarding potentially high pollutant 

exposures during commutes to and from school. However, no comprehensive studies of

children’s exposure while traveling to and from school have been performed to date in 

California, even though roadways and sidewalks have been shown to have the highest outdoor 

concentrations for many air pollutants and in-vehicle concentrations have been shown to be

higher than those measured at fixed site monitors and in some cases higher than measured along 

roadways (Shikiya et al., 1989, Rodes et al. 1998). 

Children are especially susceptible to air pollution because of their high inhalation rates 

relative to body mass, high activity concentrations, greater time spent outdoors, narrower lung 

airways, immature immune systems and rapid growth (Lipsett, 1989; Pope, 1989; Phillips et al., 

1991; Wiley et. al., 1991; U.S. EPA, 1996). In spite of this increased risk, there is a lack of data 

concerning children’s in-vehicle exposure, especially involving diesel school buses. Because of

the potential for high pollutant concentrations, school bus cabins, roadways, bus stops, and 

school yards near idling buses are critical microenvironments that must be included in accurate

assessments of children’s exposures. 

2.3 Background

California Health and Safety Code Section 39660.5 requires the ARB to assess 

Californians’ indoor exposures to toxic air pollutants as part of the ARB Toxic Air Contaminants 

Program. The ARB is also required to identify the relative contribution of indoor concentrations 

3 
to total exposure, taking into account both ambient and enclosed (e.g. indoor and in-vehicle)

environments. Senate Bill 25 (Escutia 1999), requires the ARB to identify areas where exposure 

of infants and children to air pollutants is not adequately measured by the current monitoring

network and to conduct enhanced monitoring. Because of the potential for high pollutant

concentrations, school bus cabins, roadways, bus stops, and school yards near idling buses are 

critical microenvironments that must be included in assessments of children’s exposures. 

It is well understood that gasoline-powered vehicles create significant concentrations of 

fine particles, carbon monoxide (CO), oxides of nitrogen (NOx) and volatile organic compounds 

(VOC), including aromatic VOC. In addition, diesel-powered vehicles emit greater mass per 

mile of fine particles, NOx, sulfur compounds, semi-volatile organic compounds (SVOC) and 

elemental carbon (EC), and may emit higher quantities of polycyclic aromatic hydrocarbons

(PAH), some of which are mutagenic and/or carcinogenic. Consistent with this, elevated 

concentrations of elemental carbon and PAHs have been recorded in traffic tunnels and on 

heavily traveled roadways (Hering et al., 1984; Benner et al., 1989; Venkataraman et al., 

1994a,b; Kirchstetter and Harley 1999). 

In traffic, vehicle occupants are primarily exposed to the exhaust of neighboring vehicles, 

particularly those directly ahead of the occupant’s vehicle (Rodes et al., 1998). In-vehicle 

concentrations of CO and fuel-related VOCs have been found to be significantly higher than 

those in ambient air (Shikiya et al., 1989; Ptak and Fallon, 1994; Lawryk and Weisel, 1995; 

Rodes et al., 1998; Jo and Park, 1999, Alm et al., 1999). Of all these in-vehicle studies, only the 

two (by Shikiya et al., 1989 and Rodes et al., 1998) were performed in California. Other 

variables that have been shown by previous researchers to be important influences on in-vehicle 

concentrations include traffic density, driving lane, inter-vehicle spacing, traffic speed, and the

number of stops. Secondary factors that may also be significant under certain conditions include 

wind speed and direction, inverse height, time of day, vehicle size, vehicle age, vehicle 

ventilation settings, and engine type. 

According to a recent Natural Resources Defense Council (NRDC) study (Solomon et. 

al., 2002), lack of explicit idling policies is common in Los Angeles area district schools. 

However, the ARB has recently approved an airborne toxic control measure (ATCM) that would 

limit school bus idling, and idling at or near schools, to only when necessary for safety or 

operational concerns (ARB, 2002). The ATCM to limit idling is intended to reduce children’s 

exposure to diesel exhaust particulate matter and other Toxic Air Contaminants (TAC) from 

heavy-duty motor vehicle exhaust. 

2.4 Previous In-Vehicle Studies

In traffic, vehicle occupants are primarily exposed to the exhaust of neighboring vehicles, 

particularly those directly ahead of the occupant’s vehicle (Rodes et al., 1998), and in-vehicle 

concentrations of CO and fuel-related VOCs have been found to be significantly higher than 

those in ambient air (Shikiya et al., 1989; Ptak and Fallon, 1994; Lawryk and Weisel, 1995; 

Rodes et al., 1998; Jo and Park, 1999; Alm et al., 1999; Solomon et al., 2001; Wargo et al., 

2002), including in the three studies performed in California (Shikiya et al., 1989; Rodes et al., 

1998; Solomon et al., 2001). 

4 
Children typically spend over an hour a day in transit (Phillips et al., 1991) and, as noted, 

in-transit microenvironments can exhibit high concentrations of pollutants. For example, for

aromatic VOCs, such as benzene, the in-transit microenvironment is estimated to contribute 10 

to 60 percent of a nonsmoker’s total exposure (Chan et al., 1991a,b; Weisel et al., 1992; Lawryk 

and Weisel, 1995; Fruin et al., 2001). 

In the Rodes et al. (1998) study, for most pollutants 2-hr integrated samples were

collected inside and outside a passenger car, along the roadway where the vehicle traveled, and

at ambient monitoring sites. Pollutants measured included PM10 and PM2.5, metals, and thirteen

VOCs. Continuous measurements were made of fine particles, CO and black carbon (BC). The

driving scenarios were chosen to evaluate in-vehicle pollutant concentrations as a function of 

traffic congestion, vehicle type, roadway type, time of day, and ventilation setting. 

Rodes et al. (1998) found in-vehicle pollutant concentrations were generally significantly 

higher in Los Angeles than in Sacramento, supporting the choice of Los Angeles for the present 

school bus study, which sought in part to identify high-end exposures. In general, VOC and CO 

concentrations inside or just outside the vehicles were four to ten times higher than those 

measured at the roadside or at ambient air stations (Rodes et al., 1998). Interestingly, however, 

in-vehicle PM2.5 concentrations were consistently lower than PM2.5 concentrations just outside 

the vehicles, and in some cases were also lower than roadside concentrations. PM2.5

concentrations inside or just outside the vehicles, however, were usually higher than 

concentrations measured at the nearest ambient site, however.

Also interesting was the finding by these investigators that under their study conditions, 

factors such as ventilation settings and vehicle type had little effect on in-vehicle pollutant

concentrations. In contrast, factors such as driving lane (e.g. carpool lane versus right lane), 

roadway type, congestion level, and time of day all influenced in-vehicle concentrations. For 

example, substantially higher pollutant concentrations were measured in the far right hand lane 

compared to the carpool lane and when following high-emitting “lead vehicles.” 

Further analysis of the Rodes et al. (1998) data was conducted by Fruin (2003). To 

determine which parameters had the most significant impacts on in-vehicle DPM concentrations, 

video tapes recorded during the driving commutes were used to create more refined groupings of 

the vehicles being followed, (e.g. 2, 3 or 5-axle diesel vehicles and alternate-fuel buses). Other 

factors such as exhaust location and following distance of the vehicle ahead were also included

in the analysis. Results of these analyses confirmed the Rodes et. al. (1998) conclusion that 

following a diesel-powered vehicle was associated with a significant increase in both black 

carbon and fine particle counts. In addition, Fruin (2003) found that exhaust location, following 

distance, and sometimes road type also influenced in-vehicle concentrations, while following a

gasoline-powered passenger vehicle or alternate fuel bus had weak or no associations with invehicle DPM concentrations. 

An earlier in-vehicle study conducted in California was the pioneering study by Shikiya 

et al. (1989). They demonstrated that in-vehicle concentrations of emitted criteria pollutants 

such as CO and NOx could be two to four times those measured at fixed site monitors. Of the 

5 
remaining studies discussed in the Introduction given above, only those by Ptak and Fallon 

(1994), Alm et al. (1999), and Solomon et al. (2001) measured particulate matter. 

In the recent study by Solomon et al. (2001), a team of researchers from the Natural 

Resources Defense Council and the University of California, Berkeley, investigated the 

concentrations of diesel exhaust constituents inside school buses in the Los Angeles area. They 

employed continuous measurements of PM2.5 and black carbon concentrations inside four school 

buses. For comparison, they also included measurements outside the buses and in a passenger 

car traveling ahead of the buses. They found the level of black carbon was higher in the back of 

the buses compared to the front. Furthermore, the level of black carbon increased when all

windows were closed, while it decreased with windows open. However, the level of black 

carbon in the cabin did not change, or decreased, during idling. Solomon et al. (2001) concluded 

the level of black carbon in the back of a school bus with windows closed could be up to four 

times higher than in a passenger car ahead of the bus. 

In a recent school bus study in Connecticut, Wargo et al. (2002) found concentrations of 

black carbon and PM2.5 inside commuting school buses were often 5-10 times higher than rural 

background concentrations. They found several important variables affected the concentrations 

of pollutants inside school buses, including bus ventilation via windows, bus idling behavior, and 

outdoor concentrations on bus routes. They observed the concentrations of black carbon and 

PM2.5 in moving buses were higher when windows were closed compared to when windows 

were open. However, mean concentrations of both black carbon and particulates were higher in 

idling buses when windows were open. Furthermore, idling buses had higher concentrations of

particles and black carbon than moving buses. They also found concentrations increased when 

traveling behind other buses including diesel buses (e.g., caravanning). On the other hand, they 

found black carbon and particulate concentrations did not vary by sampling location within their 

buses (i.e., front versus rear). They also employed school buses fueled with natural gas in their

study and observed CNG buses emitted 60–98% less black carbon than diesel-powered buses. 

Wargo et al. (2002) provided several recommendations for reducing children’s exposure 

to diesel exhaust particulate: prohibit bus idling; retrofit with particle traps and catalytic 

converters; use ultra-low sulfur fuels; allocate the cleanest buses to the longest route; and limit

ride duration. Solomon et al. (2001) also addressed similar recommendations, including 

retrofitting with particle traps, purchasing alternative fuel school buses (e.g., CNG), and keeping 

windows open on school buses. The South Coast Air Quality Management District (AQMD) has 

approved $10.4 million to install particulate trap filters on nearly 1,500 diesel-powered school 

buses in the summer of 2002 (AQMD, 2002). 

Of the peer-reviewed articles we found concerning exposures to pollutants in buses where 

human subjects were involved, only two specifically concerned school children. Wu et al. 

(1998) reported the use of an iridium tracer to determine soot exposure of high school students

commuting to and from school on public diesel transit buses in Baltimore. A portion of the 

Baltimore municipal fuel supply was tagged with an iridium tracer and exposure during 

commutes was monitored with personal aerosol monitors. Twenty samples were collected over

10 days while four students commuted on regularly scheduled transit buses and a fifth student 

commuted by private car. Exposures were greatest for students commuting through the 

6 
congested central business district of Baltimore. The tracer was undetectable in samples 

collected by the student commuting by car when the windows were closed, but comparable to the

other students (on transit buses) when the car windows were open during the commute. 

Wargo et al (2002) outfitted children with personal monitors that measured PM10, PM2.5, 

and VOCs. Measurements were made throughout the school day, including time spent traveling 

to and from school on diesel school buses. Average personal concentrations of PM2.5 were 

nearly three times higher than average concentrations for outdoor air in that community, while 

concentrations measured while the children were riding inside school buses were up to ten times 

greater than background. 

2.4.1 Related Traffic Exposure Studies

Adams et al. (2001) studied determinants of personal exposure to PM2.5 in transport 

microenvironments in London, UK. Four transport modes, bicycle, bus, car and underground 

rail, were involved. Meteorological variables (wind speed, wind direction, precipitation, 

temperature, atmospheric pressure and relative humidity), traffic density and route were also 

considered. Except for the underground rail, they found wind speed and route were significant

factors determining personal exposure to PM2.5, while transport mode was not significant. 

Chan and Wu (1993) studied bus commuter and pedestrian exposure to traffic pollution 

in Hong Kong. Kinney et al. (2000) measured concentrations of PM2.5 and elemental carbon on 

sidewalks in Harlem, New York City. They selected four monitoring sites ranging from heavily 

traveled roadways to a quiet residential sidewalk. PM2.5 concentration showed little association 

with the proximity to local diesel traffic, while elemental carbon concentration exhibited a strong 

spatial gradient across sites, consistent with recent studies by Zhu et al. (2002 a, b). Average 

elemental carbon concentration ranged from 1.5 ug/m3

 to 6.2 ug/m3 (a four-fold difference 

between two sites with the largest contrast in diesel traffic counts). 

Gee and Raper (1999) investigated commuter exposure to respirable particles inside 

buses in Manchester, England. This study involved using personal sampling pumps installed in 

the bus cabs to obtain an estimate of the average commuter exposure to fine particles during 

congested commutes. Conceicao et al. (1997) installed a “removal” duct in a commuter bus to 

improve ventilation rate and modeled the airflow with a simple, uni-dimensional flow model, 

predicting the air exchange rate as a function of the vehicle velocity. Tracer gas experiments 

were performed to test the model and the efficacy of the air removal duct. Scheirl and Fruhmann 

(1996) measured airborne platinum concentrations in city buses in Munich, Germany. 

Several studies in Denmark measured relationships between DNA adducts found in urine 

samples from Copenhagen bus drivers and pollutant concentrations in their buses. Loft et al. 

(1999) found increased urinary excretion of a biomarker of oxidative DNA damage in urban bus

drivers. Autrup et al. (1999) and Nielson et al. (1996) used selected biomarkers in nonsmoking 

Danish bus drivers and postal workers and found significantly higher concentrations of

carcinogen-DNA adducts in bus drivers working in the central part of Copenhagen compared 

with rural bus drivers in a control group. 

7 
Soll-Johanning et al. (1998) conducted a retrospective cohort study of cancer incidence in 

urban bus drivers in Copenhagen covering the period 1900 to 1994 and found an increased risk 

of developing several types of cancers, although risk factors other than exposure to air pollutants 

during working hours (e.g., smoking) could not be ruled out as causal factors. FernandezBremauntz and Ashmore (1995a) studied the exposure of commuters to carbon monoxide in a

range of vehicles, including transit buses, and in general found much higher in-vehicle CO 

concentrations in Mexico City than those reported for previous studies in the United States. This

study also reported mean in-vehicle CO concentrations were three times higher than fixed-site 

monitoring concentrations. 

2.5 Objectives

2.5.1 Overall Objectives

The overall objective of this study was to characterize the range of exposures experienced 

by children during their school bus commutes, especially in potentially high exposure conditions. 

By identifying factors that lead to higher exposure, this study may provide guidance for 

minimizing children’s exposures. The results may also facilitate evaluations of the direct health 

benefits of alternative fuel types and improved bus emission control technologies. 

2.5.2 Specific Objectives

The over-arching specific objective of this project was to obtain measurements of in-bus 

and near-bus pollutant concentrations during normal school bus operations across the full range 

of anticipated conditions, but with special emphasis on obtaining measurements during 

operations expected to lead to realistic high-end exposures. Diesel buses were of highest priority

because they comprise the majority of buses used by California schools. 

2.5.2.1 Pilot Study 

The specific objectives of the pilot study were to test and verify the feasibility and utility 

of the study design and proposed sampling methods prior to the main study, and to gain 

experience in all facets of conducting the experiments. Specifically, the pilot study gave us 

experience in deploying an extensive array of instruments within the demanding confines of a 

bus and a van, and allowed us to evaluate how well the various instruments performed on a

moving platform, as well as the utility of the collected data. Another objective of the pilot study 

was to evaluate the relative importance of three microenvironments, commutes, bus stops and 

loading/unloading zones to children’s total school bus related exposures. 

2.5.2.2 Main Study 

The specific objectives of the main study were to obtain measurements that can be used 

(in combination with the pilot study data) to address the following questions: 

1. What are children’s pollutant exposures during bus transit, especially for those who 

spend a large portion of their transit time in high-concentration conditions?

2. How do different bus fuel types (i.e. diesel - and natural gas-powered) affect children’s 

exposure?

3. What are the most important factors governing children’s exposure associated with 

commuting on school buses?

8 
3.0 PILOT STUDY FINDINGS AND RECOMMENDATIONS 

3.1 Introduction

The principal purpose of the pilot study was to prove methods and refine protocols. 

Thus, the pilot study was conducted to verify the feasibility of the study design and the utility of 

the instruments and sampling methods on a mobile platform. In addition, the pilot study was 

designed to provide range-finding measurements of conditions influencing high exposures to 

mobile source air pollutants in the context of school bus-related microenvironments. Three 

locations identified as potentially high exposure scenarios were explored in the pilot study with 

an emphasis on identification of conditions leading to highest exposures. These included 

exposures while waiting for the bus at a bus stop near home, while commuting on the bus in 

traffic, and/or while waiting at the school bus loading zone for the return ride home. Another 

objective was to evaluate the importance of these three microenvironments to children's total

school bus-related exposures. 

In the pilot study, we evaluated a single diesel school bus traveling along a single bus 

route. A suite of real-time and integrated instruments were used to measure concentrations of 

gaseous pollutants and fine particles inside the cabin of the bus while traveling along a 

designated route, at a selected bus stop, and in front of a school in the bus loading/unloading 

zone. The importance of variables such as location inside the bus cabin, differences between 

concentrations inside and immediately outside the bus, and the effect of window position were 

also evaluated. 

The highest exposures were expected in areas with heavy traffic congestion and during 

periods of meteorological stagnation. For this reason, the pilot study was conducted in the 

service area of the Los Angeles Unified School District(LAUSD) in November and December 

2001. We used contacts within LAUSD to identify bus-commuting routes and to gain access to 

detailed routes and stops. We replicated the characteristic patterns of loading/unloading, bus 

stops and commutes observed for a specific bus route for an appropriate school, with emphasis 

on conditions where adjacent traffic was greatest, bus idle time was longest, and where children 

were waiting for substantial periods of time to be picked up. 

3.2 Summary of Pilot Study Findings

3.2.1 Exposure in Three School Bus Commute Microenvironments

As discussed extensively in the Pilot Study Final Report (Fitz et al., 2002), three 

microenvironments were investigated concerning children's pollutant exposure due to school 

bus-related activities. "Bus commutes" refers to measurements made using the bus as a platform

for the various instruments. For this type of run a typical route transporting children from the 

inner city to BSMS (and vice versa) was followed. "Bus stops" refers to sampling at one of the 

stops along the selected route. Generally, bus stops may be near children’s residences or often, 

for reasons of safety and efficiency, at a nearby school. An instrumented van was parked in front 

of Vermont Elementary School at the corner of Vermont Avenue and 97th Street, where diesel 

buses arrived to pick up students traveling to other schools (morning) or to drop off student 

coming from other schools (afternoon). In addition, a large number of parents dropped off or 

picked up children at this "bus stop” using their personal vehicles. 

9 
"Loading/unloading" runs involved measurements with a sampling van parked next to the 

sidewalk of BSMS, in a portion of the staff parking lot, about five feet from the curb and near the 

location where children congregated briefly when leaving or boarding the buses. Typically each 

morning and afternoon, nineteen school buses lined up along this sidewalk to unload or load 

children, respectively. 

After analyzing the pollutant concentrations measured in the pilot study, as well as the

time spent by children in these three microenvironments, we concluded the relative importance 

of commutes versus stops versus loading/unloading zones was quite different. To better 

understand and quantify these differences, we estimated pollutant-specific exposure factors for 

the three microenvironments, defined as the product of the average concentration for a specific 

pollutant times the time spent by children in the particular microenvironment. Finally, we 

calculated the average ratios of exposure factors for each pair of microenvironments for each

pollutant. The average over all pollutants was taken to be a reasonably quantitative measure of 

how important one microenvironment was in relation to the other two. 

Table 3.2.1 summarizes the results for the calculation of exposure factors, exposure 

ratios, and exposure ratios averaged for the set of analyzed pollutants. Note the estimated 

average exposure ratio for bus commutes versus bus stops was 35. This indicates the 

contribution of the bus stop was about 3% of that observed for bus commutes. Similarly, the 

estimated average exposure ratio for bus commutes versu the loading/unloading zone was 90. 

Thus, the exposure contribution of the loading/unloading zone was only approximately 1% of 

that observed for bus commutes. 

From these results we concluded the loading/unloading zone microenvironment was not 

as important in terms of exposure as the other two microenvironments. There are two principal 

reasons for this. First, we observed that children generally spend a very short time on the 

sidewalk (typically as little as a minute, but on occasion as much as five minutes) before 

boarding the buses in the afternoon, or before entering the school in the morning. Second, bus 

drivers were required by school district policy to turn off their engines as soon as they arrived in 

the morning, before the children leave the bus. Similarly, in the afternoon, drivers were 

instructed to not turn on their engines before all children were aboard the buses and the entire 

fleet was prepared to depart. During the pilot study we observed nearly 100% compliance with 

these regulations (although we have anecdotal information from a BSMS teacher that this policy 

was not always rigorously enforced--J. Fogel, private communication. 2002). 

3.2.2 Conditions Leading to Highest Exposures Inside the Bus Cabin

During the pilot study, conditions associated with the highest pollutant concentrations 

inside the bus were proximity to another diesel vehicle, idling (either at bus stops or traffic lights, 

or due to traffic congestion) and closed windows. Also, the difference between pollutant 

concentrations inside the bus while traveling on the freeway versus surface streets was not found 

to be significant during the pilot study. However, this may have been due to the experimental 

design, which involved alternating between open and closed windows during the course of each 

run. Windows were typically more often open on the freeway portion of the run and more often 

closed while traveling on surface streets. Because opening windows was found to reduce

10 
Table 3.2.1 Calculated exposure factors for three school bus commute-related microenvironments.

Bus Stop Loading/Unloading Bus Routes Bus Stop Loading/Unloading Bus Routes Exposure Rati

BC (mg/m3) 4 2 14 20 10 1400 70 

PAH (ug/m3) 65 15 100 325 75 10000 31 

NO2 (ppb) 45 35 55 225 175 5500 24 

VOC (ppb) 120 70 175 600 350 17500 29 

CO (ppm) 3 1 3 15 5 300 20 

Particle Counts (#/cm3)1 30 8 43 150 40 4300 29 

PM-2.5 (ug/m3) 35 13 56 175 65 5600 32 

35 

1Between 0.3 and 0.5 um diameter2Defined as the product of average concentration and time, in minutes, spent in that particular microenvironment3Average time spent on bus runs was 100 minutes. Upper limit to time spent at bus stops was 5 minutes (approximate values)4Defined as the ratio of exposure factor for bus routes to exposure factor for bus stops5Defined as the ratio of exposure factor for bus routes to exposure factor for loading/unloading zones

Approximate Average Concentration Exposure Factor2,3 

Average Exposure Ratio:

11 
concentrations inside the bus, differences in pollutant concentrations while traveling on freeways 

versus surface streets may have been obscured during the pilot study. 

3.3 Modifications of Experimental Design for Main Study

3.3.1 Relative Importance of School Bus Commute Microenvironments

As discussed above, in the pilot study, concentrations observed during bus commutes 

were significantly higher than those at bus stops or at the loading/unloading zone. In addition, 

we observed the time children spent in these microenvironments was substantially different, 

typically a few minutes at the bus stop or loading/unloading zone versus up to 100 minutes for 

bus commutes each way. We concluded bus commutes were the most important 

microenvironment in terms of exposure, and recommended the main study focus primarily on 

that microenvironment. However, while we found no justification for expending additional 

resources in further characterization of loading/unloading zones, we determined a limited 

number of additional measurements at an additional bus stop during the main study would allow 

us to better define potential exposures associated with this microenvironment since sporadic and 

short-lived high concentrations were sometimes observed due to the arrival and departure of 

other buses. 

Specifically, although we found the bus stop contribution to exposure was minor

compared with the bus runs (about 3%), additional runs were recommended in part because a 

fairly high emitting bus was used in the pilot study. A less polluting bus may exhibit lower 

average exposure ratios, leading to a greater contribution from bus stops to total exposure. 

A second reason for a limited number of additional bus stop runs was that in the pilot 

study we observed the highest values for fine particle counts in the 0.18 um size range during the 

bus stop measurements. This was due in part to the use of the Vermont Avenue School as a bus 

stop by a number of schools other than BSMS. We observed in the neighborhoods in which our 

pilot study took place almost all the bus stops have been placed at schools (presumably in part 

out of safety considerations for the children). Thus, a substantial number of buses may stop to 

pick up students, destined for a variety of different schools. For example, during our monitoring 

periods at the Vermont Avenue School bus stop at various times as many as half a dozen buses 

would pull up in front of the school (adjacent to our instrumented van) and wait with the engine 

idling until children boarded. In some cases buses were early and waited with their engines 

idling for several minutes, while students waiting for other buses stood nearby. Buses would 

then accelerate away from the curb, often releasing an exhaust cloud of black smoke. Several of 

the highest fine particle counts we observed occurred at such times. Similarly, parents driving 

children to and from the Vermont Elementary School, in some cases in "gross emitter" vehicles

(as documented in our field-notebooks), contributed to the sporadic occurrence of high particle 

counts. 

3.3.2 Precision Data

Paired instrument data were important in determining the reliability, and precision of the 

deployed instruments. The lack of adequate precision estimates for a number of the instruments 

from the pilot study data (due to insufficient pollutant concentrations) made it difficult in some

cases to interpret differences observed in pollutant concentrations between the front and back of 

the bus, and between inside and outside the bus, with a high level of confidence. The relatively 

12 
unpolluted background conditions in West Los Angeles, where all but the NO2 precision data 

were collected, often resulted in measured pollutant concentrations that were not sufficiently 

above the detection limit. One recommendation for the main study was to perform precision 

measurements in a location where background concentrations for many of these pollutants would 

be expected to be sufficiently higher than the detection limit. In addition, the importance of 

collecting sufficient precision data before and after runs to establish comparability of 

measurements from pairs of instruments was emphasized. 

3.3.3 Gradient Tests

Based on analysis of the pilot study data, we recommended simultaneous measurements 

inside and immediately outside the bus continue during the main study. The results from the 

pilot study indicated children were exposed to higher pollutant concentrations on the bus 

compared with outside, emphasizing the importance of the school bus microenvironment to 

children's total exposure.

Although the pilot study found few differences between concentrations at the front and 

rear of the bus cabin, our recommendation for the main study was to continue front and rear 

sampling in a limited number of cleaner buses to better determine if there was a concentration

gradient between the front and rear of school bus cabins across different bus and fuel types. 

3.3.4 Conditions Leading to Highest Concentrations Inside the Bus Cabin

The data from the pilot study concerning window position were complex and 

inconclusive regarding the importance of this variable to children’s exposure. We recommended 

further assessment of this variable in the main study to obtain a more robust data set. 

In addition, although the influence of the bus's own exhaust was not directly measured 

during the pilot study, the importance of this variable in estimating children's exposure during 

bus commutes was potentially high. For the main study, we recommended the use of an SF6

tracer added to the bus exhaust as a means to evaluate the importance of a bus’ own exhaust in

children’s exposure during bus commutes. We also recommended a snap and idle test for each 

bus for the same reason. 

3.3.5 Routes

Based on the pilot study results, we elected to use the same urban route for the majority 

of commute runs, except that the route began and ended at the fifth bus stop. This shortened the 

route to about one hour duration, which was judged optimum to meet the pollutant sampling 

objectives of the main study while maintaining the diversity of roadway types and congestion 

scenarios. 

3.3.6 Summary of Pilot Study Findings

The pilot study demonstrated the proposed instrumentation and measurements were 

feasible on a moving platform such as a school bus and that the proposed routes provided both 

high concentration conditions as well as a wide range of concentrations. However, locations of 

higher concentrations for the collocated instrument comparisons were needed to ensure adequate 

signal-to-noise conditions for direct instrument comparisons. Bus stop and bus loading/ 

unloading activities were demonstrated to make small contributions to overall commute 

13 
exposures, so further characterizing these particular activities was de-emphasized in the main 

study to allow greater resources to be devoted to characterizing the actual drive portion of the 

commutes and the differences between buses. 

14 
4.0 EXPERIMENTAL METHODS AND STUDY DESIGN 

4.1 Introduction 

In this study, we obtained measurements that were used to determine children’s pollutant 

exposures during bus transit, especially for those who spend a large portion of their transit time

in high-concentration conditions. We measured a wide range of gaseous and particulate matter 

species inside school buses while traveling along in-use school bus routes. The great majority of 

these measurements were made using sophisticated real-time instruments, and successfully

operating these instruments on mobile platforms was a major experimental challenge. 

A total of 36 runs were completed, including six runs on a simulated rural/suburban 

route, 20 runs on an in-use urban route, four runs on a second in-use urban route, three window 

position tests, and two measurement periods at a bus stop along the primary urban route. Seven 

different buses were used, including three high emitting diesel buses, two typical diesel buses, 

one particle trap-outfitted diesel bus, and one CNG bus. The data collected were used to 

determine the most important factors governing children’s exposure associated with commuting 

on school buses. All data collected during this study will be submitted to the ARB for future 

additional analyses. 

4.1.1 Characterization and Justification for the Selected Buses

The primary criterion for selecting buses for testing was that they be representative of the

school bus fleet used in California with regard to age, mileage, and manufacturer, and include 

several older, dirtier buses for testing worst-case conditions. Toward this goal, we included the 

testing of three buses representative of high exhaust emissions. To evaluate state-of-the-art

control technology, we evaluated exposures on-board a newer-model bus equipped with a 

particle trap catalyst. Finally, in order to compare children’s exposure during bus commutes for

different bus fuel types, we included a CNG-fueled bus. A decision was also made to use buses 

that were in current use transporting children to school. However, the extensive suite of 

instruments employed in this study, the batteries needed to power them, and the researchers 

needed to operate them occupied almost the entire working and seating area inside the bus. 

Because of this, and for safety reasons, no children were aboard any of the buses in this study. 

We note the batteries and equipment served as approximate proxies for the children’s weight

corresponding to a fully occupied bus. 

Figure 4.1.1.1 shows the distribution by model year of school buses in California. These 

data are from the California Department of Motor Vehicles 1998 vehicle registration database. 

From this table, we see most buses in use were 1985 model year or newer. Table 4.1.1.1 gives

the distribution by bus make and use in 1998 in southern California (Los Angeles, Riverside, 

Orange and San Bernardino counties). Crown buses have not been sold since approximately 

1994, and International made chasses for Thomas. Based on informal surveys with bus 

operators, Thomas appeared to be the dominant seller in southern California. Except for a few 

older gasoline buses, almost all school buses in the 1998 fleet had diesel-powered engines. 

Selection of buses also depended on the difficulty in securing them. The major lease 

operators were unwilling to cooperate with us for this study, due to a perceived risk of adverse 

15 
Statewide Frequency Distribution of Buses by 

Model Year

0

200

400

600

800

1000

1200

195119551957195919611963196519671969197119731975197719791981198319851987198919911993199519971999

Model Year

N

u

m

ber of B

use

s

Figure 4.1.1.1 California statewide distribution of school buses by model year. 

Table 4.1.1.1 Distribution of buses in southern California by manufacturer. 

Make Number % Total

B

C

luebird 1103 14.9

arpenter 93 1.3

ollins 937 12.6

ROWN 1416 19.1

illig 576 7.8

oshen 5 0.1

nternational 1731 23.3

uperior 3 0

homas 1464 19.7

ard 26 0.4

yne 60 0.8

otal 7414 100

C

C

G

G

I

S

T

W

Wa

T

publicity based on the experience with a study by the Natural Resources Defense Council, which 

generated negative publicity concerning diesel school bus pollution (Solomon, 2001). These 

leasing companies also tended to have fleets composed primarily of relatively new buses. 

Because of these issues, we felt more appropriate sources for school buses were individual 

school districts. However, we found that many districts were also unwilling to participate for the 

same reason as the leasing companies (i.e., perceived risk of negative publicity). 

16 
Despite these problems, we were able to find two local districts willing to participate in

our study by providing buses from their in-use fleet of approximately 150 buses. Although the 

number of buses was limited, these districts had a wide variety of buses in terms of age and 

make, and had a small number of CNG buses as well. A final selection constraint was that the 

buses be large enough to carry all of the test equipment. Most of the larger buses were 10-14 

rows, which was sufficient. The smaller buses tended to be van-based with 3-4 rows. These 

were too small for our equipment and not representative since school districts tend to have only a 

small number of these for special purposes in any case. 

Table 4.1.1.2 lists the buses selected for testing. As noted earlier, the primary criterion 

for selecting buses for testing was that they be representative of the school bus fleet used in 

California. Two buses were selected to meet this criterion. The first, representative diesel 

school bus number one (RE1) was a 1998 Thomas Saf-T-Liner, while the second, representative 

diesel school bus number two (RE2) was a 1993 Thomas Saf-T-Liner. The three buses selected 

to be representative of high exhaust emissions were expected to have relatively high emissions 

based on their age and in the opinion of bus service personnel. These buses included high 

emitter diesel school bus number one (HE1), a 1985 Thomas Coach; high emitter diesel school 

bus number two (HE2), a 1985 Crown Supercoach, and high emitter diesel school bus number 

three (HE3), a 1975 Crown Supercoach. This last bus also satisfied ARB’s interest in testing a 

pre-1977 bus. Throughout this report, we refer to the two representative diesel buses (RE1 and 

RE2) and the three high emitter diesel buses (HE1, HE2 and HE3) as “conventional” diesel 

buses. We are using this term to refer to buses powered by typical diesel engines, which have 

not been modified in any way to reduce emissions beyond standard practices and as required by 

current regulations. We are not using the term “conventional” in the sense of meaning the

engine is in the front of the bus, although this designation is an industry practice. 

To test a diesel bus equipped with a particle trap catalyst, we selected a 1998 Thomas

Saf-T-Liner equipped with a Johnson Matthey Continuously Regenerating Technology (CRT®) 

particulate filter (trap-outfitted diesel school bus number one, TO1). The particle trap combined 

a platinum catalyst and a filter element to “trap” particulate matter, and operated as a passive 

emissions control system. Finally, in order to compare children’s exposure during bus commutes

for a different bus fuel type, we tested a 2002 Thomas Saf-T-Liner Compressed Natural Gas 

school bus (CNG). Photograph 1 shows two of the buses employed in the main study. 

Snap and idle opacity tests were performed on all buses. In this test the opacity of the 

exhaust was measured while the engine was brought from idle to its governed maximum rpm by 

immediately applying full throttle. The monitoring device recorded the maximum opacity. This

test was performed a minimum of three consecutive times and the results were within 5% for a 

valid test. These results are also shown in Table 4.1.1.2. As expected, the 1985 Crown 

Supercoach (HE2), which was identified by maintenance personnel as the “smokiest” in their 

fleet, clearly gave the highest value. It should be noted that it was difficult to conduct a snap and 

idle test for the CNG bus because the relatively high fraction of water in the exhaust compared to 

a diesel bus resulted in fogging of the optics in the test device, and inconsistent, but low, 

measurements resulted. 

17 
 Table 4.1.1.2 School buses selected for testing. 

Bus Number Bus Type Year Make Model Rows Engine MileageDisplacemen

854 HE1 1985 Thomas Coach 14 Cat 3208 293000 10.4 L. 851 HE2 1985 Crown Supercoach 15 Detroit Diesel 671 315000 6 L. 752 HE3 1975 Crown Supercoach 15 Cummins 290 316000 6 L. 986 RE1 1998 Thomas Saf-T-Liner 14 Cummins 250 HP 8.3 111000 8.3 L. 921 RE2 1993 Thomas Saf-T-Liner 13 Cat 3116 177000 6.6 L. 982 TO1 1998 Thomas Saf-T-Liner 14 Cummins 250 HP 8.3 78000 8.3 L. 

8 CNG 2002 Thomas Saf-T-Liner 14 John Deere 8.1 1000 8.1 L. 

1Snap and idle tests not possible due to too much water vapor. 

HE1 – HE3: High emitter conventional diesel school buses. RE1 – RE2: Representative conventional diesel school buses. TO1: Particle-Trap outfitted diesel school bus. CNG: Compressed natural gas school bus.

18 
(a) (b) 

Photograph 1. a) High emitter conventional diesel test bus parked at UCLA 

b) CNG Bus parked in front of a bus stop during an afternoon run 

4.1.1.1 Fuel Used in the Test Buses

The fuel used in all diesel buses we tested was Arco Emission Control Diesel (ECD-1).

This fuel, also sometimes called “green” diesel, has ultra-low sulfur content (<15ppm), low 

aromatics, and a high cetane number. Ultra-low sulfur fuel must be used for after-treatment

emissions control technologies (e.g. particle trap catalyst) to function properly. However, we 

used this “green” diesel on all the diesel buses we tested, with or without a particle trap catalyst. 

4.1.2 Field Sampling Procedures

In each bus tested, two identical sets of instruments used to measure a variety of gaseous

and PM species were mounted inside the bus cabin. One set was used to measure concentrations

in the rear of the bus cabin, while the second set was used to measure concentrations either at the 

front of the bus cabin, or just outside the bus. Measurements were taken while the bus traveled

along one of three designated school bus routes (described in Section 4.1.6). Each bus run lasted 

approximately one hour. A summary of the measurement methods used for this study is

presented in Table 4.1.2.1. The instrumentation included both real-time and integrated 

measurements. Although two sets of instruments were in each bus, the majority of the analyses 

in this report (including the comparison between bus types) were done using the data from a

single set of instruments, located at the rear of the bus cabin. 

4.1.2.1 Instrument Packaging and Power Supply

Figure 4.1.2.1 presents the plumbing diagram for the sampling equipment inside each 

bus. The instruments were mounted onto three plywood platform with elastic cords. In order to 

minimize the effects of vibration, foam pads were placed between the instruments and the 

plywood. Instruments that sampled from the front of the bus, or outside the bus, depending on 

the run, were mounted on one platform and instruments that sampled from the rear of the bus 

were placed on a second platform. The data logging portable computer (PC), gaseous analyzer

calibration

19 
Table 4.1.2.1 Measurement parameters and methods used in the main study. 

Code Species/Parameter Instrument Detection

Limit 

Flow Rate, 

L/min 

Amps 

110v

a PM2.5 Integrated Mass Custom/Harvard Impactor 5 μg/m3

** 20.0 * 

b PM2.5 Real-Time Mass TSI Dust Trak 1 μg/m3 1.7 0.1 

c PM10 Integrated Mass Custom/Harvard Impactor 5 μg/m3** 20.0 * 

d PM10 Real-Time Mass TSI Dust Trak 1 μg/m3 1.7 0.1 

e Fine Particle Counts Climet Spectro .3 OPC Single particle 5.0 0.1 

f Ultra-Fine Particle Counts CE-CERT SEMS Single particle 5.5 5 

g Elemental Carbon Magee Aethalometer 1 μg/m3 5.0 0.1 

i Gaseous HC (Total and 

Speciated) 

Gas Chromatographic 

Methods 

Varies Varies * 

j CO Langan electrochemical cell 

Dasibi 3003

2 ppm

1 ppm

Passive 

2 lpm

Internal 

Battery 

1.0 

k NO2

CE-CERT NO2/PAN Luminal 

GC 

TEI 42 NO/NO2/NOx

1 ppb 

1 ppb --- 

--- 

1.0 

2.0 

m Aldehydes & Ketones DNPH collection/HPLC 

analysis 1 ppb 0.8 * 

n Particle PAH EcoChem PAS 2000 3 ng/m3 4 0.25 

o Temp, RH (in-bus) Rotronics MP101A 2.0°C, 5% NA 0.1 

p Location and Speed Garmin GPS Map76 2-5 m NA 0.1 

q SF6 AeroVironment CTA 1000 0.010 ppb 0.1 2 

r Traffic Documentation Sony CXC-390 Video Camera NA NA 

Internal 

Battery 

* Supplied by common vacuum pump (10 amps) ** 1 hour sampling time 

20 
20 L/min 

PM2.5 Filter 

20 L/min 

20 L/min 

Sample Inlets 

40ml/min 40ml/min 

PM10 Filter 

PM2.5 DustTrak

PM10 DustTrak

OPC 

Tenax Tube 

DNPH

Particle PAH 

NO2/PAN GC

CO

800 ml/min

20 L/min Two 3⁄4 HP 

Vacuum Pumps 

P

P

PM

PM

Tedlar 

T

P

N

800 ml/min

Aethalometer

A

PID VOC 

Pump,

100ml/min

System at front of bus for measuring outside air orair from front of passenger compartment

System at rear of bus for measuring air at 

compartment air 

PumpPump

Sampling 

Manifold 

Cycling From

Front of Bus, 

to Rear of Bus, 

to outside Air 

SF6 Analyzer

NO/NO2/NOx

CO

System at middle of bus with 

switching, 3 location, manifold

Tedlar Bag 

Figure 4.1.2.1 Plumbing schematic for school bus sampling instrumentation.

21 
system and the instruments that were part of the “switching system” that monitored from the 

front, back and outside were mounted onto a third platform in the middle of the bus cabin. In 

this way the platforms were easily installed in the buses. The platforms were installed over the 

seat backs of the buses and tied down with ratcheting straps. All three platforms were mounted 

along the right side of the bus (facing forwards). The total flow rate for all samplers and the 

bypass for the isokinetic probe on the buses was about 150 L/min. 

A trailer mounted North Star 13000 PPG propane-powered generator with an oxidation 

catalyst was used to supply AC power for the instruments during the first week of the main

study. A hitch was installed at the back of the bus for the generator trailer. The decision was

made in the main study to convert to the use of on-board batteries and invertors for all 

subsequent buses (Runs 4-36) in order to eliminate the noise, complexities and potential 

pollution from towing a generator behind the bus. 

As discussed in more detail in Section 5.3, because of various difficulties and 

confounders encountered during the runs with the propane generator, the data for bus HE1 were

not included in the majority of comparisons between buses. 

The onboard inverter and battery system was a 7kVA (kilovolt Amp) Best Power 

uninterruptible power supply (UPS), but used only for its “inverter” capabilities on this program. 

Thirty-six deep cycle marine batteries were used for inverter input. Thirty-two of the batteries

powered the UPS directly while the remaining four batteries were used to power four DCpowered vacuum pumps. The 200 Kg UPS was placed on the floor of the bus next to a side

emergency exit. (One row of seats needed to be removed from most buses to make room for the 

UPS.) Nine wooden crates with four batteries each were placed in nine seats along the left side 

of the bus. Conventional “ground power” was used to power the instrumentation onboard the 

buses when the buses were “parked” at UCLA and CE-CERT. 

4.1.2.2 Data Collection

A schematic of the data logging design is presented in Figure 4.1.2.2. Instruments that 

internally logged data were downloaded via a PC. The clocks for all of these instruments were

synchronized at the beginning of each run using the GPS time as a reference. All of the other 

instruments had either analog or digital inputs that were connected to a PC that collected data 

using LabVIEW® software. The data logging and control PC polled and logged data from all 

instruments once per second (except for the electrochemical cell CO analyzers and portable PID 

organic compound analyzers which internally logged data). All data were downloaded and 

backed up from the data logging and control PC, the electrochemical cell CO and organic 

compound analyzers on a daily basis. For data analysis, we obtained the ten-second median 

from our one-second data for all real-time instruments. This level of resolution was justified as 

we observed rapid instrument response to changes in concentrations inside the bus on the order 

of seconds. 

4.1.2.2.1 Checkout and Installation of Instruments

Concurrent with the procurement and preparation of the school buses, the measurement

instruments were assembled, configured, and tested at the CE-CERT laboratory. This assembly 

task included all necessary calibration and data logging equipment. The instruments were also 

22 
Figure 4.1.2.2 Data logging design inside school bus platform.

23 

Instruments at Front of Bus 

ο PM2.5 Filter Sampler 

ο PM10 Filter Sampler 

ο DNPH, Carbonyl Sampler 

ο Tenax, BTX Sampler 

ο Tedlar Bag Sampler 

ο PM2.5DustTrak 

ο PM10 DustTrak 

ο OPC 

ο Range Finder 

ο GPS 

ο Aethalometer 

ο Particle PAH 

ο Electrochemical CO 

ο PID VOC 

ο UCR NO2/PAN GC 

ο Video Camera

Computer Controlled On/Off Switch for Vacuum 

Pump 

Manually Controlled On/Off Switch for Sample Pump

RS232 Signals Connected via 8-Port RS232 to 

Network Converter to DAS 

Analog Signal Output to DAS 

Internally Log Data 

Instruments at Middle of Bus

ο TEI NO/NO2/NOx

ο Dasibi CO 

ο AeroVironment CTA-1000 SF6

ο Switching Manifold 

ο Data Acquisition System

(DAS) and Control PC 

ο Exhaust SF6 injection system

ο CSI 1700 Calibrator

Digital Control from DAS; Pulse Output to DAS 

Analog Signals Output to DAS 

Cycle Controlled and Monitored by DAS PC 

Central Control and Data Logging

Mass Flow Control of 5000 ppm Cylinder 

Mass Flow Control Calibrator with GPT 

Pulse Output to DAS

Instruments at Rear of Bus 

ο PM2.5 Filter Sampler 

ο PM10 Filter Sampler 

ο DNPH, Carbonyl Sampler 

ο Tenax, BTX Sampler 

ο Tedlar Bag Sampler 

ο PM2.5 DustTrak 

ο PM10 DustTrak 

ο OPC 

ο Aethalometer 

ο SMPS

ο Particle PAH 

ο Electrochemical CO 

ο PID VOC 

ο UCR NO2/PAN GC

Computer Controlled On/Off Switch for Vacuum 

Pump 

Manually Controlled On/Off Switch for Sample Pump

RS232 Signals Connected via 4-Port RS232 to 

Network Converter to DAS or Directly to DAS PC 

Analog Signal Output to DAS 

Digital Control from DAS; Pulse Output to DAS 
tested for proper operation and proper interfacing with their respective calibration systems and 

the data-logging and control computer. 

