TCRP Web-Only Document 78/NCHRP Web-Only Document 410

Protecting Transportation Employees and the Traveling Public from Airborne Diseases

The National Academy of Sciences was established in 1863 by an Act of Congress, signed by President Lincoln, as a private, nongovernmental institution to advise the nation on issues related to science and technology. Members are elected by their peers for outstanding contributions to research. Dr. Marcia McNutt is president.

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The National Academy of Medicine (formerly the Institute of Medicine) was established in 1970 under the charter of the National Academy of Sciences to advise the nation on medical and health issues. Members are elected by their peers for distinguished contributions to medicine and health. Dr. Victor J. Dzau is president.

The three Academies work together as the National Academies of Sciences, Engineering, and Medicine to provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. The National Academies also encourage education and research, recognize outstanding contributions to knowledge, and increase public understanding in matters of science, engineering, and medicine.

Learn more about the National Academies of Sciences, Engineering, and Medicine at www.nationalacademies.org.

The Transportation Research Board is one of seven major program divisions of the National Academies of Sciences, Engineering, and Medicine. The mission of the Transportation Research Board is to mobilize expertise, experience, and knowledge to anticipate and solve complex transportation-related challenges. The Board’s varied activities annually engage about 8,500 engineers, scientists, and other transportation researchers and practitioners from the public and private sectors and academia, all of whom contribute their expertise in the public interest. The program is supported by state departments of transportation, federal agencies including the component administrations of the U.S. Department of Transportation, and other organizations and individuals interested in the development of transportation.

Learn more about the Transportation Research Board at www.TRB.org.

COOPERATIVE RESEARCH PROGRAMS

CRP STAFF FOR TCRP WEB-ONLY DOCUMENT 78/
NCHRP WEB-ONLY DOCUMENT 410

Monique R. Evans, Director, Cooperative Research Programs

Waseem Dekelbab, Deputy Director, Cooperative Research Programs, and Manager, National Cooperative Highway Research Program

Gwen Chisholm Smith, Manager, Transit Cooperative Research Program

Dianne S. Schwager, Senior Program Officer

Dajaih Bias-Johnson, Senior Program Assistant

Natalie Barnes, Director of Publications

Heather DiAngelis, Associate Director of Publications

Jennifer Correro, Assistant Editor

TCRP PROJECT F-30 PANEL
Field of Human Resources

NCHRP PROJECT 23-13(02) PANEL
Field of Administration—Area of Agency Administration

Ryan I. Daniel, St. Cloud Metro Bus, St. Cloud, MN (Chair)

Marla Blagg, Alameda-Contra Costa Transit District, Portland, OR

Kit Conway, Washington Metropolitan Area Transit Authority, Washington, DC

Avery Daugherty, Virginia Department of Rail and Public Transportation, Richmond, VA

Sean P. Fay, Johnstone & Lloyd, LLC, Salvisa, KY

Paul Goyette, USF Center for Urban Transportation Research, Spring Hill, FL

Amir Hessami, Texas A&M University-Kingsville, Kingsville, TX

Chukwuma A. Nnaji, Texas A&M University, College Station, TX

Lorena de Rodriguez, SSi, Inc., Phoenix, AZ

Brian L. Sherlock, Amalgamated Transit Union, Silver Spring, MD

Aydin Tabrizi, New York State Office of Information Technology Services, Albany, NY

Jean Landolt, FHWA Liaison

Brianna Butler, FTA Liaison

Marjorie Collins, FRA Liaison

Brian Thomas Alberts, APTA Liaison

Douglas M. Eaton, Jacobs Liaison

AUTHOR ACKNOWLEDGMENTS

The team members are grateful to the students who participated in the passenger test to determine air exchange rate in the presence of passengers. We are grateful to the Los Angeles (LA) Metro for the loan of a bus. We are also grateful to Mike Todd and his team at Bourns College of Engineering Center for Environmental Research and Technology (CE-CERT) for the loan of a bus for the project. We appreciate Dr. Oliver Chang and his team for their loan of SMPS for the test. We appreciate Dan for driving the bus during on-road testing and Alain Gomes for providing mechanical services.

Students from the MSU Mankato Twin Cities Engineering team who contributed to the work in the report include John Nutt, Cosku Kaplan, Mohamed Abdi, Brendan Dykes, Sam Merchant, Yeng Moua, Joey Stam, and Tate Putman.

Students from the UCR who contributed to the work in the report include Evan Renck, Bryan Chen, and Megan Lee plus 10 CE-CERT students who volunteered for the stationary in-cabin bus testing.

FOREWORD

By Dianne S. Schwager
Staff Officer
Transportation Research Board

This report provides information regarding strategies that mitigate exposure to airborne contagions by transportation employees and passengers on various modes, especially in buses. The COVID-19 pandemic increased awareness of airborne contagions and the importance of advanced air control systems and measures, which can lower the risk of airborne disease transmission. The primary audience for this research includes public transportation agencies (i.e., agencies that provide bus, rail, ferry, and paratransit services), departments of transportation, and practitioners.

