## CHARACTERIZING THE R ANGE 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 Je ong 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 1 0 , 2003DISCL A IMER

The statem ent and conclusions in the Repor t are those of the contractor and not necessarily those of th e California Air Resources Board. The m e ntion of commercial products, their source, or their use in conn ection with m a terial reported here in is not to be construed as actual or im plied endorsem ent of suc h products.

iACKNOWLEDGEME NTS

The contributions of the California Air Resour ces Board staff, particularly Scott F r uin, who m a de invaluable suggestions as Project Officer, and Bart Cr oes, C h ief of Research, were greatly appreciated.

We thank the staff of the Brentwo od Scie nce Magnet School, w ho provided im portant support in making this research possible, in partic ular, the school Principal, Sharon Katz, and the Science Dir ector, Phy llis Rothm a n. W e appreciate th e coop erati on of the Los Angeles Unified School District, specifically Angelo Bellom o, Dire ctor of the Environm ental Health and Safety Branch, Antonio Rodriguez, Dir ector of the Transpo r ta tion Bra n ch, and Bill Pia z za , Environm ental Assessment Coordinator in the H ealth and Sa f e ty Branch.

We wish to acknowledge valuable contri butions from Da vid Cocker, who provided technical support at the Center of Environm ental Research a nd Technology at University of California, Riverside. We al so thank Andrea Hricko, University of Southern California, who provided helpful inform a tion in select ing the bus route for the pilot study.

We especially appreciated Shonna Pierce, of the UCLA Transportation Departm e nt, for her outstanding professional driving of the school buses, and her enthusia stic sup port of the project team.

We thank Teri McL a ughlin for production and f o rm atting of this report.

We gratefully acknowledge support for this research by the California Air Resources Board, the S outh Coast Air Qualit y Managem e nt District, and the U.S. Environm ental Protection Agency. This report was submitted in pa rtial fulfillment of AR B Contract No. 00-322, “Characterizing the Range of Children’s Pollu tant Exposure During School Bus Commutes,” by the Unive r s ity of Calif o rnia, Riv e r s ide, Coll ege of Engineering Center for Environm ental Research and Technology, and the University of California, Los Angeles, School of Public Health, Environ m ental Health Sciences Depart m e nt, under the sponsorship of the California Air
Resources B o ard. W o rk was co m p leted as of October 1 0 , 2003.

iiiiiTABLE OF CONTENTS

Page

Disclaim er ............................................................................................... i Acknowledgem e nts..................................................................................... 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 Statem ent 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 R E COMMENDATIONS 3.1 Introduction.................................................................................... 9
3.2 Summ ary of Pilot Study Findings........................................................... 9 3.2.1 Exposure in Three School Bus Commute Microenvironm ents................ 9 3.2.2 Conditions Leading to Highest Ex posures Inside the Bus Cabin.............. 10 3.3 Modifications of Experim e nt al Design for Main Study................................. 12 3.3.1 Relative Importance of School Bus Commute Microenvironm e nts........... 12 3.3.2 Precision Data........................................................................ 12 3.3.3 Gradient Tests........................................................................ 13 3.3.4 Conditions Leading to Highest Concen trations Inside the Bus Cabin........ 13 3.3.5 Routes................................................................................. 13 3.3.6 Summ ary of Pilot Study Findings.................................................. 13

4.0 EXPERIME NTAL MET HOD S AND STUDY DESIGN 4.1 Introduction.................................................................................... 15
4.1.1 Characterization and Justificati on for the Selected Buses....................... 15

ivTABLE OF CONTENTS (con’t)

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4.1.1.1 Fuel Used in the Test Buses............................................. 19 4.1.2 Field Sam p ling Procedures.......................................................... 19 4.1.2.1 Instrum e nt Packaging and Power Supply.............................. 19 4.1.2.2 Data Collection............................................................ 22 4.1.2.2.1 Checkout and Installatio n of Instruments................ 22 4.1.2.2.2 Sa m p le Probe Positio ns .................................... 24 4.1.2.3 Instrum e ntation............................................................ 27
4.1.2.3.1 Integrated P M 10 and PM 2. 5 Mass Concentration........ 27 4.1.2.3.2 Real-Tim e PM 10 and PM 2. 5 Mass Concentration........ 27 4.1.2.3.3 Real-Tim e 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-Tim e Black Carbon................................... 28 4.1.2.3.5 Real-Tim e 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-Tim e Carbon Monoxide.............................. 31 4.1.2.3.9 Real-Tim e Nitrogen Dioxide.............................. 31 4.1.2.3.10 SF 6 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 Ca m e ra and Digital Cam e ra............................ 34 4.1.2.5 Docum e ntation of Bus Comm ut e, Traffic Conditions and Events During Each Run................................................. 34 4.1.3 Bus Ventilation Rate Measurem ents............................................... 34 4.1.4 Characterization and Justificat ion for the Selected School...................... 38 4.1.5 Characterization and Justificati on 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 Justificati on for the Selected Bus Stop................... 50 4.1.7 Characterization of W i nd ow Position Test........................................ 51 4.2 Data Analysis Methods........................................................................ 52 4.2.1 Goals of Tim e Series Analysis...................................................... 52 4.2.2 Data Analysis Techniques...........................................................52
4.2.3 Video Record Analysis............................................................... 53 4.2.3.1 Definitions...................................................................53

vTABLE OF CONTENTS (con’t)

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4.2.3.2 Data Generated from Videotapes........................................ 53 4.2.3.3 Videotape A n alysis P r otocol............................................. 55 4.2.3.4 Linking Events/Characteristics and Concentrations.................. 56

5.0 RESUL T S AND DISC USSION 5.1 Assessm ent of Measurem ent Methods...................................................... 57
5.1.1 Overview............................................................................... 57
5.1.2 Instrum e nt Accuracy.................................................................. 57 5.1.2.1 Audited Instrum e nts...................................................... 59 5.1.2.2 Unaudited Instrum e nts.................................................... 60 5.1.2.2.1 Particulate Matter Mass..................................... 60 5.1.2.2.1.1 Integrated P M 10 and PM 2. 5 ................. 60 5.1.2.2.1.2 Continuous PM 10 and P M 2. 5. ............... 60 5.1.2.2.1.3 Com p arison of Measurem ent Methods for PM 2. 5 and PM 10 .......................... 61 5.1.2.2.1.3.1 Descriptive Statistics......... 62 5.1.2.2.1.3.2 Assessm ent of Difference Between Samplers............ 65 5.1.2.2.2 Real-Tim e Fine Particle Counts (0.3 to 10 μm )......... 66 5.1.2.2.3 Real-Tim e Ultrafine Particle Counts (0.3 to 0.8μm ).... 66 5.1.2.2.4 Real-Tim e Black Carbon.................................... 66 5.1.2.2.5 Real-Tim e 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 Instrum e nts............................ 68 5.1.3.1 Precision Estim ation Between Paired Instrum e nts................... 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 Instrum e nts.............................. 75 5.1.3.3 Overall Ranking............................................................ 78 5.1.3.4 Use of Correlation Coefficients.......................................... 81 5.1.4 Evaluation of Sa m p ling Line Losses.............................................. 81 5.1.5 Autocorrelation....................................................................... 84
5.1.6 Nor m ality... ........................................................................... 87 5.2 Relative Contribution of School Bus Related Microenvironm ents to Children’s E xposure........................................................................... 89 5.3 Im portant Variables Governing E xposure During Bus Commutes..................... 93 5.3.1 Determ ination of Contribution of Bus’ s Own Exhaust Inside the Cabin..... 94 5.3.1.1 Analysis of SF 6 Data...................................................... 95 5.3.1.1.1 Measurem ent of SF 6 Concentrations...................... 100
5.3.1.2 Correla tion Between SF 6 versus Black Carbon and PM 2. 5 .......... 102

viTABLE OF CONTENTS (con’t)

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5.3.1.3 Estim ation of Bus Cabin Black Carbon Concentrations Using the S F 6 Data........................................................ 104 5.3.2 Effect of W i ndow Position........................................................... 105 5.3.2.1 Black Carbon.............................................................. 106 5.3.2.2 Particle Counts in the Accum u lation Mode............................ 106 5.3.2.3 Effect of W i ndow Position on Directly Em itted Pollutants versus Background Pollutants............................................ 109 5.3.3 Effect of Bus Type................................................................... 110 5.3.4 Effect of Route Type................................................................. 113 5.3.5 W ithin Run Variables In vestig at ed U s ing Real-Tim e Data and Videotape A n alysis.................................................................. 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 W ith And W ithout Visible Exhaust.............................. 121 5.3.5.2.2 The Effect of Bus Fuel Type or After-Treatm ent Technology................................................... 126
5.3.5.2.2.1 Concentrations Inside B u ses W h ile Idling at Bus Stops........................... 132 5.3.5.2.3 The Effect of Roadway Type: Freew ay versus Surface Streets....................................... 134 5.3.5.2.4 Effect of Route Type......................................... 138 5.3.6 Variables Investigated U s ing Paired Instrum e nts................................ 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 Concentrati on Inside and Imm e di ately Outside the Bus............................................................ 149 5.3.6.2.1 PAH........................................................... 150 5.3.6.2.2 Particle Counts............................................... 152 5.3.6.2.3 Im plications of the Results.................................. 153 5.3.6.2.4 Correlation Between Inside and Outside Concentrations............................................... 154 5.4 Com p arison 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 S a m p les................................................................... 159 5.4.2.1 Arom atic Hydrocarbons.................................................. 159

viiTABLE OF CONTENTS (con’t)

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5.4.2.2 1,3-Butadiene.............................................................. 160
5.4.2.3 Gaseous Aldehydes and Ketones....................................... 161

6.0 CONCLUSI ONS AND RECOMME NDATIONS 6.1 Im portance of the School Bus Microenvironm ent in Children’s Exposure During School Bus Commu te-Related Activities........................... 165 6.2 Contribution of a Bus’s Own Exhaust to Concentrations Insi de the Cabin......... 166 6.3 Variations in Exposure Across Bus Ag e, Fuel Type and After-Treatm e nt Technology. ................................................................................. 167 6.3.1 W i ndows Cl osed................................................................... 167 6.3.2 W i ndows Pa rtially Open.......................................................... 167 6.3.3 Effect of Bus Type................................................................. 168 6.4 Conditions Associated with Highest Concentrations Inside Bus Cabin............. 169 6.4.1 W ithin-Bus Variables.............................................................. 169 6.4.2 External Variables.................................................................. 169 6.5 Com p arison of Exposure to Air Pollut ants During Bus Commutes with
Am bient Air Concentrations Measured at Nearby AQMD Monitoring Sites....... 170 6.6 Recommendations........................................................................... 171

7.0 RECOMME NDATIONS FOR FUTURE RESEARCH.........

............................. 173

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

9.0 INVENTIONS REPORTED AND COPYRI GHTED MATERIALS PRODUCED..... 183 10.0 GLOSSARY OF TERMS, ABBREVIATIONS, AND SYMBOLS........................ 185

viiiixLIST OF F I GURES

Figure No. Title Page

4.1.1.1 California statewide distribution of school buses by m odel year.................. 16

4.1.2.1 Plum bing schem a tic for school bus sam p ling instrum e ntation......................... 21

4.1.2.2 Data logging design inside school bus platform

...................................... 23

4.1.2.3 Sa m p ling 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 t h e Brentwood Science Magnet School..................................... 40

4.1.5.1 Prim ary 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 Exam ple of a peak, an ou tlier and the baseline........................................... 54

4.2.3.2 PAH ti m e series for Run 23. Lower panel shows circled peak expanded and resolved into two peaks ...................................................................... 54

5.1.2.1 Com p arison between DustTr ak and HI data for PM 2. 5 (m orning only)............ 64

5.1.2.2 Com p arison between DustTr ak and HI data for PM 2. 5 (afternoon only).......... 64

5.1.3.1 Variation in absolute differences (μg/m 3 ) between paired Aeth alom eters over the course of the main study......................................................70

5.1.3.2 Variation in absolute differe nces (ppb) between paired NO 2 instrum e nts over the course of the main study....................................................... 70

5.1.3.3 Variation in absolute differences (ng/m 3 ) between paired PAH instrum e nts over the course of the main study........................................ 71

5.1.3.4 Variation in absolute differences (#/cm 3 ) between paired fine particle count instrum e nts over the course of the m a in study................................. 71

xLIST OF FIGURES (con’t)

Figure No. Title Page

5.1.3.5 Variation in absolute differences (μg/m 3 ) between the DustTrak
PM 2. 5 instrum e nts over the course of the m a in study. .................................... 72

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

5.1.5.2 Partial autocorrelation correlo gram for PAH during Run 6.......................... 85

5.1.5.3 Partial au toc o rrelation co rrelog r am for PM 2. 5 during Run 6 (data collected with the DustTrak instrum e nts)....................................... 86

5.1.6.1. Histogram of PM 2. 5 during Run14......................................................... 88

5.1.6.2 Nor m al Q-Q plots of PM 2. 5 during Run 14 (data collected with the DustTrak instrum e nts).................................................................... 88

5.3.1.1 Schem a tic of the SF 6 injection point................................................... 95

5.3.1.2 Percentage of air inside the cab in originating from the bus’s own exhaust (%).......................................................................... 99

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

5.3.1.4 Schem a tic of school bus indi cating the loca tion of the SF 6 sampling probes....................................................................................... 100

5.3.1.5 Means and 9 5 % confiden ce intervals of SF 6 concentrations m easured at front, rear, and outside lo cations during m o rning runs............................ 101

5.3.1.6 Means and 9 5 % confiden ce intervals of SF 6 concentrations m easured at front, rear, and outside locat ions during afte rnoon runs.......................... 102

5.3.1.7 Correlation coefficients between SF 6 versus black carbon and SF 6 and PM 2. 5 (data collected with the DustTrak instrum e nts)....................................... 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% conf idence intervals of black carbon (μg/m 3 )... 107

5.3.2.3. Cum u lative frequency distributio ns for black carbon during Run 32.............. 108

xiLIST OF FIGURES (con’t)

Figure No. Title Page

5.3.2.4 Mean particle counts and 95% confidence intervals in the

accum u latio n m ode from 0.3 to 0.5 μ m ( # / c m 3 ) ........................................... 108

5.3.2.5 Cum u lative frequency distributio ns for particle counts during
Run 32...................................................................................... 109

5.3.3.1 Mean concentrations and 95% c onfidence in terv als for PAH and black carbon across six bus types...................................................... 110

5.3.3.2 Mean concentrations and 95% confidence in terv als for PM 2. 5 and PC............. 111

5.3.3.3 Mean concentrations and 95% confidence in terv als for NO 2 and CO ............ 111

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

5.3.4.1 Mean concentrations and 95% conf idence intervals for black carbon ( μ g/m 3 ) and PAH (ng/m 3 ).......................................................................... 114

5.3.4.2 Mean concentrations and 95% c onf idence in terv als f o r PC (#/cm 3 ) and PM 2. 5 ( μ g/m 3 ) by bus route and tim e of da y .................................................. 114

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

5.3.5.2 Run 33 (afternoon – windows open) black ca rbon 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 w ith associated events................................. 119

5.3.5.5 Events associated with incr eases/decreases in black carbon concentrations m easured inside the C NG bus during Run 36...................... 120

5.3.5.6 The effect of visible exhaust from a di esel 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 di esel 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 f o llowing or adjacen t to d i fferent types of vehicles................................................. 125

xiiLIST OF FIGURES (con’t)

Figure No. Title Page

5.3.5.9 Concentrations of PAH inside the te st 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 ins i de the trap-o utfitted d i es el schoo l bus while idling and/or followi ng another diesel vehicle........................... 129

5.3.5.12 Concentrations of black carbon inside the CNG school bus while idling and/or following anothe r diesel vehicle................................................ 129

5.3.5.13 Concentrations of PAH inside the co nventional diesel school buses while idling and/or following a nother diesel vehicle........................................ 130

5.3.5.14 Concentrations of PAH inside the trap -outfitted diesel school bus while idling and/or following a nother diesel vehicle........................................ 130

5.3.5.15 Concentrations of PAH inside the C NG school bus while idling and/or following another diesel vehicle........................................................ 131

5.3.5.16 Concentrations of black carbon inside th e cabin for conventional diesel, trapoutfitted d i esel, and CNG school bus es while idling at bus s t ops.................. 133

5.3.5.17 Concentrations of PAH inside the cabi n for conventional dies el, trap-ou t fitted diesel, and CNG school buses wh ile idling at bus stops............................ 134

5.3.5.18 Mean and 95% confidence intervals for black carbon inside diesel school buses on different roadw a y 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 roadw a y 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 w ith or without other diesel vehicles present...................................................................................... 137

5.3.5.21 Means and 95% confidence intervals for black carbon (a) and PAH (b) concentrations m easured inside school bus RE2 while traveling on two different urban routes..................................................................... 141

xiiiLIST OF FIGURES (con’t)

Figure No. Title Page

5.3.5.22 Boxplots of black carbon (a) and PA H (b) concentrations m easured 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 m easured inside school bus RE2 for two urban bus routes, divided by roadway type................................................................. 143

5.3.6.1 PAH concentrations (ng/m 3 ) inside the cabin during Run 12....................... 145

5.3.6.2 PAH concentrations (ng/m 3 ) inside the cabin during Run 13....................... 145

5.3.6.3 Means and 95% confidence intervals of the average PAH concentrations during m o rning runs (ng/m 3 )............................................................ 146

5.3.6.4 Means and 9 5 % confiden ce intervals of the average PAH concentration s during afternoon runs (ng/m 3 )........................................................... 146

5.3.6.5 Concentrations (#/cm 3 ) of PC (0.3 to 0.5 μm ) during Run 5........................ 147

5.3.6.6. Concentrations (#/cm 3 ) 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 μ m ) at front and rear of ca bin during morning runs (#/cm 3 )............ 148

5.3.6.8 Means and 9 5 % confiden ce intervals of the average PC concentrations (0.3 to 0.5 μ m ) at front and rear of ca bin during afternoon runs (#/cm 3 )......... 149

5.3.6.9 Inside (rear) and outside concentrations (ng/m 3 ) of PAH during Run 28 (urban, m o rning, windows closed)......................................................151

5.3.6.10 Means and 95% confidence intervals of the average PAH concentrations (ng/m 3 )..................................................................................... 151

5.3.6.11 Concentrations (#/cm 3 ) of PC (0.3 to 0.5 μm ) during Run 33...................... 152

5.3.6.12 Mean and confidence intervals for PC (#/cm 3 )....................................... 153

5.4.1.1 CO concentrations (ppm ) inside bus es and at th e s e lec t ed AQMD stations..................................................................................... 157

5.4.1.2 NO2 concentrations (ppb) inside buses and at the selected AQMD stations..................................................................................... 159

xivxvLIST OF TABLES

Table No. Title Page

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

m i croenvironm ents...................................................................... 1

1.2 Mean concentrations and 95% conf idence intervals during comm utes by bus type...................................................................................... 2

3.2.1 Calculated exposure factors fo r three school bus commute-related

m i croenvironm ents........................................................................... 11

4.1.1.1 Distribution of buses in southe rn California by m a nufacturer...................... 16

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

4.1.2.1 Measurem ent param e ters and m e thods used in the m a in study.................... 20

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

4.1.3.1 Ventila tion tim e constants m easured inside buses at var i ous sp eeds and f o r open and closed windows............................................................... 37

4.1.5.1 Com p arison 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 Summ ary of m a in study exposure runs................................................ 58

5.1.2.1 Summ ary of previous studies co m p aring sam p lers em ployed for

m easure m ents of PM 2. 5 and PM 10 m a ss concentrations............................. 62

5.1.2.2 Descriptive statistics for PM 2. 5 (μg/m 3 ) m easured by HI and DustTrak.......... 63

5.1.2.3 Descriptive statistics for PM 10 (μg/m 3 ) m easured by HI and DustTrak.......... 64

5.1.3.1 General characteristics of the paired instrum e nt precision experim e nts.......... 69

5.1.3.2 Paired instrum e nt precision (average absolute and percent differences).......... 74

5.1.3.3 Estim ation of precision between paired instrum e nts using the Coefficient of Variation (%)........................................................................... 76

5.1.3.4 Correlation coefficients for paired in strum e nt precision experim e nts ............ 77

xviLIST OF TABLES (con’t)

Table No. Title Page

5.1.3.5 Regression results for the paired instrum e nt precision data ........................ 79

5.1.3.6 Bias between the instrum e nts during instrum e nt precision experim e nts.......... 80

5.1.3.7 Overall ranking of the paired inst rument precision data quality ................... 78

5.1.3.8 Correlation between PAH and other po llutants during exposure runs............. 82

5.1.3.9 Correlation between PC and other po llutants during exposure runs............... 82

5.1.4.1 Mean DustT r ak PM 10 response on ambient air with and without Bev-A-Line tubing........................................................................ 83

5.2.1 Mean concentrations in three m i croenvir onm ents in pilot and m a in studies .... 90

5.2.2 Ratios of m e an concentrations inside th e bus and at bus stops relative to the loading/unloading zone ............................................................. 91

5.2.3 Estim ated tim e spent in each m i croenvironm ent..................................... 91

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

5.2.5 Exposure ratios for the three m i croenvironm ents.................................... 93

5.3.1.1 SF 6 m easurem ent results................................................................. 98

5.3.2.1 W i ndow pos ition runs.................................................................... 105

5.3.3.1 Summ ary of m ean concentrations and 95% conf idence in terv als ins i de th e cabin for commutes with windows closed (m orning) 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 di esel 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 m easured inside th e bus cabin w h ile traveling behind different types of vehicles............................................................... 124

xviiLIST OF TABLES (con’t)

Table No. Title Page

5.3.5.4 Mean black carbon and NO 2 concentrations inside the test buses under selected conditions................................................................ 128

5.3.5.5 Mean black carbon, PAH and NO 2 concentratio ns m easured inside the test buses while idling at bus stops.......................................................... 132

5.3.5.6 Mean black carbon concentrations in side the test buses on different roadway types, stratified by the pres ence of a diesel vehicle in front of the bus................................................................................... 135

5.3.5.7 Com p arison between urban rout e one and urban route two........................ 139

5.3.5.8 Mean black carbon concentrations m easur ed inside the bus on urban route one and urban route two (Runs 20 a nd 26). Mean black carbon (μg/m 3 )......... 140

5.3.5.9 Mean PAH concentratio ns inside the bus on urban route one and urban route two (Runs 20 and 26). PAH (ng/m 3 )................................140

5.3.6.1 Front versus rear com p arison runs...................................................... 144

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

5.3.6.3 Inside/outside com p arison runs......................................................... 150

5.3.6.4 Summ ary of m a jor findings of insi de versus outside com p arisons................ 153

5.3.6.5 Observed ou tside-to -ins ide concentra tion ratios for PAH and PC for CNG and trap -outfitted buses

................................................................... 154

5.4.1.1 Mean CO concentration s (ppm ) insi de buses and at two nearby AQMD stations for the m o rning runs (windows closed) ..................................... 156

5.4.1.2 Mean CO concentration s (ppm ) insi de buses and at two nearby AQMD stations for the afternoon runs (w indows open) on urban route one ............... 156

5.4.1.3 Mean NO 2 concentrations (ppb) inside buses and at two nearby AQMD stations for the m o rning runs with windows closed on urban route one.......... 158

5.4.1.4 Mean NO 2 concentrations (ppb) inside buses and at two nearby AQMD stations for the afternoon runs w ith w i ndows open on urban route one .......... 158

xviiiLIST OF TABLES (con’t)

Table No. Title Page

5.4.2.1 Mean concentrations (ppb) of arom a tic hydrocarbons during morning runs (windows closed).......................................................................... 160

5.4.2.2 Mean concentrations (ppb) of arom a tic hydrocarbons during afternoon runs (windows open)........................................................................... 160

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

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

5.4.2.5 Mean concentrations (μg/m 3 ) of carbonyls during m o rn ing runs (windows closed)...................................................................................... 162

5.4.2.6 Mean concentrations (μg/m 3 ) of carbonyls during afte rnoon runs (windows open)........................................................................................ 162

6.1 Mean concentrations m easured in two m i croenvironm ents and in background air............................................................................ 165

6.2 Exposure factors in three school bus commute-related

m i croenvironm ents....................................................................... 166

6.3 Mean concentrations and 95% conf idence intervals during comm utes by bus type .................................................................................... 168

6.4 Morning m ean concentrations: buses ve rsus am bient air at W e st Los Angeles monitoring site............................................................................ 171

xixLIST OF PHOTOGR APHS

Photo No. Title Page

1 a) High em itter conv entional die s el test bus pa rk ed at UCLA b) CNG Bus parked in front of a bus stop during an afternoon run................ 19

2 a) Array of instrum e nts inside a test bus b) Preparing the instrum e nts before an afternoon run............................... 24

3 Inlet of the sam p ling line for outside m easure m ents of gaseous species.......... 26

4 a) Data and power cables and sam p ling lines b) Aethalom eter, PC analyzer, PAH analyzer, and Harvard Impactors.......... 29

5 (a) Power and data cables, sam p ling lines, CO m o nitors, and VOCs analyzers; (b) Cables, standard gas tanks, rotom e ters and sampling lines..................... 32

6 School bus pulling up at BSMS, at th e end of a morning run

, with children in the unloading zone.......................................................... 41

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

8 a) Departing from BSMS during an af ternoon run, school bus ahead b) Driving behind a school bus on an arterial street................................. 42

9 Another school bus passi ng our idling bus at a bus stop during a m o rning run.. 46

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

11 Caravanning after departing fr om BSMS during an afternoon run (line of 5 buses)............................................................................ 94

12 School bus ahead three-car length s (m axi m um di stance used for videotape analysis)....................................................................... 120

13 a) Sm oky diesel school bus ah ead on an arterial street. b) Driving behind a heavy-duty di esel with high exhaust em itting visible sm oke.............................................................................. 121

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

15 Typical congestion on the freew ay during an afternoon run......................... 127

xxLIST OF PHOTOGR APHS (con’t)

Photo No. Title Page

16 View out the front window of the te st bus while idling at a bus stop (at Verm ont Avenue School) during an afternoon run.............................. 132

17 West L.A. SCAQM D Station........................................................... 155

xxiABST RACT

To determ ine the range of children’s expos ures during their bus commutes, especially those conditions leading to high exposures, real- tim e and integrated m e a s urem ents of pollutant concentrations were conducted inside five conven tional diesel school buses, as well as a diesel bus outfitted with a particulate trap and a bus powered by natural gas. Meas urem ents were m a de during 20 bus comm ute s on a Los Angeles Unified School District bus rout e from South Central Los Angeles to the west side of LA, with addition a l runs on a second urban route, a rural/suburb a n route, an d to tes t the effect of window positio n.

Children’s school bus comm utes in Los Angeles appear to expose them to significantly higher concentrations of vehicle-related pollutants than ambien t air concentrations and frequently higher concentratio ns than tho s e m easured on roadways . Concentrations of diesel vehicle-relate d pollutants such as black carbon and particle-b ound PAHs were significantly higher on board conventional diesel buses when windows were close d. This was due to the intrusion of the bus’s own exhaust, as dem onstrated throug h the us e of a tracer gas added to each bus’s exh a ust. W h en windows were open, increased ventil ation rates m a rkedly reduced th is effect, although high peak concentratio ns were then observ e d when fo llowing other diesel ve hicles. On-board concentrations of vehicle-relate d pollutants were also significan tly higher on the urban routes

com p ared to the rural/suburban route, indicati ng the im portance of surrounding traffic density.

Other related exposure scen arios such as bus loading and unloading, and tim e spent waiting at b u s stops, were shown to m a ke rela tive l y in sig n if icant con t ribu tions to child ren ’ s exposure, due to the generally lower concentrati ons and the short tim e s spent at those activities

com p ared to bus commutes. Results from this study show that m i nim i zi ng commute tim e s, using the cleanest buses for the longest routes, and reducing bus caravann i n g and idlin g tim e will reduce ch ild ren’s e xposure to bus-rela ted pollutants.

xxiixxiii1.0 EXECUTI V E SUMM ARY
Background. Because children’s lun g s are still d e veloping and children are m o re susceptib le to advers e health effects from air pollution, potentially high pollutant exposures during school bus commutes are of concern. Studies of pollutant conc entrations inside vehicles show high exposures are typical, but few studies have attem p ted to character ize concentrations on-board and ne ar school buses. This study’s

m a in purpose was to determ ine the range of children ’s exposures during thei r bus comm utes, with an

em phasis on determ ining the specific factors and c onditions leading to high exposures and comparing the effects of different bus and traffic characteristics and bus operating conditions.

Methods Real-tim e and integrated m easurem e n ts of pollutant concentrations were conducted in the spring of 2002 while driving school bus routes in Los Angel e s (LA) with five conventional diesel school buses, m a nufactured fro m 1975 to 1998, a 1998 diesel bus outfitted with a particulate trap, and a 2002 bus powered by natural gas. All di esel buses used low sulfur “green” diesel fuel. Runs included ten morning and ten afternoon bus commu tes over an LA Unified School Di strict (LAUSD) bus route from South Central LA to the west side of LA, with f our additional runs on a second LAUSD urban route, seven additional runs on a rural/subu rban route, and to three addition a l ru ns to tes t th e effect of wi ndow position.

Results: Bus stop and bus loading/unloading activities were shown to m a ke s m all contributions to overall com m ute-related exposures due to the low c oncentrations found there and the short lengths of tim e involved in those activities. Ex posure factors calculated were as m u ch as two orders of m a gnitude higher for bus commutes on urban routes than for th e bus stop or loading/unloading m i croenvironm ents (Table 1.1).

Table 1.1 Average exp o sure facto r s (air concen tration * tim e) in three m i croenvironm e nts.

L o adi ng/ Unl o ading 1 B u s Stops 2 Ur ba n Com m u tes 2

Black Carb on (μg / m 3 * min ) 5 20 600

Particle-Bou nd PAH (ng / m 3 * min ) 45 230 10000

N O 2 (p pb * mi n ) 105 2 7 0 5500

Particle Cou n t s (# /cm 3 * mi n ) 25 310 10000

PM 2. 5 (μg/ m 3 * mi n ) N / A 1 3 0 3500

1 B a sed on m e asurem ent s t a ke n du ri n g pi l o t s t udy

. 2 Based on f i v e m i n

. sp en t at bu s stop an d a 7 6 m i n

. co mm u t e.

Overall, children’s school bus com m utes in Los Angeles appear to ex pose them to significantly higher concentrations of vehicle-relate d p o llutants th an am bient air con centrations and frequently higher concentrations than those m easured 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 we re closed, as was dem onstrated by on-board m easurem ents of an inert tra cer gas, SF6, added to each bus’s exhaust. Older buses show ed higher rates of exhaust in trus ion, but in trus io n was dete c t ed in all b u ses. W ith closed windows, mean concentrations of diesel vehicle-related pollutants such as black carbon and particlebound PAHs on board conventional dies el buses were m o re than double the m ean concentrations with windows open. Und e r closed window conditions, di esel vehicle-related p o llutants were also significantly higher on-board the c onventional diesel buses as com p ared to the single CNG-powered bus, while the trap-equipped bus ha d concentrations between the two (although diesel-related pollutant concentrations on board this specific trap-outfitted bus app eared to be higher than expected based on

1em issions data reported for other trap-equipped dies el vehicles). In contrast, natural gas-related pollutants such as form aldehyde were higher a board the CNG bus. W ith closed windows, concentratio ns were als o som e what higher in the rear of the bus, but front/rear differences were generally sm aller than the bus-to-bus concentr ation differences. When windows were open, the resulting high ventilation rates ap peared to strongly reduce the am o unt of self-pollution, w h ile the influence of following individual vehicles becam e m o re pr onounced, and high tr ansient conc entrations of diesel vehicle-related polluta nts w e re associated with proxim ity to other diesel vehicles.

On-board concentrations were also strongly influenced by other traffic sources. Table 1.2 presents the m ean pollutant concentrations by ru n type for the urban and rural/suburban routes, under open and closed window conditions. Bo th window position a nd s u rrounding traffic density can be seen to have strongly affected on-board conc entrations for vehicle-related pollutants, but for pollutants with a strong background com p onent, such as PM 2. 5 and fine particle cou n ts (0.3-0.5 μm ), differences betwee n routes and differences due to wind ow position were less d i s tinc t

.

Table 1.2 . Concentrations m eans a nd range of m eans by route and by window position 1

. Ur ba n R o ut e 1 R u ral/Subu rb an R o u t e

W i nd ow s Clo s ed ( a .m .) 2 W i nd ow s Op en ( p .m .) 2 W i nd ow s Op en ( p .m .) 2 P o lluta nt

Mean Range of Mea n s Mean Range of Mea n s M ean Range of Mea n s

B l ack C a r b o n ( μ g/ m 3 ) 10 2. 5 – 19 5. 2 2

. 9 – 9

. 1 2. 7 0. 9 – 4. 8

PM- bou nd PAH (ng / m 3 ) 198 64 – 400 96 33 – 147 36 14 – 66

NO 2 ( p pb) 64 34 – 110 73 39 – 120 45 23 – 68

Form al dehy de (μg/ m 3 ) 2. 1 0. 89 – 4

. 8 1. 1 0. 5 5 – 2. 1 0

. 9 3 0. 34 – 2

. 0

Particle Counts ( # /cm 3 ) 113 51 – 235 9 6 1 9 – 27 6 1 5 9 2 9 – 25 3

C a rb on M o n o x i de ( p pm ) 5. 1 3. 7 – 6. 4 2. 4 1. 9 – 2. 9 na3 na3

1 Incl u d es c o m m ut es on ur ba n r out e one o n l y

. 2 W i nd ows clo s ed in m o rn ing and o p en in afternoon , sim i lar to co nd itio ns uni fo rm ly obse r ve d i n ot her b u ses i n L o s A n gel e s f o r Ap ri l t h r o u g h J une

. 3No m easurem ent s c o n d u ct ed

.

Conclusions: Measurements m a de on-board school buses in Los Angeles indicated higher exposures are occurring during children’s commutes than am bient ai r concentrations w ould indicate. These exposures resulted prim arily from the commute itself, an d 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 h eavy; the d i rect influence of other vehicl es being followed; and the contribution of the bus ’s own e m issions. The exte nt of a bus’s own cont ribution to these high concentratio ns appeared to be hi gher when windows were closed and for older buses. However bus-tobus variability was relatively high an d theref ore with the small num ber of buses studied, and the lim ited

num b er of routes covered, the findi ngs of this study should not be view ed as inherently typical for all buses under all conditions. However, m i nim i zing co mmute tim es, using the cl eanest buses for the longest bus routes, and reducing bus “carav annin g ” and unnecessary idlin g tim e woul d reduce ch ildren’s exposures to bus-related pollutants.

22.0 INTRODUCTION AND BACKGROUND

2.1 Introduction The health of children in Calif ornia is the f o cus of intense interest by the Governor, the State Legislature, and lo cal, state an d federal hea lth agen cies, as well as by academ ic researchers. Growing ev idence ind i c a tes childre n ’s he alth m a y be affected by environm ental influences, including air pollutants. A lthough am bient (outdoor) air pollution m a y contribute to advers e health effects, th e high est expo sures to a ra n g e of air contam inants m a y occur in o t her

m i croenviro nm ents, esp ecially in v e hicles. Of pa rticular con cern to the California A i r Resource s Board (ARB) is the tim e spent by childr e n during schoo l bus commutes. Out of the six m illio n school ch ild ren in California, on e m illion are transpo r ted by public s c hool buses (Californ i a Departm e nt of Education, 2002). About 70% of the 26,000 school buses operating in California are powered by diesel engines (H orie et al., 199 4; Long, 20 00), and the ARB recen tly declared particulate m a tter in diesel exhaus t to be a toxic air con t am inant.

A recent stu dy of in-vehicle concentrations, con ducted in Sacram ento and Los Angeles using a passenger vehicle as a chase car (Rodes et al., 1998), found t h at proxim ity to diesel vehicles caused high concentratio ns of in-vehicle fine partic les and black carbon. Moreover, children m a y be exposed to high concentrations of diesel particulate m a tter (DPM) and other associated v e hicle em issions (e.g. nitrog en dio x ide and carbon m onoxide) while waiting at school bus stops, riding on buses (particularly where buses are “caravanning”), or during the tim e they are assem b led at school for loadi ng or unloading near buses that are idling.

2.2 Statem ent of the Problem Because ch ildren’s lungs are st ill dev e loping and because they are m o re susceptible to th e health effects of certain pollutants

, there is concern regarding pote ntially high pollutant exposures during commutes to and from sc hool. However, no comprehensive studies of children’s exposure while traveling to and from school have been perform e d to date in California, even though roadways and sidewalks have been shown to have the highest outdoor concentrations for m a ny air pollutants and in-veh icle concentrations have been shown to be higher than those m easured at fixed site m onitors and in som e cases higher than measured along roadways (S hikiya et al., 1989, Rodes et al. 1998).

Children are especially s u sceptible to air po llution because of their high inhalation rates relative to body m a ss, high activit y concentrations, greater tim e spent outdoors, narrower lung airways, imm a ture i mmune system s and rapid gr owth (Lipsett, 1989; Pope, 1989; Phillips et al., 1991; W iley et. al., 1991; U.S. EPA, 1996). In spite of this increased risk, th ere is a lack of data

concern i ng child ren’s in -vehicle exp o sure, esp e cial ly invo lving diesel school buses. Because of the potential for high pollutant concentrations, sc hool bus cabins, road ways, bus stops, and school yard s near idling buses are critical m i croe nvironm ents that m u st be included in accurate

assessm ents 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 Contam inants Program . The ARB is a l so required to identify th e relative contribution of indoor concentrations

3to to tal exp o sure, takin g into acco unt both ambi ent and enclosed (e.g . indoor an d in-veh icle) environm ents. Senate Bill 25 (Escutia 1999), requ ires the ARB to identify areas where exposure of infants and children to air pollutants is not adequately m easured by the curren t m onitoring network an d to condu ct enhan ced m onitoring. Because of the poten tial for high pollu tant concentrations, school bus cabins, roadways, bus stops, and school yards near idling buses are critical m i croenvironm ents that m u st be incl uded in assessm e nts of children’s exposures.

It is well understood that gaso line-powered vehicles create significant concentrations of fine particles, carbon monoxide (CO), oxides of nitrogen (NO
x ) and volatile organic com pounds (VOC), including aromatic VOC. In addition, diesel-powered vehicles em it greater m a ss per

m ile of f i ne partic les, NO
x , sulfur com pounds , se m i -volatile organic com pounds (SVOC) and elem ental carbon (EC), and m a y em it higher quant ities of polycyclic arom atic hydrocarbons (PAH), some of which are m u tagenic and/or car cinogenic. Consistent with th is, elevated concentrations of ele m e n tal ca rbon and PAHs have been recorded in traffic tunnels and on heavily traveled roadw a ys (Hering et al., 1984; Benner et al., 1989; Venkataram a n et al., 1994a,b; Kirchstetter and Harley 1999).

In traffic, vehicle occupants are prim arily e xposed to the exhaust of neighboring vehicles, particularly those directly ahead of the occupa nt’s vehicle (Rodes et al., 1998). In-vehicle concentrations of CO a nd fuel -related VOCs have been found to be significantly higher than those in ambient air (S hikiya et al., 1989; Ptak and Fall on, 1994; Lawryk and W e isel, 1995 ; Rodes et al., 1998; Jo and Park, 1999, Al m et al., 1999). Of all these in-vehicle studies, only the two (by Shikiya et al., 1989 and R odes et al., 1998) were perf orm e d in California. Other variables that have been shown by previous researchers to be im portant influences on in-vehicle concentratio ns includ e traffic de nsity, driving la ne, inte r-ve h icle spacin g, traffic sp eed, and the

num b er of stops. Secondary factor s that m a y also be significan t under certain conditions include wind speed and direction, inverse height, tim e 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 idli ng policies is comm on in Los Ange les area district schools. However, the ARB has r ecently approved an airb or ne toxic control m easure (ATCM) that would lim it school bus idling, and idling at or near sc hools, to only when necessary for safety or operational concerns (A RB, 2002). The ATCM to lim it idling is intende d to reduce children’s exposure to diesel exhaust particulate m a tter and other Toxic Air Contam inants (TAC) from heavy-duty motor vehicle exhaust.

2.4 Previous In-Vehicle Studies In traffic, vehicle occupants are prim arily e xposed to the exhaust of neighboring vehicles, particularly those directly ahead of the occupa nt’s vehicle (Rodes et al., 1998), and in-vehicle concentrations of CO a nd fuel -related VOCs have been found to be significantly higher than those in ambient air (S hikiya et al., 1989; Ptak and Fall on, 1994; Lawryk and W e isel, 1995 ; Rodes et al., 1998; Jo and Par k, 1999; Alm et al., 1999; Solom on et al., 2001; W a rgo et al., 2002), including in the three studies perform e d i n California (Shikiya et al., 1989; Rodes et al., 1998; Solomon et al., 2001).

4Children typ i cally sp end over an hour a day in transit (Phillip s et al., 1991) and, as noted, in-transit m i croenvironments can exhibit high concentrations of pollutants. For exam ple, for

arom atic VOCs, such as benzene, the in-trans it m i croenvironm ent is estim ated to contribute 10 to 60 percent of a nonsmoker’s to tal exposure (C han et al., 1991a,b; W e isel et al., 1992; Lawryk and W e isel, 1995; Frui n et al., 2001).

In the Rodes et al. (1998) study, for most pollutants 2-hr integrated sam p les wer e collected ins i de and ou ts ide a p a ssen g er car, al on g the roadway where th e vehicle traveled, an d

at am bient monitoring sites. Pollutants m easured included PM 10 and P M 2. 5 , m e tals , and th irtee n VOCs. Continuous m easurem ents were m a de of fi ne particles, CO and black carbon (BC). The driving scen arios were chosen to evaluate in -v eh icle pollutant concentra tions as a function of traffic congestion, vehicle type, roadway t ype, tim e of day, and ventilation setting.

Rodes et al. (1998) found in-vehicle pollutant concentrations were ge nerally significantly higher in Los Angeles than in Sacram ento, suppor ting the choice of Los Angeles for the pres en t school bus study, which sought in pa rt to identify high-end exposur es. In general, VOC and CO concentrations inside or just outside the vehi cles were four to ten tim e s higher than those

m easured at the roadside or at am bient air stat ions (Rodes et al., 1998). Interestingly, however, in-veh icle P M 2. 5 conc en tra tions wer e consisten t ly lower tha n PM 2. 5 con centration s just outside the vehicles, and in som e cases were also lower than roadsid e concentrations. PM 2. 5 concentrations inside or just outside the vehicles, however, were usually higher than concentratio ns m easured at the n earest am bient site, however.

Also interes ting was the finding by these i nves t igators that under their study conditio ns, factors such as ventilation sett ings and vehicle type had little effect on in-vehicle pollutant concentrations. In contrast, factors such as dr iving lane (e.g. carpool la ne versus right lane), roadway type, congestion level, and tim e of da y a ll influenced in-vehicle concentrations. For

exam ple, substantially higher pollutant concentra tions were m easured in the far right hand lane

com p ared to the carpool lane and wh en following high-em itting “lead veh i cles.”

Further analysis of the Rodes et al. ( 1998) data was conducted by Fruin (2003). To determ ine which param e ters had the most signifi cant im pacts on in-vehicle DPM concentrations, video tapes recorded during the dr iving comm ute s were used to cr eate m o re refined groupings of the vehicles being followed, (e.g. 2, 3 or 5-axle dies el vehicles and alternat e-fuel buses). Other factors su ch as exh a ust location and following d i st an ce of th e vehicle ah ead were als o includ ed in the analysis. Results of these analyses c onfirm e d the Rodes et. al. (1998) conclusion that following a diesel-pow ered veh i cle was associat ed with a significant increase in both blac k carbon and f i ne particle counts. In addition, F r uin (2003) found that exhaust location, following distan ce, an d som e ti m e s road typ e also influen ced in-v ehic le concen tr a tions, while f o llowing a gasoline-powered passenger vehicle or altern ate fuel bus ha d weak or n o associatio ns with in

vehicle DPM concentrations.

An earlier in-vehicle study conducted in Ca lifornia was the pioneering study by Shikiya et al. (1989 ). They demonstrated that in-v eh icle concen trations of em itted criteria po llutants such as CO and NO
x could be two to four tim es those m eas ured at fixed site m onitors. Of the

5rem a ining studies discussed in the Introduction given above, only those by Ptak and Fallon (1994), Alm et al. (1999), and So lomon et al. (2001) m easured particulate m a tter.

In the recent study by Solom on et al. (2001), a team of researchers from the Natural Resources Defense Council and the University of California, Berkel ey, investigated the concentratio ns of diesel exhaust con s titu ents insi de school b u ses in th e Los Angeles area. They

em ployed continuous m easurem ents of PM 2. 5 and black carbon concentra tions inside four school buses. For com p arison, they also included m e asurem ents 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 com p ared to the front. Furtherm ore, the level of black ca rbon increased when all windows were closed, while it decreased with windows open. Howe ver, the level of black carbon in the cabin did not change, or decreased, during idling. Solom o n et al. (2001) concluded the level of black carbon in the back of a school bus with windows closed could be up to four tim es higher than in a passenger car ahead of the bus.

In a recen t school bus study in Conn ecticut, W a rgo et al. (20 02) f ound concentration s of black carbon and PM 2. 5 inside com m uting school buses were of ten 5

- 1 0 tim es high er th an ru ra l background concentrations. They found several im por tant variables affected the concentrations of pollutants inside school bus es, including bus ventilation via windows, bus idling behavior, and outdoor concentrations on bus routes. They obse rved the concentrations of black carbon and
PM 2. 5 in moving buses were higher when windows were closed com p ared to when windows were open. However, mean concentrations of bot h black carbon and particul ates w e re higher in idling buses when windows were open. Furtherm ore, idling buses had hi gher concentrations of particles and black carbon than m o ving buses. They also f ound concentrations increased when traveling behind other buses including diesel bu ses (e.g., caravanning). On the other hand, they found black carbon and particulate concentrations did not vary by sam p ling location within their buses (i.e., f r ont versus rear). They also em pl oyed school buses fueled with natural gas in their study and observed CNG buses em itted 60–98% le ss black carbon than diesel-powered buses.

Wargo et al. (2002) provided several recomm endations for reducing children’s exposure to diesel exhaust particulate: prohibit bus idli ng; retrofit with particle traps and catalytic converters; use ultra-low sulfur fu els; allocate the cleanest buses to the longest route; and lim i t ride duration. Solom o n et al. (2001) also addressed sim ilar r ecommendations, including retrofitting with particle traps, purchasing alternative fuel sch ool buses (e.g., CNG), a nd keeping windows open on school buses. The South Coast Air Quality Managem e nt District (A QMD) ha s approved $1 0.4 m illion 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 con cerning exposures to pollutants in buses where hum a n subjects were involved, only two specifical ly concerned school children. W u et al. (1998) repo rted the use of an iridiu m tracer to determ ine soot exposure of high school students commuting to and from school on public diese l trans it bus es in Baltimore. A portion of th e Baltim ore municipal f u el supply was tagged w ith an iridium tracer and expo sure during

commutes was m onitored with p e rs onal ae rosol m onitors. Twenty samples wer e c o llec t ed ov e r 10 days while four students comm uted on regular ly scheduled transit buses and a fifth student commuted by private car. Exposures were greatest for students commuting through the

6congested central busin ess dis t rict of Baltim or e. The tracer was un detectable in sam p les collected by the student commuti ng by car when the windows were cl osed, but com p arable to the other students (on transit buses) when the car windows were open during the comm ut e.

Wargo et al (2002) outfitted children with personal m onitors that m easured PM 10 , PM 2. 5 , and VOCs. Measurem ents were m a de throughout the school day, including tim e spent traveling to and from school on diesel school buses. Average personal c oncentrations of PM 2. 5 were nearly three tim es higher than average concentra tions for outdoor air in that com m unity, while concentratio ns m easured while the children were riding inside school buses were up to ten times greater than background.

2.4.1 Related Traffic Exposure Studies Ada m s et al. (2001) studied determ inants of personal exposure to P M 2. 5 in tr an sport

m i croenvironm ents in London, UK. Four transport m odes, bicy cle, bus, car and underground rail, were involved. Meteor ological variables (w ind speed, wind dire ction, precipitation,

tem p erature, atm o spheric pressu re and relative hu m i dity), traffic density and route were also considered. Except for the underground rail, th ey found wi nd speed and route were significant factors determ ining personal exposure to PM 2. 5 , while transport m ode wa s not significant.

Chan and Wu (1993) studied bus commuter a nd pedestrian exposure to traffic pollution in Hong Kong. Kinney et al. (2000) m easured concentrations of PM 2. 5 and elem ental carbon on sidewalks in Harlem , New York City. They sel ected four monitoring sites ranging from heavily traveled roadways to a quiet res i de ntia l sid e walk. PM 2. 5 c oncentr ation showed little assoc i atio n with the proxim ity to local diesel traffic, wh ile elem ental carbon concentration exhibited a strong spatial gradient across sites, c onsistent with recent studies by Zhu et al. (2002 a, b). Average elem ental carbon concentra tion ranged from 1.5 ug/m 3 to 6.2 ug/m 3 (a four-fold difference between two sites with the largest co ntrast in diesel traffic counts).

Gee and Raper (1999) investigated commuter exposure to respirable particles inside buses in Manchester, England. This study involved using persona l sam p ling pum ps installed in the bus cabs to obta in an estim a te of the average comm ut e r exposure to fine particles during congested comm utes. Conceicao et al. (1997) installed a “rem oval” duct in a commuter bus to im prove ventilation rate and m ode led the airflow with a sim p le, uni-dim ensional flow m odel, predicting the air exchange rate as a function of the vehicle velo city. Tracer gas experim e nts were perform e d to test the m odel and the efficacy of the air rem oval duct. Scheirl and Fruhm a nn (1996) m easured airborne platinum c oncentra tions in city buses in Munich, Germ any.

Several studies in Denmark m easured rela tionships between DNA adducts found in urine

sam p les from Copenha gen bus drivers and pollutant concentrations in th eir bus es. Loft et al. (1999) found increased urinary ex cretion of a biom arker of oxida tive D NA da m a ge in urban bus drivers. Autrup et al. (1999) and Nielson et al . (1996) used selected biom arkers in nonsm oking Danish bus drivers and postal workers and f ound significantly higher concentrations of carcinogen-DNA adduc ts in bus drivers working in the central part of Copenhagen com p ared with rural bus driver s in a control group.

7Soll-Johanning et al. (1998) c onducted a retrospective cohort study of ca ncer 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 fa ctors other than expos ure to air pollutants during working hours (e.g., sm oking) could not be ruled out as causal factors. FernandezBrem auntz and Ashm ore (1995a) studied the ex posure of comm uters to carbon monoxide in a range of vehicles, including transit buses, a nd in general found much higher in-vehicle CO concentratio ns in Mexic o City th an those reported for previous studi es in the United States. This study also reported m ean in-vehicle CO concentra tions were three tim e s higher than fixed-site monitoring concentrations.

2.5 Objectives 2.5.1 Overall Objectives The overall objective of this study w a s to character ize the range of e xposures experienced by children during their school bus comm utes, especially in potent ially high exposure conditions. By identifying factors that l ead to higher exposure, this study m a y provide guidance for

m i ni m i zing children’s exposures. Th e results may also faci litate evaluations of the direct health benefits of alternative fu el types and im proved bus em ission control technologies.