Several shakedown runs of the measurement systems in the school bus were performed at 

the CE-CERT facility and in the Riverside area in order to assure proper operation of the 

instruments while in motion. During these shakedown runs tracer test was performed in the bus 

in order to measure the ventilation rates within the bus under different operating conditions. 

4.1.2.2.2 Sample Probe Positions 

The following descriptions apply to all of the buses employed in the main study. 

Sampling inside the bus primarily took place at two fixed locations at the front and rear of the 

cabin. The equipment at the rear of the cabin always measured concentrations at the rear of the

cabin. However the equipment at the front of the cabin measured concentration at the front of 

the cabin during 20 runs, and just outside the cabin (with the addition of longer sampling lines 

with the inlets placed outside the bus as described below) during 15 runs. The sampling probes 

were positioned to sample at the height of the breathing zone of a child seated in the bus. Large

diameter inlets were used for the interior sampling probes in order to minimize velocity changes 

and particle losses. Sampling outside the bus while in motion was performed using a specially 

fabricated isokinetic probe. Separate sampling systems were used for the gaseous and particulate 

matter (PM) sampling trains. 

The instruments were placed on platforms, consisting of three plywood sheets, which 

were strapped to the tops of the bus seats along the right side of each bus (see Photograph 2). 

The tops of the seats were approximately one meter above the bus floor. The sample inlets for 

the gaseous and particulate samplers were located on top of the instrument platforms, such that 

 (a) (b)

Photograph 2. a) Array of instruments inside a test bus 

b) Preparing the instruments before an afternoon run 

24 
the inlet heights were approximately 1.3 and 1.1 meters, respectively, above the bus floor. When 

sampling the air outside the bus, the instruments at the front of the bus were used, and the 

sampling probes were located at the base of the right side exterior driver’s mirror, approximately 

2.5 meters above the ground. 

Gaseous samples were drawn from outside, and from the front and rear of the bus, using a 

variety of inlets, depending on the instrument. The PID organic vapor analyzers and 

electrochemical cell CO analyzers located on top of the sampling platforms at the front and rear 

of the bus sampled directly from their inlets located on each instrument. The Tedlar bag 

samplers at the front and rear of the bus had their own one meter long sample lines (0.08 cm 

inner diameter (ID), PFA Teflon) placed at a height of about 1.3 meters above the floor of the

bus. The Tenax and DNPH cartridge samplers drew their air from three common sample lines 

(outside, and inside front and rear) through Teflon particulate matter filters and then through 0.65 

cm inner diameter PFA tubing to a manifold where air was directed to each of the instruments 

through a series of T-fittings. The NO2 GC’s, GFC CO, TEI NO/NO2/NOx and SF6 analyzers

also drew their air through those sample lines. However for these instruments, the manifold had 

sample lines connected to three-way solenoids, which rotated the sample path from outside the 

bus, to the front of the bus, then to the rear of the bus. The sample was drawn from each location 

for four minutes. This cycle repeated every 12 minutes. This was done to accommodate the 

sampling requirements for the SF6 tracer gas analyzer. All sample inlets for those instruments 

attached to the switching manifold were located approximately 1.3 meters above the floor of the 

bus (front and rear), or 2 meters above the ground (outside). 

PM sampling equipment was located at the front and rear of the bus. The equipment 

located at the rear of the bus was used to sample air from inside the bus near the rear only. 

Equipment located near the front of the bus alternated (depending on sampling requirements for 

specific tests) between sampling PM from inside the bus near the front and from outside the bus.

The inlets for the PM sampling equipment located at the front of the bus all drew their samples 

from a common manifold. 

For sampling outside the bus, a specially designed isokinetic probe was used (Figure 

4.1.2.3). The air entered a 1 cm ID copper tube approximately one meter long. The inlet

protruded out the front right window of the bus about 0.2 meters (see Photograph 3). The inlet 

was filed to a knife-edge and pointed directly forward to sample into the wind with minimum 

particle loss as the bus traveled down the road. The air flow rate was set to provide isokinetic 

sampling at a nominal bus speed of 20 m/s (40 mph). After the air passed through the inlet tube, 

it entered a 13 cm long section of tubing, 4 cm in diameter, and then passed into a 45 cm long “tsection” where additional bypass air, drawn to maintain isokinetic conditions, was removed. 

Sample air exited the “t-section” through a 23 cm long section of 4 cm diameter tubing into a 13 

cm long by 10 cm diameter “stilling chamber” which had seven outlet ports. Two of the outlet 

ports were approximately 30 cm long, 1 cm ID copper tubing which ran from the stilling

chamber to the Harvard Impactors. One of the outlet ports had 0.6 cm ID “nonstatic” plastic 

tubing (approximately 1 meter long) running to the front Aethalometer. The four remaining 

outlet ports had 0.4 cm ID “nonstatic” plastic tubing (each approximately one meter long) 

running form the stilling chamber to the DustTrak, OPC and PAH instruments. A tap on the side 

of the stilling chamber ran to a transducer used to monitor the pressure in the stilling chamber. 

25 
When the front system was used to sample air from inside the bus, the copper tube was 

removed from the system, allowing air to enter directly into the 4 cm diameter section and the

bypass flow was turned off. 

To 

Vacuum 

Sensors 

To 

Particle 

Samplers 

and 

Analyzers

3 cm I.D. copper tube 

used as inlet for “frontinside” sampling

~ 1 meter long, 1 cm I.D. 

copper tube used as inlet 

for “outside sampling” 

Bypass 

Flow 

Inlet (outside sampling) Cylindrical Filter

Ins

Table 5 3

Outside 

the cabin

Figure 4.1.2.3 Sampling system (including the isokinetic inlet) used at the front of the bus to 

sample inside and outside aerosol. 

Photograph 3. Inlet of the sampling line for outside measurements of gaseous species. 

The rear PM instruments all drew air from a common location next to the rear gaseous

sample inlets. No additional lines or inlets were used for the Harvard Impactor filer samplers. A 

0.5 meter long, 0.6 cm ID nonstatic plastic tube ran from the common sampling location to the 

Aethalometer. Lengths of 0.4 cm ID nonstatic tubing between 0.5 and 1 m long ran from the 

common sampling location to the DustTrak, OPC, PAH and SMPS instruments. Table 4.1.2.2 

summarizes the total length of the sampling lines used with these instruments. For the majority 

of our analyses, we focused on the rear instruments. PM2.5, black carbon, fine particle counts 

26 
and PAH (the particulate species we analyzed for this report), had sampling lines in the rear of 0, 

0.5, 0.7 and 1.2 meters, respectively. See section 5.1.4 for a discussion of potential sampling line 

losses for particulate species measured in this study. 

In the main study, bus stop measurements were conducted using an equipped bus parked 

at the bus stop. The sampling inlets for these measurements were located about 1.5 meters above 

the ground. 

Table 4.1.2.2 Length of sampling lines 

Sampling line length (meters) 

Instrument Rear Front/inside Front/outside

2

Harvard Impactor - PM10 01 0 3

Harvard Impactor - PM2.5 0 0 3 

DustTrak - PM10 1.6 0.3 3 

DustTrak - PM2.5 1.5 0.2 3 

OPC 0.7 1 3.7

Aethelometer 0.5 1 1

PAH Analyzer 1.2 1 3.7 1

Harvard Impactors were used with an open face inlet (no sampling line or adapter) for several experiments. 

4.1.2.3 Instrumentation

4.1.2.3.1 Integrated PM10 and PM2.5 Mass Concentration 

Filter samples were collected using custom sampling systems designed for portable use. 

The inlets were of the Harvard design (Turner et al., 2000), which have been shown to have 

effective cuts at 2.5 and 10 μm while sampling at 20 L/min. The flow rates were controlled by a 

needle valve and measured with a rotameter and calibrated against a volumetric flow rate sensor.

The samples were collected on 37 mm Gelman Teflon filters with a 2.0 μm pore size. A Cahn 

Model 34 microbalance at the CE-CERT laboratory was used to determine the weight of the 

filters to within ±2 μg before and after sampling. All filters were equilibrated at 23°C and 40% 

RH for at least 24 hours prior to weighing. Filters were weighed a minimum of three times

before and after sample collection. If all three weighings were not constant to within 3 μg, the 

filters were reweighed until they were. 

4.1.2.3.2 Real-Time PM10 and PM2.5 Mass Concentration

Real-time PM10 and PM2.5 measurements were made using Thermo Systems Inc. Model

8520 DustTrak Aerosol Monitors. Impactors were used to perform the necessary size cuts. The 

PM concentration that made it past the impactor was then determined by measuring the intensity

of the 90° scattering of light from a laser diode. The instrument sample flow rate was 1.7 L/min. 

The averaging time was adjustable from 1 to 60 seconds, and an averaging time of one-second 

was used. The instruments were calibrated at the factory with Arizona road dust (NIST SRM 

8632). Collocated sampling was performed each week to insure comparability. 

Throughout this report, all PM2.5 data are from the Harvard Impactor integrated samples 

unless stated otherwise (i.e., as data from the DustTrak instruments). 

27 
4.1.2.3.3 Real-Time Particle Counts

4.1.2.3.3.1 Optical Particle Counter

Climet Spectro .3 Optical Particle Counters were used for particle count concentration 

measurements in sixteen size bins from 0.3 to 10 μm. Only the size range between 0.3 – 0.5 μm 

was used for data analysis in this report. This size range was selected because the highest

number concentrations are found in the smaller size bins. Thus, this size range represents the 

majority of fine particle counts, while simplifying the analysis to just two size bins. Sample air 

was drawn through a laser beam inside the analyzer where particulate matter passes through the 

beam one particle at a time. As a particle passes through the beam it scatters light roughly in 

proportion to particle size. Optical detectors detect the scattered light and send an electrical 

signal proportional to the scattered light to the digital signal processor (DSP). The DSP performs

a pulse height analysis of the signal to determine the size of the particle. The DSP then sends the 

particle size results to one of sixteen counters (“size bins”) where the total number of particles in 

the size bins were accumulated. The instruments flow rates were set at 1.0 L/min. The 

instruments counting periods were set to fifteen seconds with a ten second “wait” period prior to 

starting a new counting period. 

4.1.2.3.3.2 SEMS

A CE-CERT Scanning Electrical Mobility Spectrometer (SEMS) was used to measure 

particles in the range of 0.03 μm to 0.8 μm electrical mobility diameter. The instrument 

consisted of three major components: a Thermo Systems Inc. (TSI) model 3077 85Kr neutralizer

which generates a known charge distribution on the aerosol; a TSI model 3081 differential

mobility analyzer long column which selects for particle sizes based on the voltage applied and

the particles’ electrical mobility; and a TSI model 3760A condensation particle counter used to

detect particles. The analyzer’s four gas flows were calibrated at CE-CERT using a primary 

flow calibrator and the particle size was calibrated using aerosolized polystyrene latex spheres. 

The analyzer operated in a ten-minute cycle mode. It spent the first 75 seconds of each cycle 

scanning the 0.03 to 0.8 μm size range in seventy-five, one-second-long increments. It then 

spent the next 525 seconds fixed (“fixed period”) on a single particle size that was expected to be 

the best indicator of the presence of diesel exhaust particulate matter. For all runs except the last 

run, the fixed-period particle diameter was 180 nm. For the last run, the fixed-period particle 

size was 50 nm. The instrument output particle count data were output and processed at a once 

per second rate. 

4.1.2.3.4 Real-Time Black Carbon

The black carbon concentrations were measured using Magee Scientific Aethalometers. 

These instruments drew sample air through a 0.5 cm2

 spot on a quartz fiber filter tape. Infrared

light at 880 nm was transmitted through the quartz tape and detected on the back side of the tape

using photodetectors. (One detector sensed the light transmitted through the spot where the air 

was drawn through and the second detected light transmitted though an unused section of tape in 

order to correct for changes in the light source intensity and changes in the tape characteristics.)

Decreases in the amount of light transmitted through the spot on the quartz tape were 

proportional to the amount of elemental carbon and “heavy” organic molecules collected. The 

instrument’s response to the change in light transmittance was reported as “black carbon” (BC). 

The instrument’s sample flow rate was maintained using mass flow controllers. The

concentration of BC in units of mass of BC per volume of air (e.g. “μg/m3

”) was determined by 

28 
the instrument from the flow rate and change in light transmittance data. When the light

transmittance through the collection spot on the quartz filter had decreased by seventy-five

percent, the quartz tape automatically advanced to a fresh section of filter. Each time the filter 

tape automatically advanced, the instrument recalibrated for approximately one minute prior to 

restarting sampling. 

4.1.2.3.5 Real-Time Particle Phase PAH

EcoChem Model PAS 2000 analyzers were used to measure the concentrations of 

particle-bound polycyclic aromatic hydrocarbons (PAH). The instrument utilized the principle of 

photoionization of total particle-bound PAH by means of an ultraviolet lamp. The wavelength of

the light was selected such that only the PAH absorbed on aerosols were ionized, while gas 

molecules and non-carbon aerosols remained neutral. The aerosol particles that had PAH 

molecules adsorbed on the surface emitted electrons, which were subsequently removed when an 

electric field was applied. Remaining positively charged particles were collected on a filter inside

an electrometer where the charge was measured. The resulting electric current established a signal 

which was proportional to the concentration of total particle-bound PAH. The lower threshold of

this method was about 3 ng/m3

 total particle-bound PAH. 

The PAH instrument’s full scale was set to read a maximum of 500 ng/m3

 in the front, 

and 1000 ng/m3

 in the rear, during the first 17 runs of this study. These runs included buses 

HE1, HE2, HE2, RE1 and RE2 (all diesel buses). Starting with Run 18, and on all subsequent 

runs (including TO1 and CNG), the scale was increased to read a maximum of 2000 ng/m3

 for 

both front and rear instruments. The implications of this change for our inter-bus comparisons of 

PAH concentrations are discussed in Section 5.3.3. 

The location of the Aethalometer, particle count analyzer, PAH analyzer and the Harvard 

Impactors in a test bus with associated power cables and sampling lines is shown in Photograph 

4. 

(a) (b)

Photograph 4. a) Data and power cables and sampling lines 

b) Aethalometer, PC analyzer, PAH analyzer, and Harvard Impactors 

29 
4.1.2.3.6 Gaseous Hydrocarbons

A multifaceted approach using both rapid response “survey” real-time monitors and 

integrated sample collection followed by subsequent laboratory analysis was used. This allowed 

for full characterization of the gaseous hydrocarbons by “time-consuming” (but highly sensitive) 

laboratory chromatographic methods and the rapid response of a real time analyzer that could 

characterize short episodes of high concentrations. 

4.1.2.3.6.1 Tenax Cartridges

Samples for aromatic hydrocarbons (benzene, toluene, and xylenes) were collected on a 

Tenax cartridge and analyzed by GC analysis, per the U.S. EPA TO-1 Method (U.S. EPA, 1988). 

The cartridges contained 0.1 gm of Tenax. The maximum target sample volume for these 

cartridges had been determined to be at least 3.0 liters before benzene breakthrough would occur; 

with the other aromatics having a longer retention time. The sample flow rate was set to 40 

cc/min in order to stay well below the breakthrough volume for the study’s projected 60 minute 

sample periods. A Hewlett Packard HP5890 II gas chromatograph with a thermal desorber, 

capillary column and flame ionization detector (FID) was used for the analysis 

4.1.2.3.6.2 Tedlar Bags 

Samples for 1,3-butadiene were collected in 8L Tedlar bags at a nominal sample flow 

rate of 100 cc/min. A potassium iodide (KI) trap was used to remove the ambient O3 before the 

sample entered the bag. The samples were hand-carried to the CE-CERT analytical laboratory

for GC analysis after the end of sampling. An aliquot from the bags were drawn through a

sample loop of known volume on the GC. A Hewlett Packard HP5890 II gas chromatograph 

with a fixed volume sample loop, capillary column and flame ionization detector (FID) was used 

for the analysis. 

4.1.2.3.6.3 Portable PID 

The total amount of aromatic and other highly conjugated aromatics were continuously 

monitored with a ppbRAE Model PGM-7240 portable photo ionization detector. Sample air was 

drawn through the instrument’s reaction chamber where it was continuously irradiated with high 

energy ultraviolet light. Compounds present that had a lower ionization potential than that of the

irradiation energy (10.6 electron volts) were ionized. The ions formed were collected in an 

electrical field, producing an ion current that was proportional to total compound concentration. 

4.1.2.3.7 Gaseous Aldehydes and Ketones

The measurement technique used was a variant of U.S. EPA Method TO-11 (U.S. EPA, 

1988) for carbonyls in which 2,4-dinitrophenylhydrazine (DNPH) was impregnated on silica 

Sep-Pak cartridges. This procedure used C18 Sep-Pak cartridges (Waters/Millipore Corp., 

Milford, MA), which had been impregnated with acidified DNPH reagent for ambient sampling.

The amounts of both the hydrazine and acid were optimized to achieve efficient collection of the

carbonyl compounds and protection from ozone destruction of the captured carbonyl derivative. 

When ambient air was drawn through the cartridge at 1 L/min, carbonyls in the air sample were

captured by reacting with DNPH to form hydrazones, which were extracted and then separated 

and quantified using HPLC (Fung and Grosjean, 1981). It had been shown that the silica 

cartridge when used alone had a significant negative ozone artifact, and that this method

measured carbonyls comparably to the silica method with an ozone removal device. 

30 
The method analyzed samples for nine individual species: formaldehyde, acetaldehyde, 

acetone, propanal, crotonaldehyde, methylethylketone, butyraldehyde, benzaldehyde, and mtolualdehyde, plus C5, C6 and >C6 aliphatic carbonyls. Aliphatic carbonyls (C5, C6, and >C6) 

were usually measurable but lower in concentrations compared with the C1-C4 carbonyls. Except

for the straight chain aldehydes (e.g. pentanal, hexanal, etc.), resolution of the other isomers of 

C5 and higher carbonyls was incomplete, and thus inaccurate. The latter were more

appropriately reported as a group by carbon number. Pure DNPH derivatives of the aldehydes 

and ketones were used to prepare calibration standard for the high performance liquid 

chromatography (HPLC). 

Field blank variability established the LQL (lower quantifiable limit), which for C1-C7

carbonyls was typically 0.5 ppb or lower (at 3 times the standard deviation of the blank 

variability).

Accuracy of this method was approximately +15% for formaldehyde based on 

comparison studies with long path spectroscopic techniques in an ambient air setting (Fung and

Wright, 1990; Lawson et al., 1990). Since the basic chemistry of the DNPH method was the 

same for all carbonyls, the accuracy for higher carbonyls was expected to be in the same range. 

4.1.2.3.8 Real-Time Carbon Monoxide

The carbon monoxide (CO) concentrations were measured using Langan electrochemical

sensors. These instruments were designed to operate in a passive environment and have no 

sample flow. The instrument had a diffusion membrane that CO could pass through. The

membrane was exposed to ambient air on one side and contained a solution that reacted with CO

on the other. Ambient CO that diffused through the membrane reacted with the solution, 

creating an electric current that was proportional to the ambient CO concentration. The

instrument was calibrated by sampling known concentrations of CO prepared in air. 

A third CO analyzer was added for the main study, a Dasibi Model 3003. This analyzer 

measured CO using an infrared detector, and is an U.S. EPA Reference Method for CO. The 

analyzer was installed near the center of the bus and connected to the switching manifold 

described in Section 4.3.11. 

The CO and VOC monitors along with associated cables, standard gas tanks, rotometers 

and sampling lines are shown in Photograph 5. 

4.1.2.3.9 Real-Time Nitrogen Dioxide

The NO2 concentrations were measured using two gas chromatographs (GCs) to measure 

NO2 and peroxyacyl nitrates (PAN). Ambient air was brought into the analyzer and drawn 

through a sample loop. Once per cycle (one minute cycle time) a valve switched and the sample 

was pushed into a capillary column using air carrier gas. NO2 and PAN were separated from 

each other in this column. The separated constituents eluted from the column onto a fabric wick 

wetted with luminol solution. NO2 and PAN react with luminol, emitting photons of light 

proportional to their concentration. The central portion of the wick was viewed by a 

photomultiplier tube (PMT). Photons of light from the reaction with luminol entered the PMT 

31 
(a) (b)

Photograph 5. (a) Power and data cables, sampling lines, CO monitors, and VOCs analyzers; 

(b) Cables, standard gas tanks, rotometers and sampling lines

where the signal was multiplied through a series of electrified plates that successively multiplied 

and converted the signal from “light energy” to “electrical energy.” This amplified electrical

signal was output to the computer controller and data logger. Because NO2 and PAN eluted 

from the instrument at different times, the data logging system could separately process and store 

the data from the PMT for these two compounds. These instruments did not detect HONO. 

A third analyzer was added to the main study to measure oxides of nitrogen. A Thermo

Environmental Instruments (TEI) Model 42 NO/NO2/NOx analyzer was installed near the center 

of the bus. It sampled from of the same switching manifold described in Section 4.1.2.2.2. This 

analyzer was added to the program for several reasons: continuous sampling coverage from three 

locations, for quality control, and for ease of calibration. The preferred method for calibrating 

the NO2/PAN GC’s for NO2 was by gas phase titration of nitric oxide. In order to perform this

type of calibration, it was necessary to have an instrument that measured NO and NOx. Thus, the

data from this instrument was used to calibrate the NO2/PAN GC’s and not for the analyses

included in the remainder of this report. 

The TEI Model 42 measures oxides of nitrogen by the chemiluminescent reaction of NO 

with ozone. The analyzer switches between two sample paths every ten seconds. The first 

sample path directs the sample directly into a reaction chamber where it was mixed with high 

concentrations of ozone (O3). The NO and O3 reaction generated photons of light proportional to 

the NO concentration that are detected and processed by the analyzer. The second sample path 

directed the sample through a converter that converted other oxides of nitrogen to NO. The 

sample was then directed to the reaction chamber. The results from the second sample path were 

reported as NOx. NO2 was calculated as the difference between the NOx and NO responses. 

Since there may be additional non-NO oxides of nitrogen present (other than NO2), this method 

can “over-report” NO2 concentrations. However, the TEI 42 is an U.S. EPA Reference Method 

for NO2. 

32 
4.1.2.3.10 SF6 Tracer Gas

The sulfur hexafluoride (SF6) concentrations were measured with an AeroVironment

Model CTA 1000 analyzer. The primary task of this analyzer was to determine if a significant 

amount of the bus exhaust was entering the passenger compartment of the bus. For this task, SF6

was injected into the bus exhaust systems as described in Section 4.1.4. A second use of the

instrument was to determine the ventilation (or air exchange) rates in the passenger compartment 

as described in Section 4.1.3. This Aerovironment instrument uses electron capture detection 

after water and oxygen are removed from the sampled air. The instrument was developed for 

operation on a moving platform and has a sensitivity of approximately 0.010 ppb with a response 

time of about three seconds. In order to account for analyzer baseline drift, it was set up to

frequently sample reference SF6-free air. 

4.1.2.3.11 Range Finder

In order to measure the distance from a vehicle being followed, a Laser Optronix DME 

200 laser range finder was acquired. This device is palm-sized and operates with a pulsed 904 

nm laser. If the reflectivity of the target is good, its range is 1-300 m, with an accuracy and 

resolution of about 1.0 m. The instrument outputs data once per second to the data logger. 

However, due to problems with trying to use the instrument on a “moving platform,” as well as 

problems trying to measure distances to vehicles that were in the field of view for often 

considerably less than one second, no viable data were obtained from this instrument. Instead 

distances to vehicles was estimated when necessary from the video tape. 

4.1.2.3.12 Bus Location

The bus location was determined with a Garmin GPS MAP76 global positioning system 

with a Wide Area Augmentation System (WAAS) corrections system. The system provides

position accuracy of about 2-3 meters. The GPS has a 12 parallel channel receiver to 

continuously track and use data from up to twelve satellites. The WAAS system is a broadcasted 

“signal integrity” signal that is determined by fixed ground-based reference stations. The GPS 

uses the WAAS correction information to increase the accuracy of the positioning information.

In addition to horizontal position (e.g. latitude and longitude or UTM coordinates), the corrected

GPS system also provides elevation and velocity data. These data were displayed on a liquid 

crystal display on the GPS and were output digitally (RS232) for logging along with the air 

quality data on the data logger. 

The GPS unit was used as a time reference during this study. The clocks on all other 

devices were set to the GPS time on a daily basis. 

4.1.2.3.13 Meteorological Data

Temperature and relative humidity were measured inside the bus using a Rotronics 

Model MP101A sensor. The sensor determined temperature using a 100 ohm platinum 

resistance temperature device (RTD) and relative humidity using a Hygrometer C94 capacitive 

humidity sensor. The instrument included internal signal conditioning to process and output two 

voltage signals that were proportional to the temperature and relative humidity, respectively. 

Hourly wind speed and direction data were obtained from the nearest AQMD fixed site 

monitoring stations for the dates of times of each bus run. These data provided an estimate of 

33 
the prevailing area-wide wind conditions during a bus run, but did not account for 

micrometeorology around the bus (e.g. street canyon effects) throughout the run. Therefore, 

tnterpretation of the data based on these measurements requires caution. In general, and as 

expected based on land/sea temperatures, there was little or no wind during the morning runs,

while the afternoon runs typically occurred during on-shore flow conditions with significant 

wind speeds. Therefore, only the afternoon wind data were useful as a potential explanatory 

variable for differences in pollutant concentrations between afternoon runs. For the primary 

urban route, hourly wind speed data from the two nearest AQMD stations (West Los Angeles 

(WLA) and downtown Los Angeles (CELA)) for 15:00–16:00 and 16:00-17:00 (the hours 

covering all runs on this route) were averaged and this value assigned to the run. The same 

procedure was used for the second urban route using the Hawthorne (HAWT) and Lynnwood 

(LYNN) AQMD stations. Wind direction measured at the AQMD stations was consistently from 

the West or Southwest for all afternoon runs. 

4.1.2.4 Use of Video Camera and Digital Camera

A Sony DXC-390 video camera was mounted at the front of the buses to record traffic 

conditions in the lane in which the bus was traveling, as well as the adjacent lanes during all 

measurement periods. This camera has an RS-232 Interface, a resolution of 800 TV lines, and a 

10-bit digital signal processing system. In addition, the camera has a variable speed electronic 

shutter, which provides the capability to capture clear images of high speed moving objects. The 

camera was set to a wide angle to view as much of the scene as possible. The camera included a

“time stamp” feature for adding date and time information to the video. The clock in the video 

camera was synchronized with the GPS master clock time prior to each run. 

4.1.2.5 Documentation of Bus Commute, Traffic Conditions and Events During Each Run

In addition to the video camera, a software program developed and tested by the UCLA 

field team was used to record traffic conditions, bus movement and other observations during 

each bus run. Detailed observations during each bus run, such as when the bus was moving, 

idling, or at a bus stop, identification of vehicles in front of the bus, and traffic conditions were 

recorded and time stamped in a data log using a laptop computer. The clock used for the timestamp was also synchronized with the master clock. Comparison of peak concentrations of 

pollutants and the observations recorded in the data log were used to identify conditions that led 

to the highest pollutant concentrations inside the bus during selected runs. All of the data 

collected during this study, will be submitted in electronic format to the ARB for further review

and analysis. 

4.1.3 Bus Ventilation Rate Measurements

We emphasize here that the experiments conducted with SF6 to measure ventilation rates 

in each bus at several speeds and with windows opened or closed (as explained below), were all 

conducted at CE-CERT prior to each bus being driven to the Westside. Thus, the use of SF6 for 

the ventilation rate measurement was completely separated in time and location from the SF6

tracer studies designed to determine the contribution of a bus’s own exhaust to within-cabin

concentrations. In short, the SF6 ventilation measurements did not affect the subsequent exhaust

tracer measurements in any way. 

34 
Exchange of the air inside bus cabins is controlled by several factors, including window 

position (i.e. closed, opened or some position in between) and penetration of outside air through 

leaks around windows, the main door, roof vents, emergency exits and other nonspecific 

locations, depending on bus speed and wind speed. Air exchange rates may also be significantly

affected by other characteristics of the bus including differences in construction specifications

for older buses versus newer buses. Some of these characteristics may increase as a bus ages

(e.g. leaks). For this study, air exchange rate or ventilation rate tests were performed with the 

windows open and the windows closed at bus speeds of 0, 20, and 40 mph. Air exchange rates 

inside the bus were measured by releasing a SF6 tracer gas inside the bus and monitoring the

concentration of the gas over time. 

High concentration (5,000 or 10,000 ppm) SF6 cylinder gas was used as the tracer. A 

plastic syringe was filled with 10-30 cc of the SF6 cylinder gas. The amount used was varied as 

a function of window position, bus speed and cylinder concentration, to obtain interior SF6

concentrations within the SF6 analyzer operating range in the available experiment time. SF6

was released from the syringe as a line source in the aisle of the cabin as the operator walked 

from the back to the front of the bus. The operator then walked back to the rear of the bus, using 

his body to help rapidly mix the SF6 inside the bus. 

The SF6 concentration was continuously monitored from the middle of the bus at the top 

edge of the seats facing the aisle. All 0 mph tests were performed in the CE-CERT parking lot 

with the bus facing north. All the 20 and 40 mph tests were performed on Riverside Drive 

between Riverside and Colton (except the CNG bus tests). Riverside Drive was a four lane road 

with a posted speed limit of 45 mph, with more than two miles between traffic stops and light 

traffic during the period we conducted the ventilation tests. Although there were variations from 

test to test, typically the test began with windows closed traveling north on Riverside Drive at 20 

mph, followed by traveling south on Riverside Drive at 40 mph. The windows remained closed 

for the duration of the test. A second test was performed in a similar manner, again traveling 

north on Riverside Drive at 20 mph, followed by traveling south on Riverside Drive at 40 mph, 

this time with the windows open for the duration of the test. Except for the tests on the CNG 

bus, ventilation tests were performed just prior to starting the rural/suburban run. Due to 

equipment problems, the ventilation tests at 20 and 40 mph for the CNG bus were performed at 

the end of the rural suburban run on Grand Avenue from Diamond Bar to Glendora and back. 

The speeds and window positions for these tests were similar to those used for previous tests on

Riverside Drive. The 0 mph tests were performed over periods ranging from ten minutes to over 

an hour. The 20 and 40 mph tests were performed over periods of two to ten minutes. 

For all tests, the amount of SF6 released resulted in concentrations beyond the 

instrument’s full scale. Typically, after a brief period beyond full scale (during which the SF6

was also mixing throughout the bus), the concentration of SF6 dropped to the monitor’s full 

scale. At this point the SF6 decay rate was measured and used to determine the air exchange rate

in the bus. 

To determine the SF6 exchange rate, we used the following equation, which assumed bus 

ventilation rates are a first order process: 

35 
(SF6)t = (SF6)0 * [1 – exp (-t/τ)] (4.1.3.1) 

where (SF6)0 is the SF6 concentration at time zero, (SF6)t is the SF6 concentration at time t, and τ

is the time constant. The SF6 removed from the bus (and the percentage of bus air exchanged) 

over time is 63%, 87%, 95%, 98%, and 99% for times equal to the first through fifth time

constants, respectively. Although there were many factors that controlled mixing and exchange

of air in the bus, inspection of the plots of SF6 versus time indicated a first order decay provided 

a reasonably accurate fit of the data. 

The results of the ventilation tests are presented in Table 4.1.3.1, which shows the time

constant, or the time required for 63% of the bus air to be exchanged. The time for essentially

complete exchange is three times (i.e. 95% exchange) to five times (i.e. 99% exchange) longer. 

The shorter the time for air to exchange, the higher the ventilation rate. The ambient wind speed 

for all stationary ventilation tests was approximately 1-3 m/sec. The ventilation rate when the 

bus was stationary with the windows closed varied from moderate for HE3 (under ten minutes

for 63% of the bus air to be exchanged) to no detectable ventilation during the fifteen to fortytwo minutes that the test was conducted (HE1, HE2, RE1, RE2, TO1, CNG). The ventilation 

rate was higher for windows open compared to windows closed (for the same bus speed). The

ventilation rate was also higher when the bus was driven at 40 mph compared to 20 mph. The

tests showed there were a significant number of “leaks” in all buses tested. As can be seen in 

Table 4.1.3.1 (excluding RE2, windows open at 0 mph), even with the windows closed, the 

ventilation rates for the buses traveling at 20 and 40 mph were higher than the ventilation rates 

for the same buses at 0 mph with the windows open. 

Significant differences in the ventilation rates between buses were observed. Because 

only a single set of ventilation tests was performed for each bus, it is not possible to determine if 

these differences were definitely due to differences between the buses, or if they were due to 

other factors, such as different wind conditions when each bus was tested. One indication of the 

possible major effect that wind speed (as opposed to bus speed) may have on ventilation rate 

comes from the windows open at 0 mph test performed on the last bus. The CNG bus had a

moderately low ventilation rate with windows open at 0 mph (seven minutes for 63% of the air 

to be exchanged), while the other buses had higher ventilation rates, (between two and four 

minutes for 63% of air to be exchanged) for 0 mph. We assume that when the bus windows are

open at 0 mph, any major differences observed are due to wind speed and not to unique 

characteristics of one bus compared to another. Hence, the most probable explanation for the 

low ventilation rate in the CNG bus with windows open at 0 mph was the calm wind conditions 

during the test. For each series of 0, 20 and 40 mph tests, there was a factor of two to a factor of 

four difference between the bus with the highest ventilation rate to the bus with the lowest 

ventilation rate. Because of the likely significant role that wind contributed to these differences 

(especially for the 0 and 20 mph tests), it is, in general, difficult to draw conclusions about 

possible bus-to-bus differences in ventilation rates. However, air exchange rates were typically 

much higher with open windows and much higher at higher speeds, regardless of window 

position. 

36 
WEEK # 1 2 3 4 5 

BUS HE1 HE2 HE3 RE1 RE2 

Response Time Response Time Response Time Response Time Response Time Res

(First Time

Constant) 

(First Time

Constant) 

(First Time

Constant) 

(First Time

Constant) 

(First Time

Constant) 

(F

C

TEST CONDITION (min:sec) (min:sec) (min:sec) (min:sec) (min:sec) (

Windows closed 0 mph > 15 min > 30 min 09:47 > 30 min > 30 min > 

Windows open 0 mph 02:39 02:38 03:16 03:57 00:40 

Windows closed 20 mph NA 01:20 01:52 01:56 03:31 Windows closed 40 mph NA 00:52 00:38 01:05 02:00 

Windows open 20 mph NA 00:31 00:58 00:48 00:47 Windows open 40 mph NA 00:16 00:29 00:17 00:37 

Number of windows 

opened for "window 

open" condition at 0 mph 

every other 

window opened 

7 cm

all windows and 

door opened 

Four windows 

on both sides (8 

total) down 4 

inches 

Three windows 

on both sides (6 

total) down 4 

inches 

3/4 of windows 

opened 4 inches 

6 of 

side 

on 

open

Number of windows 

opened for "window 

open" condition at 20

and 40 mph 

Four windows 

on left side 

down 4 inches 

Three windows 

on both sides (6 

total) down 4 

inches 

Three windows 

on both sides (6 

total) down 4 

inches 

3/4 of windows 

opened 4 inches 

6 of 

side 

on 

open

Test notes for "windows 

closed" condition at 0 

mph 

one front 

window and one 

rear window 

down 3 inches 

Test notes for "windows 

closed" condition at 20

and 40 mph 

Driver's window 

opened 1/2 way

Table 4.1.3.1 Ventilation time constants measured inside buses at various speeds and for open and closed windows (see 

37 
4.1.4 Characterization and Justification for the Selected School 

For this study we sought to identify a school with a diverse student population drawn 

from various parts of Los Angeles, and which offered a broad range of travel distance, roadway 

type, and traffic congestion scenarios associated with bus commutes. Due to resource constraints 

the present study was unable to test across a full range of possible commutes, meteorological 

conditions, bus manufacturers, model years, school districts, geographic locations in California, 

etc. Our results are “representative” only to the extent the commutes, buses, conditions, areas, 

etc. we studied are representative of southern California school districts. 

The school selected for both the pilot and main studies was the Brentwood Science 

Magnet School (BSMS), located at 740 Gretna Green Way, in West Los Angeles. The BSMS 

was in Local District D of the LAUSD. It was a K-5 facility with a total enrollment of 1,209 

students in the 1999-2000 school year. Because it was a magnet school, BSMS was racially and

ethnically diverse with a student population demographic of approximately 20% Asian, 20% 

African-American, 25% Hispanic, and 35% Caucasian. 

We felt the BSMS school provided high exposure scenarios and yet was located close to 

UCLA, allowing ready access and the opportunity to optimize time spent on acquiring data. 

Also, because BSMS was a magnet school, it provided a wide range of distance and congestion

scenarios for its bus commutes. 

Considerable initial research concerning the selected school, and its associated bus 

behavior, was conducted prior to the start of the pilot study, including a detailed analysis of 

routes, patterns, and travel times for all nineteen routes used by the BSMS. A relationship was 

established with the school through the school's principal and its science advisor, and also 

through the LAUSD Transportation Branch and its Director, Antonio Rodriguez. The school 

principal, Sharon Katz, provided approval for conducting the pilot study at BSMS, while the

LAUSD Transportation Branch provided the detailed route sheets for all the bus commutes to

and from this school. Additional support was provided by Dr. Angelo Bellomo, Director of the 

LAUSD Environmental Health and Safety Branch. 

We expected the bus commutes from the BSMS were capable of producing high 

exposure scenarios on the bus during the commute due to the long distances of the commutes and 

the areas of Los Angeles they traveled through. Figure 4.1.4.1 shows the starting locations of 

each of the bus routes bringing children to BSMS. Most of these routes involved both freeways 

and residential arterials, including those expected to have heavy traffic congestion. Children 

were bused to BSMS from over 200 schools throughout the city, with an average commute time 

and distance of 85 minutes and 23 miles, respectively. Typically, 85% of the children attending 

BSMS were transported to and from this school each day on nineteen diesel school buses. 

The school property was bounded by Gretna Green Way on the west, Montana Avenue

on the south, Bundy Avenue on the east and San Vicente Boulevard on the north. We

characterized non-freeway traffic densities for these four arterials as low, low-medium, medium, 

and medium-high, respectively, with the caveat that at the student drop-off and pick-up times 

38 
39 

$T

$T

$T

$T

$T

$T

$T

$T

$T

$T

$T

$T

$T

$T $T

$T

$T

$T

$T

r

BSMS

Los Angeles County

Route No. 3215 (U1)

Route No. 3235 (U2)

I-110

I-10

I-405

N

La county streets

Other Major Freeways

Interstate 110

Interstate 10

Interstate 405

Secondary Urban Route (No. 3235)

Primary Urban Route (No. 3215)

$T

$T BSMS Bus Route Starting Locations r Brentwood Science Magnet School

5 0 5 Kilometers

Figure 4.1.4.1 Brentwood Science Magnet School bus route starting locations. 

there was additional traffic congestion on Gretna Green Way from passenger vehicles of parents,

teachers and staff who were arriving or departing, as well as from the arrival or departure of 

school buses. Three of these streets were one lane each way, while San Vicente Blvd. was two 

lanes each way with a median strip of approximately 20 feet width. 

A key physical aspect of the school was a block-length sidewalk, approximately ten feet

wide, along the east side of Gretna Green Way. Typically, all nineteen buses bringing students 

to the school lined up along this sidewalk. Figure 4.1.4.2 shows the layout of the school and bus 

loading area. All school bus loading and unloading took place at this sidewalk. In addition,

there was head-in parking for teachers and staff (and perhaps parents) on the west side of Gretna

Green Way. 

We acquired information about loading/unloading activities in both the morning and 

early afternoon. Direct observation of BSMS during five days in December 2000 and September

and October 2001 was conducted and the following observations made. In the morning, buses 

arrived and parked on Gretna Green Way, along the sidewalk in front of the school, between 

7:40 and 8:00 (see Photograph 6). Each bus turned off its engine as soon as it parked, a key 

observation and in apparent compliance with LAUSD regulations. Children quickly unloaded 
NOT TO SCALE. ALL DISTANCES ARE APPROXIMATE. 

Bungalows

Classrooms

Office

Montana Ave.

B

u

n

d

y 

A 

v

N

San Vicente Blvd.

Playground

C

O

U

N

T

R

Y

C

L 

U

B

Parked

Buses

Parking

Parking

for staff

Main

Wind

Direction

ENTRANCE

ENTRANCE

 300 m.

e

Figure 4.1.4.2 Diagram of Brentwood Science Magnet School. 

from the buses onto the sidewalk in front of the school, where they waited until all children had

unloaded from that bus. The children then walked as a group into the school through two 

entrance ways. Buses arrived at different times, so often children were waiting on the sidewalk 

when another bus pulled up to the curb. In general, morning commutes were conducted with bus

windows up, in part due to cooler morning temperatures. 

40 
Photograph 6. School bus pulling up at BSMS, at the end of a morning run, with 

children in the unloading zone. 

In the afternoon, buses arrived and parked on Gretna Green Way, along the sidewalk in 

front of the school, between 13:00 and 14:30 (Photograph 7). Again, each bus turned off its 

engine as soon as it parked. In the afternoon, all buses had the windows pulled down about one 

quarter of the way. School ended at 14:45, and children quickly boarded a total of nineteen 

buses. Of these, four were “small” and fifteen were “full size.” Typically, children were on the 

sidewalk no more than about five minutes before they boarded, but on occasion groups of 

children were on the sidewalk considerably longer. At about 15:00, the bus engines were turned 

on simultaneously and generally all buses pulled away from the curb by about 15:05 (See 

Photograph 8). In general, no engines were turned on before all buses were boarded. The great 

majority of the buses turned right, or east, on San Vicente Boulevard. 

Photograph 7. Line of buses in front of BSMS before an afternoon run. 

41 
(a) (b)

Photograph 8. a) Departing from BSMS during an afternoon run, school bus ahead. 

b) Driving behind a school bus on an arterial street. 

During the loading period, parents driving passenger vehicles dropped off (AM) or 

picked up (PM) approximately two dozen students on Gretna Green Way. However, no children 

other than those taking the buses stood on the sidewalk in front of the school. A few children 

were picked up by parents who appeared to walk to the school. Observation of the departing 

buses in the afternoon revealed several buses often “caravanned” at least part of the way on San 

Vicente, Wilshire and the San Diego Freeway (Interstate 405), which can lead to high exposures 

in the “following” buses (Rodes et. al. 1998). 

4.1.5 Characterization and Justification for the Selected Bus Routes

Three different bus routes were used in this study. The first two were both in-use bus 

routes that traveled from highly urbanized areas of south-central Los Angeles to west Los 

Angeles. The third was a route that was fifty percent rural and fifty percent suburban, and 

traveled from rural Riverside to Diamond Bar in Los Angeles County. The principle criteria

used to select these routes were that they be representative of bus routes in Southern California 

and that they also be representative of the wide range of roadway types and traffic congestion 

scenarios encountered in the South Coast Air Basin. 

4.1.5.1 Urban Route One

During our investigation of BSMS bus commutes, the Director of the Transportation 

Branch at LAUSD provided the complete route sheets for all buses serving the BSMS. There 

were 19 different bus routes associated with the BSMS and these sheets provided the exact route 

followed, including the timing and duration of the stops, and the number of children picked up or 

dropped off at each stop. As can be seen in Figure 4.1.4.1 (triangles mark the starting locations 

of bus routes), students were bused from all parts of Los Angeles County to the BSMS, with bus

routes covering extensive portions of South Central and East Los Angeles, Hollywood, the San 

42 
Fernando Valley, and reaching as far south as Carson. Combined, these bus routes provided

comprehensive coverage of Los Angeles County west of the 710 Freeway. 

In an effort to choose a bus route for the main study that provided an appropriate range of 

roadway types and traffic conditions representative of Los Angeles County commutes in

particular, and dense urban traffic congestion in general, we investigated a selected number of 

BSMS bus routes. Four routes, including the route used during the pilot study, were evaluated. 

Since the pilot study route originated in south central Los Angeles, we selected one comparison 

route originating in north Los Angeles County (i.e. San Fernando Valley, Route No. 3222) and 

one originating in the south (i.e. Carson/Long Beach area, Route No. 3235). This was done to 

assess potential differences in route characteristics due to geographic location. The third 

comparison route selected (Route No. 3202) was one originating in an area closer to the pilot 

study route, to account for potential differences in route characteristics over similar geographic 

areas. Each of these three routes incorporated both freeway and surface streets, and included a

variety of traffic congestion scenarios. Surface streets along each route were categorized into

residential, minor arterial, and major arterials using Caltrans annual average daily traffic count 

data from 1998 and 1999. 

Table 4.1.5.1 shows the comparison of these routes, and includes the percentage (in 

distance) for each of the roadway types listed above, as well as the percentage of freeway versus 

surface streets. As can be seen from this table, Route No. 3215, the route used in the pilot study,

had a similar mix of roadway types and traffic conditions compared with the other three routes. 

Table 4.1.5.1 Comparison of the distribution of roadway types on selected BSMS bus routes. 

Route No. 3215 

(Pilot Study Route) Route No. 3202 Route No. 3222 Route No. 3235

< 5,000 10% 8% 7% 9%

5,000 - 25,000 18% 20% 42% 27%

25,001 - 100,000 34% 38% 28% 44%

> 100,000 38% 33% 22% 20%

Freeway 38% 35% 22% 26%

Surface Streets 62% 65% 78% 74%

Traffic Counts

(Annual 

Average Daily 

Traffic)

Roadway Type

Percent of Bus Route Distance

1

 For a definition of roadway type by traffic count see Table 4.1.5.2.

In addition, Table 4.1.5.2 shows the amount of time spent on each roadway type for Bus

Route 3215. Approximately 25% of the time during this bus commute was spent on each of the 

four major roadway types: residential, minor arterial, major arterial, freeway. This table clearly 

demonstrates Route No. 3215 provided a sufficient distribution of roadway types and traffic 

conditions to explore the effects of these factors on children’s exposure during bus commutes. 

43 
Moreover, based on the comparison in Table 4.1.5.1, we concluded this route contained roadway 

types and traffic conditions that were comparable to bus commutes in other parts of Los Angeles. 