Public transportation vehicles, in particular buses, can be high-risk environments since (1) passengers, who cannot socially distance themselves, are contained within a volume of air that may carry infectious diseases and (2) transportation employees are made vulnerable to airborne contagion, as their working environment inside a bus increases their risk of infection.

The research, which was jointly funded by the National Cooperative Highway Research Program (NCHRP) and the Transit Cooperative Research Program (TCRP), was conducted collaboratively by the University of California, Riverside, and Minnesota State University, Mankato. The objective of this research was to analyze and present strategies that mitigate exposure to airborne diseases to protect the health and well-being of transportation system employees while at work and to protect the traveling public. The research methodology included the following:

• A literature review was conducted to address safety and risk reduction mitigation strategies to reduce contagion on public transportation vehicles, including: 1) dilution by ventilation, 2) Personal Protective Equipment (PPE)/masks, 3) surface cleaning, 4) cabin air filtration, and 5) Plexiglas barriers.
• Bus experiments were conducted to test a range of strategies on two transit buses using COVID-like aerosolized particles, including:
  • Stationary experiments
    • Retrofit experiments: High efficiency cabin filter, effects of air exchange rates, installation of barriers, and standalone air cleaners.
    • Redesign experiments: Parallel flow ventilation system [as opposed to conventional horizontal (or longitudinal) flow ventilation system], particle arrival time, and particulate matter (PM) removal rate.
  • On-road experiments: Air exchange per hour and PM removal rate.
• Computational fluid dynamics (CFD) methods and models were used to simulate airflow in a transit bus to evaluate the impact of prevailing conditions, such as ventilation configurations, barriers, and thermal plumes.
• Closed box modeling using an Excel-based tool was used to predict the impact of variables on virus transmission in a closed space in a range of different cases.

Table of Contents

Chapter 1: Introduction, Research Objectives, and Overview of the Work Plan

Chapter 2: Literature Review

Mitigation Methods on Public Transportation

Documented Exposure in Airplanes, Buses, and Trains

Aircraft Ventilation Strategies

Chapter 3: Experimental Studies with Transit Buses

Methods

Results

Chapter 4: CFD Studies of Transit Buses

Methods

Results

Chapter 5: CFD Studies of Subway and Tram Cars

Methods

Results

Chapter 6: “Closed Box Model” can be used to Optimize Space to Reduce Virus

Methods

Results

Chapter 7: Conclusions and Suggested Research

References

Appendix A: Laboratory Tests

List of Figures and Tables

Figures

Figure 1: Continuous aerosol generator (left), and cough generator (right)

Figure 2: Bus A test vehicle with cabin seat arrangement

Figure 3: Bus B Test Vehicle with cabin seat arrangement

Figure 4: Experimental project strategy flow chart

Figure 5: A completed barrier with CAD diagram of a barrier mounted to a seat in rear of the bus

Figure 6. CAD diagram layout of the barriers inside Bus A within the parallel ventilation

Figure 7: Simulated bus driving scenerio using a utility air circulating fan near the front door

Figure 8: Airflow throughout the standard transit bus ventilation system

Figure 9: Airflow in the transit bus with the parallel flow ventilation system

Figure 10: Installation of the lower ventilation system inside the bus

Figure 11: Top view configuration used in connecting both blowers to the upper and lower ventilation system

Figure 12: Parallel air flow system set up inside Bus A equipped with rear and front blowers

Figure 13: Suggested design of the parallel air flow system for production vehicles

Figure 14: Picture of one air slot located along the roof top vent of Bus A

Figure 15: Map view of the on-road testing path in Riverside, CA

Figure 16: Wheel-based bus speed profiles during on-road baseline stop-and-go test, on road

Figure 17: Schematic of Bus A cabin with images and locations of the aerosol generator

Figure 18: Average particle number size distributions by size inside Bus B cabin with AC fans

Figure 19: Filter efficiency versus particle size for all the testing conditions

Figure 20: Bus A test bus air velocity measurement at the internal center of the duct

Figure 21: Air velocity trends with measurements at internal center points throughout Bus A

Figure 22: Air velocity measured at internal center points throughout Bus A test bus duct

Figure 23: Air velocity measured at the outlet of vertical vents throughout Bus A test bus duct

Figure 24: Air velocity measured at the outlet of lateral vents through Bus A test bus duct

Figure 25: Non-linear regression fit of Test #04 based on CO₂ decay inside Bus B cabin

Figure 26: Non-linear regression fit of Test #06 based on CO₂ decay inside Bus B cabin

Figure 27: Non-linear regression fit of Test #07 based on CO₂ decay inside Bus B cabin

Figure 28: Non-linear regression fit on Test #10 based on CO₂ decay inside Bus A cabin

Figure 29: Non-linear regression fit on Test #11 based on CO₂ decay inside Bus A cabin