2.5.2 Specific Objectives The over-arching specif i c objective of this proj ect was to obtain m easurem ents of in-bus and near-bus pollutant concentrations during norm a l school bus operations across the full range of anticipated conditions, but with speci al emphasis on obtaining m e asure m ents during operations expected to lead to re alistic high-end exposures. Diesel buses were of highest priorit y because they com p rise the m a jority of buses used by California schoo ls.

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 propos ed sam p ling m e thods prior to the m a in study, and to gain experience in all facets of conduc ting the experim e nts. Specifically, the pilo t stu dy gave us experience in deploying an exte nsive array of instrum e nts within the dem a nding confines of a bus and a van, and allowed us to evaluate how well the va rious inst rum e nts perform e d on a moving platfor m , as well as the utility of the co llected data. Another obj ective of the pilot study was to evaluate the relative im portance of th ree m i croenvironm ents, comm utes, 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 m a in study were to obtain m e asurem ents that can be used (in com b ination with the pilot study da ta) to address the follow i ng questions:
1. What are children’s pollutant exposures duri ng bus transit, especially for those who spend a large portion of th eir transit tim e in high- concentration conditions?
2. How do different bus fuel type s (i.e. diesel - and natural g a s-powered) affect child ren’s exposure? 3. What are the m o st important factors govern ing children’s exposure associated with commuting on school buses?

83.0 PILOT STUDY FI NDINGS AND RECOMMENDATIONS

3.1 Introduction The principal purpose of the pilot study was to prove methods and re fine protocols. Thus, the pilot study was conducted to verify the f easibility of the study d e sign and th e utility of the instrum e nts and sampling m e thods on a m o bile platform. In addition, the pilot study was designed to provide range-findi ng m easure m ents of conditions influencing high exposures to mobile source air pollutants in the context of school bus-related m i croenvironm ents. Three locations identified as potentially high exposure s cenarios were explored in the pilot study with

an em phasis on identif ication of conditions le ading to highest exposures. These included exposures while waiting for the bus at a bus stop near hom e , while comm uting on the bus in traffic, and/or while waiting at the school bus loading zone for th e return ride home. Another objective was to evaluate the im portance of th ese three microenv iron m e nts to childr e n' s total school bus-related exposures.

In the pilot study, we evaluated a single dies el school bus traveli ng along a single bus route. A suite of real-tim e and integrated inst rum e nts 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 th e bus loading/unloading zone. The importance of variables such as lo cation inside the bus cabin, differences between concentratio ns inside an d immediately outsid e the bus, and the effect of window position were also evaluated.

The highest exposures were e xpected in areas with heavy traffic congestion and during periods of m e teorological stagna tion. For this reason, the pilo t study was conducted in the service area of the Los Angele s Unified School District(LAUSD) in Novem b er and Dece m b er 2001. W e used contacts within LA USD to identif y bus-commuting routes an d to gain access to detailed routes and stops. W e replicated the charac teristic patterns of loading/unloading, bus stops and commutes observed for a specific bus route for an a ppropriate school, with em phasis on conditions where adjacent traffi c was greatest, bus id le tim e was longest , and where children were waiting for substantial peri ods of tim e to be picked up.

3.2 Summ ary of Pilot Study Findings
3.2.1 Exposure in Three School Bus Commute Microenvironm ents As discussed extensively in the Pilot Study F i nal Report (Fitz et al., 2002), three

m i croenvironm ents were investigated concerni ng children' s pollutant exposure due to school bus-re lated activ ities. " B us comm utes" ref e r s to m easure m ents m a de using the bus as a platform for the various instrum e nts. For this type of run a typical route transp o r ting ch ild re n f r om the inner c ity to BSMS (and vice versa) was followed. "Bus stops" refers to sam p ling at one of the stops along the selected route. Generally, bus stops m a y be near children’s residences or often, for reasons of safety and efficiency, at a nearby school. An instrum e nted van was parked in front of Ver m ont Elem entary School at the corner of Verm ont Avenue and 97 th St r e et , wher e di esel buses arrived to pick up students traveling to other schools (m orning) or to drop off student

com i ng from other sch ools (aftern oon). In ad dition, a large num ber of parents d r opped off or picked up children at this "bus stop” using their personal vehicles.

9"Loading/unloading" runs involved m easure m ents 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 congregat ed briefly when leaving or boarding the buses. Typically each morning and afternoon, nineteen school buses lined up along this sidewalk to unload or load child ren, re s p ectiv ely.

Af ter analyz ing the pollutant conce n tra tions m e asured in th e pilot stud y, as well as th e tim e spent by children in these three m i croenvir onm ents, we concluded the relative i m portance of comm ute s versus stops versus loading/unl oading zones was quite di fferent. To better understand and quantif y these differences, we es tim ated pollutant-specific exposure factors for the three m i croenvironments, defined as the produc t of the average concentration for a specific pollutant tim es the time spent by child ren in th e particular m i croenvironm ent. Finally, we calcu lated the averag e ratios of exposure facto r s for each pair of m i croenvironm ents for each pollutant. T h e average over all po llutants was taken to be a reas onably quantitative m easure of how i m portant one m i croenvironm ent was in relation to the other two.

Table 3.2.1 summ arizes the results for the ca lculation of exposure factors, exposure ratios, and exposure ratios averag ed for the set of analyzed po llutants. Note the estim a ted average exposure ratio for bus c o mm utes versus bus stops was 35. This indicates the contribution of the bus stop was about 3% of th at observed for bus commutes. Sim ilarly, the estim ated average exposure ratio for bus commu tes versu the loading/unloading zone was 90. Thus, the exposure contribution of the loading/ unloading zone was only approxim a tely 1% of that observed for bus commutes.

From these results we concluded the load ing/unloading zone m i croenvironm ent was not as im portant in term s of exposure as the other two m i croenvironm ents. There are two principal reasons for this. First, we observed that children generally spend a very short tim e on the sidewalk (typically as little as a m i nute, but on occasio n as m u ch as five m i nutes) before boarding the buses in the afternoon, or before entering the school in the m o rning. Second, bus drivers were required by school dist rict policy to turn off their e ngines as soon as they arrived in the m o rning, before the children leave the bus. Sim ilarly, in the afternoon, drivers were instru cted to not turn on their eng i n e s before all children w e re aboard the buses and the entire fleet was prepared to depart. During the p ilot study we observed nearly 100% compliance with these regulations (although we ha ve anecdotal infor m ation from a BSMS tea c her tha t this policy was not alw a ys rigorously enforced--J. Fogel, private comm unication. 2002).

3.2.2 Conditions Leading to Highest Exp o sures In sid e the Bus Cabin During the pilot study, conditions associated wi th the highest pollutant concentrations inside the bus were proxim ity to a nother diesel vehicle, idling (eithe r at bus stops or traffic lights, or due to traffic congestion) and closed windows. Also, th e difference between pollutant concentrations inside the bus while traveling on the freeway versus surface streets w a s not found to be significant during the pilot study. However, this m a y have been due to the experim e ntal design, which involved alternating between open a nd closed windows during the course of each
run. W i ndows were typically m o re often open on the freeway portion of th e run and more often closed while traveling on surface streets. Because open i ng windows was found to reduce

10Table 3.2.1Calculated exposure factors for three school bus commute-related microenvironments.BusStopLoading/UnloadingBusRoutesBusStopLoading/UnloadingBusRoutesExposure Ratio A4Exposure Ratio B5BC (mg/m3)42142010140070140PAH(ug/m3)6515100325751000031133NO2(ppb)45355522517555002431VOC(ppb)12070175600350175002950CO(ppm)3131553002060ParticleCounts(#/cm3)13084315040430029108PM-2.5(ug/m3)351356175655600328635901Between0.3 and 0.5umdiameter2Definedasthe productofaverageconcentrationand time,inminutes,spentinthatparticularmicroenvironment3Average time spenton busrunswas100 minutes.Upperlimittotimespentatbusstopswas5 minutes(approximate values)4Definedasthe ratioofexposurefactorforbusroutestoexposure factorforbusstops5Definedasthe ratioofexposurefactorforbusroutestoexposure factorforloading/unloadingzonesApproximate Average ConcentrationExposureFactor2,3Average ExposureRatio:11concentrations inside the bus, differences in pollu tant conc en tra tions while trave ling o n f r eeways

versus su rface streets may have been obscured d u ring th e pilot study.

3.3 Modifications of Experim e ntal Design for Main Study
3.3.1 Relative Importance of School Bus Commute Microenvironm e nts As discussed above, in the pilot study, c oncentrations observe d during bus commutes were significantly higher than those at bus stops or at the loading/un lo adi ng zone. In addition, we observe d the tim e child ren sp e n t in the s e m i croenvironm ents was s ubstantially different, typically a few m i nutes at the bus stop or load ing/unloading zone versus up to 100 minutes for bus commutes each way. W e concluded bus com m utes were the m o s t im portant

m i croenvironm ent in term s of exposure, and r ecomm ended the m a in study focus prim arily on that m i croenvironm ent. However, while we found no justification for expending addition a l resources in further characterization of load ing/unloading zones, we determ ined a lim ited

num b er of a ddition a l m e asurem ents at an additi o n al bus stop during the m a in study would allow us to better define potential e xposures associated with this m i croenvironm ent since sporadic and short-lived high concentrations were som e tim e s observed due to the arrival and departure of other buses.

Specifically, although we found the bus stop contribution to exposure was m i nor

com p ared with the bus runs (about 3%), additio nal runs were recomm e nded in part because a fairly h i gh em itting bus was used in the p ilo t study. A less pollu ting bus m a y exhibit lower

average exp o sure ratios, leading to a greater co ntribution from bus stops to total exposure.

A second reason for a lim ited num b er of additi onal bus stop runs wa s that in the pilot study we observed the highest values for fine part icle counts in the 0.18 um size range during the bus stop m e asurem ents. This was d u e in par t to the use of the Verm ont Avenue School as a bus stop by a number of schools othe r than BSMS. We observed in the neighborhoods in which our pilot study took place almost all th e bus stops have been placed at schools (presumably in part out of safety considerations for the children). Thus, a substantial number of buses m a y stop to pick up students, destined for a variety of diffe rent schools. For exam pl e, during our monitoring periods at the Verm ont Avenue School bus stop at various tim e s as m a ny as half a dozen buses would pull up in front of the school (adjacent to our instrumented van) a nd wait with the engine idling un til child ren bo arded. In s o m e cases bus es were early and w a ited with their eng i nes idling for several m i nutes, while students waiti ng for other buses stood nearby. Buses would then accelerate away from the cu rb, often releasing an exhaust cloud of black sm oke. Several of the highest fine particle counts we observed occurred at such tim e s. Sim ilarly, parents driving child ren to and from the Verm ont El em entary Sc hool, in som e cases in "gross em itter" veh i cles (as docum e n ted in our field-notebooks), contributed to the sporadic oc cu rrence of high particle

counts.

3.3.2 Precision Data Paired instr u m e nt data were im portant in de term ining the reliability, and precision of the deployed instrum e nts. The lack of adequate precision estim a tes fo r a num b er of the instrum e nts from the pilot study data (due to insufficient po llutant concentrations) m a de it difficult in som e cases to inte rpret d i f f e rences observ e d in pollu ta nt concentrations between the front and back of the bus, and between inside and outside the bus, w ith a high level of conf idence. The rela tive l y

12unpolluted background conditions in W e st Los Angeles, w h ere all but the NO 2 precision data

were co llec t ed, of ten re sulted in measured polluta nt concentrations that were not suffi ci entl y above the detection lim it. On e recomm endation for the main study w a s to perform precision

m easure m ents in a location wher e background concentrations for m a ny of these pollutants would be expected to be sufficiently higher than th e detection lim it. In a ddition, the importance of collecting s u fficient precis i on da ta before and after runs to establish com p arability of

m easure m ents from pairs of instrum e nts was em phasized.

3.3.3 Gradient Tests Based on analysis of the pilot study data, we recomm ended sim u ltaneous m easurements inside and immediately outside th e bus continue during the m a in study. The results from the pilot study indicated children were exposed to higher pollutant concentrations on the bus

com p ared with outside, em phasizing the im porta nce of the school bus m i croenvironm ent to child ren' s total expo sure.

Although the pilot study found few differences between con centrations at the front and rear of the bus cabin, our recomm e ndation for th e m a in study was to continue front and rear

sam p ling in a lim ited num b er of cleaner buse s to better de term ine if there was a c oncentr ation gradient between the front and rear of school bus cabins across different bus and fuel types.

3.3.4 Conditions Leading to Highest Con centration s Inside th e Bus Cabin The data from the pilot s t udy concer ning window position were com p lex and inconclusive regarding the im portance of this vari able to children’s expos ure. W e recomm ended further assessm e nt of this variable in the m a in study to obtain a m o re robust data set.

In addition, although the influence of the bus' s own exhaust was not directly m easured during the pilot study, the im portance of this vari able in estim ating children' s exposure during bus commutes was potentially high. For the main study, w e recomm ended the use of an SF 6 tracer added to the bus exhaust as a m eans to evaluate the importance of a bus’ own exhaust in children’s exposure during bus commu tes. W e also recom m e nded a s n ap and idle test for each bus for the sam e reason.

3.3.5 Routes Based on the pilot s t udy results, we elec ted to use the sam e urban route for the m a jority of comm ute runs, except that th e route began and ended at the fifth bus stop. This shortened the route to about one hour duration, which was ju dged optim um to m eet the pollutant sam p ling objectives of the m a in s t udy while m a intaining the diversity of roadway types and congestion scenarios.

3.3.6 Summ ary of Pilot Study Findings The pilot study dem o nstrated the proposed instrum e ntation and m e asurem ents were feasible on a m oving platform such as a school bus and that the proposed routes provided both high concentration conditions as we ll as a wide range of concentrations. However, locations of higher concentrations for the collocated instrum e nt com p aris ons were needed to ensu re adequ a te signal-to-noise conditions for direct instrum e nt com p arisons . Bus stop and bus loading/ unloading activities were dem onstrated to ma ke sm all contributions to overall comm ute

13exposures, so further characterizi ng these particular activities was de-em phasized in the m a in study to allow greater resources to be devoted to characterizing th e actual drive portion of the commutes and the differences between buses.

144.0 EXPERIMENTAL M ETHODS AND STUDY DESIGN

4.1 Introduction In this study, we obtained m easurements that were used to d e term ine children’ s pollu tant exposures during bus transit, especially for thos e who spend a large porti on of their transit tim e in high-concentration co nditions. We m easured a wide rang e of gaseous and particulate m a tter species inside school buses while tr aveling along in-use school bus routes. The great m a jority of these m easurem ents were m a de using sophisticated real-tim e instruments, and successfully operating these instrum e nts on m obile platform s was a m a jor experim e ntal challenge.

A tota l of 36 runs we r e com p leted

, inc l udi ng six runs on a sim u lated rura l/subu rban route, 20 runs on an in-use urba n route, four runs on a second in-use urban route, three window position tests, and two m easure m ent periods at a bus stop along the prim ary urban route. Seven dif f e rent bu ses were us ed, inc l udin g thre e high em itting d i esel bu ses, two typica l diese l buses

, one particle trap-outfitted diesel bus, and one CNG bus. The data collected were used to determ ine the m o st i m portant factors governing children’s exposure associ ated with comm uting on school b u ses. All d a ta collected during this study will be subm itted to the ARB for future additional analyses.

4.1.1 Characterization and Justifi cation for the Selected Buses The prim ary crite rion f o r

selecting buses f o r testin g was that they be repre s enta tive of the school bus fleet used in Californi a with regard to age, m ileag e, and m a nufacturer, and include several older, dirtier buses for testing worst-case conditions. Towa rd this goal, we included the testing of three buses representa tive of high exhaust em issions. To evaluate state-of-the-art control technology, we evaluate d exposures on-board a newe r-m odel bus equipped with a particle trap catalyst. Finally, in order to com p are children’s exposure during bus commutes for different bus fuel types, we included a CNG-fuel ed bus. A decision was also m a de to use buses that were in current use transporting children to school. However, the extensive suite of instrum e nts em ployed i n this study, the batteries needed to power them, and the researchers needed to o p erate them occupied alm o st the entire working and seating area inside the bus. Because of this, and for safety reaso n s, no child r en were ab oard any of the buses in this study. We note the batteries and equipm ent served as approxim a te proxi es for the children’s weigh t corresponding to a fully occupied bus.

Figure 4.1.1.1 shows the distribution by m odel year of school buses in California. These data are from the California Departm e nt of M o tor Vehicles 1998 vehicle registration database.
From this table, we see most buses in use were 1985 m odel year or newer. Table 4.1.1.1 gives the distribution by bus m a ke and use in 1998 in s outhern California (Los Angeles, Riverside, Orange and San Bernardino counties). Crown bus es have not been sold since approxim a tely 1994, and International m a de chasses for Thom as. Based on inform al surveys with bus operators, Thom as appeared to be the dom i nant se ller in southern Calif ornia. 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 di f f i culty in s e cu ring them . The m a jor lease operators were unwilling to coopera te with us for this study, due to a perceived risk of adverse

15

### S t at e w i d e Fr equency D i s t r i but i on of B u s es by
Mo d e l Y e a r

0 20 0 40 0 60 0 80 0 100 0 120 0195119551957195919611963196519671969197119731975197719791981198319851987198919911993199519971999

M ode l Y e a rNumberofBuses

Figure 4.1.1.1 California statewide distribution of school buses by m ode l year.

Table 4.1.1.1 Distribution of buses in sout hern C a lifornia by m a nufacturer.

M ake N u m b er % T o t a l

B C l uebi r d 1103 14. 9

a r pent er 93 1. 3

o llin s 9 3 7 1 2

. 6
R O W N 1416 19. 1 illig 5 7 6 7

. 8

o shen 5 0

. 1

n t e r nat i onal 1731 23. 3
uper i o r 3 0 hom as 1464 19. 7

a r d 26 0. 4

y n e 6 0 0

. 8

o t a l 7414 100 C C G G
I S
T
W
Wa

T

publicity based on the expe rience w ith a study by the Natura l Resources Defense Council, which generated negative publicity concerning diesel school bus pollution (S olom on, 2001). These leasing companies also tended to have fleets com posed prim arily of relatively new buses

. Because of these issues, we felt more approp ri ate sources for school buses were individu al school districts. However, we found that m a ny dist ricts were also unwilling to participate for the

sam e reason as the leas ing com p ani e s (i.e., perceived risk of ne gativ e pu blic ity).

16Despite thes e problem s, we were able to find two loca l dis t r i cts willing to par tic ipate in our study by providing buses from t h eir in-use fleet of approxim a tely 150 buses. Although the

num b er of buses was lim ited, these districts had a wide variety of buses in term s of age and

m a ke, and had a sm all num b er of CNG buses as we ll. A f i nal se lec tion constrain t was that the buses be large enough to carry all of the test equi pm ent. Most of the larger buses were 10-14 rows, which was sufficient. The s m aller buses tended to be van-based with 3-4 rows. These were too small for our equipm ent and not representative since schoo l districts tend to have only a

sm all num b er of these f o r special pu rposes in an y case.

Table 4.1.1.2 lists the buses selected for testin g. As noted earlier, the prim ary criterion for selecting buses for testing wa s that they be representative of the school bus fleet used in California. Two buses were selected to m eet this criterion. The first, representative diesel school bus num b er one (RE1) was a 1998 Thomas Sa f-T-Liner, while the second, representative diesel school bus num be r two (RE2) was a 1993 Thom as Saf-T-Liner. The three buses selected to be representative of high exha ust em issions were expected to have relatively high em issions based on their age and in the opinion of bus se rvice personnel. Thes e buses included high

em itter dies el schoo l bu s num ber one (HE1), a 1 985 Thom as Coach; hig h em itter diesel s c hool bus num b er two (HE2), a 1985 Crown Supercoac h, and high e m itter diesel schoo l bus num b er 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 hig h e m itter diesel buses (HE1, HE2 and HE3) a s “conventional” dies el buses. W e are using this term to ref e r to buse s powered by typical diesel engines, w h ich have not been m o dified in any way to reduce em issi ons beyond standard practices and as required by current regulations. W e are not using the term “conventional” in the sense of m eaning the engine is in the front of the bus, altho ugh this designation is an industry practice.

To test a diesel bus equipped with a particle tr ap catalyst, we selected a 1998 T h om as Saf-T-Liner equipped with a Johnson Matth ey Continuously Regenerating Technology (CRT ® ) particulate filter (trap

- outfitted d i esel school bus n u m b er one, TO1). The particle trap com b ined a platinum catalyst and a filter elem ent to “tra p” particulate m a tter, and operated as a passive

em issions control system. Fina lly, in order to com p are childre n’s exposure during bus comm ute s for a different bus fuel type, we tested a 2002 Thom as Saf-T-Liner Compressed Natural Gas school bus (CNG). Phot ograph 1 shows two of the buses employed in the m a in study.

Snap and idle opacity tests were perfor m ed on all buses. In this test the opacity of the exhaust was m easured while the engine was brought from i d le to its governed m a ximum rpm by imm e diately applying full throttle. The m onito ring device recorded the m a xi m u m opacity. This test was perfor m ed a m i nim u m of three consecutiv e tim e s and the resu lts were within 5% f o r a valid test. These results ar e also shown in Table 4.1.1.2. As expected, the 1985 Crown Supercoach (HE2), which was identified by m a i n tenance personnel as the “sm okiest” in their fleet, clearly gave the highest valu e. It should be noted that it wa s difficult to conduct a snap and idle test for the CNG bus because th e relatively hi gh fraction of water in the exhaust c o m p ared to a diesel bus resulted in fogging of the optics in the test devi ce, and inconsistent, but low,

m easurem ents resu lted.

17Table 4.1.1.2School buses selected for testing.Bus NumberBus TypeYearMakeModelRowsEngineMileageDisplacementSnap/IdleCycles854HE11985ThomasCoach14Cat 320829300010.4 L.8%4851HE21985CrownSupercoach15Detroit Diesel 6713150006 L.57%2752HE31975CrownSupercoach15Cummins 2903160006 L.18%2986RE11998ThomasSaf-T-Liner14Cummins 250 HP 8.31110008.3 L.2%4921RE21993ThomasSaf-T-Liner13Cat 31161770006.6 L.11%4982TO11998ThomasSaf-T-Liner14Cummins 250 HP 8.3780008.3 L.1%48CNG2002ThomasSaf-T-Liner14John Deere 8.110008.1 L.N/A141Snapand idletests notpossible duetotoomuch watervapor.HE1–HE3:Highemitter conventionaldiesel schoolbuses.RE1– RE2:Representative conventionaldieselschoolbuses.TO1:Particle-Trap outfitteddiesel school bus.CNG: Compressednaturalgas school bus.18(a) (b)

Photograph 1

. a) High em itter conv entional die s el test bus pa rk ed 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 dies el buses we tested was Arco Em i ssion Control Diesel (ECD-1). This fuel, also som e ti mes called “g reen” diesel , has ultra-low sulfur content (<15ppm ) , low

arom atics, and a high cetane num ber. Ultra-low sulfur fuel m u st be used for after-treatm e nt

em issions control technologies (e.g. particle tr ap catalyst) to function properly. H o wever, we used this “green” diesel on all the diesel buses we te sted, with or without a pa rticle trap catalyst.

4.1.2 Field Sam p ling Procedures In each bus tested, two identical s e ts of instrum e nts used to m e asure a variety of gas e ous and PM species were mounted inside the bus cabi n. One set was used to m easure concentrations in the rear of the bus cabin, while the second set wa s used to m easure concentrations either at the front of the bus cabin, or just outside the bus. Measurem ents we re tak e n while the bus trav ele d along one of three designated school bus routes (described in Sect ion 4.1.6). Each bus run lasted approxim a tely one hour. A summary of the m easure m ent m e thods used for this study i s presented in Table 4.1.2.1. The instrum e ntati on included both real-tim e and integrated

m easure m ents. Although two sets of instrum e nts we re in each bus, the m a jority of the analyses in this report (includi ng the com p arison between bus types) were done using the data from a single set of instrum e nts, located at the rear of the bus cabin.

4.1.2.1 Instrum e nt Packaging and P o wer Supply Figure 4.1.2.1 presents the plum bing diagram for the sam p ling equipm ent inside each bus. The instrum e nts were m ounted onto three plyw ood platform with elastic cords. In order to

m i ni m i ze the effects of vibration, foa m pads were placed between th e instrum e nts and the plywood. Instrum e nts that sam p led from the fr ont of the bus, or outside the bus, depending on the run, were m ounted on one platf o rm and instrum e nts that sam p led from the rear of the bus were placed on a second platform . The data l ogging portable com puter (PC), gaseous analyzer calib ration

19Table 4.1.2.1 Measurem ent para meters and m e thods used in the m a in study.

Co de Species/Par ameter Instrument Detec t ion Limit Flow R a te , L/min Amps 1 10v

a PM 2.5 Inte grate d Mass C u st om / H arva rd Im pact or 5 μ g/ m 3 ** 20 .0 *

b PM 2.5 Real-Time Mass TSI Dust T r ak 1 μ g/ m 3 1. 7 0. 1

c PM 10 Inte grate d Mass C u st om / H arva rd Im pact or 5 μ g/ m 3 ** 20 .0 *

d PM 10 Real-Time Mass TSI Dust T r ak 1 μ g/ m 3 1. 7 0. 1

e Fin e Particle Co un ts Clim et Spectr o .3 O P C Si ngl e pa rt i c l e 5. 0 0. 1

f Ultra-Fi n e Part icle Co un ts CE-CERT SE MS Si ngl e pa rt i c l e 5. 5 5

g Ele m en tal Carb on Magee Aethalometer 1 μ g/ m 3 5. 0 0. 1

i Gase ous HC ( T ot al an d Speciated) Gas C h r o m a t ogra p hi c Meth od s Varies Varies *

j CO Langan electrochem ical cell

D a sib i 30 03 2 ppm 1 ppm Passive

2 l p m Inter n al Battery

1. 0

k NO 2 CE-CERT NO 2 /PAN Lu m i n a l
GC

TEI 42 NO/ NO 2 /NO
x 1 pp b 1 pp b -- -

-- - 1. 0

2. 0

m Al de hy des & Ket o nes DNPH collection/ HPLC analysis 1 pp b 0. 8 *

n Particle PAH EcoC hem PAS 2 0 0 0 3 ng/ m 3 4 0. 25

o Tem p , R H (i n- bus ) Ro tr on ics MP10 1A 2. 0 ° C, 5 % NA 0. 1

p Location a n d Speed Garm in GP S M a p7 6 2- 5 m NA 0. 1

q SF 6 Aer o Vi ro nm ent CTA 10 0 0 0. 01 0 p pb 0. 1 2

r Traffic Doc u mentation So ny C X C

- 3 9 0 Vi de o C a m e ra NA NA Inter n al Battery

* S u p p l i e d by com m on vac u um pum p (1 0 a m ps) ** 1 h o u r sam p l i ng t i m e

2020 L/minPM2.5Filter20 L/min20 L/minSampleInletsSample Inlets40ml/min40ml/minPump,100ml/minPM10FilterPM2.5DustTrakPM10DustTrakOPCTenax TubeDNPHParticle PAHNO2/PANGCCO800 ml/min20 L/minTwo 3⁄4 HPVacuum PumpsPM2.5FilterPM10FilterPM2.5DustTrakPM10DustTrakOPCSEMSTedlar BagTenax TubeDNPHParticle PAHNO2/PANGCCO800ml/minAethalometerAethalometerPID VOCPIDVOCPump,100ml/minSystemat frontofbusfor measuring outsideairorair fromfrontofpassengercompartmentSystemat rearof busfor measuringairat rear of passengercompartment airPumpPumpSamplingManifoldCyclingFromFront of Bus,to Rear ofBus,to outsideAirSF6AnalyzerNO/NO2/NOxCOSystematmiddle ofbuswithswitching, 3 location,manifoldTedlar BagFigure 4.1.2.1Plumbingschematic for school bus samplinginstrumentation.21system and the instrum e nts that were part of th e “switchin g system ” that m onitored from the front, back and outside were m ount ed onto a third platform in th e m i ddle of the bus cabin. In this way the platf o rm s were easily insta lled in the buses. The platform s were installed over the seat backs of the buses and tied down with ratc heting straps. All three platform s were m ount ed along the right side of the bus (facing forwards). The total flow rate for all sam p lers and the bypass for the isokinetic probe on the buses was about 150 L/m i n.

A trailer m ounted North Star 13000 PPG propa ne-powered generator w ith an oxidation catalyst was used to supply AC power for the in strum e nts during the f i rst week of the m a i n study. A hitch was installed at the back of the bus for the generator trailer. The decision was

m a de in the m a in study to convert to the us e of on-board batteries and invertors for all subsequent buses (Runs 4-36) in order to e l im inate the noise, com p lexities an d poten tia l pollution from towing a generator behind the bus.

As discusse d in m o re detail in Section 5.3 , because of various difficultie s and confounders encountered during the runs with the propane generator, the data for bus HE1 wer e not included in the m a jority of com p a r isons between buses.

The onboard inverter and battery system wa s a 7kVA (kilovolt Amp) Best Power uninterruptible power supply (UPS), but used only for its “inverter” capabil ities on this program

. Thirty-six deep cycle m a rine batte ries were used for inverter input . Thirty-two of the batteries powered th e UPS directly while the rem a ining four batteries were used to power four DCpowered vacuum pumps. The 200 Kg UPS was placed o n the floor of the bus next to a sid e

em ergency exit. (One ro w of seats n eeded to be rem oved from most buses to m a ke room for the UPS.) Nine wooden crates with fou r batteries e ach were placed in n i ne seats along the left side of the bus. Conventional “ground power” was us ed to power the instrum e ntation onboard the buses when the buses were “parked” at UCLA and CE-CERT.

4.1.2.2 Data Collection A sche m a tic of the data logging design is presented in Figure 4.1.2.2. Instrum e nts that internally logged data w e re downloaded via a P C

. The clocks for all of these instru m e nts wer e synchronized at the beginning of ea ch run us in g the GPS tim e as a ref e rence. All of the oth e r instrum e nts had either analog or digital inputs that were connected to a PC that collected data using LabVIEW ® software. The data logging and contro l P C polled and logged data from all instrum e nts once per second (except for the electro chem ical cell CO analyzers and portable PID organic compound analyzers which internally l ogged data). All data were downloaded and backed up from the data logg ing and control PC, the electro chem ical cell CO and organ i c

com pound a n alyzers on a daily basis. For data analysis, we obtained the ten-second m e dian from our one-second data for all real-tim e instrum e nts. This level of resolution was justified as we observed rapid instrum e nt response to changes in concentrations inside the bus on the order of seconds.

4.1.2.2.1 Checkout and Installation of Instrum e nts Concurrent with the procurem ent and prepar ation of the school buses, the m easurem ent instrum e nts were assem b led, conf igured, and tested at the CE-CERT l a boratory. This assem b ly task included all necessary calibration and data l ogging equipm ent. The instrum e nts were also

22Figure 4.1.2.2 Data logging design insi de school bus platform.

23 Inst rum e nts at Front of Bus

ο PM 2.5 Filter Sam p ler

ο PM 10 Filter Sam p ler

ο DNPH, Carbony l Sampler

ο Tenax

, B T X S a m p ler

ο Tedl ar Bag S a m p ler

ο PM 2.5 DustTrak

ο PM 10 DustTrak

ο OPC

ο Range Find er

ο GPS

ο Aethalom et er

ο Particl e PAH

ο Ele c troch e m i ca l CO

ο PID VOC

ο UCR NO 2 /PAN GC

ο Video Camer a Computer Contr o lled On/Off Switch for Vacuum Pump

Manuall y Contr o lled On/Off Switch for Sam p le Pum p

RS232 Signals Connected via 8

- Port RS232 to Network Conver t er to DAS

Analog Signal O u tput to DAS

Internally Log D a ta

Inst rum e nt s at M i ddl e of B u s

ο TEI NO/NO 2 /NO
x
ο Dasibi CO

ο Aer o Vir o n m ent CT A- 1000 SF 6

ο Switching Man i f o ld

ο Data Acqu isition S y stem (DAS) and Control PC

ο Exhaust SF 6 in je ction s y st em

ο CSI 1700 Calibr ato r Digital Contro l f r om DAS; Pulse Output to DAS

Analog Signals Output to DAS

C y cle Controlled and Monitored b y DAS PC

Central Contro l and Data Loggin g

Mass Flow Control of 5000 ppm C y linder

Mass Flow Control Ca libra tor wi th GPT Pulse O utput to DAS

Inst rum e nts at Rear of B u s

ο PM 2.5 Filter Sam p ler

ο PM 10 Filter Sam p ler

ο DNPH, Carbony l Sampler

ο Tenax

, B T X S a m p ler

ο Tedl ar Bag S a m p ler

ο PM 2.5 DustTrak

ο PM 10 DustTrak

ο OPC

ο Aethalom et er

ο SMP S

ο Particl e PAH

ο Ele c troch e m i ca l CO

ο PID VOC

ο UCR N O 2 /PAN GC Computer Contr o lled On/Off Switch for Vacuum Pump

Manuall y Contr o lled On/Off Switch for Sam p le Pum p

RS232 Signals Connected via 4

- Port RS232 to Network Conver t er to DAS or Directly to DAS PC

Analog Signal O u tput to DAS

Digital Contro l f r om DAS; Pulse Output to DAStested for proper operation and pr op er in terf ac in g with their respe c tiv e calib ration s y stem s and the data-logging and control com p uter.

Several shakedown runs of the m easurem ent sy stem s in the s c hool bus were performed at the CE-CERT f acility and in the Riversid e ar ea in order to assure proper operation of the instrum e nts while in m o tion. During these shake down runs tracer test was perform e d in the bus in order to m easure the ventilation rates with in the bus under different op erating cond itions.

4.1.2.2.2 Sample Probe Positions The following descriptions apply to all of the buses employed in the m a in study.
Sa m p ling inside the bus prim arily took place at two fixed l o cation s at the front and rear of the cabin. The equipm ent at the rear of the cab in always m easured con cen tr ations at th e rear of the cabin. However the equ i pm ent at the front of th e cabin m easured concentration at the front of the cabin during 20 runs, and just outside the cab in (with the addition of longer sampling lines with the in le ts plac ed outside the bus as desc ribed below) during 15 runs. The sa m p ling probes were positio ned to sam p le a t the height of the breathing zone of a ch ild s eated in the bus. Large diam eter inlets were used for the interior sam p ling probes in order to m i nim i ze velocity changes and particle losses. Sam p ling outside the bus w h ile in m o tion was perform e d using a specially fabricated isokinetic probe. Separate sam p ling sy stem s were used for the gaseous and particulate

m a tter (PM) sam p ling trains.

The instruments were p l aced on p l atform s, consisting of three plywo od sheets, which were strapp ed to the to ps of the bus seats alon g the righ t s i de of each bus (see Ph otograph 2). The tops of the seats were appr oximately one meter above the bus floor. The sam p le inlets for the gaseous and particulate sam p lers were located on top of the instrum e nt platform s, such that

( a ) ( b )

Photograph 2

. a) Array of instrum e nts inside a test bus b) Preparing the instrum e nts before an afternoon run

24the inlet heights were approxim a tely 1.3 and 1.1 m e ters, respec tively, above the bus floor. W h en

sam p ling the air ou tsid e the bus, the instrum e nts at the front of the bus were used, and the

sam p ling probes were lo cated at the base of the right side exterior driver’s m i rror, approxim a tely
2.5 m e ters 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 instrum e nt. The PID organic vapor analyzers and electrochem i cal cell CO analyzers located on to p of the sampling platform s at the fr ont and rear of the bus sam p led directly fr om their inlets located on each instru m e nt. The Tedlar bag

sam p lers at the front and rear of the bus had their own one m e ter long sam p le lines (0.08 cm inner diam eter (ID), PF A Teflon) placed at a h e i ght of abo u t 1.3 m e ters above the floor of the bus. The Tenax and DNPH cartridge sam p lers dr ew their air from three common sam p le lines (outsid e , an d inside fron t and rear) through Tefl on particulate m a tter filters and then through 0.65 cm inner diam eter PFA tubing to a m a nifold wher e air was directed to each of the instrum e nts through a series of T-fittings. The NO 2 GC’s, GFC CO, T E I NO/NO 2 /NO
x and SF 6 analyz ers also drew th eir air throu gh those sample lines. However for these instrum e nts, the m a nifold ha d

sam p le lines connected to three-way solenoids, wh ich rotated the sam p l e 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 m i nutes. This cycle rep e ated every 1 2 m i nutes. This was done to accom m odate the

sam p ling requirem e nts for the SF 6 trac er ga s a n alyze r . All sam p le inlets f o r those instrum e nts
atta ched to the switch i n g m a nif o ld were loc a te d approxim a tely 1.3 m e ters abov e th e f l oor of th e bus (front and rear), or 2 m e ters above the ground (outside).

PM sam p ling equipm ent was located at the fr ont and rear of the bus. The equipm ent located at the rear of the bus wa s used to sam p le a i r f

r om ins i de the bus near th e rear only. Equipm ent located near the front of the bus al ternated (depending on sa mpling requ irem ents f o r specif i c tes t s) be tween s a m p ling PM f r om inside th e bus near the front and from out side the bus. The inlets f o r the PM s a m p ling equipm ent located at the f r o n t of the bus all dr ew their sam p les from a common m a nifold.

For sam p ling outside the bus, a specially de signed isokinetic probe was used (Figure 4.1.2.3). The air entered a 1 c m ID copper tube approxim a tely one m e ter long. The inlet protruded out the front right wi ndow of the bus about 0.2 m e ters (see Photograph 3). The inlet was filed to a knife-edge and poi nte d direc tly f o rward to s a mple into th e wind with m i nim u m particle lo ss as the bus traveled down the road. The air flow rate was set to provide isokinetic

sam p ling at a nom inal 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 di am eter, and then passed into a 45 cmlong “tsection” wh ere addition al bypass air, drawn to m a intain is okinetic con d itions, was rem oved.
Sa m p le air exited the “t-section” through a 23 cm long section of 4 c m diam eter tubing into a 13

cm long by 10 cm diameter “stillin g cham ber” which had seven out let ports. Two of the outlet ports were approxim a tely 30 cm long, 1 cm ID copper tubing which ran from the stilling cham ber to the Harvard I m pactors. One of th e outlet ports had 0.6 c m ID “nonstatic” plastic tubing (approxim a tely 1 m e ter long) running to th e front Aethalom eter. The four rem a ining outlet ports had 0.4 cm ID “non static” plastic tubing (each a pproxim a tely one m e ter long) running form the stilling cham ber to the DustT r ak, OPC and PAH instrum e nts. A tap on the side of

the stillin g cham ber ran to a trans ducer used to m onitor th e pressu re in the stilling cham ber.

25When the front system was used to sam p le air from inside the bus, the copper tube was

rem oved from the system, allowing air to en ter direc tly into the 4 cm diam eter sec tion and the bypass flow was turned off.

To
Vacuum Sensors To Particl e Sa mple rs

and Anal yzers

3 cm I.D. copp er tube used as in le t for “ frontinside” sam p ling ~ 1 meter long

, 1 cm I.D.

copper tube used as in let for “outside sam p ling”

By pa ss Flow Inlet (outside sampling) C y l i ndric al Filt e r Ins Ta b

l e 5 3 Outside

t h e c a b i n

Figure 4.1.2.3 Sa m p ling system (including the isokinetic inle t) u s ed at the f r ont of the bu s to

sam p le inside and outside aerosol.

Photograph 3 . Inlet of the sam p ling line for outsi de m easurem ents of gas e ous species.

The rear P M instrum e nts all drew air from a common location nex t to the rear g a seous

sam p le inlets. No additional lines o r inle ts wer e used for the Harvard Im pactor filer sam p lers. A
0.5 m e ter long, 0.6 c m I D nonstatic plastic tube ra n from the comm on sampling location to the Aethalom eter. Lengths of 0.4 c m I D nonstati c tubing between 0.5 and 1 m long ran from the

common sa mpling location to the DustTrak, OP C, PAH and SMPS ins t rum e nts. Table 4.1.2.2 summarizes the tota l len g th of the sampling lines us ed with these instru m e nt s. For the m a jority of our analyses, we focused on the rear instru m e nts. PM2.5, black carbon, fine particle counts

26and PAH (the particulate species we analyzed for th is report), had sa m p ling lines in the rear of 0, 0.5, 0.7 and 1.2 m e ters, respectively. See section 5.1.4 for a discussion of potential sampling line losses for particulate species m easured in this study.

In the m a in study, bus stop m easurem ents we re conducted using an equipped bus parked at the bus stop. The sampling inlets for these m easure m ents were located about 1.5 m e ters above the ground.

Table 4.1.2.2 Length of sam p ling lines

Sa m p ling line length (meters)

Instrum e n t R e a r Front/in side Front/outsid e 2

Harvard Im pactor - PM10 0 1 0 3
Harvard Im pactor - PM2.5 0 0 3
DustTrak - PM10 1.6 0.3 3
DustTrak - PM2.5 1.5 0.2 3

O P C 0

. 7 1 3

. 7
Aethelom e t e r 0

. 5 1 1 PAH Analyzer 1.2 1 3.7

1 Harvard Im pactors we re use d with a n ope n face i n let (n o sam p ling line or ada p ter) for several e x peri ments.

4.1.2.3 Instrum e ntation 4.1.2.3.1 Integrated PM 10 and PM 2. 5 Mass Concentra tion Filte r sam p les were collected using custom sampling syste m s designed f o r portable use. The inlets were of the Harvard design (Turner et al., 2000), which have been shown to have
effective cu ts at 2.5 and 10 μ m while sam p ling at 20 L/m i n. The flow rates were controlled by a needle v a lve and m easured with a rotam e ter and calib rated against a volum etric flo w rate s e nso r

. The sam p les were collec t ed on 37 mm Gelm an Tef l on f ilter s with a 2.0 μ m pore size. A Cahn Model 34 m i crobalance at the CE-CERT laboratory was used to dete rm ine the weight of the
f ilter s to within ±

 2 μ g b e fore and af ter sam p ling. All filters were equilib rated at 23 ° C and 40% RH for at least 24 hou rs prior to weighing. Filters were wei ghed a m i ni m u m of three tim e s bef o re and a f ter sam p le colle ction. If all th ree weighings were not co nstant to within 3 μ g, the f ilter s were r e weighed un til they were

.

4.1.2.3.2 Real-Tim e PM 10 and PM 2. 5 Mass Concentration Real-tim e PM 10 and PM 2. 5 m easurem ents were m a de using Therm o System s Inc. Model 8520 DustTrak Aerosol Monitors. Impactors were us ed to p e rform the necessary size cuts. The PM concentration that m a de it past the im pactor was th en d e term ined b y m easuring the inten s ity
of the 90 °

 scattering of light from a laser diode. The instrument sam p le flow rate was 1.7 L/m i n. The averaging tim e was adjustable from 1 to 60 seconds, and an averaging tim e of one-second was used. The instruments were c a lib rated at the f acto r y with Arizon a road dust (NIST SRM 8632). Collocated sam p ling was perfor m ed each week to insure com p arability.

Throughout this report, all PM2.5 data are from the Harvard Impactor integrated sam p les unless stated otherwise (i.e., as data from the DustTrak instrum e nts).

274.1.2.3.3 Real-Tim e Particle Coun ts 4.1.2.3.3.1 Optical Particle Counter Clim et Spec tro .3 Optical Particle Counters we re used for particle count concentration

m easure m ents in sixteen size bins from 0.3 to 10 μ m

. Only the size range between 0.3 – 0.5 μ m was used for data analy s is in th is report. Th is size rang e was selected because the highes t

num b er concentrations are found in the sm aller si ze bins. Thus, this size range represents the

m a jority of fine particle counts, while sim p lifying the analysis to just two size bins. Sa mple air was drawn through a laser beam inside the anal yzer where particulate matter passes through the beam one particle at a tim e . As a particle pa sses through the beam it scatters light roughly in proportion to particle size. Optical detectors de tect the scattered light and send an electrical signal p r opo rtion a l to the scattered lig ht to the d i gital s i gnal p r ocessor (D SP). The DSP performs a pulse height analysis of the signal to determ ine the size of the particle. The DSP then sends the particle size results to on e of sixteen counters (“si ze bins”) where th e total num b er of particles in the size bins were accum ulated. The instru m e nts flow rates were set at 1.0 L/m i n. The instrum e nts counting periods were set to fifteen seconds with a te n second “wait” period prior to starting a new counting period.

4.1.2.3.3.2 SEMS A CE-CERT Scanning Electrical Mobility Sp ectrom e ter (SEMS) was used to m e asure particles in th e range of 0.03 μ m to 0.8 μ m electric a l mobility dia m eter. The instrum e nt consisted of three m a jor com ponents: a Therm o System s Inc. (TSI) m o del 3077 85 Kr neutralizer which generates a known charge distribution on the aerosol; a TSI model 3081 differential mobility analyzer long colum n whic h selects for particle sizes based on the voltage applied and the particles ’ electrical mob ility ; an d a TSI m o del 3760A condensation particle cou n ter used to detect particles. The analyzer’s four gas fl ows were calibrated at CE-CERT using a prim ary f l ow calibra tor and the particle s i ze was calibra te d using aerosolized polyst yrene latex spheres. The analyzer operated in a ten-m i nute cycle mode. It spent the first 75 seconds of each cycle scanning the 0.03 to 0.8 μ m size range in seventy-five, one- second-long incremen ts. It then spent the next 525 seconds fixed (“fixed period”) on a single particle size that was expected to be the bes t ind i cator of the presence of diesel exhau s t particulate m a tter. Fo r all run s ex cept the last run, the fixed-period particle diam eter was 180 nm . For the last run, the fixed-period particle size was 50 nm . The instrum e nt out put particle count data were output and proces sed at a once per second rate.

4.1.2.3.4 Real-Tim e Black Carbon The black carbon concentrations were m easured using Magee Scientif ic Aethalom eters. These instrum e nts drew sam p le air through a 0.5 c m 2 spot on a quartz f i ber f ilte r tape. Inf r are d light at 880 nm was transm itted thro ugh the quartz tape and detected on the back sid e of the tape using photo d etectors. (One detecto r sensed the light trans m itted throu gh the spo t where the air was drawn through and the second detected ligh t transm itted though an unused sectio n of tape in order to correct for chan ges in th e light source intensity and changes in the tape cha r acteristics.)
Decreases in the am ount of ligh t transm itted through the spot on the quartz tape were proportional to the am ount of elemental carbon and “heavy” organic m o lecules collected. The instrum e nt’s response to the change in light tran sm ittance was reported as “black carbon” (BC). The instru m e nt’s sample f l ow r a te was m a inta ined u s ing m a ss f l ow contro lle rs. The
concentration of BC in units of m a ss of BC per volum e of a i r (e.g. “ μ g/m 3 ”) was determ ined by

28the instrument f r om the f l ow rate and change in light transm ittance data. W h en the ligh t
transm ittance through the collectio n spot on th e quartz filter had d e creased by s e venty-fiv e
percen t, the quartz tape autom a tically advanced to a fresh section of filt e r . Each tim e the f i lte r tape autom a tica lly adva nced, the in strum e nt re calibrated for approxim a tely one m i nute prior to restarting sampling.

4.1.2.3.5 Real-Tim e Particle Phase PAH EcoChe m Model PAS 2000 analyzers were used to measure the c oncentrations of

pa r t i c l e

- bound pol yc yc lic a r oma t i c hydr oc a r bons ( P AH)

. T h e in s t rum e n t u tilized th e p r in cip l e of photoionizat ion of total particle -bound PAH by means of an ultrav iolet la m p . The wavelength of the light was selected such that onl y the PAH absorbed on aerosols were ionized, while gas molec u les a nd non-carbon aeros ols re m a ine d neut ral. The aerosol particles that had PAH molecules adsorbed on the surface emitted electr ons , which were subsequen tly removed when an electric f i eld was applied. Remaining positively ch arged particles were collected on a f ilter inside an electrometer where the charge was m easured. The resulting elect ric current established a signal which was proportional to the c once n tration of tot a l particle-bound PAH. The lower threshold o f this method was a bout 3 n g / m 3 total particle-bound PAH.

The PAH instrum e nt’s full scale was set to read a m a xi m u m of 500 ng/m 3 in the front, and 1000 ng/m 3 in the rear, during the first 17 runs of this study. These runs included buses HE1, HE2, HE2, RE1 a nd RE2 (all diesel buses). Starting with Run 18, and on all subsequent runs (including TO1 and CNG), the scale was increas ed to read a m a ximum of 2000 ng/m 3 for both front and rear instrum e nts. The im plications of this change for our inter-bus comparisons of PAH concentrations are disc ussed in Section 5.3.3.

The location of the Aethalom et er, particle count analyzer, P AH analyzer and the Harvard
Im pactors 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 sam p ling lines b) Aethalom eter, PC analyzer, P AH analyzer, an d Harvard Impactors

294.1.2.3.6 Gaseous Hydrocarbons
A m u ltifaceted approach using both rapid re s ponse “surv e y” real-time m onitors and integrated sample collection followed by subsequent laboratory analysis w a s used. This allowed for full characterization of the gaseous hydrocarbons by “tim e-consum i ng” (but highly sensitive) laboratory chrom a tographic m e thods and the rapid response of a re al tim e analyzer that could characterize short ep isod es of high concentration s

.

4.1.2.3.6.1 Tenax Cartridges
Sa m p les for arom atic hydrocarbons (benzene, toluene, and xylenes) were collected on a Tenax cartridge and analyzed by GC analysis, pe r the U.S. EPA TO-1 Method (U.S. EPA, 1988). The cartridges contained 0.1 gm of Tenax. Th e m a xi mum target sam p le volum e for these cartridges had been determ ined to be at least 3.0 lite rs before benzene breakthrough w ould occur; with th e oth e r a r om atics having a lo nger r e ten t ion tim e. T h e sam p le f l ow ra te wa s set to 40 cc/m i n in order to stay well below the breakth rough volum e for the stud y’s projected 60 m i nute

sam p le periods. A Hewlett Packard HP5890 II ga s chrom a tograph with a therm a l desorber

, capillary colum n and flam e ionizatio n dete ctor (F ID) was used for the analysis

4.1.2.3.6.2 Tedlar Bags
Sa m p les for 1,3-butadiene were collected in 8L Tedlar bags at a nom inal sam p le flow rate of 100 cc/m i n. A potassium iodide (KI) trap was used to rem ove the am bient O 3 before the

sam p le entered the bag. The sa m p l e s were ha nd-carried to the CE-CERT analytical laborato r y for GC analysis after the end of sampling. An aliquot from the bags were drawn through a

sam p le loop of known volum e on the GC. A Hewlett Packard HP5890 II gas chrom a tograph with a fixed volum e sa mple loop, capillary column and flame ioni zation detector (FID) was used for the analy s is.

4.1.2.3.6.3 Portable PID The total amount of arom atic and other highl y conjugated arom atics were continuously monitored w ith a ppbRAE Model PG M-7240 portable photo ionization detector. Sam p le air was drawn through the instru m e nt’s reaction cham ber where it was continuously irradiated with high energy ultraviolet light. Com pounds present that ha d a lower ionization potent ial than that of the irradiation energy (10.6 electron volts) were ionized. The ions form e d were collected in an electrical field, producing an ion current that was proportional to total com pound concentration.