 

Our rationale for focusing primarily on a single route during the main study was to allow

us to more unambiguously investigate the importance of other variables related to pollutant 

exposures during bus commutes, without the inevitable complications and confounders 

introduced by using more than one route. Specifically, we wanted to elucidate the effects of key 

variables such as fuel type and emission control status of the bus, hence it was important to hold 

constant the nature of the route. Otherwise, potential differences in traffic density and other 

route-related variables, even if not large in most cases, may have unnecessarily complicated and 

even confounded comparisons between fuel type and vehicle emission status. It is axiomatic in 

any scientific investigation that one would like to control all but one variable at a time in order to 

elucidate the effects of the remaining changed variable (e.g. fuel type). It was challenging 

enough to establish the effects and magnitudes of changing bus emissions and fuel types without 

the complication of using many different routes. 

Table 4.1.5.2 Distribution of roadway types of BSMS bus route No. 3215. 

Percent of Time During Bus

Run

Annual Average Daily Traffic Roadway Type

Route No. 3215 

(Pilot Study Route)

< 5,000 Residential 20%

5,000 - 25,000 Minor Arterial 20%

25,001 - 100,000 Major Arterial 30%

> 100,000 Freeways 25%

Freeway 25%

Surface Streets 75%

The broader issue concerning route choice and number of routes was one of how 

“representative” were the resulting data. This is an issue inherent in any vehicle-related study of

this kind. In general, there are never sufficient resources (e.g. individual experimental runs) to 

conduct an investigation that is truly representative of the enormous variability in, for example, 

vehicle type and emissions, or roadway type and traffic characteristics. This is especially true 

for a region like southern California with more than 10 million vehicles and a geographic area of 

more than 10,000 square kilometers. Under these constraints the study design necessarily 

involved severe tradeoffs and conflicting or competing objectives. The present school bus study 

was no exception, given that only a fixed number of runs were possible within the funding, staff 

and equipment resources available. For every new run added, some other experiment had to be 

eliminated. Thus, the number of routes investigated could not have been expanded greatly 

without sacrificing some other important aspect of the study. Moreover, we felt there were 

44 
45 

significant scientific advantages in leveraging the pilot study data by continuing to use the same

bus route in the main study. 

For all of the reasons given above, Route No. 3215 (the same route used in the pilot 

study) was selected as most appropriate for the primary focus of the main study. A map of this

route and its bus stops is presented in Figure 4.1.5.1. The route was driven with the fully 

instrumented bus from the fifth stop to BSMS in the morning, and the reverse route in the 

afternoon. This somewhat shortened route relative to the pilot study was 18 miles long and 

provided approximately one hour of commute time. The percentage of time spent on the 

freeways increased to approximately 40% of the total run time with the truncated route. This 

route involved a wide variety of traffic conditions and roadway types, ranging from single lane

residential streets with little or no traffic, to heavily congested, multi-lane surface streets which 

had high traffic densities during rush hour. Additionally, portions of this route traveled on the 

two most heavily congested freeways in the United States (I-405 and I-10) during rush hour

traffic. The vehicle mix on these freeways, observed during the pilot and main studies, included 

a high percentage of medium and heavy duty diesel vehicles, including trucks and other buses. 

Particularly during the morning commutes, the percentage of diesel vehicles on the freeways was

high compared with surface streets. 

Y#

Y#

Y#

Y# Y#

r %

BSMS %

Ve mr ont

Hoover

Slauson

Exposition

Main

Weemes Elementary School Bus Stop

Normandie 51st

I-10

I-405

I-110

N

1 0 1 2 Kilometers

Los Angeles Streets

Other freeways

Interstate 110

Interstate 10

Interstate 405

Primary Urban Route No. 3215 (U1)

Y# us Stops r SMS

% QMD Monitoring Sites

B

B

A

Figure 4.1.5.1 Primary urban bus route No. 3215. 

The variety of neighborhoods along the primary route, from the west side to south central

Los Angeles, also provided a varied mix of vehicle types, ranging from predominantly passenger 

vehicles and light trucks close to Brentwood, to older cars and more transit and school buses in 
south central Los Angeles. In addition, this route traveled through inner city neighborhoods, 

making stops in locations we judged to have a racial/ethnic composition of approximately 80% 

Hispanic, 15% African American, and 5% Asian or other, thus having had relevance to concerns 

about environmental justice. 

Measurements were made on the primary route during twenty bus runs in April, May and 

June 2002, consisting of ten morning and ten afternoon commutes. Each morning run started at

6:35 at the fifth pick-up location, and ended at approximately 7:40 at BSMS. During the 

afternoon runs, the bus left BSMS at about 15:05 and reached the fifth drop-off location at 

approximately 16:10. At each bus stop, the bus pulled up to the curb, opened the doors and 

waited for one minute before driving away, to simulate the conditions of children loading or 

unloading from the bus (Photograph 9). All the windows on the bus were closed during the 

morning runs, while during the afternoon runs, approximately half the windows were partially 

opened, to simulate conditions observed on in-use school buses. 

4.1.5.2 Urban Route Two 

While we determined the route used for the pilot study was appropriate for the great 

majority of the main study bus runs, we also believed it would be interesting to investigate a 

contrast between the primary route and another urban route. Based on our evaluation of the 

BSMS bus routes, we selected a second urban route for this purpose. We concluded that because 

the primary urban route extensively covered an appropriate range of urban driving scenarios, 

including travel on the two most heavily congested freeways in the U.S. and through heavily 

congested inner-city neighborhoods, an interesting contrast would be an urban route which 

covered a wider geographic area, traveled exclusively on surface streets, and included more 

industrialized areas of the city. 

Photograph 9. Another school bus passing our idling bus at a bus stop during a morning run. 

 

46 
47 

Although the majority of the bus routes from the BSMS travel on freeways for 

approximately 20 to 40 percent of their total distance, as discussed earlier, we identified a route

which only travels on surface streets for approximately one hour of its commute time, while also

covering a different geographic area than the route selected as the primary urban route. The 

route selected was the BSMS Route No.3235 (Figure 4.1.5.2). In the morning, this route started 

in Carson, just north of Long Beach, and traveled through industrial areas with a high percentage 

of heavy duty diesel traffic. The route continued north through neighborhoods of Rosewood, 

Gardena, Hawthorne, Inglewood and Hyde Park, and included middle to lower middle income 

neighborhoods with well maintained medium density housing, to higher density economicallydisadvantaged neighborhoods. This portion of the route included 10 bus stops and, as noted, 

traveled exclusively on surface streets. After the final bus stop, the BSMS bus continued on to

the Brentwood School. However, during the main study, we truncated the route at the final stop

because this provided us with over one hour of commute time (comparable to the primary route) 

through a variety of neighborhoods and traffic densities, and included only travel on surface 

streets. We observed the racial/ethnic composition of the neighborhoods covered by this route to 

be different from the primary route, consisting of approximately 80 – 90% African American, 

with the remaining portion primarily Hispanic. The combination of the diverse neighborhoods 

covered, absence of freeway travel, and a potentially different mix of congestion scenarios on 

this route compared with Route No. 3215 allowed for interesting contrasts between bus routes 

for the main study. 

Y#

Y# Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Rosecrans

El Segundo

Century

Manchester

Western

Van Ness

Avalon

Central

Imperial

Vermont

I-710

I-405 I-110

Los Angeles Streets

Other freeways

Interstate 110

Interstate 405

Second Urban Route No. 3235 (U2)

Y# U2 Bus Stops

N

1012 Kilometers

Figure 4.1.5.2 Second urban bus route No. 3235. 
Four exposure test runs were done on the second urban route, two in the morning and two 

in the afternoon on May 29 and 30, 2002. The morning runs started at approximately 6:15 at the 

first stop in Carson, and finished at approximately 7:30 at the final stop. In the afternoon, we 

drove the route in reverse, leaving from the morning’s final stop at approximately 15:30 and 

arriving at the Carson bus stop at approximately 17:00. All conditions on the bus during the runs 

on the second urban route were identical to those on the primary route, including window 

position, the use of run logs to characterize the route, and pollutants measured. 

4.1.5.3 Rural/Suburban Route

In addition to the primary and secondary urban routes, which covered a wide range of 

urban driving conditions, we felt it was important to evaluate the concentrations inside school

buses driving in low density rural and suburban neighborhoods. We expected these driving 

conditions would provide us with a number of scenarios not encountered in urban areas, 

including driving for longer periods of time at a constant speed and reduced idling time, and with 

constant conditions (e.g. less rapidly changing composition of surrounding vehicles). In 

addition, we expected lower roadway concentrations of traffic-related pollutants on the 

rural/suburban route due to greatly reduced traffic congestion would provide additional

information for our comparison between different bus engine technologies and fuel types, and 

the influence on concentrations inside the bus. 

The criteria for the selection of the rural and suburban routes were areas of little or no

heavy duty truck traffic, no freeways within one mile of the route and lower housing and 

population densities compared to the primary urban route. The bus routes from the BSMS were 

not adequate to meet our needs for low-density driving conditions because they cover areas of 

Los Angeles County which are primarily high density, urban neighborhoods. Even the more 

suburban routes in the San Fernando Valley to and from the BSMS traveled adjacent to major 

traffic corridors (such as Interstate 405 and Interstate 5) and included heavily congested arterials 

(e.g. Ventura Blvd) during portions of their commute. 

However, because the buses were outfitted with our sampling equipment at CE-CERT in 

Riverside before being brought to UCLA for the urban bus runs, we took advantage of the low 

density rural and suburban areas close to Riverside to obtain the appropriate rural/suburban

route. The route selected (Figure 4.1.5.3) provided us with about one hour of commute time and 

traveled first through predominantly rural areas, and continued on to low density suburban areas. 

The selected route started on Mission Boulevard in Riverside and traveled through rural parts of 

Riverside, Jurupa, Mira Loma and Chino, with approximately two-thirds of the route passing by 

dairy farms and through undeveloped areas (see Photograph 10). This section of the route 

included predominantly light traffic (split evenly between trucks and passenger cars) on roads 

with two lanes in each direction. The route became more suburban as it turned north through

Chino Hills and Diamond Bar, with areas of residential housing and small businesses (e.g., gas 

stations and strip malls). 

Traffic conditions were heavier in the suburban portion of the route, but included very 

few heavy duty trucks. The route ended at the 60 Freeway in Diamond Bar. 

48 
Los Angeles County San Bernardino County

Orange County Riverside County

Rural/Suburban Bus Route

N

Figure 4.1.5.3 Rural/suburban bus route. 

(a) (b) 

Photograph 10. Rural (a) and suburban (b) segments of the rural/suburban route. 

49 
Due to logistical considerations, the exposure test runs on the rural/suburban route were 

conducted in the afternoon. One run on the rural/suburban route was conducted for each bus 

evaluated in the main study, generally one day prior to the test runs on the primary urban route 

for the same bus. A total of seven rural/suburban exposure test runs were completed, one during 

each week of the main study beginning the week of April 22, and ending the week of June 11 

(not including the week of May 29). The start times for the rural/suburban test runs varied from

approximately 11:30 to 15:00, although most started after 12:30. For each test on the 

rural/suburban route, approximately half the windows on the bus were partially opened. The 

video camera was operated on three of the seven rural test runs and notes about traffic events

during the rural/suburban runs were maintained in a notebook by the field technician. 

4.1.6 Characterization and Justification for the Selected Bus Stop

Concentrations observed in the pilot study during bus commutes were generally 

significantly higher than those at the Vermont Elementary School bus stop (the first stop along 

the pilot study bus route) or at the loading/unloading zone at the BSMS. Based on this finding, 

in combination with our observations children typically spent only short periods of time waiting 

at bus stops or in loading/unloading zones, we recommended the main study focus primarily on 

bus commutes. We determined, however, that a limited number of additional measurements at a 

second bus stop during the main study would allow us to better define potential exposures 

associated with this microenvironment. These additional runs were recommended in part 

because a fairly high-emitting bus was involved in the pilot study. We felt a lower-emitting bus, 

of the kinds used in the main study, might exhibit lower average exposure ratios, for the 

commute versus the bus stop, leading to a greater relative contribution from bus stops to total 

exposure. 

Since this study was designed to include range-finding for high-end exposure conditions, 

the second bus stop along the primary route was selected for factors such as high adjacent traffic, 

long bus idle time, and where children were waiting for long periods of time to be picked up by 

the bus. By observation we determined which stops on the inner city portion of the primary 

route involved these high exposure scenarios. By following several buses over different routes 

on several different days we determined the range of relevant variables, such as the time spent by 

the bus at a given stop and whether the bus idled or turned off its engine. Logistical

considerations such as site security and positioning of the monitoring equipment were also 

factors included in selecting the bus stop location. 

Based on these criteria, the second bus stop selected along the primary route was the

Weemes Elementary School, located on 36th Place, three blocks south of Vermont Avenue near

downtown Los Angeles. Similar to the first bus stop measured during the pilot study, this stop 

was characterized by increasing traffic congestion during peak periods, and served as a bus stop 

for several bus routes. Diesel buses arrived to pick up or drop off children frequently during the

period just before this school started and after it let out. The average time spent by a bus at stops 

was 60 seconds, with the engine of the bus remaining on after the bus pulled up to the curb and 

children loaded/unloaded. In addition, this stop had an appropriate place to set up the monitoring 

equipment, and was located in a relatively secure area. 

50 
Measurements at the bus stop were made during one morning and one afternoon on May 

23, 2002. Each measurement period started at least 30 minutes before the expected arrival time

of the BSMS bus, and continued for at least 30 minutes after that time. The fully instrumented 

bus was parked (engine off) next to the sidewalk on the street in front of the school. Other buses 

and passenger vehicles pulled in front of or behind the instrumented bus to drop off/pick-up 

children at the school. 

4.1.7 Characterization of Window Position Test

The objective of the window position tests was to establish the effect of window position

on concentrations inside the bus cabin. Two sets of real time instruments were used, one set 

sampled inside the bus and the other sampled immediately outside the bus. During window 

position tests, we alternated between open and closed windows for designated intervals. 

However, during all other bus commutes, the position of the windows was fixed, with the 

windows closed during all morning commutes, and the windows open during all afternoon 

commutes. 

We selected the I-405 (north/south direction) between Wilshire and Century Boulevards 

as the route for the window position tests. Although there could have been ambient air pollutant 

concentration gradients along this north/south route, we expected them to be smaller than those 

observed for east/west routes. Specifically, during the pilot study we observed background 

concentrations tended to increase as the bus approached the inner city (heading east) and 

decreased approaching the Westside and Brentwood (heading west), likely due to changes in 

traffic density and composition, as well as meteorological effects. The I-405 runs approximately 

north/south, with more uniform traffic densities and meteorological conditions, therefore, we 

anticipated relatively minor ambient air pollutant concentration gradients and measurements 

taken outside the bus during the test allowed us to characterize any concentration gradients along

the route. 

Typically, we began a window position test on I-405 at Wilshire Boulevard traveling 

south to Century Boulevard, which took approximately 20 minutes, followed by the more 

congested return back up the I-405 north for about 40 minutes, for a total sampling time of one 

hour. During the test we opened the windows for eight minutes, then closed the windows for

eight minutes, alternating between the two positions every eight minutes for the duration of the 

test. This amount of time was short enough to allow several tests under similar traffic conditions 

(i.e., north and south directions) but long enough to allow stabilization of conditions inside the 

bus. During open window times, on the driver’s side of the bus, every other window was opened 

about ten inches, while on the other side of the bus, where the instruments were located, every 

other window was opened only two inches. 

The window position tests were conducted immediately following the morning bus 

commutes on May 8, and June 5 and 6, 2002, between 8:15 and 9:15. Different average speeds 

along the route allowed us to test the effect of window position for different ventilation rates 

inside the bus cabin. 

51 
4.2 Data Analysis Methods

The data collected during this study can be categorized as continuous, non-stationary and 

deterministic. We used time-series analysis techniques as well as conventional statistical

procedures to analyze the data set. This section presents a detailed explanation of all procedures 

used to analyze the main study data. 

 

4.2.1 Goals of Time Series Analysis

Time series analysis can be used to accomplish two different goals: identification of the 

nature of the phenomenon represented by the sequence, and the forecasting or prediction of 

future values of the time series variable. In this study, we focused on the first objective. 

The presence of stochastic events that modified the general pattern occurred constantly in

our time series (e.g., encounters with other HD diesel vehicles; random stops). In addition, the 

data did not exhibit deterministic cyclic patterns. Finally, the concentrations measured by the 

instruments were due to the contribution of “background pollution” concentrations as well as 

primary emissions from vehicles. 

Cross correlation (establishment of relationships between different time series and their 

implications) was used to establish precision between paired instruments measuring the same

pollutant simultaneously and to identify correlations between different pollutants (Section 5.1.3). 

4.2.2 Data Analysis Techniques

To provide a measure of the importance of a given microenvironment in terms of 

exposure, we calculated exposure factors for school bus commutes and bus stops and 

loading/unloading zone (from the pilot study). An exposure factor was defined as the product of

the average concentration for a specific pollutant multiplied by the time spent by children in that 

microenvironment. 

To determine the effects of fixed variables on concentrations inside the bus during each 

run, parametric and nonparametric statistical tools and graphical methods were used to identify 

variables governing exposure during bus commutes (Section 5.3). The data from different runs 

were grouped into categories by variable types. The objectives were to understand the contrasts 

defined by the study's experimental design as well as the relative importance of the variables 

(e.g. type of bus, type of route, windows position, time of day, position within the cabin, type of 

roadway). Basic descriptive statistics, such as the median and interquartile ranges, and the 95% 

confidence intervals were calculated for the grouped data. Graphical representations were used 

to describe the contrast between data sets, including cumulative frequency distributions and 

boxplots. 

Tests for autocorrelation were applied, to assure the validity of subsequent statistical 

procedures. Since the concentration at a particular time is dependent on the concentration a 

period of time before, the consideration of autocorrelation was mandatory in the data analysis. 

The time-lag (k) within which there is correlation was established for several data sets. 

Correlograms (a plot of the autocorrelation coefficient (rk) in terms of the lag (k)) provided a 

visual inspection of a range of correlation coefficients at relevant time lags so that significant

values were identified. An rk value of +-2/(N)1/2 denotes a significant difference from zero and

52 
signifies the presence of autocorrelation (Norusis, 2002). Note also that as k becomes larger, rk

becomes smaller. 

Finally, we included more detailed analysis for a limited number of bus runs, which 

involved identification of events influencing real-time concentrations inside the bus during a run. 

This effort included the most comprehensive analysis and was performed for a selected set of 

runs. This included a description of events (including a picture gallery); an

explanation/correlation of peak pollutant concentrations inside the bus with events (e.g. idling or 

driving behind another diesel bus) using video-camera records and activity logs; analysis of 

meteorological data and their effects on the selected runs; and compilation of selected video clips 

to demonstrate major events during runs. 

Our goal for this analysis was to be as methodical and consistent as possible. We

developed a specific protocol for identification of peak concentrations, as well as their 

correlation with external events recorded on the video-camera and/or the activity log. This 

formality was necessary as the assessment and interpretation of data can be affected by the

reporting practices (Henry et al., 2000). 

4.2.3 Video Record Analysis

An 8mm high-resolution video camera was mounted at the front of the test bus to record

traffic conditions and other exposure-related events occurring in front of and adjacent to the test 

bus during most of the measurement periods. All video camera records (created for 28 out of 36 

runs) were digitized into MPEG format, which is considered state-of-the-art for long-term

preservation and future migration to new formats. Selected videotape records were carefully 

examined to relate peaks in pollutant concentrations with traffic conditions, type of vehicle being 

followed, and other variables that might affect the concentrations inside the cabin of the bus. 

4.2.3.1 Definitions

In principle, a peak occurs when the concentration time series changes behavior during a 

short period, exhibiting prominent high values compared with the baseline. This is not the same 

as an outlier, which is defined as measurements that are extremely large or small relative to the 

remaining data and, therefore, are suspected of misrepresenting the population from which they 

were collected (U.S. EPA, 2000). See Figure 4.2.3.1 for a schematic representation of a peak, an

outlier, and the baseline. 

4.2.3.2 Data Generated from Videotapes

Two basic approaches can be taken when linking time series concentrations with events

recorded by the video camera. The first method includes a formal definition of relevant

concentration behavior in a time series with which one can try to define what constitutes a peak 

using a formal and systematic procedure and attempt to explain such a peak by association with 

an event. For example, the use of time-series intervention analysis (McDowall et al., 1980)

mathematically determines if an event has an impact on a time series. This method uses 

hypothesis-testing in conjunction with ARIMA models (Box and Jenkins, 1976; Box and Tiao, 

1975). 

53 
The second approach, which we adopted in our analysis, focused on identifying all 

conditions throughout a run, instead of focusing only on periods of peak concentrations. This

was necessary since a single peak may be generated by several events, and one event may cause

a signal that lasts for more than a few seconds. These concepts are illustrated in Figure 4.2.3.2. 

This time series corresponds to the PAH concentrations measured in the rear of the bus during 

T im e

Concentration 

Species i

Peak O utlier

Figure X . Schem atic representation of a tim e series. N ote that outliers

refer to discrete instantaneous data points w hile peaks refer to intervals

of tim e.

Baseline

Figure 4.2.3.1 Example of a peak, an outlier and the baseline. 

 

PAH - Rear (RUN23 - AM - RE2 - U2)

0.0 00 0

100.0 00 0

200.0 00 0

300.0 00 0

400.0 00 0

500.0 00 0

600.0 00 0

700.0 00 0

0 6:21:36 06:2 8:48 06 :36:0 0 06:43 :12 0 6:50 :24 0 6:5 7:36 07:04:48 07:12:00 07:19 :12 0 7:26:24 07:33:36 07:40:48

Tim e

Conc en tra tion

PAH - Rear (RUN23 - AM - RE2 - U2)

0

10 0

20 0

30 0

40 0

50 0

60 0

70 0

06:32:00 06:32:17 06:32:35 06:32:52 06:33:09 06:33:2 6 06:3 3:44 06:34 :01 06 :34:1 8

Time

C oncen tration

Following HD 

Diesel Truck

Diesel truck

pulled away

Bus stop

700 

600 

400 

300 

200 

500 

0 

100 

Figure 4.2.3.2 PAH time series for Run 23. Lower panel shows circled peak expanded and 

resolved into two peaks.

54 
Run 23 where a major peak can be observed at the beginning of the run. After analyzing the 

events occurring during this peak, we were able to divide the peak into three distinct regions. 

The first region showed a monotonous increasing concentration truncated at about 6:32:40, 

followed by a decrease for several tens of seconds, and was associated with the presence of a

heavy duty diesel truck. We started following this vehicle at 6:32:00 and continued following it 

until 6:32:40 (increasing trend) where a red light allowed the truck to pull away from us 

(truncated signal). The second region included another increasing concentration that reached a 

maximum value at about 6:33:00. During this period, we were again following a heavy duty 

diesel truck. The third region showed a flat behavior that lasted for about thirty seconds. At 

6:33:00 we stopped at a school bus stop where we stayed for about one minute (flat line). This 

example illustrates how multiple sources can contribute to a single peak concentrations. 

Therefore, to properly correlate and explain peak concentrations with events we attempted to 

describe all relevant events and characteristics during a run using the protocol described below. 

4.2.3.3 Videotape Analysis Protocol

The goal of our videotape analysis was to gain a better understanding of the factors that 

affect pollutant concentrations inside the bus, such as the effect of surrounding vehicles in

combination with the contribution from the bus’s own exhaust for different fuel types and engine 

after-treatment technology. Based on the experience obtained during the pilot study and the 

results of the initial analysis of the videotapes from the main study, a set of events or 

characteristics with the potential to influence in-vehicle concentrations was defined. These 

events or characteristics were the basis for the final videotape analysis during the main study, 

and included the following: 

1. Presence of any diesel-powered vehicle in front of or adjacent to the test bus. 

Only those vehicles within approximately three car lengths (based on a qualitative

assessment of distance) were considered. 

2. Exhaust location of the vehicle being followed (high, low). 

3. Presence of visible emissions or smell of exhaust from the vehicle being followed, or 

from the test bus. 

4. Idling at red lights, stop signs, in traffic or at bus stops. 

5. Level of traffic congestion (heavy, moderate, light). 

6. Roadway type (freeway, major/minor arterial, small residential streets). 

7. The direction the test bus was facing (e.g. north, northeast, etc.). 

As discussed previously, we used the ten-second concentration data (time resolution 

established for the data acquisition system in the field) for each pollutant inside the test bus

during each run for the following analyses. Using primarily the videotaped record of an 

exposure run, which showed the view of the road in front of the test bus, we systematically 

identified the events and characteristics (described above) that occurred in the area surrounding 

the test bus during each ten second period for a selected number of runs. We also used the 

activity logs created during the runs to supplement the video record. The use of these logs was 

necessary since important events were not always easily visible, in part due to the quality of the 

digitized videotape records (e.g., smoke coming out of an exhaust pipe), but were noted by the 

55 
field technician during the run and recorded in the run logs. In addition, there were events that

could not be identified by the videotape records (e.g., diesel exhaust odors). 

Every ten-second time interval during a given run was defined by the 

events/characteristics that occurred during the interval. An event/characteristic (based on the 

criteria noted above) was assigned to a ten-second time interval if the event/characteristic

occurred for at least five seconds during that interval (50% of the gap). If it continued for more

than ten seconds, the event was assigned to all intervals during which it occurred. For example, 

if a diesel truck was followed for 30 seconds, this event was assigned to three time intervals. It 

was also possible for more than one event to occur during a given ten-second interval. If the 

events were different, multiple assignments were made. For example, if the test bus was idling 

behind another school bus at a traffic light for one interval, both following a diesel vehicle and 

idling were assigned to that time interval. However, if two different diesel vehicles were in front 

of the test bus within three car lengths, only the closest one was assigned to the interval. 

An assignment of the type of vehicle being followed was made for every ten-second 

interval during a run. Vehicles were classified into one of five vehicle types: heavy-duty diesel 

truck (3-5 axles), medium duty diesel truck (2 axles), diesel school bus, diesel transit bus and 

passenger car/no target (Fruin, 2003). The location of the exhaust pipe on all diesel vehicles and

the presence of visible emissions or odors was noted when possible. 

Because of the subjective nature of the videotape analysis, every effort was made to 

assure consistent, repeatable and accurate assignments of events were made. The distance to the 

vehicle in front was the most subjective portion of the analysis. Vehicles more than 

approximately three car lengths (about sixty feet) away from the test bus were not included in the 

event assignments. In order to validate this criterion, each run was analyzed by two different 

individuals, and any discrepancies between the results were carefully scrutinized before a final 

assignment was made to assure consistency. In addition, assignments of idling were validated 

using the bus’s speed (also measured in real-time during every run). Finally, to assure unbiased 

assignment of events/characteristics based solely on the criteria described above, pollutant 

concentrations were not considered until after the videotapes had been reviewed and all event 

assignments made. 

4.2.3.4 Linking Events/Characteristics and Concentrations

The final step was to identify significant concentration differences between time periods

associated with each type of event or characteristic. For example, we were interested in 

describing any difference found between concentrations in the bus when idling, when traveling 

behind another diesel vehicle, between vehicles followed which had high or low exhaust, or 

when not following any diesel vehicles. We combined all the time periods with a given

event/characteristic assignment, such as when following a diesel school bus, or behind a heavy 

duty diesel vehicle, and made comparisons of pollutant concentrations associated with those 

events. In addition, we produced graphs similar to Figure 4.2.3.2, representing the time series 

for a given pollutant and a set of explanatory textboxes indicating the type of events associated 

with the real-time concentration behavior. 

56 
5.0 RESULTS AND DISCUSSION 

Our discussion of the results from this study begins with a brief assessment of our 

measurement methods. This involved both an assessment of the (a) accuracy of our instruments,

as determined by external audits or best estimates for those instruments for which known 

standards were not available; and (b) an assessment of precision, based on measurements from 

paired instruments. 

Although these measurement method evaluations were important, the primary approach 

to data analysis used in this study, and upon which the majority of our important findings were 

based, relied on the relative concentration differences measured during different exposure runs

using the same set of instruments. Specifically, all the measurements made during the main 

study that were used to evaluate the importance of test bus type, roadway type, window position,

surrounding traffic and events, and the bus stop vs. commute microenvironments, were made

with the same set of instruments. These comparisons relied on the assumption the same 

instrument operated in the same manner during the course of the eight weeks of the main study. 

In addition, a limited number of experiments were done in which measurements were 

taken using paired instruments, including the comparison of the front vs. the back of the bus, and 

the inside vs. the outside of the bus. The results of our precision estimates were important for 

these comparisons, and only those instrument pairs with adequate precision (as described in 

Section 5.1.3) were used for these comparisons. 

It is important to recognize in interpreting results presented in this chapter that, as shown 

in Table 5.1, with one exception (HE3), all front/rear contrasts were conducted only for the 

conventional diesel buses (Runs 1 to 20 and 23 to 26), and that all inside/outside contrasts were 

made only for the trap-outfitted and CNG buses (Runs 27 to 36). 

As mentioned before, throughout this report, PM2.5 data are from the Harvard Impactor

integrated sampler unless stated otherwise (i.e., data from the DustTrak instrument) 

5.1 Assessment of Measurement Methods

5.1.1 Overview

In this study an important objective was to compare measurements made with pairs of 

instruments for each pollutant for contrasts such as inside/outside and front/rear of the test bus. 

To evaluate such comparisons, the statistical variability of the measurements under 

collocated sampling conditions was needed to determine the probability at a stated significance 

level, that the paired measurements were significantly different. Thus, determination of 

collocation comparisons was the focus rather than establishing detection limits per se. A 

comprehensive discussion and analysis of collocated sampling data is given in Section 5.1.3. 

5.1.2 Instrument Accuracy

Instrument accuracy is normally defined by comparisons to known standards, preferably 

traceable to the National Institute of Standards and Technology (NIST). Accuracy is defined as 

absence of bias from the known standard, while for the purpose of this study was defined based 

57 
Table 5.1 Summary of main study exposure runs. 

Run Number Date Time of Day Type of Bus Contrast Type of Route

1 4/22/2002 Afternoon HE1 Front/rear Rural/suburban

2 4/23/2002 Morning HE1 Front/rear Urban 1

3 4/23/2002 Afternoon HE1 Front/rear Urban 1

4 4/30/2002 Afternoon HE2 Front/rear Rural/suburban

5 5/1/2002 Morning HE2 Front/rear Urban 1

6 5/1/2002 Afternoon HE2 Front/rear Urban 1

7 5/7/2002 Afternoon HE3 Inside/outside Rural/suburban

8 5/8/2002 Morning HE3 Inside/outside Urban 1

9 5/8/2002 Morning HE3 Inside/outside Window position test

10 5/8/2002 Afternoon HE3 Inside/outside Urban 1

11 5/13/2002 Afternoon RE1 Front/rear Rural/suburban

12 5/14/2002 Morning RE1 Front/rear Urban 1

13 5/14/2002 Afternoon RE1 Front/rear Urban 1

14 5/16/2002 Morning RE1 Front/rear Urban 1

15 5/16/2002 Afternoon RE1 Front/rear Urban 1

16 5/20/2002 Afternoon RE2 Front/rear Rural/suburban

17 5/21/2002 Morning RE2 Front/rear Urban 1

18 5/21/2002 Afternoon RE2 Front/rear Urban 1

19 5/22/2002 Morning RE2 Front/rear Urban 1

20 5/22/2002 Afternoon RE2 Front/rear Urban 1

21 5/23/2002 Morning N/A N/A Bus Stop

22 5/23/2002 Afternoon N/A N/A Bus Stop

23 5/29/2002 Morning RE2 Front/rear Urban 2

24 5/29/2002 Afternoon RE2 Front/rear Urban 2

25 5/30/2002 Morning RE2 Front/rear Urban 2

26 5/30/2002 Afternoon RE2 Front/rear Urban 2

27 6/4/2002 Afternoon TO1 Inside/outside Rural/suburban

28 6/5/2002 Morning TO1 Inside/outside Urban 1

29 6/5/2002 Morning TO1 Inside/outside Window position test

30 6/5/2002 Afternoon TO1 Inside/outside Urban 1

31 6/6/2002 Morning TO1 Inside/outside Urban 1

32 6/6/2002 Morning TO1 Inside/outside Window position test

33 6/6/2002 Afternoon TO1 Inside/outside Urban 1

34 6/11/2002 Afternoon CNG Inside/outside Rural/suburban

35 6/12/2002 Morning CNG Inside/outside Urban 1

36 6/12/2002 Afternoon CNG Inside/outside Urban 1

on the comparisons of measurements made with identical pairs of instruments (see Section

5.1.3). Poor precision can limit the assessment of accuracy, although a measurement can be very

accurate even with poor instrument precision if enough measurements are made. For 

measurements of the kind conducted in the present bus study, accuracy is normally assessed 

58 
during an independent audit of the instrument performance where known standards were 

available. For many of the instruments used in this study such standards were not available or 

did not exist. The following section describes our best estimation of the accuracy of the 

measurements and the limitations involved. 

5.1.2.1 Audited Instruments

A system and performance audit was performed May 10, 2002 following the procedures 

described in the project Quality Integrated Work Plan (QIWP) (Gemmill, 2002a). The audit was

performed on most of the instruments which measured gaseous pollutants. The system audit 

included an inspection and evaluation of the overall monitoring system setup in a bus being 

prepared for use in the study. The accuracy of the CO, NO/NOx, and NO2 analyzers were 

assessed by this performance audit. The performance audit consisted of challenging the 

analyzers with a test atmosphere from an independent calibration source. The test gas 

concentrations entered each instrument through the same sampling system used during sample 

runs. After the instruments’ responses had equilibrated, their outputs were read from the data

logger, correction factors previously determined by the system operator were applied, and these 

corrected outputs were compared to the known test gas inputs. The following is a summary of 

the audit findings: 

1. Continuous Analyzers: The audit results for the Thermal Electron Instruments (TEI) 

NO/NOx and CO analyzers indicated the project accuracy data quality objectives (see QWIP, 

Gemmill, 2002a) were met for those instruments. However, the objectives for the UCR NO2

analyzers were not met, probably due to the difficult moving platform environment in which 

they were operated. An audit was not performed on the SF6 analyzer because it was not 

operational at the time of the audit. 

2. System: The general system condition, operations, and maintenance were evaluated. A 

completed system evaluation report is presented in the Quality Assurance Audit Report 

which indicated that no significant unsatisfactory audit results were encountered (Gemmill, 

2002b). The procedures followed in the execution of this project were consistent with those 

described in the QIWP (Gemmill, 2002a). Overall, the monitoring equipment was securely

installed in the bus and was in satisfactory operational condition. All instrument calibrations 

were done in a proper manner and all documentation was complete, concise, and up to date. 

3. Calibration Results: A review of the calibration results for the main study indicated the data 

quality objectives were generally met for the TEI Model 42 NO/NOx analyzer, but were not

met for the CE-CERT NO2 and Dasibi 3003 CO analyzers (see QWIP—Gemmill, 2002b). 

Most of these data were considered valid. 

Complete details of the audit, including procedures and findings are provided in a separate audit

report (Gemmill, 2002b). 

59 
5.1.2.2 Unaudited Instruments

5.1.2.2.1 Particulate Matter Mass

5.1.2.2.1.1 Integrated PM10 and PM2.5

The largest sources of error for these instruments were the measurement and control of 

the flow rates and filter handling. The flow rates were controlled with needle valves and 

measured with a rotameter. The accuracy of the flow maintenance was defined by the 

characterization of volume, which depends on both temperature and pressure. The flow was set 

to a specific volumetric rate since the inlet was designed to operate under those conditions. 

While temperature and pressure changed during the course of sampling, the specified volumetric 

flow setting for these devices may deviate was believed to stay within approximately 5-10% of 

true volumetric flow. 

Minor sources of error included the size-cut characteristics of the PM inlet and weighing 

accuracy. Since evaluation of the size-cut characteristics were beyond the scope of this project,

we relied on previous comparisons. The inlet design we employed has been used in many other 

exposure related studies; and provided results comparable with other research. It should be 

noted that any inertial size separation is not exact, and that designation of a size cut off is an 

operational definition. Specifically, the size cut of an inlet is defined as the size at which 50% of

the mass of the specified aerodynamic size penetrates the inlet. The sharpness of the size cut 

(particles greater or less than the specified aerodynamic size will always penetrate the inlet) 

depends on the design of the inlet. 

We believe the error in filter mass weighing was minor due to our weighing procedure. 

We used a class M calibration standard before and after every weighing session. The calibration

standard was weighed to the nearest 0.1 μg, and little deviation was observed (typically +/-

0.1ug). It should be noted that in practice it is not possible to weigh filters to closer than 1 μg as 

the weight will fluctuate due to factors such as humidity much more than a metallic standard. 

All filters were weighed until three weighings agreed to within 3 μg. Since typical loadings were 

50-100 μg, the error in the weighing process should not have exceeded a few percent. The errors

due to filter handling are random and therefore related to the precision of the measurement. 

5.1.2.2.1.2 Continuous PM10 and PM2.5

The DustTrak instruments used for this measurement are based on light scattering of 

particles. The amount of light scattered is dependent not only on concentration, but also is 

highly dependent on the particle size distribution and composition. In order to relate the light

scattering to a concentration it is necessary to choose a particle size distribution since these 

distributions are constantly changing in ambient air. The response of these instruments was

calibrated by the manufacturer based on the particle size distribution of Arizona road dust, which 

was chosen because it is an NIST standard reference material. Thus the inherent accuracy can be

related to an NIST standard, but the accuracy at the time measurements are made must be 

established by comparing the measurements with mass measurements from collected PM. The 

accuracy determination for the measurements made in this study was therefore limited to that of

the integrated filter collection methods described above. In addition the accuracy of the 

instrument varies directly with the flow rate. A rotameter and needle valve were also used to 

adjust this flow and the estimated uncertainty introduced was five to ten percent as discussed 

60 
above. See the next section for details of a comparison of the DustTrak against a reference 

method. 

5.1.2.2.1.3 Comparison of Measurement Methods for PM2.5 and PM10

The present study compared two instruments that measured the mass concentration of 

PM2.5 and PM10: the Thermo Systems Inc. Model 8520 DustTrak Aerosol Monitor (DustTrak)

and the Harvard Impactor (HI). The DustTrak measured the PM concentration continuously in 

real-time while the Harvard Impactor used standard filter-based sampling methods. Continuous 

monitors for airborne particulate matter measurements have an advantage over traditional filterbased sampling methods that do not provide real-time data and cannot characterize short-term 

high concentrations (Babich et al., 2000; Chung et al., 2001). However, continuous monitors 

currently in use may suffer from problems of accuracy and appear to be more useful on a relative 

rather than absolute basis (Ramachandran et al., 2000; Chung et al., 2001; Moosmuller et al., 

2001; Yanosky and Maclntosh 2001; Yanosky et al., 2002). 

Several studies have been conducted comparing different PM2.5 or PM10 samplers 

(Babich et al., 2000; Ramachandran et al., 2000; Chung et al., 2001; Yanosky and Maclntosh 

2001; Yanosky et al., 2002). Babich et al. (2000) and Yanosky and Maclntosh (2001) compared 

PM2.5 mass concentrations from the HI against U.S. Environmental Protection Agencydesignated Federal Reference Method (FRM) PM2.5 samplers. Yanosky et al. (2002) 

investigated the performance of the DustTrak measuring PM2.5 concentrations of indoor air in

comparison with the FRM. The gravimetric method, (i.e. FRM) was used as the standard for 

evaluation of the real-time continuous monitors because it has proven to be stable and accurate.

In Ramachandran et al. (2000) DustTrak measurements of PM2.5 concentrations indoors and 

outdoors were compared with gravimetric measurements (MSP, Inc. PM2.5 inlet for indoors;

Andersen RAAS2.5-300 sampler for outdoors). Chung et al. (2001) compared measurements of 

PM10 by the DustTrak with those by a size-selective inlet sampler (SSI). 

All these studies employed simple linear regression analyses in order to calculate 

differences between sampler types. When the concentrations from a sampler, y, are regressed on 

those from a reference sampler, x, the intercept should be zero and the slope on x should be 

unity, i.e. y = 0 + 1*x, if the measurements between the two samplers coincide. If the intercept 

estimate is significantly different from zero statistically, there is a systematic bias between

samplers. A statistically significant difference in slope suggests a proportional bias between 

samplers (Yanosky and Maclntosh 2001). The regression results of the above-mentioned studies 

are summarized in Table 5.1.2.1. 

 

From Table 5.1.2.1, the first two studies showed excellent agreement between the HI and 

FRM methods, indicating the Harvard impactor could serve as a reference method for the 

DustTrak instruments employed in the present study. Three other studies showed the DustTrak 

overestimated PM2.5 or PM10 mass concentration by factors of two to three (the slope column in 

Table 5.1.2.1) compared with filter-based methods, although no systematic biases were observed 

in these three studies. These findings are consistent with the fact that the DustTrak is a

nephelometer, which measures mass concentration of an aerosol stream by converting the 

amount of light scattered from many particles at once. Thus, the DustTrak output depends on 

particle size distribution and refractive index (Moosmuller et al. 2001) and the response of the 

61 
instrument for an aerosol stream consisting mainly of finer particles can produce errors in 

concentration of a factor of two or more (Hinds 1999; Ramachandran et al., 2000; Chung et al., 

2001). 

Table 5.1.2.1 Summary of previous studies comparing samplers employed for measurements of 

PM2.5 and PM10 mass concentrations. 

Reference PM size Samplers n Intercept1 Slope R2 

Babich et al. (2000) PM2.5 HI/FRM 81 1.36 0.972 0.99 

Yanosky and 

Maclntosh (2001) 

PM2.5 HI/FRM 21 -0.06 1.032 0.96 

Yanosky et al. 

(2002) 

PM2.5 DustTrak/FRM 17 -1.73 2.57 0.86

Ramachandran et al. 

(2000) 

PM2.5 DustTrak/Andersen3 

DustTrak/MSP4 

N/A 

N/A 

N/A

N/A

2.99 

1.94 

0.92

0.76

Chung et al. (2001) PM10 DustTrak/SSI 60 -13.60 3.00 0.92

1. The intercept was not significantly different from zero (α = 0.05). 

2. The slope was not significantly different from zero (α = 0.05). 

3. Gravimetric sampler for outdoor measurement 

4. Gravimetric sampler for indoor measurement 

5. N/A: statistics are not available. 

5.1.2.2.1.3.1 Descriptive Statistics

Descriptive statistics for PM2.5 and PM10 measurements by the DustTrak and the HI are 

given in Tables 5.1.2.2 and 5.1.2.3, respectively. Real-time ten second interval measurements by 

the DustTrak were averaged to properly match the integrated sampling times of the HI 

measurements. Difference between the means and medians of the DustTrak data were modest 

for both PM2.5 and PM10, indicating the distributions were not skewed. However, there were

significant disparities between the DustTrak and HI samplers. For instance, in the morning runs 

(from 23 to 35) and afternoon runs (24, 27, 30, and 33), integrated PM2.5 concentrations from the 

HI fell outside the range (i.e. maximum – minimum) of the time-resolved DustTrak

measurements. 

The ratios of the DustTrak-measured PM2.5 mean concentration divided by the HImeasured PM2.5 concentration varied substantially. Seven runs (23, 24, 25, 27, 30, 31, and 33) 

involving three types of routes (RS, U1, and U2) and two types of buses (RE2 and TO1) had 

DustTrak/HI PM2.5 ratios greater than 2. No obvious patterns were seen across bus type, 

including for the CNG bus or route. Figures 5.1.2.1 and 5.1.2.2 show the differences between 

the DustTrak and HI samplers across morning and afternoon runs, respectively. 

62 
Table 5.1.2.2 Descriptive statistics for PM2.5 (μg/m3

) measured by HI and DustTrak. 

Ratio1 Run Date Time Week Bus Route DustTrak DustTrak DustTrak DustTrak HI

Minimum Maximum Median Mean 

4 04/30/02 PM 2 HE2 RS 9.0 34.0 14.0 14.9 40.6 0.4

5 05/01/02 AM 2 HE2 U1 35.0 148.0 53.0 58.8 40.6 1.4

6 05/01/02 PM 2 HE2 U1 8.5 115.5 15.0 20.3 12.5 1.6

7 05/07/02 PM 3 HE3 RS 51.0 161.0 74.0 76.5 44.4 1.7

10 05/08/02 PM 3 HE3 U1 29.0 70.5 37.0 39.0 29.0 1.3

11 05/13/02 PM 4 RE1 RS 29.5 161.5 47.0 51.7 47.1 1.1

12 05/14/02 AM 4 RE1 U1 33.0 61.0 43.5 44.0 37.8 1.2

14 05/16/02 AM 4 RE1 U1 37.5 89.5 61.0 61.8 42.0 1.5

15 05/16/02 PM 4 RE1 U1 26.0 157.0 41.0 47.5 31.5 1.5

16 05/20/02 PM 4 RE1 RS 4.0 43.0 11.0 13.4 18.1 0.7

17 05/21/02 AM 5 RE2 U1 41.0 147.0 75.3 78.0 50.9 1.5

18 05/21/02 PM 5 RE2 U1 3.5 167.0 11.0 15.4 21.2 0.7

19 05/22/02 AM 5 RE2 U1 25 146.5 78.5 78.8 48.5 1.6

20 05/22/02 PM 5 RE2 U1 12.0 204.5 24.0 32.5 22.4 1.5

23 05/29/02 AM 6 RE2 U2 59.5 148.0 112.8 111.0 49.2 2.3

24 05/29/02 PM 6 RE2 U2 58.5 227.0 109.0 109.0 41.6 2.6

25 05/30/02 AM 6 RE2 U2 74.0 180.5 129.5 128.7 48.9 2.6

26 05/30/02 PM 6 RE2 U2 35.0 219.5 72.5 74.2 41.3 1.8

27 06/04/02 PM 7 TO1 RS 64.5 211.5 118.5 117.3 40.3 2.9

28 06/05/02 AM 7 TO1 U1 79.0 128.5 100.0 99.8 57.0 1.8

30 06/05/02 PM 7 TO1 U1 105.0 163.5 119.0 123.0 61.8 2.0

31 06/06/02 AM 7 TO1 U1 97.0 158.0 127.5 128.0 62.1 2.1

33 06/06/02 PM 7 TO1 U1 78.0 224.0 103.0 106.0 49.6 2.1

34 PM 8 CNG RS 57.5 211.0 91.0 89.6 57.1 1.6

35 

06/11/02

06/12/02 AM 8 CNG U1 39.5 68.5 48.5 49.0 36.1

36 06/12/02 PM 8 CNG U1 13.5 117.5 21.0 27.8 26.7

1.4 

1.0 

1. Ratio = (DustTrak PM2.5 mean concentration) / (HI PM2.5) 

For PM10 measurements, the overall differences between the DustTrak and HI samplers 

were less pronounced than for PM2.5. Only two runs (30 and 33) showed HI integrated 

concentrations outside the range of the DustTrak time-resolved data (Table5.1.2.3) and the 

DustTrak/HI PM10 ratios ranged from 0.4 to 1.7, more closely distributed around unity than for 

PM2.5. Fourteen of twenty-six runs (i.e. 50% of all bus commute runs) had ratios less than 1,

while only three runs for PM2.5 had ratios less than 1. 

Another problem with certain DustTrak data in this study was that mean PM2.5 mass 

concentrations were higher than mean PM10 mass concentrations for several morning and 

afternoon runs (16, 19, 20, 23, 24, and 25). 

63 
Table 5.1.2.3 Descriptive statistics for PM10 (μg/m3

) measured by HI and DustTrak. 

Run Date Time Week Bus Route DustTrak

Minimum 

DustTrak

Maximum 

DustTrak

Median

DustTrak

Mean 

HI Ratio1 

4 04/30/02 PM 2 HE2 RS 13.0 75.5 23.5 24.4 37.0 0.7

5 05/01/02 AM 2 HE2 U1 33.5 123.5 50.5 54.8 63.7 0.9

6 05/01/02 PM 2 HE2 U1 9.5 102.0 17.0 21.5 28.2 0.8

7 05/07/02 PM 3 HE3 RS 53.5 145.5 76.5 77.5 65.6 1.2

10 05/08/02 PM 3 HE3 U1 35.0 74.0 44.5 45.8 52.0 0.9

11 05/13/02 PM 4 RE1 RS 32.0 870.5 57.0 69.3 105.5 0.7

12 05/14/02 AM 4 RE1 U1 37.0 71.5 50.0 50.9 78.3 0.6

14 05/16/02 AM 4 RE1 U1 38.5 101.0 61.5 62.1 77.3 0.8

15 05/16/02 PM 4 RE1 U1 29.5 142.0 45.5 50.2 50.9 1.0

16 05/20/02 PM 4 RE1 RS 2.0 43.0 10.0 11.7 27.3 0.4

17 05/21/02 AM 5 RE2 U1 51.0 157.5 76.8 81.4 86.3 0.9

18 05/21/02 PM 5 RE2 U1 6.0 159.5 16.0 20.4 24.9 0.8

19 05/22/02 AM 5 RE2 U1 24.5 146.5 76.0 78.1 86.9

20 05/22/02 PM 5 RE2 U1 11.0 173.0 23.8 32.3 47.0

0.9 

0.7 

23 05/29/02 AM 6 RE2 U2 50.0 154.5 104.0 103.2 80.3 1.3

24 05/29/02 PM 6 RE2 U2 57.5 258.0 102.0 104.9 71.5 1.5

25 05/30/02 AM 6 RE2 U2 67.5 171.5 125.0 122.6 72.8 1.7

26 05/30/02 PM 6 RE2 U2 40.5 238.5 73.0 76.4 66.7 1.1

27 06/04/02 PM 7 TO1 RS 77.0 242.0 140.0 139.9 98.3 1.4

28 06/05/02 AM 7 TO1 U1 80.5 139.0 100.5 101.5 104.8 1.0

30 06/05/02 PM 7 TO1 U1 102.5 174.0 123.0 126.4 97.3 1.3

31 06/06/02 AM 7 TO1 U1 98.0 166.0 129.5 130.5 99.4 1.3

33 06/06/02 PM 7 TO1 U1 80.5 230.0 106.0 109.5 73.7 1.5

34 06/11/02 PM 8 CNG RS 61.5 450.0 100.5 101.3 101.8 1.0

35 06/12/02 AM 8 CNG U1 41.0 73.0 53.0 53.3 68.8 0.8

36 06/12/02 PM 8 CNG U1 24.5 121.5 31.5 37.1 50.3 0.7

5 14

19

23

25

28

31

35

17

12

0

20

40

60

80

100

120

140

0 5 10 15 20 25 30 35 40

Run

P

M2.5(ug/m3)

Harvard

DustTrak

1. Ratio = (DustTrak PM10 mean concentration) / (HI PM10) 

Figure 5.1.2.1 Comparison between DustTrak and HI data for PM2.5 (morning only). 

64 
4

6

7

10

11 15

16 18

20

24

26

27

30

33

34

36

0

20

40

60

80

100

120

140

0 5 10 15 20 25 30 35 40

Run

PM2.5(ug/m3)

Harvard

DustTrak

 

Figure 5.1.2.2 Comparison between DustTrak and HI data for PM2.5 (afternoon only). 

5.1.2.2.1.3.2 Assessment of Difference Between Samplers

To assess the difference between samplers quantitatively, simple linear regressions were 

performed for both PM2.5 and PM10 data. As noted earlier, in previous studies (Ramachandran et 

al., 2000; Chung et al., 2001; Yanosky et al., 2002), the DustTrak overestimated the 

concentration of airborne particulate matter by factors of two to three (Table 5.1.2.1) compared 

to filter-based sampling techniques and a similar result was observed in the present study for 

PM2.5 but not for PM10. 

The intercept terms were not found to be statistically significant (i.e., no systematic bias) 

at the significance level of 0.05 for both PM2.5 and PM10. However, the estimated slopes were 

significantly different from zero at the significance level of 0.05, indicating a proportional bias. 

The slope of 2.34 for PM2.5 fell within the range observed in previous studies (Ramachandran et 

al., 2000; Yanosky et al., 2002), while the slope of 1.25 for PM10 was lower than the value of 3 

found by Chung et al. (2001), who employed the SSI gravimetric method, different from the HI 

used in the present study. 

The correlations observed in the present study were modest (R2

 of 0.66 for PM2.5 and 

0.67 for PM10), compared with previous studies (R2

 from 0.76 to 0.92) reported in Table 5.1.2.1. 

However, in the present study data were collected on moving platforms, school buses, while the 

previous studies were conducted conventionally on roofs or inside of buildings. A degradation

in correlation for instruments operated on moving platforms might be expected, although it is 

interesting that the patterns of differences in measurements between the DustTrak and the HI

samplers were similar in the present study to the previous studies for PM2.5. 

Based on the results presented above, and considering the HIs are the Federal reference 

method, we decided to separate the use of the data obtained by these and the DustTrak

65 
instruments. The HI integrated samples data were used to provide absolute concentrations which 

could be compared with other studies. The data collected with the DustTrak instruments were

used only on a relative basis or when real-time series were required for the analysis. 

5.1.2.2.2 Real-Time Fine Particle Counts (0.3 to 10.0 μm)

Optical particle counters count pulses of scattered light with high efficiency. The 

intensity of the pulse is directly (but not linearly) related to particle size. The output pulses are 

sorted into bins that are therefore related to particle size. The intensity of the pulse is dependent 

on the physical characteristics of the particle scattering the light. Refractive index and shape are

two characteristics that have the greatest affect on particle size. The size bins from the output of 

optical particle counters were calibrated by the manufacturer by sampling aerosol consisting of

spherical latex particles of known size and refractive index. While this calibration can be highly 

accurate (latex particle sizes are known to better than 1%), ambient particles have a wide variety 

of shapes and refractive indices. Optical particle counters therefore provide a size output that is 

operationally defined, assuming the particles scatter light in the same manner as latex beads. 

Measurement accuracy can only be determined for latex beads and the accuracy in size

determination of ambient aerosol cannot be measured. 

5.1.2.2.3 Real-Time Ultrafine Particle Counts (0.03 to 0.8 μm)

A CE-CERT Scanning Electrical Mobility Spectrometer (SEMS) was used to measure

particles in the range of 0.03 to 0.8 um electrical mobility diameter. These analyzers charge

particles and then separate them by size based on their electrical mobility. The particles are then

detected with a condensation nuclei counter (CNC). Only a small fraction of the particles 

become charged, with the charging efficiency dependent on particle size, shape, and 

composition. The CNC can count the particles essentially quantitatively. For the SEMS, the size

range is determined by sampling aerosol particles of known size, shape and composition, 

therefore, it is not possible to establish a meaningful accuracy when sampling ambient aerosol

since the size, shape and composition can vary widely. This imposes significant restrictions and 

complexities in the analysis of the data generated by the instrument. Although we collected

samples using the SEMS for the majority of the bus runs, analyses of these data were beyond the

scope of the present project. 

5.1.2.2.4 Real-Time Black Carbon 

These analyzers (Aethalometers) determine the concentration of black carbon by 

determining the light absorption after collection on a filter. Black carbon is generally the only 

significant component of ambient particulate that absorbs light at the near IR wavelength used. 

The calibration cannot be determined directly, but must be established empirically by 

comparison with PM collected on filters and analyzed by thermal decomposition. This analysis

method also has inherent errors since the amount of elemental carbon can change during analysis 

by charring and pyrolysis, the degree of which is dependent on the chemical composition of the 

PM. The measurement of elemental carbon in ambient air is therefore an operational definition, 

and accuracy is difficult to establish and depends on the aerosol composition. Thermal methods 

sampling ambient air generally agree to within about 10 percent and this may be used to estimate 

accuracy. The real time analyzers generally agree to within about ten percent of that determined 

by filter collection followed by thermal analysis. The overall accuracy therefore is expected to 

be in the range of twenty percent. 

66 
5.1.2.2.5 Real-Time Particle Phase PAH

This analyzer does not directly measure PAHs, but rather their photoionization products. 

The manufacturer’s calibration is based on an empirical relationship, with PAH measured in 

ambient air by filter collection followed by extraction and gas chromatographic analysis. Since

the response depends on the composition of the PM, it is not possible to establish the accuracy of 

the method. Based on the manufacturers data, we expect ambient air accuracy to generally be 

fifteen percent.

5.1.2.2.6 Gaseous Analyses

Tedlar bag samples were analyzed for 1,3-butadiene by gas chromatography with flame

ionization detection. The accuracy is dependent on sample injection accuracy, loss of compound 

in the bag, and calibration standards used as the chromatographic standard. Sample injection is

done with a loop of fixed size, which changes volume only as a result of atmospheric pressure. 

We have shown that losses of 1, 3-butadiene are within the precision of the measurement

(several percent) for periods of up to four days. This was done by collecting ambient samples 

enriched in 1,3-butadiene by sampling near an idling diesel engine and then periodically 

analyzing them. The GC accuracy was established by calibrating using a gas cylinder certified

by the manufacturer to +/- 2% based on NIST standards. The overall accuracy is estimated to be 

within five percent. 

Tenax samples were collected using needle valves for flow control and rotameters for 

flow measurement. As with the filter collection the accuracy of flow control is estimated to be

within five to ten percent. Gas chromatography with flame ionization was used to measure 

aromatic compounds desorbed from the Tenax. The calibration of the measurement was based 

on a compressed gas standard certified by the manufacturer to within two percent. The overall 

measurement accuracy was estimated to be within ten percent.

Carbonyl compounds were measured by collecting them as a 2,4-dinitrophenyl hydrazine 

adduct and analyzing the adduct by high performance liquid chromatography (HPLC). The flow 

rate was controlled by needle valves and measured with a rotameter. As with the filter collection

the accuracy of flow control is estimated to be within five to ten percent. Gaseous calibration

standards are not available for carbonyls and the calibration is established by weighing the 

adduct. Weighing and dilution accuracy is estimated from methods traceable to the NIST and 

based on these procedures the accuracy of the standards is estimated to be within ten percent. 

Overall accuracy of the method is estimated to be twenty percent based on this analysis and field

comparisons with spectroscopic methods. 

5.1.2.2.7 Portable Carbon Monoxide Analyzers 

These analyzers were not audited, but their accuracy may be estimated by comparison 

with the infrared analyzer that was audited. The accuracy may be affected when sampling 

ambient air due to interferences, since electrochemical cells do not have high specificity. The 

interference due to hydrogen also needs to be addressed since this is a known interferent and the 

lead-acid batteries carried in the bus may have been a source of hydrogen. 

67 
5.1.3 Evaluation of Precision Between Paired Instruments

Two contrasts tested in this study were differences in concentrations at the front and the

rear of the bus cabin, and between inside and outside the bus. To help validate these contrasts, 

we assessed the precision of the pairs of instruments which were used to simultaneously measure 

at the front and rear of the bus cabin, or inside and outside the bus during a commute. If there 

was not substantial agreement between the readings of the pair of the same instruments when 

they were sampling the same volume of air under common sampling conditions, we would be 

limited in the inferences we could draw from the measurements made when sampling different 

air parcels (e.g., inside versus outside). Thus the results of our precision experiments were used 

to determine which pairs of instruments had an acceptable level of precision for evaluating 

simultaneous measurements at the front and rear of the bus cabin, or inside and outside the bus. 

It is important to note, for all comparisons which relied solely on a single set of instruments 

instead of paired instruments (e.g. comparisons between bus types, windows open or closed, 

different microenvironments, comparisons to ambient air), the precision results reported here do

not apply. 

During the pilot study, low pollutant concentrations during the precision checks for a 

number of the instruments made it difficult to interpret differences observed in pollutant 

concentrations between the front and back of the cabin, and between inside and outside the bus. 

One of the improvements of the main study experimental design was that we performed 

instrument precision measurements in locations where the higher concentrations of many of the 

analyzed pollutants were expected to provide more robust precision assessment. In general, all 

instruments improved their precision when exposed to higher concentrations. Therefore, the 

precision was potentially better during the bus commutes since the values observed during these 

tests were much higher than during the precision experiments. 

Precision between paired Aethalometers, however, did not improve during the main study 

precision tests. The relatively clean background conditions in West Los Angeles, even a few

tens of meters from the I-405, often resulted in measured black carbon concentrations that were

not sufficiently above the detection limit. During the collocation experiments, the measured 

concentrations for this pollutant were most of the time below 4 ug/m3

 and for these values the

signal-to-noise ratio produced by the Aethalometers did not yield appropriate precision results. 

Even so, the post-study controlled experiments (see below) showed the precision between the 

pairs of Aethalometers improved substantially at higher concentrations. Therefore we believe

that at the peak black carbon concentrations encountered during the main study runs, the 

precision between the paired Aethalometers was substantially better than that indicated by the 

precision experiments. 

During the main study, seventeen instrument precision experiments were conducted when 

the bus was parked with the engine off and the windows fully opened. For these experiments, 

the instruments were sampling inside concentrations and the sample inlets from each pair of 

instruments were separated by the length of the bus. That is, one set of instruments had its 

sample inlets in the rear of the cabin while the other set had them in the front. This arrangement 

was necessary since the logistics of relocating the sampling inlets to be adjacent to each other 

was too complex to be performed once the bus was instrumented for the runs. 

68 
Although the sampling inlets were not exactly in the same location, the fact that the bus 

was at rest and fully ventilated was expected to lead to uniform pollutant concentrations from

ambient air entrained throughout the cabin. Thus, under these conditions, no significant 

differences were expected in measurements by the pairs of instruments. 

Six instrument precision experiments were conducted with the test bus parked close to 

the intersection of two congested streets (Sepulveda Blvd and Wilshire Blvd) and near the I-405 

freeway in West Los Angeles. The remaining instrument precision experiments were conducted 

either inside a parking lot at UCLA, in front of the Brentwood Science Magnet School, or in 

front of several of the schools which served as bus stops on the routes used for the bus runs. 

We also conducted a post-study experiment to assess the precision between the paired 

instruments at much higher concentrations in a more controlled laboratory environment. Table 

5.1.3.1 summarizes the main characteristics of the instrument precision experiments and Figures 

5.1.3.1 to 5.1.3.5 show a series of boxplots illustrating, as discussed below, the variation in 

differences between the pairs of instruments over the course of the study. With the exception of 

the Aethalometers, these graphs were plotted using the same numerical scale (although in several

cases with different units) to emphasize the variation of the differences not only within runs but 

also between instruments. Figures 5.1.3.1 to 5.1.3.5 depict the results only in terms of absolute 

precision. Relative precision analyses are presented in Section 5.1.3.1.1. 

Table 5.1.3.1 General characteristics of the paired instrument precision experiments. 

Collocation Run 

Number Date Time Start Time Stop Time Location Week Number

101 04/23/02 PM 10:00 12:00 UCLA 1

201 05/01/02 PM 17:32 18:25 UCLA 2

301 05/07/02 PM 15:00 15:33 Sepulveda Blvd 3

401 05/13/02 PM 14:51 15:21 Sepulveda Blvd 4

402 05/14/02 AM 6:22 6:36 First Bus Stop 4

403 05/14/02 PM 14:50 14:59 Brentwood 4

404 05/16/02 AM 8:32 9:34 UCLA 4

405 05/16/02 PM 11:00 14:10 UCLA 4

501 05/20/02 PM 15:00 15:30 Sepulveda Blvd 4

502 05/20/02 PM 15:56 16:57 Sepulveda Blvd 5

503 05/22/02 AM 6:10 6:29 First Bus Stop 5

504 05/23/02 PM 15:04 15:19 Brentwood 5

601 05/29/02 PM 14:55 15:25 First Bus Stop 6

602 05/30/02 PM 15:08 15:27 First Bus Stop 6

701 06/04/02 PM 15:23 16:02 Sepulveda Blvd 7

702 06/05/02 AM 6:15 6:29 First Bus Stop 7

801 06/11/02 PM 15:04 16:43 Sepulveda Blvd 8

802 06/17/02 PM 12:55 16:21 Laboratory

 

69 
70 

N = 53 33 30 14 62 12 12 48 30 19 39 14 98

Run

201 301 401 402 404 405 501 502 601 602 701 702 801

BC_

DIF

25

15

5

-5

-15

-25

 

Figure 5.1.3.1 Variation in absolute differences (μg/m3

) between paired Aethalometers over 

the course of the main study. 

N = 90 33 28 27 12 9 57 151 24 41 12 12 26 15 29 12 32

Run

801

702

701

602

601

504

503

502

501

405

404

403

402

401

301

201

101

NO2_DIF

50

40

30

20

10

0

-10

-20

-30

-40

-50

Figure 5.1.3.2 Variation in absolute differences (ppb) between paired NO2 instruments 

over the course of the main study. 
N = 119 53 33 30 14 9 62 190 30 43 19 5 14 19 39 14 73

Run

801

702

701

602

601

504

503

502

501

405

404

403

402

401

301

201

101

PAH_DIF

50

40

30

20

10

0

-10

-20

-30

-40

-50

Figure 5.1.3.3 Variation in absolute differences (ng/m3

) between paired PAH 

instruments over the course of the main study. 

N = 51 33 30 14 9 60 189 30 48 19 15 30 18 39 14 98

Run

801

702

701

602

601

504

503

502

501

405

404

403

402

401

301

201

PC_DIF

50

40

30

20

10

0

-10

-20

-30

-40

-50

Figure 5.1.3.4 Variation in absolute differences (#/cm3

) between paired fine particle 

count instruments over the course of the main study. 

71 
N = 120 53 33 30 14 9 62 190 30 48 19 15 30 19 35 98

Run

801

701

602

601

504

503

502

501

405

404

403

402

401

301

201

101

PM25_DIF

50

40

30

20

10

0

-10

-20

-30

-40

-50

Figure 5.1.3.5 Variation in absolute differences (μg/m3

) between the DustTrak 

 PM2.5 instruments over the course of the main study. 

The subsequent assessment of the instrument precision data quality was performed via 

two different methods: precision estimation and correlation between pairs of instruments. 

5.1.3.1 Precision Estimation Between Paired Instruments

The simplest way to establish the quality of the paired instrument precision data was to

compute the precision of the paired instruments, which we did by using absolute differences, 

percent differences and coefficients of variation. 

To perform these calculations, the original ten-second data were smoothed using a oneminute moving average. This procedure was used only for this analysis. The remaining 

calculations presented in this report used the validated 10-second data. We tested different 

periods for the moving average, ranging from 30-second to 10-minute averages. After analyzing 

the effects of such averaging periods on the results of the precision calculations, we selected the 

1-minute averaging time since it provided enough time resolution while minimizing noise in the 

concentration-time series. Although this procedure improved the results of the precision 

experiments, it did not affect the outcome of our analyses. The paired instrument precision

values were only used on a relative basis to rank the performance of the paired instruments and 

to determine their suitability for evaluating front/rear and inside/outside contrasts (see Section 

5.1.3.3). 

72 
5.1.3.1.1 Absolute and Percent Differences

We used equation (5.1.3.1) to calculate the average absolute precision (P) between paired 

instruments: 

( ) 2

σ μ ± • − = ∑ 1.96

c c

P 1 (5.1.3.1) 

e

mple size, and 

N

where c1 is the concentration of a pollutant obtained with one of the instruments at a given time, 

c2 is the concentration of the sam pollutant obtained with the second instrument at the same

time, N is the sa σ μ is the standard deviation of the distribution of differences 

tanda

.3.2 summarizes these results for the pollutants that 

recision was 13% while the

, we did not use the data from the NO2 precision experiments conducted during 

, roadway type, and window position, which were done 

ately 65% for black carbon, 20% for NO2, 10% 

r PAH

erefore not all of the seventeen runs mentioned in Table 5.1.3.1 

are presented in Table 5.1.3.2. 

(s rd error). 

To give context to the results, we computed the median of all bus run data for each 

pollutant. We then calculated the ratio between the average absolute precision between paired 

instruments during precision experiments, and the median value of the bus runs. We refer to this

ratio as the percentage precision. Table 5.1

were considered for the real-time analyses. 

We observed from Table 5.1.3.2, that in terms of percentage precision between paired 

instruments, the pair of instruments reading fine particle counts in the size range of 0.3–0.5 μm 

(PC) exhibited the best behavior (5% precision), closely followed by the PAH and DustTrak 

PM2.5 analyzers (8 and 9%, respectively). The NO2 instruments p

Aethalometer (measuring black carbon) exhibited a 53% precision. 

During weeks four and five, the NO2 analyzers experienced technical problems that made 

the interpretation of the data difficult. Hence, for the estimation of precision of NO2 

measurements

those weeks.

The poor precision for Aethalometers (53% precision) limited front-to-back and insideto-outside comparisons of black carbon bus commute data. Therefore, for black carbon, we 

focused on comparisons that did not require the use of paired instruments, including our

assessment of variables such as bus type

using the same instrument on all buses. 

The results of the laboratory post-study precision experiment confirmed findings from the 

bus-based precision measurements discussed above. The precisions obtained during the poststudy tests were similar to those obtained during the weekly instrument precision experiments for 

black carbon, NO2, PAH, and PC instruments. For the PM2.5 analyzers (DustTrak), the precision 

improved considerably under laboratory conditions (see Table 5.1.3.2). The relative precision 

obtained during the laboratory test was approxim

fo , 7.5% for PC, and about 1% for PM2.5.

Several of the instrument precision experiments had sample sizes too small to allow 

meaningful calculations and th

73 
Table 5.1.3.2 Paired instrument precision (average absolute and percent differences) (*)

Units 101 201 301 401 404 405 501 502 601 701 801 802 (1)

Range of 

controlled 

experiment 

(2)

Percent 

difference 

controlled 

experiment 

(%)

Avera

absolu

precisio

Black carbon ug/m3 N/A -1.15 ± 0.1 -1.7 ± 0.1 -1.6 ± 0.3 -1.7 ± 0.1 -2.5 ± 0.3 -0.9 ± 0.2 -1.5 ± 0.1 -0.6 ± 0.1 -2.3 ± 0.1 -1.3 ± 0.1 -17.5 ± 5.6 0.2 - 52 24 ± 1.

NO2 ppb 1.4 ± 1.0 -10.1 ± 2.4 -15.7 ± 0.8 -2 ± 1.3 40 ± 1.2 28.3 ± 0.6 16.5 ± 1.1 12.7 ± 1.0 8.4 ± 0.9 1.8 ± 3.2 -4.9 ± 1.1 -3.1 ± 0.7 7 - 29.3 17 ± 7 

PAH ng/m3 0.18 ± 0.2 1.78 ± 0.5 9.6 ± 2.4 -4.1 ± 1.6 -1.2 ± 0.6 0.6 ± 0.3 1.4 ± 0.6 1.4 ± 0.4 -0.8 ± 0.6 9.6 ± 1.3 0.7 ± 0.1 -98.9 ± 14.3 0.5 - 2000 10 ± 3.0 

PC #/cm3 N/A 0.52 ± 0.2 6.2 ± 1.2 -13.4 ± 0.4 6.7 ± 0.5 -11.2 ± 0.3 0.85 ± 0.6 -0.5 ± 0.1 5.4 ± 0.7 5.3 ± 1.0 2.9 ± 0.6 -52.1 ± 19.9 6 - 1400 7 ± 5.2 

PM2.5 ug/m3 3.5 ± 0.3 -3.9 ± 0.2 2.7 ± 0.6 -6.6 ± 0.3 5 ± 0.1 3.1 ± 0.1 0.5 ± 0.3 0.3 ± 0.1 5.8 ± 0.9 -5.8 ± 1.9 -9.8 ± 0.4 13.8 ± 140 29 - 31600 1 ± 4.

(*) These values correspond to the mean and the confidence interval of the absolute difference distributions among the instrument precision experimeThe absolute difference was computed subtracting the concentrations in thee front from the concentrations in the rear of the cabThe original 10-seconds data were smoothed using a 1-minute moving averag

1 Instrument precision experiment performed under controlled conditions in the laboratory (post-study test).2 Range of values observed during the instrument precision experiments performed in the laboratory (Run 802).3 Average precision of experiments performed inside the bus. Does not include run 802.4 Median of the entire data set, including all the runs.5 Range of peak concentrations observed during exposure runs.6 The NO2 instruments experienced technical problems during weeks four and five, therefore, the instrument precision results for these weeks were not considered for this calculation.
5.1.3.1.2 Coefficient of Variation

U.S. EPA's guidelines (U.S. EPA, 2000) recommend the use of the coefficient of

variation as a measure of precision between instruments sampling the same volume of air under

the same sampling conditions. The first step of this method is to calculate the percent difference 

(Di) between each measurement using equation (5.1.3.2): 

( ) 100

2

Y X

D

i i

i i

i • + = (5.1.3.2) 

here Yi is the concentration measured by one of the instruments at a given time and Xi is th

Y − X

e

oncentration measured by the other instrument at the same time. The percent difference is then

used to calculate the coefficient of variat equation (5.1.3.3):

w

c

ion (CVi) for each data point using

2

D

CV i

i = (5.1.3.3)

Finally, we computed the overall coefficient of variation (CV) as the root mean square of 

the distribution of individual coefficients using equation (5.1.3.4): 

N

CV

CV ∑ i = (5.1.3.4) 

These results are summarized in Table 5.1.3.3, which shows that in terms of the 

oefficient of variation, the pair of instruments measuring fine particle counts in the size range of 

% precision), the PM analyzers (DustTrak) 

.1.3.2 Correlation Between Pairs of Instruments

c

0.3 – 0.5 μm (PC) exhibited the best behavior (7 2.5

ranked second (14% precision), and the PAH and NO2 instruments ranked third and fourth (23 

and 33%, respectively). Again the Aethalometers ranked last (52%). 

5

marizes the correlation coefficients found for the instrument precision 

xperiments. During these tests both instruments in each pair were supposed to behave similarly, 

For the correlation coefficients analysis, the laboratory post-study experiments showed 

different results than the weekly instrument precision experiments. PM2.5 exhibited the best 

correlation during the laboratory tests (0.90), while the correlation coefficient associated with 

Precision is one means of measuring agreement. Another is tests of correlation. We used

the Pearson's correlation coefficient to describe the strength of the linear association between the 

readings of the pairs of instruments. 

Table 5.1.3.4 sum

e

therefore, the values of the correlation coefficients were expected to be close to one. Additional 

logarithmic transformations of the data distributions (not shown) did not appreciably improve the 

values presented below. 

The highest average correlation coefficient was obtained for the PC data (0.70), closely 

followed by PAH (0.65) and then by NO2 (0.57) and PM2.5 (0.54). Once again, the poorest 

results were obtained for the BC data (0.18). 

75 
Table 5.1.3.3 Estimation of precision between paired instruments using the coefficient of variation (%) (*). 

101 201 301 401 404 405 501 502 601 701 

Black Carbon N/A 67 ± 12 34 ± 18 33 ± 22 55 ± 10 61 ± 22 39 ± 26 74 ± 16 29 ± 16 54 ± 11 

NO2 N/A 26 ± 9 33 ± 27 15 ± 4 50 ± 3 58 ± 3 20 ± 11 23 ± 12 31 ± 30 48 ± 17 

PAH N/A 23 ± 7 23 ± 11 14 ± 7 25 ± 10 17 ± 16 22 ± 16 35 ± 15 31 ± 18 20 ± 8 

PC N/A 3 ± 2 7 ± 2 23 ± 4 8 ± 2 12 ± 5 4 ± 2 4 ± 1 2 ± 1 5 ± 3 

PM2.5 N/A 30 ± 3 8 ± 3 17 ± 4 13 ± 2 6 ± 3 10 ± 8 9 ± 4 4 ± 2 12 ± 4 

(*) The values in the table represent the coefficient of variation and the 95% confidence intervalThe original 10-seconds data were smoothed using a 1-minute moving average

76 
Table 5.1.3.4. Correlation coefficients for paired instrument precision experiments 

Table 5.1.3.4. Correlation coefficients for instrument precision experiments

101 201 301 401 404 405 501 502 601 701 801 

Black 

Carbon N/A 0.13 0.34 0.03 0.10 0.04 0.10 0.04 0.24 0.65 0.14 

PAH 0.45 0.76 0.69 0.76 0.73 0.71 0.83 0.13 0.46 0.79 0.85 

NO2 0.88 0.83 0.14 0.08 0.91 0.89 0.60 0.89 0.56 0.12 0.42 

PC N/A 0.84 0.44 0.34 0.59 0.93 0.88 0.88 0.76 0.39 0.98 

PM2.52 0.83 0.35 0.12 0.54 0.51 0.87 0.67 0.34 0.41 0.34 0.98 

1 Instrument precision experiments performed under controlled conditions in the laboratory2 Data obtained using the DustTraks3 Not applicable (see text)

77 
PAH was 0.89, and for both BC and PC, 0.62. We were not able to establish a meaningful

correlation coefficient for the NO2 data due to the previously mentioned problem with the NO2

instrument part way through the study. 

The results for the higher concentration post-study experiments show that the limitations

experienced during the in-field instrument precision experiments, in particular with the 

Aethalometer, are associated with the relatively low concentrations encountered during the busbased precision experiments. 

In addition to the correlation coefficient, which was used as a measure of variability 

between pairs of instruments, we considered the slope obtained from the linear regression. This

quantity is a measure of bias between instruments and can be used to estimate correction factors

if necessary. Table 5.1.3.5 summarizes these findings. 

To better understand the implications of these results, we created Table 5.1.3.6, which 

shows the bias between instruments, expressed as the percent difference between the expected 

slope (one) and the slope observed from the linear regression (Table 5.1.3.5). From Table 

5.1.3.6 we find, again, the PAH and the PC analyzers exhibited the best behavior along the 

course of the study. The average bias for those instruments was about 30%. PM2.5 and NO2 had 

biases of 46% and 61%, respectively, while black carbon measurements showed a bias of 85%. 

5.1.3.3 Overall Ranking

Based on the results presented above, we generated an overall ranking for the instrument

precision data. As described in Table 5.1.3.7, the best instrument pairs in terms of precision 

were the OPC (PC) and the PAH analyzer, which ranked first and second respectively. The 

DustTraks (PM2.5) ranked third, the NO2 analyzers fourth, and the Aethalometer (BC) fifth. 

Although the DustTraks exhibited a relative high precision, these instruments also 

showed low accuracy (see Section 5.1.2.2). These results were somewhat expected since this 

type of instrument is known to be less accurate (Hinds, 1999). 

Table 5.1.3.7 Overall ranking of the paired instrument precision data quality. 

Instrument Pollutant Precision (%) Coefficient of

Variation Correlation Overall Ranking

OPC Climet

Fine Particle Counts

(0.3 to 0.5 um) 1 1 1 1

Ecochem PAS 2000 Particle-bound PAH 2 3 2 2

DustTrak PM2.5 Mass 2 2 4 3

UCR analyzer NO2 4 4 3 4

Aethalometer Black Carbon 5 5 5 5

CRITERIA

78 
Table 5.1.3.5 Regression results for the paired instrument precision data

Table 5.1.3.5 Regression results for the instrument precision data (*).

101 201 301 401 404 405 501 502 601 70

Black 

Carbon N/A 0.32 3.38 0.19 0.30 0.07 0.45 0.13 0.28 1.5

NO2 0.74 0.91 0.08 -0.40 0.48 0.39 0.34 0.80 0.42 0.0

PAH 0.73 0.48 0.72 1.13 0.68 0.66 0.65 0.36 0.81 0.9

PC N/A 0.85 0.66 0.51 0.52 1.01 0.18 1.00 0.76 0.6

PM2.5 0.74 0.38 0.40 1.00 0.44 0.75 0.21 0.27 0.42 0.5

( *) The values in the table represent the slope calculated for the linear regression model.The values in color represent cases where the p-values indicated non-significant results.

79 
80 

Table 5.1.3.6 Bias between the instruments during instrument precision experiments (*).

101 201 301 401 404 405 501 502 601 701 

Black 

Carbon N/A 68% 238% 81% 70% 93% 55% 87% 72% 50% 

NO2 26% 9% 92% 140% 52% 61% 66% 20% 58% 94% 

PAH 27% 52% 28% 13% 32% 34% 35% 64% 19% 8% 

PC N/A 15% 34% 49% 48% 1% 82% 0% 24% 34% 

PM2.5 26% 62% 60% 0% 56% 25% 79% 73% 58% 50% 

(*) The values in the table represent the difference between the expected (one) and the observed slope from the linear regression.
As mentioned above, the poor results obtained for instrument precision for black carbon 

measurements are likely to be related to relatively low concentrations encountered during those 

tests. As stated earlier however, these precision estimates only apply to the precision between 

pairs of instruments. Paired instruments were only used to evaluate concentration differences 

between the front and rear of the bus cabin, and between the inside and outside of the bus during 

the same run. Paired instruments were not used to evaluate the differences in concentrations

between buses. For each pollutant, the same instrument was used to measure concentrations at

the rear of the bus cabin for all buses tested, and this measurement was used for the comparison

between bus types (and all other comparisons reported except front/rear and inside/outside). 

Thus, the precision results reported here allowed us to select the best pairs of instruments for 

studying the front/rear and inside/outside contrasts, but do not represent an overall measure of 

precision of a given instrument. 

5.1.3.4 Use of Correlation Coefficients

Table 5.1.3.7 shows the overall ranking of the instrument precision data quality, 

according to the criteria discussed above. PC and PAH measurements, during instrument 

precision experiments, ranked first and second respectively. These results suggest the PC and 

PAH analyzers provided the most reliable data for observing contrasts between pairs of

instruments. For this reason, we focused on these two pollutants when performing the

comparisons between pollutant concentrations measured at different locations during the bus 

runs (e.g., outside versus inside). 

The results of the instrument precision experiments were used to understand the 

limitations of the data and to determine which instruments were suitable for certain analyses

developed in the study. We did not apply any corrections to make one instrument equivalent to 

the other. 

However, we also included the other pollutants as appropriate. For the bus runs, we

found reasonably strong correlations between PAH and black carbon and between PC and PM2.5. 

Tables 5.1.3.8 and 5.1.3.9 summarize these findings and again, suggest the relatively poor 

precision of the pair of Aethalometers was probably due to low concentrations encountered 

during the instrument precision experiments. Tables 5.1.3.8 and 5.1.3.9 show that black carbon 

and PAH tended to exhibit similar behaviors during the bus runs, while PM2.5 and PC also 

exhibited similar trends and responses to the variables determining pollutant concentrations. 

Hence, for certain comparison we assumed black carbon and PM2.5 concentrations could be 

explained based on the results obtained for PAH and PC, respectively. 

 Because the NO2 data gathered during instrument precision experiments generally did not 

exhibit good precision we did not include NO2 in the analyses of front/rear and inside/outside

contrasts. 

5.1.4 Evaluation of Sampling Line Losses

We have previously evaluated losses of PM10 through the sampling lines used in this 

study (1/4 OD polyethylene impregnated with metal to reduce static charge buildup). To do this, 

we used three collocated DustTraks equipped with PM10 inlets. They sampled ambient air for 

two hours, alternating ten minutes without tubing and ten minutes with tubing (1.7 m in length). 

81 
Table 5.1.3.8. Correlation between PAH and other pollutants during exposure runs

HE1 HE2

Run 2 5 6 8 10 12 13 14 15 17 18 19 20 23 24 25 26 28 30 31 33 

NO2 0.01 0.01 0.27 0.02 0.25 0.02 0.31 0.01 0.40 0.28 0.16 0.01 0.08 0.10 0.05 0.21 0.29 0.41 0.13 0.57 0.18 

BC 0.57 0.41 0.65 0.60 0.81 0.33 0.56 0.90 0.91 0.71 0.40 0.45 0.69 0.68 0.75 0.91 0.84 0.57 0.66 0.76 0.80 

PM2.5 0.01 0.56 0.53 0.48 0.69 0.81 0.35 0.45 0.85 0.03 0.10 0.01 0.57 0.01 0.05 0.76 0.02 0.18 0.48 0.24 0.64 

PC 0.30 0.63 0.51 0.36 0.28 0.77 0.01 0.10 0.25 0.01 0.15 0.06 0.44 0.01 0.01 0.73 0.03 0.01 0.23 0.01 0.54 

Table 5.1.3.9. Correlation between PC and other pollutants during exposure runs

HE1 HE2

Run 2 5 6 8 10 12 13 14 15 17 18 19 20 23 24 25 26 28 30 31 33 

NO2 0.02 0.04 0.11 0.04 0.10 0.01 0.01 0.34 0.41 0.23 0.02 0.28 0.03 0.01 0.03 0.54 0.01 0.01 0.10 0.01 0.23 

BC 0.36 0.45 0.51 0.56 0.24 0.20 0.01 0.18 0.21 0.03 0.19 0.33 0.39 0.02 0.04 0.73 0.04 0.01 0.24 0.02 0.52 

PM2.5 0.10 0.81 0.60 0.84 0.35 0.71 0.07 0.66 0.41 0.63 0.62 0.76 0.57 0.12 0.60 0.82 0.62 0.01 0.39 0.38 0.66 

1 Arithmetic average of runs presented in the table2 Correlation coefficient estimated using the entire data set

HE3 RE1 RE2 TO1 HE3 RE1 RE2 TO1 
Data were collected as thirty-second averages. The data are summarized in Table 5.1.4.1. Based 

on the means, the tubing caused a loss of PM ranging from 21% to 29% depending on which 

DustTrak was evaluated. 

Table 5.1.4.1 Mean DustTrak PM10 response on ambient air with and without Bev-A-Line 

tubing.

Mean DustTrak 1 

(mg/m3

) w/o tubing 

 Mean DustTrak 1 

(mg/m3) w/ tubing 

Difference DT 1 

(w/o-w) 

% Difference DT 1 

(w/o-w) SD of Difference 

0.269 0.213 0.056 20.8% 0.117

 

Mean DustTrak 2 

(mg/m3

) w/o tubing 

 Mean DustTrak 2 

(mg/m3) w/ tubing 

Difference DT 2 

(w/o-w) 

% Difference DT 2 

(w/o-w) SD of Difference 

0.359 0.272 0.087 24.2% 0.132

 

 Mean DustTrak 3 

(mg/m3

) w/o tubing 

 Mean DustTrak 3 

(mg/m3) w/ tubing 

Difference DT 3 

(w/o-w) 

% Difference DT 3 

(w/o-w) SD of Difference 

0.253 0.179 0.074 29.3% 0.110

In addition, the data sets for each DustTrak were compared with and without the tubing

using the Wilcoxan (Mendenhall, 1971) non-parametric ranking test (in this test the data do not

have to be normally distributed). For all three DustTraks the data sets were shown to be

statistically different. The loss of PM10 due to the tubing, therefore, is significant and should be 

considered in future PM10 data analysis. 

However, we did not evaluate sampling line losses for any pollutants analyzed in this

report, or make corrections to our data to account for sampling line losses because sampling line 

losses will be highly dependent on the particle size distribution and therefore will vary depending 

on the characteristics of the PM being sampled, and because we believe any losses were small. 

Specifically, the estimates of sampling line losses given here for PM10 represent a worst-case, 

upper-limit. The PM sampling lines we used inside the buses ranged from a few centimeters 

(integrated PM2.5 mass both front and rear) up to 1and 1.5 meters in length at the front and rear of 

the bus, respectively, for real-time PM2.5 mass and fine particle counts in the range of 0.3 – 0.5 

μm in diameter, the PM size ranges our analyses focused on (see Section 4.1.2.2.2 for a

discussion of sampling line lengths). Based on results from other studies, we expect lower 

sampling line losses for particles in these size ranges compared with PM10. In addition, we also 

expect the shorter sampling lines used inside the buses compared with the study cited above 

would result in lower sampling line losses. Moreover, it should be emphasized that most of our

analyses were based on relative comparisons between buses and did not use absolute

concentrations or particle counts. In summary, we expect particle losses were small, particularly 

for measurements made inside the buses, since we focused our main study analysis on particles <

2.5 μm in diameter. 

83 
5.1.5 Autocorrelation

Autocorrelation, also known as serial correlation, is the statistic that measures the

correlation of a variable with itself over time. It can be calculated for lags (k) of any size, where 

the effective sample size (n) of paired values will decrease by one of each lag. 

Assessing the autocorrelation is important since data sets with this attribute are more 

complex to analyze. Several statistical procedures, such as significance tests, assume the data 

are free of autocorrelation (i.e., the series of observations are independent of one another). 

Among other effects, autocorrelation can severely reduce the effective sample size and degrees 

of freedom in a time series. 

In any time series containing non-random patterns of behavior, it is likely that any 

particular item in the series is somewhat related to other items in the same series. For example, 

ambient air data are, in general, likely to exhibit autocorrelation. Autocorrelation can be 

measured by estimating the sample autocorrelation coefficients (rk), where values of 

n

± 2

denote autocorrelations significantly different from zero (Norusis, 2002). We used equation 

(5.1.5.1) to compute the simple autocorrelation: 

[( )( )]

∑( ) yi − y

where y is the value of the object series, x is a lagged value of it, and 

∑

2

i x - (5.1.5.1) 

x yi − y

y and x are the respective 

means; there are n-k terms in the numerator summation, and n terms in the denominator 

ummation. Where simple autocorrelations are computed, no account is taken of the correlation 

een excluded. The results of 

is type of analysis are often portrayed graphically in the form of a correlogram, which is a plot 

of the a

ral, our data did not exhibit significant autocorrelation. 

ence, we did not perform any type of transformation or statistical treatment to account for

utocorrelation during our data analysis. 

s

between other pairs of entries. 

An alternative method for assessing autocorrelation is via the partial autocorrelation 

coefficient (for order k), which measures the strength of correlation among pairs of entries in the

time series while accounting for (i.e., removing the effects of) all autocorrelations below order k.

For example, the partial autocorrelation coefficient for order k=5 is computed in such a manner 

that the effects of the k=1, 2, 3, and 4 partial autocorrelations have b

th

utocorrelation coefficient (simple or partial) against the lag. 

We generated simple autocorrelation correlograms and partial correlation correlograms 

for all of the exposure runs. Figures 5.1.5.1 to 5.1.5.3 illustrate examples of partial correlograms 

obtained for three pollutants. The partial autocorrelation correlograms exhibit non-zero

coefficients for only one or two lags, beyond which they are not significantly different from zero. 

Therefore we assumed insignificant autocorrelation among these variables. Similar results were

obtained for most of the runs and, in gene

H

a

84 
BC_I_R

Lag Number

29

27

25

23

21

19

17

15

13

11

9

7

5

3

1

Partial ACF

1.0

.5

0.0

-.5

-1.0

Confidence Limits

Coefficient

Figure 5.1.5.1 Partial autocorrelation correlogram for black carbon during Run 6. 

Figure 5.1.5.2 Partial autocorrelation correlogram for PAH during Run 6. 

PAH_I_R

Lag Number

29

27

25

23

21

19

17

15

13

11

9

7

5

3

1

Partial ACF

1.0

.5

0.0

-.5

-1.0

Confidence Limits

Coefficient

Outside 

the cabin 

85 
PM25_I_R

Lag Number

29

27

25

23

21

19

17

15

13

11

9

7

5

3

1

Partial ACF

1.0

.5

0.0

-.5

-1.0

Confidence Limits

Coefficient

Figure 5.1.5.3 Partial autocorrelation correlogram for PM2.5 during Run 6 (data 

collected with the DustTrak instruments). 

side the cabin; hence, the time

ries were, to some extent, dominated by these high emissions. 

analyses suggest their effect was not as

important as the random encounters with high emitters. 

lation. These methods are

omplex and were deemed to be beyond the scope of this report. 