4.1.2.3.7 Gaseous Aldehydes and Ketones The m easurem ent technique used w a s a vari ant of U.S. EPA Method TO-11 (U.S. EPA, 1988) for carbonyls in which 2,4- dinitrophenylhydrazine (DNPH) was im pregnated on silica Sep-Pak cartridg e s. This procedu r e used C1 8 Sep-Pak cartridges (Wat ers/Millipore Corp., Milford, MA), which had been im pregnated with acidified DNPH reagent for am bi ent sam p ling. The a m ounts of both the hydrazine and acid were optim ized to achieve efficient collection of the carbonyl compounds and protection from ozone destruc tion of the captured carbonyl derivative. When am bi ent air was drawn thro ugh the cartridge at 1 L / m i n, car bonyls in the air sam p le were captured by reacting with DNPH to for m hydrazone s, which were extracted and then separated and quantified using H P LC (Fung and Grosjean, 1981). It had been shown that the silica cartridge when used alone ha d a significant negativ e ozone artifact, and th at this m e thod

m easured carbonyls com p arably to the sili ca m e thod with an ozone rem oval device.

30The m e thod analyzed samples for nine indi vidu al species: fo rm aldehyde, acetaldehy de, acetone, p r opanal, crotonaldehyde, m e thylethy lk etone, bu tyraldehyd e, benzaldehy de, and m

tolualdehyde, plus C 5 , C 6 and >C 6 aliphatic carbonyls. Aliphatic carbonyls (C 5 , C 6 , and >C 6 ) were usually m easurable but lower in concentratio ns com p ared with the C 1 -C 4 carbonyls. Except for the straight chain aldehydes (e.g. pentanal, hexa nal, etc.), resolution of the other isom ers of C 5 and higher carbon yls was in com p lete, and thus in accurate. The latter were m o r e appropriately reported as a group by carbon number. Pure DNPH derivatives of the aldehydes and ketones were used to prepare calibrati on standard for the high performance liquid chrom a tography (HPLC).

Field blank variability established the LQ L (lower quantifiable lim it), which for C 1 -C 7 carbonyls was typically 0.5 ppb or lower (at 3 tim es the standard devi ation of the blank variab ility).

Accuracy of this m e thod was approxim a tely + 15% for for m aldehyde based on

com p arison studies with long path spectroscopic techniques in an am bien t air setting (Fung and
W r ight, 1990; Lawson et al., 1990). Since the basic chemistry of the DNPH m e thod was the

sam e for all carbonyls, the accuracy for higher carbonyls was expected to be in the sam e range.

4.1.2.3.8 Real-Tim e Carbon Monoxide The carbon monoxide (CO) concentrations were m easured using Langan electrochem i cal sensors. These instrum e nts were designed to operate in a passive environm ent and have no

sam p le flow. The instrum e nt had a diffusion m e m b rane that CO c ould pass through. The

m e mbrane was exposed to am bient air on one side and contained a solution that reacted with C O on the other. Am bient CO that diffused thro ugh the m e mbrane reacted with the solution, creating an electric cu rrent that was proportional to the am bient CO concentration. The instrum e nt was calibrated by sam p ling known c oncentrations of CO pre p ared in air.

A third CO analyzer was added for the m a in study, a Dasibi Model 3003. This analyzer

m easured CO using an infrared detector, and is an U.S. EPA Re ference Method for CO. The analyzer was installed near th e center of th e bus and co nnect ed to the switchin g m a nif o ld described in Section 4.3.11.

The CO and VOC m onitors along with associat ed cables, standard gas tanks, rotom e ters and sam p ling lines are shown in Photograph 5.

4.1.2.3.9 Real-Tim e Nitrogen Dioxide The NO 2 concentration s were m easured us ing tw o gas chromatographs (GCs) to m easure NO 2 and peroxyacyl nitrates (PAN). Am bient air was brought into the analyzer and drawn through a sample loop. Once per cycle (one m i nut e cycle tim e ) a valve sw itched and the sam p le was pushed into a capillary colum n using air carrier gas. N O 2 and PAN were separated from each other in this column. The separated cons titu ents elu t ed from the colum n onto a fabric wick wetted with lum i nol solution. NO 2 and PAN react with lum i nol , emitting photons of light proportional to their concentration. The cen tral portion of the wi ck was viewed by a photom ultiplier tube (PMT). Photo n s of light fro m the reaction with lum i nol en tered the PMT

31( a ) ( b )

Photograph 5

. (a) Power and data cables, sam p ling lines, CO m o nitors, and VOCs analyzers; (b) Cables, standard gas tanks , rotom e ters and sa mpling line s

where the signal was m u ltip lied th ro ugh a series of electrified plates that success i vely m u ltiplied and converted the signal from “ light energy” to “electrical energy.” T h is am plified electrical signal was output to th e com puter cont ro ller and data log g er. Becau se NO 2 and PAN eluted f r om the instrum e nt at dif f e rent tim es, the data logging system could separately process and store the data from the PMT for these tw o com pounds. These instrum e nts did not detect HONO.

A third analyzer was added to the m a in study to m easure oxides of nitrogen. A Therm o Environm ental Instrum e nts (TEI) Model 42 NO/NO 2 /NO
x analyzer was installed near the center of the bus. It sam p led from of the sam e switc hing m a nifold described in Section 4.1.2.2.2. This analyzer was added to the program for several reasons: continuous sam p ling coverage from three locations, for quality control, and for ease of calibration. T h e preferred m e thod for calibrating the NO 2 /PAN GC’s for NO 2 was by gas phase titra tion of nitric oxide. I n order to perf orm this type of calib ration, it was necessary to ha ve an in strum e nt that m easured NO and NO
x . Thus, the data f r om this ins t rum e nt was used to calibra te the NO2/PAN GC’s and not f o r the analyse s included in the rem a inder of this report.

The TEI Model 42 m eas ures oxides of nitrogen by the chemilum i nescent reaction of NO with ozone. The analyzer switch e s between two sam p le paths every ten second s. The first

sam p le path directs th e sam p le directly into a reaction chamber where it was m i xed with high concentrations of ozone (O 3 ). The N O and O 3 reaction generated photons of light proportional to the NO concentration that are detected and proc essed by the analyzer. The second sam p le path directed the sam p le through a converter that c onverted other oxides of nitrogen to NO. The

sam p le was then directed to the reaction cham ber. The results from the second sam p le path were reported as NO
x . NO 2 was calculated as the difference between the NO
x and NO responses. Since there m a y be additional non-NO oxide s of nitrogen present (other than NO 2 ), this m e thod can “over-report” NO 2 concentrations. However, the TEI 42 is an U.S. EPA Reference Method for NO 2

.

324.1.2.3.10 SF 6 Tracer Gas The sulfur hexafluoride (SF 6 ) concentrations were m easured with an AeroVironm ent Model CTA 1000 analyzer. The prim ary task of th is analyzer was to determ ine if a significant

am ount of the bus exhaust was en tering the passenger com p artm ent of the bus. For this task, SF 6 was injected into the bus exha ust system s as described in S ection 4.1.4. A second use of the instrum e nt was to deter m ine the ventilation (or air exch ange) rates in the passenger com p artm ent as described in Section 4.1.3. Th is Aerovironm ent instrum e nt us es electron capture detection after water and oxygen are rem oved from the samp led air. The instrument was developed for operation on a m oving platform and has a sensit ivity of approxim a tely 0.010 ppb with a response tim e of about three seconds. In order to acco unt for analyzer baselin e drift, it w a s set up to frequently sam p le reference SF 6 -free air.

4.1.2.3.11 Range Finder In order to m easure the distance fro m a vehicle being followed, a Laser Optronix DM E 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 go od, its rang e is 1-300 m, with an accuracy an d resolution of about 1.0 m. The instrum e nt output s data once per second to the data logger. However, due to problem s with trying to use the instrum e nt on a “ m oving platform ,” as well as problem s trying to m easure d i stan ces to v e hicles that w e re in the f i eld of view for often considerably less than one second, no viable data were obtained from t h is instrum e nt. Instead distan ces to vehicles was estim ated when necess a ry from the video tap e

.

4.1.2.3.12 Bus Location The bus loc a tion was d e term ined with a Garm in GPS MAP76 global position i ng system with a W i de Area Aug m entation System ( W AAS) corrections system . The system provides position accuracy of about 2-3 meters. The GP S has a 12 parallel ch annel receiver to continuously track and use data from up to twelve satellites. The WAAS system is a broadcasted “signal integrity” signal that is determ ined by fixed ground-based reference stations. The GPS uses the W AAS correction inf o rm ation to incre a se the accu racy of the position i ng inf o rm ation. In addition to horizontal position (e .g. latitude and longitude or UTM coordinates) , th e corrected GPS system also prov id es 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 tim e reference during this study. Th e clocks on all other devices were set to the GPS ti m e on a daily basis.

4.1.2.3.13 Meteorological Data
Tem p erature and relativ e hum i dity were m easured inside the bus using a Rotronics Model MP 101A sensor. The sensor determin ed tem p erature using a 100 ohm platinum resis t ance temperature d e vice (RTD) and relati ve hum idity using a Hygrom eter C94 capacitive hum i dity se nsor. The instrum e nt included inter n al signa l condition i ng to process an d output two voltage signals that were propor tional to the tem p erature and re lative hum idity, respectively.

Hour l y wi nd s p e e d a n d di r e ct i o n da t a we re obtained from the nearest AQMD fixed site monitoring stations for the dates of tim es of each bus run. These data provided an estim ate of

33the prev ailing area-wide wi nd conditions d u ring a bu s run, but did not account for

m i crom eteorology around the bus (e.g. street canyon effects) throughout th e run. Therefore,

tnte rpre tatio n of the data based on these m eas urem ents requires caution. In general, and as expected ba sed on land/sea tem p eratures, th ere was little or no wind during the m o rning runs, while the af ternoon run s typically occurred dur ing on-sho r e flow conditions with significan t wind speeds. Therefore, only the afternoon wind data were useful as a potential explanatory variable for differences in pollutant concentrat ions between afternoon runs. For the prim ary urban route, hourly wind speed data from th e two nearest AQMD stations (W est 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 averag ed and this value a ssign ed to the run. The same procedure w a s used for the s econd urban route using the H a wthorne (HAWT) and Lynnwood (LYNN) AQMD stations. W i nd direction m easured at the AQMD stations wa s consistently from the W e st or Southwest for all afternoon runs.

4.1.2.4 Use of Video Ca m e ra and Digital Cam e r a A Sony DXC-390 video cam era was m ounted a t th e front of the buses to record traffic conditions in the lan e in which the bus was tr aveling, as well as the adjacen t lan e s during all

m easure m ent periods. T h is cam era has an RS-2 32 Interface, a resolution of 800 TV lines, and a 10-bit digital signal processing sy stem. In addition, the camera ha s a variable speed electronic shutter, which provides the capabil ity to captu re clear im ages of high speed m oving objects. The

cam era was set to a wide angle to v i ew as m u ch of the scene as possib l e. The cam e ra included a “tim e stam p” feature for adding date and tim e info r m ation to the video. The clock in the video

cam era was synchronized with the G PS m a ster clock tim e prior to each run.

4.1.2.5 Docum e ntation of Bus Comm ute, Traffi c Conditions and Events During E ach Run In addition to the video cam era, a software program developed and tested by the UCLA field team was used to record traffic conditio ns, bus m ove m e nt and other observations during each bus run. Detailed observation s during each bus run, s u ch as when the bus was m oving, idling, or at a bus stop, identifica tion of vehicles in front of th e bus, and traffic conditions were recorded and tim e sta m ped in a data log using a laptop computer. The clock used for the tim estam p was also syn c hronized with the m a ster cl ock. Comparison of p eak concentrations of pollutants and the observations reco rded in the data log were used to identify condi tions that led to the high est pollu tan t concentrations insid e the bus during selected runs. All of the data collected du ring th is stu dy, will be s u bm itted in el ectronic form at to the ARB for fur t her review and analysis.

4.1.3 Bus Ventilation Rate Measurem ents We em phasi ze here that the ex perim e nts conducted with SF 6 to m e asure ventilation rates in each bus at several sp eeds and with windows ope ned or closed (as exp l ained belo w), were all conducted at CE-CERT prior to e ach bus being driven to the W e stside. Thus, the use of SF 6 fo r the ventilation rate m easurem ent was com p letely sepa rated in tim e and location f r om the SF 6 tracer studies designed to determ ine the contri bution of a bus’s own e xhaust to within -cab in concentrations. In short, the SF 6 ventilation m easurem ents did not affect the subsequent exhaust

tracer m easurem ents in any way.

34Exchange of the air ins i de bus cabins is co ntrolled by several fact ors, including window position (i.e. closed, opened or so m e position in between) and penetr ation of outside air through leaks around windows, the m a in door, roof ve nts, em ergency exits and other nonspecific locations, depending on bus speed and wind speed. Air exchange rates m a y also be significan tly affected by other ch aracteristics of the bus incl uding differences in cons truc tion sp ecif ica tions for older bu ses versu s n e wer buses. Som e of t h ese characteris tics m a y increase as a bus ages (e.g. leaks). For this st udy, air exchange rate or ventilation rate tests w e re performed with the windows open and the windows closed at bus speeds of 0, 20, and 40 mph. Air exchange rates inside the b u s were m e asured by releas ing a S F 6 trac er ga s inside th e bus and monitoring the concentration of the gas over tim e

.
High concentration (5,000 or 10,000 ppm ) SF 6 cylind e r gas was used as the tracer. A plastic syr i n g e was f ille d with 10-30 cc of the SF 6 cylinder gas. The amount used was varied as a function of window position, bu s speed and cyli nder concentration

, to ob tain interior SF 6 concentratio ns within th e SF 6 analy zer operatin g ra nge in the availab l e experim e nt tim e. SF 6 was released from the s y ringe as a l i n e sour ce in t h e ai sl e of the cab in as th e ope ra tor wa lked from the bac k to the front of the bus. The operato r then walked back to the rear of the bus, using his body to help rapidly m i x the SF 6 inside the bus.

The SF 6 con centr ation was continuo usly m onitore d from the m i ddle of the bus at the top edge of the seats facing the ais l e. All 0 m ph tests were perf orm e d in the CE-CERT parking lot with the bus facing north. All the 20 and 40 mph tests w e re performed on Riverside Drive between Riverside and C o lton (except the CNG bus te sts). Riverside Drive was a four lane road with a poste d speed lim it of 45 mph, with m o re than two m iles between traffic stops and light traffic during the period we conducted the ventilation tests. A lthough there were variations from test to test, typically the test began with window s closed traveling north on Riverside Drive at 20 mph, followed by traveling south on Riverside Dr ive at 40 mph. The windows rem a ined closed for the duration of the test. A second test was perform e d in a sim ilar m a nner, again traveling north on R i verside D r iv e at 20 m ph, f o llow e d by trave ling s outh on R i verside D r ive at 40 m ph, this tim e with the wind ows open f o r the dura tio n of the te st. Except f o r the tes t s o n the CNG bus, ventilation tests were performed just prio r to starting the rural/suburban run. Due to equipm ent problem s, the ventilation tests at 20 and 40 m ph for the CNG bus were perform e d at the end of the rural suburban run on Grand Av enue from Diam ond Bar to Glendora and back. The speeds and window positions for these tes t s were sim ilar to those used for previous tests on Riverside Drive. The 0 mph tests were perfor med over periods ranging from ten m i nutes to over an hour. The 20 and 40 mph tests were perf ormed over periods of two to ten m i nutes.

For all tests, the am ount of SF 6 released resulted in concentrations beyond the instrum e nt’s full scale. Typically, after a br ief period beyond full scale (during which the SF 6 was also m i xing throughout the bus), the concentration of SF 6 dropped to the m onitor’s full scale. At this point the S F 6 decay rate was m easured and used to determ ine the air ex change ra te in the bus.

To determ ine the SF 6 exchange rate, we used the fo llowing equation, which assum e d bus ventilation rates are a first order process:

35(SF 6 ) t = (SF 6 ) 0 * [1 – exp (-t/ τ )] (4.1.3.1)

where (SF 6 ) 0 is the SF 6 concentratio n at tim e zero, (SF 6 ) t is the SF 6 concentration at tim e t, and τ is the tim e c onstant. Th e SF 6 rem o ved from the bus (and the pe rcentage of bus air exchanged) over tim e is 63%, 87%, 95%, 98 %, and 99% for tim e s equal to the first through fifth tim e constants, respectively. Although there were m a ny factors that controlled m i xing and exchange of air in the bus, inspec tion of the plots of SF 6 versus tim e indicated a first order decay provided

a reason ably accurate fit of the data.

The results of the ventilati on tests are presented in Tabl e 4.1.3.1, which shows the tim e constant, or the tim e required for 63% of the bus air to be exchanged. The tim e for essentially

com p lete exchange is three tim es (i.e. 95% excha nge) to five tim es (i.e. 99% exchange) longer. The shorter the tim e for air to exch ange, the h i g h er the ventilation rate. The am bient wind speed for all stationary ventilation te s t s w a s approx imately 1-3 m / sec. Th e ventilation ra te when th e bus was stationary with the windows closed vari ed from moderate for HE3 (under ten m i nute s for 63% of the bus air to be exch anged) to no detectab le ve ntilation dur ing th e f i f

t e e n to f o rtytwo m i nutes that the test was conducted (H E1, HE2, RE1, RE2, TO1, C NG). The ventilation rate was higher for windows open compared to wi ndows closed (for the sam e bus speed). The ventilation rate was also higher when the bus w a s driven at 40 m ph compared to 20 m ph. The tests showed there were a significant num ber of “leaks” in all buses tested. As can be seen in Table 4.1.3.1 (excluding RE2, windows open at 0 m ph), even with the windows closed, the ventilation rates for the buses traveling at 20 an d 40 m ph were higher than the ventilation rates for the sam e buses at 0 mph with the windows open.

Significant differences in the ventilation ra tes between bus es were observed. Because only a sing le set of ventilation tests was performe d for each bus, it is no t possible to determ ine if these differences were definitely due to differen ces between the buses, or if they were due to other factors, such as dif f eren t wind conditions when each b u s was tested. One ind i cation of the possible m a jor effect that wind speed (as opposed to bus speed) m a y have on ventilation rate

com e s from the windows open at 0 m ph test perf orm e d on the last bus. The CNG bus had a moderately low ventilation rate with windows open at 0 m ph (seven m i nutes for 63% of the air to be exchanged), while the other buses had hi gher ventilation rates, (b etween two and four

m i nutes for 63% of air to be exchanged) for 0 m ph. W e assum e that when the bus windows are open at 0 mph, any m a jor differences observe d are due to wind s p eed and not to unique characteristics of one bus com p ared to another. Hence, the m o st probable explanation for the low ventilation rate in the CNG bus with wi ndows open at 0 m ph was the calm wind conditions during the test. For each series of 0, 20 and 40 mph te sts, there was a factor of two to a factor of four difference between the bus with the highest ven tilation r a te to the bus with the lo west ventilation rate. Becaus e of the likely significan t role that w i nd contribu ted to th ese differences (especially for the 0 and 20 m ph t e sts), it is, in general, difficult to draw conclusions about possible bus-to-bus differences in ventilation rates. However, ai r ex chan ge rates were typically much higher with open windows and m u ch highe r at higher speeds, regardless of window position.

36WEEK#1234578BUSHE1HE2HE3RE1RE2TO1CNGResponse TimeResponse TimeResponse TimeResponse TimeResponse TimeResponse TimeResponse Time(First TimeConstant)(First TimeConstant)(First TimeConstant)(First TimeConstant)(First TimeConstant)(First TimeConstant)(First TimeConstant)TEST CONDITION(min:sec)(min:sec)(min:sec)(min:sec)(min:sec)(min:sec)(min:sec)Windows closed 0 mph> 15 min> 30 min09:47> 30 min> 30 min> 15 min> 42 minWindowsopen0mph02:3902:3803:1603:5700:4002:1807:00Windowsclosed20mphNA01:2001:5201:5603:3104:3802:00Windowsclosed40mphNA00:5200:3801:0502:0001:2201:21Windowsopen20mphNA00:3100:5800:4800:4700:2300:26Windowsopen40mphNA00:1600:2900:1700:3700:1200:23Number of windowsopened for "windowopen" condition at 0 mpheveryotherwindow opened7 cmall windows anddoor openedFour windowson both sides(8total) down 4inchesThree windowson both sides(6total) down 4inches3/4 of windowsopened 4 inches6 of 12of rightside and 7 of13on left sideopened 4 inchesSix windowsoneach side (12total) opened3inchesNumber of windowsopened for "windowopen" condition at 20and 40 mphFour windowson left sidedown 4 inchesThree windowson both sides(6total) down 4inchesThree windowson both sides(6total) down 4inches3/4 of windowsopened 4 inches6 of 12of rightside and 7 of13on left sideopened 4 inchesSix windowsoneach side (12total) opened3inchesTest notes for "windowsclosed" condition at 0mphone frontwindow andonerear windowdown 3 inchesTest notes for "windowsclosed" condition at 20and 40 mphDriver's windowopened 1/2 wayTable 4.1.3.1Ventilation time constants measured insidebuses at various speeds andfor open and closed windows (see text).374.1.4 Characterization and Justifi cation for the Selected School For this study we sought to identify a sc hool with a diverse st udent population drawn from various parts of Los Angeles, and which o ffered a broad range of travel distance, roadway type, and traffic congestion scenar ios associated with bus commutes. Due to resource constraints the present study was unable to test across a full range of possible co mmutes, m e teorological conditions, bus m a nufa cturers, m o de l years, school districts, geogr aphic 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 m a in studies was the Brentwood Science Magnet School (BSMS), located at740 Gretna Green W a y, in W e st Los Angeles. The BSMS was in Local District D of the LAUSD. It wa s a K-5 facility with a to tal enrollm e nt of 1,209 students in the 1999-200 0 school year. Becau se it was a m a gnet schoo l, BSMS was racially and ethnically diverse with a stud ent population dem ographic of approxim a tely 20% Asian, 20% African-Americ a n, 25% Hispan ic, and 35% Caucasian.

We felt the BSMS school provid ed high exposure scenarios a nd yet was located close to UCLA, allowing ready access and the opportu nity to op tim i ze tim e spent on acq u iring data. Also, becau se BSMS was a m a gnet school, it p r ovi ded a wide rang e of distan ce and congestion scenarios for its bu s co mmutes.

Considerab le in itial res earch concerning th e s e lected s c h ool, and its associated bus behavior, w a s conducted prior to the start of the pilot study, including a detailed analysis of routes, patterns, and travel tim e s for all n i neteen routes us ed by the BSMS. A relatio n ship was established with the school thr ough the school' s principal and its science advisor, and also through the LAUSD Transportation Branch and its Director, Antonio Rodriguez. The school principal, S h aron Katz, provided approval for conducting the pilot study at BSMS, while the LAUSD Transportation Branch pr ov ided th e de tailed rou t e sheets f o r all the bus co mmutes to and from this school. Addition a l su pport was provided by Dr. Angelo Bellom o, Director of the LAUS D En vironm ental Hea lth 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 t h e starting locations of each of the bus routes b r inging ch ildren to BSMS . Most of these rout es involved both freeways and residential arterials, includ ing those expected to have h eavy traffic congestion. Children were bused to BSMS fr om over 200 schools thr oughout the city, with an average comm ute time and distance of 85 minutes and 23 miles, respectively. Typicall y, 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 Gr een W a y on the west, Montana Avenue on the south, Bundy Avenue on the east and San Vicente Boulevar d on the north. W e characterized non-freeway traffic densities for thes e four arterials as low, low-m e dium, m e dium

, and m e dium-high, respectively, with the caveat that at the student drop-of f and pick-up tim es

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5 0 5 K i l o m et er s

Figure 4.1.4.1 Brentwood Science Magnet School bus route starting locations.

there was ad dition a l traf f i c cong estio n on Gretn a Green W a y f r om passenger veh i cles of parents, teachers and staff who were arriv i n g or dep a rting, as well as fr om the arrival or d e partu r e of school buses. Three of these streets were one lane each way

, while San Vicente Blv d. was two lanes each way with a median stri p of approxim a tely 20 feet width.

A key physical aspect of the school was a bloc k-length sidewalk, approxim ately ten feet wide, along the east side of Gret na Green W a y. Typically, all nineteen buses bringing students to the school lined up along this sidewalk. Figure 4.1.4.2 shows the la yout of the school and bus loading area. All schoo l bus lo adin g and unloadi ng took p l ace at th is sidewalk. In addition, there was head-in parkin g for teachers and staff (a nd perhaps parents ) on the west side of Gretn a Green W a y.

We acquired inform ation about loading/unlo ading activities in both the m o rning and early afterno on. Direct o b servation o f BSMS during five days in Decem ber 2000 and Septem ber and October 2001 was conducted and the following observations m a de. In the m o rning, buses arrived and parked on Gretna Gr een W a y, 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 com p liance with L AUSD regulations. Children quickly unloadedNOT TO S C A LE. ALL DIS T AN CES A R E APP R O X I M ATE

. Bung al o w s

Cl as s r o o m s

Of fi c e

Mo n t an a A v e

. B

u

n
d

y

A

v N Sa n Vi c e n t e B l vd

.

Pl a y g r ou n d

C

O

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N

T

R

Y

C

L

U

B Pa rk e d

Bus e s

Pa rk i n g Pa rk i n g
fo r st af f Ma i n

Wi n d
Direc t i o n E N T RAN CE

E N T RAN CE 300 m

.

e

Figure 4.1.4.2 Diagram of Brentwood Science Magnet School.

from the buses onto the sidewalk in front of th e school, where they waited until all children had unloaded from that bus. The children then walked as a group into the school through tw o entran ce ways. Buses arrived at d i fferent tim e s, so often children were waiting on the sidewalk when another bus pulled up to the curb. In gene ral, m o rning comm utes we re conducted with bus windows up, in part due to cooler m o rning tem p eratures.

40Photograph 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). Ag ain, each bus turned off its engine as soon as it parked. In the after noon, all buses had the windows pulled down about one quarter of the way. School ende d at 14:45, and children quickly boarded a total of nineteen buses. Of these, four w e re “sm a ll” and fifteen were “full size.” Typ i ca lly, ch ild ren were on the sidewalk no m o re than about five m i nutes before they b o arded, but on occasio n groups of children were on the sidewalk cons iderably longer. At about 15:00, the bus e ngines were turned on sim u ltaneously and generally all buses pu lled away from the curb by about 15:05 (See Photograph 8). In general, no e ngines were turned on before all buses were boarded. The g r eat

m a jority 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 af ternoon run, school bus ahead. b) Driving behind a school bus on an arterial street.

During the loading period, pare nts driving passenger vehicl es dropped off (AM) or picked up (P M) approxim ately two dozen student s on Gretna Green W a y. However, no childre n other than those taking the buses stood on the sidewalk in front of the school. A f e w children were picked up by parents who appeared to walk to the school. Observation of the departi n g buses in the after noon revealed several buses often “caravanned” at leas t part of the way on San Vicente, W i lshire and the San Dieg o Freeway (Interstate 405), whic h can lead to high exposures in the “following” buses (Rodes et. al. 1998).

4.1.5 Characterization and Jus t ificati on 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 ar eas of south-central L o s Angeles to west Los Angeles. The third was a route that was fift y percent rural and fifty percent suburban, and traveled from rural Riverside to Diam ond Bar in Los Angeles County. The principle criteri a used to s e lect these rou t es were that they be re presentative of bus routes in Southern California and that they also be representative of the wi de range of roadway types and traffic congestion scenarios encountered in the South C o ast Air Basin.

4.1.5.1 Urban Route One During our investigation of BSMS bus comm utes, the Director of the Transportation Branch at L AUSD provided th e co mplete rou t e sheets for all bus es serving the BS MS. There were 19 different bus routes asso ciated with the BSMS and these sheets provided the exact route followed, including the tim i ng and duration of the stops, and the num ber of children picked up or dropped off at each stop. As can be seen in Figure 4.1.4.1 (triangl es m a rk the starting locations of bus routes), students were bused from all pa rts of Los Angeles County to the BS MS, with bus routes covering extensive portions of South Ce ntral and East Los Angeles, Hollywood, the San

42Fernando Valley, and reaching as f a r south as Carson. Combined, these bus routes provided

com p rehensive coverage of Los Ange le s County west of the 710 Freeway.

In an effort to choose a bus route for the m a in study that provided an appropriate range of roadway types and traffic conditions represen tative of Los Angeles County comm utes i n particular, and dense urban traffi c congestion in general, we inve stigated a selected num b er of BSMS bus r outes. Four routes, including the rout e used during the pilot study, were evaluated. Since the pilot study route originated in south ce ntral Los Angeles, we selected one com p arison route originating in north Lo s Angeles County (i.e. San Fern ando Valley, Route No. 3222) and one originating in the south (i.e. Carson/Long B each area, R oute No. 3235). This was done to assess potential differences in route characteristics due to geographic location. The third

com p arison 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 sim ilar geographic

areas. Each of these three rou t es in corporated b o th freeway and surface streets, and includ ed a variety of traffic congestion scen arios. Surface streets alon g each route were categorized into residential, m i nor arterial, and m a jor arterials using Caltrans a nnual average daily traffic count data from 1998 and 1999.

Table 4.1.5.1 shows the com p arison of these routes, and includes the percentage (in distan ce) for each of the roadway types listed abo v e, as well as the percen tage of freeway versus surface streets. As can b e seen from this table, R oute No. 3215, the rou t e used in the pilot study, had a sim ilar m i x of roa d way types and traffic cond itions co mpared with the other three routes.

Table 4.1.5.1 Com p arison of the distribution of roadwa y types on selected BSMS bus routes.

Rou t e N o

. 32 15 (Pilot Stud y Rou t e) Rou t e No . 32 02 Route N o

. 322 2 R o u te No . 3 235

< 5

, 00 0 1 0% 8 % 7% 9 %

5

, 00 0 - 25, 000 18 % 20% 4 2 % 2 7 %

25 ,00 1 - 100 ,00 0 34 % 38% 2 8 % 4 4 %

> 10 0, 0 0 0 3 8% 3 3 % 22 % 2 0%

Freeway 3 8% 3 5 % 22 % 2 6%

S u rface St reet s 6 2% 6 5 % 78 % 7 4% Traff i c Counts (A nnu al Average Dai l y Traffic)

Roa d w a y Type P e rcent of Bus R o ut e Dis t a n ce

1 For a d e fin itio n of ro adway typ e b y traffic co un t see Tab l e 4

. 1

. 5

. 2

.

In addition, Table 4.1.5.2 shows the am ount of tim e spent on each roadway type for Bus Route 3215. Approximately 25 % o f the tim e during th is b u s commute was spent o n each of th e four m a jor roadway types: resident ial, m i nor arteria l , m a jor arte ri al, freeway. This table clearly dem onstrates Route No. 3215 provided a sufficien t distribution of roadway types and traffic conditions to explore the effects of these fact ors on children’s exposure during bus comm utes.

43Moreover, based on the com p arison in Table 4.1.5.1, we concluded this ro ute contained roadway types and tra f f i c conditio ns that wer e com p arable to bus commutes in oth e r par t s of Los Angeles.

Our rationale for focusing prim arily on a singl e route during the m a in stu dy was to allow

us to m o re unam b iguously investigate the im porta nce of other variables related to pollutant exposures during bus commutes, without th e inevitable com p lica tions and confounders introduced by using m o r e than one route. Specif i cally, we wanted to elu c idate the ef fects of key variab les su ch as fuel ty pe and em ission control status of the bus, hence it was im portant to hold constant the nature of the route. Otherwise, potential d i fferences in traffic density and other route-related variables, even if not large in m o st cases, m a y have unnecessarily complicated and even confounded com p arisons between fuel type and vehicle em ission status. It is axiom a tic in any scientific investigation that one would like to c ontrol all but one variable at a time in order to elucidate the effect s of the rem a ining changed variable (e .g. fuel type). It was challenging enough to establish the effects a nd magnitudes of changing bus em issions and fuel types without the com p lication of using m a ny diffe rent routes.

Table 4.1.5.2 Distribution of roadway types of BSMS bus route No. 3215.

P e rcent of Ti me Du ri ng B u s Ru n

A nnua l Ave r age D a ily Tr a f f i c R o a dw ay Typ e Ro ute No . 32 15 ( P ilot Stu dy Route )

< 5,000 Reside ntia l 2 0%

5, 00 0 - 25 ,0 0 0 Mi no r Art e ri al 20 %

2 5 ,0 01 - 1 00,00 0 M ajor Ar te r i al 3 0 %

> 1 00,000 Fr ee wa ys 2 5 %

Fr e e wa y 2 5%

S u rface S t reet s 7 5 %

The broader issue con cerning rou t e choice and num b er of routes was one of how “representative” were the resu lting data. Th is is an issue inherent in any v e hicle-related study o f this kind. In general, there ar e never sufficient resources (e.g. i ndividual experim e ntal runs) to conduct an investigation that is truly representativ e of the enorm ous variability in, for exam ple, vehicle type and em issions, or roadway type and tr affic characteristics. This is esp ecially true f o r a reg i on like southe r n Calif ornia with m o re th an 10 m illion vehic l es and a geog r a phic area of more than 10,000 square kilom e ters. Under th ese constraints the st udy design necessarily involved severe tradeoffs and conflicting or com p eting objectives. The present school bus study was no exception, given that only a fixed num ber of runs were possible within the funding, staff and equipm ent resources available. For every ne w run added, som e other experim e nt had to be elim inated. Thus, the num b er of routes inve stigated could not have been expanded greatly without sacrificing som e other im portant aspect of the study. Moreover, we felt there were

4445 significant scientific advantages in leveraging the pilot study data by continuing to use the sam e bus route in the m a in study.

For all of the reasons given above, Route No . 3215 (the sam e route used in the pilot study) was selected as m o st appropriate for the prim ary focus of the m a in study. A m a p of this route and its bus stops is pres ented in Figure 4.1.5.1. T h e rout e was driven wi th the fully instrum e nted bus from the fifth stop to BSM S in the morning, and the reverse route in the afternoon. This som e what shortened route rela tive to the pilot study was 18 m iles long and provided approxim a tely one hour of comm ute tim e. The per centage of tim e spent on the f r eeways in creas ed to a pproxim a tely 40% of the tota l run tim e with the trunca t ed route. This route involv e d a wide v a riety of traffic conditio ns and road way types, ranging from single lan e residen t ial s t reets with little or no traffic, to heavily conges t ed, m u lti-lane surface s t reets which had high traffic densities during ru s h hour. Addition a lly, p o rtions of th is route trav eled on th e two m o st heavily congested freeways in the United States (I-405 and I-10) during rush hour traffic. The vehicle m i x on these freeways, obs erved during the pilot and m a in studies, included a high percentage of m e dium and heavy duty diesel vehicles, including trucks and other buses. Particularly during the morning co mmutes, the percentage of dies el vehicles on the freeways was high com p ared with surf ace streets.

# Y #

Y

# Y

# Y # Y r % % BSM S

V e

r m o n

t H o o v e

r

Sl a u s o n E

x p

o s i t i o n

M a

i n W

e e m e s E l e m en t a r y S c ho ol B u s S t o p

N o

r m a n d

i e 51 s t I- 1 0

I- 4 0

5

I- 1 1 0 N

1 0 1 2 K i l o m e t e r s

L o s

A nge l e s S t r eet s O t h e r f r eew ay

s I n

te r s ta te 1 1 0 I n

te r s ta te 1 0 I n

te r s ta te 4 0 5 P r im ar y U r ban R out e N o

. 32 15 ( U 1) # Y u s St o p s r S M S % Q M D M o ni t o r i ng S i t e s

B B A

Figure 4.1.5.1 Prim ary urban bus route No. 3215.

The variety of neighborhoods along the prim ary r oute, from the west side to south central Los Angeles, also provided a varied m i x of vehi cle types, ranging from predom inantly passenger vehicles and light trucks close to Brentwood, to ol der cars and m o re transit and school buses insouth central Los Angeles. In addition, this route traveled through i nner city neighborhoods,

m a king stops in location s we judged to have a racial/ethnic com position of approxim a tely 80% Hispanic, 15% African Am erican, and 5% Asian or other, thus having had relevance to concerns about environm ental justice.

Measurem ents were m a de on the prim ary route during twenty bus runs in April, May and June 2002, consisting of ten m o rning and ten afte rnoon commutes. Each m o rning run started a t 6:35 at the fifth pick-up location, and ended at approxim a tely 7:40 at BSMS. During the afternoon runs, the bus left BSMS at about 15: 05 and reached the fifth drop-off location at approxim a tely 16:10. At each bus stop, the b u s pulled u p to the curb, opened the doors an d waited for one m i nute before driv ing away, to sim u late the condi tions of children loading or unloading from the bus (Photogr aph 9). All the windows on the bus were closed during the morning runs, while during the afternoon runs, a pproxim a tely half the windows were partially opened, to simulate conditions observed on in-use school buses.

4.1.5.2 Urban Route Two While we determ ined the route used for the pilot study was appropriate for the great

m a jority of the m a in study bus runs, we also be lieved it w ould be interesting to investigate a contrast between the prim ary route and another ur ba n r o ute

. Ba s e d on our e v a l ua t i o n of t h e BSMS bus routes, we selected a second urban rout e for this purpose. We concluded that because the prim ary urban route extensively covered an appropriate range of ur ban driving scenarios, including travel on the two m o st heavily congested freew ays in the U.S. and through heavily congested inner-city neighborhoods, an interesting contrast woul d be an urban route which covered a wider geographic area, traveled excl usively on surface streets, and included m o re industrialized areas of the city.

Photograph 9 . Another school bus passi ng our idling bus at a bus stop during a m o rning run.

4647 Although the m a jority of the bus routes from the BSMS travel on freeways for approxim a tely 20 to 40 percent of th eir total dis t ance, as dis c ussed earlier, we iden tified a route which only travels on su rface streets for approxim a te ly one hour of its comm ute time, while also covering a different geogra phic area than the route selected as the prim ary urban route. The route selected was the BSMS Route No.3235 (Figur e 4.1.5.2). In the m o rning, this route started in Carson, just north of Long Beach, and traveled through in dustrial areas with a hig h percen tage of heavy duty diesel traffic. The r oute c ontinued north through nei ghborhoods of Rosewood, Gardena, Hawthorne, Inglewood and Hyde Park, and included m i ddle to lower middle income neighborhoods with well m a intained m e dium de nsity housing, to higher density econom i callydisadvantaged neighborhoods. This portion of th e 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 m a in st udy, we truncated the route at the final stop because th is provided us with over one hour of c o mm ute ti me (com parable to the prim ary route) through a variety of neighborhoods and traffic densities, and in cluded only travel on surface
streets. W e observed the raci al/ethnic com position of the neighborhoods covered by this route to be different from the prim ary route, consisti ng of approxim a tely 80 – 90% African Am erican, with the rem a ining portion prim arily Hispanic. The com b ination of the diverse neighborhoods covered, absence of freeway travel, and a poten tially different m i x of congestion scenarios on this route com p ared with Route No. 3215 allowed for interesting contrasts between bus routes for the m a in study.

# Y # Y # Y # Y #

Y # Y # Y #

Y # Y # Y # Y

R o se cr a n s E l S e g u n d o

C e n t u r y M a n c h e s t e r

W

e s
t e

r n

V a n N e s s A v a

l o n C e n

t r a

l I m pe r i al V e

r m o n

t

I- 7 1 0 I- 1 1 0 I- 4 0 5

Lo s A n g e l e s S t r e e t s Ot h e r f r e e w a y s In te r s ta te 11 0 In te r s ta te 40 5 S e c o nd U r b a n R o u t e N o . 3 2 3 5 ( U 2) # Y U2 B u s S t o p s N

1012 K i l o m e t e r s

Figure 4.1.5.2 Second urban bus route No. 3235.Four exposure test runs were done on the sec ond urban route, two in the morning and two in the afternoon on May 29 and 30, 2002. The morning runs started at appr oxim a tely 6:15 at the first stop in Carson, and finished at approxim a tely 7:30 at the fi nal stop. In the afternoon, we drove the route in reverse, leaving from the morning’s final stop at approxim a tely 15:30 and arriv i ng at the Carson bus stop at ap proxim a tely 17:00. All conditions on the bus during the runs on the second urban rout e were identical to those on the prim ary ro ute, including window position, the use of run logs to characte rize the route, and pollutants m easured.

4.1.5.3 Rural/Suburban Route In addition to the prim ary and secondary ur ban routes, which covered a wide range of urban driv in g condition s , we felt it was im portant to evalu a te the concentrations in s i de school buses driving in low density rural and suburban neighborhoods. W e expected these driving conditions would provide us with a num b er of scenarios not encounter ed in urban areas, including driving for longer peri ods of tim e at a constant speed and reduced idling tim e , and with constant conditions (e.g. less rapidly changing com position of surrounding vehicles). In addition, we expected lower roadway concentr ations of traffic-related pollutants on the rural/suburb a n route due to greatly reduced traffic congestion would provid e addition a l inform ation for our comparison between different bus engine technologies and fuel types, and the influence on concentrations inside the bus.

The criter ia f o r the selec tion of the r u ral and sub u rban routes were are a s of little or n o heavy duty truck traffic, no freeways within one m ile of the route and lower housing and population densities com p ared to the prim ary urban route. The bus routes from the BSMS were not adequate to m eet our needs for low-density driving conditions because they cover areas of Los Angeles County w h ich are prim arily high density, urban neighborhoods. Even the m o re suburban routes in the San Fern ando Valley to and from the BSMS traveled adjacent to m a jor traffic corridors (such as Intersta te 405 and Interstate 5) and in cluded heavily congested arterials (e.g. Ventura Blvd) during portions of their com m ute.

However, because the b u ses were o u tfitted wi th our sam p ling equipm ent at CE-CERT in Riverside before being brought to U C LA for the urban bus runs, we took advantage of the low density rural and suburban area s c l ose to Riv e rside to ob tain the app r opria te rur a l/subu rban route. The route selected (Figure 4.1.5.3) provi ded us with about one hour of commute tim e a nd traveled first through predom inantly rural areas, a nd 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 Lom a a nd Chino, with approxim a tely two- thirds of the route passing by dairy farm s and through undeveloped areas (see Photograph 10). T h is section of the route included predom inantly light traffic (split even ly between trucks and passenger cars) on roads with two lanes in each directi on. The route b ecam e m o re suburban as it turned n o rth through Chino Hills and Diam ond Bar, with areas of res i dential hous ing and sm a ll busin esses (e.g., gas stations and strip m a lls).

Traffic conditions were heavier in the suburba n portion of the route, but included very few heavy duty trucks. The route ended at the 60 Freeway in Diam ond Bar.

48L o s A n g e le s C oun ty S an B e r n a r di n o C o unt y

R i v e r s i d e C o unt y O r ange C o un t y R u r a l/ S ubur ba n B u s R o ute N

Figure 4.1.5.3 Rural/suburban bus route.

(a) (b)

Photograph 10 . Rural (a) and suburban (b) segm ents of the rural/suburban route.

49Due to logistical considerations, the exposure test runs on the rura l/suburban route were conducted in the aftern oon. One r un on the rural/suburban route was conducted for each bus evaluated in the m a in study, generally one day prio r to the test runs on the prim ary urban route for the sam e bus. A total of seven rural/suburba n exposure test runs were com p leted, one during each week of the m a in study begin n ing the week of April 22, and ending the week of June 11 (not including the week of May 29). The start tim e s for the ru ral/suburban test r uns varied from approxim a tely 11:30 to 15:00, although m o st st arted after 12:30. For each test on the rural/suburban route, approxim a tely half the windows on the bus were partially opened. The video cam era was operated on three of the seven ru ral test runs and notes about traffic events during the rural/suburban runs were m a intain ed in a notebook by the field technician.

4.1.6 Characterization and Justificat ion for the Selected Bus Stop Concentrations observed in the pilot st udy during bus commutes were generally significantly higher than those at the Verm ont El em entary 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 com b ination with our observations children ty pically spent only short periods of tim e waiting at bus stops or in lo ading/unloading zones, we recommended the m a in study focus prim arily on bus commutes. W e determ ined, however, tha t a lim ited num b er of additio nal m easurem ents at a second bus stop during the m a in study would allo w us to better define potential exposures associa t ed with th is microenv ironm e nt. These additional runs were recomm ended in part because a fairly high -em itting bus was involved in the pilo t s t udy. W e felt a lower-em itting bus, of the kinds used in the m a in study, m i ght e xhibit low e r average e xposure ratios, for the commute versus the bus stop, leading to a greater relative contribution fr om bus st ops to total

exposure.

Since this study was designed to include ra nge-finding for high-end exposure conditions, the second bus stop along the prim ary route was sele cted for factors such as high adjacent traffic, long bus idle tim e, and where children were waiti ng for long periods of tim e to be picked up by the bus. By observation we determ ined which stops on the inner city portion of the prim ary route involved these high exposur e scenarios. By following seve ral buses over different routes on several different days we determ ined the range of relevant variab les, s u ch as the tim e spent by the bus at a given stop and whether the bus id led or turned off its engine. Logistical considerations such as site security and pos itioning of the m onitoring equipm ent were also factors in clu d ed in select ing the bus stop location.

Based on these criteria, the second bus stop selected along the prim ary route was the
Wee m es Elem entary School, located on 36th Place, three blocks south of Verm ont Avenue near downtown Los Angeles. Sim ilar to the first bus stop m easured dur ing the pilot study, this stop was characterized by increasing traf fic 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 star ted and after it let out. The av erage tim e spent by a bus at stops was 60 seconds, with the engine of the bus rem a ining on af ter the bus pulled up to the curb and children loaded/unloaded. In add ition, this stop had an appropriate place to set up the m onitoring equipm ent, and was located in a relatively secure area.

50Measurem ents at the bus stop were m a de during one m o rning and one afternoon on May 23, 2002. Each m easurem ent period started at least 30 m i nutes before the expected arrival tim e of the BSMS bus, and continued fo r at leas t 30 m i nutes after that tim e . The fully in strum e nted 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 instrum e nted bus to drop off/pick-up children at the school.

4.1.7 Characterization of W i ndow Position Test The objectiv e of the win dow positio n tes t s was to estab lish the effect of window positio n on concentrations inside the bus cabin. Two sets of real tim e instruments were used, one set

sam p led inside the bus and the other sam p le d imm e diately outside the bus. During window position tests, we alternated betw een open a nd closed windows for designated intervals. However, during all other bus co mmutes, the position of the windows was fixed, with the windows closed during all m o rning commutes, and the w i ndows open during all afternoon

commutes.

We selected the I-405 (north/s outh direction) between W ils hire and Century Boulevards as the route for the window position tests. Altho ugh there co uld have been a m bient air pollutant concentration gradients along this north/south route, we expected them to be sm aller than those observed for east/west r outes. Specifically, during the pilot study we observed background concentratio ns tended to increase as the bus approached the inner city (head i n g east) and decreased approaching the W e stside and Brentw ood (heading west), likely due to changes in traffic density and com p osition, as well as m e teor ological effects. The I-405 runs approxim a tely north/sou th, with m o re unif o rm traf f i c densities and m e teorological conditions, therefore, we antic ipated rela tive l y m i nor am bient a i r po llu ta nt concentration grad ients and m e asurem ents taken outside the bus duri ng the tes t allowed u s to characte r iz e any concentration gradients along the rou t e.

Typically, we began a window position tes t o n I-405 at W ilshire Boulevard trav eling south to Century Boulevard, which took appr oxim ately 20 m i nutes, followed by the m o re congested return back up the I-405 north for about 40 m i nutes, for a total sam p ling tim e of one hour. During the test we opene d the windows for eight m i nutes, then closed the windows for eight m i nutes, alternating between the two positions every eight m i nutes for the duration of the test. This amount of tim e was short enough to allow several test s under sim ilar traf fic conditions (i.e., north and south direction s ) but long enough to allow stabilization of condition s inside the bus. During open window ti m e s, on the driver’s side of the bus, every other window was opened about ten inches, while on the other side of th e bus, where the instrum e nts were located, every other window was opened only two inches.

The window position tests were conducted immediately following the m o rning bus commutes on May 8, and June 5 and 6, 2002, between 8:15 and 9:15. Diffe rent average speeds along the ro ute allowed us to test the effect of window position for different ventilation rates inside the bus cabin.

514.2 Data Analysis Methods The data collected during this study can be cat egorized as continuous , non-stationary and determ inistic. W e used tim e-series analysis techniques as well as c onventional statistical procedures to analyze th e data set. This section presents a detailed explanation of all procedures used to analyze the m a in study data.

4.2.1 Goals of Tim e Series Analysis Tim e series analys is can be used to accom p lish two different goals: iden tification of the nature of the phenom enon represented by the se quence, and the forecasting or prediction of future values of the tim e series variable. In this study, we focused on the first objective.

The presence of stochastic events that m odifi ed the genera l p a tte rn occu rr ed constan t ly in our tim e series (e.g., en counters with other HD dies el vehicles; random stops). In addition, the data did not exhibit determ inistic cyclic patter ns. Finally, the concentr ations m easured by the instrum e nts were due to the contribution of “b ackground pollution” concentrations as well as prim ary em i ssions from vehicles.

Cross correlation (establishm e nt of r e lationshi ps between different tim e series and their im plications) was used to establish precision be tween paired instrum e nts m easuring the sam e pollutant simultaneously and to identify correla tions between different pollutants (Section 5.1.3).

4.2.2 Data Analysis Techniques To provide a m easure of the i m portance of a given microenvironm e nt in term s of exposure, we calculated exposure factors for school bus commute s and bus stops and loading/unloading zone (from the pilot study). An exposure factor was de fined as the product of the ave r age concentratio n f o r a spe c if ic pollu tant m u ltiplied by the tim e spen t by children in that

m i croenvironm ent.

To determ ine the effects of fixed va riables on concentrations inside the bus during each

run, param e tric and nonparam e tric statistical tools and graphical m e thods were used to identify variables governing exposure during bus commutes (Section 5.3). The data from different runs were grouped into categories by va riable types. The objectiv es we re to understand t h e c ontr a st s defined by the study' s experim e ntal design as well as the relativ e im portance of the variables (e.g. type of bus, type of route, windows position, tim e of day, position with in the cabin, type of roadway). Basic descriptive statistics, such as the m e dian and interquartile ranges, and the 95% confidence intervals were calculated for the g r o upe d data. Graphical repr esentations were used to desc rib e the con t ra st between d a ta s e ts, including cumulative freq uency distributions and boxplots.

Tests for au tocorrelation were applied, to assu re the validity of subsequent statistical procedures. Since the concentratio n at a part icular tim e is dependent on the concentration a period of tim e before, the consideration of autoco rrelation was m a nda tory in the data analysis. The tim e-la g (k) with in which th ere is cor r e l at ion was established for several data sets. Correlograms (a p l ot of the au tocorrelation co efficient (r k ) in term s of the lag (k)) provided a visual inspection of a range of co rrelation coef ficients at relevan t tim e lags so tha t sign if icant values were identif ied. An r k value of +-2/(N) 1/2 denotes a significant dif f erence from zero and

52signifies the presence of autocorrelation (Norusis , 2002). Note also that as k becom e s larger, r k becom e s smaller.

Finally, we included more detailed analysis for a lim ited num b er of bus runs, which involved identification of events influencing real- tim e concentrations inside the bus during a run. This effort included the m o st com p rehensive anal ysis and was perform e d for a selected se t of runs. This included a description of ev ents (including a pi ctu r e gallery); an explanation/correlation of peak pollu tant concentrations inside the bus with events (e.g. idling or driving behind another diesel bus ) using video-cam era records a nd activity logs; analysis of

m e teorological data and their eff ects on the selected runs; and com p ilation of selected video clips to dem onstrate m a jor events during runs.

Our goal f o r this ana l y s is was to be as m e thodical and consistent as possible. W e developed a specific protocol f o r iden tif ic ation of pea k concentra tions, as w e ll as th eir correlation with external events recorded on th e video-cam e ra and/or the activity log. This for m ality was necess a ry as the ass e ssm ent and interp retation 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 m o unted at the front of the test bus to r e cord traffic conditions and other exposure-related events occurring in front of a nd adjacent to the test bus during most of the m easure m en t periods. All video camera reco rds (created for 28 out of 36 runs) were digitized into MPEG form at, which is considered state-of-the-art for long-term preservation and future m i gration to new for m a t s. Selected videotape records were carefully

exam ined to relate peaks in pollu tant concentrations with traffic conditions, type of vehicle being followed, and other variables that m i ght affect th e concentrations inside the cabin of the bus.

4.2.3.1 Definitions In prin ciple, a peak occu rs when the concen tration tim e series changes behavior during a short period, exhibiting prom inent high values com p ar ed with the baseline. This is not the same as an outlier, which is defined as measurem ents that are ex trem ely large or sm all relative to the

rem a ining data and, therefore, are suspected of m i srepresenting the population from which they were collected (U.S. EPA, 2000). See Figure 4.2.3.1 for a s c hem a tic representation of a peak, a n outlier, and the baseline.

4.2.3.2 Data Generated from Videotapes Two basic approaches can be taken when linki n g tim e series concentrations with ev ents record ed by the video cam era. The first m e t hod includes a for m al definition o f relevan t concentration behavior in a tim e series with whic h one can try to define what constitutes a p e ak using a form al and system atic procedure and atte m p t to explain such a peak by ass o ciation with
an event. For exam pl e, the use of ti m e -ser ies intervention analysis (McDowall et al., 1980)

m a the m atically determ ines if an event has an im pact on a tim e series. This m e thod uses hypothesis-testing in conjunction with ARIMA models (Box and Jenkins, 1976; B ox and Tiao, 1975).

53The 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 genera ted by several events, and one event m a y cause a signal that lasts for more than a few seconds. These concepts are illustrated in F i gure 4.2.3.2. This tim e series corresponds to the PAH concentrations m easured in the rear of the bus during

T i m e C onc e n t r a t i on S p eci es i

P eak O u tlie r

F i g u r e X

. S c h e m a tic r e p r e s e n ta tio n o f a tim e s e r i e s

. N o te th a t o u tlie r s

r e f e r to d i s c r e te in s t a n ta n e o u s d a ta p o i n t s w h ile p e a k s r e f e r to in te r v a l s

o f

tim e

. B a s e lin e

Figure 4.2.3.1 Exam ple of a peak, an outlier and the baseline.

P A H

- Re a r ( R U N 2 3 - AM - R E 2

- U2 )

0

. 0 0 0 0 10 0. 0 0 0 0 20 0. 0 0 0 0 30 0. 0 0 0 0 40 0. 0 0 0 0 50 0. 0 0 0 0 60 0. 0 0 0 0 70 0. 0 0 0 0

0 6 : 2 1: 3 6 06 : 2 8 : 4 8 0 6 : 3 6: 0 0 06 : 4 3 : 1 2 0 6 : 5 0 : 2 4 0 6 : 5 7 : 3 6 0 7 : 0 4: 48 07 : 1 2: 0 0 07 : 1 9 : 1 2 0 7 : 2 6 : 2 4 0 7 : 3 3: 36 07 : 4 0: 4 8

Ti m eConcentration

P AH

- Re a r ( R U N 2 3 - AM - R E 2

- U2 )

0 10 0 20 0 30 0 40 0 50 0 60 0 70 0

0 6 : 3 2 : 00 06 : 3 2: 1 7 0 6 : 3 2: 35 0 6 : 3 2 : 5 2 06 : 3 3: 0 9 0 6 : 3 3 : 2 6 06 : 3 3 : 4 4 0 6 : 3 4 : 01 0 6 : 3 4: 1 8

Ti m eC oncentration

Fo llo w i n g HD
Di es el Truck Die s el truc k

pu l l e d aw a y

B u s sto p 7 00

6 00

4 00

3 00

2 00 5 00

0 1 00

Figure 4.2.3.2 PAH ti m e s e ries for Run 23. Lower panel shows circled peak expanded and resolved into two peaks.

54Run 23 whe r e a m a jor peak can be observed at th e beginning of the run. After analyzing the

events occu rring dur ing this peak, we were able to divide the peak into three distinct regions. The first region showed a m onotonous increasing concentration truncated at about 6:32:40, followed by a decrease for several tens of seconds , and was associated with th e presence of a heavy duty diesel truck. W e st ar ted f

o llowing this veh i cle a t 6: 32:00 and continued following it until 6 : 32:4 0 (inc reas in g trend ) where a red li ght allowed the truck to pull aw ay f r om us (truncated signal). The second region included a nother increasing concentration that reached a

m a xi m u m value at about 6:33: 00. During this period, we were again following a heavy duty diesel truck. The third region show ed a flat beha vior that lasted for about thirty seconds. At 6:33:00 we stopped at a sc hool bus stop where we stayed for a bout one m i nute (fla t line). This

exam ple illustra t es ho w m u ltiple sources ca n c ontr i bute to a s i n g le peak c oncentr ation s

. Therefore, to properly correlate an d explain p eak concentrations with events we attem p ted to describ e all relev a nt eve n ts and cha r acte r is tics during a run using the prot ocol described below.

4.2.3.3 Videotape A n alysis P r otocol The goal of our videotape analysis was to ga in a better understanding of the factors that affect pollutant concentrations inside the bus, such as the effect of surrounding vehicles in

com b ination with the co ntribu tion f

r om the bus ’ s own exhaust for different fuel types and engine after-treatment t echnology. Based on the experience obt ained during the p ilot study and the results of the initial analysis of the videotap es from the m a in study, a set of events or charac ter i stics with the potentia l to inf l uence in-vehicle concentrations was defined. These events or characteristics were the basis for the final videotape analysis during the m a in study, and included the following:

1. Presence of any diesel-p owered vehicle in front of or adjacent to the test bus. Only those vehicles within approxim ately three car length s (based on a qualitativ e

assessm ent of distance) were considered.
2. Exhaust location of the vehicle being followed (high, low).
3. Presence of visible em issions or sm ell of exhaust from the vehicle being f o llowed, or from the test bus.
4. Idling at red lights, stop signs, in traf fic or at bus stops.
5. Level of traf fic congestion (heavy, moderate, light).
6. Roadway type (freeway, m a jor/m i nor ar terial, sm all resid e ntial streets).
7. The direction the test bus was f acing (e.g. north, northeast, etc.).

As discussed previously, we used the ten- second concentration data (tim e resolution estab lished for the data acquisition system in th e field) for each pollu tant ins i de the test bu s during each run for the following analyses. Using prim arily th e videotap ed record of an exposure run, which showed the view of the road in front of th e test bus, we system atically identified the events and characteristics (descr ibed above) that occurred in the area surrounding the test bus during each ten second period for a selected num b er of runs. W e also used the activity logs created duri ng the runs to supplem ent the video r ecord. The use of these logs was

necessary s i nce im portant even ts were no t alway s eas ily visible, in part due to the q u ality of the digitized videotape records (e.g., smoke com i ng out of an exhaust pipe), but were noted by the

55field technician during the run and recorded in the run logs. In a ddition

, there were events tha t could not be identified by the videot ape records (e.g., dies el exhaust odors).

Every ten-second tim e interval during a given run was defined by the

events /characteristics th at occu rred during the interval. An ev ent/characteristic (based on the criteria noted above) w a s assigned to a ten-s econd tim e interval if th e event/characteristic occurred for at least five seconds during that inte rval (50% of the gap). I f it continued for more than ten seconds, the event was assigned to all in tervals during which it occurred. For exam ple, if a diesel truck was followed for 30 seconds, this event was assigned to three tim e intervals. It was also possible for m o re than one event to o ccur during a given ten-se cond interval. If the

events we re dif f e rent, m u ltip le a ssig n m e nts wer e m a de. For exam ple, if the tes t bu s was id ling behind another school bus at a traffic light for one interval, both followi ng a diesel vehicle and idling wer e assigned to that tim e inte rval. However, if two different diesel vehicles were in front of the test bus within three car lengths, only the closest one was assi gned to the in terva l

.
An assignment of the type of vehicle be ing followed was m a de for every ten-second interval during a run. Vehicles we re classified into one of fi ve vehicle types: heavy-duty diesel truck (3-5 axles), m e dium 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 em issions or odors was noted when possible.

Because of the subjective natu re o f the vide o t ape ana l ysis , every effort was m a de to assure consistent, repeatable and accurate ass i gn m e nts of events were made. The distance to the vehicle in front was the m o st subjective po rtion of the analysis. Vehicles m o re than approxim a tely three car lengths (about sixty feet) away from t h e test bus were not included in the event assign m e nts. In order to validate this criterion, each run was analyzed by two different individuals, and any discrepancie s b e tween the results were carefully s c rutinized before a final assignm ent was m a de to assure consistency. In addition, assignm ents of idling were validated using the bus’s speed (also m easur ed in real-tim e during every run) . Finally, to assure unbiased assignm ent of events/characteristics based sole ly on the criteria described above, pollutant concentrations were not consider ed until after the video t ap es ha d b een reviewed and all even t assignm ents m a de.

4.2.3.4 Linking Events/Characteris tics and Concentrations The f i nal s t e p was to id e n tif y signif i cant co ncentration differences between tim e periods associated with each type of event or characte ristic. F o r exam ple, we were interes t ed in describing any difference found between concentra tions in the bus when idling, when traveling behind another diesel vehicle, betw een vehicles followed which had high or low exhaust, or when not following any diesel vehicles. W e com b ined all the tim e periods with a give n event/characteristic as sig n m e nt, such as when following a diesel schoo l b u s, or beh i n d a heavy duty diesel vehicle, and m a de co mparisons of pollutant concentr ation s assoc i ated with tho s e events. In addition, we produced graphs sim ilar to Figure 4.2.3.2, repres enting the tim e series for a given pollutant and a set of explanatory te xtboxes indicating the type of events associated with the real-tim e concentration behavior.

565.0 RESULTS AND DISCUSSION

Our discussion of the results from this st udy begins with a brief assessm ent of our

m easure m ent m e thods. This involved both an asse ssm ent of the (a) accu racy of our instrum e nts, as determ ined by external aud its or best estim ates for those instrum e nts for which known standards were not available; and (b) an asse ssm ent of precision, based on m easurem ents from paired instrum e nts.

Although these m easurem ent m e thod evaluations were im portant, the prim ary approach to data analysis used in this study, and upon w h ich the m a jority of our im portant findings were based, relied on the relative concentration differe nces m easured during di fferent exposure runs using the sam e set of instrum e nts. Specifica lly, all the m easurem ents m a de during the m a in study that w e re used to evaluate the importance o f test bus ty pe, roadway type, windo w position, surrounding traffic and events, and the bus stop vs. comm ute m i croenvironm ents, were m a de with the sa m e set of instrum e nts. These comparisons r e lied on th e assum p tion the same instrum e nt operated in the sam e m a nner during th e course of the eight weeks of the m a in study.

In addition, a lim ited num b er of e xperim e nt s were done in which m e asurem ents were taken using paired instrum e nts, inclu d ing th e co mparison of the fr ont vs. the back of the bus, and the inside vs. the outside of the bus . The results of our pre c ision estim ates were important for these com p arisons, and only those instrum e nt pa ir s with adequate pr eci sion (as described in Section 5.1.3) were used for these comparisons.

It is im portant to recognize in interpreting results presented in this chapter that, as shown in Table 5.1, with one exception (HE3), all fr ont/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

m a de only for the trap -o utf itted a nd CNG buses (Runs 27 to 36).

As m e ntioned before, throughout this report, P M 2.5 data are from the Harvard Impactor integ r ated sampler unles s stated otherwise (i.e., data from the DustTrak instrum e nt)

5.1 Assessm ent of Measurem ent Methods 5.1.1 Overview In this study an i m portant objective was to compare m easurem ents made with pairs of instrum e nts for each pollutant for con t ras t s such as inside/outs i de and front/rear of the test bus.

To evaluate such com p arisons, the statisti cal variability of th e m easurem ents under colloc a ted s a m p ling conditions was needed to d e te rm ine the probability at a s t ated s i gnif i canc e level, tha t the paired m easurem ents were s i gnificantly different. T hus, determination of colloc a tion com p arisons was the f o cus rath er than estab l ishing de tec tion lim its per se. A

com p rehensive discussion and analysis of collo cated sam p ling data is given in Section 5.1.3.

5.1.2 Instrum e nt Accuracy
Instrum e nt accuracy is norm ally defined by comparisons to known standards, preferably traceable to the National Institut e of Standards and Technology (NIS T). Accuracy is defined as absence of bias from the known standard, while for the purpose of this study was defined based

57Table 5.1 Summ ary of m a in study exposure runs.

Run Num b er D a te T i m e o f Da y T y p e of B u s C ontrast T y pe of Route

1 4 /22/20 02 A f ternoon HE 1 F ront/rear Rural/su burban

2 4 /23/20 02 Morning H E 1 Front/rear U r ban 1
3 4 /23/20 02 A f ternoon HE 1 F ront/rear U r ban 1

4 4 /30/20 02 A f ternoon HE 2 F ront/rear Rural/su burban

5 5 /1/2002 Morning H E 2 Front/rear U r ban 1
6 5 /1/2002 A f ternoon HE 2 F ront/rear U r ban 1

7 5 /7/2002 A f ternoon HE 3 I nside/outside Rural/su burban
8 5 /8/2002 Morning H E 3 Inside/outside U r ban 1
9 5 /8/2002 Morning H E 3 Inside/outside W i n dow position test
10 5/8/2002 A f ternoon HE 3 I nside/outside U r ban 1

11 5/13/20 02 A f ternoon RE 1 F ront/rear Rural/su burban

12 5/14/20 02 Morning R E 1 Front/rear U r ban 1
13 5/14/20 02 A f ternoon RE 1 F ront/rear U r ban 1

14 5/16/20 02 Morning R E 1 Front/rear U r ban 1
15 5/16/20 02 A f ternoon RE 1 F ront/rear U r ban 1
16 5/20/20 02 A f ternoon RE 2 F ront/rear Rural/su burban
17 5/21/20 02 Morning R E 2 Front/rear U r ban 1
18 5/21/20 02 A f ternoon RE 2 F ront/rear U r ban 1
19 5/22/20 02 Morning R E 2 Front/rear U r ban 1
20 5/22/20 02 A f ternoon RE 2 F ront/rear U r ban 1

21 5/23/20 02 Morning N /A N / A B us Stop
22 5/23/20 02 A f ternoon N/A N /A Bus Stop
23 5/29/20 02 Morning R E 2 Front/rear U r ban 2

24 5/29/20 02 A f ternoon RE 2 F ront/rear U r ban 2
25 5/30/20 02 Morning R E 2 Front/rear U r ban 2
26 5/30/20 02 A f ternoon RE 2 F ront/rear U r ban 2
27 6/4/2002 A f ternoon TO 1 I nside/outside Rural/su burban
28 6/5/2002 Morning T O 1 Inside/outside U r ban 1
29 6/5/2002 Morning T O 1 Inside/outside W i n dow position test
30 6/5/2002 A f ternoon TO 1 I nside/outside U r ban 1
31 6/6/2002 Morning T O 1 Inside/outside U r ban 1
32 6/6/2002 Morning T O 1 Inside/outside W i n dow position test
33 6/6/2002 A f ternoon TO 1 I nside/outside U r ban 1
34 6/11/20 02 A f ternoon CNG I nside/outside Rural/su burban
35 6/12/20 02 Morning C NG Inside/outside U r ban 1
36 6/12/20 02 A f ternoon CNG I nside/outside U r ban 1

on the comparisons of m easurem e n ts m a de with identical pairs of in strum e nts (s ee Section 5.1.3). Poor precision can lim it the assessm ent of accuracy, although a m e asurem ent can be very

accurate ev en with poor instrum e nt precis ion if enough m easurements are made. For

m easure m ents of the kind conducted in the present bus stu dy, accuracy is nor m a lly assessed

58during an independent audit of the instrum e nt perform a nce where known standards were available. For m a ny of the instrum e nts used in this study such standards were not available or did not ex ist. The following section describe s our best estim ation of the accuracy of the

m easure m ents and the lim itations involved.

5.1.2.1 Audited Instrum e nts A system and perform a nce audit was perfor m e d May 10, 2002 following the procedures describ e d in the project Quality In tegrated W o rk Plan (QIWP) (Gemm ill , 2002a). The audit was perform e d on m o st of the instrum e nts which m easured gaseous pollutants. The system audit included an inspection and evalua tion of the overall m onitoring system setup in a bus being prepared for use in the study. The accuracy o f the CO, NO/NO
x , and NO 2 analyzers were assessed by this perform a nce audit. The perfor m ance audit consisted of challenging the analyzers with a tes t atm o sphere from an i ndependent calibration source. The test gas concentrations entered each in strument through the sam e sampling system used during sam p le runs. After the instrum e nts’ responses had equi librated, their outputs were read from the data logger, correction factors previ ously determ ined by the system operator were applied, and these corrected outputs were com p ared to the known test gas inputs. The following is a summ ary of the audit f i n d ings:

1. Continuous Analyzers: The audit results for the Therm a l Electron Instrum e nts (TEI) NO/NO
x and CO analyz ers indicated the projec t accura cy data quality objectiv es (see QW IP, Ge mm ill, 2002a) were met for those instrum e nts. However, the objectives for the UCR NO 2 analyzers w e re not m e t, probably due to the difficult m oving platform environm ent in which they were operated. An audit was not perform e d on the SF 6 analyzer becau se it was no t operational at the tim e of the audit.

2. System : The general system condition, operati ons, and m a intenance were evaluated. A

com p leted system evaluation report is presen ted in the Quality Assurance Audit Report which indicated that no signifi cant unsatisfactory audit results were encounter ed (G emm ill

, 2002b). The procedures followed in the execution of this project were consistent with those describ e d in the QI W P (Ge m m ill, 2002a). Over all, the m o nitoring equ i pm ent was securel y installed in the bus and was in satisf actory ope rational condition. All in strum e nt calibrations were done in a proper m a nner and all docum enta tion was complete, concise, and up to date.

3. Calibration Results: A review of the calibration results for the m a in study indicated the data quality obje c tive s were genera lly m e t f o r the T E I Model 4 2 NO/NO
x analyzer, but were not

m e t f o r the CE-CERT NO 2 and Dasibi 3003 CO analyzers (see QW I P —Ge m m ill, 2002b). Most of these data were considered valid.

Com p lete d e tails of th e audit, inc l ud ing proc edu r es and findings are provid ed in a s e parate aud i t report (Gemm ill, 2002b).

595.1.2.2 Unaudited Instrum e nts 5.1.2.2.1 Particulate Matter Mass 5.1.2.2.1.1 Integrated P M 10 and PM 2. 5 The larges t sources of error for thes e instrum e nts were the m easurem ent and contro l of the flow rates and filter handl ing. The flow rates were cont rolled with need le valves and

m easured with a ro tam e ter. The accuracy of the flow m a intena nce was defined by th e characterization of volum e, which depends on both tem p erature and p r es sure. The flow was set to a specific volum etric rate since the inlet was designed to operate under those conditions. While tem p erature and pressure changed during th e course of sa m p ling, the specified volum etric f l ow setting f o r these devices m a y deviate was be liev e d to stay within approxim a tely 5-10% of true volum e tric flow.

Minor sources of error included the size-cut ch aracteristics of the PM inlet and weighing accuracy. S i nce evalu a tion of the size-cu t characte ristics were beyond th e scope of this pro j ect, we relied on previous comparis ons. The inlet design we employe d has been used in m a ny other exposure related studies; and provi ded results com p arable with ot her research. It should be noted tha t a ny iner tia l s i ze sep a ra tion is not ex act , and that designation of a size cut off is an operational definition. Specifically, the size cut of an inle t is defined as the size at w h ich 50% of the m a ss of the specified aerodynam i c size penetrat es the inlet. The sh arpness of the size cut (particles greater or less than the specified aerodynam i c size will always penetrate the inlet) depends on the design of the inlet.

W e believe the erro r in f ilter m a ss weighing was m i nor due to our weighing proce dure. We used a class M calib ration stand a rd before and after every weighing session. Th e ca libr a tion standard was weighed to the nearest 0.1 μ g, and little deviation was observed (typically +/- 0.1ug). It should be noted that in practice it is not possible to weigh filt ers to closer than 1 μ g as the weight will f l uctu ate due to f actors such a s hum i dity much m o re than a m e tallic stand a rd

. All f ilte rs were weighed until three w e ighings ag r eed to within 3 μ g. Sinc e typical loadings were
50-100 μ g, the error in the weighing proces s sho u ld not have exceed ed a few percent. The erro rs due to filter handling are random and therefore related to the precision of the m easurem ent.

5.1.2.2.1.2 Continuous PM 10 and P M 2. 5 The DustTrak instrum e nts used for this m easurem ent are based on light scatterin g of particles. T h e am ount of light scattered is de pendent not only on concen tration, but also is highly dependent on the particle si ze distribution and composition. In order to relate the light scattering to a concentra tion it is necessary to choose a particle size distribution since these distributions are constantly changing in am bie n t air. The response of these instrum e nts was calibrated by the m a nufacturer based on the particle size distribution of Arizona road dust, which was chosen because it is an NIST standard refere nce m a terial. Thus the inherent accu racy can be related to an NIST standard, but the accuracy at the tim e m easurem ents are m a de m u st be estab lished by com p aring the m easurem ents with m a ss m e asurem ents from collected PM. The accuracy determ ination for the m easurem ents made in this study was th erefore lim ited to th at o f the integrated filter co llection m e thods descri bed above. In addition the accuracy of the instrum e nt varies d i r ectly with the f l ow rate. A rotam e ter and n eedle valve were also used to adjust th is f l ow and the estim ated uncertainty introduced w a s f i ve to te n percent as discussed

60above. See the next section for details of a c o m p arison of the Dust Trak against a reference

m e thod.

5.1.2.2.1.3 Com p arison of Measurem ent Methods for PM 2. 5 and PM 10 The present study compared two instrum e nts that m easured the m a ss concentration of
PM 2. 5 and P M 10 : the Therm o System s Inc. Model 8520 D u stTrak Aerosol Monitor (DustTrak) and the Harvard Im pactor (HI). The DustTrak m easured the PM concen tration continuously in real-tim e while the Harvard Im pactor used stan dard filter-b ased sam p ling m e thods. Continuous monitors for airborne particulate m a tter m easurem ents have an advantage over traditional filterbased sam p ling m e thods that do not provide real-t im e data and cannot char acterize short-term high concentrations (Babich et al., 2000; Chung et al., 2001). However, continuous m onitors

curren tly in use m a y suffer from problem s of accuracy and appear to be more useful on a relative rather than absolute basis (Ram achandran et al., 2000; Chung et al., 2001; Moosmuller et al., 2001; Yanosky and Maclntosh 2001; Yanosky et al., 2002).

Several studies have been c onducted com p aring different PM 2. 5 or PM 10 sam p lers (Babich et al., 2000; Ram achandran et al., 2000; Chung et al., 2001; Y a nosky and Maclntosh 2001; Yanosky et al., 2002). Babi ch et al. (2000) a nd Yanosky and Maclntosh (2001) com p ared
PM 2. 5 m a ss concentrations from t h e HI agains t U.S. Environm ental Protec tionAgencydesignated Federal Reference Method (FRM) PM 2. 5 samplers. Yanosky et al. (2002) investigated the perform a nce of the DustTrak m easuring PM 2. 5 concentrations of indoor air i n

com p arison with the FRM. The gravim etric m e thod, (i.e. F R M) was used as the standard for evaluation o f the real-tim e continuous m onitors b ecause it h a s proven to be stable and accurate.
In Ram achandran et al. (2000) DustTrak m easurem ents of PM 2. 5 concentrations indoors and outdoors were com p ared with gravim etric m e a s urem ents (MSP, Inc. PM 2. 5 inlet for indoors; Andersen RAAS2.5-300 sam p ler for outdoors). C hung et al. (2001) compared m easurem ents of
PM 10 by the DustTrak with those by a si ze- selective inle t sam p ler (SSI ).

All these studies em ployed sim p l e linear re gression analyses in order to calculate differences between sampler types. W h en the concentratio ns from a sampler, y , are regressed on those from a reference sam p ler, x, the intercept should be zero and the slope on x should be unity, i.e. y = 0 + 1* x , if the m easurem ents between the two sam p lers coincide. If the inter c ept estim ate is significan tly different from zero sta tistically, th ere is a sys t em atic bias between

sam p lers. A statistically significant differenc e in slope suggests a proportional bias between

sam p lers (Yanosky and Maclntosh 2001). The regr ession results of the above-m entioned studies

are summ arized in Table 5.1.2.1.

From Table 5.1.2.1, the first two studies showed excellent agreem ent between the HI and
FRM m e thods, indicating the Harvard im pactor could serve as a reference m e thod for the DustTrak instrum e nts employed in the present st udy. Three other studies showed the DustTrak overestim ated PM 2. 5 or PM 10 m a ss concentration by factors of tw o to three (the slope colum n in Table 5.1.2.1) com p ared with filter-based m e t hods, although no system atic biases were observed in thes e th ree stud ies. These find ings are co ns isten t with the f act that the DustTrak is a nephelom e ter, which m easures m a ss concentrat ion of an aerosol stream by converting the

am ount of light scattered from m a ny particles at once. Thus, the DustTrak output depends on particle size distribution and refractive index (Moosm u ller et al . 2001) and the response of the

61instrum e nt for an aerosol stream consisting mainly of finer particles can produce errors in concentration of a fact or of two or more (Hinds 1999; Ra machandran et al., 2000; Chung et al., 2001).

Table 5.1.2.1 Summ ary of previous studies com p aring sam p lers em ployed for m easurem ents of
PM 2. 5 and PM 10 m a ss concentration s

. R e f e r e n c e P M s i z e Sam p l e r s n Intercept 1 S l o p e R 2 Babich et al. (2000) PM 2. 5 H I / F R M 8 1 1.36 0.97 2 0.99 Yanosky and Maclntosh (2001) PM 2. 5 H I / F R M 2 1 -0.06 1.03 2 0.96

Yanosky et al. (2002) PM 2. 5 D u s t T r a k / F R M 1 7 -1.73 2

. 5 7 0

. 8 6

Ram achandran et al. (2000) PM 2. 5 D u s t T r a k / A ndersen 3

DustTrak/MSP 4 N/A N/A N/A N/A 2.99 1.94 0.92 0.76 Chung et al. (2001) PM 10 DustTrak/S S I 6 0 -13.60 3

. 0 0 0

. 9 2

1. Th e i n tercep t was n o t sign ifi can tly d i fferen t fro m zero ( α = 0.05).
2. The sl ope wa s not si g n i f i cant l y di ffe re nt f

r o m zero ( α = 0. 05 ). 3. Gra v im etric sa m p ler for outdoor m easurem e n t 4. Gra v im etric sa m p ler for indoor m easurem en t 5. N/A: statistics are n o t av ailable.

5.1.2.2.1.3.1 Descriptive Statistics Descriptive statistics for PM 2. 5 and PM 10 m easurem ents by the DustTrak and the HI are given in Tables 5.1.2.2 and 5.1.2.3, respectively. Real-tim e ten second interval m easurem ents by the DustTrak were averaged to properly m a tch the integ r ated s a m p l i ng tim es of the HI

m easurem ents. Differen ce between the m eans and m e dians of the DustTrak data were m odest for both PM 2. 5 and PM 10 , indicating the distributions were not skewed. However, there were significan t d i sparities between the DustTrak and HI sam p lers. For instan ce, in the morning runs (from 23 to 35) and afternoon runs (24, 27, 30, and 33), integrated PM 2. 5 concentratio ns from the HI fell outside the range (i.e. m a xi m u m – mi n i mu m) o f t h e t i me

- r e s o l v e d D u s t T r a k

m easure m ents.

The ratio s of the DustTrak-m easured PM 2. 5 mean concentration divided by the HIm easured PM 2. 5 concentration varied s ubstantially. 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 PM 2. 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 m o rning and afternoon runs, respectively.

62Table 5.1.2.2 Descriptive statistics for PM 2. 5 ( μ g/m 3 ) m easured by HI and DustTrak.

R a t i o 1 Ru n D a t e T i m e W e e k Bu s Ro u t e Du stTrak DustT r a k DustT r a k DustT r a k H I
M i n i mu m M a x i mu m Med i an Mean
4 0 4 / 30/ 02 PM 2 HE 2 R S 9. 0 34

. 0 14

. 0 14

. 9 40 .6 0. 4
5 0 5 / 01/ 02 AM 2 HE 2 U 1 35

. 0 14 8. 0 53

. 0 58

. 8 40 .6 1. 4 6 0 5 / 01/ 02 PM 2 HE 2 U 1 8. 5 11 5. 5 15

. 0 20

. 3 12 .5 1. 6
7 0 5 / 07/ 02 PM 3 HE 3 R S 51

. 0 16 1. 0 74

. 0 76

. 5 44 .4 1. 7 1 0 0 5 / 08/ 02 P M 3 HE 3 U 1 29

. 0 70

. 5 37

. 0 39

. 0 29 .0 1. 3
1 1 0 5 / 13/ 02 P M 4 R E 1 R S 29

. 5 16 1. 5 47

. 0 51

. 7 47 .1 1. 1
1 2 0 5 / 14/ 02 AM 4 R E 1 U 1 33

. 0 61

. 0 43

. 5 44

. 0 37 .8 1. 2
1 4 0 5 / 16/ 02 AM 4 R E 1 U 1 37

. 5 89

. 5 61

. 0 61

. 8 42 .0 1. 5 1 5 0 5 / 16/ 02 P M 4 R E 1 U 1 26

. 0 15 7. 0 41

. 0 47

. 5 31 .5 1. 5 1 6 0 5 / 20/ 02 P M 4 R E 1 R S 4. 0 43

. 0 11

. 0 13

. 4 18 .1 0. 7
1 7 0 5 / 21/ 02 AM 5 R E 2 U 1 41

. 0 14 7. 0 75

. 3 78

. 0 50 .9 1. 5 1 8 0 5 / 21/ 02 P M 5 R E 2 U 1 3. 5 16 7. 0 11

. 0 15

. 4 21 .2 0. 7 1 9 0 5 / 22/ 02 AM 5 R E 2 U 1 2 5 14 6. 5 78

. 5 78

. 8 48 .5 1. 6
2 0 0 5 / 22/ 02 P M 5 R E 2 U 1 12

. 0 20 4. 5 24

. 0 32

. 5 22 .4 1. 5
2 3 0 5 / 29/ 02 AM 6 R E 2 U 2 59

. 5 14 8. 0 11 2. 8 11 1. 0 49 .2 2. 3
2 4 0 5 / 29/ 02 P M 6 R E 2 U 2 58

. 5 22 7. 0 10 9. 0 10 9. 0 41 .6 2. 6
2 5 0 5 / 30/ 02 AM 6 R E 2 U 2 74

. 0 18 0. 5 12 9. 5 12 8. 7 48 .9 2. 6
2 6 0 5 / 30/ 02 P M 6 R E 2 U 2 35

. 0 21 9. 5 72

. 5 74

. 2 41 .3 1. 8
2 7 0 6 / 04/ 02 P M 7 TO 1 R S 64

. 5 21 1. 5 11 8. 5 11 7. 3 40 .3 2. 9
2 8 0 6 / 05/ 02 AM 7 TO 1 U 1 79

. 0 12 8. 5 10 0. 0 99

. 8 57 .0 1. 8 3 0 0 6 / 05/ 02 P M 7 TO 1 U 1 10 5. 0 16 3. 5 11 9. 0 12 3. 0 61 .8 2. 0 3 1 0 6 / 06/ 02 AM 7 TO 1 U 1 97

. 0 15 8. 0 12 7. 5 12 8. 0 62 .1 2. 1 3 3 0 6 / 06/ 02 P M 7 TO 1 U 1 78

. 0 22 4. 0 10 3. 0 10 6. 0 49 .6 2. 1
3 4 P M 8 C N G R S 57

. 5 21 1. 0 91

. 0 89

. 6 57 .1 1. 6 35 06/ 11/ 02 06/ 12/ 02 A M 8 C N G U 1 39

. 5 68

. 5 48

. 5 49

. 0 36 .1
3 6 0 6 / 12/ 02 P M 8 C N G U 1 13

. 5 11 7. 5 21

. 0 27

. 8 26 .7 1. 4 1. 0

1. Ratio = (DustTrak PM 2.5 m e a n co n c en tration ) / (HI PM 2.5 )

For PM 10 measurem ents , the overall differences be tween the DustTrak and HI sam p lers were less pronounced than for PM 2. 5 . Only two runs (30 and 33) showed HI integrated concentrations outside the range of the Dust T r ak tim e-resolved data (Table5.1.2.3) and the DustTrak/HI PM 10 ratios ranged from 0.4 to 1.7, m o re clos ely distributed ar ound unity than for
PM 2. 5 . Fourteen of twenty-six runs (i.e. 50% of all bus commute runs) had ra tios less th an 1, while only three runs for PM 2. 5 had ratios less than 1.

Another problem with certain DustTrak da ta in this s t udy was th at m ean PM 2. 5 m a ss concentratio ns were higher than m ean PM 10 m a ss concentrations for several morning and afternoon runs (16, 19, 20, 23, 24, and 25).

63Table 5.1.2.3 Descriptive statistics for PM 10 ( μ g/m 3 ) m easured by HI and DustTrak.

Ru n D a t e Ti m e W e e k Bu s Ro u t e Du stTrak
M i n i mu m DustT r a k
M a x i mu m DustT r a k Med i an DustT r a k
Mean H I R a t i o 1

4 0 4 / 30/ 02 PM 2 HE 2 R S 13

. 0 75

. 5 23

. 5 24

. 4 37

. 0 0. 7
5 0 5 / 01/ 02 AM 2 HE 2 U 1 33

. 5 12 3. 5 50

. 5 54

. 8 63

. 7 0. 9 6 0 5 / 01/ 02 PM 2 HE 2 U 1 9. 5 10 2. 0 17

. 0 21

. 5 28

. 2 0. 8
7 05/ 07/ 02 P M 3 HE 3 R S 53

. 5 14 5. 5 76

. 5 77

. 5 65

. 6 1. 2
1 0 0 5 / 08/ 02 PM 3 HE 3 U 1 35

. 0 74

. 0 44

. 5 45

. 8 52

. 0 0. 9
1 1 0 5 / 13/ 02 PM 4 R E 1 R S 32

. 0 87 0. 5 57

. 0 69

. 3 10 5. 5 0. 7 1 2 0 5 / 14/ 02 AM 4 R E 1 U 1 37

. 0 71

. 5 50

. 0 50

. 9 78

. 3 0. 6
1 4 0 5 / 16/ 02 AM 4 R E 1 U 1 38

. 5 10 1. 0 61

. 5 62

. 1 77

. 3 0. 8
1 5 0 5 / 16/ 02 PM 4 R E 1 U 1 29

. 5 14 2. 0 45

. 5 50

. 2 50

. 9 1. 0
1 6 0 5 / 20/ 02 PM 4 R E 1 R S 2. 0 43

. 0 10

. 0 11

. 7 27

. 3 0. 4
1 7 0 5 / 21/ 02 AM 5 R E 2 U 1 51

. 0 15 7. 5 76

. 8 81

. 4 86

. 3 0. 9
1 8 0 5 / 21/ 02 PM 5 R E 2 U 1 6. 0 15 9. 5 16

. 0 20

. 4 24

. 9 0. 8
1 9 0 5 / 22/ 02 AM 5 R E 2 U 1 24

. 5 14 6. 5 76

. 0 78

. 1 86

. 9
2 0 0 5 / 22/ 02 PM 5 R E 2 U 1 11

. 0 17 3. 0 23

. 8 32

. 3 47

. 0 0. 9 0. 7
2 3 0 5 / 29/ 02 AM 6 R E 2 U 2 50

. 0 15 4. 5 10 4. 0 10 3. 2 80

. 3 1. 3
2 4 0 5 / 29/ 02 PM 6 R E 2 U 2 57

. 5 25 8. 0 10 2. 0 10 4. 9 71

. 5 1. 5 2 5 0 5 / 30/ 02 AM 6 R E 2 U 2 67

. 5 17 1. 5 12 5. 0 12 2. 6 72

. 8 1. 7 2 6 0 5 / 30/ 02 PM 6 R E 2 U 2 40

. 5 23 8. 5 73

. 0 76

. 4 66

. 7 1. 1
2 7 0 6 / 04/ 02 PM 7 TO 1 R S 77

. 0 24 2. 0 14 0. 0 13 9. 9 98

. 3 1. 4
2 8 0 6 / 05/ 02 AM 7 TO 1 U 1 80

. 5 13 9. 0 10 0. 5 10 1. 5 10 4. 8 1. 0 3 0 0 6 / 05/ 02 PM 7 TO 1 U 1 10 2. 5 17 4. 0 12 3. 0 12 6. 4 97

. 3 1. 3 3 1 0 6 / 06/ 02 AM 7 TO 1 U 1 98

. 0 16 6. 0 12 9. 5 13 0. 5 99

. 4 1. 3 3 3 0 6 / 06/ 02 PM 7 TO 1 U 1 80

. 5 23 0. 0 10 6. 0 10 9. 5 73

. 7 1. 5
3 4 0 6 / 11/ 02 PM 8 C N G R S 61

. 5 45 0. 0 10 0. 5 10 1. 3 10 1. 8 1. 0 3 5 0 6 / 12/ 02 AM 8 C N G U 1 41

. 0 73

. 0 53

. 0 53

. 3 68

. 8 0. 8 3 6 0 6 / 12/ 02 PM 8 C N G U 1 24

. 5 12 1. 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 4 0

Ru nPM2.5(ug/m3)

Ha r v a r d

Du s t T r a k 1. Ratio = (DustTrak PM 10 m ean concent r ation) / (HI PM 10 )

Figure 5.1.2.1 Com p arison between DustTrak and HI data for PM 2. 5 (m orning only).

644 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

RunPM2.5(ug/m3)

Harv ard

Dust T r ak

Figure 5.1.2.2 Com p arison between DustTrak and HI data for PM 2. 5 (afternoon only).

5.1.2.2.1.3.2 Assessm ent of Difference Between Sam p lers To assess the difference between samplers qua n titativ ely, s imple linea r regres sions were perform e d for both PM 2. 5 and PM 10 data. As noted earlier, in pr evious studies (Ram a c handran et al., 2000; Chung et al., 2001; Ya nosky et al., 2002), the DustTrak overestim ated the concentration of airborne particulate m a tter by f actors of two to three (Table 5.1.2.1) com p ared to filter-b as ed sam p ling techniqu es and a sim ilar resu lt was observed in the presen t study for
PM 2. 5 but not for PM 10

.
The intercept term s were not f ound to be statistically significa nt (i.e., no system atic bias) at the significance level of 0.05 for both PM 2. 5 and PM 10 . However, the estim a ted slopes were significantly different from zero at the signifi cance level of 0.05, indicati ng a proportional bias. The slope of 2.34 for P M 2. 5 fell within the range observed in previous studies (Ramachandran et al., 2000; Yanosky et al., 2002), wh ile the slope of 1.25 for PM 10 was lower than the value of 3 found by Chung et al. (2001), who e m ployed the SSI gravi m etric m e thod, different fr om the HI used in the present study.

The correlations observed in the present study were m odest (R 2 of 0.66 for PM 2. 5 and 0.67 for PM 10 ), com p ared with p r evious stud ies (R 2 from 0.76 to 0.92) re ported in Table 5.1.2.1. However, in the present study data were collect ed on m oving platform s, school buses, while the previous studies were conducted conventionally on roofs or inside of buildings. A degradation in correlation for instrum e nts operated on m ovi ng platform s m i ght be expected, although it is interesting that the p a tterns of differences in m easurem ents between the DustTrak and the HI

sam p lers were sim ilar in the presen t study to the previous studies for PM 2. 5

.
Based on the results presented above, and c onsidering the HIs are the Federal reference

m e thod, we decided to separate the use of t h e data obtained by these and the DustTra k

65instrum e nts. The HI integrated sam p les data were used to provide absolu te conce n tr a tions which could be compared with other st ud ies. The d a ta co llec t ed with the DustTrak instr u m e nts wer e used only on a relative basis or when real-t im e series were required for the analysis.

5.1.2.2.2 Real-Tim e Fine Particle C ounts (0.3 to 10.0 μ m) Optical particle counters count pulses of scattered light with high efficiency. The intens ity of the puls e is direc tly (but not linearly) re lated to particle siz e . The output pulses are sorted into b i ns that are therefor e r e la ted to pa rticle size. The in tensity of the pulse is dependent on the physical characteristics of the particle scattering the light. Refractive index and shape are two chara c te ristics tha t h a ve the gre a t est af f ect o n particle size. The size bins from the output of optica l pa rticle coun ters were ca libr a ted by th e m a nufacturer by sam p li ng aerosol consisting of spherical latex particles of known size and refrac tive index. W h ile this calibration can be highly accurate (latex particle sizes are known to better th an 1%), ambient particles have a wide variety of shapes and refractive indices. Optical particle count ers therefore provide a size output that is operationally defined, assum i ng the particles scatte r light in the sam e m a nner as l a tex beads.
Measurem ent accu racy can only be determ ined for latex beads an d the accuracy in size determ ination of a m bient aerosol cannot be m easured.

5.1.2.2.3 Real-Tim e Ultrafine Part icle Counts (0.03 to 0.8 μ m) A CE-CERT Scanning Electrical Mobility Sp ectrom e ter (SEMS) was used to m e asure particles in the range of 0.03 to 0.8 um electrical m obility diam eter. These analy zers charg e particle s and then sepa rate them by size based on th eir ele c tr ic al m obility. The particle s are th en detected with a condensation nuclei counter (CNC ). Only a s m all fract ion of the particles becom e charged, with the charg i ng efficien cy dependent on particle size, s h ape, and

com position. The CNC can count th e particles essentially quantitat ively. For the SEMS, the size range is determ ined by sam p ling aerosol pa rticles of known size, shape and com position, therefore, it is no t poss i ble to es tablish a m eaningful accuracy when sampling am bient aeroso l since the siz e , shape and com positio n can vary widely. This im poses signif i cant re s t riction s and

com p lexities in the analysis of the data gene rated by the instrum e nt. Although we collected

sam p les using the SEMS for the m a jority of the bus runs, analyses of these data were beyond the scope of the present project.

5.1.2.2.4 Real-Tim e Black Carbon These analyzers (Aeth a lom e ters) determ in e the concentration of black carbon by determ ining the ligh t ab sorption af te r collection on a f ilter. Black carbo n is genera lly the only significant com ponent of a m bient particulate that absorbs light at the near IR wavelength used. The calibration cannot be determ ined directl y, but m u s t be established em pirically by

com p arison with PM collected on filters and analyzed by therm a l decomposition. T h is analys is

m e thod also has inherent errors since the am ount of elem ental ca rbon can change during analysis by char ring and pyro l ysis, the degr e e of which is dependen t on the chem ical com position of the PM. The measurem ent of elem ental carbon in ambi ent air is therefore an operational definition, and accu racy is d i fficult to es tab lish and depend s on the aerosol com position. Therm a l m e thods

sam p ling ambient air generally agr ee to within about 10 percent and this m a y be used to estim ate accuracy. The real tim e analyzers generally agree to within about ten percent of that determ ined by f ilter collection f o llo w ed by ther m a l analysis . The overa ll ac cura cy theref ore is e xpected to be in the range of twenty percent.

665.1.2.2.5 Real-Tim e Particle Phase P A H This analyzer does not directly m easure PAHs , but rather their photo ionization products. The m a nufa cturer’s calibration is based on an em pirical relati onship, with PAH m easured in

am bient air by f ilter co llection f o llo wed by extr action and g a s chrom a tographic an alysis. Since the respon se depends on the com position of the P M

, it is not possible to establish th e accuracy of the m e thod. Based on the m a nufacturers data, we expect ambient air accu racy to generally be fi ft een percent

.
5.1.2.2.6 Gaseous Analyses Tedlar bag sam p les were analyzed for 1,3-butadiene by gas chrom a tography with fla m e ionization d e tection. The accuracy is dependent on sam p le injection accuracy, loss o f com pound in the bag, and calibration standa rd s used as the chrom a tographic stand a r d. Sam p le injection is done with a loop of fixed size, which changes vol um e only as a result of at m o spheric pressure. We have shown that losses of 1, 3-butadiene are with in the precis ion of the m easurem ent (several percent) for periods of up to four days. This was done by collecting am bient sam p les enriched in 1,3-butadiene by sam p ling near an idling diesel e ngine and then periodically analyzing th em . The GC accuracy was estab lis hed by calib rating using a gas cy lin der certified by the m a nufacturer to +/- 2% based on NIST standa rds. The overall a c c u racy is es tim a ted to be within f i ve p e rcen t.

Tenax sam p les were collected using needle v a lves for flow control and rotam e ters for flow m easurem ent. As with the filter co llection the accuracy of fl ow control is estim ated to be within five to ten percent. Gas chrom a togr aphy with fla m e ionization was used to m e asure

arom atic compounds de sorbed from the Tenax. The calibration of the m e asure m ent was based

on a com p ressed gas standard certified by the manuf acture r to within two percen t. The overall

m easurem ent accu racy w a s estim a ted to be with in ten percent.

Carbonyl compounds we re m easured by collec ting them as a 2,4-dinitrophenyl hydrazine adduct and analyzing the adduct by high performa nce liquid chrom a tography (HPLC) . The flow

rate was con t rolled by needle valves and m easured with a ro ta m e ter. As with the f i lter collec tion the accu racy of flow control is estim a ted to be w ithin five to ten percent. Gaseo u s calib ratio n standards are not available for carbonyls and the calibration is established by weighing the adduct. W e ighing and dilution accuracy is esti m a ted from m e thods traceable to th e NIST and based on th ese procedu r es the accu racy of the sta ndard s is estim ated to be within ten perc ent. Overall accu racy of th e m e thod is estim ated to b e twenty percent b a sed o n this analysis and field

com p arisons with spectroscopic m e thods.

5.1.2.2.7 Portable Carbon Monoxide Analyzers These analy zers were n o t aud ited, but th eir accuracy m a y be es tim ated by com p arison with the inf r ared an aly zer that was audited. The accuracy m a y be affected when sam p ling

am bient air due to in terf erences, sin ce electroch em ical cells do not hav e high spe c ificity. The interference due to hydrogen also needs to be addr essed since this is a known interferent and the lead-acid batteries carried in the bus m a y have be en a source of hydrogen.

675.1.3 Evaluation of Precision Be tween Paired Instrum e nts Two contrasts tested in this study w e re differe nces in concentrations at the front and the rear of the b u s cabin, an d between inside and o u tside the b u s. To help valida t e these contrasts, we assessed the p r ecisio n of the pairs of instru ments which were used to sim u ltaneously m easure at the front and rear of the bus cabin, or inside and outside the bus duri ng a commute. If there was not sub s tantial agreem ent between the read ings of the pair of the sam e instru m e nts when they were sam p ling the sam e volum e of air under common sam p ling conditions, we would be lim ited in th e inf e renc es we could d r aw f r om the m easurements m a de when sam p l i ng different air parcels (e.g., inside versus outs ide). Thus the results of our precision experim e nts were used to determ ine which pairs of instrum e nts had an acceptable level of precision for evaluating sim u ltaneous m easurements at the front and rear of the bus cabin, or inside and out side the bus. It is im portant to no te, f o r all com p arisons wh ich re lied s o lely on a s i ngle s e t of instrum e nts instead of paired instrum e nt s (e.g. com p arisons between bus types, windows open or closed, different m i croenvironments, com p arisons to am bien t air ) , th e prec ision r e sults repo rted here do not apply.

During the pilot study, low pollutant concentr ations during the precision checks for a

num b er of the ins t ruments m a de it dif f i cult to interpret differences observed in pollutant concentratio ns between the front and back of th e cabin, and between inside and outside the bus. One of the im prove m e nts of the m a in study e xperim e ntal design was that w e perform e d instrum e nt precis i on m e asurem ents in locations where the higher concen tr ations of m a ny of the analyzed pollutants were expected to provide mo re robust precision assessm ent. In general, all instrum e nts im proved their precision when exposed to higher concentrations. Therefore, the precision was potentially better during the bus co mm utes since the values observed during these tests were much higher than duri ng the precision experim e nts.

Precision between paired Aethalom eters, how ever, did not improve during the m a in study precision tests. The relatively clean background conditions in W e st Los Angeles, even a few tens of m e ters from the I-405, often resulted in m easured black carbon concentrations that were not sufficiently above the detection lim it. Duri ng the collocation experim e nts, the m e asured concentratio ns f o r this p o llutant wer e m o st of the tim e belo w 4 ug/m 3 and f o r these values the signal-to-noise ratio produced by the Aethalometer s did not yield appropriate precision results. Even so, the post-study controll ed experim e nts (see below) s howed the precision between the pairs of Aethalom eters improved substantially at higher concentrations. Therefore we believe that at the peak black carbon concentrations encountered during th e m a in study runs, the precis i on between the p a ired Aeth alom eters was s ubstantially better tha n that indic a ted by the precision experim e nts.

During the m a in study, seventeen instrum e nt precision experim e nts were conducted w h en the bus was parked with the engine off and th e windows fully opened. For these experim e nts, the ins t rum e nts were s a m p ling inside concen trati ons and the sam p le inlets from each pair of instrum e nts were separated by the length of the bus. That is, one set of instruments had its

sam p le inlets in th e re ar of the cabin while th e other set had them in th e front. This arrangem e nt was necessary since th e logistics of relocating th e sam p ling inlets to be adjacen t to each other was too complex to be perform e d once the bus w a s instrum e nted for the runs.

68Although the sam p ling inlets were not exactly in the sam e loca tion, the fact that the bus

was at res t and fully ventilated was expected to lead to unifor m pollutant concentrations from

am bient air entrained throughout the cabin. Thus, und er th ese co nditions, no significant differences were expected in m easurem ents by the pairs of instrum e nts.

Six instrum e nt precision experim e nts were co n ducted with the tes t bus parked close to the intersection of two congested streets (Sepulve da Blvd and W ilshire Bl vd) and near the I-405 freeway in West Los Angeles. The rem a ining instrum e nt precision experim e nts were conducted either inside a parking lot at UCLA, in front of the Brentwood Science Magnet S c hool, 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 experim e nt to assess the precision between the paired instrum e nts at m u ch higher concentrations in a more controlled laboratory environm ent. Table
5.1.3.1 summ arizes the m a in characteristics of th e instrum e nt precision e xperim e nts and Figures 5.1.3.1 to 5.1.3.5 show a series of boxplots illustrati ng, as discussed below, the variation in differences between the pairs of instrum e nts over th e course of the study. W ith the exception of the Aethalo m eters, thes e graphs were plo tted u s ing the s a m e num erical s cale (although in several cases with d i fferent units) to em phasize th e varia tion of the differences not only within runs but also between instrum e nts. Figur es 5.1.3.1 to 5.1.3.5 depict the results only in term s of absolute precision. R e lative precision analys es are presented in Section 5.1.3.1.1.

Table 5.1.3.1 General c h arac ter i stic s of the pair ed instrum e nt precision experim e nts.