One possible explanation for the relative lack of autocorrelation in our data is related to 

the frequent presence of high-emitting vehicles in the surroundings of the bus. Encounters with

heavy duty vehicles (particularly other school buses ) occurred randomly many times during the 

runs and, as described in Section 5.3.5, the presence of these high emitting vehicles was one of 

the most important factors determining pollutant concentrations in

se

It was clear from the fieldwork that encounters with high emitting vehicles were largely

random occurrences except when we were caravanning with other BSMS buses at the beginning 

of the afternoon runs. Although events such as red lights, stop signs, and bus stops had the 

potential to be not entirely random, the autocorrelation

The analysis described above addresses only the autocorrelation within runs. It does not 

deal with the autocorrelation between runs if we combined them for a particular analysis. For 

example, it could be interesting to combine data for all morning runs and compare them to the 

combined data for all afternoon runs but this procedure is complicated. Combining all morning

runs, for instance, would create an interrupted time series, which requires the use of pooled timeseries cross-section models (TSCS) in order to assess autocorre

c

In summary, we did not use time series analysis for data sets comprised of more than one 

exposure run. However, combining data from different runs was required for other types of 

analyses such as comparisons between bus types and bus routes. Assuming the data distributions 

86 
were similar from runs conducted with the same type of bus under similar traffic and

environmental conditions, and considering the lack of autocorrelation within runs as discussed 

above, we were able to combine data from different runs to calculate means and confidence 

intervals and then used these results to determine significant differences between the analyzed 

variables (e.g., bus types). 

5.1.6 Normality

Most statistical procedures assume the distributions of the variables under consideration

come from a normal population, or the sample sizes are large enough so the distributions of 

sample means are approximately normal. Our data sets were, in general, large enough to satisfy 

this assumption (greater than 50); however, we ran formal tests to determine if the pollutant

oncentration time series fit normal distributions. 

ng the logarithmic transformation, the data related

the bus runs were not normally distributed. 

e scope of the present project. However, this would be an interesting topic for 

further study. 

ain is consistent with the use of confidence intervals 

(which assume independent samples). 

c

We used the Kolmogorov-Smirnov and Shapiro-Wilk Criteria and found most of the data 

sets (dominated by the bus runs) failed the normality tests (the only exception being the black 

carbon data collected during Run 19). Thus, the data obtained during the bus runs were 

generally not normally distributed. The logarithmic transformation is a widely used procedure 

that could, in principle, apply to our case since many studies have shown ambient air

concentrations tend to follow log-normal distributions. We used the base 10 transformation and 

repeated the tests. However, even after applyi

to

Figure 5.1.6.1 shows the histogram of the data collected with the PM2.5 analyzer 

(DustTrak) located at the rear of the bus during Run 14 (RE1-AM). The distribution of these 

data is a representative example of the majority of the bus run data for most pollutants. Figure 

5.1.6.1 exhibits a somewhat tri-modal distribution where each mode could be related to different 

conditions experienced during the bus runs. One possible interpretation of these results is that 

the first mode (low concentration) was related to low-traffic areas in West Los Angeles, the

second mode was related to higher traffic-density streets in the inner city, and the third mode

(high concentration) was related to concentration peaks resulting from self pollution or 

encounters with high emitting sources in both low-traffic and high-traffic areas (see Section 

5.3.5). Proving this hypothesis would require a series of thorough and detailed analyses that 

were beyond th

As mentioned above our data sets were in all cases sufficiently large (greater than 50 data 

points) that the violation of the normality assumption was not a factor for the type of analyses we 

performed. According to R. Berk (Personal Communication, 2002), for this dataset, basic

analyses were the most useful. We therefore focused our analyses on the use of confidence 

intervals and central tendency measures. For these analyses, the means are statistically 

significantly different if the confidence intervals do not overlap (although the reverse is not 

necessarily true). As explained in Section 5.1.5, in general, our data set did not exhibit 

significant serial correlation, which ag

87 
Finally, it is noteworthy that if future analyses of our data involving more advanced 

statistical tools were to be developed, it would be necessary to reconsider the issues of normality 

and serial correlation. 

PM25_I_R

87.5

82.5

77.5

72.5

67.5

62.5

42.47.552.5

57.5

5

37.5

Histogram

Frequency

70

60

50

40

30

20

10

0

Std. Dev = 12.02 

Mean = 61.4

N = 415.00

Figure 5.1.6.1 Histogram of PM2.5 during Run 14. 

Figures 5.1.6.2 show the normal Q-Q plot and the de-trended normal Q-Q plot, 

respectively, for PM2.5 during Run 14. These representations can be used to further describe the

not-normal characteristics of the data sets collected during the bus runs. Figure 5.1.6.2b, in 

particular, re-emphasizes the tri-modal characteristics of the observed data distributions. 

 

Normal Q-Q Plot of PM25_I_R

Observed Value

20 30 40 50 60 70 80 90 100

Expected Normal

3

2

1

0

-1

-2

-3

Detrended Normal Q-Q Plot of PM25_I_R

Observed Value

30 40 50 60 70 80 90

Dev from Normal

.6

.4

.2

-.0

-.2

-.4

-.6

(a) (b) 

Figure 5.1.6.2 Normal Q-Q plots of PM2.5 during Run 14 (data collected with the DustTrak 

instruments). 

88 
5.2. Relative Contribution of School Bus-Related Microenvironments to Children’s

 Exposure

As described in previous sections, three microenvironments were investigated concerning 

children's pollutant exposure due to school bus-related activities: urban bus routes, bus stops, and 

a loading/unloading zone at BSMS. Specifically, two urban routes to the BSMS were 

investigated; and two of the stops along urban route one (U1) were studied (Vermont Elementary 

School and Weemes Elementary), where diesel buses arrived to pick up or drop off students 

traveling to or from other schools, and a large number of parents dropped off or picked up 

children using their personal vehicles. The loading/unloading zone was investigated during the 

pilot study by conducting measurements with an instrumented van parked at the sidewalk of the 

BSMS. During the main study, the same instruments were used for all bus stop and bus 

commute measurements. 

Calculations of pollutant-specific exposure factors, defined as the product of the average 

concentration for a specific pollutant times the time spent by children in the particular 

microenvironment, were made for the three microenvironments, based on pilot study data. These 

initial calculations suggested the relative importance of bus commutes versus bus stops versus 

the loading/unloading zone were quite different. Specifically, from the pilot study data, we

found the exposure contributions of the bus stop and loading/unloading zone were about 3% and 

1%, respectively, of the bus commute exposures. We concluded the loading/unloading zone 

microenvironment is generally not as important as the other two (in part because of the small

amount of time children spent at the loading/unloading zone). 

In order to confirm these findings, we conducted measurements at an additional bus stop 

during the main study, in part because a dirty bus was involved in the pilot study. Cleaner buses, 

of the kind used in several runs of the main study were expected to exhibit lower commute

exposures, potentially leading to a greater exposure contribution from bus stops. 

However, except for black carbon, average commute concentrations were higher during 

the main study (see Table 5.2.1). These results could be explained by several factors, including 

differences in meteorological conditions between the two sampling periods. The pilot study was 

conducted during winter time, when wind speeds and ventilation of the basin are generally 

higher than during the spring/summer season, when the main study was conducted. During the 

main study, meteorological conditions were consistent throughout the eight weeks of the 

sampling period in May and June. 

Another potential explanation could be the shortening of the route used during the main 

compared with the pilot study. The pilot study route started at 6:00 A.M. on small residential 

streets with almost no traffic for a considerable part of the commute. This may have reduced the

average concentrations during the morning commutes with windows closed despite involving a 

“dirty” bus. Finally, it is also possible concentrations averaged over the entire main study period 

were dominated by the high concentrations resulting from the several high emitter buses

employed. 

Table 5.2.1 summarizes the mean concentrations observed in the three 

microenvironments, over appropriate periods of time, for both the main and pilot studies. These

89 
values are commonly referred to in the literature as average exposure (Monn, C., 2001). The

values reported for the loading/unloading zone runs during the pilot study and for the collocation 

experiments during the main study may be considered as urban background concentrations for 

the times and locations at which the experiments were conducted. 

Table 5.2.1 Mean concentrations in three microenvironments in pilot and main studies. 

Collocation

1 Loading/ 

unloading Zone

2 Bus Stops Bus

Commutes

3,4

BC (ug/m3) 2 2 4 3 - 19 (8)

PAH (ng/m3

) 23 15 47 64 - 400 (134)

NO2 (ppb) 64 35 54 34 - 110 (73)

PC (#/cm3

) 72 8 62 70 - 212 (130)

PM2.5 (ug/m3

) 235 N/A 26 21 - 62 (43)

1

 These values were measured during collocation experiments and likely typify urban background concentrations during the main study

2 These measurements were conducted during the pilot study and likely typify urban background concentrations during the pilot study

3 The ranges are associated with the different bus types (see Table 5.3.3.1 )

4

 The values within parentheses are the averages for all runs

5 2001 annual arithmetic mean from AQMD station at Central Los Angeles

Average Concentrations

Table 5.2.1 includes the results of the pilot study but does not include the rural/suburban 

route data from the main study. The relevance of this latter microenvironment is described 

below (see Section 5.3.5.2.4). 

The results obtained during the main study were, in general, similar to those obtained

during the pilot study except for PC, for which the average concentration measured at the main

study bus stop (Weemes ES) was about two times the average concentration measured at the 

pilot study bus stop (Vermont ES). 

Similarly, the average PC concentration inside the bus during main study urban route 

commute was about three times the average concentration measured inside the bus during the 

pilot study commutes. The majority of these differences could be explained by a change in the 

particle counts analyzer; during the main study we used a more sophisticated device than in the 

pilot study. 

The average BC concentration inside the cabin for the pilot study route (14 μg/m3

) was 

about two times that measured for the main study urban routes (8 μg/m3

). The BC results, both 

for the pilot and main studies were consistent with the findings of a recent Natural Resources

Defense Council (NRDC) study (Solomon et. al., 2001). The NRDC study also found higher 

concentrations in the rear of the bus and when the windows were closed, similar to our results as 

described in Sections 5.3.6.2 and 5.3.2. 

90 
Table 5.2.1 shows the loading/unloading microenvironment in front of BSMS was quite 

clean compared to the urban route commutes or bus stops, with the average concentration

observed at the BSMS within, and slightly below, the range of urban background values reported 

in the literature. 

We calculated the ratios of the average concentrations in the bus runs and stops relative 

to the loading/unloading zone microenvironment and these results are presented in Table 5.2.2. 

Average concentrations at the bus stop microenvironment were between two and eight times 

higher than at the loading/unloading zone microenvironment (dependent upon the pollutant) 

while average concentrations inside the bus on the urban routes were between 2 and 16 times

higher than at the loading/unloading zone. Average concentrations inside the bus on the urban 

routes were on average 1.3 to 2.8 times higher than at the bus stops. 

Table 5.2.2 Ratios of mean concentrations inside the bus and at bus stops relative to 

the loading/unloading zone. 

M icroenvironment Time spent (min.)

Loading/unloading 

(each time) 3

Bus stops 

(each one) 2 - 5

Urban route 

(each way) 76

Loading/ 

unloading

Bus stops

Urban routes (main 

study)

BC 124

PAH 139

NO2 122

PC 1 8 16

PM2.5 1 N/A N/A

Ratios of Average Concentrations (relative to L/U)

Table 5.2.3 shows our estimations of the time spent in each microenvironment. The 

commute time, or time spent inside the bus was the mean duration of the urban commute routes 

used during the main study (in the case of U1 representing the midway point between the first 

and last child on the full route used in the Pilot Study--see Section 4.1.5.1). The 

loading/unloading and bus stop times were estimated from observations in the field. These 

values are consistent with the results reported in the South Coast School Bus Idling Study 

(Solomon et. al., 2002). In our study, time spent in commuting on the bus was between 15 and 

38 times greater than for the other two microenvironments. 

Table 5.2.3 Estimated time spent in each microenvironment. 

91 
Table 5.2.4 shows the exposure factors resulting from the product of the average 

concentration and the time spent in each microenvironment. Across all pollutants the exposure

during bus commutes on urban routes was much higher than for the other two 

microenvironments. These results confirmed the findings of the pilot study that in general the

loading/unloading microenvironment is the least important in terms of school bus related 

exposure, both because children generally spend a short time on the sidewalk (three minutes or 

less) before entering the school in the morning or boarding the buses in the afternoon, and 

because bus drivers were required by school district policy to turn off their engines as soon as 

they arrived in the morning, before the children left the buses. Similarly, in the afternoon, 

drivers are not supposed to turn on their engines before all children are aboard the buses and the 

entire fleet is prepared to depart. During both the pilot study and the main study we observed a 

near 100% of compliance with these regulations in the morning with somewhat less than full 

compliance in the afternoon, due to buses leaving while some children were still waiting to 

board. However, we have anecdotal evidence from a BSMS teacher that this policy is not always 

rigorously enforced (Fogel, 2002). In addition, according to a recent NRDC study (Solomon et. 

al., 2002), lack of explicit idling policies is common in Los Angeles area district schools. 

During our monitoring periods at both the Vermont Avenue School and the Weemes 

Elementary School bus stops, at various times as many as half a dozen buses would pull up in 

front of the school and wait with the engine idling until children boarded. In several cases, buses 

were early and waited with their engines idling for several minutes, while students waiting for

other buses stood nearby. Buses would then accelerate away from the curb, often releasing an 

exhaust cloud of black smoke. Similarly, parents dropping off or picking up children at those 

schools serving as bus stops, in a number of cases were driving what appeared to be high emitter

vehicles (as documented by us in our field notebooks). 

Table 5.2.4 Mean exposure factor calculations. 

Loading/ 

unloading Bus stops1 Urban commutes

BC (ug/m

3

-min) 5 8 - 20 600

PAH (ng/m

3

-min) 45 95 - 230 10000

NO2 (ppb-min) 105 110 - 270 5500

PC (#/cm

3

-min) 25 125 - 310 10000

PM2.5 (ug/m

3

-min) N/A 50 - 130 3500

1

 The ranges are associated with the different values for time spent in each

 microenvironment used in the calculations (see Table 5.2.3 )

Exposure Factors

92 
The calculated ratios of exposure factors for the three microenvironments and these 

results are summarized in Table 5.2.5. 

Table 5.2.5 Exposure ratios for the three microenvironments. 

Bus stops / 

L/U 2

Urban commutes / 

L/U 2

Urban commutes / 

Bus stop1

BC 1.3 - 3.5 100 30 - 75

PAH 2.1 - 5.2 220 45 - 110

NO2 1.0 - 2.6 50 20 - 50

PC 5.2 - 13 400 30 - 80

PM2.5 N/A N/A 25 - 70

1

 The ranges are associated with the different values for time spent in each

icroenvironment used in the calculations (see Table 5.2.3 )

L/U = loading/unloading zone

Exposure Ratios

 

 m

2 

In terms of exposure, the bus stops microenvironment was between one and thirteen 

times more important than the loading/unloading microenvironment, while the urban bus 

commutes were between 50 and 400 times more important than the loading/unloading 

microenvironment. The urban bus commutes led to between 20 and 100 times higher exposures 

than at the bus stops, depending upon the pollutant. 

5.3 Important Variables Governing Exposure During Bus Commutes

The design of this study allowed us to investigate the importance of multiple variables

governing children’s exposure to vehicle-related pollutants during school bus commutes. The 

variables analyzed in this study included contribution of the bus’s own exhaust, window position 

(open versus closed), bus type (based on engine type and age, fuel, and after-treatment 

technology), route (rural/suburban versus urban and between urban routes), roadway type 

(freeway versus non-freeway), the effect of following other diesel vehicles, the effect of idling, 

position within the bus (front versus rear) and the difference between inside and immediately 

outside the bus. Only the front/rear and inside/outside comparisons involved using paired 

instruments. All other variables were evaluated based on measurements taken at the rear of the 

bus cabin with the same instruments (e.g. the same Aethalometer was used for all black carbon 

comparisons between buses, etc.). 

The exposure runs on HE1, performed during the first week of the main study, were 

different than those conducted in subsequent weeks for a number of reasons, and thus were

generally not included in our analyses. First, the instruments on the bus were powered by a 

93 
propane-fueled generator which was towed behind the bus, as was done in the pilot study. This 

created several problems. During the rural/suburban run, the generator became disconnected

from the bus, likely due to the vigorous motion of the bus at higher speeds. During the morning 

run on the primary urban route, the muffler fell off the generator while the bus was traveling on 

the freeway, which resulted in the bus sitting on the side of the freeway for several minutes with 

the windows open and the instruments still measuring pollutants in the bus. In addition, during 

these runs, the window position on the bus was not fixed. We alternated between windows 

opened and windows closed for both runs on the primary urban route. By contrast, the window 

position was fixed on all subsequent buses, with windows in the morning always closed, and

windows in the afternoon always partially opened, reflecting the practices we observed on in-use 

school buses. Finally, during the afternoon run, the BSMS let out one hour earlier than usual on

this day. In an attempt to mimic regular BSMS bus commutes, we decided to run the route at 

this earlier time because we felt it was important to capture the effect of caravanning (which

occurs when leaving the school with the nineteen BSMS buses as shown in Photograph 11). 

However, without the muffler, the noise from the generator was so great the BSMS officials 

would not allow our bus to wait in front of the school and we had to leave immediately, without 

waiting for the other buses. Because the run started an hour earlier than usual, traffic conditions

on the route were considerably lighter than encountered at the regular time. Because of these 

difficulties and confounding factors, we felt the exposure runs on HE1 should not be included in 

the majority of our comparisons with the other buses. However, these runs were still valuable 

because they provided data on bus commutes during periods of lighter traffic and allowed us to 

perfect our sampling strategy for the subsequent bus runs. 

For the analyses described in the remainder of Section 5.3, only data from the second 

through eighth weeks of the study were used (e.g. HE2, HE3, RE1, RE2, TO1, CNG). 

Photograph 11. Caravanning after departing from BSMS during an afternoon run 

 (line of 5 buses). 

5.3.1 Determination of Contribution of Bus’s Own Exhaust Inside the Cabin

Because a potentially important source of children’s exposure to pollutants from vehicle

exhaust is the introduction of a bus's own exhaust into the passenger compartment, our study 

included a method to determine the percentage of air in the bus cabin originating from the bus’s

94 
own exhaust. A tracer gas, SF6, was metered into the bus’s exhaust system from a high 

concentration cylinder using a mass flow controller whose flow rate was logged by the data 

acquisition system and processed with the rest of the measurement data. The concentration of

SF6 and the flow rate were selected to detect if at least 0.1 percent of the air in the passenger 

compartment of the bus was from the vehicle’s exhaust. We emphasize these experiments were

completely independent of, and not affected by the SF6 experiments conducted to determine the 

ventilation rates in the passenger compartment (see Section 4.1.3). 

 The SF6 injection probe extended ∼ 15 cm into the bus’s exhaust pipe in order to provide 

reasonable mixing of the SF6 without attempting to snake the injection probe around the bends in 

the exhaust system. At the injection point, the maximum exhaust temperature of the bus was 

expected to be about 200o

C, well below the temperature at which SF6 decomposes. 

 The SF6 concentration in the bus was measured on a continuous basis using a CTA-1000 

continuous tracer gas analyzer whose inlet was connected to a series of solenoids that switched

the sample inlet between the front and rear of the bus cabin. To account for baseline drift of the 

CTA-1000 additional solenoids switched the instrument to SF6-free air drawn through a line 

located outside the bus next to the right rear-view mirror. Although this third sample line

generally provided a reference zero value, it also yielded data indicating that under the right wind 

conditions (i.e., wind from the rear) when the bus was stopped and idling, significant amounts of 

the bus’s own exhaust reached the location next to the passenger door at the front of the bus. 

Control of the switching and logging of the solenoid position was performed by the data 

acquisition system and switching times from 2-4 minutes were used during the initial bus runs.

After reviewing the system’s response, a switching time of four minutes per position (twelve 

minutes per cycle) was determined to provide the optimum data and this switching time was used 

for all subsequent buses. When the solenoids switched from one position to the next, they

generated a pressure pulse in the sample line that temporarily threw the CTA-1000 off scale. 

The instrument’s recovery time from the solenoid switching was determined to be close to three 

minutes. As a consequence, the first three minutes of data after each solenoid position change 

were voided and only the final minute of data was employed in the following analysis. 

5.3.1.1 Analysis of SF6 Data

Figure 5.3.1.1 depicts the conditions at the point of injection (A): 

A

Qeng, Ceng Qexh, Cexh 

Qcyl, Ccyl

Figure 5.3.1.1 Schematic of the SF6 injection point. 

95 
where: 

Qeng : Flow rate of the engine gases 

Ceng : Concentration of SF6 in the engine gases (zero) 

Qcyl : Flow rate of the high-concentration SF6 cylinder 

Ccyl : Concentration of SF6 in the cylinder 

Qexh : Flow rate of the exhaust (end of tailpipe) 

Cexh : Concentration of SF6 in the exhaust 

The mass flow rates were expressed as: 

Mcyl Qcyl Ccyl = • (5.3.1.1) 

where: 

 cyl M : Mass flow rate of SF6 from the cylinder

NOTE: similar equations may be written for the mass flow rates from both, the engine and the 

exhaust.

From the mass conservation principle (at the point of injection) we know 

Mexh = Meng + Mcyl (5.3.1.2) 

considering 5.3.1.2 and solving for Cexh yields 

cyl

exh

exh Q

To estimate the concentration of SF6

cyl C Q C = • (5.3.1.3) 

 in the exhaust, we need to know the flow rate of 

xhaust which is given by equation (5.3.1.4), a commonly used expression in mechanical 

engineering (Western Filter Co, Inc., 2002) 

e

C

E D V

Q rpm eff

exh

• • = (5.3.1.4) 

:

h ses

r minute 

eff : Volumetric efficiency 

: Engine's cycle factor

where

Qex : Flow rate of exhaust ga

Erpm : Engine's revolutions pe

D : Engine's displacement

V

C

96 
For a turbocharged diesel engine, the average volumetric efficiency (measure of the 

charge that enters the cylinder compared to ideal conditions) is about 1.5. Two-cycle engines 

displace their engine size every revolution while 4-cycle engines displace their engine size every 

two revolution; therefore, the engine's cycle factor (C), a measure of the number of spark-plug 

fires per revolution, is one and two for 2-cycle and a 4-cycle engines, respectively. Five of the 

seven buses used in the study had 4-cycle engines and the buses used in the study had engine

displacements ranging from six to 10 liters. Diesel engines typically operate around 1500 rpm 

sed Run 31 to illustrate our calculations. The bus used for this run had an 8.3 liter, 

-cycle, turbocharged engine. Using equation (5.3.1.4) and the values mentioned above we 

estimated the average flow rate of exhaust gases uring Run 31 to be approximately 9000 liters

per minute. 

for a variety of speed/gear combinations ( this number was selected after reviewing several diesel 

engine specifications and performance curves). 

We u

4

d

min

L

rev

1

min

rev

exh 9000

2

1500 8.3L 1.5

Q ≅ • • =

The release rate of SF6 from the high-concentration gas cylinder varied between runs and

even within runs. The release rate during Run 31 was about 2.3 liters per minute. The 

concentration of SF6 in the canister was 5000 ppm for the first five weeks of the study and 10000 

ppm for the last three weeks. During Run 31, we used a 10000 ppm cylinder. 

Using equation (5.3.1.3) we estimated the concentration of SF6 n the exhaust during Run 

31 to be approximately 2.5 ppm (the average concentration of SF6 measured inside the cabin 

as about 1000 ppt or 1 ppb).

i

during Run 31 w

10000ppm 2.5ppm 9

2.3

C min

L

exh-exhaust = • ≅

000 min

L

The ratio (R), which represents the p ation inside the cabin 

origina

ercentage of pollutant concentr

ting from the exhaust, or the concentration of SF6 in the cabin divided by the

concentration of SF6 in the exhaust was 

0.04%

1x10 ppt

1ppm

2.5ppm

1000ppt

C

C R 6

Exhaust

cabin = = • ≅ (5.3.1.5) 

 

Table 5.3.1.1 summarizes the results obtained for all of the bus runs (except from the first

eek) using the procedure described above for SF6 measurements at the rear of the cabin. The 

results presented in this table as well as in Figure 5.3.1.2 show the percentage of air inside the 

cabin originating from the exhaust were a function of bus type as well as window position. 

Values were substantially higher during morning runs, when all the windows of the bus were 

closed compared with afternoon runs, when the windows were partially opened. Thus, the SF6

data suggest exposure to pollutants originating from the bus's own emissions were higher when 

w

97 
Table 5.3.1.1. SF6 measurement results

Bus type Displacement (L) Cycles Exhaust flow rate (lpm) 1 SF6 in the cylinder (ppm) Run number Median release rate (lpm) 2 Average SF6 concentration at the tailpipe (ppm) 3 Average 

concentratio

6 2 13500 5000 5 1.9 0.704 922 6 2 13500 5000 6 4.4 1.630 620 6 2 13500 5000 8 1.1 0.407 1186 2 13500 5000 10 1.1 0.407 439 

8.3 4 9338 5000 12 1.0 0.535 N/A 8.3 4 9338 5000 13 1.0 0.535 N/A 8.3 4 9338 5000 14 1.1 0.589 N/A 8.3 4 9338 5000 15 5.5 2.945 966 6.6 4 7425 10020 17 3.8 5.128 1026.6 4 7425 10020 18 3.9 5.263 304 6.6 4 7425 10020 19 3.9 5.263 815 6.6 4 7425 10020 20 3.9 5.263 868 6.6 4 7425 10020 23 3.9 5.263 997 6.6 4 7425 10020 24 3.9 5.263 741 6.6 4 7425 10020 25 2.7 3.644 1136.6 4 7425 10020 26 2.9 3.914 N/A 8.3 4 9338 10020 28 2.0 2.146 827 8.3 4 9338 10020 30 4.3 4.614 848 8.3 4 9338 10020 31 2.3 2.447 801 8.3 4 9338 10020 33 2.0 2.146 519 8.1 4 9113 10020 35 2.9 3.189 1168.1 4 9113 10020 36 2.9 3.189 807 

1 Calculated using equation 5.3.1.42 Calculated as the median of the 10-seconds release rate data for each run.3 Calculated using equation 5.3.1.34 Calculated as the mean of the concentrations measured at the rear of the cabin for each run5 Calculated using equation 5.3.1.5

TO1

CNG

HE2HE3

RE1RE2
0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

HE3 (1975) HE2 (1985) RE2 (1993) RE1 (1998) TO1 (1998) CNG (2002)

Windows Closed (morning)

Windows Open (afternoon)

Figure 5.3.1.2 Percentage of air inside the cabin originating from the bus's own exhaust (%). 

the windows were closed. It is also important to consider that, as described in Section 4.1.3, the 

ventilation rate of the bus cabin was determined, primarily, by the position of the windows, since

each bus was checked for exhaust system leaks. 

Figure 5.3.1.2 also shows the importance of bus type. The older buses HE2 (1985) and 

HE3 (1975) showed a larger percentage of their own exhaust entering into the cabin compared

with newer buses RE2 (1993), TO1 (1998), CNG (2002). These results are consistent with the 

older buses not being as well isolated (airtight) from outside air compared with the newer buses, 

due to design or construction. However, other factors have to be considered as well. 

Figure 5.3.1.3 presents the results of the snap-and-idle tests we conducted (see Section 

4.1.1). The HE2 bus exhibited the highest emission factors among the buses used in the study,

followed by HE3, RE2 and HE1. The TO1 and the RE1 buses were the cleanest according to 

those tests (it was not possible to perform the test for the CNG bus). Thus, even without 

considering the pollutant concentration data, we would expect exposures associated with HE2 to 

be among the highest for the buses tested. 

The snap-and-idle test is an experiment convenient and easy to perform, however, these 

results can only be used as qualitative measures. We included these results in our analyses since 

they were the only source of information about the tested buses emissions. 

99 
0.0

0.1

0.2

0.3

0.4

0.5

0.6

HE1 HE2 HE3 RE1 RE2 TO1

Opacity

 

Figure 5.3.1.3 Snap-and-idle test results expressed in terms of opacity (fraction). 

5.3.1.1.1 Measurements of SF6 Concentrations

As mentioned above, three sampling positions were employed: inside/front, inside/rear 

and outside/front. Figure 5.3.1.4 shows a schematic of the location of these sampling probes. 

 

Exhaust

(SF6) 

Sampling 

(Inside/rear) 

Sampling 

(Inside/front)

Sampling 

(Outside/front) 

CROWN

Figure 5.3.1.4 Schematic of school bus indicating the location of the SF6 sampling probes.

100 
Figure 5.3.1.5 shows the means and 95% confidence intervals for the average SF6

concentrations measured at the three locations (front, rear, and outside) during the morning runs, 

indicating that, in general, there was a substantial difference between the measurements at the 

front and the rear of the cabin. Specifically, the SF6 data indicated exposure to pollutants from 

the bus's own emissions were higher at the rear of the cabin than at the front. The influence of 

position inside the cabin is further analyzed in Section 5.3.6.2. 

Figure 5.3.1.5 also shows there were detectable SF6 concentrations outside at the front of 

the bus at times during all runs, although, these concentrations were much smaller than the 

concentrations inside the bus. Since we injected the tracer gas in the tailpipe and there cannot be 

any other source of it, these results show the bus’s exhaust was able to reach the sampling line

located at the front (outside) of the bus, for example, when buses were idling (e.g., at a red light

or a bus stop) and the wind was blowing from the rear to the front of the bus. This also shows 

there are two different means for the exhaust gases to enter the cabin of the buses. First, leaks

from the engine’s compartment into the cabin and second the exhaust plume traveling from the 

rear of the bus and entering through the windows (if these are open). Because the SF6

concentrations measured at the front (outside) of the bus were substantially lower than inside the

bus (either front or back), the second mechanism appeared to be less important than the first. 

Run

5 8 17 19 23 25 28 31 35

SF6 mean and 95% C.I.

2500

2000

1500

1000

500

0

LOCATION

Front

Rear

Outside

Figure 5.3.1.5 Means and 95% confidence intervals of SF6 concentrations (ppt) 

measured at front, rear, and outside locations during morning runs. 

101 
Figure 5.3.1.6 shows the 95% confidence intervals for the SF6 concentrations measured at 

the three locations during the afternoon runs, and these were, in general, somewhat lower than 

during morning runs. Figure 5.3.1.6 also shows the differences between the SF6 concentrations

measured at the three locations are smaller during the afternoon runs compared to differences 

measured during morning runs. Thus, when the windows of the buses were partially open, for 

the majority of the runs there were no significant differences between measurements at the front 

of the cabin, at the rear of the cabin, and outside the bus. 

Run

6 10 15 18 20 24 30 33 36

SF6 mean and 95% C.I.

2500

2000

1500

1000

500

0

LOCATION

Front

Rear

Outside

Figure 5.3.1.6 Means and 95% confidence intervals of SF6 concentrations (ppt) 

measured at front, rear, and outside locations during afternoon runs. 

5.3.1.2 Correlation between SF6 versus Black Carbon and PM2.5.

Section 5.1.3.2 describes how the correlations between black carbon and PAH were 

higher than those between PM2.5 and PC (between 0.3 and 0.5 μm). PM2.5 and PC exhibited 

different behavior but followed similar patterns between them, consistent with multiple other

sources and a generally high elevated background of PM2.5 (Zhu et al., 2002, a,b). 

We selected black carbon and PM2.5 (one pollutant from each of the two groups) to 

analyze the implications of the SF6 data in terms of the differences between emissions among the 

different buses. As mentioned above, based on the results of the correlation coefficients between 

the analyzed pollutants (see Tables 5.1.3.8 and 5.1.3.9), we assumed black carbon and PM2.5

concentrations may be used to explain the behavior of PAH and PC, respectively. It is important 

to note that although we show in Section 5.1.3 that the precision between paired Aethalometers 

(used to measure black carbon) was low, these results only apply to analyses which used paired 

instruments. The correlations between SF6 and black carbon were estimated using data from a 

102 
single Aethalometer (the rear instrument), therefore the precision estimates for paired 

instruments described in Section 5.1.3 do not apply to the following analysis. 

To obtain the results presented below we normalized the data using the maximum 

concentrations. For example, during Run 6 the maximum concentration of black carbon was 

about 40 μg/m3

 and we divided every data point of Run 6 by this value. This procedure was 

applied for all runs for black carbon and PM2.5. Since it is easier to interpret the differences 

between absolute quantities, rather than the differing concentrations of the different species, we 

eliminated the units of mass per volume. 

Figure 5.3.1.7 summarizes the results obtained for the correlation coefficients between 

SF6 and black carbon (normalized) and between SF6 and PM2.5 (normalized). To avoid 

confounding factors we only analyzed the morning runs in which, as mentioned elsewhere, all 

the windows were closed. The calculated Pearson's regression coefficients ranged from 0.1 to 

0.6 for PM2.5 whereas for black carbon they were about 0.5 for the two runs with available data. 

The consistent, relatively high correlations between SF6 and black carbon suggested a stronger 

relationship between black carbon and bus emissions than between PM2.5 and bus emissions. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

HE3 - Windows Closed RE2 - Windows Closed TO1 - Windows Closed CNG - Windows Closed

Correlation Coefficient (black carbon)

Correlation Coefficient (PM2.5)

Figure 5.3.1.7 Correlation coefficients between SF6 versus black carbon and between SF6 

 versus PM2.5 (data collected with the DustTrak instruments) 

103 
5.3.1.3 Estimation of Bus Cabin Black Carbon Concentrations Using the SF6 Data

As just shown, among the pollutants measured during the study, black carbon correlated 

well with the bus’s own exhaust. Since all of the SF6 found in the cabin was from the bus’s own 

exhaust, we could use the ratio of the concentration of SF6 in the cabin to the concentration of

SF6 in the exhaust to obtain a rough estimate of the amount of black carbon inside the cabin 

originating from the bus’s own exhaust. 

Our first-order-approximation model started with an estimate by Miguel et al. (1998) that 

black carbon emissions from heavy-duty diesel vehicles are 1440 ± 160 milligrams per kilogram

of fuel burned. We used the upper end of this range (1600 mg/Kg) as the emission rate for the 

bus used during Run 25 (RE2-AM) and assumed a typical fuel consumption rate of 10 

miles/gallon. For this bus commute, the distance covered was about 20 miles and the total time

of the run was about 80 minutes. From these values, we estimated the amount of fuel burned per 

unit of time to be about 0.025 gallons per minute. 

minute

gallons 0.025

10 miles

1gallon

80 minutes

20 miles • =

We also know the density of diesel fuel: 

gallon

Kg ρ 830 3 3.15 m

Kg

Diesel ≅ ≅

Thus, the fuel burned per time can be expressed as 

min

Kg -fuel 0.08

gallon

Kg 3.15

min

gallon 0.025 • ≅

We used this value and the emission rate reported by Miguel et al. (1998) to estimate the mass 

flow rate of black carbon ( M ) BC

min

mg BC

min

Kg Fuel

Kg Fuel

mg BC MBC 1600 0.08 128 − −

−

− = • =

Similar to equation (5.3.1.1): 

MBC CBC-Exhaust QExh = • (5.3.1.6)

Solving for the concentration of black carbon in the exhaust (CBC) 

3

9000 1mg 1m3

min

L Exhaust BC-Exh − m 14000

128 1000 g 1000L C min

mg BC

μ μg = • • ≅

−

104 
During Run 25, the percentage of air inside the cabin originating from the exhaust was

about 0.03% (see Table 5.3.1.1). Hence, we estimated the concentration of black carbon inside

the cabin to be about 4 micrograms per cubic meter. 

m 3

μg

BC Cabin

m

μg C − = 14000 3 • 0.0003 ≅ 4

he fieldwork.

The mean of the real-time black carbon concentrations during Run 25 was about 9 μg/m3, 

which is of the same order of magnitude as the estimated value of 4 μg/m3

. Agreement to within

a factor of two between the calculated and measured black carbon concentrations tends to 

validate both the SF6 and the black carbon data obtained during t

We emphasize again that the approach employed in the previous calculations was not 

intended to produce accurate values that can be extrapolated. The estimates were meant to be 

only first-order approximations aimed at validating the data gathered by two different

instruments, as well as identifying major trends in our data set. 

5.3.2 Effect of Window Position

The position of the windows was one of the most complex variables analyzed in this 

study. Window position contributed importantly to the ventilation rate inside the cabin leading

n Sections 5.3.6.3 and

5.3.6.2, respectively. 

These experiments were performed on the I-405 freeway between 8 and 9 AM, traveling 

tes.

f about seven minutes. During the first cycle all windows 

were closed. During the second cycle every other window was opened about 10 inches on the 

dr side, where the instruments were located. 

Table 5.3.2. ar e char he w sition experiments. 

Ta .3 ow tion t

Run 

to substantially different exposures. Assessment of the influence of the window position

required consideration of other variables such as position of the sampling probe (inside versus

outside) and its location inside the cabin (front versus rear) as discussed i

Due to the relevance of windows position, demonstrated in part during the pilot study, we 

performed three additional tests (Runs 9, 29, and 32) specifically designed to test the effect of the 

window position (See Section 4.1.7) on the concentrations inside the bus. 

south from Wilshire Avenue (close to UCLA) to Century Blvd (close to LAX) and then returning 

north to UCLA on I-405. During these runs we experienced substantial differences in congestion 

in the north/south directions. Since differences in average speeds led to different ventilation 

rates, we effectively tested the effect of window position for different ventilation ra

We utilized alternating cycles o

iver's side and about two inches on the opposite

1 summ izes th acteristics of t indow po

ble 5 .2.1 Wind posi ests. 

Date Time Type of Bus Week 

Number

9 05/08/02 AM Conventional diesel 3 

29 06/05/02 AM Trap-outfitted 7 

32 06/06/02 AM Trap-outfitted 7

105 
Inspection of the time series graphs for each run allowed identification of changes in the

concen

to link

em directly to emissions. In summary, the particle counts measured in the accumulation mode 

more like a “background” pollutant, and this is confirmed by the strong 

.3.2.1 Black Carbon

trations that could be correlated with the position of the windows and calculation of the

95% confidence intervals of the average concentrations demonstrated exposures inside the cabin 

depended significantly on the position of the windows. This finding was confirmed when we 

analyzed the cumulative frequency distribution of the data. 

As mentioned in several sections of this report, the influence of the window position

varied between pollutants. Directly emitted, diesel vehicle-related pollutants with low

background concentrations, such as black carbon, PAH, and NO2 exhibited a different behavior 

than those such as PC and PM2.5 that had strong “background” contributions. Recent studies 

(Zhu et. al. 2002 a,b) suggest that PM2.5 is a “background” pollutant substantially affected by 

regional sources, secondary formation and meteorological conditions and not a sensitive

signature for direct emissions from vehicles. As mentioned before, our analyses of the particle 

count data refer only to the size range between 0.3 and 0.5 μm, near the peak of the accumulation

mode for the particle size distributions by volume and mass (Hinds, 1999). Although number

concentrations of ultra-fine particles are closely related to direct emissions from mobiles sources

(Zhu et al., 2002 a,b), once these particles grow into the accumulation mode it is difficult

th

would tend to behave

correlations we found between PC (0.3 to 0.5 μm) and PM2.5. For the following analysis, we 

selected one pollutant we will refer to as primarily directly emitted (e.g. black carbon or PAH),

and one (e.g. PM2.5 or fine particle counts) we will refer to as a “background” pollutant. 

5

he results of the 95% confidence interval calculations were also consistent between the 

ally higher than during Runs 29 and 32 (on the trap-outfitted diesel

us), due to the different bus types as discussed in Section 5.3.1. 

The concentration time series for black carbon followed similar trends for all three

window position tests. The concentrations inside the cabin tended to decrease when the

windows were opened and tended to increase when the windows were closed. Figure 5.3.2.1 

shows the ten-second data for black carbon concentrations during Run 32. 

T

three runs. In all cases, the average concentrations were higher when the windows were closed 

(see Figure 5.3.2.2). However, the average concentrations during Run 9 (on a conventional 

diesel bus) were substanti

b

Figure 5.3.2.3 shows the cumulative frequency distributions of the concentrations with 

the windows open and closed during Run 32, demonstrating the concentrations were higher when 

the windows were closed. 

5.3.2.2 Particle Counts in the Accumulation Mode

For particle counts in the size range of 0.3 – 0.5 μm (PC), the concentration time series

lso followed similar trends for all three runs, but different to those observed for black carbon. 

he concentrations inside the cabin tended to increase when the windows were opened and 

nded to decrease when the windows were closed. Figure 5.3.2.1 shows the ten-second data for 

C in a portion of the accumulation mode (0.3 to 0.5 um) during Run 32. 

a

T

te

P

106 
150

170

190

210

230

250

270

290

310

330

350

0

Particle Counts (#/cm3)

.

2.

4.

6.

8.

10.

12.

14.

16.

Black Carbon (ug/m3)

7:56 08:03 08:10 08:17 08:24 08:32 08:39

Time (hh:mm)

Particle Counts

Black Carbon

Windows

Open

Windows

Closed

Windows

Open

Windows

Open

Windows

Closed

Window

s Closed

F

Run

Convent. diesel (9) Trap-outfitted (29) Trap-outfitted (32)

95% ac

k

o

n

20

18

16

14

12

10

8

6

2

Windows

Closed

 CI Bl

4

Open

C

arb

igure 5.3.2.1 Black carbon and particle counts (0.3 to 0.5 μm) concentrations during 

Run 32 on the particle-trap outfitted diesel bus.

Figure 5.3.2.2 Mean concentrations and 95% confidence intervals of black carbon (μg/m3

). 

107 
20.

40.

50.

60.

70.

80.

90.

100.

Cumulative Frequency

Windows Open

Windows Closed

108 

.

10.

. 2. 4. 6. 8. 10. 12. 14. 16.

Concentration (ug/m3)

30.

Run

9 29 32

95% CI Par

0 Closed

ticle Counts

400

300

200

100

Windows

Open

he 95% confidence intervals for PC (0.3 to 0.5 um) were again consistent between the 

three runs but opposite of those for black carbon. In all cases, the average concentrations were

higher when the windows were opened. Figure 5.3.2.4 depicts these results. The average 

concentrations during Run 9 were substantially lower than during Runs 29 and 32. These

differences could be related to differences in background concentrations of particles in the 

accumu tion mode. 

Figure 5.3.2.4 ean particle counts and 95% confidence intervals

the accumulation mode from 0.3 to 0.5μm (#/cm3

). 

Figure 5.3.2.3 Cumulative frequency distributions for black carbon during Run 32. 

T

la

M

in
0

10

30

40

50

60

70

80

90

100

50 100 150 200 250 300 350

Cumulative Frequency

20

0

Concentration (#/cm3)

Windows Open

Windows Closed

Figure 5.3.2.5 shows the cumulative frequency distributions of the particle counts 

concentrations with the windows open and closed during Run 32, confirming the previous 

ndings that the concentrations were higher when the windows were opened. 

ffect of Window Position on Directly Emitted Pollutants versus Background

fi

Figure 5.3.2.5 Cumulative frequency distributions for particle counts during Run 32. 

5.3.2.3 E

Pollutants

ese runs the main source of pollution

was the bus’s own exhaust. Concentrations tended to increase when the buses were partially 

isolated

oth open and closed windows could lead to undesirable exposure scenarios, depending 

upon th

particularly for dirty buses. On the other hand, for “background” pollutants 

losing the windows would yield lower average concentrations inside the cabin. 

Figure 5.3.2.1 shows the inverse behavior between black carbon and particle counts. For 

directly-emitted, diesel-vehicle pollutants it appears in th

from the outside, whereas increased ventilation rates caused dilution with the windows 

opened. 

For “background” pollutants, concentrations were lower when windows were closed, 

slowing the entrance of pollutants into the cabin.

B

e pollutants involved and the degree of self pollution. For directly emitted pollutants,

having the windows open could lead to short-term high exposures from other vehicles whereas 

closing the windows could lead to longer-term elevated exposures with higher average

concentrations,

c

These findings emphasize the importance of transitioning as soon as possible to “cleaner” 

buses, such as the CNG bus we tested, to minimize pollution inside the cabin due to the bus’s 

own exhaust, as well as to reduce impacts on and from other nearby vehicles, as discussed in 

Section 5.3.5. 

109 
110 

N = 445 496 449 750 805 789

Bus

CNG HE2 HE3 RE1 RE2 TO1

Black Carbon (ug/m^3) 20

15

10

5

0

N = 445 500 469 860 803 800

HE3 RE1 RE2 TO

0

CNG HE2 1

PAH (ng/m^3)

0

400

0

200

100

Bus

50

30

5.3.3 Effect of Bus Type

In Section 5.2 we combined the results from two urban routes and the seven bus types to 

establish the overall average differences in exposure between the three microenvironments. 

However, the data also suggested that variables such as bus type and route type (see Section 

5.3.4)

s bus types investigated for several key

pollutants. Confidence intervals were calculated using the standard error of the mean, and are 

not related to the precision estimates in Section 5.1.3. To minimize confounding factors, these 

plots were based only on the measurements in the rear/inside of the cabin for urban route one 

during commutes with windows on the bus closed. Again, as only one instrument, the rear 

instrum nt, was used for all analyses in the comparison between bus types, the results of the 

precision estimates in Section 5.1.3 do not apply here. 

Figure 5.3.3.1 Mean concentrations and 95% confidence intervals for PAH and black carbon 

across six bus types. 

Figure 5.3.3.4 shows the results for the VOC data. Although the differences between 

uses appeared to be significant, the average concentrations were quite low relative to previously 

sured ambient total VOC concentrations (mostly below 200 ppb). 

played a significant role in determining the exposure inside the cabin. This section 

focuses on the effect of different bus types on concentrations inside the bus cabin when the

windows of the bus were closed. However other variables, such as roadway type and the 

presence of other diesel vehicles, may have also influenced concentrations inside the cabin, and

are discussed in other sections of this report (see Section 5.3.4 and 5.3.5). 