Col l o c a tio n Ru n Nu mber Da te T i me Sta r t Time Stop Time L o c a t ion W e e k Numbe r

101 04 /2 3/ 02 P M 10: 0 0 12: 0 0 UCL A 1

201 05 /0 1/ 02 P M 17: 3 2 18: 2 5 UCL A 2

301 05 /0 7/ 02 P M 15: 0 0 15: 3 3 Sepu l v eda B l vd 3

401 05 /1 3/ 02 P M 14: 5 1 15: 2 1 Sepu l v eda B l vd 4

402 05 /1 4/ 02 AM 6 : 22 6: 36 F i r s t B u s S t op 4

403 05 /1 4/ 02 P M 14: 5 0 14: 5 9 B r ent w ood 4

404 05 /1 6/ 02 AM 8 : 32 9: 34 UCL A 4

405 05 /1 6/ 02 P M 11: 0 0 14: 1 0 UCL A 4

501 05 /2 0/ 02 P M 15: 0 0 15: 3 0 Sepu l v eda B l vd 4

502 05 /2 0/ 02 P M 15: 5 6 16: 5 7 Sepu l v eda B l vd 5

503 05 /2 2/ 02 AM 6 : 10 6: 29 F i r s t B u s S t op 5

504 05 /2 3/ 02 P M 15: 0 4 15: 1 9 B r ent w ood 5

601 05 /2 9/ 02 P M 14: 5 5 15: 2 5 F i r s t B u s S t op 6

602 05 /3 0/ 02 P M 15: 0 8 15: 2 7 F i r s t B u s S t op 6

701 06 /0 4/ 02 P M 15: 2 3 16: 0 2 Sepu l v eda B l vd 7

702 06 /0 5/ 02 AM 6 : 15 6: 29 F i r s t B u s S t op 7

801 06 /1 1/ 02 P M 15: 0 4 16: 4 3 Sepu l v eda B l vd 8

802 06 /1 7/ 02 P M 12: 5 5 16: 2 1 L a bo r a t o r y

6970 98 14 39 19 30 48 12 12 62 14 30 33 53 N =

Ru n 80 1 70 2 70 1 60 2 60 1 50 2 50 1 40 5 40 4 40 2 401 30 1 20 1BC_DIF 25

15

5

-5

-1 5

-2 5

Figure 5.1.3.1 Variation in absolute differences (μg/m 3 ) between paired Aeth alom eters o v er the course of the m a in study.

32 12 29 15 26 12 12 41 24 151 57 9 12 27 28 33 90 N =

Run 801

702 701

602 601

504 503

502 501

405 404

403 402

401 301

201 101NO2_DIF 50

40

30

20

10

0

-10

-20

-30

-40

-50

Figure 5.1.3.2 Variation in absolute diffe rences (ppb) between paired NO 2 instrum e nts over the course of the main study.73 14 39 19 14 5 19 43 30 190 62 9 14 30 33 53 119 N =

Run 801

702 701

602 601

504 503

502 501

405 404

403 402

401 301

201 101PAH_DIF 50

40

30

20

10

0

-10

-20

-30

-40

-50

Figure 5.1.3.3 Variation in absolute differences (ng/m 3 ) between paired PAH instrum e nts over the course of the main study.

98 14 39 18 30 15 19 48 30 189 60 9 14 30 33 51 N =

Run 801 702

701 602

601 504

503 502

501 405

404 403

402 401

301 201PC_DIF 50

40

30

20

10

0

-10

-20

-30

-40

-50

Figure 5.1.3.4 Variation in absolute differences (#/cm 3 ) between paired fine particle count instrum e nts over the course of the m a in study.

7198 35 19 30 15 19 48 30 190 62 9 14 30 33 53 120 N =

Run 801 701

602 601

504 503

502 501

405 404

403 402

401 301

201 101PM25_DIF 50

40

30

20

10

0

-10

-20

-30

-40

-50

Figure 5.1.3.5 Variation in absolute differences (μg/m 3 ) between the DustTrak
PM 2. 5 instrum e nts over the course of the m a in study.

The subsequent assessm e nt of the instrum e nt precision data quality w a s perform e d via two different m e thods: precision estim a tion and correlation between pairs of instrum e nts.

5.1.3.1 Precision Estim ation Between Paired Instrum e nts The sim p lest way to establish the q u ality of the pair ed ins t r u m e nt prec ision da ta w a s to

com pute the precision o f the paired instrum e nt s, which we did by using absolute differences, percent differences and coe fficients of variation.

To perfor m these calculations , the original ten-second data were sm oot hed using a onem i nute m o ving average. This pr ocedure was used only for th is an alysis. Th e rem a ining calculations presented in this report used the validated 10-second data. W e tested different periods for the m oving a v erage, ranging from 30- second to 10-m inute aver ages. After analyzing the effects of such averaging periods on the result s of the precision calculations, we selected the
1-m i nute averaging tim e since it provided enoug h tim e resolution while m i ni m i zing noise in the concentration-tim e series. Although this pr ocedure im proved the re sults of the precision experim e nts, it did not affect the outcom e of our analy s es. The paired instrum e nt precision values were only used on a rela tiv e basis to ran k the perform a nce of the pa ir ed ins t rum e nts and to dete rm ine their suita b ility f o r ev aluating f r o n t/rear and inside/outside contrasts (see Section 5.1.3.3).

725.1.3.1.1 Absolute and Percent Differences We used equation (5.1.3.1) to calculate the averag e absolute precision (P ) between paired instrum e nts:


# ( ) 2

μ σ • ± −

= 

# ∑ 96

. 1 c c P 1 (5.1.3.1)

e

m

p le size, and N

where c 1 is the concentration of a pollutant obtained with on e of the ins t ru m e nts at a given tim e , c 2 is the concentration of the sa m pollutant obt ained with the second instrum e nt at the sam e tim e, N is t h e s a μ 

### σ is the standard deviation of the distribution of differences

t a n d a

. 3

. 2 s u m m arizes these results for the pollutants that

r e c i s i o n w a s 1 3 % w h i l e t h e

, w e d i d n o t u s e t h e d a t a f r o m the NO 2 precision experiments conducted during

, r o adw a y type, and window position, which were done

a t e l y 6 5 % f o r black carbon, 20% for NO 2 , 10%

r P A H

e r e f o r e n o t a l l o f t h e s e v e n t een runs m e ntioned in Table 5.1.3.1 are presented in Table 5.1.3.2. ( s rd e r ror).

To give context to the results, we com put ed the m e dian of all bus run data for each pollutant. We then calculated the ratio betwee n the averag e absolute p r ecis i on be tween paired instrum e nts during precision experim e nts, and the m e di an value of the b u s runs. W e ref e r to this

a t i o a s t h e p e r c e n t a g e p r e c i s i o n

. T a b l e 5

. 1 r were consid ered for the real-tim e analyses.

We observed from Table 5.1.3.2, that in term s of percentage precision between paired
instrum e nts, the pair of instrum e nts reading fine particle counts in the size range of 0.3–0.5 μ m (PC) exhibited the best behavi or (5% precision), closely fo llowed by the PAH and DustTrak

M 2. 5 analy zers (8 and 9%, resp ectively). The NO 2 instrum e n t s p P Aethalom eter (m easuring black carb on) exhibited a 53% precision.

During weeks four and five, the NO 2 analyzers experien ced tec hnica l p r o b lem s that m a de the in terp re tation of the data dif f i cult. Hence, for the estim a tion of precision of NO 2

easure m e n t s m those weeks.

The poor precision for Aethalom eters (53% pr ecision) lim ited front-to-back and insideto-outside com p arisons of black carbon bus co mmute data. Therefore, for black carbon, we focused on com p arisons that did not require th e use of paired instrum e n t s , i n c l u d i n g o u r

s s e s s m e n t o f v a r i a b l e s s u c h a s b u s t y p e a using the sam e instrum e nt on all buses.

The resu lts of the labo ra tory post-study precision experim e nt confirm e d fi ndings from the bus-based precision m e asurem ents discussed above . The precisions obtained during the poststudy tests were sim ilar to those obtained during the weekly inst rum e nt precision experim e nts for black carbon, NO 2 , PAH, and PC instrum e nts. For the PM 2. 5 analyzers (DustTrak )

, the p r ecisio n im proved considerably under labora tory conditions (see Table 5.1.3.2). The relative precision b t a i n e d d u r i n g t h e l a b o r a t o r y t e s t w a s a p p r o x i m o f o , 7

. 5 % f o r P C

, a n d a b o u t 1 % f o r P M 2. 5.

Several of the instrum e nt precision experi m e nts had sam p le sizes too sm all to allow

m eaningful calculations a n d t h

73Table 5.1.3.2Paired instrument precision (average absolute and percent differences)(*)Units101201301401404405501502601701801802 (1)Range ofcontrolledexperiment(2)Percentdifferencecontrolledexperiment(%)Averageabsoluteprecision (3)Median(4)Percentdifference(%)Typical peak(5)Black carbonug/m3N/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 ± 5.60.2 - 5224± 1.6353%20 - 40NO2ppb1.4 ± 1.0-10.1 ± 2.4-15.7 ± 0.8-2 ± 1.340 ± 1.228.3 ± 0.616.5 ± 1.112.7 ± 1.08.4 ± 0.91.8 ± 3.2-4.9 ± 1.1-3.1 ± 0.77 - 29.317± 75513% (6)150PAHng/m30.18 ± 0.21.78 ± 0.59.6 ± 2.4-4.1 ± 1.6-1.2 ± 0.60.6 ± 0.31.4 ± 0.61.4 ± 0.4-0.8 ± 0.69.6 ± 1.30.7 ± 0.1-98.9 ± 14.30.5 - 200010± 3.0408%500 - 1000PC#/cm3N/A0.52 ± 0.26.2 ± 1.2-13.4 ± 0.46.7 ± 0.5-11.2 ± 0.30.85 ± 0.6-0.5 ± 0.15.4 ± 0.75.3 ± 1.02.9 ± 0.6-52.1 ± 19.96 - 14007± 5.2955%150 - 200PM2.5ug/m33.5 ± 0.3-3.9 ± 0.22.7 ± 0.6-6.6 ± 0.35 ± 0.13.1 ± 0.10.5 ± 0.30.3 ± 0.15.8 ± 0.9-5.8 ± 1.9-9.8 ± 0.413.8 ± 14029 - 316001± 4.3509%100 - 200(*) 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 averag1Instrument precision experiment performed under controlled conditions in the laboratory (post-study test).2Range of values observed during the instrument precision experiments performed in the laboratory (Run 802).3Average precision of experiments performed inside the bus. Does not include run 802.4Median of the entire data set, including all the runs.5Range of peak concentrations observed during exposure runs.6The NO2instruments 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) re comm end the use of the coefficient of variation as a m easure of precision between in strum e nts sampling the sam e volum e of air under the sam e sampling conditions. The first step of this m e thod is to calculate the percent difference (D i ) between each m easurem ent using equation (5.1.3.2):



## ( ) 100

2 X Y D

i i i i i • + = (5.1.3.2)

h e r e Y i is the concentration m easured by one of the instrum e nts at a given tim e and X i i s t h X

− Y

e

o n c e n t r a t i o n m easured by the other instrum e nt at the sam e tim e. The percen t difference is then used to calculate the coe f f i c i e n t o f v a r i a t e q u a t i o n ( 5

. 1

. 3

. 3 ) : w

c i o n ( C V i ) for each d a ta poin t u s i n g

2 D CV i
i = ( 5

. 1

. 3

. 3 )

Finally, we com p u t e d t h e o v e r a t V ) a s t h e root m ean square of l l c o e f f i c i e n o f v a r i a t i o n ( C the distribution of individual coe fficients using equation (5.1.3.4):

N CV CV i 

## ∑

= (5.1.3.4)

These results are summ arized in Table 5

. 1

. 3

. 3

, w h i c h s h o w s t h a t in term s of the

o e f f i c i e n t o f v a r i a t i o n , t h e p a i r of instrum e nts measuring fine par ticle counts in th e size range of
% p r e c i s i o n )

, t h e P M analyzers (DustTrak)

. 1

. 3

. 2 C o r r e l a t i o n B e t w een Pairs of Instrum e nts c 0.3 – 0.5 μ m (PC) exhi b i t e d t h e b e s t b e h a v i o r ( 7 2. 5 ranked second (14% precis ion), and the PAH and NO 2 instrum e nts ranked third and fourth (23 and 33%, respectively). Again the Aethalom eters ranked last (52%).

5

m a r i z e s t h e c o r r e l a t i o n c o e f f i cients found for the instrum e nt precision

x p e r i m e nts. During these tests both instrum e nts in each pair were suppo sed to behav e sim ilarly,

For the correlation coefficients analysis, the laboratory post-study experim e nts showed dif f e rent re s u lts than th e weekly in strum e nt precision experim e nts. P M 2. 5 exhibited the best correlation during the laboratory te sts (0.90), while the correlation coefficient associated with Precision is one m eans of m easuring agreem ent. Another is tests of correlation. W e u s e d the Pearson' s correlation coefficient to describe the strength of the linear association between th e readings of the pairs of instrum e nts.

T a b l e 5

. 1

. 3

. 4 s u m

e therefore, th e values of the corr elatio n coefficients were expected to be close to one. Additional logarithm i c transform a tions of the data distributi ons (not shown) did not appreciably im prove 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 NO 2 (0.57) and PM 2. 5 (0.54). Once again, the poorest results were obtained fo r the BC data (0.18).

75Table 5.1.3.3Estimation of precisionbetween paired instruments using the coefficient of variation (%)(*).101201301401404405501502601701801AverageVariationBlackCarbonN/A67± 1234± 1833± 2255 ±1061± 2239± 2674± 1629 ±1654± 1168± 1152%NO2N/A26± 933± 2715 ± 450 ±358± 320± 1123± 1231 ±3048± 1726± 1433%PAHN/A23± 723± 1114 ± 725 ±1017± 1622± 1635± 1531 ±1820± 819± 1223%PCN/A3± 27± 223 ± 48±212± 54± 24± 12±15± 36± 27%PM2.5N/A30± 38± 317 ± 413 ±26± 310± 89± 44±212± 429± 414%(*) The valuesinthetablerepresentthe coefficientofvariationandthe 95%confidenceintervalTheoriginal10-secondsdataweresmoothedusinga 1-minutemovingaverage76Table 5.1.3.4. Correlation coefficients for paired instrument precision experimentsable 5.1.3.4.Correlationcoefficientsfor instrument precisionexperimentsT101201301401404405501502601701801802 (1)AverageBlackCarbonN/A0.130.340.030.100.040.100.040.240.650.140.620.18PAH0.450.760.690.760.730.710.830.130.460.790.850.880.65NO20.880.830.140.080.910.890.600.890.560.120.42N/A30.57PCN/A0.840.440.340.590.930.880.880.760.390.980.620.70PM2.520.830.350.120.540.510.870.670.340.410.340.980.880.541Instrument precision experiments performed undercontrolled conditions in the laboratory2Data obtained usingthe DustTraks3Not applicable(seetext)77PAH was 0.89, and for both BC and PC, 0.62. W e we re not able to establish a m e aningful correlation coefficient for the NO 2 data due to the previously m e ntioned problem wi th the NO 2 instrum e nt part way through the study.

The results f o r the higher concentration post- study experim e nts show th at the lim itations experienced during the in-field instrum e nt precision experim e nt s, in particu l ar with th e Aethalom eter, are associated with th e relativel y low concentrations encountered during the busbased precision experiments.

In addition to the co rr e l ation coef f i cient, which was used as a m easure of variab ility between pairs of instrum e nts, we considered the slope obtained from the lin ear regression. Thi s quantity is a m easure of bias be tween instrum e nts and can be used to es tim a te correction factors if necessary. Table 5.1.3.5 summarizes these findings.

To better understand the im plications of these result s, we created Table 5.1.3.6, w h ich shows the bias between instrum e nts, 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 analyzer s exhibited the best behavior along the course of the study. The average bias for those instrum e nts was about 30%. PM 2. 5 and NO 2 ha d biases of 46% and 61%, respectiv ely, while black carbon m easurem ents showed a bias of 85%.

5.1.3.3 Overall Ranking Based on the results presented above, we ge nerated an overall ranki ng for the instrum e nt precision data. As described in Table 5.1.3.7, the be st instrum e nt pairs in term s of precision were the O P C (PC) and the PAH analyzer, whic h ranked first and se cond respectively. The DustTraks (PM 2. 5 ) ranked third, the NO 2 analyzers fourth, and the Aethalom eter (BC) fifth.

Although the DustTraks exhibited a relative high precision, these instrum e nts also showed low accuracy (see Section 5.1.2.2). These re sults were som e what expected since this type of instrum e nt is kn own to be less accurate (Hinds, 1999).

Table 5.1.3.7 Overall ranking of the paired instrum e nt precision data quality.

I n s t ru m e n t P o l l u t an t P reci si o n (% ) C o ef f i c i en t o f
V ari a t io n C o r r e l a tion O ve rall R a n k ing

O P C Clime t F i ne P a rt i c le C oun t s ( 0

. 3 t o 0.5 um ) 1 1 1 1

E c oc h e m P A S 2 000 P a r ticle

- b o und P A H 2 3 2 2

Du st Trak PM 2. 5 Ma ss 2 2 4 3

UC R an a l y z e r NO 2 4 4 3 4

A e t h a l o m et e r Bl ac k C arb o n 5 5 5 5 CR I T E R I A

78Table 5.1.3.5Regressionresults for the paired instrument precision dataTable 5.1.3.5Regression results for the instrument precision data(*).101201301401404405501502601701801BlackCarbonN/A0.323.380.190.300.070.450.130.281.500.63NO20.740.910.08-0.400.480.390.340.800.420.060.47PAH0.730.480.721.130.680.660.650.360.810.920.96PCN/A0.850.660.510.521.010.181.000.760.660.79PM2.50.740.380.401.000.440.750.210.270.420.500.80( *)The valuesinthe table represent the slope calculated for the linear regressionmodel.The valuesincolor represent caseswhere the p-valuesindicated non-significant results.7980Table 5.1.3.6Bias between the instruments duringinstrument precision experiments(*).101201301401404405501502601701801AverageDifferenceBlackCarbonN/A68%238%81%70%93%55%87%72%50%37%85%NO226%9%92%140%52%61%66%20%58%94%53%61%PAH27%52%28%13%32%34%35%64%19%8%4%29%PCN/A15%34%49%48%1%82%0%24%34%21%31%PM2.526%62%60%0%56%25%79%73%58%50%20%46%(*) The valuesinthe table represent the difference betweenthe expected (one) and the observed slope fromthe linear regression.As m e ntioned above, the poor results obtained for instrum e nt precision for black carbon

m easure m ents are likely to be related to relativ ely low concentrations encountered during those tests. As stated earlier howev er, th ese precisio n estim ates only apply to the precision between pairs of instrum e nts. Paired ins t ru m e nts were only used to evaluate c oncentration differences between the front and rear of the bus cabin, and between the inside and outside of the bus during the sam e run. Paired instrum e nts were not used to evaluate the differences in co ncentration s between buses. For each pollutant, the sam e instru m e nt was used to m e asure concentrations at the rear of the bus cabin for all buses tested, an d this m easurem ent was used for th e c o m p arison between bus types (and all other com p arisons re ported except front/rear and inside/outside). Thus, the precision results reported here allowed us to sele ct th e best pairs of instrum e nts for studying the front/rear and inside/o utside contrasts, bu t do not represent an overall m easure of precision of a given instrum e nt.

5.1.3.4 Use of Correlation Coefficients Table 5.1.3.7 shows the overall ranking of th e instrument precision data quality, accord ing to the criteria discussed above. P C and PAH m easurem ents, during instrum e nt precision experim e nts, ranked firs t and second respectively. Thes e results suggest the PC and PAH analyzers p r ovid e d the m o st reliab l e d a ta for observing contrasts between pairs of instrum e nts. For this reason, w e focused on these two pollutants when perform i ng the

com p arisons between p o llutant con centration s m easured at different locations during the bus runs (e.g., outside versus inside).

The results of the instrum e nt precision experim e nts were used to understand the lim itations of the data and to dete r m ine which instrum e nts were suita ble f o r certain analy s es developed in the study. W e did not apply any corre ctions to m a ke one instrum e nt equivalent to the other.

However, we also inclu d ed the o t h e r polluta nts as appropriate. For the bus runs, we found reasonably strong correl ations between PAH and black carbon and between PC and PM 2. 5

. Tables 5.1.3.8 and 5.1.3.9 summ a r ize these findings and again, suggest the relatively poor precision of the pair of Aethalom eters was pr obably due to low concentrations encountered during the instrum e nt precision experim e nts. Tables 5.1.3.8 and 5.1.3.9 show that black carbon and PAH te nded to exhibit sim ilar behaviors during the bus runs, while PM 2. 5 and PC also exhibited sim ilar trends and res pon ses to the v a riab les de term in ing pollutant concentrations. Hence, for certain comparison we assum e d bl ack carbon and PM 2. 5 concentrations could be explained based on the results obtain ed for PAH a nd PC, respectively.

Because the NO 2 data gathered during instrum e nt pr ecision experim e nts generally did not exhibit good precision we did not include NO 2 in the analyses of front/rear and inside/outside

contrasts.

5.1.4 Evaluation of Sa m p ling Line Losses We have previously evaluated losses of PM 10 through the sam p ling lines used in this study (1/4 O D polyethylene im pregnated with metal to reduce static charge buildup). To do this, we used three collocated DustTraks equipped with PM 10 in lets. They sam p led am bient air f o r two hours, alternating ten m i nutes without tubing and ten m i nutes with tubing (1.7 m in length).

81Table 5.1.3.8. Correlation between PAH and other pollutants during exposure runsHE1 HE2Run 2 5 6 8 10 12 13 14 15 17 18 19 20 23 24 25 26 28 30 31 33 35 36Average (1) All (2)NO20.01 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 0.45 0.09 0.19 0.03BC 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 0.56 0.87 0.67 0.51PM2.50.01 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 0.12 0.84 0.38 0.03PC 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 0.01 0.60 0.26 0.01Table 5.1.3.9. Correlation between PC and other pollutants during exposure runsHE1 HE2Run 2 5 6 8 10 12 13 14 15 17 18 19 20 23 24 25 26 28 30 31 33 35 36Average (1) All (2)NO20.02 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 0.11 0.10 0.12 0.01BC 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 0.01 0.61 0.26 0.01PM2.50.10 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 0.68 0.69 0.53 0.811Arithmetic average of runs presented in the table2Correlation coefficient estimated using the entire data setCNGHE3 RE1 RE2 TO1CNGHE3 RE1 RE2 TO1Data were collected as thirty-s econd averages. The data are su mm arized in Table 5.1.4.1. Based on the m eans, the tubing caused a loss of PM r a nging from 21% to 29% depending on which DustTrak was evalu a ted

.

Table 5.1.4.1 Mean DustTrak PM 10 response on am bient air w ith and without Bev-A-Line tubing

. Mean DustT r ak 1
(mg/m 3 ) w/o t ubing Mean Du stT r ak 1
(mg/m 3 ) w/ tu bing Differenc e DT 1
(w/o -w ) % Differenc e DT 1
(w/o -w ) SD of Differe nce

0

. 2 6 9 0

. 2 1 3 0

. 0 5 6 2 0

. 8 % 0

. 1 1 7

Mean DustT r ak 2
(mg/m 3 ) w/o t ubing Mean Du stT r ak 2
(mg/m 3 ) w/ tu bing Differenc e DT 2
(w/o -w ) % Differenc e DT 2
(w/o -w ) SD of Differe nce

0

. 3 5 9 0

. 2 7 2 0

. 0 8 7 2 4

. 2 % 0

. 1 3 2

Mean Du stT r ak 3
(mg/m 3 ) w/o t ubing Mean Du stT r ak 3
(mg/m 3 ) w/ tu bing Differenc e DT 3
(w/o -w ) % Differenc e DT 3
(w/o -w ) SD of Differe nce

0

. 2 5 3 0

. 1 7 9 0

. 0 7 4 2 9

. 3 % 0

. 1 1 0

In addition, the data sets for each D u stTrak were com p ared with and without th e tu bing using the Wilcoxan (Mendenhall, 1971) non-param e tric ranking test (in this test the data do not have to be norm a lly distributed). For all thre e DustTraks the data sets were shown to be statistically different. The loss of PM 10 due to the tubing, therefore, is significant and should be considered in future PM 10 data analysis.

However, we did not evaluate sampling line losses f o r any pollutan t s analyzed in this report, o r make corrections to our d a ta to a ccou n t for sam p l i ng line loss es because s a m p ling line losses will b e highly dep e ndent on th e particle si z e distributio n and theref ore will va ry depending on the characteristics of the PM bein g sam p led, a nd because we believe any losses were sm all. Specif i cally, the estim a tes of sam p ling line los s e s given her e f o r PM 10 repres ent a worst-case, upper-lim it. The PM s a m p ling lines we used inside the buses ranged from a few centim eters (integra ted PM 2. 5 m a ss both front and rear) up to 1and 1.5 m e ters in length at the front and rear of the bus, respectively, for real-tim e PM 2. 5 m a ss and fine particle coun ts in the range of 0.3 – 0.5

μm in diam eter, the PM size ranges our analyses focused on (see Section 4.1.2.2.2 for a discussion of sa m p ling line lengths). Based on results from other studies, we expect lower

sam p ling line losses f o r particle s in these size ra nges com p ared with PM 10 . In addition, we also expect the shorter sam p ling lines used inside the buses compared with the study cited above would result in lower sa mpling line losses. Mo r e over, it should be em phasized that most of our analyses w e re based on relative com p arison s between buses and did not use absolute concentrations or particle counts. In summ ary, we expect particle losses were s m all, particularly for m easurem ents m a de inside the buses, since we focused our m a in study analysis on particles < 2.5 μm in diam eter.

835.1.5 Autocorrelation Autocorrelation, also known as serial correl ation, is the statistic that m e asures the

corre lation o f a variable with itself o v er tim e . It can be calcu l ated for la g s (k) of any size, where the effective sam p le size (n) of paired values will decrease by one of each lag.

Assessing the autocorrelation is important sin ce data se ts with this attribute are m o re

com p lex to analyze. Several stat istical procedu r es, such as significan ce tests, assu m e the data are free of autocorrelation (i.e., th e series of observations are independ ent of one another).
Am ong other effects, autocorrela tion can severely reduce the eff ective sample size and degrees of freedom in a tim e series.

In any tim e series containing non-random patterns of behavior, it is likely that any particula r item in the se ries is som e what re lated to o t her item s in the sam e series. For exam ple,

am bient air data are, in general, likely to exhibit autocorrelation. Autocorrelation can be

m easured by estim ating the sam p le autocorrelation coefficients (r k ), w h ere valu es of

n 2 ±

denote autocorrelations significantl y different from zero (Norusis , 2002). W e used equation (5.1.5.1) to com pute the sim p le autocorrelation:



## ( ) ( ) 

## ] [ ( ) 

## ∑

− i y y

where y is the value of the object se ries, x is a lagged value of it, and 

## ∑

2 i - x (5.1.5.1) − i y y x

y and x are the respective

m eans; there are n-k term s i n t h e n u m e rator summ ation, and n term s in the denom inator

u m m a t i o n

. W h ere simple autocorrelations are co m puted, no account is taken of the correlation

e e n e x c l u d e d

. T h e results of i s t y p e o f a n a l y s i s a r e o f t e n portrayed graphically in the form of a correlogram , which is a plot

o f t h e a

r a l

, o u r data did not exhibit signi ficant autocorrelation.

e n c e , w e d i d n o t p e r f orm any type of transf orm a tion or statistical treatm e nt to account for

u t o c o r r e l a t i o n d u r i n g our data analysis. s between other pairs of entries.

An alternative m e thod for assessing autoco rrelation is via the partial autocorrelation coefficient (for order k), which m eas ures the strengt h of correlation am o n g p a i r s o f e n t r i e s i n t h e tim e series while accou n ting for (i.e., rem oving the e ffects of) all autoco rrelations b e l o w o r d e r k

. For exam ple, the partial autocorr elation coefficient for order k=5 is com puted in such a m a nner that the effects of the k=1, 2, 3, and 4 partial autocorrelations have b

t h

u t o c o r r e l a t i o n c o efficient (s imple or partial) against the lag.

We generated sim p le autocorrelation corre logram s and partial co rre lation corre lograms for all of the exposure ru ns. Figures 5.1.5.1 to 5.1.5.3 illus t rate exam ples of partial co rrelog r am s obtained for three pollutants. The partial autocorrelation correlogram s e x h i b i t n o n

- z e r o coefficients for only one or two lags, beyond which they are not signi ficantly different from zero. Therefore we assum e d i n significant autocorrelati on a m ong these variables. Si m ilar results wer e obtained for m o s t o f t h e r u n s a n d

, i n g e n e H

a

84

### BC_I_R

Lag Number 29

27 25

23 21

19 17

15 13

11 9

7 5

3 1Partial ACF 1.0

.5

0.0

-.5

-1.0 Confidence Limits

Coefficient

Figure 5.1.5.1 Partial autocorrelation correl ogram for black carbon during Run 6.

Figure 5.1.5.2 Partial au tocorrelation co rrelogram for PAH during Run 6. 

### PAH_I_R

Lag Number 29

27 25

23 21

19 17

15 13

11 9

7 5

3 1Partial 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 1Partial ACF 1.0

.5

0.0

-.5

-1.0 Confidence Limits

Coefficient

F Partial au toc o rrelation co rrelog r am f o r P M i g u r e 5

. 1

. 5

. 3 2. 5 during Run 6 (data collected with the DustT r ak ins t rum e nts).

s i d e t h e c a b i n ; h e n c e , t h e t i m e

r i e s w e r e , t o s o m e extent, dom inated by these high em issions.

a n a l y s e s s u g g e s t t h e i r e f f e c t w a s n o t a s im portant as the random encounters with high em itters.

l a t i o n

. T h e s e m e t h o d s a r e

o m p lex and were deemed to be bey ond the scope of this report. One possible explanatio n for the relative lack of autocorrelation in our data is re late d to the frequent presence of high-em itting vehicles in the su rro undings of the bus. Encounters with heavy duty vehicles (particularly other school buses ) occurred random ly m a ny tim e s during the runs and, as described in Section 5.3.5, the pres ence of these high em itting vehicles was one of the m o st importan t f acto r s dete rm ining pollutan t c o n c e n t r a t i o n s i n

s e

It was clear from the fieldwork that encount ers with high em itting vehicles were largely random occ u rrences except when we were carava nning with other BSMS buses at the beginning of the afternoon runs. Although ev ents such as red lights, stop signs, and bus stops had the potential to be not entirely random , the autocorrel a t i o n

The analysis described above addresses only th e autocorrelation within runs. It does not deal with th e autocor r elation betwe e n runs if we com b ined them for a particula r analysis. For

exam ple, it could be interesting to com b ine data for all m o r n ing runs an d com p are them to the

com b ined data for all afternoon r uns but this procedur e is complicated. Com b ining all m o r n i n g runs, for instance, would create an interrupted tim e series, which requires the use of pooled tim eseries cross-section m o dels (TSCS) in order t o a s s e s s a u t o c o r r e

c

In summ ary, we did not use tim e series analy s is f o r data sets com p rised of m o re than one exposure run. However, com b ining data from di fferent runs was requir ed for other types of analyses such as com p ar isons between bus types and bus routes. Assum i ng the data distributions

86were sim ilar from runs conducted with the sa m e type of bus under sim i l a r t r a f f i c a n d environm ental conditions, and considering the lack of autocorrelation within runs as discussed above, we were able to com b ine data from diffe rent runs to calculate m eans and confidence intervals an d t h e n u s e d t h e s e r e s u l t s t o d e t e r m ine significant differences between the analyz ed variables (e.g., bus types).

5.1.6 Nor m ality
Most s t atistical p r ocedures assum e the dis t ributions of the variab les und er cons ider a tion

com e from a norm a l population, or the sam p le si zes are large enough so the distributions of

sam p le m eans are app r o x im ately no rm al. Our d a ta sets were, in general, large enough to satisfy this as sum p tion (g rea t er than 50); h o w e v e r , w e r a n f o rm al tests to determ ine if the pollutan t

o n c e n t r a t i o n tim e series f it norm a l distributions.

ng the logarithm i c transform a tion, the data related

t h e b u s r u n s w e r e not norm a lly distributed.

e s c o p e o f t h e p r e s ent project. However, this woul d be an interesting topic for further study.

a i n i s c o n s i s t e n t w i t h t h e use of confidence intervals (which assum e independent sam p les). c

We used the Kolm ogorov-Sm i rnov and Shapiro- W ilk Criteria and found most of the data sets (dom inated by the bus runs) f a iled the nor m a lity tests (the only ex ception be in g the black carbon data collected during Run 19). Thus, th e data obtained during the bus runs were generally not norm a lly distributed. The logarith m i c transform a tion is a widely used procedure that could, in principle, apply to our cas e since m a ny studies have shown am b i e n t a i r concentratio ns tend to f o llow lo g-norm a l distributions. W e used the base 10 transform a tion and

r e p e a t e d t h e t e s t s

. H o w e v e r , e v e n a f t e r a p p l y i

t o

Figure 5.1.6.1 shows the histogram of the data collected with the PM 2. 5 analyzer (DustTrak) located at the rear of the bus dur ing Run 14 (RE1-AM). Th e distribution of these data is a rep r esentative exam ple of t h e m a jority of the bus run data for mo st pollutants. Figure 5.1.6.1 exhibits a som e what tri-m o dal distribution wh ere each m ode could be related to different conditions experienced during the bu s runs. One possible interpreta tion of these results is that the f i rs t m o de (low con centr ation ) was rela ted to low-traf f i c area s in W e st Los Angeles, the second m o de was related to higher traffic-density streets in the inner city, and the third m ode (high concentration) was related to concentr ation peaks resulting fr om self pollution or encounters with high em itting sources in both lo w-traffic and high-traffic areas (see Section 5.3.5). Proving this hypothesis would require a se ries of thorough and deta iled analyses that

w e r e b e y o n d t h

As m e ntioned above our data sets were in all cases sufficiently large (greater than 50 data points) that the violation of the normality assum p tion was not a factor for the type of analyses we perform e d. According to R. Berk (Personal Comm unic a tion, 2002), for this dataset, basic analyses were th e m o st useful. We therefore focused our analys es o n the us e o f confidence intervals an d central tendency m easures. F o r these an alyses, the m eans are statistically significantly different if the c onfidence 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, whic h a g

87Finally, it is noteworthy that if future anal yses of our data involving m o re advanced statistica l to ols were to be deve loped, it would be nece ssary to recons id er th e issues of norm a lity and serial correlation.

PM25_I_R 87 .5 82

.5 77

.5 7 2.5 6 7.5 6 2.5 57.5 52.5 47.5 42.

5 37.

5 

### HistogramFrequency 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 PM 2. 5 during Run 14.

Figures 5.1.6.2 show the norm a l Q-Q plot and the de-trended norm a l Q-Q plot,

respec tive l y, for PM 2. 5 during Run 14. These representations can be used to further describe the

not-norm a l characteristics of the data sets collected during the bu s runs. Figure 5.1.6.2b, in particular, re-em phasizes the tri-m odal charact eristics of th e o b served data distributions.

Normal Q-Q Plot of PM25_I_R

Observed Value 100 90 80 70 60 50 40 30 20Expected Normal 3

2

1

0

-1

-2

-3 Detrended Normal Q-Q Plot of PM25_I_R

Observed Value 90 80 70 60 50 40 30Devfrom Normal .6

.4

.2

-.0

-.2

-.4

-.6

(a) (b)

Figure 5.1.6.2 Nor m al Q-Q plots of PM 2. 5 during Run 14 (data collected with the DustTrak instrum e nts).

885.2. Relative Contribution of School Bus- Related Microenvironments to Children’s Exposure As described in previous sections, three m i croenvironm ents were investigated concerning children' s pollutant exposure due to school bus-rel ated activities: urban bus routes, bus stops, and a loading/u n loading zo ne at BSMS. Specifi cally, two urban routes to the BSMS were investigated; and two of the stops along urban route one (U1) were studied (Verm ont Elem entary School and Wee m es Elem entary), where diesel bus es arrived to pick up or drop off students traveling to or from ot her schools, and a larg e num b er of parents dropped off or picked up children using their personal vehicles. The loadi ng/unloading zone was i nvestigated during the pilot study by conducting m easurements with an in strum e nted van parked at the sidewalk of the BSMS. During the main study, the sam e instru m e nts were used for all bus stop and bus

commute measurem ents

.
Calculations of pollutant-specific exposure f actors, defined as the product of the average concentratio n for a sp ecific po llu tant tim es th e tim e spent by children in th e par ticu l ar

m i croenviro nm ent, were m a de for the three m i croe nvironm ents, based on pilot study data. Thes e initial calculations suggested the relative im portance of bus commutes versus bus stops versus the loading/unloading zone were q u ite d i f f e rent. Spec if ic ally, f r om the pilo t s t u dy 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 . W e concluded the loading/unloading zone

m i croenviro nm ent is generally not as im portant as the other two (in part becau se o f the sm al l

am ount of ti m e children spent at the loading/un loading zone).

In order to confirm these findings, w e conducte d m easure m ents at an ad dition a l bus stop during the m a in study, in part beca use a dirty bus was involved in the pilot study. Cleaner buses, of the kind used in several runs of the m a in study were expected to exhibit lower comm ute exposures, potentially leading to a greater exposure contribution from bus stops.

However, except for black carbon, average co mmute concentrations were higher during the m a in study (see Table 5.2.1). These results coul d be explained by severa l factors, including differences in m e teorological conditions between the two sam p li ng periods. The pilot study was conducted during winter tim e, when wind speeds and ventilation of the basin are generally higher than during the spring/summer season, w h en the m a in study was conducted. During the

m a in study, m e teorological cond itio ns were con s isten t th rou ghout the eight weeks of the

sam p ling period in May and June.

Another potential explanation could be the s hortening of the route used during the m a in

com p ared with the pilot study. The pilot study r oute started at 6:00 A.M. on sm all residential streets with alm o st no traffic for a considerab le pa rt of the comm ute. This m a y have reduced the average concentrations during the morning commutes with window s closed despite involving a “dirty” bus. Finally, it is also possible concentrations averaged over the entire m a in study period were dom inated by the high concentra tions r e sulting f r om the several high e m itter buse s

em ployed.

Table 5.2.1 summarizes the m ean con centrations observed in the three

m i croenvironm ents, over appropriate periods of tim e, f o r both the m a in and pilot s t u d ies. These

89values are comm only referred to in the litera ture as average exposure (Monn, C., 2001). The values reported for the loading/unloading zone runs during the pilot study and for the collocation experim e nts during the m a in study m a y be consid ered as urban background concentrations for the tim e s and locations at which the experim e nts were conducted.

Table 5.2.1 Mean concentrations in three m i croe nvironm ents in pilot and m a in studies.

Co ll o c at io n 1 L o a d i n g /

un lo a d i n g Z o n e 2 Bu s S t o p s Bu s
Commute s 3,4

BC (ug / m 3 ) 2 2 4 3 - 1 9 ( 8 )

PA H ( n g / m 3 ) 23 15 47 6 4 - 40 0 ( 1 3 4 )

NO 2 (p pb ) 64 35 54 34 - 1 1 0 (73 )

PC ( # /c m 3 ) 72 8 6 2 7 0 - 21 2 ( 1 3 0 )

PM 2.5 (u g/ m 3 ) 23 5 N/ A 2 6 2 1 - 62 ( 4 3)

1 Thes e v a lues w e re m e asured d

u rin g co llo catio n exp e rim e n t s and li k e ly ty

pify urban back g r ound conc en tration s du ring the m a in s t udy

2 Thes e m e asurem ents wer e co nducted during th e pilot stud y and lik ely ty

p i fy ur

ban b a ck g r ou nd co ncentrations during the pilot st ud

y

3 T h e r a nges ar e as soci at ed wi t h the dif f e r e nt bus t y pes ( s ee T a bl e 5. 3. 3. 1 )

4 T h e v a lues wit h i n pa r e nt he se s a r e t h e av e r ages f o r

al l r u ns

5 20 01 an nual arithm etic m e an fro m AQM D s t a tio n at C e nt ral L o s A n g e les Averag e Co ncent r ati o n s

Table 5.2.1 includes the results of the pilot study but does no t include the rural/suburban route data from the m a in study. T h e relevance of this latter m i croenvironm ent is described below (see S ection 5.3.5.2.4).

The results obtained during the m a in study were, in general, s i m ilar to those ob tained during the pilot study except for PC , for which the average concentr ation m easured at the m a i n study bus stop (W ee m e s ES) was about two tim es the average concentra tion m e asured at the pilot study bus stop (Vermont ES).

Sim ilarly, the average PC concentration in side the bus during m a in study urban route commute was about three tim es the average conc entration m easured insi de the bus during the pilot study comm utes. The m a jority of these diffe rences could be explained by a change in the particle counts analyzer; during the m a in study we used a more sophisticated device than in the pilot study.

The average BC concentration inside the cabin for the pi lot study route (14 μ g/m 3 ) was
about two tim es that m easured for the m a in study urban routes (8 μ g/m 3 ). The BC results, b o th for the pilot and m a in studies were consistent with the findings of a recent Natural Resources Defense Council (NRDC) study (Solom on et. al ., 2001). The NRDC study also f ound higher concentrations in the rear of th e bus and when the windows were cl osed, sim ilar to our results as described in Sections 5.3.6.2 and 5.3.2.

90Table 5.2.1 shows the loading/un loading m i croenvironm ent in front of BSMS was quite clean com p ared to the urban rou t e commutes or bus sto p s, with th e averag e concentration observed at the BSMS within, and slightly belo w, the range of urban background values reported in the litera t ure.

We calculated the ratio s of the average concentrations in th e bus runs and stops relative to the loading/unloading zone m i croenvironm ent and these results are presented in Table 5.2.2. Average co ncentration s at the bus stop m i croe nvironm ent were between two and eight tim e s higher than at the loading/unloading zone m i croenvironm ent (dependent upon the pollutant) while average concentrations ins i d e the bus on the urban routes were between 2 and 16 tim e s higher than at the loading/unloading zone. Aver age concentrations inside the bus on the urban routes were on average 1.3 to 2.8 tim es higher than at the bus stops.

Table 5.2.2 Ratios of mean concentrations inside the bus and at bus stop s rela tive to the loading/unloading zone.

M i c r o e n v ir o n m e n t T i m e s p e n t (m in

. )

L o a d i n g / unl oa di ng (e a c h tim e ) 3

B u s s t o p s ( e ach o n e) 2 - 5

U r b a n r o ut e ( e ach w a y ) 76 L oading /
unlo a din g Bus st o p s Ur b a n routes (m a i n stud y)

B C 124

P A H 139

NO 2 122

PC 1 8 16

PM 2. 5 1 N / A N / A R a tio s of Averag e Co nce ntrations (re lative to L / U)

Table 5.2.3 shows our estim ations of the tim e spent in each m i croenvironm ent. The commute tim e, or tim e spent insid e the bus was the m ean duration of the urban commute routes used during the m a in study (in the case of U1 re presenting the m i dwa y point between the first and last child on the full route used in the Pilot St udy--see Section 4.1.5.1). T h e loading/unloading and bus stop times were estim a ted from observations in the field. These values are consistent with the results repo rted in the South Coast School Bus Idling Study (Solom on et . al., 2002). In our study, tim e spent in commut i ng on the bus was between 15 and 38 tim es greater than for the other tw o m i croenvironm ents.

Table 5.2.3 Estim ated tim e spent in each m i croenvironm ent.

91Table 5.2.4 shows the exposure factors resu lting from t h e product of the average concentratio n and th e tim e spent in each m i croe nvironm ent. Across all pollutants th e exposure during bus commutes on urban routes w a s m u ch higher than for the other two

m i croenvironm ents. These result s confirm e d the findings of the pilot study that in general the loading/unloading m i croenvironm ent is the leas t im portant in term s of school bus related exposure, b o th because child ren gen e rally spend a short time on the sid e walk (th r ee m i nutes or less) before entering the school in the m o rni ng or boarding the buses in the afternoon, and because bus drivers were required b y school d i stri ct po licy to turn off their eng i nes as soon as they arrived in the m o rning, before the children left the buses. Sim ilarly, in the afternoon, drivers are not supposed to turn on their engines before all childre n are aboard the buses and the entire fleet is prepared to de part. D u ring both the pilot study and the main study w e observed a near 100% of com p liance with th ese regulations in the m o rning with som e what less than full

com p liance in the afternoon, due to buses leavi ng while som e children we re still waiting to board. However, we hav e anecdotal evidence fro m a BSMS t eacher that this policy is not always rigorously enforced (Fo g el, 2002). In addition, accord ing to a recent N R DC study (Solom on 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 W e em es Elem entary School bus stops, at various tim e s as m a ny as half a dozen buses would pull up in front of the school and wait with the engine idling until children boa rded. In several cases, buses

were ea rly a nd waited with their eng i nes idli ng f o r seve ra l m i nutes, while studen t s waiting f o r other buses stood nearby. Buses w ould then accelerate away from the curb, often releasing an exhaust cloud of black sm oke. Si m ilarly, parent s dropping off or pic k ing up children at those schools serv ing as bus s t ops, in a nu mber of cases were driv ing what ap peared to be high em itter vehicles (as docum ented by us in our field notebooks).

Table 5.2.4 Mean exposure factor calculations.

L o ading / unloading B u s stops 1 Urban commutes

BC (u g/ m 3 -m i n ) 5 8 - 20 600

P AH (ng / m 3 -m i n ) 45 95 - 230 10000

NO2 (ppb-min) 105 110 - 270 5500

PC ( # /c m 3 -m i n ) 25 125 - 310 10000

PM2.5 (ug / m 3 -m i n ) N/A 50 - 130 3500

1 T h e ran g e s are as s o ciated w ith th e dif f e ren t v a lu es f o r t i m e s p en t in each

m i croen v i ron m en t u s ed in th e calcu lation s (s ee T a ble 5.2.3 ) Ex posure F actors

92The calculated ratios of exposure factors fo r the three microenvironm e nts and these results are summ arized in Table 5.2.5.

Table 5.2.5 Exposure ratios for the three m i croenvironm ents.

B u s stops /

L/ U 2 Urban commutes /

L/ U 2 Urban commutes /

B u s stop 1

B C 1.3 - 3.5 100 30 - 75

PAH 2.1 - 5.2 220 45 - 110

NO2 1.0 - 2.6 5 0 20 - 50

PC 5.2 - 13 400 30 - 80

PM2.5 N /A N/A 25 - 70

1 T h e ran g e s are as s o ciated w ith th e dif f e ren t v a lu es f o r t i m e s p en t in each

i croen v i ron m en t u s ed in th e calcu lation s (s ee T a ble 5.2.3 )

L/U = lo a d ing/unlo a d i ng z o ne Ex posure Ratios

m

2

In term s of exposure, the bus stops m i croenvironm ent wa s between one and thirteen tim es m o re im portant than the lo ading/un lo ading m i croenvironm ent, while the urban bus commutes were between 50 and 400 ti m e s m o re i m portant than the loading/unloading

m i croenvironm ent. The urban bus comm utes led to between 20 and 100 tim es higher exposures than at the bus stops, depe nding upon the pollutant.

5.3 Im portant Variables Governi ng Exposure During Bus Commutes The design of this s t ud y allowed u s to inve stig ate the im portanc e of multiple var i able s governing children’s exposure to vehicle-relate d pollutants during school bus commutes. The variab les an alyzed in this study inclu d ed contri bution of the bus’s own ex haust, window position (open versus closed), bus type (based on engi ne type and age, fuel, and after-treatm e nt technology), route (rura l/suburban versus urban and between urban routes), roadway type (freeway versus non-freeway), the effect of followi ng other diesel vehicles, the effect of idling, position within the bus (front versus rear) a nd the difference between inside and immediately outside the bus. Only the front /rear and inside/out side com p arisons i nvolved using paired instrum e nts. All other variables were evaluated based on measurem ents taken at the rear of the bus cabin w ith the sam e instrum e nts (e.g. the sa m e Aethalom eter was used for all black carbon

com p arisons between buses, etc.).

The exposure runs on HE1, perform e d during the first week of the m a in study, were different than those conducted in subsequent weeks for a num b er of reasons, and thus were generally not included in our analyses. First, the instrum e nts on the bus were powered by a

93propane-fueled generator which wa s towed behind the bus, as was done in the pilot study. This created several prob lems. During the rural/s ub urban run, the generato r becam e disconnected from the bus, likely due to the vigorous m o tion of the bus at higher speeds. During the m o rning run on the prim ary urban route, the muffler fell o ff the gene rator while the bus was traveling on the freeway, which resulted in the bu s sitting on th e side of th e freeway for several m i nutes with the windows open and the instrum e nts still m easur ing pollu tants in the bus. In addition, during these runs, the window position on the bus was not fixed. W e alternated between windows opened and windows closed for both runs on the prim ary urban rout e. By contrast, the window position was fixed on all subs equen t buses, with windows in the m o rning always closed, an d windows in the afternoon always partially opened, refl ecting 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 attem p t to m i m i c regular BSMS bus commutes, we decide d to run the route at this earlier tim e because we felt it was im portant to cap ture the effect of caravanning (which occurs when leaving the school with the ninet een 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 imm e diately, without waiting for the other bus es. Because the run started an hour earlier th an usual, traffic conditions on the route were considerably ligh ter than enc ountered at the regular tim e . Because of these difficulties and confounding factors, we felt the e xposure run s on HE1 should not be included in the m a jority of our comparisons with the other bus es. However, these runs were still valuable because they provided data on bus commutes during period s of lighter traffic and allowed us to perfect our sam p ling strategy fo r the subsequent bus runs.

For the analyses described in the rem a inder of Section 5.3, only data from the second through eighth weeks of the study were us ed (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 Determ ination of Contribution of Bus’s Own Exhaust Inside the Cabin Because a p o tentially important so urce of ch ildren’s expo sure to pollutants from vehicle exhaust is the introduction of a bus ' s own exha ust into the passenger com p artm ent, our study included a m e thod to determ ine the percentage of air in the bus cabin originating from the bus’ s

94own exhaust. A tracer gas, SF 6 , was m e tered into the bus’s exhaust system fr om a high concentration cylinder using a m a s s flow contro ller whose flow rate was logged by the data acquis ition system and processed with the res t of the m easurem ent data. The con c entr ation o f SF 6 and the flow rate were selected to detect if at least 0.1 per cen t of the air in the passeng er

com p art m ent of the bus was from the vehicle’s exhaust. W e e m phasize these experim e nts were

com p letely independent of, and not affected by the SF 6 experim e nts conducted to determ ine the ventilation rates in the passenge r compartm ent (see Section 4.1.3).

The SF 6 injection probe extended

∼ 15 cm into the bus’s exhaus t pipe in order to provide reasonable m i xing of the SF 6 without attem p ting to snake the injection probe around the bends in the exhaust system . At the injection point, th e m a xi m u m exhaust temperature of the bus was expected to be about 200 o C, well be low the tem p eratu r e a t which SF 6 decom poses.

The SF 6 concentration in the bus was m easured on a continuous basis using a CTA-1000 continuous tracer gas analyzer whose inlet was connected to a series of solenoids that switched the sam p le inlet between the front and rear of th e bus cabin. To account fo r baseline drift of the CTA-1000 additional solenoids sw itched the instrum e nt to SF 6 -free air drawn through a line located outside the bus next to the right rear -view m i rror. Although this third sam p le line generally provided a ref e rence 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 am ounts of the bus’s ow n exhaust reached the location next to the passenger door at the front of the bus.