Different bus models and ages correspond to different construction and cabin designs and 

the results of our SF6 analyses suggested these differences may result in a wide range of pollutant 

exposure across bus types. Figures 5.3.3.1 to 5.3.3.4 show the observed mean concentrations 

and 95% confidence intervals for the mean for variou

e

(a) PAH (b) Black carbon 

b

mea
N = 444 517 469 857 802 800

CNG HE2 HE3 RE1 RE2

0

Bus

TO1

Fine Particle Counts (#/cm/3) 250

200

150

100

50

N = 445 517 469 860 805 800

CNG HE2 HE3 RE1 RE2 TO1

0

Bus

120

100

80

60

40

PM2.5 Mass (ug/m^3)

20

N = 445 517 469 775 854 800

Bus

CNG HE2 HE3 RE1 RE2 TO1

Carbon Monoxide (ppm) 10.0

8.0

6.0

4.0

2.0

0.0

N = 445 511 433 794 740 704

CNG HE2 HE3 RE1 RE2 TO1

Nitrogen Dioxide (ppb) 120

100

80

60

40

20

Bus

0

(a) PM2.5 (b) Fine Particle Counts (0.3 – 0.5 μm) 

(Data collected with the DustTrak instrument) 

re 5.3.3.2 Mean concentrations and 95% confidence intervals for PM2.5 and PC. 

CO.

These figures illustrate the complexity of the data set, where differences across bus types 

re pollutant-dependent. To better understand the similarities and differences between pollutants, 

e summarized the findings presented in Figures 5.3.3.1 to 5.3.3.4 in Table 5.3.3.1 in terms of 

ean concentrations and 95% confidence intervals for the means, by bus type. 

Figu

a) NO2 (b) Carbon Monoxide 

Figure 5.3.3.3 Mean concentrations and 95% confidence intervals for NO2 and

a

w

m

111 
N = 379 469 715 836 385

CNG HE3 RE1 RE2 TO1

VOC (ppb) 400

300

200

Bus

100

0

Figure 5.3.3.4

Tab ary s c in sid n 

for commutes with windows closed (morning) by bus type . 

Mean concentrations and 95% confidence intervals for VOC.

le 5.3.3.1 Summ of mean concentration and 95% onfidence tervals in

1,2

e the cabi

HE2 HE3 RE1 RE2 TO1 CNG

Black Carbon (ug/m3) 5 ± 0.3 19 ± 0.6 13 ± 0.5 12 ± 0.2 7.2 ± 0.2 2.5 ± 0.1

PAH (ng/m3)

3 400 ± 15 220 ± 6.4 162 ± 5.8 167 ± 3.6 201 ± 7 64 ± 3.3 

NO2 (ppb) 50 ± 0.5 110 ± 1.6 74 ± 1.4 76 ± 0.3 42 ± 0.4 34 ± 0.4 

CO (ppm) 4.5 ± 0.1 4 ± 0.1 5 ± 0.1 5 ± 0.1 6 ± 0.1 6 ± 0.2 

PC (#/cm3)

4 83 ± 2.5 78 ± 1.7 70 ± 1.4 111 ± 2.1 211 ± 1.9 97 ± 1.4 

VOC (ppb) N/A 93 ± 2.8 121 ± 2.3 171 ± 1.7 197 ± 2.4 334 ± 2.5 1

Conventional diesel buses = HE2, HE3, RE1 and RE2. 2

This ta e groups two types of pollutants analyzed in this study: pollutants primarily or entirely emitted from

s (BC, PAH and NO2), and “background” pollutants considered to be dominated by atmospheric 

sources other than diesel (e.g. CO from light duty motor vehicles, “background” accumulation

ode particles). 

ents of PAH inside conventional diesel buses may be biased low due to the instrument’s maximum

tting during these commutes. See Sections 4.1.2.3.5 and 5.3.3 for specific details. 

ze range from 0.3 – 0.5 μm. 

bl

combustion source

transformations or 

m

3

Measurem

se

4

In si

 

112 
According to the results found for emissions of PAH (with the exception of HE2), black 

carbon, and NO2, in general, the buses that exhibited high opacity and a high percentage of air 

inside the cabin coming from the exhaust, also exhibited the highest average inside/rear

concentrations. In other words, for BC, PAH, and NO2, the average inside/rear exposure results

ould be explained by the snap-and-idle test and SF6 data. 

ses likely

understated

c

In contrast, the snap-and-idle test and the SF6 data did not adequately explain the

behavior of PC and PM2.5 average concentrations inside the cabin. These results suggest that it is 

necessary to consider additional variables in order to explain the behavior of PC and PM2.5, such 

as the background contribution to concentrations of these pollutants inside the bus cabin. 

In addition, as noted in Section 4.1.2.3.5, the PAH instrument’s full scale was set to read 

a maximum of 500 ng/m3

 in the front, and 1000 ng/ m3 in the rear during the first 17 runs of this 

study, all of which involved diesel buses. During Run 18 and all subsequent runs (including the 

TO1 and CNG buses), the scale was increased to read a maximum of 2000 ng/m3

 for both front 

and rear instruments. This created a potentially substantial bias in our comparison of PAH 

concentrations by bus type. PAH data for the diesel buses were biased low, because any

concentrations higher than the maximum scale setting were recorded as the preset maximum of 

500 or 1000 ng/m3

. In the later runs involving the trap-outfitted and CNG buses, the

concentrations of PAH inside the bus cabin often exceeded 500 or 1000 ng/m3

. Thus, the 

differences we saw between the diesel buses versus the trap-outfitted or CNG bu

 the benefits of reduced PAH concentrations for the trap and CNG buses. We expect 

both hi

Overall, the results presented in Table 5.3.3.1 demonstrate the importance of bus type for

rticularly for directly emitted pollutants such as black carbon and 

PAH.

gher peaks and higher average concentrations likely occurred inside the diesel buses

during Runs 1 – 17 and therefore average PAH concentrations for diesel buses almost certainly

would have been higher had the maximum scale been set to 2000 ng/m3 as in the case of the

trap-outfitted and CNG buses. Thus, if anything, the benefits of the trap and CNG technologies 

over the diesel with respect to PAH concentrations are under-estimated in our study. 

exposures inside the cabin, pa

The ratios between the minimum and maximum average concentrations across bus type 

ranged from 1.6 to 6.3; hence, the differences in average exposure between buses were

significant. For example, for black carbon, the average exposure associated with the HE3 bus

was more than six times the exposure associated with the CNG bus. 

5.3.3 Effect of Route Type 

Figures 5.3.4.1 and 5.3.4.2 show the observed mean pollutant concentrations and 95% 

confidence intervals for the three types of bus routes used during the main study (rural/suburban,

urban route one, and urban route two). The confidence intervals were calculated based on the 

standard error of the mean. To avoid confounding factors only data from the RE2 bus were used 

since this was the only test bus used on all three routes, and only from measurements collected 

by sampling at the rear of the cabin. 

113 
114 

N = 391 752 1052 805 730

Windows Open Windows Closed

95% C.I. Bla

4 U1

ck Carbon 14

12

10

8

6

2

Route

U2

RS

N = 390 761 1051 803 739

Windows Open Windows Closed

95% 180

160

140

120

100

40

20

Route

RS

U1

U2

 C.I. PAH

60

80

N = 391 761 1051 802 803

Windows Open Windows Closed

95% C.I. PC 300

200

0

Route

RS

U1

U2

100

N = 391 763 1052 805 807

2.5 140

120

100

80

60

40

0

Route

Windows Open Windows Closed

95% C.I. PM

20 U1

U2

RS

(a) Black carbon (b) PAH 

igure 5.3.4.1 Mean concentrations and 95% confidence intervals for black carbon (μg/m3

) 

and PAH (ng/m3

). 

s and 95% confidence intervals for PC (#/cm ) and PM2.5 

suburban runs were conducted in the

afternoon with the windows open. 

F

(a) PM2.5 (b) PC

(Data collected with the DustTrak instrument) 

Figure 5.3.4.2 Mean concentration 3

(μg/m3) by bus route and time of day. 

Figure 5.3.4.1 again shows similar behavior between black carbon and PAH. As

expected, based in part on lower concentrations, the rural/suburban route (RS) exhibited

significantly lower mean exposure than the urban routes. These differences could only be 

established for runs with windows open, since all rural/
During the runs with windows open, the mean concentrations for black carbon and PAH 

were similar between the two urban routes (see Section 5.3.5.2.4). However, during runs with 

windows closed, U1 exhibited higher mean concentrations than U2. Differences in traffic 

conditions between the two urban routes could partially explain this result, where in general, the 

U2 route traveled trough less congested streets. As explained elsewhere this difference was only 

visible for morning runs where the windows were closed. 

haved

similar

Figure 5.3.4.2 shows, when analyzing the type of route, PC and PM2.5 also be

ly. The rural/suburban route and the U1 route exhibited similar average concentrations.

The U2 route showed higher average concentrations than U1 during both morning and afternoon 

runs (in part due to higher speeds and greater ventilation on the freeway portion of U1, and 

greater density of diesel trucks on surface streets for U2).. These results demonstrate, again, the

differences between directly emitted versus background pollutants. 

5.3.5 Within Run Variables Investigated Using Real-Time Data and Videotape Analysis

The videotapes in conjunction with the activity logs recorded during each run were used 

to determine the influence of surrounding vehicles on black carbon and PAH concentrations 

inside the test buses during selected exposure runs. Black carbon and PAH were the focus of this 

analysis for three main reasons. The first was that these pollutants are both highly associated 

with diesel exhaust. Black carbon is typically used as a marker for diesel exhaust particulate 

(Rodes et. al., 1998; Fruin, 2003), and results from both the pilot and main studies found 

concentrations of black carbon and PAH were highly correlated (see Section 5.1.3). Second, we 

expected both pollutants resulted primarily from direct emissions, and had low background 

concen

uments was placed at the rear of the bus (e.g. all runs had 

the sam Aethalometer measuring black carbon at the rear of the bus). This is an important point 

Aethalo

trations. This was important because high background concentrations could obscure our 

ability to identify the important variables contributing to exposure on the time scale of seconds 

and tens of seconds during the course of a commute. This also relates to the third major reason, 

which was the fast response of the instruments used to measure these pollutants allowed us to see

changes in concentrations within seconds of an event. We used ten second data for all the

videotape analyses. 

All measurements of black carbon and PAH used in the following analyses were based on 

the measurements taken inside the cabin at the rear of the bus. For all runs in this study, the 

same instrument from each pair of instr

e

because although in Section 5.1.3 we estimated the precision between the front and rear 

meters was low, the between-run comparisons in the videotape analyses which follow 

were based only on one instrument, the rear Aethalometer. Thus we were able to make relative

comparisons between buses, without concerns of inadequate precision (for example between our 

front and rear Aethalometers), because these comparisons involved only one instrument for each 

pollutant (e.g. black carbon or PAH). 

5.3.5.1 Selection of Runs for Videotape Analysis

The primary focus of this analysis was the effect of surrounding traffic on concentrations 

inside the bus, and less on the effect of the particular bus being tested (i.e. self pollution). In 

addition, because of the nature of the analysis, it was necessary to have highly variable 

concentrations inside the cabin over the course of a commute, in order to associate changes in

115 
concentration with the events/characteristics that also changed during the commute. For these 

two reasons, we determined only afternoon runs, with windows partially open, were suitable for 

comp

 series 

Figure 5.3.5.1 Run 31 (morning – windows closed) black carbon and PAH 

were

more influenced by the outside air because the windows were open during all afternoon runs. As

noted during the window position tests (see Section 5.3.2), when the windows were open, mean 

concentrations inside the bus tended to decrease, and were similar to concentrations outside the

bus for directly emitted pollutants, such as black carbon and PAH. The high ventilation rate 

inside the bus cabin eliminated build up of concentrations inside the cabin over the course of the 

commute. High, but short-lived, peak concentrations inside the bus were observed only when 

the windows were open, indicating open windows allowed high concentrations from other 

videotape analysis. Afternoon runs had generally low baseline pollutant concentrations 

ared with the morning runs (windows closed), but the greatest concentration variability, 

including high peaks. Figures 5.3.5.1 – 5.3.5.2 show examples of the types of time

obtained with morning and afternoon runs, respectively. The rapidly changing concentrations 

inside the bus from very low baseline concentrations to much higher peak concentrations found 

during afternoon runs provided the opportunity to investigate the factors influencing high 

concentrations inside the bus. 

concentrations. 

By contrast, during the morning runs with windows closed, pollutant concentrations 

inside the bus were less variable, and increased/decreased only gradually over much longer 

periods of time, making it more difficult to assess the factors causing those changes on a realtime basis. 

In addition, focusing on afternoon runs for the videotape analysis increased our ability to

assess the effects of surrounding traffic because these runs had higher ventilation rates and

0

10

6:30 6:37 6:44 6:51 6:58 7:06 7:13 7:20 7:27 7:34

Time

-300

20

40

50

60

70

80

90

100

Black Carbon (ug/m3

)

0

600

900

1200

1500

1800

2100

PAH (ng/m3

)

Black Carbon

PAH

20 Second Moving Average (BC)

20 Second Moving Average (PAH)

30

300

116 
0

10

20

30

40

50

60

70

80

90

100

15:14 15:21 15:28 15:35 15:42 15:50 15:57 16:04 16:11 16:18

Time

Black Carbon (ug/m3

)

-300

0

300

600

900

1200

1500

1800

2100

PAH (ng/m3

)

Black Carbon

PAH

2 per. Mov. Avg. (PAH)

2 per. Mov. Avg. (Black Carbon)

Figure 5.3.5.2 Run 33 (afternoon – windows open) black carbon and PAH concentrations. 

sources to easily penetrate the bus cabin, although only for very short periods of time due to the 

high ventilation rates. 

It is also important to note that meteorological conditions during the afternoon were

consistent across all bus runs, with onshore flow conditions typical of the Los Angeles basin

during May and June. 

Out of a total of sixteen afternoon runs with windows open that were videotaped, seven 

were selected for complete videotape analysis, including a variety of bus types. Table 5.3.5.1 

describes the runs analyzed using the videotapes. Four runs were on diesel buses, including Run 

6 on a high emitter bus (HE2), Run 15 on a representative bus (RE1), Runs 20 and 26 on a

second representative bus (RE2), Runs 30 and 33 on a particle-trap outfitted diesel bus (TO1), 

and Run 36 on a CNG bus (CNG). All of these runs were completed on the primary urban route, 

except one of the two runs on the representative diesel bus, RE2, which was done on the 

secondary urban route. Again, for all runs analyzed using the videotapes, the windows of the bus

were open. 

5.3.5.2 Results of the Videotape Analysis

Figures 5.3.5.3 – 5.3.5.4 show examples of time-series for black carbon and PAH, 

respectively, with events assigned to the concentration peaks based on the results of the 

videotape analysis. These figures are provided primarily to demonstrate the nature of the realtime data collected during this study, and the association between events and peak 

concentrations. However, all event assignments are not shown in these graphs. During the 

videotape analysis, every ten-second period within a run was given an event assignment 

according to the criteria described in Section 4.2.3.3. The event assignments shown in Figures

5.3.5.3–5.3.5.4 only represent the events occurring during major peak concentrations, and not all 

117 
events during a run, providing a qualitative demonstration of the value of the videotape analysis. 

However, all subsequent quantitative analyses include the event assignments for all ten-second

periods during each run analyzed. 

Table 5.3.5.1 Bus runs selected for videotape analysis.

Run 

Number Bus ID Bus Fuel Type

Bus

Route

Time of

Day

Average

Bus Speed 

(km/hr)

Total Run 

Time 

(hr:min)

 Average

Wind

Speed 

(km/hr)

6 HE2 Diesel U1 PM 22.7 1:11 15

15 RE1 Diesel U1 PM 23.8 0:57 9

20 RE2 Diesel U1 PM 26.3 1:00 17

26 RE2 Diesel U2 PM 21.4 1:21 9

30 TO1 Trap-Outfitted Diesel U1 PM 24.8 0:52 14

33 TO1 Trap-Outfitted Diesel U1 PM 23.1 1:10 10

36 CNG CNG U1 PM 19.8 1:24 15

0

10

20

30

40

50

60

15:10 15:15 15:21 15:27 15:33 15:38 15:44 15:50 15:56 16:02 16:07

Time

Black Carbon (ug/m3

)

Black Carbon

20 Second Moving Average

Traveling behind a diesel school 

bus - intermixed with periods of

idling in traffic on surface streets

Traveling behind a 

medium duty diesel

truck on freeway

Traveling behind

a diesel school 

bus on freeway

Diesel school bus 

emitted large cloud of

black smoke while 

accelerating past our

bus (see picture)

Figure 5.3.5.3 Run 20 black carbon concentrations with associated events. 

118 
0

200

400

600

800

1000

1200

1400

1600

1800

2000

15:14 15:21 15:28 15:35 15:42 15:50 15:57 16:04 16:11 16:18

Time

PAH (ng/m3

)

PAH

20 Second Moving Average

Diesel school bus

ahead directly in 

front emits puff of

black smoke on 

acceleration

Traveling behind a 

smokey diesel 

school bus

Traveling behind a diesel 

school bus which 

periodically emits visible 

black smoke

No other diesel 

vehicles present

No other

diesel 

vehicles

present

Heavy duty and 

medium duty

diesel trucks

ahead

Figure 5.3.5.4 Run 33 PAH concentrations with associated events. 

Small adjustments were made following the assignments of events based on further 

analysis of the data and inspection of the concentration time series. This was necessary because 

in a small number of instances, the criteria for assignment of an event described in Section 

4.2.3.3 did not adequately characterize an association between pollutant concentrations inside the 

cabin and outside events. Inspection of the concentration time series revealed small portions of 

peak concentrations, typically the down-side of a peak, were not always identified with a given 

event, although they were likely associated with that event. Figure 5.3.5.5 provides an example, 

with an expanded time scale, during Run 36 where the original criteria did not result in the

assignment of all elevated concentrations to the appropriate event. At the beginning of the 

period shown in Figure 5.3.5.5, a low baseline concentration of black carbon was observed, and 

no diesel vehicles were near the bus. At approximately 16:07, two events occurred nearly 

simultaneously. A passenger car with noticeable odors moved directly in front of the bus, and a 

diesel truck, which was ahead of the passenger car, came within about four to five car lengths of

the bus. Although this distance was just outside our three car length limit (see Photograph 12), 

careful inspection of the black carbon time series during this period showed a dramatic increase

in concentrations as soon as the truck and the passenger car both moved in front of the bus, with 

a further increase as the bus pulled closer to the diesel truck. 

During the three minute period from about 16:07:30 to 16:10:00, we saw a series of peaks 

and valleys (although the valleys were still elevated well above the baseline) as the bus traveled 

behind the diesel truck. Although concentrations dipped slightly when the passenger car moved 

away at about 16:08:50, they increased again as the bus moved closer to the diesel truck. The 

truck moved in and out of a three car length distance ahead of the bus over the course of this time

period, and the maximum concentration was reached when the diesel truck was within about one 

119 
0

10

20

30

40

50

60

16:04:30 16:05:56 16:07:23 16:08:49 16:10:16 16:11:42

Time

Black Carbon (ug/

m3

)

Black Carbon

2-Min Moving Average

Diesel truck ahead of 

passenger car (~5 car lengths

ahead of bus).

Passenger car with 

smelly exhaust moves 

in front of bus.

Smelly 

passenger car 

moves away

Bus is now directly

behind diesel truck

Diesel truck passes

through a light, while 

bus stops

Bus speed 

increases

Bus moves closer 

to Diesel Truck

Bus starts moving again, 

no other vehciles are in 

front 

Bus is stopped at a red 

light with no other 

vehicles in front of it

Figure 5.3.5.5 Events associated with increases/decreases in black carbon 

concentrations measured inside the CNG bus during Run 36. 

Photograph 12. School bus ahead three-car lengths (maximum distance used for 

 videotape analysis). 

car length of the bus (at this point the passenger car was gone). Finally, the diesel truck passed 

through a traffic light, while the bus did not. At this point, according to our original criteria, the 

diesel truck was no longer associated with any subsequent time intervals. However, we saw 

from the black carbon time series that although the concentrations immediately started to

decrease after the diesel truck was gone, it took approximately thirty seconds before the 

120 
concentrations were again close to the concentrations observed inside the bus prior to the

encounter with the diesel truck. In this case, the highest concentrations were identified by our 

original criteria, but the elevated concentrations in the final tail of the peak, before background 

concentrations were again reached, were not. The event assignments in the few instances where 

this occurred were adjusted, and these periods of time were also assigned to the original event 

associated with the major peak. However, these adjustments accounted for only 3% of all 

vehicle assignments made during the videotape analysis for all seven runs. The great majority of 

the events during each run were identified solely on the basis of the original criteria described in 

Section 4.2.3.3. 

5.3.5.2.1 The Effects of Following Diesel Vehicles With and Without Visible Exhaust

In order to assess the impact of diesel vehicles emitting visible exhaust, the time interval 

during which visible exhaust or odor from a diesel vehicle in front of, or adjacent to, the test bus 

was first noted, and the following one minute period after the exhaust was noted were assigned 

to the vehicle that emitted the exhaust. In order to determine the effect such an event had on

concentrations inside the bus cabin, a comparison was done of all concentrations measured when 

no visible exhaust from surrounding vehicles was present, with the first ten second period during 

which exhaust was seen, and the one minute period following the emission of visible exhaust

(see Photograph 13). The results of this comparison, stratified by the test bus type (diesel, trapoutfitted diesel and CNG) and including all seven runs analyzed with the videotapes, are shown 

in Table 5.3.5.2. 

 

 (a) (b) 

 

Photograph 13. a) Smoky diesel school bus ahead on an arterial street. 

b) Driving behind a heavy-duty diesel with high exhaust emitting visible 

 smoke. 

121 
Table 5.3.5.2 The effect of visible exhaust from a diesel vehicle in front of or adjacent to 

the test bus on concentrations of black carbon and PAH inside the bus cabin. 

Presence of Visible Exhaust Diesel Buses

Trap Outfitted

Diesel Bus CNG Bus Diesel Buses

Trap Outfitted

Diesel Bus CNGBus

No Visible Exhaust Present 5 3 2 76 78 70

Visible Exhaust First Emitted 

from a Diesel Vehicle in Front

of the Test Bus

24 9 13 464 370 473

One Minute Period Following

the Emission of Visible

Exhaust from a Diesel Vehicle 

in Front of the Test Bus

28 11 14 586 362 517

Mean Black CarbonConcentrationsInside the Test

Buses (ug/m3

)

Mean PAH Concentrations Inside the Test Bus

(ng/m3)

Figures 5.3.5.6 and 5.3.5.7 show the boxplots of the same comparison for each of the bus 

types tested for black carbon and PAH, respectively. The boxplots show the median, quartiles, 

and extreme values. The box represents the interquartile range which contains 50% of the 

values. The whiskers are lines that extend from the box to the highest and lowest values. The 

Bus Type

CNG Diesel Trap

Black Carbon (ug/m^3)

60

50

40

30

20

10

0

Visible Exhaust

Not Present

First Emitted

1 Minute Following

Exhaust Emission

Figure 5.3.5.6 The effect of visible exhaust from a diesel vehicle in front of or adjacent to the 

test bus on concentrations of black carbon inside the bus cabin. 

122 
Bus Type

CNG Diesel Trap

PAH (ng/m^3)

2500

2000

1500

1000

500

0

Visible Exhaust

Not Present

First Emitted

1 Minute Following

Exhaust Emission

Figure 5.3.5.7 The effect of visible exhaust from a diesel vehicle (in front of, or adjacent 

to, the test bus) on concentrations of PAH inside the bus cabin. 

line across the box indicates the median, and outliers or extreme values are included as 

individual points. Mean black carbon and PAH concentrations inside the test bus increased four 

to eight times when a diesel vehicle in front of the test bus emitted visible black smoke. These 

results indicated the presence of visible exhaust from a diesel vehicle near the test bus was a 

major predictor of high concentrations of black carbon and PAH inside the cabin. 

In an effort to further characterize the effect of the presence of other diesel vehicles in 

front of, or adjacent to, the test bus (see Photograph 14) during the course of a bus commute, a 

comparison was done of the observed concentrations inside the test bus while following different 

vehicle types, with or without visible exhaust. All seven runs (including the three different bus 

types) were included in this analysis. Five categories of vehicles being followed were used: 

diesel school buses with visible exhaust; other diesel vehicles with visible exhaust; diesel school 

buses without visible exhaust; other diesel vehicles without visible exhaust; and gasoline 

vehicles or no target. The category of other diesel vehicles included both medium and heavyduty vehicles (2,3 or 5 axles). Although during the video analysis separate categories for 

medium and heavy duty diesel vehicles were used, for this analysis these two categories were 

collapsed into one. The reasons for doing this were first, to increase the sample size, because the 

number of times these vehicles were encountered was low, and second, because the distinction

between medium and heavy duty diesel vehicles was not always apparent from the videotapes. 

The assignment to one category or the other was often difficult to make and collapsing the 

categories eliminated possible misclassification between the two categories. Diesel transit buses 

were not included because the number of times they were encountered during these bus 

commutes was low (N<20 for all runs combined). Most of the transit buses encountered were 

fueled by CNG. 

123 
Photograph 14. Medium duty diesel vehicle ahead on the freeway. 

Table 5.3.5.3 shows the results of this comparison. Mean black carbon and PAH 

concentrations inside the test buses were highest when following a diesel school bus which 

emitted visible exhaust, and lowest when following a gasoline vehicle or no target. When 

following a smoky diesel school bus, the concentrations measured inside the bus cabin were 

Table 5.3.5.3 Concentrations measured inside the bus cabin while traveling behind 

different types of vehicles. 

Mean Concentration Inside the Test Buses1

Type of Vehicle Being Followed: Black Carbon (ug/m3) PAH (ng/m3) 

Diesel School Bus Emitting Visible 

Exhaust 23 552

Other Diesel Vehicle Emitting 

Visible Exhaust2 133 429c

Diesel School Bus 12 271 

Other Diesel Vehicle2,4 4 104

Gasoline Vehicle or No Target 3 48 

Notes:

 1Selected exposure runs included 4 runs on 3 diesel buses, 2 runs on a trap-outfitted diesel bus,

 and 1 run on a CNG bus. Only afternoon runs with windows open were included in this analysis. 

 2

Diesel vehicles with 2, 3 or 5 axels were combined into one category to increase sample size. 

 3

Small sample size (N<50).

 4

Transit buses were not assessed due to small sample size (N<10).

124 
eight and twelve times greater for black carbon and PAH, respectively, compared with following 

a gasoline vehicle or no target. When following a diesel school bus that was not emitting visible 

exhaust, black carbon and PAH concentrations inside the test buses increased four and six times,

respectively, compared with following a gasoline vehicle or no target. 

Figures 5.3.5.8 and 5.3.5.9 show the boxplots of the black carbon and PAH 

concentrations, respectively, measured inside the test buses while following different types of 

vehicles. The type of diesel vehicle encountered most frequently during all the bus runs was a 

diesel school bus. On average, during more than one-quarter of a bus commute, another diesel

vehicle was within three car lengths in front of or adjacent to, our bus, with diesel school buses 

responsible for over sixty percent of that time. The highest concentrations inside all the test 

buses were observed when following, or adjacent to, a diesel school bus. This demonstrated that 

the type of school bus a child rides on is not the only determinant of exposure. Another major

factor is the type of vehicles encountered during the commute. Under conditions similar to our 

study conditions (e.g. high density urban setting, with many children commuting to school by 

bus), the impact of other diesel school buses in the surrounding area on the exposure of a 

commuting child would be significant, regardless of the type of bus that child was riding on. 

This was a particularly important finding as we observed during the course of the study that

school buses tended to caravan with each other, especially when leaving the school at the same

time in the afternoon. 

Type of Vehicle in Front of Test Bus

Diesel School Bus

Diesel Transit Bus

Medium Duty Diesel

Heavy Duty Diesel

Gasoline/No Target

Black Carbon (ug/m^3)

60

50

40

30

20

10

0

Figure 5.3.5.8 Concentrations of black carbon inside the test buses while following 

or adjacent to different types of vehicles. 

125 
Type of Vehicle in Front of Test Bus

Diesel School BusDiesel Transit BusMedium Duty Diesel

Heavy Duty Diesel

Gasoline/No Target

PA

H (

n

g/m^

3)

2500

2000

1500

1000

500

0

Figure 5.3.5.9 Concentrations of PAH inside the test buses while following or adjacent to 

different types of vehicles. 

5.3.5.2.2 The Effect of Bus Fuel Type or After-Treatment Technology 

Although the primary purpose of the videotape analysis was to investigate the effects of 

surrounding traffic and outside events on concentrations inside the bus, we were also able to 

investigate differences between bus fuel types and after-treatment technology. As stated 

previously, we tested three different bus types: conventional diesel, trap-outfitted diesel, and

CNG. For these analyses, we used only the six runs on the primary urban route. Despite the 

effect of open windows and consequently high ventilation rates in the bus during these afternoon 

runs, which reduced build up of any of the bus’s own emissions inside the bus, we were still able

to see differences in concentrations during the commutes based solely on the type of test bus, 

irrespective of surrounding events and traffic conditions. In order to show this, we first 

investigated differences in traffic congestion levels, frequency of encounters with other diesel 

vehicles, and average wind speeds, to verify these conditions were similar between runs and thus 

not responsible for any differences observed between bus types. 

One major indicator of traffic congestion levels (Photograph 15) on these commutes was

the average speed and the total time to complete the run. The run on the second urban route was 

not included in the comparisons between test bus types because any differences due to the route 

may have confounded the analysis. Consequently, all runs considered in this section followed 

the same route, our primary urban route, and were performed at approximately the same time of 

day, on either Wednesday (four runs) or Thursday (two runs), over the course of seven 

consecutive weeks. We would expect similar average speeds and total time to complete the route 

126 
between runs if traffic congestion levels were similar. Table 5.3.5.1 shows the average speeds 

and total time to complete the route for each of the six runs on the primary urban route. Table

5.3.5.4 shows the mean black carbon and NO2 concentrations, categorized into periods of idling

when no other diesel vehicles were present for these six runs. Run 36, the CNG bus, had the

slowest average speed, and the longest total commute time, indicating traffic congestion levels

were heavier on this run than on all other runs. Yet, the black carbon concentrations over an

entire commute were almost twice as high on the diesel bus with the highest average speed (Run 

20). 

Photograph 15. Typical congestion on the freeway during an afternoon run. 

When the black carbon concentrations were broken down into periods of idling with no 

other diesel vehicles present, we also saw the CNG bus again had the lowest mean concentration

of black carbon, while the conventional diesel buses had concentrations that were two to four 

times higher than either the CNG or trap-outfitted diesel buses. Both the trap-outfitted diesel and 

conventional diesel buses had concentrations of NO2 that were up to two to three times higher, 

respectively, than the CNG bus. 

Figures 5.3.5.10 – 5.3.5.12 and 5.3.5.13 – 5.3.5.15 show the boxplots of the breakdown 

of black carbon and PAH concentrations, respectively, by idling with or without other diesel

vehicles present, for each of the three bus types. These graphs demonstrate the importance of the 

presence of a diesel vehicle in front of the test bus in determining the highest concentrations

inside the bus cabin. However, we also saw a difference between test bus types as well. For the 

conventional diesel buses, the highest peak concentrations were obtained when the test bus was 

idling behind another diesel vehicle. But high peak concentrations were also obtained during

periods of idling, without other diesel vehicles present, and even during periods when the bus 

was not idling and no other diesel vehicles were present. 

127 
Table 5.3.5.4 Mean black carbon and NO2 concentrations inside the test buses under selected 

conditions. 

 Black Carbon (ug/m3) NO2 (ppb) 

Run

Number 

Bus Fuel 

Type Entire Run

Not Idling

NO Vehicle in

Front 

Idling with 

NO Vehicle in

Front Entire Run

Not Idling NO

Vehicle in 

Front 

Idling with 

NO Vehicle in

Front 

6 

Conventional 

Diesel 5.1 2.5 7 54 50 50

15 

Conventional 

Diesel 11.5 3 3.4 122 106 102

20 

Conventional 

Diesel 7.2 4.1 6.2 88 83 79

26 

Conventional 

Diesel 6.6 3.9 4.7 50 44 51

30 

Trap-Outfitted 

Diesel 2.7 2.2 1.9 82 81 82

33 

Trap-Outfitted 

Diesel 4.5 2.6 1.7 90 85 80

36 CNG 3.9 1.5 1.7 40 38 33

N = 600 250 175 75

Test Bus Status

NOT IDLING IDLING

Black Carbon (ug/m^3)

60

50

40

30

20

10

0

Vehicle Ahead of Bus

No Diesel Vehicle

Present

Diesel Vehicle 

Present

Figure 5.3.5.10 Concentrations of black carbon inside the conventional diesel school buses 

while idling and/or following another diesel vehicle. 

128 
N = 386 206 123 19

Test Bus Status

NOT IDLING IDLING

Black C

arb

on (ug/m^3)

60

50

40

30

20

10

0

Vehicle Ahead of Bus

No Diesel Vehicle

Present

Diesel Vehicle

Present

Figure 5.3.5.11 Concentrations of black carbon inside the trap-outfitted diesel school bus 

while idling and/or following another diesel vehicle. 

N = 300 124 56 27

Test Bus Status

NOT IDLING IDLING

Black Carbon (ug/m^3)

60

50

40

30

20

10

0

Vehicle Ahead of Bus

No Diesel Vehicle

Present

Diesel Vehicle

Present

Figure 5.3.5.12 Concentrations of black carbon inside the CNG school bus while idling and/or 

following another diesel vehicle. 

129 
N = 600 251 175 75

Test Bus Status

NOT IDLING IDLING

PAH (ng/m^3)

2500

2000

1500

1000

500

0

Vehicle Ahead of Bus

No Diesel Vehicle

Present

Diesel Vehicle 

Present

Figure 5.3.5.13 Concentrations of PAH inside the conventional diesel school 

buses while idling and/or following another diesel vehicle. 

N = 386 206 123 19

Test Bus Status

NOT IDLING IDLING

PAH (ng/m^3)

2500

2000

1500

1000

500

0

Vehicle Ahead of Bus

No Diesel Vehicle

Present

Diesel Vehicle

Present

Figure 5.3.5.14 Concentrations of PAH inside the trap-outfitted diesel school bus while idling 

and/or following another diesel vehicle 

130 
N = 300 124 56 27

Test Bus Status

NOT IDLING IDLING

PAH (ng/m^3)

2500

2000

1500

1000

500

0

Vehicle Ahead of Bus

No Diesel Vehicle 

Present

Diesel Vehicle

Present

Figure 5.3.5.15 Concentrations of PAH inside the CNG school bus while 

idling and/or following another diesel vehicle. 

By comparison, the trap-outfitted diesel bus and CNG bus had high peak concentrations 

only while traveling behind a diesel vehicle. In addition, the highest peaks obtained on the CNG 

bus (which occurred while traveling behind a diesel vehicle) were less than 30 μg/m3

, while on 

the conventional diesel buses, the highest peaks reached were over 50 μg/m3

, almost twice as

high. Concentrations over 50 μg/m3

were observed inside the conventional diesel test buses not 

only when following another diesel vehicle, but also during periods when no other diesel

vehicles were present. This indicated even with the high ventilation rates and low background 

concentrations observed during the afternoon runs with the windows open, the type of bus had a 

significant impact on concentrations inside the bus cabin, in particular, on peak concentrations. 

Moreover, the differences found between bus types could not be attributed to differences 

in average wind speed between runs. Table 5.3.5.1 shows the average wind speeds, measured at 

the nearest AQMD stations (see Section 4.1.2.3.13), on all runs used in this analysis. The 

averages by bus type ranged from 12 km/hr (trap-outfitted diesel bus) to 15 km/hr (CNG bus),

with the average speed for the diesel buses at 14 km/hr. 

Finally, we also calculated the amount of time spent behind or adjacent to other diesel 

vehicles, to determine if there were differences between bus types based on this variable. We

found that for each of the bus types tested, the amount of time spent behind or adjacent to other 

diesel vehicles was 25%, 30%, and 31% for the conventional diesel, CNG, and trap-outfitted 

diesel buses, respectively. Thus, we concluded differences found between bus types were not 

due to differences in the number of encounters with other diesel vehicles during the commutes. 

131 
In fact, the runs with the least number of encounters with other diesel vehicles were on the diesel 

test buses, the buses with the highest concentrations of black carbon and PAH concentrations 

inside the cabin. 

5.3.5.2.2.1 Concentrations Inside Buses While Idling at Bus Stops

Investigation of concentrations inside the buses while idling at bus stops provided another 

opportunity to evaluate differences between bus types. Because the bus stops along the primary 

urban route were all located on small residential streets, usually in front of elementary schools, 

there were few other vehicles in the surrounding area during the afternoon runs (see Photograph 

16). Thus, we expected concentrations inside the bus cabin during bus stops were dominated by 

the bus’s own exhaust, with a minimal contribution from other vehicles. 

Photograph 16. View out the front window of the test bus while idling at a bus stop (at Vermont

Avenue School) during an afternoon run. 

Table 5.3.5.5 shows the mean black carbon, and PAH, and NO2 concentrations measured

inside the test buses while idling at bus stops, categorized by bus type. On average, black carbon 

Table 5.3.5.5. Mean black carbon, PAH and NO2 concentrations measured inside the test buses 

while idling at bus stops in residential neighborhoods (urban route one). 

Test Bus Type Black Carbon (ug/m3) PAH (ng/m3) NO2 (ppb) 

Conventional Diesel 8.6 +/- 1.9 106 +/- 26 64 +/- 6 

Trap-Outfitted Diesel1 1.9 +/- 0.2 20 +/- 4 78 +/- 6 

CNG 1.2 +/- 0.2 27 +/- 16 37 +/- 3 1

 Trap outfitted diesel bus was the only bus to encounter another diesel vehicle at a bus stop for runs included here. 

132 
concentrations were four and six times higher inside the diesel buses at bus stops compared with 

the trap outfitted diesel bus and the CNG bus, respectively, while PAH concentrations were four

to five times higher inside the conventional diesel buses. Concentrations of NO2 were twice as

high for both the conventional diesel and trap-outfitted buses than for the CNG bus. 

Inspection of the boxplots in Figures 5.3.5.16 and 5.3.5.17 for black carbon and PAH 

concentrations, respectively, during bus stops revealed a much wider range of differences for the 

diesel buses tested. These figures showed both the CNG and the trap-outfitted diesel buses had 

relatively low concentrations and no high peaks while idling at bus stops. For these two bus 

types, the concentrations measured at the bus stops were lower than the average concentrations

measured on these buses in congested traffic conditions when other diesel vehicles were not 

present. This finding indicated the contribution to black carbon and PAH from the CNG and 

diesel trap bus’s own exhaust was minimal. 

N = 19 106 61

TEST BUS TYPE

CNG Diesel Trap Diesel

Bl

a

c

k Carb

o

n (ug/m

^3)

60

50

40

30

20

10

0

Figure 5.3.5.16 Concentrations of black carbon inside the cabin for conventional diesel, 

trap-outfitted diesel, and CNG school buses while idling at bus stops. 

However, for the conventional diesel buses, we found a different result. First, we found a 

wider range of concentrations measured inside the bus cabin at bus stops. For black carbon, the 

range of concentrations was similar to what was observed during other portions of the bus 

commute. This was an interesting finding since the period of time spent idling at the bus stops

had the least impact from traffic congestion and the presence of other diesel vehicles. In fact, 

only the trap-outfitted diesel bus had any other diesel vehicles present at any of the bus stops, 

and that was only for a short thirty-second period. Thus, the concentrations measured at the bus 

stops were not due to other vehicles or general traffic congestion. The differences seen between 

133 
N = 19 106 61

Test Bus Type

CNG Diesel Trap Diesel

PAH (ng/m^3)

1000

750

500

250

0

Figure 5.3.5.17 Concentrations of PAH inside the cabin for conventional diesel, 

trap-outfitted diesel, and CNG school buses while idling at bus stops. 

bus types clearly indicated the conventional diesel buses contributed higher concentrations of 

black carbon and PAH to concentrations inside the bus cabin compared with either trap-outfitted

diesel or CNG buses. These results also suggested the exhaust from the diesel test buses was a 

significant source of black carbon and PAH inside the cabin during the commute. In addition, 

both the conventional diesel and trap-outfitted diesel buses contributed approximately twice as 

much NO2 to concentrations inside the bus cabin compared with the CNG bus. 

5.3.5.2.3 The Effect of Roadway Type: Freeway versus Surface Streets

Again, only those runs on the primary urban route (U1) were included in the analysis of 

the effect of roadway type (freeway versus surface streets) on concentrations inside the bus. The 

second urban route (U2) consisted of all surface streets, and no freeway travel, so no comparison

of roadway type was possible for that route. Mean concentrations of black carbon were 

approximately three times higher on surface streets compared with the freeway for diesel buses

and the CNG bus, while for the trap-outfitted diesel bus, concentrations were higher on the

freeway. In order to explain these differences, it was necessary to account for other potentially 

influential variables, such as the type of vehicle in front of the bus (see below). 

Table 5.3.5.6 shows the black carbon concentrations for surface streets and freeways on 

the three different test bus types, grouped into periods with or without a diesel vehicle in front of 

the bus. The most interesting result we found was that the presence of a diesel vehicle in front of 

the bus was a more dominant determinant of bus cabin concentrations than roadway type. First, 

134 
we found that the trap-outfitted diesel bus had fewer encounters with other diesel vehicles on 

surface streets than on the freeway, while the opposite was true for the diesel buses and the CNG 

bus, which had more encounters with other diesel vehicles on surface streets. As encounters 

with other diesel vehicles appear to be the dominant determinant of high peak concentrations 

inside the test buses, this result helped explain why when looking only at mean concentrations on 

each roadway type without further separation by presence of other diesel vehicles, the diesel 

buses and the CNG bus were higher on surface streets (where they had more encounters with 

other diesel vehicles), while the trap-outfitted diesel was higher on the freeway (where it had 

more encounters with other diesel vehicles). 

Roadway Type

Diesel Vehicle Ahead of Test

Bus Diesel Buses

Trap Outfitted

Diesel Bus CNG Bus

SURFACE STREETS NO 4.3 1.7 1.5

FREEWAY NO 3.2 3.4 1.6

SURFACE STREETS YES1 23.2 

(19%, 93%)

10.5 

(10%, 91%)

13.1 

(19%, 56%)

REEWAY YES1 4.6 

(10%, 26%)

5.9 

(20%, 44%)

2.4 

(10%, 32%)

Notes:

1: First number in parenthesis is the percent of the total commute time represented by this category. 

 Second number in parenthesis is the percentage of the vehicle encounters in this category that were diesel school buses.

.6 Mean black carbon concentrations inside the test buses on different roadway types, stratified

by the presence of a diesel vehicle in front of the bus.

Mean Black Carbon Concentrations Inside the 

Test Buses (ug/m3

)

Table 5.3.5

F

Table 5.3.5.6 Mean black carbon concentrations inside the test buses on different roadway

types, stratified by the presence of a diesel vehicle in front of the bus. 

In addition, the concentrations measured inside the buses were highest for all three bus 

types when diesel vehicles were encountered on surface streets. This was likely due to the type 

of diesel vehicle typically encountered on surface streets versus freeway. For all the test bus 

types, the majority of encounters with other diesel vehicles on surface streets were with diesel

school buses (between 56 and 93% of all encounters on surface streets), while the majority of 

encounters on the freeway were with medium or heavy duty diesel trucks. This finding again 

demonstrated the potential of diesel school buses to contribute high concentrations of black 

135 
carbon (and by implication, other diesel-related pollutants) to a commuting child’s exposure

regardless of the type of vehicle the child may be riding in. Exhaust pipe location may also have 

played a role here, as most diesel school buses encountered had low exhaust pipes, while most

heavy duty diesel trucks had high exhaust pipes. Other studies have found the position of a 

diesel vehicle’s exhaust pipe was an important determinant of pollutant concentrations in a 

passenger car traveling behind a diesel vehicle (Fruin, 2003). 

Mixed results between bus types were obtained for the comparison of roadway type when 

no diesel vehicles were present (Table 5.3.5.6, Figures 5.3.5.18 – 5.3.5.20). Statistical tests of

significance were done to compare surface streets versus freeways when no other diesel vehicles 

were present for each of the test bus types. The CNG bus had no significant difference between 

the two roadway types, while both the diesel buses and the trap-outfitted bus were significantly

different, although in the opposite direction (e.g. surface streets were higher on the diesel buses,

while the freeway was higher on the trap bus). 

One explanation for this difference between buses was likely due in part to differences in 

average speeds on the different roadway types in combination with the different contribution 

from each bus’s own exhaust, depending on bus type. Notice that the mean concentrations of

black carbon measured inside both the diesel buses and the trap-outfitted diesel bus while

traveling on the freeway when other diesel vehicles were not present was approximately the 

same (3.2 and 3.4 ug/m3, respectively). For both of these bus types, the average speed on the 

freeway was about double the average speed on surface streets. Thus, we would expect much 

higher ventilation rates, and as the windows were open, we would expect the outside air, or the 

N = 454 214 321 111

Roadway Type

Surface Streets Freeway

95% CI for Black Carbon (ug/m^3)

30

20

10

0

Vehicle Ahead of Bus

No Diesel Vehicle

Present

Diesel Vehicle

Present

Figure 5.3.5.18 Mean and 95% confidence intervals for black carbon inside diesel school buses 

on different roadway types with or without other diesel vehicles present. 

136 
N = 330 76 179 149

Roadway Type

Surface Streets Freeway

95

% CI f

or Black C

arb

on (ug/

m

^3) 30

20

10

0

Vehicle Ahead of Bus

No Diesel Vehicle 

Present

Diesel Vehicle

Present

Figure 5.3.5.19 Mean and 95% confidence intervals for black carbon inside the trap-outfitted 

diesel school bus for different roadway types with or without other diesel 

vehicles present 

N = 164 98 192 53

Roadway Type

Surface Streets Freeway

95% CI for Black Carbon (ug/m^3)

30

20

10

0

Vehicle Ahead of Bus

No Diesel Vehicle

Present

Diesel Vehicle

Present

Figure 5.3.5.20 Mean and 95% confidence intervals for black carbon inside the CNG school bus 

 for different roadway types with or without other diesel vehicles present. 