Control of the switching and logging of th e solenoid position was perform e d by the data acquis ition s y stem and s w itching tim e s f r om 2-4 m i nutes were us ed du r i ng th e in itial bus runs. After reviewing the system ’s response, a swit ching tim e of four m i nutes per pos ition (twelv e

m i nutes per cycle) was determ ined to provide th e optim um data and this switching time was used f o r all subs equent buse s . W h en the soleno id s switched f r om one position to the next, th e y generated a pressure pulse in the sample line that tem porarily threw the CTA-1000 off scale. The instrument’s recovery tim e from the solenoid switch i n g was dete r m ined to be close to th re e

m i nutes. As a co nsequ e nce, the first th ree m i nut es of data after each s o lenoid position change were voided and only the final m i nute of data was em ployed in the following analysis.

5.3.1.1 Analysis of SF 6 Data
Figure 5.3.1.1 depicts the conditions at the point of injection (A):

A Q eng , C eng Q exh , C exh

Q cyl , C cy l
Figure 5.3.1.1 Schem a tic of the SF 6 injec tion po int.

95where:

Q eng : Flow rate of the engine gases
C eng : Concentration of SF 6 in the engine gases (zero) Q cyl : Flow rate of the high-concentration SF 6 cylinder
C cyl : Concentration of SF 6 in the cylinder Q exh : Flow rate of the exhaust (end of tailpipe)
C exh : Concentration of SF 6 in the exhaust

The m a ss flow rates were expressed as:

cyl cyl cyl 

### C Q M • = (5.3.1.1)

where:

cyl : Mass flow rate of SF 6 f r om the cylin d 

### M e r

NOTE: sim ilar equ a tion s m a y be written f o r the m a ss flow rates from both, the engine and the

x h a u s t

. e

From the m a ss conservation principle (a t the point of injection) we know

cyl eng exh 

### M M M + = (5.3.1.2)

c o n s i d e r i n g 5

. 3

. 1

. 2 a n d s o l v i n g f o r C exh yields

cyl
exh exh Q

To estim ate the concentration of SF 6 cyl C Q C • = (5.3.1.3)

in the exhaust, we need to know the flow rate of

x h a u s t w h i c h i s g i v e n b y e q u a t i o n ( 5

. 3

. 1

. 4 )

, a c o mm only used expression in m echanical engineering (W estern Fi lter Co, Inc., 2002) e

C V D E Q eff rpm exh • •

= (5.3.1.4)

:

h s e s

r m i nute

ef

f : Volum e tric ef f i ciency : Engine' s cycle facto r w h e r e Q e x : F l o w r a t e o f e x h a u s t g a E rp m : Engine' s r e v o l u t i o n s p e D : Engine' s displacem e n t V C

96For a tu rbo c harged d i esel eng i ne, the aver age volum etric efficiency (m easure of the charge that enters the cylinder compared to id eal conditions) is about 1.5. Two-c y cle engines displace their engine size every re volution while 4-cycle engines di sp lace their engin e size ev ery two revolution; therefore, the engin e ' s cycle fact or (C), a measure of th e num b er of spark-plug fires per revolution, is one and two for 2-cycle a nd a 4-cycle engines, respectively. Five of the

s e v e n b u s e s u s e d i n t h e s t u d y h a d 4

- c y c l e e n g i n e s a n d t h e b u s e s u s e d i n t h e s t u d y h a d e n g i n e displacem ents rangin g from six to 10 liters. Diesel eng i nes typically o p erate aroun d 1500 rpm

s e d R u n 3 1 t o i l l u s t rate our calculations. The bus used for this run had an 8.3 liter,

- c y c l e , t u r

b o c h a r g e d e n g i n e

. Using equation (5.3.1.4) and th e values m e ntioned above we estim ated th e average flow rate of e xhaust gases u r i n g R u n 3 1 t o b e a p p r o x i m a tely 9000 liters

per m i nute. for a variety of speed/gear com b inations ( this nu mber was selected after reviewing several d i esel engine specifications an d perform a nce curves).

W e u 4 d

min L

rev 1 min rev

exh 9000 2 1.5 8.3L 1500 Q ≅ • •

=

The release rate of SF 6 from the high-concentration gas cy linder varied between run s and even within runs. The release rate during R un 31 was about 2.3 liters per m i nute. The concentratio n of SF 6 in the canister was 5000 ppm for the first five weeks of the study and 10000

p m for the last three weeks. During R u n 3 1

, p w e u s e d a 1 0 0 0 0 p p m cylinder.

Using equation (5.3.1.3) we esti m a ted the concentration of SF 6 n t h e e x h a u s t d u r i n g R un 31 to be approxim a tely 2.5 ppm (t he average concentration of SF 6 measured in side the cabin

a s a b o u t 1 0 0 0 p p t o r 1 p p b )

. i

d

u r i n g R u n 3 1 w

2.5ppm 10000ppm 9

2.3 C min L

exhaust

- exh ≅ • = 0 0 0 min L

o ( R )

, w h i c h r e p r esents the p ation inside the c a b in T h e r a t i

o r i g i n a e rcen tage of pollutan t co ncentr
t i n g f r o m the exhaust, or the concentration of SF 6 in the cabin div i d e d b y t h e concentration of SF 6 in the exhaust was

% 04

. 0 ppt 1x10

1ppm 2.5ppm 1000ppt

C C R 6 Exhaust cabin ≅ • = = (5.3.1.5)

Table 5.3.1.1 summ arizes the results obtained f o r all of the bus runs (e xcept from t h e f i r s t

e e k ) u s i n g t h e p r o c e d u r e d e s c r i b e d a b o v e f o r S F 6 m easurem ents at the rear of the cabin. Th e results presented in this table as well as in Fi gure 5.3.1.2 show the percentage of air inside the cabin orig in ating from t h e exhaust were a func tion of bus type as well as window position. Values were substantially highe r during m o rning runs, when a ll the windows of the bus were closed com p ared with af ternoon runs, when th e windows were partiall y opened. Thus, the SF 6 data suggest exposure to pollutants originating from the bus' s own e m issions were higher when w

97Table 5.3.1.1. SF6measurement resultsBus typeDisplacement(L)CyclesExhaust flowrate (lpm)1SF6in thecylinder (ppm)RunnumberMedian release rate(lpm)2Average SF6concentration atthe tailpipe (ppm)3Average SF6concentration (ppt)4Cabin air from theexhaust (%)56 2 13500 5000 5 1.9 0.704 922 0.136 2 13500 5000 6 4.4 1.630 620 0.046 2 13500 5000 8 1.1 0.407 1184 0.296 2 13500 5000 10 1.1 0.407 439 0.118.3 4 9338 5000 12 1.0 0.535 N/A N/A8.3 4 9338 5000 13 1.0 0.535 N/A N/A8.3 4 9338 5000 14 1.1 0.589 N/A N/A8.3 4 9338 5000 15 5.5 2.945 966 0.036.6 4 7425 10020 17 3.8 5.128 1026 0.026.6 4 7425 10020 18 3.9 5.263 304 0.016.6 4 7425 10020 19 3.9 5.263 815 0.026.6 4 7425 10020 20 3.9 5.263 868 0.026.6 4 7425 10020 23 3.9 5.263 997 0.026.6 4 7425 10020 24 3.9 5.263 741 0.016.6 4 7425 10020 25 2.7 3.644 1134 0.036.6 4 7425 10020 26 2.9 3.914 N/A N/A8.3 4 9338 10020 28 2.0 2.146 827 0.048.3 4 9338 10020 30 4.3 4.614 848 0.028.3 4 9338 10020 31 2.3 2.447 801 0.038.3 4 9338 10020 33 2.0 2.146 519 0.028.1 4 9113 10020 35 2.9 3.189 1164 0.048.1 4 9113 10020 36 2.9 3.189 807 0.031Calculated using equation 5.3.1.42Calculated as the median of the 10-seconds release rate data for each run.3Calculated using equation 5.3.1.34Calculated as the mean of the concentrations measured at the rear of the cabin for each run5Calculated using equation 5.3.1.5TO1CNGHE2HE3RE1RE20.00% 0.05% 0.10% 0.15% 0.20% 0.25% 0.30%

HE3 ( 1975) HE2 (1985) RE2 ( 1993 ) R E1 ( 1998) TO1 (1998) CNG ( 2002 ) Windows C l osed ( m orn i ng)

Windows O p en (afte r no on)

Figure 5.3.1.2 Percentage of air inside the cabin or iginating f r om 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 r a te of the bus cabin was determ ined, prim arily, by the positio n of the windows, since each bus was checked fo r exhaust sy stem leaks.

Figure 5.3.1.2 also show s the im portance of bus type. The older buses HE2 (1985) and HE3 (1975) showed a larger perc entage of their own exhaust en tering into the cabin com p are d with newer buses RE2 (1993), TO1 (1998), CNG (2002) . These results are consistent with the older buses not being as well isol ated (airtight) from outside air com p ared with th e n e wer 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 em ission factors am ong 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 concentr ation data, we would expect expos ures associated w ith HE2 to be am ong the highest for the buses tested.

The snap-and-idle test is an experiment c onvenient and easy to perform

, however, these results can o n ly be u s ed as qualitativ e m easures. W e included these result s in our analyses since they were the only source of infor m a tion about the tested buses em issions.

990.0 0.1 0.2 0.3 0.4 0.5 0.6

HE1 H E 2 HE3 R E1 RE 2 T O1 Opac i t y

Figure 5.3.1.3 Snap-and-idle test results expres sed in term s of opacity (f raction).

5.3.1.1.1 Measurem ents of SF 6 Concentr ation s As m e ntioned above, three sam p ling positions were em ployed: inside/front, inside/rear and outside/front. Figure 5.3.1.4 shows a schem a tic of the location of th ese sam p ling probes.

Exhaust

(SF 6 ) Sa m p ling (Insid e /rear) Sa m p ling (Insid e /front) Sa m p ling (Outside/front)

CRO WN

Figure 5.3.1.4 Schem a tic of school bus indicating the location of the SF 6 sam p ling probes.

100Figure 5.3.1.5 shows the m eans and 95% c onfidence intervals for the average SF 6 concentratio ns m easured at the three locations (front, rear, and outside) during the morning runs, indicating that, in general, ther e was a subs tan tial differen ce between the m e asurem ents at the front and the rear of the cabin. Specifically, the SF 6 data indicated exposure to pollutants from the bus' s own em issions were higher at th e rear of the cabin than at the front. The influence of position inside the cabin is furt her analyzed in Section 5.3.6.2.

Figure 5.3.1.5 also shows there were detectable SF 6 concentrations outside at the front of the bus at tim e s during all runs, although, these concentrations were much s m aller than the concentrations inside the bus. Si nce we injected the tr acer gas in the tailpip e and there cannot be any other source of it, these resu lts s how the bus’s exhaus t was able to reach th e sa mpling lin e located at the front (outside) of the bus, for ex ample, when buses were idling (e.g., at a red light or a bus stop) and the wind was bl owing from the rear to the front of the bus. This also shows there are two different m eans for t h e exhaust gases to enter the cabin of the buse s . First, lea k s from the engine’s com p artm ent into the cabin and second the exhaust plum e traveling from the rear of the bus and e n tering through the windows (if these are open). Because the SF 6 concentratio ns m easured at the fr ont (outside) of the bus were subs tan tia lly lower than inside the bus (either f r ont or back), the se cond m echanism appeared to be le ss im portan t than th e f i rst.

Run 35 31 28 25 23 19 17 8 5SF6 mean and 95% C.I. 2500

2000

1500

1000

500

0 LOCATION

Front

Rear

Outside

Figure 5.3.1.5 Means and 9 5 % confiden ce intervals of SF 6 concentrations (ppt)

m easured at front, rear, and outsi de locations during m o rning runs.

101Figure 5.3.1.6 shows the 95% conf idence intervals for the SF 6 concentratio ns m easured at the three locations duri ng the afternoon runs, and these were, in general, som e what lower than during m o rning runs. F i gure 5.3.1.6 also shows the differences between the SF 6 con centration s

m easured at the three locations are s m aller dur ing the afternoon runs com p ared to differences

m easured during m o rning runs. Thus, when the windows of the buses were partially open, for the m a jority of the runs there wer e no signif i can t differences between m e asurem ents at the fron t of the cabin, at the rear o f the cabin, and outside the bus.

Run 36 33 30 24 20 18 15 10 6SF6 mean and 95% C.I. 2500

2000

1500

1000

500

0 LOCATION

Front

Rear

Outside

Figure 5.3.1.6 Means and 9 5 % confiden ce intervals of SF 6 concentrations (ppt)

m easured at front, rear, and outside locations during afternoon runs.

5.3.1.2 Correlation between SF 6 versus Black Carbon and PM 2.5. Section 5.1.3.2 describes how the correlations between black carbon and PAH were higher than those between PM 2.5 and PC (betw een 0.3 and 0.5 μ m)

. P M 2.5 and P C exhibited dif f e rent be havior but f o llowed s i mila r pa tte rns between th em , consiste nt with m u ltiple other sources and a generally high elevated background of PM 2.5 (Zhu et al., 2002, a,b).

We selected black carb on and PM 2.5 (one po llutant from each of the two group s) to analyze the implications of the SF 6 data in term s of the differences between em issions am ong the different buses. As m e ntioned above, based on the results of the correlatio n coefficients between the analyzed pollutants (see Tables 5.1.3.8 and 5.1.3.9), we assum e d black carbon and PM 2.5 concentratio ns m a y be used to exp l ain the behav i or of PAH a nd PC, respectively. It is im portant to note that although we show in Section 5.1.3 that the precision between paired Aethalom eters (used to m e asure black carbon) was low, these resu lts only apply to analyses which used paired instrum e nts. The correlations between SF6 and black carbon were estim a ted using data from a

102single Aeth alom eter (the rear in strum e nt), therefore th e precis ion estim ates for paired instrum e nts described in Section 5.1.3 do not apply to the following analysis.

To obtain the results presented below we norm a lized the data u s ing the m a ximu m concentrations. For example, during Run 6 the m a xi m u m concentration of black carbon was
about 40 μ g/m 3 and we divided every data point of Run 6 by this value. This procedure was applied for all runs for black carbon and PM 2.5 . Since it is easier to interpret the differences between abs o lute quan tities, ra ther th an the dif f e ring concentrations of the different species, we elim inated the units of m a ss per volum e.

Figure 5.3.1.7 summ arizes the results obtained for the correlation coefficients between
SF 6 and black carbon (norm a lized) and between SF 6 and PM 2.5 (norm a lized ). To avo i d confounding factors we only analyzed the m o rning 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 PM 2.5 whereas for black carbon they were about 0.5 for the two runs w ith available data. The consistent, relatively high correlations betw een SF 6 and black carbon suggested a stronge r relationship between black carbon an d bus em issions than between PM 2.5 and bus em issions.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

HE3 - Wind ows Clo s ed RE2 - Wind ows Closed TO 1

- Wi nd ow s C l os ed C N G - Wind o ws C l o s e d Corre l ation Coefficie n t (bla ck c a r bon)

Corre l ation Coefficie n t (PM2.5)

Figure 5.3.1.7 Correlation coefficients between SF 6 versus black carbon and between SF 6 versus PM 2. 5 (data collected with th e DustTrak instrum e nts)

1035.3.1.3 Estim a tion of Bus Cabin Black Ca rbon Concentrations Using the SF 6 Data As just shown, a m ong t h e pollutants m easur ed during the study, bl ack carbon correlated well with th e bus’s own exhaust. Si nce all of th e SF 6 found in the cabin wa s from the bus’s own exhaust, we could use the ratio of the concentration of SF 6 in the cab in to the con centration o f SF 6 in the exhaust to obtain a rough estim ate of the a m ount of black carbon inside the cabin originating f r om the bus’s own exhaust.

Our first-order-approxim a tion m odel started w ith an estim ate by Miguel et al. (1998) that black carbo n em issions from heavy-duty dies el vehicles are 1440 ± 160 m illigram s per kilogram of fuel burned. W e use d the upper end of this range (1600 mg/Kg) as the em ission rate for the bus used during Run 25 (RE2-AM) and assum e d a typical fuel consum ption rate of 10

m iles/gallon. For this bus commute, the distance covered was about 20 m iles and the total tim e of the run was about 80 m i nutes. From these valu es, we estim a ted the amount of fuel burned per unit of tim e to be about 0.025 gallons per m i nute.

minute gallons 0.025 miles 10 1gallon

minutes 80 miles

20

= •

We also know the density of diesel fuel:

gallon Kg 3.15 830 ρ 3 m Kg
Diesel ≅ ≅

h u s , t h e f u e l b u r n e d p e r t i m e can be expressed as T

min fuel

- Kg 0.08 gallon

Kg 3.15 min gallon 0.025 ≅ •

W e u s e d t h i s v a l u e a n d t h e m e ission rate repor ted by Miguel et al. (1998) to estim a te the m a ss

o w r a t e o f b l a c k c a r b o n f l ( BC M )

min BC mg min

Fuel Kg
Fuel Kg BC mg BC 128 0.08 1600 M − −

− − = • =

Sim ilar to equation (5.3.1.1):

Exh Exhaust

- BC BC Q C M • = ( 5

. 3

. 1

. 6 )

Solving for the concentration of black carbon in the exhaust (C BC )

3 1m 1mg 9000 3
min Exhaust L Exh

- BC m

− 14000 1000L g 1000 128 C min BC mg
g μ μ ≅ • • = −

104During Run 25, the percentage of air inside the cabin orig inating from the exhaust was about 0.03% (see Table 5.3.1.1). Hence, we estim at ed the concentration of black carbon inside the cabin to be about 4 m i crogram s per cubic meter.

3 m μ g Cabin BC m μ g 4 0.0003 14000 C ≅ • = 3

−

h e f i e l d w o r k

. The m ean of the real-time black carbon concentrations during Run 25 was about 9 μ g/ m 3

, which is of the sam e order of m a gn itude as the estim ated value of 4 μ g/m 3 . Agreement to with in a factor of two betwee n the calculated and m easured blac k carbon concentrations tends to validate both the SF 6 a n d t h e b l a c k c a r b o n d a t a o b t a i n e d d u r i n g t

We em phasize ag ain th a t t h e appro ach em ploye d in the previous calculations was not i n t e n d e d t o p r o d u c e a c c u r a t e v a l u e s that can be extrapo l ated. The estim a tes were m eant to be only first-order approxim ations aim e d at validating the data gathered by tw o d i f f e r e n t instrum e nts, as well as identifying m a jor trends in our data set.

5.3.2 Effect of W i ndow Position
T h e p o s i t i o n o f t h e w i n d o w s w a s one of the most com p l e x variables analyzed in this study. W i ndow positio n contributed im portantly to th e ven tilation rate inside the cabin leading

n S e c t i o n s 5

. 3

. 6

. 3 a n d 5.3.6.2, respectively.

These experim e nts were perform e d on the I- 405 freeway between 8 and 9 AM, traveling

t e s

.

f abo u t seve n m i nutes. During the f i rs t c y cle a ll win dows were closed. During the second cycle every other window was ope ned about 10 inches on the d r side, where the instrum e nts were located. Table 5.3.2. a r e c h a r h e w s i t i o n e x p e rim e nts.

T a
. 3 o w t i o n t

Run t o s u b s t a n t i a l l y d i f f e r e n t e x p o s u r e s

. A s s e s s m e n t o f t h e i n f l u e n c e o f t h e w i n d o w p o s i t i o n required consideration of other va riables such as position of th e sam p ling probe (inside versus outside) and its location inside th e cabin (front versus rear) as d i s c u s s e d i

Due to th e relevance of windows position, dem o ns trated in p a rt du ring th e pilo t s t udy , we perform e d t h ree additional tests (Runs 9, 29, and 32) specifically designed to te st the effect of the window position (See Section 4.1.7 ) on the concentrations ins i de the bu s.

south from W ilshire Avenue (close to UCLA) to Century Blvd (close to LAX) and then returning north to UCLA on I-405. During these runs we e xperienced substantial di fferences in congestion in the north/south directions. S i nce differences in average speed s led to different ventilation rates, we effectiv ely tested the effect of window position for d i f f e r e n t v e n t i l a t i o n r a

W e utilized alte rnating c y c l e s o

i v e r ' s side and about two inches on the opposite
1 s u m m i z e s t h a c t e r i s t i c s o f t i n d o w p o

b l e 5

. 2

. 1 W i n d p o s i e s t s.

Date Tim e Type of Bus Week
Nu m b er
9 05/08/02 AM Conventional diesel 3

29 06/05/02 AM Trap-outfitted 7

32 06/06/02 AM Trap-outfitted 7

105I n s p e c t i o n o f the tim e series graphs for each run allowed identification o f changes in th e

c o n c e n

t o l i n k

e m directly to em issio n s. In summ ary, the particle counts m easured in the accumulation m o de

m o r e l i k e a “ b a c k g r o u n d ” p o l lutant, and this is c onfirm e d by the strong

. 3

. 2

. 1 B l a c k C a r b o n t r a t i o n s t h a t c o u l d b e c o rrelated with the posit ion of the window s and calculation of the 95% confid ence in terv als of the av erage con cen tra tions demonstrated exposures inside the cabin depended significantly on the positi on of the windows. This findi ng was confirm e d when we analyzed the cum u lative frequency distribution of the data.

As m e ntioned in several sections of this report, the influ e nce of the window positio n varied be tween pollu tants. Dir ectly em itte d

, d i e s e l v e h i c l e

- r e l a t e d p o l l u t a n t s w i t h l o w background concentrations, such as black carbon, PAH, and NO 2 exhibited a different behavior than tho s e s u ch as PC and PM 2.5 that had strong “background” c ontributions. Recent studies (Zhu et. al. 2002 a,b) suggest that PM 2.5 is a “background” pollutant substantially affected by regional sources, secondary fo rmation and m e teorological c onditions and not a sensitive signature for direct em issions from vehicles. As m e ntioned before , our analyses of the particle count data refer only to the size range between 0.3 and 0.5 μ m, near the peak of the accum ulatio n mode f o r th e par tic le size di stributions by volum e and m a ss (Hinds, 1999). Although num be r concentrations of ultra-fine par tic les are c l ose l y r e lated to d i r ect em ission s f r om mobiles sou r ces (Zhu et al., 2002 a,b), once these particles grow into the accum u l a t i o n m o d e i t i s d i f f i c u l t
t h

w o u l d t e n d t o b e h a v e correlations we found be tween PC (0.3 to 0.5 μ m ) and PM 2.5. For the followin g analys is, we selected one pollu tant w e will refer to as prim arily directly em itted (e.g. black carbo n or PAH), and one (e.g. PM 2.5 or fine particle counts) we will refe r to as a “backgrou nd” pollu tan t

.

5

h e r e s u l t s o f t h e 9 5 % c o n f i d e n c e i n t e r v a l c a l culations were also consistent between the

a lly high er than du ring Runs 29 and 32 (on the tr ap-ou t f itted d i ese l

u s )

, d u e t o t h e d i f f e r e n t b u s t ypes as discussed in Section 5.3.1. The concen tration tim e series for black ca rbo n f o llowed sim ilar tren ds f o r a l l t h r e e window position tes t s. The concentrations in side the cabin tended to decreas e w h e n t h e windows were opened and tended to increase when the w i ndow s 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 concentrat ions were higher when the windows were closed (see Figure 5.3.2.2). However, the average con centrations during Run 9 (on a conventional diese l bus) were substa n t i b

Figure 5.3.2.3 shows the cum u lative frequency di stributions of the concentrations with the windows open and closed during Run 32, demonstr ating 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 μ m (PC), the conc entr ation tim e serie s l s o f o llowe d sim ilar tre nds f o r all three runs, b u t different to those observed for black carbon. h e c o n c e n t r a t i o n s i n s i d e the cabin tended to increase when the windows were opened and

n d e d t o d e c r e a s e w h e n t h e w i n d o w s w e r e c l o s e d. Figure 5.3.2.1 shows the ten-second data for
C i n a p o r t i o n o f t h e a c c u m u lation mode (0.3 to 0.5 um ) during Run 32. a T

t e P

10615 0 17 0 19 0 21 0 23 0 25 0 27 0 29 0 31 0 33 0 35 0

0ParticleCounts(#/cm3)

. 2. 4. 6. 8. 10. 12. 14. 16.BlackCarbon(ug/m3)

7 :56 08:03 08: 10 08 :17 08:2 4 08: 32 0 8 :39

Ti m e ( h h: m m ) Partic le Cou n ts

Bl ac k Ca rbon W i ndows

O pen W i ndows C l os ed W i ndows O pen W i ndow s

O pen W i ndow s C l os ed W i ndow

s C l os ed

F

Run T r ap

- o u t f i t t e d ( 3 2) T r a p

- o ut f i t t e d ( 2 9 ) C

o nv en t

. di es el ( 9 )95%ackon 20

18

16

14

12

10

8

6

2 W i ndow s

C l os edCI Bl

4 Op e nCarb i g u r e 5

. 3

. 2

. 1 Black carbon and particle counts (0.3 to 0.5 μ m ) concentrations during
R

u n 3 2 o n t h e p a r t i c l e

- t r a p o u t f i t t e d d i e s e l b u s

.

Figure 5.3.2.2 Mean concentrations and 95% conf idence intervals of black carbon (μg/m 3 ).

10720

. 40

. 50

. 60

. 70

. 80

. 90

. 100

.CumulativeFrequency W i ndow s O pen

W i ndow s Clos ed

108 . 10

.

. 2

. 4

. 6

. 8

. 10. 12. 14. 16.

Conc e n tr a t ion (ug/m3 ) 30

.

Run 32 29 9 0 Closed95% CI Particle Counts 400

300

200

100 Windows

Open h e 9 5 % c o n f i d e n c e i n t e r v a l s f o r P C ( 0

. 3 t o 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 R un 9 were substantia lly lower than during Runs 29 and 32. These differences could be related to dif f erences in background concentrations of particles in the

accum u t i o n m ode.

Figure 5.3.2.4 e a n p a r t i c l e c o u n t s a n d 9 5 % c o n f i d e n c e i n t e r v a l s
t h e a c c u m u l a t i o n m o de from 0.3 to 0.5 μ m ( # / c m 3 ). Figure 5.3.2.3 Cum u lative frequency distribut ions for black carbon during Run 32.

T

l a

M i n0 10 30 40 50 60 70 80 90 100

5 0 1 0 0 150 200 2 50 300 3 5 0CumulativeFrequency

20

0

C onc entr a t ion (# /c m3)

W i ndow s O pen

W i ndow s C l os ed Figure 5.3.2.5 shows the cum u lative frequency distributions of the particle counts concentrations with the windows open and cl osed during Run 32, conf irm i ng the previous

n d i n g s t h a t t h e c o n c e n t r a t i o n s w e r e higher when the windows were opened.

f f e c t o f W i ndow Position on Directly Em itted Pollutants versus Backgro und f i

Figure 5.3.2.5 Cum u lative frequency distributio ns for particle counts during Run 32.

5

. 3

. 2

. 3 E

o l l u t a n t s P

e s e runs the m a in source of pollutio n was the bus’s own exhaust. Conc e n tra tions ten d ed to inc r e a se when th e buses wer e par tia lly i s o l a t e d

o t h o p e n a n d c l o s e d w i n d o w s c o u l d l e a d t o undesirable exposure scenarios, depending

u

p o n t h

p a r t i c u l a r l y for dirty buses. On the othe r hand, for “background” pollutants l o s i n g t h e w i n d o w s w o u l d y i e l d lower average concentratio ns inside the cabin. Figure 5.3.2.1 shows the inverse behavior betwee n black carbon and part icle counts. For direc tly -em itted, d i ese l

- v ehicle pollutants it ap p ears in th

f r o m t h e o u t s i d e , w h e r e a s i n c r e a s e d v e ntilation rates caused dilution with the windows opened.

For “background” pollutants, concentrations w e re lower when windows were closed, slowing the entrance of po llutants in to the c a bin.

B

e p o llutants invo lved and th e degree of se lf pollu tio n. For d i rectly em itted pollu tants, having the windows open could lead to short-term high exposures from other vehicles whereas closing the windows could lead to longer-term e l e v a t e d e x p o s u r e s w i t h h i g h e r a v e r a g e

c o n c e n t r a t i o n s , c

These findin g s em phasize the im portance of tran sition i ng as soon as poss i ble to “cleaner” buses, such as the CNG bus we tested, to m i ni m i ze pollution inside the cabin due to the bus’s own exhaust, as well as to re duce impacts on and from other near by vehicles, as discussed in Section 5.3.5.

109110 789 805 750 449 496 445 N =

Bus TO1 RE2 RE1 HE3 HE2 CNGBlack Carbon (ug/m^3) 20

15

10

5

0

8 0 0 803 860 469 500 445 N =

T O RE2 RE1 HE3 0

1 HE2 CNGPAH (ng/m^3) 0

400

0

200

100

Bus 5 0

3 0 5.3.3 Effect of Bus Type In Section 5.2 we com b i n ed the results from two urban routes and the seven bus types to establish the overall average di fferences in exposure between t h e t h r e e m i croenvironm ents. However, the data also suggested that variables such as bus t ype and route type (see Section
5

. 3

. 4 )

s b u s t y p e s i n v e s t i g a t e d f o r s e v e r a l k e y pollutants. Conf idence inte rvals we re calcula te d using the standard error of the mean, and are not related to the precision es tim ates in Section 5.1.3. To m i ni m i ze confounding factors, these plots were based only on the m easurem ents in th e rear/insid e of the cab in for urban route one during com m utes with windows on the bus closed. Again, as only one instrum e nt, the rear instrum nt, was used fo r all an alys es in the co mparison between bus type s, the re sults of the precision estim a tes in Secti on 5.1.3 do not apply here.

Figure 5.3.3.1 Mean concentrations and 95% conf idence intervals for PAH and black carbon across six b u s types.

Figure 5.3.3.4 shows the results for the VOC data. Al though the differences betw een

u s e s a p p e a r e d t o b e s i g n i f i c a n t
, the average con centration s were quite low relative to previously

s u r e d a m b i e n t t o t a l V O C c o n c e n trations (mostly below 200 ppb). p l a y e d a s i g n i f i c a n t role in determ ining the exposure inside th e cabin. T h is section focuses on the effect o f different bus types on concentrations i n s i d e t h e b u s c a b i n w h e n t h e windows of the bus w e re closed. However othe r variables, such as roadway type and the presence of other diesel vehicles , m a y have also influenced con centr ation s insid e th e cabin, an d are discussed in other sections of th is report (see Section 5.3.4 and 5.3.5).

Different bus m odels and ages correspond to dif f erent construction and cabin designs and the results of our SF 6 analyses suggested these differences ma y result in a wide range of pollutant exposure across bus types. Figures 5.3.3.1 to 5.3.3.4 show the observed m e an concentrations and 95% co nfidence intervals for th e m ean for va r i o u

e

(a) PAH (b) Black carbon

b

m e a800 802 857 469 517 444 N =

RE2 RE1 HE3 HE2 CNG 0

Bus TO1Fine Particle Counts (#/cm/3) 250

200

150

100

50

800 805 860 469 5 1 7 445 N =
TO1 RE2 RE1 HE3 HE2 CNG 0

Bus 120

100

80

60

40PM2.5 Mass (ug/m^3)

20

800 854 775 469 517 445 N =

Bus TO1 RE2 RE1 HE3 HE2 CNGCarbon Monoxide (ppm) 10.0

8.0

6.0

4.0

2.0

0.0

704 740 794 433 511 445 N =
TO1 RE2 RE1 HE3 HE2 CNGNitrogen Dioxide (ppb) 120

100

80

60

40

20

Bus 0 (a) PM 2.5 (b) Fine Particle Counts (0.3 – 0.5 μm ) (Data collected with th e DustT r ak instrum e nt)

r e 5

. 3

. 3

. 2 Mean concentrations and 95% confidence in terv als for PM 2.5 and PC.

C O

.

These f i gure s illus t r a te the com p lexity of the data set, where dif f e rences across bus types

r e p o l l u t a n t

- d e p e n d e n t

. T o better un derstand th e sim ilaritie s and differences be tween pollu tants,

e s u m m a r i z e d t h e f i n d i n g s p r e s e n t e d i n F i gures 5.3.3.1 to 5.3.3.4 in T a bl e 5.3.3.1 in term s of ean concentrations and 95% confidence in tervals for the m eans, by bus type. F i g u

a) NO 2 (b) Carbon Monoxide

i g u r e 5

. 3

. 3

. 3 Mean concentrations and 95% confidence in terv als for NO 2 a n d F

a

w

m

111385 836 715 469 379 N =
TO1 RE2 RE1 HE3 CNGVOC (ppb) 400

300

200

Bus 100

0

Figure 5.3.3.4

T a b a r y s c i n s i d n for comm ut es with windows closed (morning) by bus type

. M e a n c o ncentration s and 95% co nfidence intervals for V O C

.

l e 5

. 3

. 3

. 1 S u m m o f m e a n c o n c e n t r a t i o n and 9 5 % o n f i d e n c e t e r v a l s i n 1,2 e the cab i

H E 2 H E 3 R E 1 R E 2 T O 1 C N G

Black Carbon (ug/m 3 ) 7 2 5 ± 0.3 1 9 ± 0

. 6 1 3 ± 0

. 5 1 2 ± 0

. 2

. 2 ± 0

. 2

. 5 ± 0

. 1

P A H ( n g / m 3 ) 3 400 ± 15 2 2 0 ± 6.4 1 6 2 ± 5.8 167 ± 3.6 201 ± 7 64 ± 3.3

NO (ppb) 2 5 0 ± 0

. 5 1 1 0 ± 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 (#/cm 3 ) 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 C o n v ent i o nal di esel b u ses = HE 2, HE 3, R E 1 a n d R E 2

. 2 Th i s t a e g r o u p s t w o typ e s o f po llu tan t s an alyzed in t h is stud y: po llu tan t s prim ari l y o r en tirely e m it ted fro m

s (B C

, PA H a n d N O 2 ), a n d “bac k g r o un d” pol l u t a nt s c onsi d ere d t o b e d o m i nat e d b y atm o sphe ri c sources ot her t h an diesel (e.g. CO from light duty m o tor vehi cles, “background” accum u lation ode part i c l e s )

. ents of PAH insi de conventional diesel buse s m a y be biased low due to t h e i n s t r u me n t ’ s ma x i mu m t t i ng du ri n g t h ese c o m m ut es. See Sect i o ns 4. 1. 2. 3. 5 a n d 5

. 3. 3 f o r s p eci fi c det a i l s

. ze ra nge f r om 0. 3 – 0. 5 μm

. b l

com bust i o n so u r c e

a n s f o r m a t i ons or t r

m 3 Measurem

s e 4 In si

112According to the results found for em issions of PAH (with the excep tion of HE2), black carbon, and NO 2 , in general, the buses that exhibited high opacity and a high percentage of air inside th e cabin com i ng from the exhaust, al so exhibited the highest average inside/rear concentrations. In other words, for BC, PAH, a nd NO 2 , the average ins i de/rear exp o s u r e r e s u lts

o u l d b e e x p l a i n e d b y t h e snap-and-idle test and SF 6 data.

s e s l i k e l y understated c

In contras t
, the sn ap-a nd-idle test and the S F 6 d a t a d i d n o t a d e q u a t e l y e x p l a i n t h e behavior of PC and PM 2.5 average co ncentration s inside the cabin. These results sugg est that it is

necessa ry to consid er ad dition a l v a r i ables in o r d e r to exp l ain the behav i o r of PC and PM 2.5, 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 P AH instrument’s full scale was set to read

a m a xi m u m of 500 ng/m 3 in the front, and 1000 ng/ m 3 in the rear du ring the first 17 runs of this study, all of which involved diesel buses. During Run 18 and all subseque nt runs (including the TO1 and CNG buses), the scale was incr eased to read a m a xim u m of 2000 ng/m 3 for both front and rear ins t rum e nts. This created a potentia lly substantial bias in our com p arison of PAH concentratio ns by bus type. PAH data for th e diesel bu ses were b i ased low, b ecause any concentratio ns higher th an the m a xi mum scale sett ing were recorded as the preset maxim u m of 500 or 1000 ng/m 3 . In the later runs involving the t r a p

- o u t f i t t e d a n d C N G b u s e s , t h e concentratio ns of PAH inside the b u s cabin often exceed ed 500 or 1 000 ng/m 3 . Thus, the differences we saw between the diesel bus es ve rsus the trap-outfitted o r C N G b u the benefits of reduced PAH concentrations for the trap and CNG buses. W e expect b o t h h i

Overall, the results presented in Table 5.3.3.1 demonstrate the im porta nce of bus type for

r t i c u l arly for d i r ectly em itted pollutants s u ch as b l ack carbon and
P A H

. g h e r p e a k s a n d h i g h e r a v e r a g e concen trations likely occurred inside the d i esel buses during Runs 1 – 17 and therefore av erage PAH concentratio ns for diesel buses alm o st ce rtainly would have been higher had the m a xim u m scale been set to 2000 ng/m 3 as in the case of th e trap-outfitted and CNG buses. Thus, if anything , the benefits of the trap and CNG technologies over the diesel with respect to PAH concen trations are under-estim ated in our study.

exposures in side the cab i n , p a T h e r a t i o s b e t w e e n t h e mi n i mu m a n d ma x i mu m a v e r a g e c oncentrations ac ross bus type ranged from 1.6 to 6.3; hence, the differences in average exposure between buses were significant. For exa m ple, for black carbon, the av erage exposure associated with the HE3 bus

was m o re than six tim e s the exposure associated with the CNG bus.

5.3.3 Effect of Route Type
F i g u r e s 5

. 3

. 4

. 1 a n d 5

. 3

. 4

. 2 s h o w t h e o b s e r v e d m ean pollutant concentrations and 95% confidence intervals for the three types of bus routes used during the m a in study (rural/suburban, urban route one, and urban route two). The confidence interval s were calculated based on the standard error of the m e an. To avoid confounding factors only data from the RE2 bus were used since this was the only test bus used on all thre e routes, and only from m easure m ents collected by sam ling at the rear of the cabin. p

113114 730 1052 805 752 391 N =
Windows Closed Windows Open95%C.I. Bla

4 U1ck Carbon 14

12

10

8

6

2 Route

U2 RS

739 1051 803 761 390 N =
Windows Closed Windows Open95% 180

160

140

120

100

40

20 Route

RS

U1

U2C.I. PAH 60 80

803 1051 802 761 391 N =
Windows Closed Windows Open95% C.I. PC 300

200

0 Route

RS

U1

U2 100

807 1052 805 763 391 N =2.5 140

120

100

80

60

40

0 Route

Windows Closed Windows Open95% C.I. PM

20 U1

U2 RS (a) Black carbon (b) PAH

i g u r e 5

. 3

. 4

. 1 Mean concentrations and 95% conf idence intervals for black carbon ( μ g/m 3 ) and PAH (ng/m 3 ).

s a n d 9 5 % confidence in terv als for PC (#/cm ) and PM 2.5

s u b u r b a n r u n s w e r e c o n d u c t e d i n t h e afternoon with the windows open. F

(a) PM 2.5 ( b ) P C (Data c o llected with t h e DustTrak instrum e nt)

Figure 5.3.4.2 M e a n c o n c e n t r a t i o n 3 ( μ g/m 3 ) by bus route and tim e of da y.

Figure 5.3.4.1 again shows sim ilar behavior between black carbon and PAH. As expected, based in part on lower concentrati o n s , t h e r u r a l / s ub u r b a n r o u t e ( R S ) e x h i b i t e d significantly lower m ean exposur e than the urban routes. These differences could only be established for runs with windows open, since a l l r u r a l /During the runs with windows open, the m ean concentrations for black carbon and PAH were sim ilar between the two urban routes (see Section 5.3.5.2.4). However, during runs with windows closed, U1 exhibited higher m ean c oncen trations than U2. Differences in traffic conditions between the two urban routes could partia lly explain this result, where in general, the U2 route traveled trough less c ongested streets. As explained elsewhere this difference was only visible for morning runs where the w i ndows were closed.

h a v e d sim i l a r Figure 5.3.4.2 shows, when analyzing the type of route, PC and PM 2.5 a l s o b e l y

. T h e r u r a l / s u b u rban rou t e and the U1 route exhibited s i m ilar average con centration s

. The U2 route showed higher aver age concentrations than U1 dur ing both m o rning and afternoon runs (in part due to higher speeds and greater ventilation on the freeway portion of U1, and greater dens ity of diesel trucks on su rface streets for U2).. These resu lts dem onstrate, again, the differences between directly em itted versus background pollutan t s.

5.3.5 W ithin Run Variables Investigated U s i ng Real-Tim e Data and Videotape Analysis The videota p es in con j u n ction with the ac tiv ity logs recorded during each run were used to determ ine the influence of surrounding vehi cles on black carbon and PAH c oncentrations inside the test buses during sele cted exposure runs. Black carbon and PAH were the focus of this analys is for three m a in reasons. Th e first was that thes e po llutants ar e both highly associated with diesel exhaust. Black carbon is typically us ed as a marker for dies el exhaust particulate (Rodes et. al., 1998; Fruin, 2003), and results fr om both the pilot and m a in studies found

c o n c e n t r a t i o n s o f b l a c k carbon and PAH were highly correlated (see Section 5.1.3 ). Second, we expected bo th pollutan t s resu lted p r im arily f r om direct em issions, and had low background

c o n c e n

u m e nts wa s placed at the rear o f the bus (e.g. all runs h a d

t h e s a m Aethalom eter m easuring black carbon at the rear of the bus). This is an im portant point

A e t h a l o t r a t i o ns. This was im portant because hi g h backgroun d concentrations could o b scure our ability to identify the important variables contributing to expos ure on the tim e scale of seconds and tens of seconds during the cour se of a commute. This also re lates to the third m a jor reason, which was the fast respo n se of the in strum e nts us ed to m easure thes e pollu tants allowed us to see changes in concentrations within seconds of an event. We used ten second data for all the videotap e an alyses.

All m easurem ents of black carbon and PAH used in the follow i ng analyses were based on the m easurem ents taken inside the cabin at the r ear of the bus. For all runs in this study, the

sam e instrum e nt from e a c h p a i r o f i n s t r

e because although in Section 5.1.3 we estim a t ed the precision between the front and rear

m eters was low, the between-run com p aris ons in the videotape analyses which follow were based only on one instrum e nt, the rear Aethal om eter. Thus we w e re able to m a ke relativ e

com p arisons between buses, without concerns of inadequate precision (for exam ple be tween our front and rear Aethalom eters), becau se these co mparisons involved only one instru m e nt for each pollutant (e.g. black carbon or PAH).

5.3.5.1 Selection of Runs for Videotape Analysis The prim ary focus of this analysis w a s the e ffect of surrounding traffic on concentrations inside the bus, and less on the effect of the part icular bus being tested (i .e. self pollution). In addition, because of the nature of the analys is, it was necessary to have highly variable concentratio ns inside th e cabin over the course of a commute, in order to associate changes in

115concentratio n with the e v ents/chara c t er istics th at also chang e d during th e commute. For these two reasons, we determ i n e d o n l y a f t e r n o o n r u n s , with windows partially open, were suitable for

com p series

Figure 5.3.5.1 Run 31 (morning – windows closed) black carbon and PAH

w e r e more influenced by the outside air b ecause the windows were open du ring all aftern oon runs. As noted durin g the window position tests (see S ection 5.3.2), when the windows were open, m e an concentratio ns insid e th e bus tend e d to dec r eas e, and were sim ilar to c oncentr ation s outsid e th e bus for directly em itted pollutants, such as bl ack carbon an d PAH. The high ven t ilation rate inside the bus cabin elim inated build up of concen tra tions in side the c a bi n over the course of the commute. High, but short-lived, peak concentrat ions inside the bus were observed only when the windows were open, indicating open windows allowed high concentrations from other videotape analysis. A f ternoon runs had genera lly low baseline pollu tant concentrations ared with the m o r n ing runs (windows closed ), but the greatest c oncentration variability, including high peaks. Figures 5.3.5.1 – 5.3.5.2 show examples of the types of tim e obtained with m o rning and afternoon runs, resp ectively. T h e rapidly changing concentrations inside the bus from very low baseline concentra tions to m u ch higher peak concentrations found during afternoon runs provided the opportunity to investigate the factors influencing high concentrations inside the bus.

concentratio ns.

By contrast, during the m o rning runs with windows closed, pollutant concentrations inside the bus were less variable, and incr eased/decreased only gra dually over much longer periods of tim e , m a king it m o re difficult to a ssess the factors causing th ose changes on a realtim e basis.

In addition, focusing on afternoon ru ns for the vi deotape an alysis increased our ability to assess th e effects of surrounding traf fic because th ese runs had higher ven t i l a t i o n r a t e s a n d 0 10

6: 30 6: 3 7 6: 44 6: 5 1 6: 58 7: 0 6 7: 1 3 7: 20 7: 2 7 7: 34
Ti m e -30 0 20 40 50 60 70 80 90 10 0Black Carbon (ug/m3)

0 60 0 90 0 12 00 15 00 18 00 21 00PAH (ng/m3) Blac k Ca rbo n

PAH

20 Sec ond M o v i ng A v erage (B C )

20 Sec ond M o v i ng A v erage (P AH )

30

30 0

1160 10 20 30 40 50 60 70 80 90 10 0

1 5 : 1 4 1 5: 21 1 5 : 2 8 1 5 : 35 15 :42 1 5 : 50 1 5 : 5 7 1 6 : 04 16 :11 1 6 : 18
Ti m eBlack Carbon (ug/m3)

-3 00 0 30 0 60 0 90 0 12 00 15 00 18 00 21 00PAH (ng/m3) B l ac k C a r b on

PA H

2 p e r

. M o v. Avg. ( PAH )

2 pe r. Mo v. A v g

. (B la c k Carb on )

Figure 5.3.5.2 Run 33 (afternoon – windows open) blac k carbon and PAH concentrations.

sources to e a sily pene tr ate the bus cabin, although only for very sh ort periods of tim e due to the high ventilation rates.

It is also important to not e that m e teorological conditions during the afternoon were consistent a c ross a ll bu s runs, with onshore f l o w condition s typica l of the Los Angeles bas i n during May and June.

Out of a total of sixteen afternoon runs w ith windows open that were videotaped, seven were selected for com p lete videotape analysis , including a variety of bus types. T a ble 5.3.5.1 describ e s th e runs analy zed usi ng the videotapes. Four runs we re on diesel buses, including Run 6 on a h i gh em itter bu s (HE2), Ru n 15 on a r e presen tativ e bus (RE1), Runs 20 a nd 26 on a second representative b u s (RE2), Runs 30 and 33 on a particle-trap outfitte d diesel bus (TO1), and Run 36 on a CNG bus (CNG). A ll of these runs were c o m p lete d on the prim ary urban route, except one of the two runs on the representati ve diesel bus, RE2, which was done on the secondary urban route. Again, for all runs anal yzed 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 exa m ples of tim e-series for black carbon and PAH,

respec tive l y, with ev ents assign ed to the c oncentration peaks based on the resu lts of the videotape analysis. These figures are provided prim arily to demons trate th e natur e of the rea l tim e data collected during this study, and th e association between events and peak concentratio ns. However, all ev ent assignm ents are not shown in these graphs. During the videotape analysis, every ten- second period within a run was given an event assignm ent accord ing to the criteria describ e d in Section 4.2 .3.3. The event assign m e nts shown in Figures 5.3.5.3–5.3.5.4 only represent the events occurring duri ng major peak concentrations, and not all

117events during a run, providing a qualitative dem onstration of the va lue of the v i deotape analy s is. However, all subsequent quantita tive analyses in clude the ev ent assignm ents f o r all ten-secon d periods du ring each run analyzed.

T a ble 5.3.5.1 B u s runs selec t e d fo r videotape ana l y s is.

Run
Number Bus ID B u s F u el T yp e Bu s Route Ti m e of
Da y A v er age B u s Speed

(km/hr) Tota l R un Ti me (h r:m i n ) A v e r age Win d Sp eed (km/hr)

6 H E 2 D i esel U 1 PM 2 2

. 7 1 :1 1 1 5

15 RE1 D i e se l U 1 P M 23.8 0 : 5 7 9

20 RE2 D i e se l U 1 P M 26.3 1 : 0 0 1 7

26 RE2 D i e se l U 2 P M 21.4 1 : 2 1 9

3 0 T O 1 T rap

- O u tfitte d D i esel U 1 P M 2 4

. 8 0 :5 2 1 4

3 3 T O 1 T rap

- O u tfitte d D i esel U 1 P M 2 3

. 1 1 :1 0 1 0

36 CNG C N G U1 PM 19.8 1 : 2 4 1 5

0 10 20 30 40 50 60

15: 10 15: 15 15: 21 15: 27 15:3 3 15: 38 1 5 :44 15: 50 15 :56 16: 02 16: 07
TimeBlack Carbon (ug/m3) Bl ack C a rb on

20 Sec ond M o v i n g Av er ag e Tra v e l i n g be hi nd a di es el s c h o o l
bu s - i n te rm i x ed wit h p e ri od s of
i d l i n g in tr a f f i c on s u r f ac e st r e e t s

T r av el in g be h i n d a

m e di um du ty di es el

t r uck o n fr ee w a y T r avel in g b e h i n d
a di es el sch oo l
bu s on f r e e w a y D i es el s c h o o l bu s

e m i t te d l a r g e cl ou d o f
b l a ck s m ok e w h il e

a cce le r a t i ng pa st o u r bu s (se e pic t u r e)

Figure 5.3.5.3 Run 20 black carbon concentrations with associated events.

1180 20 0 40 0 60 0 80 0 100 0 120 0 140 0 160 0 180 0 200 0

15:14 15 :21 15:28 15 :35 15:42 15 :50 15:57 16 :04 16:11 16:1 8

Ti m ePAH (ng/m3) PAH

20 Sec o n d M o v i n g A v er ag e D i e s el s c hool bus
ahead di r e c t ly in
f r ont em its puf f of
blac k s m ok e on

a cce l e ra t i o n

T r aveling be hi n d a

s m ok e y dies el
sch oo l b u s T r a v e l ing b ehind a dies el

s c h ool bus whic h

pe riodic a lly em its vi s i ble
blac k s m ok e

No other di e s el

v e hi c l es pres en t N o other
dies el
vehic l es pre s ent H eavy duty an d

m edium duty
dies e l tr uc k s ah ead

Figure 5.3.5.4 Run 33 PAH concentrations with associated events.

Sm all adjustm e nts were m a de following the assignm ents of events based on further analys is of the data and inspection o f the concen tra tion tim e serie s . Th is was necessary because in a sm all num b er of instances, th e criteria fo r assignm ent of an event described in Section 4.2.3.3 did not adequately characterize an associati on between pollutant con centrations inside the cabin and o u tside even ts. Insp ectio n of the con centration tim e series revealed sm all portions of peak conc en tra tions, typ i cally th e down-side of a peak, were not always identif ied with a g i ven event, although they were likely associated with that event. Figure 5.3.5.5 provides an exam ple, with an exp a nded tim e scale, during Run 36 where the origin al cr ite ria did not result in the assignm ent of all elevated concentrations to th e appropriate event. At the beginning of the period show n in Figure 5.3.5.5, a low baseline con centration of black carbon was observed, and no diesel vehicles were near the bus. At approxim a tely 16:07, two events occurred nearly sim u ltaneously. A passenger car with noticeab le odors m oved directly in front of th e bus, and a diesel truck, which was ahead of the passenger car, cam e within about four to five car lengths of the bus. Although this distance wa s just outside our three car length lim it (see Photograph 12), careful insp ection of the black carbo n tim e series during this period showed a dramatic increase in concen trations as soo n as the truck and the passenger car both m oved in front of the bus, with a further increase as the bus pulled closer to the d i esel tru c k.

During the three m i nute 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 m oved away at about 16:08:50, they increased again as the bus m o ved closer to the diesel truck. The truck m oved in and out of a three car length distan ce ahead of the bus over th e course of this tim e period, and the m a xi m u m concentration was reached when the diesel truck was within about one

1190 10 20 30 40 50 60

16 :0 4: 30 16 :05 : 5 6 16: 07 :2 3 16: 08 :49 1 6: 10: 16 1 6 : 11: 42

Ti meBlack Carbon (ug/m3) B l ack Ca rbon

2- M i n M o vi ng A v er a g e

Dies el tr uc k ahead of
pas seng er c a r (

~ 5 c a r l ength s ahead of bus )

.

P a s s e nger car w i th

sm ell y ex haus t mov e s in fr on t of bus

. Sm e l ly
pas seng er c a r

moves aw ay B u s is now di r e ctl y behi nd di ese l truc k

Dies el tr u c k pas ses through a li ght, w h il e bus sto p s

B u s sp eed
inc r ease s Bus moves cl oser to Di esel T r uc k

B u s s t arts m o v i ng agai n,

no other v ehc il es ar e in front B u s i s s t oppe d at a red
l i ght w i th no o t her vehic l e s i n front of i t

Figure 5.3.5.5 Events associated with increases/decreases in black carbon concentrations m easured inside the C NG bus during Run 36.

Photograph 12

. School bus ahead three-car leng ths (m axi m um di stance used for videotape analysis).

car length of the bus (at th is point the passenger ca r was gone). F i nally, the diesel truck passed through a traffic light, while the bu s did not. At th is poin t , accord ing to our original criteria, the diesel truck was no longer associated with any subsequent tim e intervals. However, we saw from the black carbon tim e seri es that although the concentrations imm e diately started t o decrease after the diesel tr uck was gone, it took approxim a tely thirty seconds before the

120concentratio ns were ag ain clo s e to the concen tra tions obs erved in sid e the bus p r ior to th e 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 concentratio ns were again reached, were not. The event ass i gnm ents in the few instances where this occurred were adjusted, and th ese periods of ti m e wer e also assig n ed to the origin al even t associated with the m a jor peak. However, these adjustm e nts accounted for only 3% of all vehicle assignm e nts m a de during th e videotape analys is for all seve n runs. The great m a jority of the even ts d u ring e ach r un were id e n tif ied so le ly on the basis of the original criteria described in Section 4.2.3.3.

5.3.5.2.1 The Effects of Following Diesel Vehicl es W ith and W ithout Visible Exhaust In order to a ssess the im pact of diesel vehic l es e m itting visible exhaust, the tim e interval during which visible exhaust or odor from a die s el ve hicle in front of, or ad jac e nt to , the tes t bus was first noted, and the following one m i nute peri od after the exhaust was noted were assigned to the veh i c l e tha t em itted the exha ust. In orde r to determ ine the effect such an ev ent had on concentrations inside the bus cab in, a com p arison was done of all concentrations m easured when no visible exhaust from surrounding vehicles was present, with the first ten second period during which exhaust was seen, and the one m i nute pe riod follow i ng the em ission of visible exhaust (see Photograph 13). T h e results of this com p aris on, stratified by the test bus type (diesel, trapoutf itted die s el and CN G) and inc l u d ing all sev e n runs an alyzed with th e videotapes , are shown in Table 5.3.5.2.

(a) (b)

Photograph 13 . a) Sm oky diesel school bus ahead on an arterial street. b) Driving beh i nd a heavy- duty dies el with high ex haust em itting visible

sm oke.

121Table 5.3.5.2 The effect of visible exhaust from a di esel vehicle in front of or adjacent to the test bus on concentrations of bl ack carbon and PAH inside the bus cabin.

P

r es e n

c e

of V i s i bl e E

xhaus t D i e s e l B

u

s e s T r a p O

u tf

itt e d
Di e s e l B

u

s C NG Bu s D i e s e l B

u

s e s T r a p O

u tf

itt e d

D

i esel B

u

s C N

G B u

s

N

o V i s i b l e E x h

a u

s t P

r esen t 5 3 2 7 6 7 8 7 0

V

i s i

bl e E x ha

u

s t F i r s t E

m

i t

t e d
f r om

a D i e s el V e hi cl e i n F

r on

t of

t

h

e Te s t B

u

s 24 9 1 3 4 64 370 47 3

O

n

e M

i nu

t e

P e

r i od F o l l ow i n

g
t h

e E

m

i s s i on of V

i s i bl e E

xhaus t f

r om

a D i e s e l V e hi c l e i n

F

r on

t of

t

h

e Tes t B

u

s 28 1 1 14 5 8 6 362 51 7 M

e an B l a c k C

a r bon C onc ent r a t i ons Ins i de t

h

e T e s t

Bu

s e s

(

u

g / m

3 ) M ean P A

H

C

o n

cen t r at i o n

s I

n

s i d

e t

h

e T e st B u

s (n

g / m

3 )

Figures 5.3. 5.6 and 5.3.5.7 show the boxplots o f the sam e c o m p arison for each of the bus types tested for black carbon and PAH, respectiv ely. The boxplots show the m e dian, quartiles, and extreme values. The box represents the in terquartile range which contains 50% of the values. The whiskers are lines that extend from the box to the hi ghest and lowest values. The

Bus Type Trap

Diesel CNGBlack 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 o f visible exh a ust from a dies el vehicle in front of or adjacent to the test bus on concentrations of bl ack carbon inside the bus cabin.

122Bus Type Trap Diesel CNGPAH (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 dies el vehicle (in front of , or adjacent to, the test bus) on concentrations of PAH inside the bus cabin.

line across the box indicates th e m e dian, and outliers or extr em e values are included as individual points. Mean black carbon and PAH c oncentrations inside the test bus increased four to eight tim es when a diese l vehic l e in f r ont of the test bus e m itted visible black sm oke. These results indicated the p r esence of visible exhaust from a diesel v e hicle near the tes t bus was a

m a jor 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 adjacen t to, the tes t bus (see Photogr aph 14) during the cours e of a bus comm ute, a

com p arison was done of the observed concentrations inside the test bus while following different vehicle types, with or without vi sible exhaust. All seven runs (i ncluding the three different bus types) were included in this analysis. Five cat egories of vehicles being followed were used: diesel school buses with visible ex haust; other diesel vehicles with visible exhaust; diesel school buses without visible exhaust; other diesel ve hicles without visible exhaust; and gasoline vehicles or no target. The category of other di esel vehicles included both m e dium and heavyduty vehicles (2,3 or 5 axles) . Although during the video anal ysis separate categories for

m e dium and heavy duty diesel vehicles were use d, f o r this analys is th e s e two ca te gories we re collap s ed in to one. The reasons for doing this w e re firs t, to increas e the sam p le size, because th e

num b er of tim es these vehicles were encountered was low, and second , because th e distinction between m e dium and heavy duty dies el vehicles was not always a pparent from the videotapes. The assignm e nt to one category or the other was often difficult to m a ke and c o llapsing the catego r ies elim inated possible m i sclassification betw een the two categories. Diesel transit buses were not included because the num b er of tim es they were encou n tered du ring these b u s commutes was low (N <20 for all runs com b ined). Most o f the trans i t buses en cou n tered were fueled by CNG.

123Photograph 14 . Medium duty diesel vehi cle ahead on the freeway.

Table 5.3.5.3 shows the results of this com p arison. Mean black carbon and PAH concentrations inside the test buses were hi ghest when following a diesel school bus which

em itted visible exhaus t, and lowest when f o llowing a gas o line veh i cle or no ta r g et. W h en following a sm oky diesel school bus, the concentrat ions m easured inside the bus cabin were

Table 5.3.5.3 Concentrations m easured inside the bus cabin w h ile traveling behind different types of vehicles.

Mean Conce n tration Inside the Test Buses 1
Type of Vehicle Being Followed: Black Carbon (ug/m3) PAH (ng/m 3 )

Diesel School Bus E m itting Visible Exhaust 2 3 5 5 2

Other Dies el Vehicle E m itt ing Visible Exhaust 2 13 3 4 2 9 c

Diesel School Bus 12 271

Other Dies el Vehicle 2, 4 4 1 0 4

Gasoline Veh i cle or No Target 3 48

Notes:

1 Selected exp o s ure run s i n clu d e d 4 run s on 3 d i esel bu ses, 2 run s o n a t r ap-ou t fitted d i esel bu s,

an d 1 run on a C N G b u s

. O n ly af ter noo n run s w ith win d o w s op en wer e in cl u d e d i n th is an alysis.

2 Diesel ve hi cles with 2, 3 or 5 a x els we re com b ined i n to one category t o inc r ease sample size.

3 Sm all sa m p le size (N<50 )

. 4 Tran sit bu ses were n o t assessed du e to small sa m p le si ze (N<10 )

.

124eight and twelve tim e s greater for black carbon and PAH, respectiv ely, com p ared with following a gasoline vehicle or no target. W h e n following a diesel school bus that was not em i tting visible exhaust, black carbon and PAH concen trations inside the test buses increased four and six tim e s , respec tive l y, com p ared with f o llowing 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, m easured inside th e test buses while following different types of vehicles. T h e type of diesel ve hicle encountered m o st frequently during all the bus runs was a diesel school bus. On average, during m o re than one-quarter of a bus co mm ute, another diesel vehicle was within three car leng ths in front of or ad jacen t to
, our bus, w ith d i esel school buses responsible for over sixty percent of that tim e

. The highest concentrations inside all the test buses were observed wh en following , or adjacen t t o, a d i esel school bus. This dem o nstrated that the type of school bus a child ri des on is not the only determ inan t of exposure. Another m a jor factor is the type of vehicles encoun tered during the comm ute. Under conditions sim ilar to our study conditions (e.g. high density urban setting, with m a ny children comm uting 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 im porta nt finding as we observed during the course of the study tha t school bu ses tend ed to caravan with each other, especially when leav in g the schoo l at the sam e tim e in the afternoon.

Type of Vehicle in Front of Test Bus Di e se

l S

ch

o o l B

u s Di

e se

l Tr

a n sit

Bu

s Me

di

u m

D

u ty

D

ie

se

l He

a vy Du ty

D

i es

e l Ga

so

li n e / N o Ta

r ge

tBlack 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.

125Ty p e o f V e h i c l e i n Fr o n t o f Te st B u s Di e s e l Sc

ho

o l B us D

i e s el

Tr

an

sit

B u s M

e di

um D

u t y

D

ies

e l H

e av

y

Du

t y

Di

e s e l Gas

o li ne

/N

o

T a rg

e tPAH(ng/m^3) 25 00

20 00

15 00

10 00

50 0

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-Treatm e nt Technology Although the prim ary purpose of the videotape an alysis was to investigate the effects of surrounding traffic and outside even ts on concentrations inside the bus, we were also able to investigate differences between bus fuel types and after-tr eatm ent technology. As stated previously, we tested th ree different bus types: conventiona l d i esel, trap-outfitted dies el, and
CNG. For t h ese analys es, we used only the six runs on the prim ary urban route. Despite the effect of ope n windows and consequently high ven tilation rates in the bus during these afternoon runs, which reduced build up of a ny of the bus’s own em issions insi de the bus, we we r e still ab le to see differences in co ncentration s during the commutes based solely on the type of test bus, irrespective of surrounding events and traffic c onditions. In order to show this, we first investigated differences in tra ffic congestion levels, frequency of encounters with other diesel vehicles, an d average wind speeds, to verif y thes e condition s were sim ilar between ru ns and thus not responsible for any differences observed between bus types.

One m a jor indicator of traffic congestion le vels (Photograph 15) on these comm utes wa s the average speed and th e total tim e to com p lete the run. The run on the second urban route was not included in the com p arisons between test bus types becau se any differences due to the route

m a y have confounded the analysis. Consequently, all runs considered in this section followed the sam e route, our prim ary urban route, and we re performed at approxim ately the sam e tim e of day, on either W e dnes d ay (four runs) or Thursd ay (two runs), over the course of seven consecutive weeks. W e would expect sim ilar av e r age speed s and tota l tim e to com p lete the route

126between runs if traffic congestion levels were sim ilar. Table 5.3.5.1 sh ows the average speeds and total tim e to complete the rou t e for each of the six run s on the prim ary urban route. Table 5.3.5.4 shows the m ean black carbon and NO 2 concentr ation s , catego r iz e d into pe rio d s of idling when no ot her diesel vehicles were present fo r these six runs. Run 36, the CNG bus, had the slowest average speed, and the l ong est tota l co mmute tim e, indic a ting traf f i c cong estion lev e ls were heavier on this run than on all other runs. Yet, the black carbon concentrations over an entire commute were a lmost twice a s high on th e diesel bus with the h i g h est average speed (Run 20).

Photograph 15. Typical congestion on the freew ay during an afternoon run.

When the black carbon concentrations were br oken down into periods of idling with no other d i es el vehicles p r esent, we als o saw the CNG bus again had the lowe st m ean concentration of black carbon, while the conventional diesel buses had concentrations that were two to four tim es higher than e ithe r the CNG or trap-outf i tted di esel buses . Both the tr ap-outfitted diesel and conventional diesel buses had concentrations of NO 2 that we re up to two to three times highe r, respectively, than the CN G 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, respectiv ely, by idling with or without other diesel vehicles present, for each of th e three bus types. These graphs dem onstrate the im portance of the presence of a dies el v e h i cle in fron t of the te st bus in d e te r m ining th e highest concentrations inside the bus cabin. However, we also saw a d i fference between te s t bu s types as w e ll. For th e conventional diesel buses, the high est peak concentrations w e re obtained when the test bus was idling behind another diesel vehicle. But high peak concen trations were also ob tained 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.

127Table 5.3.5.4 Mean black carbon and NO 2 concentrations inside the test buses under selected conditions.

B l ack Car b o n (u g/ m 3 ) N O 2 (pp b )

Run Number Bus Fuel
T y p e E n t i r e R u n Not Idling NO Vehicle in
Fron t Idling with NO Vehicle in
Fron t Entire Run No t I d ling NO Vehicle in

Fron t Idling with NO Vehicle in
Fron t

6 Co nv en tio n a l Diesel 5. 1 2

. 5 7 5 4 5 0 5 0

15 Co nv en tio n a l Di esel 1 1

. 5 3 3. 4 12 2 10 6 10 2

20 Co nv en tio n a l Diesel 7. 2 4

. 1 6. 2 8 8 8 3 7 9

26 Co nv en tio n a l Di esel 6

. 6 3. 9 4. 7 5 0 4 4 5 1

30 Trap-Ou t fitted Diesel 2. 7 2

. 2 1. 9 8 2 8 1 8 2

33 Trap-Ou t fitted Di esel 4

. 5 2. 6 1. 7 9 0 8 5 8 0

3 6 C N G 3. 9 1. 5 1. 7 4 0 3 8 3 3

75 250 175 600 N =

Test Bus Status IDLING NOT IDLINGBlack 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.

12819 20 6 1 2 3 38 6 N =

T e s t B u s S t at us ID L I N G NO T I D L I NGBlackCarbon(ug/m^3) 60

50

40

30

20

10

0 V e hi c l e A h ea d of B u s

N o D i es el V e hi c l e

Pr e s e n t

D i e s el V e hi c l e

Pr e s e n t

Figure 5.3.5.11 Concentrations of black carbon inside the trap-outfitted diesel school bus while idling and/or following another diesel vehicle.

27 124 56 300 N =

Test Bus Status IDLING NOT IDLINGBlack 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 wh ile idling and/or following another diesel vehicle.

12975 251 175 600 N =

Test Bus Status IDLING NOT IDLINGPAH (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 insi de the conventional diesel school buses while idling and/or follo wing another diesel vehicle.

19 206 123 386 N =

Test Bus Status IDLING NOT IDLINGPAH (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 insi de the trap-outfitted dies el school bus while idling and/or following another diesel vehicle

13027 124 56 300 N =

Test Bus Status IDLING NOT IDLINGPAH (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 C NG school bus while idling and/or following a nother diesel vehicle.

By com p arison, the trap-outfitted diesel bus and CNG bus had high peak concentrations only while traveling behind a dies el vehicle. In addition, the highest peaks obtained on the CNG bus (which occurred while traveling behind a diesel vehicle) were less than 30 μg/m 3 , while on the conventional diesel buses, the highest peaks reached were over 50 μg/m 3 , alm o st twice as high. Conc entrations over 50 μg/ m 3 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 th e afternoon runs with the window s open, the type of bus had a significan t impact on co ncentration s inside the b u s cab in, in particular, on p eak concentrations.

Moreover, the differences found between bus type s could not be attributed to differences in average wind speed between runs. Table 5.3 .5.1 shows the average wind speeds, m easured at the nearest AQMD stations (see S ection 4.1.2.3.13), on all runs used in this analysis. The averages by bus type ranged from 12 km /hr (t rap-outfitted diesel bus) to 15 km /hr (CNG bus)

, with the av erage speed for the d i esel buses at 14 km /hr.

Finally, we also calcu l ated the am ount of tim e spent beh i nd or ad jacen t to oth e r d i esel vehicles, to determ ine if there were differences between bus types based on th is v a riable. W e found that f o r each of the bus typ e s tested, the am ount of tim e spent behind or adjacent to oth e r diesel vehicles was 25 %, 30%, and 31% for the conventiona l diesel, CNG, and trap-outfitted diesel buses, respectively. Thus, we conclude d differences found between bus types were not due to differences in the num b er of encounters with other diesel vehicles during the commutes.

131In fact, the runs with th e least num ber of encounters with other diesel vehicles were on the diesel test buses, the buses with the highest concentr ations of black carbon and PAH concentrations inside the cabin.

5.3.5.2.2.1 Concentrations Inside B u ses W h ile Idling at Bus Stops Investigation of concentrations inside the bus es while idling at bus stops provided another opportunity to evaluate differences between bus types. Bec a use the bus stops along the prim ary urban route were all located on sm a ll residential st reets, usually in front of ele m entary schools, there were few other vehicles in the surroundin g area during the aftern oon runs (see Photograph 16). Thus, we expected concentrations inside the bus cabin during bus stops were dom i nated by the bus’s ow n exhaust, w ith a m i nim a l contribution from other vehicles.

Photograph 16. View out the front window of the test bu s while idling at a bus stop (at Verm ont Avenue School) during an afternoon run.

Table 5.3.5.5 shows the m ean black carbon, and PAH, and NO 2 concentrations m easured inside the test buses while idling at bus stops, ca tegorized by bus type. On average, black carbon

Table 5.3.5.5 . Mean black carbon, P AH and NO 2 concentrations m easured inside the test buses while idling at bus stops in resident ial neighborhoods (u rban route one).

Test Bus T y pe Bla c k Ca rb o n (u g/m3 ) PAH (n g/m 3 ) NO 2 (pp b )

C o n v e n t i onal Di esel 8. 6 +/

- 1

. 9 10 6 +/ - 26 64 +/ - 6

Trap-Ou t fitted Diesel 1 1. 9 +/

- 0

. 2 20 +/ - 4 78 +/ - 6

C NG 1. 2 +/

- 0

. 2 27 +/ - 1 6 37 +/ - 3

1 Trap o u t fitted d i esel b u s was th e o n l y b u s t o en coun ter ano t h e r d i esel v e h i cle at a b u s sto p fo r ru n s in clu d e d h e re.

132concentrations were four and six times higher inside the diesel bus es at bu s stops compared with the trap outf itted diesel bus and the CNG bus, re spectively, while PAH concentratio ns were four to five tim e s higher inside the conventi onal diesel buses. Concentrations of NO 2 were twice as high for both the conven tional dies el and tr ap -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 P A H concentrations, respectively, during bus stops revealed a m u ch wider range of differences for the diese l buses tested. The s e f i gures sh owed both the CNG and the trap -outf itted die s el buses had relatively low concentrations and no high peaks while idling at bus stops. For these two bus types, the concentration s m easur ed at the bus stops were lo wer than the average co ncentration s

m easured on these buses in congested traffic c onditions when other dies el vehicles were not present. This finding indicat ed the contribution to black ca rbon and PAH from the CNG and diesel trap bus’s own exhaust was minim a l.

61 10 6 19 N =

T E S T B U S T YPE Tr a p D i esel Di e s el CN GBlackCarbon(ug/m^3) 60

50

40

30

20

10

0

Figure 5.3.5.16 Concentrations of black carbon insi de the cabin for conventional diesel, trap-ou t fitted diesel, and CNG sc hool buses while idling at bus stops.

However, for the conventional diesel buses, we found a diffe rent result. First, we found a wider range of concentrations m easured inside the bus cabin at bus stops. For black carbon, the range of co ncentr ation s was sim ilar to what was observed during other portions of the bus commute. This was an interesting finding since th e period of ti m e spent idling at the bus stops had the least im pact from traffic congestion and th e presence of other diesel vehicles. In fact, only the trap-outfitted diesel bus had any other diesel veh i cles p r esen t at any of th e bus s t ops, and that was only for a short thir ty-second period. Thus, the concen trations m easured at the bus stops were not due to other vehicles or general traffic congestion. The differences seen between

13361 106 19 N =

Test Bus Type Trap Diesel Diesel CNGPAH (ng/m^3) 1000

750

500

250

0

Figure 5.3.5.17 Concentrations of PAH inside the cabin for conventional diesel, trap-ou t fitted diesel, and CNG sc hool buses while idling at bus stops.

bus types clearly indicated the conventional dies el buses contributed higher concentrations of black carbo n and PAH to concentr ations ins i de the bus cab in com p ared with either trap-outfitted diesel or C NG buses. These results also suggested the exhaust from the di esel test buses was a significant source of black carbon and PAH inside the cabin during the co mm ute. In addition, both the conventional diesel and tr ap-outfitted diesel buses cont ributed approxim a tely twice as much NO 2 to concentrations inside the bus cabin com p ared with the CNG bus.

5.3.5.2.3 The Effect of Roadway Type: Freew ay versus Su rface Streets Again, only those runs on the prim ary urban ro ute (U1) were included in the ana l ysis of the effect of roadway ty pe (freeway versus surfa ce streets ) o n concentrations insid e the bus. The second u r ban route (U2) consisted of all su rface streets, and n o fr eeway travel, so no com p arison of roadway type was possible for that route. Mean concentrations of black carbon were approxim a tely three times higher on surface street s com p ared with the freeway for diesel buses and the CNG bus, while for the trap-outfitted di esel bus, concentrations were higher on the freeway. In order to ex plain th ese differences, it was neces sary to acco unt for other potentially inf l uential v a riab les, su c h as the type of vehicle in front of the bus (see below).

Table 5.3.5. 6 shows the black carbo n concen tr ations for su rface streets and freeways on the three different test bus types, grouped into peri ods with or without a diesel vehicle in front of the bus. The m o st interesting result we found was th at the presence of a diesel vehicle in front of the bus was a m o re dominant dete rminant of bus cabin conc e n tra ti ons than roadway type. First,

134we found th at the trap -o utfitted dies el bus had f e wer encou n ters with o t her dies el vehicles on surface s t reets th an on th e freeway, while th e op pos ite was true for the d i esel bu ses a nd the CNG bus, which had m o re encounters with other diesel vehicles on surface st reets. As encounters with other diesel vehicles app ear to be the dom inant determinan t of high peak concentrations inside the te st buses, this result helped explain why when looki ng only at m e an concentrations on each ro adway type without further separation by pr esence of other diesel veh i cles, the d i esel buses and the CNG bus were higher on surface st reets (w here they had m o re encounters with other diesel vehicles), while th e trap-outfitted diesel was higher on the fr eeway (where it had more encounters with other diesel vehicles).

R oadw ay Type D i esel V e hicle A h ead of T e s t
Bu s D ies e l Bu s e s T r a p Ou tf itted
Di e s e l B u s CNG B u s

SURF ACE STREETS NO 4.3 1 .7 1.5

F R EEW AY NO 3.2 3 .4 1.6

SURF ACE STREETS YE S 1 23.2 (19%, 93%) 10.5 (10%, 91%) 13.1 (19%, 56%)

R EEWAY YE S 1 4. 6 (10%, 26%) 5.9 (20%, 44%) 2.4 (10%, 32%)

N ote s : 1 : Fi rst num be r i n pa r e nt hesi s i s t h e p e rc e n t o f t h e t o ta l c o m m

ute ti m e r e p r e s e n te d b y t h is ca t e go ry

. S e con d n u m be r i n paren t h e s i s i s t h e perc en ta g e of th e v e h i cl e en c o u n t e r s in t h i s cat eg ory t h at w e re di es el s c h o ol bu s e s

. . 6 Me an b l ac k ca rbo n conc ent r a t i ons i n si de t h e t e st b u ses on di ff er ent ro adway t y p e s, s t r a t i f i ed by th e presence of a di esel vehicle in fro n t o f t h e bu s.

Mean B l ack C a rbon C o ncentrati o ns Insi de the

T e st B u ses (ug/m 3 ) Table 5.3.5

F Table 5.3.5.6 Mean black carbon concentrations insi d e t h e t e s t b u s e s o n d i f f e r e n t r o a d w a y diesel vehicle in front of the bus. types, stratified by the presence of a

In addition, the concentrations m easured insi de the buses were high est for all three bus types when diesel vehicles were encountered on surface streets. This was likely du e to the type of diesel vehicle typically enc ountered on surface streets v e rsus freew ay. For all the test bus types, the majority of encounters with other dies el vehicles on surface stre ets were with diesel school buses (between 56 and 93% of all enco unt ers on su rface streets), while the m a jority of encounters on the freeway were w ith m e dium or heavy duty diesel trucks. This finding again dem onstrated the potential of di esel school buses to contribute high concentrations of black

135carbon (and by im plication, other diesel-related pollutants) to a commuting child’s exposure regardless of the type of vehicle the child m a y be riding in. Exhaust pipe location m a y also have played a role here, as m o st diesel school buses encountered had low exhaust pipes, while m o st heavy duty diesel truck s had high exhaust pip e s. Other studies have found the position of a diesel veh i cle’s e xhaust pipe was an im portant determ inan t of pollutant con centrations in a
passenger car traveling behind a diesel vehicle (Fruin, 2003).

Mixed results between bus types were obtaine d for the com p arison of roadway type when no diesel vehicles were pres ent (Table 5.3.5.6, Figures 5.3.5.18 – 5.3.5 .20). Statistical tests of significan ce were done to com p are surface streets ve rsus freeways when no other d i esel veh i cles

were presen t for each of the tes t bus types. The CNG bus had no significant difference between the two roadway types, while both the diesel buse s and the trap-ou t f itte d bus were signif i can tly different, although in the opposite d i rection (e.g. surface streets were h i gh er on th e 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 com b ination with the different contribution from each bus’s own exhaust, depen d ing on bus t ype. Notice that the m ean concentrations of black carbo n m easured inside both the diese l buses and the trap -ou t f itted d i ese l bus while traveling on the freeway when oth e r diesel ve hicles were not present was approxim ately the

sam e (3.2 a nd 3.4 ug/ m3, respectively). For both of these bus types, the average speed on the freeway was about dou ble th e average speed o n su rface streets. Thus, we would expect m u ch higher ventilation rates, and as the windows were open, we would expect the outside air, or the

111 214 321 454 N =

Roadway Type Freeway Surface Streets95% 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 w it hout other diesel vehicles present.

13614 9 76 17 9 33 0 N =

Ro a d w a y T y p e Fr

e e w a y Su r f a c e S t r e et s95%CIforBlackCarbon(ug/m^3) 30

20

10

0 V e hi c l e A h ea d of B u s

N o D i es e l V e hi c l e

Pr e s e n t

D i e s el V e hi c l e

Pr e s e n t

Figure 5.3.5.19 Mean and 95% confidence intervals for black carbon in side the trap-outfitted diesel school bus for different roadway types with or without other diesel vehicles present

53 98 192 164 N =

Roadway Type Freeway Surface Streets95% 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 fo r black carbon inside the CNG school bus for different roadway types with or without ot her diesel vehicles present.

137average con centration in the line sou r ce, was the dom inant determ inant o f concentra t ions insid e the bus, when other diesel vehicles were not present and when the buses were m oving at relatively high speeds (greater than 30 km/h r). Under these conditions, the dom i nant determ inant of concentrations inside the bus cabin was th e average c oncentr ation in the lin e

source.

By contrast, on surface streets, with m u ch lo wer speeds and m o re perio d s of idling, the concentrations inside the bus, when other diesel vehicles were not present, were dom i n ated both by averag e concentratio ns in the ro ad, and by th e contribution of the bu s’s own exhaust. The trap-ou t fitted diesel bus likely h a d lower concen trations on surface stre ets, under th e conditions describ e d ab ove, becaus e the contrib u tion from the bus itself was lower. The diesel buses’ own exhaust contributed m o re black carbon to the co ncentration s 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 freew ays also had sim ilar av erage speeds on both roadway types (17 and 22 km /hr), indicating sim ilar traffic congestion levels, and thus sim ilar line sour ce concentrations.

These resu lts indicated the im portance of road way type on concentrations inside the bus cabin depended on the type of bus. For relativel y clean buses (e.g. CNG, trap-outfitted diesel),

trave ling in a line sourc e with high er concen tr ations, such as on freeways, tended to increase concentratio ns ins i de th e bus. For a dirtier bus es (e.g. dies el), b ecause the bus ’s o w n exhaus t

was a s i gnificant con t ributor to co ncentration s ins i de the bus, inc r eas ing th e ven t ila tion ra te inside the bus, which was m o re likely to occur on freeways, tended to decrease co ncentration s inside the bus.

5.3.5.2.4 Effect of Route Type Two different urban routes were driven by the sam e diesel bus (RE2) during two consecutive weeks. For the comparison of r outes, only these runs (Run 20 - driven on the prim ary urban route (U1) and Run 26 - driven on urban route 2 (U2)) were used. By m a intaining the sam e bus on both routes, we hoped to elim inate any confounding due to differences between buses.

Table 5.3.5.7 shows the com p arison between U1 a nd U2 for variables such as distribution of road typ e s (freeway , m a jor/m i nor arterials and sm all r e siden tia l st reets ) , av erage speed, percent of tim e spent following other diesel vehi cles and behind diesel school buses, num b er of bus stops, etc. One of the m a jor differences be tween these two routes was that U2 involved only surface streets, while U1 was a m i x of surface streets and fr eeways. In Section 5 .3.5.2.3 we concluded for diesel buses, freeway travel which allowed h i g h er sp eeds and increased ventilation in the bus cabin resulted in lo wer concentrations of black car bon. Given that result, we m i ght expect a bu s route with n o freeway trav el to have higher concentrations.

138Table 5.3.5

. 7 Comparis on between urban rout e on e an d urb a n rout e t w o

.

Urban Rout e O n e (U 1) Urban Rout e Tw o (U 2)

D i stri bution of R oadw ay Types:

Freew ay 42% 0

Maj o r/ Mi nor Art e ri al s 37% 56%

Sm al l Resi dent i a l St reet s 21% 44%

Speed:

A v erag e Speed - Entire C o m m

u te (k m

/ hr) 26.3 21.4

A v erag e Speed - Freew a y 39.5 N / A

A v erag e Speed - Maj o r/Minor A r terials 20.4 27.4

A v erag e Speed - R e sid e ntial Streets 11.4 13.6

Other:

N u m b er of Bus St ops 5 1 0

T i m e Spent at B u s Stops 7% 14%

T i m e Spent B e hin d O t her D i esel Vehicles 28% 13%

T i m e Spent Behi n d O t her D i esel School Buses 18% 11%

Tim e S p en t I d lin g 19% 36%

Tables 5.3.5.8 and 5.3.5.9 show the concentrat ions of black carbon and PAH m e asured 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 m easured on the entire routes. W e found the m e an concentrations for the en tire runs were not significantly differe nt between routes. W e also found sim ilar ranges of concentrations on both routes (Figure 5.3.5.22). However it was also interesting to com p are the concentrations m eas ured on different roadway types for each route (see T a bles 5.3.5.8-5.3.5.9 and Figure 5.3.5.23). First we found the fr eeway portion of Run 20 on U1 had lower concentrations compared to the other tw o roadway types, as we would expect based on our results from Section 5.3.5.2.3 in wh ich we found lower concentrations on the freeway versus surface streets for diesel buses. Next, concentrations o f both black carbon and PAH were sim ilar on m a jor/m i nor arterial roads on U1 and U2, although s lightly higher on U1. The m a jor/m i nor arterial streets on both routes had sim i lar m i xes of vehicles, but when we

exam ined average speeds on the different road t ypes, we found slightly reduced average speeds for the sam e roadway ty pes on U1 com p ared with U2, indicating higher con centr ation s of traf f i c congestion on U1. This is consis tent with the fact that although both routes were in urban areas, U1 had higher density neighborhoods com p ared to U2. This also explains why although both routes traveled through sm all residential streets for a porti on of the run, U1 had higher

139140 . 9

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o 2) Diesel Table 5.3.5.8 Mean black carbon concentrations m easur ed inside the bus on urban route one and urban route two (Runs 20 a nd 26). Mean black carbon (ug/m ∧ 3).

Table 5.3.5.9 Mean PAH concentratio ns inside the bus on urban route one and urban route two (Runs 20 and 26). PAH (ng/m 3 ).

concentratio ns on these streets. Again, average speeds were slightly low e r on res i de ntia l streets on U1, and the residential streets were generally located between highly congested arterials. By

m i nor differences, however, our r e sults show that bo th urban routes were

com p arable in term s of in-bus concentrations over the cours e of the commute. These results als o
p h a s

, f r o m the comparisons in Tabl e 5.3.5.7, we also found that for both routes, concentratio ns ins i de th e bus cabin increased five to six tim es when following an other d i es el

v e h i c l e contrast, on U2, the m a j o rity of the residential streets were in neighbor hoods with low density housing and few other vehicles, and were separated from congested arterials by greater distances

com p ared with U1.

D e s p i t e t h e s e

e m i z e o u r p r e v i o u s f i n d i n g t h a t f r e e w a y t r a v el, which increas es the average sp eed of the bus, tended to reduce concentrations inside di e s e l b u s e s

. I n t h e c a s e o f U 1

, t h e r e d u c e d concentrations on the freeway portion of the ru n were offs et by higher concentrations on the surface streets. U2, which had no freeway travel, had slightly lower surface street con centration s

com p ared with U1, bu t these con cen trations were still high er than those experienced on U1 while on the freew ay.

I n a d d i t i o n

. E n c o u n t e r s w ith other diesel vehicles were less frequent on U2 than on U1 as a percentage of the total run. Ho wever, 82% of all encounters with other diesel vehicles on U2 were with diesel school buses, while on U1, there were m o re total encounters with other diesel vehicles, but only 63% of these were with other diesel school buses. For both routes, a sim ilar(b)
Figure 5.3.5.21 Means and 95% confidence intervals for black carbon (a) and PAH (b) concentrations m easured inside school bus RE2 while traveling on two different urban routes. (a)

30

487 363 N =

ROUTE U2 U195% CI for Black Carbon (ug/m^3)

20

10

0

487 363 N =

ROUTE U2 U195% CI for PAH(ng/m^3) 300

200

100

0

141487 363 N =
U2 U1Black Carbon (ug/m^3) 60

50

40

30

20

10

0

ROUTE
( a

(b)

inside school bus RE2 while trave )

487 363 N =

ROUTE U2 U1PAH (ng/m^3) 2500

2000

1500

1000

500

0

Figure 5.3.5.22 B o x p l o t s o f b l a c k c a r b o n ( a ) a n d PAH (b) concentrations m easured ling on two different urban routes.

142(a) 205 78 257 136 149 N =

ROUTE U2 U195% CI for Black Carbon (ug/m^3) 20

15

10

5

0 Road Type

Freeway

Major/Minor Arterial

Residential Street

205 78 257 136 149 N =

ROUTE U2 U195% 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 interv als for black carbon (a) and PAH (b)

o u t e s , concentrations m easured inside scho o l b u s R E 2 f o r t w o u r b a n b u s r divided by roadway type.

143Run D a t e T ime T y p e of B u s Ty pe of
Route Window
Po s i t i o n W eek
Num b er 5 05/01/02 AM HE2 U 1 C losed 2
6 05/01/02 PM HE2 U 1 O pen 2
1 2 05/14/02 AM RE1 U 1 C losed 4
1 3 05/14/02 PM RE1 U 1 O pen 4
1 4 05/16/02 AM RE1 U 1 C losed 4
1 5 05/16/02 PM RE1 U 1 O pen 4
1 7 05/21/02 AM RE2 U 1 C losed 5
1 8 05/21/02 PM RE2 U 1 O pen 5
1 9 05/22/02 AM RE2 U 1 C losed 5
2 0 05/22/02 PM R E 2 U 1 O pen 5 am ount of tim e was spent traveling b e hind o r ad jacen t to diesel school bu ses, with ap proxim a tely ten and eight m i nutes, respectively, of U1 and U2 spent behind or adjacent to another diesel school bus.

The m o st interesting aspect of the comparison of the two urban routes was that we found both routes to have sim ilar concentrations in side the bus, both in term s of the ranges of concentratio ns experienced, and the average over th e commute. Th is d e spite th e f a ct th at bo th routes had a variety of differe nt features, including the m i x of roa d way types and traffic congestion scenarios. Fe atures which were sim ilar between ro utes, such as encounters with other diesel vehicles, particularly di esel school buses, dom inated the highest peak concentrations on both routes.

5.3.6 Variables Investigated U s ing Paired Instrum e nts For a selected num ber of runs, two sets of in strum e nts were us ed to sim u ltaneously

m easure the front and rear of the bus. Sectio n 5.1.3 of t h is report describes the results of collocation m easure m ents taken each week during th e study for the two sets of instrum e nts.

5.3.6.1 Differences in C oncentration Between the Front and the Rear of the Cabin Pollutan t co ncentr ation s at the f r ont and re ar of the cab in were m easured during 10 of the 36 exposure runs, including five afternoon and five m o rning runs, all on urban route one. Three conventional diesel buses were used for thes e m easurem ents: HE2, RE1, and RE2. Table 5.3.6.1 summarizes the characteristics of these runs.

We used two approaches to analyze the di f f e ren ces between f

r ont/re a r c oncentr ation s : a study of the tim e series graphs an d the calculation of the 95% confidence intervals of the average concentratio ns.

Table 5.3.6.1 Front versus rear comparison runs.

5.3.6.1.1 PAH d u r i n g t h e f i v e m o r n i n g r u n s , t h e front/rear time series ten d ed to track each th e rear of the cabin showed I n g e n e r a l

, other, however, concentrations at m o re fre quent and higher peaks, leading to s l ightly higher average co ncentration s . Run 14 was an exception in that the two tim e series did not track each other an d the baseline of the tim e series for the rear of the cab in was

144substantially higher. Figure 5.3.6.1 shows the PAH measurem ents during Run 12 (RE1-U1- AM), an example of a representative morning run

. As discussed below, exhaust entered into the

c a b i n c r e a t i n

TIME 7 :48:4

0 7:4

5:0

0 7:41:

2 0 7:37:4

0 7 :34:0

0 7:3

0:2

0 7:26

:4 0 7:23

:0 0 7:19:2

0 7:15:4

0 7 :12:0

0 7:0

8:2

0 7:04:

4 0 7:01:0

0 6 :57:2

0 6:5

3:4

0 6:50

:0 0 6:46:2

0 6:42:4

0 6 :39:0

0 400

300

200

100

0 Front

Rear

TIME 15:57:0

0 15:

53:5

0 15:50:4

0 1 5:47:3

0 15:4

4:2

0 1 5:41:1

0 15

:38:0

0 15:34:

5 0 15

:31:4

0 15:2

8:3

0 15:25:2

0 15

:22:1

0 15:19
:0 0 15

:15:5

0 15:1

2:4

0 15:09:3

0 15:0

6:2

0 15:03

:1 0 1 5:00:0

0 700

600

500

400

300

200

100

0 Front

Rear g t h e c o n c e n t r a t i o n g r adient. S i n ce th e rear of the cabin was closer to the eng i n e and its exhaust, the time series for this location exhibited a m o re dynam i c behavior com p ared

w i t h t h

s i d e t h e c a b i n d u r i n g R u n 1 2

.

v e a f t e r n o o n r u n , t h e 1 0

- s e c onds data for P AH during Run 13 (RE1-U1-PM). e f r o n t.

i g u r e 5

. 3

. 6

. 1 PAH concentrations (ng/m 3 ) i n F
In general, the five afternoon runs showed sim ilar trends, with the tim e series tracking each oth e r (with the exception of a few concentr ation spikes). Figu re 5. 3.6.2 shows an exam ple

o f a r e p r e s e n t a t i

Figure 5.3.6.2 PAH concentrations (ng/m 3 ) inside the cabin during Run 13.

145146 Run 19 17 14 12 595%CI 400

300

200

100

0 Front

Rear 500 Figure 5.3.6.3 shows the m eans and 95% conf i d e n c e i n t e r v a l s o f t h e a v e r a g e P A H concentrations during the select ed morning runs, with the window s closed. In general, the concentratio ns at the rear of the cabin were si gnificantly higher than thos e at the front of the cabin for the conventional diesel buses.

Run 20 18 15 13 695%CI 200

100

0 Front

Rear Figure 5.3.6.3 Means and 9 5 % confiden ce intervals of the average PAH concentration s during m o rning runs (ng/m 3 ).

i g u r e 5

. 3

. 6

. 4 Means and 9 5 % confiden ce intervals of the average PAH concentration s Figure 5.3.6.4 shows the 95% confidence interval s of the average PAH concentrations during the selected afternoon runs, with the windows opened. In general, the differences between the concen trations m easured at the two location s were less substan t ial than du rin g morning runs and in m o s t cases those differe nces were not statistically sign if icant.

F during afternoon runs (ng/m 3 ).5

. 3

. 6

. 1

. 2 P a r t i c l e C o u n t s During the morning runs, for particle counts, the pairs of tim e series followed sim ilar trends and exhibited a sim ilar num ber of peaks. However, the tim e series for the rear of the

c a b i n s h o w e d h i g h e r b a s e l i n e s a n d h i g h

TIME 7:5 9:20 7:5

5:00 7:50:

40 7:46:20 7:42:00 7:37:40 7 :33:20 7:2

9:00 7:24:4

0 7:20:

20 7:16:00 7:11:40 7: 07:20 7 :03:00 6:58

:40 6:54:2

0 6:50:00 6:45:40 6:41:20 6: 37:00 300

200

100

0 Front

Rear

TIME 1 5:57:00 15:53:5

0 1 5:50:40 15:47:3

0 15:44:20 15:4

1:10 15:38:00 15:3

4:50 15:31:40 15:2

8:30 15:25:20 15:2

2:10 15:19:00 15:1

5:50 15:12:40 15:0

9:30 15:06:20 15:0

3:10 15:00:00 90

80

70

60

50

40

30 Front

Rear e r p e a k s , yielding higher average c oncentrations (excep t for Run 17 which between the two tim e 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).

h e p a i r s o f tim e series for PC (0.3 to 0.5 μ m ) 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 0.5 μ m ) during Run 5.

Figure 5.3.6.6 Concentrations (#/cm 3 ) of PC (0.3 to 0.5 μ m ) during Run 13. T

3

147148 F i g u r e 5

. 3

. 6

. 7 s h o

Run 19 17 14 12 595% CI 140

1 2 0

100

80

60

40 Front

Rear w s t h e m eans and 95% conf idence intervals of the average PC

i g u r e 5

. 3

. 6

. 7 Means and 9 5 % confiden ce intervals of the average PC concentrations (0.3 to 0.5 μ m ) at front and rear of cabin during morning runs (#/cm 3 ).

Figure 5.3.6.8 shows the m eans and 95% conf idence intervals of the average PC

o n c e n t r a t i o n s ( 0

. 3 t o 0

. 5 μ m ) during the select ed afternoon runs, with the windows partially opened. In general, the differenc es between the concen trations m easured at front versus rear

e r e n o t s i g n ificant (ex cept for Run 20 where the concentrations at the rear of the cabin were significantly higher than those at the front). After reviewing the real tim e data for this run, we

e t e r m ined this was due to a few of high concentrations peaks the instrum e nts at the front did not

a p t u r e , p r o b a b l y c a u s e d b y a m o mentary m a lfunction.

In summary, both pollutants PAH and PC exhibite d high er concentratio ns at the rear of the cabin w h en the win dows were closed (m orning r uns), due to exhaust leak ing into the cabin a s

e m onstrated by the analysis of the SF 6 data (see Section 5.3.1).

For the runs where the windows were part ially opened (afternoon), the front/rear concentratio ns were simila r f o r the two pollutan t s, consistent with other analyses reported here suggesting that when the windows were opened, higher ventilation rates prom oted uniform

m i xing within the cabin concentrations during the select ed morning runs, with the window s closed. In general, the concentratio ns at the rear of the cabin were si gnificantly higher than thos e at the front of the cabin, for all bus types.

F

c

w

d

c

dRun 20 18 13 695% CI 60

50

40

30

20

10 Front

Rear

Figure 5.3.6.8 Means and 9 5 % confiden ce intervals of the average PC concentrations (0.3 to 0.5 μ m ) at front and rear of cabin during afternoon runs (#/cm 3 ).

. 3

. 6

. 1

. 3 C o r r e l a t i o n B e t w e e n F r o n t a n d R e a r C oncentrations 5

P AH R a tio (rea r to front) P C R a tio (re ar to front)

Morning ( w indows cl osed) 1.4 ± 0.07 1

. 4 ± 0

. 03

A f t e rno on ( w indows o 1.3 ± 0.17 1

. 1 ± 0

. 06 p en) As described in previous sections we could quantify the differences betw een the front and

a r u s i n g t h e r a t i o o f t h e a v e r age concentrations. Table 5.3.6.2 su mmarizes these results. The %

Table 5.3.6.2

5.3.6.2 Diffe rences in Concentration Insi de and Immediately Outside the Bus r e

r a t i o s w e r e c a l c u lated u s ing th e 10 -second d a ta of the selected runs. In add ition

, the 95

c o n f i d e n c e i n t e v r a l s o f t h e a v e r a g e o f thos e ratios were com p uted and also shown.

Observed rear-to-front ratios.

o f t h e m with windows open). Three bus types were used for these

m e a s u r ents, including a conventional diesel bus (HE3), the particle trap-outfitted diesel bus (TO1) and the CNG bus . Table 5.3. 6.3 summarizes th e characteristics of thes e runs. Because of Pollutant concentrations inside and immediat ely outside the bus were measured during 10 of the 36 exposure runs. These experim e nts cons isted of four afternoon runs with windows open (using U1), three m o rning runs with windows cl osed (using U1), and three runs using th e

r u r a l / s ub u r b a n r o u t e ( a l l

e m

149150 R

u n D at e T i m e T y p e of Bu s Ty pe of
Route W i ndo w P o sition W eek
Number

7 05/07 / 0 2 P M H E3 RS Ope n 3

1 0 05/08 / 0 2 P M H E3 U1 Ope n 3
2 7 06/04 / 0 2 P M T O1 RS Ope n 7
2 8 06/05 / 0 2 A M T O1 U1 Clo s e d 7
3 0 06/05 / 0 2 P M T O1 U1 Ope n 7
3 1 06/06 / 0 2 A M T O1 U1 Clo s e d 7
3 3 06/06 / 0 2 P M T O1 U1 Ope n 7
3 4 06/11 / 0 2 P M C NG RS Ope n 8
3 5 06/12 / 0 2 A M C NG U1 Clo s e d 8
3 6 06/12 / 0 2 P M C NG U1 Ope n 8 a problem with the sampling line, outside m e as urem ents were not made on the conventional diesel bus (HE3) during any runs with the windows closed.

Table 5.3.6.3 Inside/outside com p arison runs

W e u s e g r a p h s for each run to study th e concentration variatio ns within ru ns and also to exam ine whether th e

e c a l c u l a t e d t h e 9 5 % c o n f i d e n c e inte r v a

d t h e h i g h e s t

r e c i s i o n b e t w e e n p a i r e d i n s t r u m e nts used in th e study. In the sam e section, we discussed the

c a r b o n a n d b e t w e e n P C a n d P M 2.5 . Hence, we

orm i ng the com p arisons between the inside

n d o u t s i d e c o n c e n t r a t i o n s

. B a sed on the analysis of the corre lation coefficients, we assum e d

c a r b o n m i m i c the behavior of PC and PAH, respectively. d t w o m e thods to analy ze this set of runs. First, we produced tim e s e r i e s

two series (inside and o u tside) track ed each oth e r. Second, w l s of the conce n tra tion m eans to es ta blish if the dif f e rences between the ave r ag e concentrations (inside versus outside ) were sta tis tically signif i cant.

As discussed in Section 5.1.3.3, the PC and t h e P A H a n a l y z e r s e x h i b i t e
p strong correlations betw een PAH a n d b l a c k focused on PC and PAH m easurem ents when perf

a PM 2.5 a n d b l a c k

5

. 3

. 6

. 2

. 1 P A H During the three rural/suburba n runs with the windows open, the inside versus outside n tim e series tended to track each ot her: when the outside values were high, th e

s i d e v a l u e s w e r e a l s o h i g h a n d v i c e v e r s a

. Ho wever, the o u tside tim e serie s exhib i ted a m o re
y n a m i c be havior, with m o re frequent and higher peaks. The four runs on U1 with the windows ilar trends, with the outside tim e series exhibiting m o re variability.

n t h e p a r t i c l e

- t r a p concentratio i n d open showed sim

During the three m o rning runs on U1 with t h e w i n d o w s c l o s e d ( o

u t f i t t e o d die s el and the CNG buses), the insid e tim e serie s exhibited f e w peaks but the ou tsid e tim e series again showed m o re dynam i c behavior with a variety of peaks. As an example, Figure 5.3.6.9 shows 10-second resolution data fo r PAH during Run 28 (AM-TO1-U1).TIME 7: 36

: 3 0 7:

3 3 :0 0 7:

2 9 :3 0 7:

26

: 0 0 7:

22

: 3 0 7:

19

: 0 0 7:

1 5 :3 0 7 :1 2:0

0 7:

08

: 3 0 7:

05

: 0 0 7:

0 1 :3 0 6 :5 8:0

0 6:

54

: 3 0 6:

51

: 0 0 6 :4 7:3

0 6 :4 4:0

0 6 :4 0:3

0 6:

37

: 0 0 6 :3 3:3

0 6 :3 0:0

0 2000

1750

1500

1250

1000

750

500

250

0 Inside

Outside

Run 36 35 34 33 31 30 28 27 10 7 Outside95%CI

0 250

2 0 0

150

100

50 Inside

CNG (35) TO1 (31) TO1 (28)95%C

0 Outside.I. PAH 100

50 250

200

150

Inside i g u r e 5

. 3

. 6

. 9 Inside (rear) and outsi de concentrations (ng/m 3 ) of PAH during Run 28

3 5 )

, c o n c e n t r a t i o n s o u t s i d e

e b u s

d o n l y

i g u r e 5

. 3

. 6

. 1 0 Means and 9 5 % confiden ce intervals of the average PAH concentration s (ng/m 3 ) for selec t ed runs. F (urban, m o rning, windows closed).

Figure 5.3.6.10 shows the m eans and 95% c onfidence intervals of the average P A H concentratio ns for selected runs . During Runs 28 and 31 on the pa rticle trap-outfi tted diesel bus with the windows closed, the concen trations inside the cabin were clearly higher than outside.

o w e v e r , f o r t h e r u n o n t h e C N G b u s w i t h t h e w i n d o w s c l o s e d ( R u n H

t h w e r e h i g h e r , i n d i c a t i n g lo wer contrib u tion f r om self pollu tio n insid e th e cabin of th e CNG bus.