137 
average concentration in the line source, was the dominant determinant of concentrations inside

the bus, when other diesel vehicles were not present and when the buses were moving at 

relatively high speeds (greater than 30 km/hr). Under these conditions, the dominant 

determinant of concentrations inside the bus cabin was the average concentration in the line

source. 

By contrast, on surface streets, with much lower speeds and more periods of idling, the 

concentrations inside the bus, when other diesel vehicles were not present, were dominated both 

by average concentrations in the road, and by the contribution of the bus’s own exhaust. The

trap-outfitted diesel bus likely had lower concentrations on surface streets, under the conditions 

described above, because the contribution from the bus itself was lower. The diesel buses’ own 

exhaust contributed more black carbon to the concentrations inside the cabin while on surface 

streets than did the trap-outfitted or CNG bus, resulting in higher concentrations on surface 

streets inside the bus cabin. The CNG bus, which showed no significant difference between 

surface streets and freeways also had similar average speeds on both roadway types (17 and 22

km/hr), indicating similar traffic congestion levels, and thus similar line source concentrations. 

These results indicated the importance of roadway type on concentrations inside the bus 

cabin depended on the type of bus. For relatively clean buses (e.g. CNG, trap-outfitted diesel), 

traveling in a line source with higher concentrations, such as on freeways, tended to increase 

concentrations inside the bus. For a dirtier buses (e.g. diesel), because the bus’s own exhaust 

was a significant contributor to concentrations inside the bus, increasing the ventilation rate

inside the bus, which was more likely to occur on freeways, tended to decrease concentrations

inside the bus. 

5.3.5.2.4 Effect of Route Type

Two different urban routes were driven by the same diesel bus (RE2) during two 

consecutive weeks. For the comparison of routes, only these runs (Run 20 - driven on the 

primary urban route (U1) and Run 26 - driven on urban route 2 (U2)) were used. By maintaining 

the same bus on both routes, we hoped to eliminate any confounding due to differences between 

buses. 

Table 5.3.5.7 shows the comparison between U1 and U2 for variables such as distribution 

of road types (freeway, major/minor arterials and small residential streets), average speed,

percent of time spent following other diesel vehicles and behind diesel school buses, number of 

bus stops, etc. One of the major differences between these two routes was that U2 involved only 

surface streets, while U1 was a mix of surface streets and freeways. In Section 5.3.5.2.3 we 

concluded for diesel buses, freeway travel which allowed higher speeds and increased ventilation 

in the bus cabin resulted in lower concentrations of black carbon. Given that result, we might

expect a bus route with no freeway travel to have higher concentrations. 

138 
Table 5.3.5.7 Comparison between urban route one and urban route two.

Urban Route One 

(U1)

Urban Route Two 

(U2)

Distribution of Roadway Types:

Freeway 42% 0

Major/Minor Arterials 37% 56%

Small Residential Streets 21% 44%

Speed:

Average Speed - Entire Commute (km/hr) 26.3 21.4

Average Speed - Freeway 39.5 N/A

Average Speed - Major/Minor Arterials 20.4 27.4

Average Speed - Residential Streets 11.4 13.6

Other:

Number of Bus Stops 5 10

Time Spent at Bus Stops 7% 14%

Time Spent Behind Other Diesel Vehicles 28% 13%

Time Spent Behind Other Diesel School Buses 18% 11%

Time Spent Idling 19% 36%

Tables 5.3.5.8 and 5.3.5.9 show the concentrations of black carbon and PAH measured 

inside the bus on U1 and U2. Figures 5.3.5.21 and 5.3.5.22 show the 95% confidence intervals 

and boxplots for the concentrations measured on the entire routes. We found the mean 

concentrations for the entire runs were not significantly different between routes. We also found 

similar ranges of concentrations on both routes (Figure 5.3.5.22). However it was also 

interesting to compare the concentrations measured on different roadway types for each route 

(see Tables 5.3.5.8-5.3.5.9 and Figure 5.3.5.23). First we found the freeway portion of Run 20 

on U1 had lower concentrations compared to the other two roadway types, as we would expect 

based on our results from Section 5.3.5.2.3 in which we found lower concentrations on the 

freeway versus surface streets for diesel buses. Next, concentrations of both black carbon and

PAH were similar on major/minor arterial roads on U1 and U2, although slightly higher on U1. 

The major/minor arterial streets on both routes had similar mixes of vehicles, but when we 

examined average speeds on the different road types, we found slightly reduced average speeds

for the same roadway types on U1 compared with U2, indicating higher concentrations of traffic 

congestion on U1. This is consistent with the fact that although both routes were in urban areas, 

U1 had higher density neighborhoods compared to U2. This also explains why although both 

routes traveled through small residential streets for a portion of the run, U1 had higher 

139 
140 

.9 Mean PAH Concentrations Inside the Bus on Urban Route One and Urban Route Two 

Residential 

Streets

No Vehicle

in Front

No Vehicle

in Front

Vehicle in

Front

Urban R ute

One (U ) 113 46 188 111 55 70 365

Urban R ute

Two (U 122 N/A 192 26 41 51 508

(Runs 20 and 26). PAH (ng/m

3

).

Table 5.3.5

Total Run Freeway

Major/Minor

Arterials

Small Not Idling Idling 

o

1

o

2)

 

T

Total Run Freeway

Major/Minor

Arterials

Residential

Streets

No Vehicle in

Front

No Vehicle in

Front

Vehicle in

Front

UrbanR ute

One (U ) 7.2 4.1 10.8 6.9 4.1 6.2 19.3

UrbanR ute

Two (U 7.3 N/A 10.2 3.4 3.9 4.7 22.8

able 5.3.5.8 Mean black carbon concentrations measured inside the bus on urban route one and urban route two 

Small Not Idling Idling 

(Runs 20 and 26). Mean Black Carbon (ug/m^3).

o

1

o

2)

 Diesel

Table 5.3.5.8 Mean black carbon concentrations measured inside the bus on urban route one 

and urban route two (Runs 20 and 26). Mean black carbon (ug/m∧3). 

Table 5.3.5.9 Mean PAH concentrations inside the bus on urban route one and urban route two 

(Runs 20 and 26). PAH (ng/m3

). 

concentrations on these streets. Again, average speeds were slightly lower on residential streets 

on U1, and the residential streets were generally located between highly congested arterials. By 

minor differences, however, our results show that both urban routes were 

comparable in terms of in-bus concentrations over the course of the commute. These results also

phas

, from the comparisons in Table 5.3.5.7, we also found that for both routes, 

concentrations inside the bus cabin increased five to six times when following another diesel 

vehicle

contrast, on U2, the majority of the residential streets were in neighborhoods with low density 

housing and few other vehicles, and were separated from congested arterials by greater distances 

compared with U1. 

Despite these

em ize our previous finding that freeway travel, which increases the average speed of the 

bus, tended to reduce concentrations inside diesel buses. In the case of U1, the reduced

concentrations on the freeway portion of the run were offset by higher concentrations on the 

surface streets. U2, which had no freeway travel, had slightly lower surface street concentrations

compared with U1, but these concentrations were still higher than those experienced on U1 while 

on the freeway. 

In addition

. Encounters with other diesel vehicles were less frequent on U2 than on U1 as a

percentage of the total run. However, 82% of all encounters with other diesel vehicles on U2 

were with diesel school buses, while on U1, there were more total encounters with other diesel 

vehicles, but only 63% of these were with other diesel school buses. For both routes, a similar 
(b) 

Figure 5.3.5.21 Means and 95% confidence intervals for black carbon (a) and PAH (b) 

concentrations measured inside school bus RE2 while traveling on two 

different urban routes. 

(a) 

30

N = 363 487

ROUTE

U1 U2

95% CI for Black Carbon (ug/m^3) 20

10

0

N = 363 487

ROUTE

U1 U2

95% CI for P

A

H (ng/m^3)

300

200

100

0

141 
N = 363 487

U1 U2

Black Carbon (ug/m^3)

60

50

40

30

20

10

0

ROUTE

(a

(b) 

inside school bus RE2 while trave

)

N = 363 487

ROUTE

U1 U2

PAH (ng/m^3)

2500

2000

1500

1000

500

0

Figure 5.3.5.22 Boxplots of black carbon (a) and PAH (b) concentrations measured 

ling on two different urban routes. 

142 
(a) 

N = 149 136 78 257 205

ROUTE

U1 U2

95% CI for Black Carbon (ug/m^3)

20

15

10

5

0

Road Type

Freeway

Major/Minor Arterial

Residential Street

N = 149 136 78 257 205

ROUTE

U1 U2

95% CI for PAH (ng/m^3)

300

200

100

0

Road Type

Freeway

Major/Minor Arterial

Residential Street

(b) 

Figure 5.3.5.23 Means and 95% confidence intervals for black carbon (a) and PAH (b) 

concentrations measured inside school bus RE2 for two urban bus routes,

divided by roadway type. 

143 
Run Date Time Type of Bus Type of 

Route

Window 

Position

Week 

Number

5 05/01/02 AM HE2 U1 Closed 2

6 05/01/02 PM HE2 U1 Open 2

12 05/14/02 AM RE1 U1 Closed 4

13 05/14/02 PM RE1 U1 Open 4

14 05/16/02 AM RE1 U1 Closed 4

15 05/16/02 PM RE1 U1 Open 4

17 05/21/02 AM RE2 U1 Closed 5

18 05/21/02 PM RE2 U1 Open 5

19 05/22/02 AM RE2 U1 Closed 5

20 05/22/02 PM RE2 U1 Open 5

amount of time was spent traveling behind or adjacent to diesel school buses, with approximately

ten and eight minutes, respectively, of U1 and U2 spent behind or adjacent to another diesel 

school bus. 

The most interesting aspect of the comparison of the two urban routes was that we found 

both routes to have similar concentrations inside the bus, both in terms of the ranges of 

concentrations experienced, and the average over the commute. This despite the fact that both

routes had a variety of different features, including the mix of roadway types and traffic 

congestion scenarios. Features which were similar between routes, such as encounters with other 

diesel vehicles, particularly diesel school buses, dominated the highest peak concentrations on 

both routes. 

5.3.6 Variables Investigated Using Paired Instruments 

For a selected number of runs, two sets of instruments were used to simultaneously

measure the front and rear of the bus. Section 5.1.3 of this report describes the results of 

collocation measurements taken each week during the study for the two sets of instruments. 

5.3.6.1 Differences in Concentration Between the Front and the Rear of the Cabin

Pollutant concentrations at the front and rear of the cabin were measured during 10 of the

36 exposure runs, including five afternoon and five morning runs, all on urban route one. Three 

conventional diesel buses were used for these measurements: HE2, RE1, and RE2. Table 5.3.6.1 

summarizes the characteristics of these runs. 

We used two approaches to analyze the differences between front/rear concentrations: a

study of the time series graphs and the calculation of the 95% confidence intervals of the average 

concentrations. 

Table 5.3.6.1 Front versus rear comparison runs. 

5.3.6.1.1 PAH

during the five morning runs, the front/rear time series tended to track each 

 the rear of the cabin showed 

In general,

other, however, concentrations at more frequent and higher peaks, 

leading to slightly higher average concentrations. Run 14 was an exception in that the two time

series did not track each other and the baseline of the time series for the rear of the cabin was 

144 
substantially higher. Figure 5.3.6.1 shows the PAH measurements during Run 12 (RE1-U1-

AM), an example of a representative morning run. As discussed below, exhaust entered into the

cabin creatin

TIME

7:48:40

7:45:00

7:41:20

7:37:407:34:00

7:30:20

7:26:40

7:23:00

7:19:207:15:407:12:00

7:08:20

7:04:40

7:01:006:57:20

6:53:40

6:50:00

6:46:206:42:406:39:00

400

300

200

100

0

Front

Rear

TIME

15:57:0015:53:5015:50:4015:47:30

15:44:20

15:41:1015:38:0015:34:50

15:31:40

15:28:3015:25:2015:22:1015:19:00

15:15:50

15:12:4015:09:3015:06:20

15:03:10

15:00:00

700

600

500

400

300

200

100

0

Front

Rear

g the concentration gradient. Since the rear of the cabin was closer to the engine

and its exhaust, the time series for this location exhibited a more dynamic behavior compared 

with th

side the cabin during Run 12.

ve afternoon run, the 10-seconds data for PAH during Run 13 (RE1-U1-PM). 

e front. 

igure 5.3.6.1 PAH concentrations (ng/m3 F ) in

In general, the five afternoon runs showed similar trends, with the time series tracking 

each other (with the exception of a few concentration spikes). Figure 5.3.6.2 shows an example 

of a representati

Figure 5.3.6.2 PAH concentrations (ng/m3

) inside the cabin during Run 13. 

145 
146 

Run

5 12 14 17 19

95% CI 400

300

200

100

0

Front

Rear

500

Figure 5.3.6.3 shows the means and 95% confidence intervals of the average PAH

concentrations during the selected morning runs, with the windows closed. In general, the 

concentrations at the rear of the cabin were significantly higher than those at the front of the 

cabin for the conventional diesel buses.

Run

6 13 15 18 20

95% CI 200

100

0

Front

Rear

Figure 5.3.6.3 Means and 95% confidence intervals of the average PAH concentrations 

during morning runs (ng/m3

). 

igure 5.3.6.4 Means and 95% confidence intervals of the average PAH concentrations 

Figure 5.3.6.4 shows the 95% confidence intervals of the average PAH concentrations 

during the selected afternoon runs, with the windows opened. In general, the differences 

between the concentrations measured at the two locations were less substantial than during

morning runs and in most cases those differences were not statistically significant. 

F

during afternoon runs (ng/m3

). 
5.3.6.1.2 Particle Counts

During the morning runs, for particle counts, the pairs of time series followed similar 

trends and exhibited a similar number of peaks. However, the time series for the rear of the

cabin showed higher baselines and high

TIME

7:59:20

7:55:007:50:40

77:37:407:42:007:46:20

:33:20

7:29:007:24:40

7:20:20

7:7:11:407:16:00

07:20

7:03:006:58:406:54:206:6:41:206:45:406:50:00

37:00

300

200

100

0

Front

Rear

TIME

15:57:00

15:53:5015:50:40

15:47:30

15:415:44:20

1:10

15:315:38:00

4:50

15:215:31:40

8:30

15:215:25:20

2:10

15:115:19:00

5:50

15:015:12:40

9:30

15:015:06:20

3:10

15:00:00

90

80

70

60

50

40

30

Front

Rear

er peaks, yielding higher average concentrations (except 

for Run 17 which between the two time series did not show a high degree of correlation). Figure 

5.3.6.5 shows the 10-seconds data for PC during Run 5 (HE2-U1-AM). 

he pairs of time series for PC (0.3 to 0.5 μm) to the five afternoon runs exhibited strong 

overlap, as shown in Figure 5.3.6.6, the 10-second data for Run 13 (RE1-U1-PM). 

 

Figure 5.3.6.5 Concentrations (#/cm ) of PC (0.3 to 0.5μm) during Run 5. 

Figure 5.3.6.6 Concentrations (#/cm3

) of PC (0.3 to 0.5μm) during Run 13. 

T

3

147 
148 

Figure 5.3.6.7 sho

Run

5 12 14 17 19

95% CI

140

120

100

80

60

40

Front

Rear

ws the means and 95% confidence intervals of the average PC 

igure 5.3.6.7 Means and 95% confidence intervals of the average PC concentrations 

(0.3 to 0.5 μm) at front and rear of cabin during morning runs (#/cm3

). 

Figure 5.3.6.8 shows the means and 95% confidence intervals of the average PC 

oncentrations (0.3 to 0.5 μm) during the selected afternoon runs, with the windows partially 

opened. In general, the differences between the concentrations measured at front versus rear 

ere not significant (except for Run 20 where the concentrations at the rear of the cabin were

significantly higher than those at the front). After reviewing the real time data for this run, we 

etermined this was due to a few of high concentrations peaks the instruments at the front did not

apture, probably caused by a momentary malfunction. 

In summary, both pollutants PAH and PC exhibited higher concentrations at the rear of 

the cabin when the windows were closed (morning runs), due to exhaust leaking into the cabin as

emonstrated by the analysis of the SF6 data (see Section 5.3.1). 

For the runs where the windows were partially opened (afternoon), the front/rear 

concentrations were similar for the two pollutants, consistent with other analyses reported here 

suggesting that when the windows were opened, higher ventilation rates promoted uniform

mixing within the cabin 

concentrations during the selected morning runs, with the windows closed. In general, the 

concentrations at the rear of the cabin were significantly higher than those at the front of the 

cabin, for all bus types. 

F

c

w

d

c

d
Run

6 13 18 20

95% CI

60

50

40

30

20

10

Front

Rear

Figure 5.3.6.8 Means and 95% confidence intervals of the average PC concentrations 

(0.3 to 0.5 μm) at front and rear of cabin during afternoon runs (#/cm3

). 

5.3.6.1.3 Correlation Between Front and Rear Concentrations

PAH Ratio (rear to front) PC Ratio (rear to front)

Morning 

(windows closed) 1.4 ± 0.07 1.4 ± 0.03

Afternoon 

(windows o 1.3 ± 0.17 1.1 ± 0.06pen)

As described in previous sections we could quantify the differences between the front and 

ar using the ratio of the average concentrations. Table 5.3.6.2 summarizes these results. The 

% 

Table 5.3.6.2

5.3.6.2 Differences in Concentration Inside and Immediately Outside the Bus

re

ratios were calculated using the 10-second data of the selected runs. In addition, the 95

confidence intervals of the average of those ratios were computed and also shown. 

 Observed rear-to-front ratios. 

 

 

of them with windows open). Three bus types were used for these 

measur ents, including a conventional diesel bus (HE3), the particle trap-outfitted diesel bus 

(TO1) and the CNG bus. Table 5.3.6.3 summarizes the characteristics of these runs. Because of 

Pollutant concentrations inside and immediately outside the bus were measured during 10 

of the 36 exposure runs. These experiments consisted of four afternoon runs with windows open 

(using U1), three morning runs with windows closed (using U1), and three runs using the 

rural/suburban route (all

em

149 
150 

Run Date Time Type of Bus Type of 

Route

Window 

Position

Week

Number

7 05/07/02 PM HE3 RS Open 3

10 05/08/02 PM HE3 U1 Open 3

27 06/04/02 PM TO1 RS Open 7

28 06/05/02 AM TO1 U1 Closed 7

30 06/05/02 PM TO1 U1 Open 7

31 06/06/02 AM TO1 U1 Closed 7

33 06/06/02 PM TO1 U1 Open 7

34 06/11/02 PM CNG RS Open 8

35 06/12/02 AM CNG U1 Closed 8

36 06/12/02 PM CNG U1 Open 8

a problem with the sampling line, outside measurements were not made on the conventional

diesel bus (HE3) during any runs with the windows closed. 

Table 5.3.6.3 Inside/outside comparison runs 

We use graphs

for each run to study the concentration variations within runs and also to examine whether the 

e calculated the 95% confidence

interva

d the highest

recision between paired instruments used in the study. In the same section, we discussed the 

carbon and between PC and PM2.5. Hence, we

orming the comparisons between the inside 

nd outside concentrations. Based on the analysis of the correlation coefficients, we assumed 

carbon mimic the behavior of PC and PAH, respectively. 

d two methods to analyze this set of runs. First, we produced time series

two series (inside and outside) tracked each other. Second, w

ls of the concentration means to establish if the differences between the average

concentrations (inside versus outside) were statistically significant. 

As discussed in Section 5.1.3.3, the PC and the PAH analyzers exhibite

p

strong correlations between PAH and black

focused on PC and PAH measurements when perf

a

PM2.5 and black

5.3.6.2.1 PAH

During the three rural/suburban runs with the windows open, the inside versus outside 

n time series tended to track each other: when the outside values were high, the 

side values were also high and vice versa. However, the outside time series exhibited a more 

ynamic behavior, with more frequent and higher peaks. The four runs on U1 with the windows 

ilar trends, with the outside time series exhibiting more variability. 

n the particle-trap

concentratio

in

d

open showed sim

During the three morning runs on U1 with the windows closed (o

outfitted diesel and the CNG buses), the inside time series exhibited few peaks but the outside

time series again showed more dynamic behavior with a variety of peaks. As an example, Figure 

5.3.6.9 shows 10-second resolution data for PAH during Run 28 (AM-TO1-U1). 
TIME

7:36:307:33:00

7:29:30

7:26:007:22:307:19:007:15:307:12:00

7:08:307:05:007:01:30

6:58:00

6:54:306:51:006:47:30

6:44:00

6:40:30

6:37:006:33:30

6:30:00

2000

1750

1500

1250

1000

750

500

250

0

Inside

Outside

Run

7 10 27 28 30 31 33 34 35 36

9

Outside 5%

CI

0

250

200

150

100

50 Inside

TO1 (28) TO1 (31) CNG (35)

95% C

0 Outside

.I. PAH 100

50

250

200

150

Inside

igure 5.3.6.9 Inside (rear) and outside concentrations (ng/m3

) of PAH during Run 28 

35), concentrations outside

e bus

d only

igure 5.3.6.10 Means and 95% confidence intervals of the average PAH concentrations

(ng/m3

) for selected runs.

F

(urban, morning, windows closed). 

Figure 5.3.6.10 shows the means and 95% confidence intervals of the average PAH 

concentrations for selected runs. During Runs 28 and 31 on the particle trap-outfitted diesel bus 

with the windows closed, the concentrations inside the cabin were clearly higher than outside. 

However, for the run on the CNG bus with the windows closed (Run

th were higher, indicating lower contribution from self pollution inside the cabin of the 

CNG bus. 

 

(a) All available runs (see table 5.3.6.3) (b) Runs with windows close

F

151 
152 

For the remaining afternoon runs with the windows open, differences in concentrations 

etween inside and outside were not significant. 

.3.6.2.2 Particle Counts

b

5

TIME

16:20:5016:17:00

16:13:10

16:09:20

16:05:30

16:01:4015:57:50

15:54:00

15:50:10

15:46:20

15:42:3015:38:4015:34:50

15:31:00

15:27:10

15:23:20

15:19:3015:15:40

15:11:50

15:08:00

700

600

500

400

300

200

Inside

Outside

For PC, for the three rural/suburban runs with the windows open, the inside and outside 

oncentration time series behaved similar to each other, following the same trends and with 

milar magnitudes of peak concentrations. 

The four afternoon runs for U1 with the windows open exhibited almost the same trends

s those for the rural/suburban route, with inside/outside time series following similar patterns

xcept that, in general, the outside time series showed slightly more dynamic behavior for U1. 

During the three morning runs on U1 with the windows closed (includes the particle traputfitted diesel and the CNG buses), the pairs of time series again followed similar trends; 

owever, the outside concentrations (both baseline and peaks) were higher than the 

oncentrations inside the cabin, the opposite of what we found for PAH. 

data for

C

c

si

a

e

o

h

c

Figure 5.3.6.11 shows an example of a time series based on 10-second resolution

PC during Run 33 (PM-TO1-U1-windows open). 

Figure 5.3.6.12 shows the 95% confidence intervals of the average PC concentrations 

during the selected runs. For the morning runs with windows closed (28, 31, 35), the differences 

between the inside and outside concentrations were not only significant but also substantial 

(outside higher). 

Figure 5.3.6.11 oncentrations (#/cm3

) of PC (0.3 to 0.5μm) during Run 33. 
400

Run

7 10 27 28 30 31 33 34 35 36

95% CI

utside

100

0

Inside

O

300

200

Figure 5.3.6.12 Mean and 95% confidence intervals for PC (#/cm3

). 

tside the cabin were significantly higher. 

.3.6.2.3. Implications of the Results

During the afternoon runs with windows open, the differences between inside versus

outside were, in general, not statistically significant, except for Runs 27 and 33, where the 

concentrations ou

5

Inside Outside

osed

)

Flat time series 

Higher average concentrations (except CNG)

Dynamic time series 

Lower average concentrations

en Time series similar to outside 

Average concentrations similar to outside

Time series slightly more dyna

Average concentrations similar t

Inside Outside

osed

Windows cl

(morning

 

Windows op

(afternoon)

mic 

o inside

Windows cl

)

Time series similar to outside 

Average concentrations lower than outside

Time series slightly more dynamic 

Average concentrations higher than inside

Windows open 

(afternoon)

Time series similar to outside 

Average concentrations similar to outside

Time series slightly more dynamic 

Average concentrations similar to inside

PC in the accumulation mode (Background pollutant)

PAH (Directly emitted pollutant)

(morning

Table 5.3.6.4 summarizes the major findings of the analyses described in this section. 

Table 5.3.6.4 Summary of major findings of inside versus outside comparisons. 

153 
These results also suggest there is a significant difference in behavior between directly 

mitted pollutants, such as BC, PAH, and NO2, and “background” pollutants such as PM2.5 and 

C (in the accumulation mode), consistent with analyses presented in other sections of this report 

.1.3, 5.2.1, and 5.3.1) (although we emphasize that both PM2.5 and PC can have a direct 

missions component). These findings demonstrate the complex implications for effects of 

indows position, as explained in Section 5.2.2.3. 

.3.6.2.4 Correlation between Inside and Outside Concentrations

e

P

(5

e

w

5

We have described the key factors governing the differences in pollutant concentrations 

side and outside the cabin. However, the analysis above did not include the quantification of 

ose differences.

Outdoor-to-indoor ratios are widely used in the analysis of indoor air pollutant data 

(Fischer et al., 2000; Janssen et al, 2001; Cha 2002). We used an 

analogous parameter defined as the ratio of outside-to-inside bus cabin concentrations. 

in

th

loulakoua and Mavroidis,

Inside,j

Outside,j

j C

C

α = (5.3.6.2.1) 

where is the outside-to-inside concentrations ratio for the pollutant j; COutside,j is the

bus; and Cinside,j is the

concentration of pollutant j measured inside the cabin. 

36 (U1 with windows open on TO1

nd CNG). In addition, the 95% confidence intervals of the mean of these ratios were computed. 

and demonstrates the outside-to-inside ratio is a function 

of seve ive

sults support the findings described in the preceding sections. 

Table 5.3.6.5 Observed outside-to-inside concentration ratios for PAH and PC for CNG and 

particle trap-outfitted buses. 

 PAH (ng/m3

) PC (#/cm3) 

α j

concentration of pollutant j measured immediately outside the

These ratios were calculated using the 10-seconds data of Runs 28, 31, and 35 (U1 with 

windows closed on TO1 and CNG), and Runs 30, 33, and

a

Table 5.3.6.5 summarizes these results

ral factors, including type of pollutant, bus type, and window position. These quantitat

re

 

Windows closed 

(morning) 

0.75 ± 0.2 (TO1) 

1.54 ± 0.2 (CNG) 

1.40 ± 0.04 (TO1) 

2.14 ± 0.04 (CNG) 

Windows open 

(afternoon) 0.99 ± 0.2 (TO1) 1.06 ± 0.08 (TO1) 

154 
5.4 Comparison of Pollutant Concentrations Measured Inside the Buses and at Nearby 

AQMD Monitoring Sites

5.4.1 Carbon Monoxide and Nitrogen Dioxide

CO and NO2 ambient monitoring data were obtained from the South Coast Air Quality 

Management District (SCAQMD) for comparisons between inside bus concentrations and 

ambient concentrations. The Central LA and West LA SCAQMD stations (see Photograph 17) 

e took the average of the data 

om th

lts for CO and NO2, respectively. 

5.4.1.1 Carbon

were selected as the closest monitoring sites to urban route one. W

fr e two monitoring sites as best representing ambient concentrations near urban route one. 

We chose AQMD data corresponding to the date and time of each run. Sections 5.4.1.1 and 

5.4.1.2 discuss resu

Photograph 17. West L.A. SCAQMD Station 

Monoxide

Tables 5.4.1.1 and 5.4.1.2 show the mean CO concentrations measured inside the buses 

and at the two earest AQMD stati g (windows closed on (windows

open) runs on ban route one, respe

orning versus afternoon, since 

earlier studies showed in-vehicle/ er for an

for the m s, with fixed wer c on

(Petersen and Allen 1982; Fernandez-Bremauntz and Ashmore 1995b). However, in the present 

study we found no difference between m ternoon in-vehicle/fix

on aver f 0.7. n in Tables 5. e absolute

concentrations of CO were significantly different between morning and afternoon, with higher 

oncentrations both inside the buses and at the AQMD stations in the morning. The overall 

ean CO concentration inside buses in the morning was 5.2 ppm versus 2.4 ppm in the 

fternoon, while the overall mean of the two AQMD stations in the morning was 6.9 ppm versus 

.6 ppm in the afternoon. 

n

ur

ons for mornin

ctively.

) and afterno

Mean CO concentrations were compared 

orning hour

separately, m

fixed site ratios were high

-site monitors exhibiting lo

afternoon/evening hours th

oncentrations in the afterno

orning and af

However, as show

ed-site ratios, with 

age the same ratio o 4.1.1 and 5.4.1.2, th

c

m

a

3

155 
Table 5.4.1.1 Mean CO concentrations (ppm) inside buses and at two nearby AQMD stations

for the morning runs (windows closed) on urban route one. 

Date Run Week Bus Inside West

LA 

Central 

LA 

Mean1 Ratio2 

bus 

05/01/02 5 2 HE2 4.5 6.5 6.5 6.5 0.7 

05/08/02 8 3 HE3 4.0 4.0 5.5 4.8 0.8 

05/14/02 12 4 RE1 5.8 9.5 7.0 8.3 0.7 

05/16/02 14 4 RE1 4.3 2.5 4.5 3.5 1.2 

05/21/02 17 5 RE2 6.0 4.0 6.0 5.0 1.2 

05/22/02 19 5 RE2 3.7 7.0 8.0 7.5 0.5 

06/05/02 28 7 TO1 5.7 6.0 12.0 9.0 0.6 

06/06/02 31 7 TO1 6.4 8.5 9.0 8.8 0.7 

06/12/02 35 8 CNG 6.2 3.0 15.0 9.0 0.7 

 Mean 5.2 6.9 0.7 

1. Arithmetic mean of west LA and central LA stations. 

2. Ratio = (mean of inside bus) / (mean of two stations) 

Table 5.4.1.2 Mean CO concentrations (ppm) inside buses and at two nearby AQMD stations 

for the afternoon runs (windows open) on urban route one. 

Date Run Week Bus Inside 

bus 

West

LA 

Central 

LA 

Mean1 Ratio2 

05/01/02 6 2 HE2 2.1 2.0 3.0 2.5 0.8 

05/08/02 10 3 HE3 N/A 2.0 4.0 3.0 

05/14/02 13 4 RE1 2.6 2.5 6.0 4.3 0.6 

05/16/02 15 4 RE1 2.1 2.0 5.0 3.5 0.6 

05/21/02 18 5 RE2 2.0 2.0 3.0 2.5 0.8 

5 RE2 1.9 2.0 3.0 2.5 0.8 

7 TO1 2.9 3.0 8.5 5.8 0.5 

3.5 7.0 5.3 0.6 

8 CNG 2.8 1.5 4.0 2.8 1.0 

Mean 2.4 3.6 0.7 

05/22/02 20 

06/05/02 30 

06/06/02 33 7 TO1 2.9

06/12/02 36 

 

1. Arithmetic mean of west LA and central LA stations. 

2. Ratio = (mean of inside bus) / (mean of two stations) 

Interestingly, the trends of CO concentrations inside the buses and at the ambient sites

tracked each other over the main study period (see Figures 5.4.1.1 (a) and (b) for morning and 

afternoon, respectively). Figure 5.4.1.1 shows CO concentrations inside the buses were

consistently lower than those at the ambient sites except for Run 14 and 17 in the morning and 

Run 36 in the afternoon. 

156 
5

8

12

28 31 9.0 35

14

5

12

14

31 35

8.0

4

n

17

19

8

17

19

28

0.0

1.0

2.0

3.0

4.0

CO(p

5.0 p

6.0

m)

7.0

10.0

0 10 20 30 0

Ru

AQMD sites

s

6

10

13

15

13

15 36

36

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0

0 0 2 40

18 20

30

33

6 18 20

30 33

7.

10.0

8.0

9.0

Inside bu

1 0 30

Run

CO(ppm)

AQMD sites

Inside bus

(b) Afternoon 

5.4.1.2 Nitrogen Dioxide

(a) Morning

Figure 5.4.1.1 CO concentrations (ppm) inside buses and at the selected AQMD stations. 

les 4.1.3 5.4 esen n NO centra measured inside buses and at 

l L nd W A st for ing and afternoon runs, re 2 

ion side bus re c tently higher than those measured at ambient

mo g ru the l mean concent n insid s 64 p ersus 28

A D sta ns. erag 2 concentrations de the buses were more than 

hi than e am con ations in the morning during the main study. The 

an centr on in us in afternoon was 76 ppb, higher than inside the buses 

nin d mo es higher than the overall ambient level of about 22 ppb. 

pos to NO insi s em ed in main study ranged from 34 to 110 ppb in 

the n significan ly highe n in e r studies

of e reported NO2 concentrations measured in 

ighteen cities in fifteen countries around the world. The range of personal exposures was from

11 ppb to 51.5 ppb. T

Tab 5. and .1.4 pr t mea 2 con tions

the Centra A a L ations morn spectively. Mean NO

concentrat s in the es we onsis the

sites. For rnin ns, overal ratio e the buses wa pb v

ppb at the QM tio On av e, NO insi

two times gher th bient centr

overall me con ati side b the

in the mor g an re than three tim

Ex ure 2 de buse ploy the

morning and from 39 to 115 ppb in the afternoo , t r tha arlie

oth r microenvironments. Levy et al. (1998)

e

he highest NO2 concentration reported in their study was 106 ppb in 

workplace exposure in Mexico City. Stevenson et al. (2001) also reported the average NO2

concentration during 1997 in UK was 23 ppb at curbside locations. They defined points within 

one to five meters from the curb of a busy road as curbside locations, reflecting NO2

concentrations on a busy street. 

157 
Table 5.4.1.3 Mean NO2 concentrations (ppb) inside buses and at two nearby AQMD stations 

for the morning runs with windows closed on urban route one. 

Date Run Week Bus Inside 

bus 

West

LA 

Central 

LA 

Mean1 Ratio2 

05/01/02 5 2 HE2 49.8 28.5 28.5 28.5 1.7 

05/08/02 8 3 HE3 109.9 17 29.5 23.3 4.7 

12 4 RE1 55.4 40.5 33.5 37 1.5 

4 RE1 91.8 14 28.5 21.3 4.3 

17 5 RE2 74 19.5 25.5 22.5 3.3 

5 RE2 78.2 30.5 25 27.8 2.8 

7 TO1 38.7 25 45 35 1.1 

7 TO1 45.9 30 35 32.5 1.4 

8 CNG 33.8 25 30 27.5 1.2 

 Mean 64.2 28.4 2.3 

05/14/02 

05/16/02 14 

05/21/02 

05/22/02 19 

06/05/02 28 

06/06/02 31 

06/12/02 35 

1

Arithmetic mean of west LA and central LA stations. 2

Ratio = (mean of inside bus) / (mean of two stations). 

able 5.4.1.4 Mean NO2 concentrations (ppb) inside buses and at two nearby AQMD stations 

Date Run 

bus LA LA 

T

for the afternoon runs with windows open on urban route one. 

Week Bus Inside West Central Mean1 Ratio2 

05/01/02 6 2 HE2 48.1 28.5 9.5 19 2.5

05/08/02 10 

05/14/02 13 

3 HE3 65.2 17 14.5 15.8 4.1 

4 RE1 75.6 40.5 26 33.3 2.3 

05/16/02 15 4 RE1 115.1 14 20.5 17.3 6.7 

05/21/02 18 5 RE2 80.4 19.5 7.5 13.5 6.0 

05/22/02 20 5 RE2 86.9 30.5 9.5 20 4.3 

06/05/02 30 7 TO1 84 25 30 27.5 3.1 

06/06/02 33 7 TO1 89.2 30 35 32.5 2.7 

06/12/02 36 8 CNG 39.4 25 10 17.5 2.3 

 Mean 76 21.8 3.5 1

Arithmetic mean of west LA and central LA stations. 2

Ratio = (mean of inside bus) / (mean of two stations). 

Figure 5.4.1.2 shows clear differences in NO2 concentrations between inside the buses 

and at the ambient sites. It is noteworthy that the CNG bus had the lowest NO2 concentrations

among bus types in the morning and afternoon runs. However, the trends in concentrations over 

the study period less closely tracked each other compared with CO (as discussed in the previous

section). Furthermore, there was little difference in average NO2 concentrations between 

morning and afternoon runs (64 versus 76 ppb inside buses and 28 versus 22 ppb at the ambient 

sites), while CO concentrations in the morning were about two times higher than those in the

afternoon in both microenvironments. 

158 
5

8 0.0

12

14 17

19

28 31

17

19

28

31

2

4

6

80.0

100.0

120.0

0 20 30 4

Run

NO2(ppb)

35

5

15

100.0

120.0

8

12

14

35

0.0

0 1

0.0

0.0

0

AQMD sites

6 10

13

15

30

10

13

30

0.0

0

0

.0

0 10 30 40

18

20

33

36

6

18

20 33

36

20.0

40.

60.

NO

80

20

Run

2(ppb)

Inside bus AQMD sites

Inside bus

(b) Afternoon 

5.4.2 nteg

(a) Morning

Figure 5.4.1.2 NO2 concentrations (ppb) inside buses and at the selected AQMD stations. 

I rated Samples

5.4.2.1 Aromatic Hydrocarbons

During the main study we measu he con atic hydrocarbons 

 the AQMD station locate west Angeles

he n co ntrations of target pollutants are shown for morning runs and afternoon 

runs in Tables 5.4.2.1 and 5.4.2.2, respectively. Ratios of mean concentrations between inside 

the bus and the WLA AQMD site varied by pollutant. It is also interesting that the ratios for

each pollutant varied between morning and afternoon sampling periods. 

Several other studies have also compared concentrations of aromatic hydrocarbons inside 

vehicles versus fixed monitoring sites. Rodes et al. (1998) measured concentrations of all of the

above-mentioned pollutants (except n-octane) inside a typical California diesel school bus in 

Sacramento and then compared those concentrations to ambient concentrations. The ratios 

between inside the school bus and the ambient site were available only for m and p-xylene (ratio

= 2.5) and toluene (ratio = 2) in the morning and only for toluene (ratio = 1.2) in the afternoon. 

The higher ratios found for the same pollutants in the present study are presumably due to 

differences in traffic densities and vehicle mixes between Sacramento and Los Angeles as found 

by Rodes et al. (1998) and Fruin (2003). 

Jo and Park (1999) also reported in-diesel bus/fixed site ratios for aromatic hydrocarbons: 

benzene (3), ethylbenzene (4.2), m and p-xylene (5), o-xylene (3.8), toluene (2). They conducted 

measurements of those compounds during morning and evening rush hours in Taegu, the third 

largest city in Korea. Ratios for benzene, ethylbenzene, and m and p-xylene were comparable to 

ratios we found in the present study, while ratios for o-xylene and toluene were much lower than

our results. 

red t centrations of several arom

both inside the school buses and at the site of d in Los

(WLA). T mea nce

159 
Table 5.4.2.1 Mean concentrations (ppb) of aromatic hydrocarbons during morning runs 

 (windows closed). 

Compound Inside bus West LA AQMD site Ratio1 

Benzene 2.9 0.5 5.8 

Ethylbenzene 1.1 0.1 11 

m and p-Xylenes 4.4 0.7 6.3 

o-Xylene 1.7 0.2 8.5 

n-Octane2 1.5 1.0 1.5 

Toluene 17.7 2.3 7.7 

1. Ratio = (mean concentration inside bus) / (mean concentration at WLA AQMD site) 

2. n-octane is not an aromatic hydrocarbons but is reported for completeness.

3. Detection limit for all compounds except toluene was 0.01 ppb. 

Table 5.4.2.2 Mean concentrations (ppb) of aromatic hydrocarbons during afternoon runs 

 (windows open). 

Compound Inside bus West LA AQMD Ratio1 

Benzene 0.9 0.6 1.5 

Ethylbenzene 0.4 0.1 4 

m and p-Xylenes 2 0.2 10 

o-Xylene 0.6 0.05 12 

n-Octane2 1.9 0.1 19 

Toluene 14.4 0.7 20.6 

1. Ratio = (mean concentration inside bus) / (mean concentration at WLA AQMD site) 

2. n-octane is not an aromatic hydrocarbons but is reported for completeness.

3. Detection limit for all compounds except toluene was 0.01 ppb. 

5.4.2.2 1,3-Butadiene

Concentrations of 1,3-butadiene were also measured inside the school buses and at the 

west LA AQMD site during the main study. Several other studies have also investigated invehicle concentration of 1,3-butadiene (Chan et al., 1991a; Duffy and Nelson 1997; Rodes et al., 

1998). However, 1,3-butadiene has high photochemical reactivity and will undergo rapid 

photooxidation during the day (Carter and Atkinson, 1989). Thus, in the study by Duffy and 

Nelson (1997), significant concentrations of 1,3-butadiene were only observed inside vehicles 

during peak traffic hours, while concentrations of 1,3-butadiene in the samples collected during 

non-peak traffic times or in ambient air were near, or below, the detection limit of 0.1 ppb. Chan

et al. (1991a) also reported 30% of in-vehicle samples and 100% of fixed-site and sidewalk 

samples below the detection limit of 1 ppb. In the present study, 70% of the 1,3-butadiene data 

collected during the bus commute runs were below the detection limit of 0.5 ppb. 

Table 5.4.2.3 shows concentrations of 1,3-butadiene inside the buses and at the west LA 

AQMD site. In-cabin concentrations of 1,3-butadiene were above the detection limit of 0.5 ppb 

for only eight morning runs out of thirty-one bus commute runs. For the west LA AQMD site, 

only two values of 0.5 ppb and 0.6 ppb were at, or above, the detection limit of 0.5 ppb, both in 

the afternoon. 

160 
Table 5.4.2.3 Concentrations of 1,3-butadiene (ppb) during the main study. 

Date Run Time Location Concentration 

4/23/02 2 AM Bus 1.3 

5/8/02 8 AM Bus 0.9 

5/14/02 12 AM Bus 0.8 

5/16/02 14 AM Bus 0.8 

5/21/02 17 AM Bus 0.8 

5/22/02 19 AM Bus 0.6 

6/6/02 31 AM Bus 1.2 

6/12/02 35 AM Bus 1.1 

5/21/02 PM WLA AQMD site 0.5 

5/29/02 PM WLA AQMD site 0.6 

Table 5.4.2.4 summarizes in-vehicle mean concentrations of 1,3-butadiene measured in 

the present study as well as in earlier studies. Two studies compared in-vehicle concentrations to 

ambient concentrations: the present study (0.9 ppb average inside bus and 0.5 ppb or less average 

at the west LA AQMD station) and Chan et al. (1.5 ppb in-vehicle and 0.5 ppb at a fixed-site). 

According to Seiber (1996), the statewide population-weighted exposure to ambient airborne 

1,3-butadiene for California was estimated to be 0.37 ppb. Consequently, on average, children 

on the school buses employed in the present study would have been exposed to 1,3-butadiene at 

about two and a half times the statewide population-weighted level during the times of their 

commute. 

Table 5.4.2.4 Studies measuring 1,3-butadiene (ppb) inside vehicles. 

Reference Year Location Vehicle type Concentration 

This study 2002 Los Angeles, CA School bus (diesel) 0.9 ± 0.2 

Chan et al. (1991) 1988 Raleigh, NC Passenger car 

(gasoline) 

1.5 ± 1.0 

Duffy and Nelson 

(1997) 

1996 Sydney, Australia Bus (diesel) 2.2 

Rodes et al. (1998) 1997 Sacramento, CA School bus (diesel) 0.4 

5.4.2.3 Gaseous Aldehydes and Ketones 

Gaseous aldehydes and ketones (i.e., carbonyls) emitted by vehicles have been identified 

as hazardous air pollutants, with compounds such as formaldehyde being a known human

carcinogen (Kean et al. 2001). Grosjean et al. (2001) showed formaldehyde, acetaldehyde, and

acetone were the three most abundant carbonyls emitted by vehicles. However, there are few 

data on children’s exposure to speciated carbonyls inside school buses powered by diesel fuel. 

During the main study the concentrations of fourteen carbonyl species including formaldehyde, 

acetaldehyde, and acetone, were measured inside the buses. For comparisons, these fourteen 

carbonyls were also measured concurrently at the west LA AQMD site. 

Tables 5.4.2.5 and 5.4.2.6 show the mean concentrations of each individual species for 

morning runs and afternoon runs, respectively. Acrolein and methacrolein were not reported

because all the measurements of both species were below the detection limit of 0.03 ug/sample. 

161 
Table 5.4.2.5 Mean concentrations (μg/m3

) of carbonyls during morning runs (windows closed). 

Inside bus cabin WLA AQMD Site Ratio1 

Formaldehyde 2.1 0.4 5.3 

Acetaldehyde 28.2 6.7 4.2 

Acetone 18.6 9.5 2 

Propanal 4.8 1.9 2.5 

Crotonaldehyde 2.7 0.7 3.9 

Methylethylketone2 32.7 3.3 9.9 

Butanal3 4.6 3 1.5 

Benzaldehyde 0.9 0.8 1.1 

Pentanal 1.5 1.4 1.1 

m-Tolualdehyde 1.4 0.5 2.8 

Hexanal 4.4 2.2 2 

Pentanones 1.2 N/A N/A 1

Ratio = (Inside school buses) / (WLA AQMD site) 2methylethylketone (MEK) = 2-butanone 3butanal = butyraldehyde 4N/A: not available. 

Table 5.4.2.6 Mean concentrations (ug/m3

) of carbonyls during afternoon runs (windows open). 