( a ) A l l a v a i l a b l e r u n s ( s e e table 5.3.6.3) ( b ) R u n s w i t h w i n d o w s c l o s e

F

151152 For the remaining afternoon runs with the windows open, differences in concentrations

e t w e e n i n s i d e a n d o u t s i d e were not significant.

. 3

. 6

. 2

. 2 P a r t i c l e C o u n t s b

5

TIME 16:2 0 :50 1 6:1

7 :00 1 6:1

3 :10 16

:0

9:2

0 16:

0 5:3

0 16:0
1 :40 1 5:5

7 :50 1 5:5

4 :00 15

:5

0:1

0 15

:4

6:2

0 15:4

2 :30 15:3

8 :40 1 5:3

4 :50 15

:3

1: 00 15

:2

7:1

0 15:2

3 :2

0 15:1

9 :30 1 5:1

5 :40 1 5:1

1 :50 15

:0

8:0

0 700

600

500

400

300

200 Inside

Outside For PC, for the three rural/s uburban runs with the windows open, the in side and outside

o n c e n t r a t i o n t i m e series behaved sim ilar to ea ch other, following the sam e trends and with

m ilar m a gnitudes of p eak concen trations.

The four afternoon runs for U1 with the wi ndow s open exhibited alm o st the sam e trends

s t h o s e f o r t h e r u r a l / s u b u r b a n r o u t e, with in side/outs i de tim e series f o llowing s i mila r pattern s

x c e p t t h a t
, i n g e n e r a l

, t h e o u t s i d e t i m e series showed slightly m o re dynam i c behavior for U1.

During the three m o rning runs on U 1 with the windows closed (includes the particle trapu t f itted die s el and the CNG buses), the pairs of tim e series again f

o llowed s i m ilar trends ;

o w e v e r , t h e o u t s i d e c o n c e n t r a t i o n s ( b o t h baseline and peaks) were higher than the

o n c e n t r a t i o n s i n s i d e t h e c a b i n , t h e opposite of what we found for PAH.

d a t a f o r

C c

s i

a

e

o h

c

Figure 5.3.6.11 shows an exam ple of a tim e se ries based on 10-second r e s o l u t i o n PC during Run 33 (PM-TO1-U1-windows open).

Figure 5.3.6.12 shows t h e 95% confidence interval s of the average PC concentrations during the selected runs. For the m o rning runs with windows closed ( 28, 31, 35), the differences between th e inside and outside co ncentr ation s were not o n ly sign if ic ant but also substan tia l (outside higher).

Figure 5.3.6.11 o n c e n t r a t i o n s ( # / c m 3 ) o f P C ( 0

. 3 t o 0

. 5 μ m ) during Run 33.400

Run 36 35 34 33 31 30 28 27 10 7 u t s i d e95% CI 100

0 Inside

O 300

200

F i g u r e 5

. 3

. 6

. 1 2 Mean and 95% confiden ce inte r v als f o r PC (#/cm 3 ).

t s i d e t h e cab in were significantly higher.

. 3

. 6

. 2

. 3

. I m p l i c a t i o n s o f t h e R e s u l t s During the afternoon runs w ith windows open, the differen ces between inside versus outside were, in general, not statistically si gnificant, except for R uns 27 and 33, where the concentratio n s o u

5

I n sid e O u tsid e

o s ed ) F l at time series
Hi ghe r av era g e co ncen t r at i o n s ( e x cep t C NG) Dy namic tim e series

L o wer a v er age c onc ent r a t i o ns

en T i me s e r i e s s i mila r to ou ts ide
Av era g e con cen tr at i ons s i m i la r t o out s i de Time series slig htly more d y n a Av erage con cen tr at i ons s i m i l a r t

I n sid e O u tsid e

o s ed W i n dows cl (m orn i ng

W i nd ows op

(aft erno on) mic

o i n si de

W i n dows cl ) T i me s e r i e s s i mila r to ou ts ide
A v e r ag e c o nc e n t r a tio n s lo w e r th an ou ts ide Time series slig htly more d y n a mic
Av erage con cen tr at i ons h i gh er t h an i n si de

W i nd ows op en

(aft erno on) T i me s e r i e s s i mila r to ou ts ide
Av era g e con cen tr at i ons s i m i la r t o out s i de Time series slig htly more d y n a mic
Av erage con cen tr at i ons s i m i l a r t o i n si de PC i n t h e accumu l ati o n mo de (Back g r ou nd po ll ut an t ) PA H ( D ire c tly e m itt ed po llu ta n t )

(m orn i n g Table 5.3.6.4 summarizes the m a jor findings of th e analyses described in this section.

a b l e 5

. 3

. 6

. 4 Summ ary of m a jor findings of in side versus outside com p a r isons. T

153These results also suggest there is a signifi cant difference in behavior between directly

m itted pollutants, such as BC, PAH, and NO 2 , and “background” pollutants such as PM 2.5 and
C ( i n t h e a c c u m u lation m ode), cons isten t with analyses pr e s ented in other sections of this report

. 1

. 3

, 5

. 2

. 1

, a n d 5

. 3

. 1 ) ( a l t h o u g h w e em phasize that both PM 2.5 and PC can have a direct

m issions com ponent). These findings dem onstr ate the com p lex im plica tions for effects of i n d o w s p o s i t i o n , a s e x plained in S ection 5.2.2. 3.

. 3

. 6

. 2

. 4 C o r r e l a t i o n b e t w e e n I n s i d e a n d O u t s i d e C o n c e n t r a t i o n s e P ( 5

e

w

5 We have described the key f actors governing the differences in pollutant concentrations

s i d e a n d o u t s i d e t h e c a b i n

. H o w e v e r , t h e a n a l ysis above did not include the quantification of

o s e d i f f e r e n c e s

.

Outdoor-to-indoor ratios are wide ly used in the analysis of indoor air pollutant data ( F i s c h e r e t a l

. , 2 0 0 0 ; J a n s s e n e t a l

, 2 0 0 1 ; C h a 2002). We used an analogous param e ter defined as the ratio of outside-to-inside bus cabin concentrations. i n
t h

l oulakoua and Mavroidis,

j Inside, j Outside, j C C

α = (5.3.6.2.1)

where is the ou tside

- to-ins ide co ncentr a tion s ra tio f o r

the pollutan t j; C Outside,j is the

b u s ; a n d C ins i de,j is the concentration of pollutant j m easured inside the cabin.

3 6 ( U 1 w i t h w i n d o w s o p e n o n T O 1

n d C N G )

. I n a d d i t i o n , t h e 9 5 % c o nfidence intervals of the m ean of these ratios were com puted.

a n d d e m o n s t r a t es the outside-to-inside ratio is a function

o f s e v e i v e

s u l t s s u p p o r t t h e f i n d i n g s d e s c r i bed in the preceding sections.

Table 5.3.6.5 Observed outside-to-inside concentr ation ratios for PAH and PC for CNG and particle trap-outfitted buses.

PAH (ng/ m 3 ) PC (#/cm 3 ) j 

### α concentration of pollutant j m easured imm e d i a t e l y o u t s i d e t h e

These ratios were calculated using the 10-s econds data of Runs 28, 31, and 35 (U1 with windows closed on TO1 and CNG)

, a n d R u n s 3 0

, 3 3

, a n d

a T a b l e 5

. 3

. 6

. 5 s u m m a r i z e s t h e s e r e s u l t s

r a l f a c t o r s , i n c l u d i n g t ype of pollutant, bus type, and wi n d o w p o s i t i o n

. T h e s e q u a n t i t a t

r e

W i ndows closed (m orning) 0.75 ± 0.2 (TO1) 1.54 ± 0.2 (CNG) 1.40 ± 0.04 (TO1) 2.14 ± 0.04 (CNG)

W i ndows open

(afternoon) 0.99 ± 0.2 (TO1) 1.06 ± 0.08 (TO1)

1545.4 Com p arison of Pollutant Concentrations M easured Inside the Buses and at Nearby AQMD Mo nitoring Sites

5.4.1 Carbon Monoxide and Nitrogen Dioxide CO and NO 2 am bient monitoring data were obtaine d from t h e South Coast Air Quality Managem e nt District (SCAQM D) for co m p a r is ons between inside bus concentrations and

am bient concentration s . The Central LA and West LA SCAQMD stations (see Photograph 17)

e took the average of the data

om t h

l t s f o r C O a n d N O 2 , respectively.

5

. 4

. 1

. 1 C a r b o n were se lec t e d as the clos est m onitoring sites to u r b a n r o u t e o n e

. W f r e t w o m onitorin g sites as be st repre s enti ng a m bient concentrations near urban route one. We chose AQMD data correspond ing to the date and tim e of each run. Sections 5 .4.1.1 and
5

. 4

. 1

. 2 d i s c u s s r e s u

P h o t o g r a p h 1 7

. W e st L.A. SCAQM D Station

M o n o x i d e Tables 5.4.1.1 and 5.4.1.2 show the m ean CO c oncentrations m easured inside the buses

a n d a t t h e t w o e a r e s t A Q M D s t a t i g ( w i n d o w s c l o s e d o n ( w i n d o w s

o p e n ) r u n s o n b a n r o u t e o n e , r e s p e

o rning versus afternoon, since earlier studies showed in-vehicle / e r f o r a n for the m s , w i t h f i x e d w e r c o n (Petersen and Allen 1982; Fernandez-Brem auntz and Ashmore 1995b). However, in the present study we found no difference between m ternoon in-vehicle/fix

o n a v e r f 0

. 7

. n i n T a b l e s 5

. e a b s o l u t e concentrations of CO were si gnificantly different between morning and afternoon, with higher

o n c e n t r a t i o n s b o t h i n s i d e t h e b u s e s a n d a t t h e AQMD stations in the m o rning. The overall
ean CO c oncentration inside buses in th e morning was 5.2 ppm ve rsus 2.4 ppm in the f t e r n o o n , w h i l e t h e o v e r a l l m ean of t h e two AQMD stations in the m o rning was 6.9 ppm versus

. 6 p p m in the afternoon. n

u r o n s f o r m o r n i n

c t i v e l y

. ) a n d a f t e r n o

Mean CO concentrations were compared

o r n i n g h o u r separately, m f i x e d s i t e r a t i o s w e r e h i g h

- s i t e m onitors e x h i b i t i n g l o a f t e r n o o n / e v e n i n g h o u r s t h

o n c e n t r a t i o n s i n t h e a f t e r n o

o rning and af
H o w e v e r , a s s h o w ed-site ratios, with
4

. 1

. 1 a n d 5

. 4

. 1

. 2

, t h a g e t h e s a m e r a t i o o

c

m

a 3

155Table 5.4.1.1 Mean CO concentration s (ppm ) inside buses and at two nearby AQMD station s for the m o r n i n g r u n s ( w i n d o w s closed) on urban route one.

Date Run Week Bus Inside West LA Central
LA Mean 1 Ratio 2

bus
0 5 / 0 1 / 0 2 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. Arithm e tic m e an of west LA and cent r al L A stations. 2. R a t i o = (m ean of i n si de b u s ) / (m ean of t w o st at i ons)

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 Mean 1 Ratio 2

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. Arithm e tic m e an of west LA and cent r al L A stations. 2. R a t i o = (m ean of i n si de b u s ) / (m ean of t w o st at i ons)

Inter e stingly

, the trend s of CO concentra tions in side the bus es and at th e am bient sites track e d e a c h other over the m a in study period (see Figures 5 .4.1.1 (a) an d (b) for m o rning and afternoon, respectively). Figure 5.4.1.1 shows CO c o n c e n t r a t i o n s i n s i d e t h e b u s e s w e r e consistently lower than those at the am bient sites except for Run 14 and 17 in the morning and Run 36 in the afternoon.

1565

8 12 28 31 35 9. 0

14 5 12

14 31 35 8. 0

4

n 17 19

8 17

1 9 28

0. 0 1. 0 2. 0 3. 0 4. 0CO( 5. 0pp 6. 0m) 7. 0 10

. 0

0 1 0 2 0 3 0 0

R u AQ M D s i t e s

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 4 0 18 2 0 30

33

6 18 2 0 30 33 7. 10

. 0

8. 0 9. 0

I n si de b u

1 0 3 0

Ru nCO(ppm) AQ M D s i t e s
I n si de bus

( b ) Afternoo n

5.4.1.2 Nitrogen Dioxide (a) Mo rning

F i g u r e 5

. 4

. 1

. 1 CO concentrations (ppm ) inside buses and at th e selected A Q MD statio ns.

l e s 4

. 1

. 3 5

. 4 e s e n n N O c e n t r a m easured inside buses and at l L n d W A s t f o r i ng and afternoon runs, re 2 i o n s i d e b u s r e c t e n t ly higher th an those measured a t a m bient

m o g r u t h e l m ean con c e n t n ins i d s 6 4 p e r s u s 2 8
A D s t a n s

. e r a g 2 concentratio n s de th e buses were m o re than h i t h a n e a m c o n a t i o ns in the morning during the m a in study. The

a n c e n t r o n i n u s i n afternoon was 76 ppb, higher than inside the buses

n i n d m o es highe r than the overall am bient level of about 22 ppb.

p o s t o N O i n s i s e m e d i n m a in study ranged from 34 to 110 ppb in

t h e n s i g n i f i c a n l y h i g h e n i n e r s t u d i e s

o f e r e p o r t e d N O 2 concentrations m easured in

i g h t e e n c i t i e s i n f i f

t e e n c o u n t r i e s a r o u n d t h e w o r l d. The range of personal exposures was fr o m 11 ppb to 51.5 ppb. T T a b 5

. a n d

. 1

. 4 p r t m e a 2 c o n t i o n s
t h e C e n t r a A a L a t i o n s m o r n spectively. Mean NO

c o n c e n t r a t s i n t h e e s w e o n s i s t h e

s i t e s

. F o r r n i n n s , o v e r a l r a t i o e t h e buse s w a p b v
p p b a t t h e Q M t i o O n a v e , N O i n s i two tim e s g h e r t h b i e n t c e n t r overall m e c o n a t i s i d e b t h e in the m o r g a n re than three tim

E x u r e 2 d e b u s e p l o y t h e

, m o rning and from 39 to 115 ppb in the after n o o t r t h a a r l i e

o t h r m i croenvironments. Le v y e t a l

. ( 1 9 9 8 )

e h e highest NO 2 concentration reported in their study was 106 ppb in

w o r k p l a c e e x p o s u r e i n M e x i c o C i t y

. S t e v e n s on et al. (2 001) also reported the average NO 2 concentration during 1997 in UK was 23 ppb at curbsi de locations. They defined points within one to five m e ters from the curb of a bus y r o ad as cur b si de l o cat i ons, r e fl ect i ng NO 2 concentrations on a busy street.

157Table 5.4.1.3 Mean NO 2 concentrations (ppb) inside buses and at two nearby AQMD stations for the m o rning runs with window s closed on urban route one.

Date Run Week Bus Inside bus West LA Central
LA Mean 1 Ratio 2

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 Arithm e tic m e an of west LA and cent r al L A stations.

2 Ratio = (m ean of in si d e b u s) / (m ean o f t

w o statio n s ).

a b l e 5

. 4

. 1

. 4 Mean NO 2 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 Mean 1 Ratio 2

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
RE1 115.1 14 20.5 17.3 6.7 05/16/02 15 4 05/21/02 18 5 RE2 80.4 19.5 7.5 13.5 6.0 0 5 / 2 2 / 0 2 2 0 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 Arithm e tic m e an of west LA and cent r al L A stations.

2 Ratio = (m ean of in si d e b u s) / (m ean o f t

w o statio n s ).

Figure 5.4.1.2 shows clear differences in NO 2 concentratio ns betw een inside the buses and at the a m bient sites

. It is no te worthy that the CNG bus had the lowest NO 2 c oncentr ation s

am ong bus types in the morning and afternoon runs. However, the trends in concentrations over the study p e riod less clo s ely tracked each oth e r com p ared with CO (as d i scussed in the previou s section). Furtherm ore, there was little dif f e rence in av erage NO 2 concentratio ns between morning and afternoon runs (64 versus 76 ppb insi de buses and 28 versus 22 ppb at the am bient sites ) , while CO concen tra tions in the m o rning were abou t two tim es h i gher than those in the afternoon in both m i croenvironm ents.

1585

8 0

. 0 1 2

14 17 19 28 31 17 19

28 31

2 4 6 80.0 100.0 120.0

0 2 0 3 0 4

Ru nNO2(ppb)

35 5 15

100.0 120.0

8

1 2 14

35

0

. 0

0 1 0

. 0 0

. 0

0 A Q M D s i t e s

6 10 13

15 30 10 13 30

0.0

0 0 . 0

0 1 0 3 0 4 0 18 20 33

36 6 18 20 33

36

20.0 4 0

. 6 0

.NO 8 0

2 0

Run2(ppb)

I n side bus A Q MD sites

I n side bus

( b ) Afternoo n

5.4.2 n t e g (a) Mo rning

Figure 5.4.1.2 NO 2 concentrations (ppb) inside buses and at the selected AQMD stations.

t e d S a m p les I r a 5

. r o m y o n

4

. 2

. 1 A a t i c H d r o c a r b s During the m a in study we m e a s u h e c o n atic hydrocarbons the AQMD station loca te w e s t A n g e l e s h e n c o n t r a t i o n s of target pollutants are shown for m o rning runs and afternoon runs in Tables 5.4.2.1 and 5.4.2.2, resp ectively. Ratios of m ean c oncentrations between inside the bus and the W L A AQMD site varied by pollutant. I t is also inter e sting that the ratios f o r each pollutant varied between m o rn ing and afternoon sam p ling periods.

Several other studies have also com p ared co ncentrations of arom a tic hydrocarbons inside vehicles versus fixed monitoring sites. Rodes e t al. (1998) m easured concentrations of all of the above-m entioned pollutants (except n-octane) inside a typical California diesel school bus in
Sacram ento and th en com p ared th ose concen trations to ambient con centration s

. The ratio s between inside the school bus and th e am bient site were av ailable on ly f o r m and p-xylene ( r atio = 2.5) and toluene (ratio = 2) in the m o rning and only for toluene (ratio = 1.2) in the afternoon. The higher ratios found for the sa m e pollutants in the present study are presumably due to differences in traffic densities and vehicle m i xes between Sacram ento 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 arom a tic hydrocarbons: benzene (3), ethylbenzene (4.2), m and p-xylene (5), o-xylene (3.8) , toluene (2). They conducted

m easure m ents of those com pounds during m o rning and evening rush hour s in T aegu, the third larges t city in Korea. Ratios for ben z ene, ethy lbenzene, and m and p-xylene were co mparable to ratios we found in the present study, while ratios for o-xylene and toluene were m u c h lower than our results. r e d t c e n trations of several arom both inside the school buses and at the site of d i n L o s ( W L A )

. T m e a n c e

159Table 5.4.2.1 Mean concentrations (ppb) of arom atic hydrocarbons during m o rning runs (windows closed).
Com pound Inside bus West LA AQMD site Ratio 1

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-Octane 2 1.5 1.0 1.5

Toluene 17.7 2.3 7.7

1. Ratio = (m ean co n c en tration i n sid e bu s) / (m ean c once n trati o n at WLA AQMD site) 2. n- oct a ne i s n o t an a r om at i c hy dr ocar b o n s but i s re po rt ed f

o r

com p l e t e ness. 3. Detectio n lim it fo r all co m p o u n d s ex cep t t o luen e was 0

. 01 pp b.

Table 5.4.2.2 Mean concentrations (ppb) of arom a tic hydrocarbons during afternoon runs (windows open).
Com pound Inside bus West LA AQMD Ratio 1

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-Octane 2 1.9 0.1 19

Toluene 14.4 0.7 20.6

1. Ratio = (m ean co n c en tration i n sid e bu s) / (m ean c once n trati o n at WLA AQMD site) 2. n- oct a ne i s n o t an a r om at i c hy dr ocar b o n s but i s re po rt ed f

o r

com p l e t e ness. 3. Detectio n lim it fo r all co m p o u n d s ex cep t t o luen e was 0

. 01 pp b.

5.4.2.2 1,3-Butadiene Concentrations of 1,3-butadiene were also m easured inside the school buses and at the west LA AQMD site during the main study. Seve ral other studies have also investigated invehicle concentration of 1,3-but adiene (Chan et al., 1991a; Duffy and Nelson 1997; Rodes et al., 1998). However, 1,3-butadiene has high photoc hem i cal reactivity and will undergo rapid photooxidation during the day (Carter and Atki nson, 1989). Thus, in the study by Duffy and Nelson (1997), significant concen trations of 1,3-butadiene were only observed inside vehicles during peak traffic hours, while concentrations of 1,3-butadiene in the sam p les collected during non-peak traffic ti m e s or in a m bient air were near , or below, the detection lim it of 0.1 ppb. Cha n et al. (1991a) also reported 30% of in-vehicle sam p les and 100% of fi xed-site and sidewalk

sam p les below the detection lim it of 1 ppb. In the present study, 70% of the 1,3-butadiene data collected during the bus commute runs were below the detection lim it of 0.5 ppb.

Table 5.4.2.3 shows concentratio ns of 1,3-butadiene inside th e buses and at the west LA AQMD site. In-cabin concentrations of 1,3-butad iene were above the detection lim i t of 0.5 ppb for only eight m o rning 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 lim it of 0.5 ppb, both in the afternoon.

160Table 5.4.2.3 Concentrations of 1,3-butadie ne (ppb) during the m a in study.
Date Run Tim e 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 AQM D site 0.5 5/29/02 PM WLA AQM D site 0.6

Table 5.4.2.4 summarizes in-vehicle m ean con centrations of 1,3-butadiene m easured in the present study as well as in ea rlier studies. Two studies compared in-vehicle concentrations to

am bient concentrations: the pres ent study (0.9 ppb aver age 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 popul ation-weighted exposure to am bient airborne 1,3-butadiene for California was estim a ted to be 0.37 ppb. Consequently, on average, children on the school buses em ployed in the present study would have b een exposed to 1,3-butadiene at about two and a half ti m e s the statewide population-weighted level during the tim e s of their

commute.

Table 5.4.2.4 Studies m easuring 1,3-butadie ne (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 Sacram en to, CA School bus (diesel) 0.4

5.4.2.3 Gaseous Aldehydes and Ketones Gaseous ald e hydes and ketones (i.e.

, carbonyls ) em itted by vehicles have been identif ied as hazardou s air pollu tants, with com pounds s u ch as form aldehyde being a known hum a n carcinogen (Kean et al. 2001). Gr o s jean et al. (2001) showed form alde hyde, acetaldehyde, and acetone were the three most abundant carbony ls em itted by vehicles. However, there are few data on children’s exposure to speciated carbonyls inside school bus es powered by diesel fuel. During the m a in study t h e concentrations of fo urteen carbonyl species including form aldehyde, acetaldehyd e, and aceto n e, were m easured ins i de the bu ses. For com p arisons, th ese fourteen carbonyls w e re also m easured concurrently at the west LA AQMD site.

Tables 5.4.2.5 and 5.4.2.6 show the m ean conc en trations o f each individual species for morning runs and afternoon runs, respectively. Acrolein and m e thacrolein were not reported because all the m easurem ents of bot h species were below th e detection lim it of 0.03 ug/sam p le.

161Table 5.4.2.5 Mean con centration s (μg/m 3 ) of ca rbonyls during m o rning runs (windows closed). Inside bus cabin WLA AQM D Site Ratio 1

Form aldehyde 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 Methylethylketone 2 32.7 3.3 9.9 Butanal 3 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 = (In s i d e schoo l bu ses) / (WLA AQM D site)

2 m e t h y l et hy l k et one (M E K ) = 2- b u t a n one 3 but a n al = but y r al de hy de

4 N/ A: not a v ai l a bl e.

Table 5.4.2.6 Mean con centration s (ug/m 3 ) of ca rbonyls during after noon runs (windows open). Inside bus cabin At W e st LA AQMD Site Ratio 1

Form aldehyde 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 Methylethylketone 2 76.3 5 15.3 Butanal 3 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 = (In s i d e schoo l bu ses) / (WLA AQM D site)

2 m e t h y l et hy l k et one (M E K ) = 2- b u t a n one 3 but a n al = but y r al de hy de

As shown in Table 5.4.2.5, the m e an in-cabin concentrations of acetaldehyde, acetone and m e thylethylketone were subs tantially higher than for the other compounds. Moreover, the

m ean conce n tration of each carbon yl inside th e bus cabin divided by the corresp onding m e a n concentration at the W L A AQMD si te, varied w i de ly by carbonyl species in the m o rning runs. Benzaldehyde, pentanal, and butanal showed lit tle difference, while the concentration of

m e thylethylketone inside the bus cabin was on av erage abo u t ten tim e s higher com p ared to the west LA AQMD station. Form aldehyde a nd acetald e hyde also showed much higher concentratio ns ins i de th e buses than at the WL A AQMD site with ratios of five and four f o r for m aldehyde and acetaldehyde, respectively. The rem a ini ng com p ounds showed average

162concentrations inside the bus cabin two to four tim e s higher than in am bi ent air m easured at the WLA AQM D site.

For the afternoon runs, the m ean concentr ations of m e t hylethylketone (MEK) and acetaldehyd e were m u ch higher insid e the bus es com p ared with other sp ecies. As in th e m o rning runs, the ratio of m ean MEK concentrations insi de th e bus cabin to the m ean concentra tion in

am bient air was m u ch higher than for the othe r com pounds and about 50% higher than the morning ratio. For most other com pounds th ere was reasonable consistency between the morning and afternoon in the ratios of the inside cabin/W LA site m ean c oncentrations, given the uncertainties in these m easurem ents. An ex ception was form aldehyde for which the afternoon inside cabin concentration was half the m o rning concentration while th e m ean concentration at the W L A AQMD site was the sam e in the m o rning and afternoon.

1631646.0 CONCL US IONS AND RECOMMENDATIONS

6.1 Im portance of the School Bus Microenvir onm ent in Children’s Exposure During School Bus Commute-Related A c tiv ities
Across a ll pollutants, c oncentr ation s m eas ured during bus commu tes on urban routes were higher than background concen trations m easured during precis ion experim e nts or at the bus stop (Table 6.1). For exam ple, the highest m ean concentrations of black carbon and PAH

m easured during a bus comm ute were nearly factor s of ten and twenty, respectively, greater than

m ean concentrations m easured for these pollu tants during instrum e nt precision assessm ents

m a de near the W e st Los Angeles A Q MD site. The m ean bus comm ute concentrations over all bus runs (in parentheses) were approxim a tely a factor of two or m o re (except for NO 2 ) higher than m ean concentrations at the bus stops studied.

Table 6.1. Mean concentrations m easured in two m i croenvironm ents and in background air.

Am bient air 1 Bus stop Bus comm ut es 2,3

Black Carbon (ug/m 3 ) 2 ± 0.1 4 ± 0.4 3 - 19 (8)

PAH (ng/ m 3 ) 27 ± 1.5 44 ± 4.5 64 - 400 (134)

NO 2 (ppb) 49 ± 1.0 54 ± 1.9 34 - 110 (73)

PC (#/cm 3 ) 5 83 ± 3.1 62 ± 1.8 70 - 236 (130)

PM 2.5 (ug/m 3 ) 23 4 26 21 - 62 (43)

1 Measured during precision experiments near the West Los Angeles AQMD st ation.

2 Ranges include all bus ty pes (se e Table 5.3.3.1).

3 Values within parentheses are th e m e an for all bus commute s. 4 2001 annual arith m e tic me an from the C e ntral Los Angeles AQM D station.

5 In 0.3 – 0

. 5um size r a n g e

. Exposure factors, calculated by multiply in g the m ean concentration in a given

m i croenviro nm ent by th e average tim e spent in th at m i croenvironm ent, were highest for the bus commute (Table 6.2). Bus stop m i croenvironment exposures were up to thirteen tim e s greater than loading/unloading zone m i croenvironm ent exposures, while the urban bus comm ute exposures were between 50 and 400 tim es greater than those due to the loading/unloading

m i croenvironm ent. The urban bus comm utes led to between 20 and 100 tim es higher exposures than at the bus stops, depending upon the pollutant. Therefore, the bus commute

m i croenvironm ent dom i n ated school bus commute-related exposures, and the bus stop and loading/unloading zone m i croenvironm ents were near ly ins i gnif i can t c ontribu tors to commuterelated exposures.

165Table 6.2. Exposure factors in three school bus commute-related m i croenvironm ents.

Exposure Factors

Loading/ unloading 1 Bus stops Urban commutes

BC (ug/ m 3 * m i n) 5 20 600

PAH (ng/ m 3 * m i n) 45 230 10000

NO 2 (ppb * m i n) 105 270 5500

PC (#/cm 3 * m i n) 25 310 10000

PM 2.5 (ug/m 3 * m i n) N/A 130 3500

1 Exposure factor calculations for the lo a d ing/unloading zone were based on measurements taken durin g the pil o t stu d y u nder wint er conditions.

6.2 Contribution of Bus’s Own Exhaust to Concentrations Inside the Cabin Our study used an SF 6 trace r gas meter e d into the bus’s exh a ust sys t em to dete rm ine the

percen tage of air in the bus cabin origin ating f r om the bus ’s own exhaust and this percentage varied sign if icantly be tween buses a nd also de pe nded on win dow positio n. For all b u ses tested

, the am ount of the bus’s own exhaust inside the cabin was substantially higher with all windows closed compared with the windows partially open ed. Moreover, older buses showed a larger percentage of their own exha ust en ter i ng into the cabin co m p ared with newer buses.

SF 6 concentrations in sid e the bu s cabin correl ated reason ably well with n o rm alized black carbon concentrations m easured in side the conventional diesel and trap-outfitted diesel bus cabins. This correlation was stronger than that between SF 6 and PM 2.5 mass when the windows were closed.

Different bus m odels and ages correspond to different construction and cabin designs, and the results of our S F 6 analyses suggested th ese differences m a y 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., conventi onal diesel buses) and a high percentage of SF 6 insid e the cabin, also exh i bited the h i ghest average inside/rear concentration s of black carbon, PAH a nd NO 2 in runs with the windows cl ose d. Although the higher inside/rear exposures of these pollutants could be pr edicted by the snap-and-idle tests and SF 6 da ta, th is was not the case for particle counts (i n the size range from 0.3 to 0.5 μ m) o r P M 2.5 mass average concentrations inside the cabin.

1666.3. Conditions Associated with Highes t Concentrations Ins i de the Bus Cabin
6.3.1 W ithin-Bus Variables
W i ndow Position. The influence of window pos ition dep e nded on whether pollutants were prim arily d i rectly -em itted (e. g., black carbon and PAH) or had strong “background ” contributions (e.g., PM 2.5 ). Concentrations of black carbon in side the cabin tended to decrease when the windows were partially opened but in crease w h en the windows were closed. In contrast, for particle counts w ithin the size range 0.3 to 0.5 μm , concentrations inside the cabin tended to in creas e when the windo ws were partially opened but te nded to decrease when the windows were closed. T hus, for directly -em itted pollutants it appears increased ven t ilation rates with the windows opened caused dilution of the “self pollution,” while for “background” pollutants closed windows inhibited entran ce of these pollutant s into the cabin.

Location Inside the Bus Cabin . W ith the windows closed th e 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 cab in across all conventional diesel buses tested. W ith partially opened windows, there were no statistically si gnificant differences between m e asurem ents at the front and rear of th e cabin. These resu lts are cons istent with th e preced ing analysis sug g esting that when th e windows were open, h i gher v e ntilation rates

prom oted uniform m i xing within the cabin.

6.3.2 Externa l Variab les Type of Vehicle in Fro n t of, or Adjacen t to, th e 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 whe n f o llowing a diese l sch ool bus which em itted visible exhau s t, and lowe st when following a gasoline vehicle or no target . W h en following a smoky diesel school bus, concentratio ns ins i de th e cabin were on averag e eight and twelve tim es higher for black carbon and PAH, respectively, com p ared with followi ng a gasoline vehicle or no target. W h en following a diesel school bus th at was not emitting visible e xhaust, black carbon and PAH concentratio ns ins i de th e tes t buses were on average four and six tim es higher, respectively,

com p ared with follow i ng a gasoline vehicle or no target. These results were f ound during commutes when the windows of the bus were open.

This finding is im portant because the type of diesel vehicle we encountered most frequently during all bus runs was a diesel sc hool bus. On average in the afternoon runs analyzed using the v i deo t apes, anot h e r diesel ve hicle was within th ree car lengths in front of or adjacen t to, our bus, d u ring m o re than one-q uart er the comm ute, with diesel school buses responsible for over sixty percent of these enco unters. This high incidence of following other diesel school buses was in part due to caravan ning behind other buses leaving the BS MS.

These results dem onstrated that the type of school bus a child rides on is not the only determ inant of exposure and that conventional diesel school buses can have a double exposure im pact on comm uting children: first, the influen ce of the bus’s own exha ust on con centr ation s inside the cabin and second, exposures to the exhaust from other nearby conventional diesel school buses. These encounters m a y cause high c oncentrations of black carbon and PAH inside the cabin of the school bus, particul arly if the bus windows are open.

167Roadway Type The degree of influence of road way type on pollutant concentrations inside the bus cabin depended on the type of bus, the level of traffic c ongestion and whether the bus windows were open or closed . For relatively clean buses (i .e., the CNG bus, and the trapoutf itted d i e s el bus), tra v eling on co ngested f r ee ways with higher pollu ta nt concentra tions tended to increase concentrations inside the bus. For relatively dirtier buses (i.e., conventional diesel), because th e bus’s own exhaust was a significant contributor to concentrations ins i de the bus, incre a sing the ventila tio n ra te insid e the bus, wh ic h was m o r e likely to o ccur on freeways when the windows were open, tended to decrease concentrations inside the bus.

Route Type Two different urban routes, U1 and U 2, (as well as an rural/suburban route) were driven by the sam e “repre sentative” conventional diesel bus on two consecutive weeks and for the comparison of urban routes U1 and U 2, only these runs were used. We found t h e concentrations of black carbon and PAH averaged over the entire runs we re not significantly different between the tw o different urban routes while the rural/suburba n route exhibited lower concentrations of both black carbon and PAH. This was interesting since U1 spent approxim a tely 40% of the tim e on the freeway and 60% of t h e tim e on surface streets, while U2 was entirely on surface streets. Features which were sim ilar between ro utes, such as encounters with other diesel vehicles, pa rticularly diesel school buses , dom i n ated the highest peak concentrations of black carbon and P AH on both routes.

For PC (in the size range from 0.3 to 0.5 um ) and PM 2.5 mass, the rural/suburban route and U1 exhibited sim ilar average within -bus co ncentration s , while U2 showed higher averag e concentrations than U1 during both morning and afternoon commutes with windows closed and open, respectively. These results demonstrate, ag ain, the differences between the inf l uences of directly em itted versus “background” pollutants on concentrations inside the bus cabin.

Table 6.3 presents the m ean pollutant concentrati ons by run type for the urban and rural/suburban routes, und er open an d clos ed window conditions. Both window p o sition and surround in g traffic density can be seen to have strongl y affected on-board conc entrations for vehicle -related pollutants, but for pollutants with a strong background com p onent, su ch as PM2.5 and fine particle counts (0.3-0.5

μm ), differences between routes and differen ces due to window positi on were less d i s tinc t

.

168Table 6.3 . Concentrations m eans a nd range of m eans by route and by window position 1

.

Urban Route 1 Rural/Suburban Route

W i ndows Cl osed (m orning) 2 W i ndows Open (afternoon) 2 W i ndows Open (afternoon) 2 Pollutan t
Mean Range of
Means Mean Range of
Means Mean Range of
Means Black Carbon (μg/m 3 ) 10 2.5 – 19 5.2 2.9 – 9.1 2.7 0.9 – 4.8

PM-bound PAH (ng/m 3 ) 198 64 – 400 96 33 – 147 36 14 – 66

NO 2 (ppb) 64 34 – 110 73 39 – 120 45 23 – 68

Form aldehyde (μg/m 3 ) 2.1 0.89 – 4.8 1.1 0.55 – 2.1 0.93 0.34 – 2.0

Particle Cou n ts (#/cm 3 ) 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 co mmutes on ur ban route o n e onl y

. 2 Windows closed in m o rning and open i n after noon, s i m ilar to conditions unifor mly observed in other buses in Los Angeles for April through June. 3No measurements conducted.

6.4 Variations in Exposure Across Fuel Type and After-T reatm e nt Technology
6.4.1 W i ndows Cl osed For directly-em itted pollutants such as bl ack carbon and PAH, with the windows closed we found the lowest m e an concentrations inside the CNG bus, and the highest concentrations inside the conventional diesel buses. Com p ar ed with the CNG bus, concentrations of black carbon and PAH were two-to-three tim es higher inside the trap-out fitted diesel bus, and two-tosix tim es higher inside the conventional diesel buses. W e also found the conventional diesel buses had the highest average NO 2 concentrations. Thus, in our study, the highest exposures to black carbon, PAH and NO 2 occurred while riding on a conven tional diesel school bus, followed next by the trap-outfitted diesel bus, with the lowest exposures to thes e pollutants occurring on the CNG bus.

For pollutants such as part icle counts (in the size range from 0.3 to 0.5 μm ) and PM 2.5

m a ss, di ffer e nces observed between bus types were not easily explained, although these pollutants tend to have a hi gher “background” influence.

Finally, the type of bus we tested did not appear to be an im portant variable in determ ining in-c abin c oncentr ation s of CO, with com p arable, low in-bus con c entra tions

m easured in conventional diesel, trap -outfitted diesel, and C NG buses.

1696.4.2 W i ndows Pa rtially Open During afternoon runs, the windows in our test s buses were partially open, consistent with the behavior of a majority of school buses we observed in the aftern oon in Los Angeles. A s discussed in Section 5.3.5 and 6.3.2, we found th e dom i n ant variable associated with high concentrations of black carbon, PA H and NO 2 inside the bu s cabin when the windows of the bus were open was the presence of anot her dies el vehicle, while the contribution from the bus’s own exhaust was reduced under these conditions.

During periods of idling, with windows partiall y open and with no other diesel vehicles present, the CNG bus again had the lowest m ean concentrations of bl ack carbon and PAH, while the conventional diesel buses had concentrations that were two-to-four ti m e s higher than either the CNG or trap-ou t fitted dies el bus es. Fo r NO 2 , the trap-ou t fitted and conventional diesel buses both had m e an concentrations ne arly double those m easured on th e CNG bus. No real tim e data were available for form al dehyde, so we were not able to exam ine subsets of the data.

For all buses tested, we found the highest p eak concentrations were observed when the test bus was idling behind another diesel vehicle. The trap-out fitted diesel bus and CNG bus generally exhibited high peak conc e n tra tions on ly while trav eling behind a diesel vehicle, while the conventional diesel buses exhibited high peak s both when following other diesel vehicles and while idling without another diesel vehicle in front of the bus.

6.4.3 Summ ary of Effect of Bus Type
In summ ary, during com m utes when the windows of the bus were closed, we found substantial differences in concentrations m eas ured inside the bus cabin depending on the fuel type or after-treatm ent technology of the test bus , while the impact of outside sources was less im portant. In contrast, when the windows of the bus were open, differences between buses were dom i nated by the influence of outside sources (e.g. other di esel vehicles in front of the test bus). However, for comm utes with windows open, inves tigation of concentrations inside the bus during periods of idling on sm all residential streets, when no other diesel ve hicles were present, showed significant differences between buses based on bus type. Overall, however, the

m a gnitudes of the differences were m a rked ly reduced com p ared with windows closed.

Table 6.4 provides a summ ary of the m ean c oncentrations m easured inside the buses during com m utes on urban route one. For co mm utes with windows open, the first num ber

repres ents the m ean concentration from the en tir e commute, while the num b er in parenth e ses
repres ents the m ean concentration during perio d s of the co mmute when the buses were idling in residential areas, and no other diesel vehicles were present (See Section 5.3.5). Both num bers were included to dem o nstrate the importance of outside sources when the windows of the bus were open. W h en the windows of the bus were cl osed, outside sources such as other diesel vehicles, did not have a discer nable influence on concentratio ns inside the test bus.

170Table 6.4 Mean concentrations and 95% confid ence intervals during comm utes by bus type 1,2

.

W i ndows Cl osed (m orning) W i ndows 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/m 3 ) 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/ m 3 ) 5 64 ± 3 201 ± 7 219 ± 5 119 ± 18 (27 ± 16) 105 ± 12 (20 ± 4) 87 ± 7 (106 ± 26)

NO 2 (ppb) 34 ± 0.4 42 ± 0.4 76 ± 1 39 ± 1 (37 ± 3) 86 ± 2 (78 ± 6) 77 ± 1 (64 ± 6)

Form aldehyde 6 (ug/m 3 ) 4.8 2.2 1.4 1.2 1.0 1.0

1 Includes bus commutes o n urban r oute one onl y. 2 The num ber of buses teste d in each cate gory included: one CNG bus; one particle trap-outfitted diesel bus; five conventional dies el buses.

3 With windows open, concentrations inside bus were dom inated by outside sources (s ee Se ct ion 5.3.5 and 6.4.2), thus re ducing t h e inf l uence of the bus t y pe. 4 Num b ers in parenthesis re present m e an concentrations during periods of the comm ut e when the bus was idling i n residential neighborhoods with windows ope n, and no ot her diesel vehicles were present. Includes selected afternoon runs on HE2, RE1, RE2, TO1 and CNG (see S ection 5.3.5.1).

5 Measurem e n t s of PAH inside conventional diesel bu ses may be biased low due t o the instrum e nt’ s maxi m u m set ting during these co mmute s. See Sec tio ns 4.1.2.3.5 and 5.3.3 for specific details

. 6 Confidence intervals are not presented for fo rm aldeh yde because of small sa mple size.

Across all of our various m easurements, the highest concentrations of black carbon and PAH were consistently found inside the conventi onal diesel test buses com p ared to the trapoutf itted d i e s el or CNG buses, with the CNG bus having the lowes t concentrations of these pollutants. The conventional dies el and trap-ou t fitted bu ses also had th e highes t con centration s of NO 2 . For for m aldehyde the CNG bus exhibited th e highest integrated concentration of any bus type, twice the average concentration for the trap-ou t fitted bus and three tim es the averag e for the conventional diesel buses when the windows of the bus were closed. Results for the trapoutfitted bu s were gen e rally in between the convention a l diesel bus es and the CNG bus. However, based on emission data reported for other trap-equipped diesel vehicles (Johnson, 2001); d i esel-related po llutan t concentrations on boa rd our sp ecific trap -o utfitted bus appeared to be higher than expected. In addition, it is e ssential to cons id er tha t bus-to-bus variations were large, so to fully characterize the differences in exp o sures due to bus fuel type would require a m u ch larger

num b er of buses.

1716.5 Com p arison of Exposure to Air Pollutant s During Bus Commutes with Am bient Air Concentrations Measured at Nearby AQMD Monitoring Sites

In the course of the m a in study, we com p ared measurem ents taken inside the bus cabin for runs on U1 with m easure m ents take n sim u ltaneously at the SCAQM D am bient air monitoring sites in downtown and west Los Angele s (W LA). Different re sults were obtained for different pollutants as shown in Table 6.5. On av erage, am bient concentrat ions of CO m easured at th e nea r b y m onitoring stations w e re s ligh tly higher than those m easured inside the bus cabin, but within-cabin CO con centrations were relatively low duri ng all runs (2-3 ppm in the afternoon and 4-6 ppm in the morning). C o mm utes on dies el school buses do not app ear to result in higher exposures to CO than would be encountered in nearby am bient air.

In con t ras t , NO 2 concentrations inside the buses we re, on average, m o re than two and three tim e s higher than the am bi ent concen trations m e asured at th e SCAQMD sites in th e morning and afternoon, respectively. Concentra tions of arom atic hydrocarbons, including benzene, were four to ten tim es higher inside the buses w e tested than at the WLA am bient monitoring site during morning commutes. Other arom atic hydrocarbons were three to twelve tim es higher inside the buses and within-cabin concentration of 1,3-butadiene (only found above detection lim its for about 30% of the bus commu tes) were higher than those m easured at the
WLA a m bient site.

During m o r n ing runs, concentrations of aldehydes and ketones m e asured inside the bus cabins were generally higher than those m e a s ured at the W L A a m bient m onitoring site.
Form aldehyde, acetaldehyde and methylethylketone we re five, four and nine tim e s, respectively, higher insid e the buses com p ared with am bient concentrations. Sim ilar ratios were obtained during the afternoon (excep t for formaldehyde).

These results indicate children riding on sc hool buses m a y be e xposed to elevated concentratio ns of NO 2 , arom atic hydrocarbons, 1,3-butadiene , and a variety of aldehydes and ketones (including formaldehyde) a nd that estim ates 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 summ arizes the results for the comparison of in-bus and

am bient air concentrations during mo rning com m utes on urban route one.

Table 6.5 Morning mean concentrations: buses ve rsus ambient air at W e st Los Angeles monitoring s ite.

Bus Cabin Concentration West Los Angeles Monitor i ng Site

Concentration Bus-toAm bient Ratio

CO (ppm ) 5.1 ± 0.1 6.9 0.7

NO 2 (ppb) 64 ± 1 28 2.3

Benzene (ppb) 2.9 0.5 5.8

Form aldehyde (μg/m 3 ) 2.1 0.4 5.4

1726.6 Recommendations Although th e pres ent s t udy was ambitious in it s scope and approach, it was neces s arily lim ited to a single schoo l, a relative ly sm all num ber of bus es, and a given region of California (i.e., the So CAB), as well as a lim ited num ber of bus routes and bus stops. However, based on the results of this study it is a pparent several steps can be taken to reduce exposures of the public in general and comm uting children in particular due to conventional diesel school buses. First, reducing the num ber of conventiona l diesel school buses, by repl acing them with cleaner buses, such as C NG-fueled or trap-outfitted diesel buses, will substantially reduce exposures of child ren, co mmuters an d the g e neral public to the d i rectly -em itted pollutan t s m easured in th is study, such as black carbon, PAHs and NO 2 . Moreover, comm uting children would be doubly benefited by phasing out current di esel engines since both pollutan ts from the bus’s own exhaust and from nearby buses would be m i ni m i zed. Ho wever, as long as a significant num b er of conventional diesel school buses rem a in in service, th e cleanest buses shoul d be assigned to the longest routes.

Next, school bus drivers should be instructed to avoid other dies el school buses, and especially to avoid caravanning. This would provide an imm e diate reduction of children’s exposures to diesel exhaust. In particular, at schools which have m o re than one bus transporting children to and from school, instit uting a policy of sta ggered departure tim es of buses at that school would significantly reduce children’s exposures to diesel-related pollutants. W ith such a policy, bus drivers would be required to wait a m i nute or two after the bus ahead of the m has departed before they pull away from the school. If conventional diesel buses ar e not full, children should be en couraged to sit nearer the front of the bus than the rear. In addition, if strategies are avai lable to shorten comm ut e tim es for children such strategies shoul d be pursued, since on averag e exposure is proportional to commute tim e

.

Another m a jor recommendation is that in-use diesel school bus engines be m a intained properly by school districts to elim inate visible e xhaust, 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 estim ated that between one fourth to one third of all dies el s c hool buses we encountered em itted black smoke or visib l e exhaus t. Thus, we believe the prevalence of “dirty” buses in Southern California is high.

Finally, we want to r e inf o r ce school district policies wh ich m i ni m i ze children ’s time spent on sidewalks in front of schools when dies el school buses are arri ving or departing and urge enf o rcem ent of the policy tha t driver s imm e diately turn off their engines on arrival at a school and do not turn their eng i n e s on until ready to depart a scho ol. In the course of the present stud y we receiv e d anecdotal evidence that such policies are no t always strictly enforced.

1737.0 RECOMMENDATIONS FOR FUTURE RESEARCH

Further research could be undertaken to interpret the m e a s urem ents from the present study. For exam ple, the laborious analysis of the data obtained with the S E MS instrum e nts at the

sm allest particle size ranges, from 30 to 100 nm , was beyond the scope of the present project. However, such data would provide important in f o rm ation 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 com p arisons could be m a de between the data obtained in the present study and recen t data obtain e d, or being obt ain e d, for near-roadway an d in-roadway

m easure m ents of black carbon and other vehicle -related species. For exam ple, som e of the

current m e a s urem ents by the Southern Californ ia Par tic le Center a nd Supersite (SCPCS) researchers as a function of distance from freeways would be of interest re lative to certain of our in-roadway and within bus-cabin measurem ents

. Additional com p arisons could also be m a de between th e data gen e r a ted in the curren t pro j ect for gaseous and particulate pollutants and corresponding am bient air m easurem ents m a de away from heavily traveled arterials f o r the sam e pollutants.

A follow-on school bus field study m odeled on the present project but using on-board particle concentrators and a focused set of pollutant anal yzers should be conducted for the express purpose of collecting part iculate and gas phase sam p les for toxicological anal yses. Such a study could be built around the expertise of th e SCPCS, including the Center’s developm ent 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 co mmutes, m e teorological conditions, bus m a nufa cturers, m o de l years, school districts, geogr aphic locations in California, etc. Our results are “representative” only to the extent the commutes, buses, conditions, areas, etc. we studied are rep r esentative of southern Cali fornia school districts. Additional field studies could be conducted to broaden the range o f conditions investigated, although substan tial resources are required to conduc t studies of this m a gnitude. Potential cost savings include elim inating the PM f ilte r sam p les (and elem ental analys is) a nd the integ r ated VOC sam p les, as they were frequently at low concentrations and pr ovided relatively little infor m ation com p ared to the other study m easurem ents.

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1811829.0 INVENTIONS REPOR TED AND COPYRIGHTED MA TERIALS PRODUCED

None.

18318410.0 GLOSSARY OF TERMS, ABBREVIATIONS AND SYMBOLS

AQMD South Coast Air Qualit y Managem e nt District

ARB California A i r Resources Board

ATCM airborn e tox i c control m easure

BC black carbo n

BSMS Brentwood Science Magnet School

CE-CERT College of Engineering – Cent er of Environm ental Research and
Technology

CNG com p ressed natural gas

CNC condensation nucleus counter

CO carbon m o noxide

DPM diesel particulate m a tter

EC elem ental carbon

EPA Environm ental Protection Agency (U.S.)

FRM Federal reference m e thod

GC gas chrom a tography

HE1 high em itter convention a l dies el bus num b er one

HE2 high em itter convention a l dies el bus num b er two

HE3 high em itter convention a l dies el bus num b er three

ID` inner d i am eter

LAUSD Los Angeles Unified School District

NO 2 nitrogen dioxide

NO x oxides of nitrogen (NO + NO 2 )

NRDC Natural Resources Defences Council

OD outer d i am eter

OPC optical particle counter

PAH polycyclic arom atic hydrocarbons
PAN peroxyacetyl nitrate

PC fine particle counts (0.3 – 0.5 μm diam eter)
PM particula t e m a tter

185PM 2.5 particulate m a tter less than 2.5 μm in diam eter

PM 10 particulate m a tter less than 10 μm in diam eter

RE1 representative conventional diesel bus num ber one

RE2 representative conventional diesel bus num ber two

SEMS scanning electrical m obility particle sizer

SF 6 sulfur hexafluoride

SVOC sem i -volatile organic compound

TAC toxic air con t am inant

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 Ca lifornia, Riverside

VOC volatile organic com pound

186