Inside bus cabin At West LA AQMD Site Ratio1 

Formaldehyde 1.1 0.4 2.8 

Acetaldehyde 62.5 15.1 4.1 

Acetone 20.4 9.6 2.1 

Propanal 10.9 3.1 3.5 

Crotonaldehyde 2.8 1 2.8 

Methylethylketone2 76.3 5 15.3 

Butanal3 10.7 5.1 2.1 

Benzaldehyde 0.5 0.6 0.8 

Pentanal 3.5 3.1 1.1 

m-Tolualdehyde 1.4 1.3 1.1 

Hexanal 5.2 4.1 1.3 

Pentanones 2.5 3.1 0.8 1

Ratio = (Inside school buses) / (WLA AQMD site) 2

methylethylketone (MEK) = 2-butanone 3

butanal = butyraldehyde 

As shown in Table 5.4.2.5, the mean in-cabin concentrations of acetaldehyde, acetone 

and methylethylketone were substantially higher than for the other compounds. Moreover, the 

mean concentration of each carbonyl inside the bus cabin divided by the corresponding mean

concentration at the WLA AQMD site, varied widely by carbonyl species in the morning runs. 

Benzaldehyde, pentanal, and butanal showed little difference, while the concentration of 

methylethylketone inside the bus cabin was on average about ten times higher compared to the 

west LA AQMD station. Formaldehyde and acetaldehyde also showed much higher 

concentrations inside the buses than at the WLA AQMD site with ratios of five and four for

formaldehyde and acetaldehyde, respectively. The remaining compounds showed average

162 
concentrations inside the bus cabin two to four times higher than in ambient air measured at the 

WLA AQMD site. 

For the afternoon runs, the mean concentrations of methylethylketone (MEK) and 

acetaldehyde were much higher inside the buses compared with other species. As in the morning 

runs, the ratio of mean MEK concentrations inside the bus cabin to the mean concentration in 

ambient air was much higher than for the other compounds and about 50% higher than the 

morning ratio. For most other compounds there was reasonable consistency between the 

morning and afternoon in the ratios of the inside cabin/WLA site mean concentrations, given the 

uncertainties in these measurements. An exception was formaldehyde for which the afternoon 

inside cabin concentration was half the morning concentration while the mean concentration at

the WLA AQMD site was the same in the morning and afternoon. 

163 
164 
6.0 CONCLUSIONS AND RECOMMENDATIONS 

6.1 Importance of the School Bus Microenvironment in Children’s Exposure During School 

Bus Commute-Related Activities

Across all pollutants, concentrations measured during bus commutes on urban routes 

were higher than background concentrations measured during precision experiments or at the bus 

stop (Table 6.1). For example, the highest mean concentrations of black carbon and PAH 

measured during a bus commute were nearly factors of ten and twenty, respectively, greater than 

mean concentrations measured for these pollutants during instrument precision assessments 

made near the West Los Angeles AQMD site. The mean bus commute concentrations over all 

bus runs (in parentheses) were approximately a factor of two or more (except for NO2) higher 

than mean concentrations at the bus stops studied. 

Table 6.1. Mean concentrations measured in two microenvironments and in background air. 

 Ambient air1 Bus stop Bus commutes2,3

Black Carbon (ug/m3) 2 ± 0.1 4 ± 0.4 3 - 19 (8) 

PAH (ng/m3) 27 ± 1.5 44 ± 4.5 64 - 400 (134) 

NO2 (ppb) 49 ± 1.0 54 ± 1.9 34 - 110 (73) 

PC (#/cm3)

5 83 ± 3.1 62 ± 1.8 70 - 236 (130) 

PM2.5 (ug/m3) 234 26 21 - 62 (43) 

1

Measured during precision experiments near the West Los Angeles AQMD station. 

2

Ranges include all bus types (see Table 5.3.3.1). 

3

Values within parentheses are the mean for all bus commutes. 

4

2001 annual arithmetic mean from the Central Los Angeles AQMD station. 

5

In 0.3 – 0.5um size range.

Exposure factors, calculated by multiplying the mean concentration in a given 

microenvironment by the average time spent in that microenvironment, were highest for the bus

commute (Table 6.2). Bus stop microenvironment exposures were up to thirteen times greater 

than loading/unloading zone microenvironment exposures, while the urban bus commute

exposures were between 50 and 400 times greater than those due to the loading/unloading 

microenvironment. The urban bus commutes led to between 20 and 100 times higher exposures 

than at the bus stops, depending upon the pollutant. Therefore, the bus commute 

microenvironment dominated school bus commute-related exposures, and the bus stop and 

loading/unloading zone microenvironments were nearly insignificant contributors to commuterelated exposures. 

165 
Table 6.2. Exposure factors in three school bus commute-related microenvironments. 

 Exposure Factors 

Loading/ 

unloading1 Bus stops Urban commutes

BC (ug/m3

* min) 5 20 600 

PAH (ng/m3

* min) 45 230 10000 

NO2 (ppb * min) 105 270 5500 

PC (#/cm3

* min) 25 310 10000 

PM2.5 (ug/m3

* min) N/A 130 3500 

1

Exposure factor calculations for the loading/unloading zone were based on 

measurements taken during the pilot study under winter conditions.

6.2 Contribution of Bus’s Own Exhaust to Concentrations Inside the Cabin

Our study used an SF6 tracer gas metered into the bus’s exhaust system to determine the 

percentage of air in the bus cabin originating from the bus’s own exhaust and this percentage 

varied significantly between buses and also depended on window position. For all buses tested,

the amount of the bus’s own exhaust inside the cabin was substantially higher with all windows

closed compared with the windows partially opened. Moreover, older buses showed a larger 

percentage of their own exhaust entering into the cabin compared with newer buses. 

SF6 concentrations inside the bus cabin correlated reasonably well with normalized black

carbon concentrations measured inside the conventional diesel and trap-outfitted diesel bus 

cabins. This correlation was stronger than that between SF6 and PM2.5 mass when the windows

were closed. 

Different bus models and ages correspond to different construction and cabin designs, 

and the results of our SF6 analyses suggested these differences may result in a wide range of 

pollutant exposures across bus types, depending on the pollutant. In general, the buses that 

exhibited high opacity during snap-and-idle tests (i.e., conventional diesel buses) and a high 

percentage of SF6 inside the cabin, also exhibited the highest average inside/rear concentrations

of black carbon, PAH and NO2 in runs with the windows closed. Although the higher inside/rear 

exposures of these pollutants could be predicted by the snap-and-idle tests and SF6 data, this was 

not the case for particle counts (in the size range from 0.3 to 0.5 μm) or PM2.5 mass average 

concentrations inside the cabin. 

166 
6.3. Conditions Associated with Highest Concentrations Inside the Bus Cabin

6.3.1 Within-Bus Variables

Window Position. The influence of window position depended on whether pollutants 

were primarily directly-emitted (e.g., black carbon and PAH) or had strong “background” 

contributions (e.g., PM2.5). Concentrations of black carbon inside the cabin tended to decrease 

when the windows were partially opened but increase when the windows were closed. In

contrast, for particle counts within the size range 0.3 to 0.5 μm, concentrations inside the cabin 

tended to increase when the windows were partially opened but tended to decrease when the

windows were closed. Thus, for directly-emitted pollutants it appears increased ventilation rates 

with the windows opened caused dilution of the “self pollution,” while for “background” 

pollutants closed windows inhibited entrance of these pollutants into the cabin. 

Location Inside the Bus Cabin. With the windows closed the concentrations of PAH and 

particle counts (in the range of 0.3 – 0.5 μm) at the rear of the cabin were statistically 

significantly higher than those at the front of the cabin across all conventional diesel buses

tested. With partially opened windows, there were no statistically significant differences

between measurements at the front and rear of the cabin. These results are consistent with the

preceding analysis suggesting that when the windows were open, higher ventilation rates

promoted uniform mixing within the cabin. 

6.3.2 External Variables

Type of Vehicle in Front of, or Adjacent to, the Test Bus. Using the videotapes of the

bus runs with windows open, we found black carbon and PAH concentrations inside the test 

buses were highest when following a diesel school bus which emitted visible exhaust, and lowest 

when following a gasoline vehicle or no target. When following a smoky diesel school bus, 

concentrations inside the cabin were on average eight and twelve times higher for black carbon 

and PAH, respectively, compared with following a gasoline vehicle or no target. When 

following a diesel school bus that was not emitting visible exhaust, black carbon and PAH 

concentrations inside the test buses were on average four and six times higher, respectively, 

compared with following a gasoline vehicle or no target. These results were found during 

commutes when the windows of the bus were open. 

This finding is important because the type of diesel vehicle we encountered most 

frequently during all bus runs was a diesel school bus. On average in the afternoon runs 

analyzed using the videotapes, another diesel vehicle was within three car lengths in front of or 

adjacent to, our bus, during more than one-quarter the commute, with diesel school buses 

responsible for over sixty percent of these encounters. This high incidence of following other 

diesel school buses was in part due to caravanning behind other buses leaving the BSMS. 

These results demonstrated that the type of school bus a child rides on is not the only 

determinant of exposure and that conventional diesel school buses can have a double exposure 

impact on commuting children: first, the influence of the bus’s own exhaust on concentrations

inside the cabin and second, exposures to the exhaust from other nearby conventional diesel 

school buses. These encounters may cause high concentrations of black carbon and PAH inside 

the cabin of the school bus, particularly if the bus windows are open. 

167 
Roadway Type The degree of influence of roadway type on pollutant concentrations 

inside the bus cabin depended on the type of bus, the level of traffic congestion and whether the 

bus windows were open or closed. For relatively clean buses (i.e., the CNG bus, and the trapoutfitted diesel bus), traveling on congested freeways with higher pollutant concentrations tended

to increase concentrations inside the bus. For relatively dirtier buses (i.e., conventional diesel), 

because the bus’s own exhaust was a significant contributor to concentrations inside the bus,

increasing the ventilation rate inside the bus, which was more likely to occur on freeways when 

the windows were open, tended to decrease concentrations inside the bus. 

Route Type Two different urban routes, U1 and U2, (as well as an rural/suburban route) 

were driven by the same “representative” conventional diesel bus on two consecutive weeks and 

for the comparison of urban routes U1 and U2, only these runs were used. We found the 

concentrations of black carbon and PAH averaged over the entire runs were not significantly 

different between the two different urban routes while the rural/suburban route exhibited lower 

concentrations of both black carbon and PAH. This was interesting since U1 spent 

approximately 40% of the time on the freeway and 60% of the time on surface streets, while U2 

was entirely on surface streets. Features which were similar between routes, such as encounters 

with other diesel vehicles, particularly diesel school buses, dominated the highest peak 

concentrations of black carbon and PAH on both routes. 

For PC (in the size range from 0.3 to 0.5 um) and PM2.5 mass, the rural/suburban route

and U1 exhibited similar average within-bus concentrations, while U2 showed higher average 

concentrations than U1 during both morning and afternoon commutes with windows closed and 

open, respectively. These results demonstrate, again, the differences between the influences of 

directly emitted versus “background” pollutants on concentrations inside the bus cabin. 

Table 6.3 presents the mean pollutant concentrations by run type for the urban and rural/suburban 

routes, under open and closed window conditions. Both window position and surrounding traffic

density can be seen to have strongly affected on-board concentrations for vehicle-related pollutants, but 

for pollutants with a strong background component, such as PM2.5 and fine particle counts (0.3-0.5 

μm), differences between routes and differences due to window position were less distinct.

168 
Table 6.3. Concentrations means and range of means by route and by window position1

. 

Urban Route1

 Rural/Suburban Route 

Windows Closed 

(morning)2

Windows Open 

(afternoon)2

Windows Open 

(afternoon)2

Pollutant

Mean Range of 

Means Mean Range of Means Mean Range of Means 

Black Carbon 

(μg/m3) 10 2.5 – 19 5.2 2.9 – 9.1 2.7 0.9 – 4.8 

PM-bound PAH 

(ng/m3

) 198 64 – 400 96 33 – 147 36 14 – 66 

NO2 (ppb) 64 34 – 110 73 39 – 120 45 23 – 68 

Formaldehyde 

(μg/m3

) 2.1 0.89 – 4.8 1.1 0.55 – 2.1 0.93 0.34 – 2.0 

Particle Counts 

(#/cm3) 113 51 – 235 96 19 – 276 159 29 – 253 

Carbon Monoxide 

(ppm) 5.1 3.7 – 6.4 2.4 1.9 – 2.9 na3 na3 

1

Includes commutes on urban route one only. 2

Windows closed in morning and open in afternoon, similar to conditions uniformly observed 

in other buses in Los Angeles for April through June. 

3No measurements conducted. 

6.4 Variations in Exposure Across Fuel Type and After-Treatment Technology

6.4.1 Windows Closed

For directly-emitted pollutants such as black carbon and PAH, with the windows closed 

we found the lowest mean concentrations inside the CNG bus, and the highest concentrations 

inside the conventional diesel buses. Compared with the CNG bus, concentrations of black 

carbon and PAH were two-to-three times higher inside the trap-outfitted diesel bus, and two-tosix times higher inside the conventional diesel buses. We also found the conventional diesel 

buses had the highest average NO2 concentrations. Thus, in our study, the highest exposures to 

black carbon, PAH and NO2 occurred while riding on a conventional diesel school bus, followed 

next by the trap-outfitted diesel bus, with the lowest exposures to these pollutants occurring on 

the CNG bus. 

For pollutants such as particle counts (in the size range from 0.3 to 0.5 μm) and PM2.5

mass, differences observed between bus types were not easily explained, although these 

pollutants tend to have a higher “background” influence. 

Finally, the type of bus we tested did not appear to be an important variable in 

determining in-cabin concentrations of CO, with comparable, low in-bus concentrations

measured in conventional diesel, trap-outfitted diesel, and CNG buses. 

169 
6.4.2 Windows Partially Open

During afternoon runs, the windows in our tests buses were partially open, consistent 

with the behavior of a majority of school buses we observed in the afternoon in Los Angeles. As

discussed in Section 5.3.5 and 6.3.2, we found the dominant variable associated with high 

concentrations of black carbon, PAH and NO2 inside the bus cabin when the windows of the bus

were open was the presence of another diesel vehicle, while the contribution from the bus’s own 

exhaust was reduced under these conditions. 

During periods of idling, with windows partially open and with no other diesel vehicles 

present, the CNG bus again had the lowest mean concentrations of black carbon and PAH, while

the conventional diesel buses had concentrations that were two-to-four times higher than either 

the CNG or trap-outfitted diesel buses. For NO2, the trap-outfitted and conventional diesel buses 

both had mean concentrations nearly double those measured on the CNG bus. No real time data 

were available for formaldehyde, so we were not able to examine subsets of the data. 

For all buses tested, we found the highest peak concentrations were observed when the 

test bus was idling behind another diesel vehicle. The trap-outfitted diesel bus and CNG bus

generally exhibited high peak concentrations only while traveling behind a diesel vehicle, while 

the conventional diesel buses exhibited high peaks both when following other diesel vehicles and 

while idling without another diesel vehicle in front of the bus. 

6.4.3 Summary of Effect of Bus Type

In summary, during commutes when the windows of the bus were closed, we found 

substantial differences in concentrations measured inside the bus cabin depending on the fuel 

type or after-treatment technology of the test bus, while the impact of outside sources was less 

important. In contrast, when the windows of the bus were open, differences between buses were 

dominated by the influence of outside sources (e.g. other diesel vehicles in front of the test bus). 

However, for commutes with windows open, investigation of concentrations inside the bus 

during periods of idling on small residential streets, when no other diesel vehicles were present, 

showed significant differences between buses based on bus type. Overall, however, the 

magnitudes of the differences were markedly reduced compared with windows closed. 

Table 6.4 provides a summary of the mean concentrations measured inside the buses 

during commutes on urban route one. For commutes with windows open, the first number 

represents the mean concentration from the entire commute, while the number in parentheses

represents the mean concentration during periods of the commute when the buses were idling in 

residential areas, and no other diesel vehicles were present (See Section 5.3.5). Both numbers

were included to demonstrate the importance of outside sources when the windows of the bus

were open. When the windows of the bus were closed, outside sources such as other diesel 

vehicles, did not have a discernable influence on concentrations inside the test bus. 

170 
Table 6.4 Mean concentrations and 95% confidence intervals during commutes by bus type1,2. 

Windows Closed (morning) Windows Open (afternoon)3,4

 CNG Bus Trap-Outfitted 

Diesel Bus 

Conventional 

Diesel Buses CNG Bus Trap-Outfitted Diesel Bus 

Conventional 

Diesel Buses

Black Carbon 

(ug/m3

) 2.5 ± 0.1 7.2 ± 0.2 12.5 ± 0.3 3.5 ± 0.4 (1.2 ± 0.2) 

4.2 ± 0.3 

(1.9 ± 0.2) 

5.9 ± 0.3 

(8.6 ± 1.9) 

PAH (ng/m3)

5 64 ± 3 201 ± 7 219 ± 5 119 ± 18 

(27 ± 16) 

105 ± 12 

(20 ± 4) 

87 ± 7 

(106 ± 26) 

NO2 (ppb) 34 ± 0.4 42 ± 0.4 76 ± 1 39 ± 1 

(37 ± 3) 

86 ± 2 

(78 ± 6) 

77 ± 1 

(64 ± 6) 

Formaldehyde6

(ug/m3

) 4.8 2.2 1.4 1.2 1.0 1.0 

1

Includes bus commutes on urban route one only. 

2

The number of buses tested in each category included: one CNG bus; one particle trap-outfitted diesel 

bus; five conventional diesel buses. 

3

With windows open, concentrations inside bus were dominated by outside sources (see Section 5.3.5 and 

6.4.2), thus reducing the influence of the bus type. 

4

Numbers in parenthesis represent mean concentrations during periods of the commute when the bus was 

idling in residential neighborhoods with windows open, and no other diesel vehicles were present. 

Includes selected afternoon runs on HE2, RE1, RE2, TO1 and CNG (see Section 5.3.5.1). 

5

Measurements of PAH inside conventional diesel buses may be biased low due to the instrument’s

maximum setting during these commutes. See Sections 4.1.2.3.5 and 5.3.3 for specific details. 6

Confidence intervals are not presented for formaldehyde because of small sample size.

Across all of our various measurements, the highest concentrations of black carbon and 

PAH were consistently found inside the conventional diesel test buses compared to the trapoutfitted diesel or CNG buses, with the CNG bus having the lowest concentrations of these 

pollutants. The conventional diesel and trap-outfitted buses also had the highest concentrations

of NO2. For formaldehyde the CNG bus exhibited the highest integrated concentration of any 

bus type, twice the average concentration for the trap-outfitted bus and three times the average 

for the conventional diesel buses when the windows of the bus were closed. Results for the trapoutfitted bus were generally in between the conventional diesel buses and the CNG bus. 

However, based on emission data reported for other trap-equipped diesel vehicles (Johnson, 

2001); diesel-related pollutant concentrations on board our specific trap-outfitted bus appeared to 

be higher than expected. 

In addition, it is essential to consider that bus-to-bus variations were large, so to fully 

characterize the differences in exposures due to bus fuel type would require a much larger

number of buses. 

171 
6.5 Comparison of Exposure to Air Pollutants During Bus Commutes with Ambient Air 

Concentrations Measured at Nearby AQMD Monitoring Sites

In the course of the main study, we compared measurements taken inside the bus cabin 

for runs on U1 with measurements taken simultaneously at the SCAQMD ambient air

monitoring sites in downtown and west Los Angeles (WLA). Different results were obtained for 

different pollutants as shown in Table 6.5. On average, ambient concentrations of CO measured 

at the nearby monitoring stations were slightly higher than those measured inside the bus cabin, 

but within-cabin CO concentrations were relatively low during all runs (2-3 ppm in the afternoon 

and 4-6 ppm in the morning). Commutes on diesel school buses do not appear to result in

higher exposures to CO than would be encountered in nearby ambient air. 

In contrast, NO2 concentrations inside the buses were, on average, more than two and

three times higher than the ambient concentrations measured at the SCAQMD sites in the 

morning and afternoon, respectively. Concentrations of aromatic hydrocarbons, including 

benzene, were four to ten times higher inside the buses we tested than at the WLA ambient

monitoring site during morning commutes. Other aromatic hydrocarbons were three to twelve 

times higher inside the buses and within-cabin concentration of 1,3-butadiene (only found above 

detection limits for about 30% of the bus commutes) were higher than those measured at the 

WLA ambient site. 

During morning runs, concentrations of aldehydes and ketones measured inside the bus

cabins were generally higher than those measured at the WLA ambient monitoring site. 

Formaldehyde, acetaldehyde and methylethylketone were five, four and nine times, respectively, 

higher inside the buses compared with ambient concentrations. Similar ratios were obtained 

during the afternoon (except for formaldehyde). 

These results indicate children riding on school buses may be exposed to elevated 

concentrations of NO2, aromatic hydrocarbons, 1,3-butadiene, and a variety of aldehydes and 

ketones (including formaldehyde) and that estimates of children’s exposure to these pollutants 

based on ambient air concentrations alone are unlikely to account for the elevated exposures 

occurring on bus commutes. Table 6.5 summarizes the results for the comparison of in-bus and 

ambient air concentrations during morning commutes on urban route one. 

Table 6.5 Morning mean concentrations: buses versus ambient air at West Los Angeles 

monitoring site.

Bus Cabin 

Concentration 

West Los Angeles 

Monitoring Site 

Concentration 

Bus-toAmbient Ratio

CO (ppm) 5.1 ± 0.1 6.9 0.7 

NO2 (ppb) 64 ± 1 28 2.3 

Benzene (ppb) 2.9 0.5 5.8 

Formaldehyde (μg/m3) 2.1 0.4 5.4 

172 
6.6 Recommendations

Although the present study was ambitious in its scope and approach, it was necessarily 

limited to a single school, a relatively small number of buses, and a given region of California

(i.e., the SoCAB), as well as a limited number of bus routes and bus stops. However, based on 

the results of this study it is apparent several steps can be taken to reduce exposures of the public 

in general and commuting children in particular due to conventional diesel school buses. First, 

reducing the number of conventional diesel school buses, by replacing them with cleaner buses, 

such as CNG-fueled or trap-outfitted diesel buses, will substantially reduce exposures of 

children, commuters and the general public to the directly-emitted pollutants measured in this

study, such as black carbon, PAHs and NO2. Moreover, commuting children would be doubly 

benefited by phasing out current diesel engines since both pollutants from the bus’s own exhaust

and from nearby buses would be minimized. However, as long as a significant number of 

conventional diesel school buses remain in service, the cleanest buses should be assigned to the 

longest routes. 

Next, school bus drivers should be instructed to avoid other diesel school buses, and 

especially to avoid caravanning. This would provide an immediate reduction of children’s

exposures to diesel exhaust. In particular, at schools which have more than one bus transporting 

children to and from school, instituting a policy of staggered departure times of buses at that 

school would significantly reduce children’s exposures to diesel-related pollutants. With such a 

policy, bus drivers would be required to wait a minute or two after the bus ahead of them has

departed before they pull away from the school. 

If conventional diesel buses are not full, children should be encouraged to sit nearer the 

front of the bus than the rear. In addition, if strategies are available to shorten commute times for 

children such strategies should be pursued, since on average exposure is proportional to 

commute time. 

Another major recommendation is that in-use diesel school bus engines be maintained 

properly by school districts to eliminate visible exhaust, since the presence of diesel school buses

with visible exhaust resulted in the highest peak concentrations observed inside the test buses 

during this study. Moreover, in the course of this study we estimated that between one fourth to 

one third of all diesel school buses we encountered emitted black smoke or visible exhaust.

Thus, we believe the prevalence of “dirty” buses in Southern California is high. 

Finally, we want to reinforce school district policies which minimize children’s time 

spent on sidewalks in front of schools when diesel school buses are arriving or departing and 

urge enforcement of the policy that drivers immediately turn off their engines on arrival at a 

school and do not turn their engines on until ready to depart a school. In the course of the 

present study we received anecdotal evidence that such policies are not always strictly enforced. 

173 
7.0 RECOMMENDATIONS FOR FUTURE RESEARCH

Further research could be undertaken to interpret the measurements from the present 

study. For example, the laborious analysis of the data obtained with the SEMS instruments at the 

smallest particle size ranges, from 30 to 100 nm, was beyond the scope of the present project. 

However, such data would provide important information about roadway and in-bus exposure to 

ultra-fine particles in this size range, and this analysis should be undertaken as soon as possible. 

More extensive comparisons could be made between the data obtained in the present 

study and recent data obtained, or being obtained, for near-roadway and in-roadway 

measurements of black carbon and other vehicle-related species. For example, some of the

current measurements by the Southern California Particle Center and Supersite (SCPCS) 

researchers as a function of distance from freeways would be of interest relative to certain of our 

in-roadway and within bus-cabin measurements. Additional comparisons could also be made

between the data generated in the current project for gaseous and particulate pollutants and 

corresponding ambient air measurements made away from heavily traveled arterials for the same

pollutants. 

A follow-on school bus field study modeled on the present project but using on-board 

particle concentrators and a focused set of pollutant analyzers should be conducted for the 

express purpose of collecting particulate and gas phase samples for toxicological analyses. Such 

a study could be built around the expertise of the SCPCS, including the Center’s development 

and application of a range of toxicological assays. 

Finally, as we have acknowledged elsewhere in this report, due to resource constraints 

the present study was unable to test across a full range of possible commutes, meteorological 

conditions, bus manufacturers, model years, school districts, geographic locations in California, 

etc. Our results are “representative” only to the extent the commutes, buses, conditions, areas, 

etc. we studied are representative of southern California school districts. Additional field studies 

could be conducted to broaden the range of conditions investigated, although substantial 

resources are required to conduct studies of this magnitude. Potential cost savings include 

eliminating the PM filter samples (and elemental analysis) and the integrated VOC samples, as 

they were frequently at low concentrations and provided relatively little information compared to 

the other study measurements. 

174 
8.0 REFERENCES 

Adams, H.S., Nieuwenhuijsen, M.J., and R.N. Colvile. 2001. Determinants of Fine Particle 

(PM2.5) Personal Exposure Levels in Transport Microenvironments, London, UK. Atmospheric 

Environment, 35: 4557-4566 

Alm, S., Jantunen, M.J., and M. Vartianen. 1999. Urban Commuter Exposure to Particle Matter 

and Carbon Monoxide Inside an Automobile. Journal of Exposure Analysis and Environmental 

Epidemiology. 9: 237-244. 

AQMD Advisor, The News Publication of the South Coast Air Quality Management District, vol 

9 (No. 5), August 2002. 

ARB. 2002. http://www.arb.ca.gov/toxics/sbidling/sbidling.htm. 

Autrup, H. et al. 1999. Biomarkers for Exposure to Ambient Air Pollution—Comparison of 

Carcinogenic-DNA Adduct Levels With Other Exposure Markers and Markers for Oxidative 

Stress.” Environmental Health Perspectives. 107: 233-238. 

Babich, P., Davey, M., Allen, G., and P. Koutrakis. 2000. Method Comparisons for Particulate 

Nitrate, Elemental Carbon, and PM2.5 Mass in Seven U.S. Cities. Journal of Air and Waste 

Management Association, 50: 1095-1105. 

Benner, B.A., Gordon, G.E., and S.A. Wise. 1989. Mobile Sources of Atmospheric Polycyclic 

Aromatic Hydrocarbons: A Roadway Tunnel Study. Environmental Science and Technology. 

23: 1269-1278. 

Berk, R. A. 2002. Personal communication 

Box, G.E.P. and G.M. Jenkins. 1976. Time Series Analysis: Forecasting and Control. HoldenDay, San Francisco. 

Box, G.E.P. and G.C. Tiao. 1975. Intervention Analysis With Applications to Economic and 

Environmental Problems. Journal of the American Statistical Association. 70: 70-92. 

California Department of Education, Office of School Transportation. 2002. 2002 Student

Transportation Information, available at: http://www.cde.ca.gov/bus/fastfacts.pdf. 

Carter, W.P.L. and R. Atkinson. 1989. Computer Modeling Study of Incremental Hydrocarbon 

Reactivity. Environ. Sci. Technol. 23: 864-880. 

Chaloulakoua, A. and I. Mavroidis. 2002. Comparison of Indoor and Outdoor Concentrations of 

CO at a Public School. Evaluation of an Indoor Air Quality Model. Atmospheric Environment. 

36: 1769-1781. 

175 
Chan, C.C., Ozkaynak, C.H., Spengler, J.D. and L. Sheldon. 1991a. Driver Exposure to Volatile 

Organic Compounds, CO, Ozone, NO2 Under Different Driving Conditions. Environmental 

Science and Technology. 25: 964-972. 

Chan, C.C., Spengler, J.D., Ozkaynak, H. and M. Lefkopoulous. 1991b. Commuter Exposures 

to VOCs in Boston, Massachusetts. Journal of the Air and Waste Management Association. 41: 

1594-1600. 

Chan, L.Y. and H.W. Wu. 1993. A Study of Bus Commuter and Pedestrian Exposure to Traffic 

Air Pollution in Hong Kong. Environment International. 19: 121-132. 

Chung, A., Chang, D.P.Y., Kleeman, M.J., Perry, K.D., Cahill, T.A., Dutcher, D., McDougall,

E.M., and K. Stroud. 2001. Comparison of Real-Time Instruments Used to Monitor Airborne 

Particulate Matter. Journal of Air and Waste Management Association, 51: 109-120. 

Conceicao, E.Z., Silva, M.C. and D.X. Viegas. 1997. Air Quality Inside the Passenger 

Compartment of a Bus. Journal of Exposure Analysis and Environmental Epidemiology. 7:

521-534. 

Duffy, B.L. and P.F. Nelson. 1997. Exposure to Emissions of 1,3-butadiene and Benzene in the 

Cabins of Moving Motor Vehicles and Buses in Sydney, Australia. Atmospheric Environment, 

31 (23): 3877-3885. 

Escutia, 1999. Senate Bill 25. 

Fernandez-Bermauntz, A.A. and M.R. Ashmore. 1995a. Exposure of Commuters to Carbon 

Monoxide in Mexico City—I. Measurements on In-Vehicle Concentrations. Atmospheric 

Environment. 29: 525-532. 

Fernandez-Bremauntz, A.A. and M.R. Ashmore. 1995 b. Exposure of Commuters to Carbon 

Monoxide in Mexico City II. Comparison of In-Vehicle and Fixed-Site Concentrations. Journal 

of Exposure Analysis and Environmental Epidemiology, 5(4): 497-510. 

Fischer, P.H. et al. 2000. Traffic-Related Differences in Outdoor and Indoor Concentrations of 

Particles and Volatile Organic Compounds in Amsterdam. Atmospheric Environment. 34: 

3713–3722. 

Fitz, D., Winer, A.M. and S. Colome. 2002. Pilot Study Report to California Air Resources 

Board on Characterizing the Range of Children’s Pollutant Exposure During School Bus

Commutes, April. 

Fogel, J. 2002. Private communication. 

Fruin, S., St. Denis, M.J., Winer, A.M., Colome, S.D. and F. W. Lurmann. 2001. Reductions in 

Human Benzene Exposure in the California South Coast Air Basin. Atmospheric Environment, 

35: 1069-1077. 

176 
Fruin, S. 2003. Black Carbon Concentrations Inside Vehicles: Implications for Refined Diesel 

Particulate Matter Exposures. Doctoral thesis, University of California, Los Angeles. 

Fung, K. and D. Grosjean. 1981. Determination of Nanogram Amounts of Carbonyls as 2,4-

dinitrophenylhydrazones by High Performance Liquid Chromatography. Anal. Chem. 53: 168. 

Fung, K. and B. Wright. 1990. Measurement of Formaldehyde and Acetaldehyde Using 2,4-

Dinitrophenylhydrazine-Impregnated Cartridges During the Carbonaceous Species Methods 

Comparison Study. Aero. Sci. Technol. 12: 44. 

Gee, I.L. and D.W. Raper. 1999. Commuter Exposure to Respirable Particles Inside Buses and 

by Bicycle.” Science of the Total Environment. 235: 403-405. 

Gemmill, D. 2002a. Quality Integrated Work Plan for Pollutant Exposure During School Bus

Commutes: Main Study. Center for Environmental Research and Technology, University of

California, Riverside. May 17, 2002. 

Gemmill, D. 2002b. Quality Assurance Audit Report, School Bus Measurement System, 

Pollutant Exposure During School Bus Commutes Program. Center for Environmental Research 

and Technology, University of California, Riverside. July 26, 2002. 

Grosjean, D., Grosjean, E., and A.W. Gertler. 2001. On-Road Emissions of Carbonyls from 

Light-Duty and Heavy-Duty Vehicles. Environ. Sci. Technol. 35: 45-53. 

Henry, R.F., Rao, S.T., Zurbenko, I.G. and P.S. Porter. 2000. Effects of Changes in Data 

Reporting Practices on Trend Assessments. Atmospheric Environment. 34: 2659-2662. 

Hering, S.V., Miguel, A.H. and R. L. Dod. 1984. Tunnel Measurements of the PAH Carbon 

Therogram and Elemental Source Signature for Vehicular Exhaust. Science of the Total 

Environment. 36: 3945. 

Hinds, W.C. 1999. Aerosol Technology. Properties, Behavior, and Measurements of Airborne 

Particles. Second Edition. Wiley-Interscience, New York. 

Horie, Y., Tranby, C. and S. Sidawi. 1994. On-Road Motor Vehicle Activity Data. Volume I—

Bus Population and Activity Pattern. ARB Final Report A 132-182. California Air Resources 

Board, Research Division, Sacramento, CA. 

Janssen, N.A.H., Vliet, P.H.N.van, Aarts, F., Harssema, H. and B. Brunekreef. 2001. 

Assessment of Exposure to Traffic Related Air Pollution of Children Attending Schools Near 

Motorways. Atmospheric Environment. 35: 3875–3884. 

Johnson, T. V. 2001. Diesel Emission Control in Review. SAE Technical paper. 2001-01-0184. 

177 
Jo, W.K. and K.H. Park. 1999. Commuter Exposure to Volatile Organic Compounds Under 

Different Driving Conditions. Atmospheric Environment. 33: 409-417. 

Kean, A.J., Grosjean, E., Grosjean, D., and R.A. Harley. 2001. On-Road Measurement of 

Carbonyls in California Light-Duty Vehicle Emissions. Environ. Sci. Technol. 35: 4198-4204. 

Kinney, P.L., Aggarwal, M., Northridge, M.E., Janssen, N.A.H., and P. Shepard. 2000. 

Airborne Concentrations of PM2.5 and Diesel Exhaust Particles on Harlem Sidewalks: A

Community-Based Pilot Study. Environmental Health Perspectives, 108 (3): 213-217. 

Kirchshetter, T.W. and R.A. Harley. 1999. Impact of Reformulated Fuel on Particle and GasPhase Emissions from Motor Vehicles Final Report, Contract No. 95-330. California Air 

Resources Board, Research Division, Sacramento, CA. 

Lawryk, N.J. and C.P. Weisel. 1995. Exposure to Volatile Organic Compounds in the Passenger 

Compartment of Automobiles During Periods of Normal and Malfunctioning Operation. Journal 

of Exposure Analysis and Environmental Epidemiology. 5: 511-531. 

Lawson, D.R. et al. 1990. Formaldehyde Measurement Methods Evaluation During the

Carbonaceous Species Methods Comparison Study. Aero. Sci. Technol. 12: 20. 

Levy, J.I. et al. 1998. Impact of Residential Nitrogen Dioxide Exposure on Personal Exposure:

An International Study. Journal of Air and Waste Management Association, 48: 553-560. 

Lipsett, M. 1989. The Hazards of Air Pollution to Children. Environmental Medicine. S. 

Brooks et al. Eds. Mosby. St. Louis. 

Loft, S., Poulsen, H.E., Vistisen, K. and L.E. Knudsen. 1999. Increased Urinary Excretion of 8-

oxo-2’-deoxyguanosine, A Biomarker of Oxidative DNA Damage in Urban Bus Drivers. 

Mutation Research. 441: 11-19. 

Long, J. 2000. Personal Communication to California Air Resources Board, Mobile Source

Control Division, Analysis Section, El Monte, CA. 

McDowall, D., McCleary, R., Meidinger, E.E. and R.A. Hay. 1980. Interrupted Time Series 

Analysis. Series: Quantitative Applications in the Social Sciences. Sage Publications, Beverly 

Hills. 

Miguel, A.H., Kirchstetter, T.W. and R.A. Harley. 1998. On-Road Emission of Particulate 

Polycyclic Aromatic Hydrocarbons and Black Carbon From Gasoline and Diesel Vehicles.

Environmental Sciences and Technology. 32: 450-455. 

Monn, C. 2001. Exposure Assessment of Air Pollutants: A Review on Spatial Heterogeneity 

and Indoor/Outdoor/Personal Exposure to Suspend Particulate Matter, Nitrogen Dioxide and 

Ozone. Atmospheric Environment. 35: 1-32. 

178 
Moosmuller, H., Arnott, W.P., Rogers, C.F., Bowen, J.L., Gillies, J.A., Pierson, W.R., Collins,

J.F., Durbin, T.D., and J.M. Norbeck. 2001. Time Resolved Characterization of Diesel 

Particulate Emissions. 1. Instruments for Particle Mass Measurements. Environ. Sci. Technol. 

35: 781-787. 

Nielsen, P.S., de Pater, N., Okkels, H. and H. Autrup. 1996. Environmental Air Pollution and 

DNA Adducts in Copenhagen Bus Drivers—Effect of GSTM1 and NAT2 Genotypes on Adduct 

Levels. Carcinogenesis. 17: 1021-1027. 

Norusis, M. J., 2002. SPSS®11.0. Guide to Data Analysis. Prentice Hall. Upper Saddle River, 

New Jersey. 

Petersen, W.B. and R. Allen. 1982. Carbon Monoxide Exposures to Los Angeles Area

Commuters. J. Air.Pollut.Control. Assoc., 32: 826-833. 

Phillips, T.J., Jenkins, P.L. and E.J. Mulberg. 1991. Children in California: Activity Patterns

and Presence of Pollutant Sources. Proceedings of the 84th Annual Meeting of the Air and 

Waste Management Association, Pittsburgh, PA. 

Pope, A. 1989. Respiratory Disease Associated with Community Air Pollution and a Steel Mill. 

Utah Valley. American Journal of Public Health. 79: 623-628. 

Ptak, T.J. and S.L. Fallon. 1994. Particulate Concentration in Automobile Passenger

Compartments. Particulate Science and Technology. 12: 313-322. 

Ramachandran, G., Adgate, J.L., Hill, N., Sexton, K., Pratt, G.C., and D. Bock. 2000. 

Comparison of Short-Term Variations (15-minute averages) in Outdoor and Indoor PM2.5

Concentrations. Journal of Air and Waste Management Association, 50: 1157-1166. 

Rodes, C. et al. 1998. Measuring Concentrations of Selected Air Pollutants Inside California 

Vehicles. Final Report, Contract No. 95-339. California Air Resources Board, Research 

Division, Sacramento, CA. 

Schierl, R. and G. Fruhmann. 1996. Airborne Platinum Concentrations in Munich City Buses. 

Science of the Total Environment. 182: 21-23. 

Seiber, J.N. 1996. Toxic Air Contaminants in Urban Atmospheres: Experience in California. 

Atmospheric Environment, 30 (5): 751-756. 

Shikiya, D.C., Liu, C.S., Hahn, M.I., Juarros, J. and W. Barcikowski. 1989. In-Vehicle Air

Toxics Characterization Study in the South Coast Air Basin. Final Report. South Coast Air 

Quality Management District, El Monte, CA. 

Soll-Johanning, H., Bach, E., Olsen, J.H. and F. Tuchsen. 1998. Cancer Incidence in Urban Bus 

Drivers and Tramway Employees: A Retrospective Cohort Study. Occupational and 

Environmental Medicine. 55: 594-598. 

179 
Solomon, G.M., Campbell, T.R., Feuer, G.R., Masters, J., Samkian, A. and K.A. Paul. 2001. No 

Breathing in the Aisles: Diesel Exhaust Inside School Buses. Natural Resources Defense Council 

and Coalition for Clean Air. New York. 

Solomon, G.M., Bailey, D., Campbell, T., Masters, J. and E. Slavin. 2002. South Coast School 

Bus Idling Study, November Draft. 

Stevenson, K., Bush, T., and D. Mooney. 2001. Five Years of Nitrogen Dioxide Measurement 

with Diffusion Tube Samplers at Over 1000 Sites in the UK. Atmospheric Environment, 35: 281-

287. 

Turner, W.A., Olson, B.A. and G. A. Allen. 2000. Calibration of Sharp Cut Impactors for 

Indoor and Outdoor Particle Sampling. J. Air and Waste Management Assoc. 50: 484-487. 

U.S. Environmental Protection Agency. 1978. Screening Procedures for Ambient Air Quality

Data. Document EPA-450/2-78-037. Office of Air Quality Planning and Standards, Research

Triangle Park, NC 27711. 

U.S. Environmental Protection Agency. 1980. Validation of Air Monitoring Data. Document

EPA-600/4-80-030. Environmental Monitoring and Support Laboratory, Research Triangle 

Park, NC 27711. 

U.S. Environmental Protection Agency. 1984. Guidance for Preparation of Combined 

Work/Quality Assurance Project Plans for Environmental Modeling. EPA Document OERSQA-1. 

U.S. Environmental Protection Agency. 1996. Environmental Health Threats to Children. U.S. 

Environmental Protection Agency, September. 

U.S. Environmental Protection Agency. 2000. Guidance for Data Quality Assessment.

Practical Methods for Data Analysis. EPA QA/G-9. 

Venkataraman, C., Lyons, J.M. and S. K. Friedlander. 1994a. Size Distributions of Polycyclic 

Aromatic Hydrocarbons and Elemental Carbon. 1. Sampling, Measurement Methods and Source 

Characterization. Environmental Science and Technology. 28: 555-562. 

Venkataraman, C., Lyons, J.M. and S. K. Friedlander. 1994b. Size Distributions of Polycyclic 

Aromatic Hydrocarbons and Elemental Carbon. 2. Ambient Measurements and Effects of 

Atmospheric Processes.” Environmental Science and Technology. 28: 563-572. 

Wargo, J., Brown, D., Cullen, M., Addiss, S., and N. Alderman. 2002. Children’s Exposure to 

Diesel Exhaust on School Buses. Environmental and Human Health, Inc., North Haven, CT. 

Weisel, C.P., Lawryk, N.J. and P. J. Lioy. 1992. Exposure to Emissions From Gasoline Within 

Automobile Cabins.” Journal of Exposure Analysis and Environmental Epidemiology. 4: 7996. 

180 
Western Filter Co, Inc. 2002. Nett Technologies Inc., 1997; MER Equipment. Exhaust Flow

Rate Equation Available at: http://www.westernfilterco.com, http://www.nett.ca, 

http://www.merequipment.com. 

Wiley, J.A., Robinson, J.P., Cheng, Y.T., Piazza, T., Stork, L. and K. Plasden. 1991. Study of 

Children’s Activity Patterns. Final Report. Contract No. A773-149. California Air Resources 

Board, Research Division, Sacramento, CA. 

Wu, C.C. et al. 1998. Application of an Ir Tracer to Determine Soot Exposure to Students 

Commuting to School on Baltimore Public Buses.” Atmospheric Environment. 32: 1911-1919. 

Yanosky, J.D. and D. L. Maclntosh. 2001. A Comparison of Four Gravimetric Fine Particle 

Sampling Methods. Journal of Air and Waste Management Association, 51: 878-884. 

Yanosky, J.D., Williams, P.L., and D.L. Maclntosh. 2002. A Comparison of Two DirectReading Aerosol Monitors With the Federal Reference Method for PM2.5 in Indoor Air. 

Atmospheric Environment, 36: 107-113. 

Zhu, Y., Hinds, W.C., Kim, S., and C. Sioutas. 2002a. Concentration and Size Distribution of 

Ultrafine Particles Near a Major Highway. Journal of the Air and Waste Management 

Association, 52: 1032-1042. 

Zhu, Y., Hinds, W.C., Kim, S., Shen, S., and C. Sioutas. 2002b. Study of Ultrafine Particles 

Near a Major Highway With Heavy-Duty Diesel Traffic. Atmospheric Environment, 36: 4323-

4335. 

181 
182 
9.0 INVENTIONS REPORTED AND COPYRIGHTED MATERIALS PRODUCED 

None. 

183 
184 
10.0 GLOSSARY OF TERMS, ABBREVIATIONS AND SYMBOLS 

AQMD South Coast Air Quality Management District 

ARB California Air Resources Board 

ATCM airborne toxic control measure 

BC black carbon

BSMS Brentwood Science Magnet School 

CE-CERT College of Engineering – Center of Environmental Research and 

Technology 

CNG compressed natural gas 

CNC condensation nucleus counter 

CO carbon monoxide 

DPM diesel particulate matter 

EC elemental carbon 

EPA Environmental Protection Agency (U.S.) 

FRM Federal reference method 

GC gas chromatography 

HE1 high emitter conventional diesel bus number one 

HE2 high emitter conventional diesel bus number two 

HE3 high emitter conventional diesel bus number three 

ID` inner diameter 

LAUSD Los Angeles Unified School District 

NO2 nitrogen dioxide 

NOx oxides of nitrogen (NO + NO2) 

NRDC Natural Resources Defences Council 

OD outer diameter 

OPC optical particle counter 

PAH polycyclic aromatic hydrocarbons 

PAN peroxyacetyl nitrate 

PC fine particle counts (0.3 – 0.5 μm diameter) 

PM particulate matter 

185 
PM2.5 particulate matter less than 2.5 μm in diameter 

PM10 particulate matter less than 10 μm in diameter 

RE1 representative conventional diesel bus number one 

RE2 representative conventional diesel bus number two 

SEMS scanning electrical mobility particle sizer 

SF6 sulfur hexafluoride 

SVOC semi-volatile organic compound 

TAC toxic air contaminant 

TO1 particle trap-outfitted diesel bus 

U1 urban bus route one 

U2 urban bus route two 

UCLA University of California, Los Angeles 

UCR University of California, Riverside 

VOC volatile organic compound 

186