Figure 30: Non-linear regression fit on Test #12 based on CO₂ decay inside Bus A cabin

Figure 31: Non-linear regression fit on Test #13 based on CO₂ decay inside Bus A cabin

Figure 32: On-road test CO₂ concentration and speed profile with aerosol generator location at front of bus

Figure 33: On-road test CO₂ concentration and speed profile with aerosol generator location at middle of bus

Figure 34: On-road test CO₂ concentration and speed profile with aerosol generator at front and driver fans on

Figure 35: On-road stop-and-go test CO₂ concentration and speed profile with aerosol

Figure 35: PM arrival time, max PM, and ½ max PM from a sensor measurement inside bus

Figure 36: Test # 1 Particle concentration removal rate after aerosol generator is turned off

Figure 37: Test #2 Particle concentration removal rate after aerosol generator is turned off

Figure 38: Test #3 Particle concentration removal rate after aerosol generator is turned off

Figure 39: Test #9 Particle concentration removal rate after aerosol generator is turned off and

Figure 40: Test #10 Particle concentration removal rate after aerosol generator is turned off

Figure 41: Test #11 Particle concentration removal rate after aerosol generator is turned off

Figure 42: Test #12 Particle concentration removal rate after aerosol generator is turned off

Figure 43: Schematic of Bus B with locations of passengers and sensors for the Stationary Passenger test

Figure 44: Bus B cabin CO₂ concentration measured by sensors #1 - #10 during the Stationary Passenger test

Figure 45: Experimental setup used for the isolated ventilation system study

Figure 46: Line used to analyze the velocity and pressure across the length of the ventilation

Figure 47: Actual velocity distribution profile of the bus ventilation system

Figure 48: Pressure profile across the length of the ventilation system

Figure 49: Modified ventilation system with the equivalent inlet volume flow geometry

Figure 50: Lines used for the upper and lower longitudinal xy-plots to determine mesh independence

Figure 51: Automatic mesh level 1

Figure 52: Automatic mesh level 2 and 3

Figure 53: Automatic mesh level 4 with the added local mesh

Figure 54: Mesh study results from the lower longitudinal xy-plot

Figure 55: Mesh study results from the upper longitudinal xy-plot

Figure 56: The 11 rows of the bus and the definition of their boundaries

Figure 57: Test matrix of the different conditions applied for each case study

Figure 58: Visual representation of how the bus is separated into rows

Figure 59: Air velocity in the x-direction for cases 1 and 3

Figure 60: Fluid flow through a cross section of the bus for cases 1 and 3

Figure 61: Relationship between the particles absorbed in the bus versus the particles removed via ventilation

Figure 62: Air velocity in the x-direction for cases 2 and 4

Figure 63: Fluid flow through a cross section of the bus for case 2 and 4

Figure 64: Relationship between the particles absorbed in the bus versus the particles removed for cases 2 and 4

Figure 65: Air velocity in the x-direction for cases 5 and 7

Figure 66: Air velocity in the y-direction located on the driver side of the bus for cases 5 and 7

Figure 67: Fluid flow through a cross section of the bus within row 2 for cases 5 and 7

Figure 68: Relationship between the particles absorbed in the bus versus the particles removed for cases 5 and 7

Figure 69: Air velocity in the x-direction for cases 6 and 8

Figure 70: Air velocity in the y-direction for cases 6 and 8

Figure 71: Influence of thermal plume on the fluid flow surrounding the mannequins for cases 6 and 8

Figure 72: Relationship between the particles absorbed in the bus versus the particles removed for cases 6 and 8

Figure 73: Air velocity in the x-direction for cases 9 and 11

Figure 74: Air velocity in the y-direction for cases 9 and 11

Figure 75: Cross sectional view of an air barrier and two distinct recirculation zones in cases

Figure 76: Relationship between the particles absorbed versus the particles removed for cases 9 and 11

Figure 77: Air velocity in the x-direction for cases 10 and 12

Figure 78: Air velocity in the y-direction located at the middle plane of the bus for cases 10 and 12

Figure 79: Fluid flow through a cross section of the bus within row 10 for cases 10 and 12

Figure 80: Air velocity in the y-direction for cases 10 and 12

Figure 81: Relationship between the particles absorbed versus the particles removed for cases 10 and 12

Figure 82: Air velocity in the x-direction for cases 13 and 15

Figure 83: Air velocity in the y-direction for cases 13 and 15

Figure 84: Fluid flow through a cross section in the rear of the bus for cases 13 and 15

Figure 85: Relationship between the particles absorbed versus particles removed for cases 13 and 15

Figure 86: Air velocity in the x-direction for cases 14 and 16

Figure 87: Air velocity in the y-direction for cases 14 and 16

Figure 88: Fluid flow through a cross section in the rear of the bus for cases 14 and 16

Figure 89: Relationship between the particles absorbed versus particles removed for cases 14 and 16

Figure 90: Air velocity in the x-direction for cases 17 and 19

Figure 91: Air velocity in the y-direction for cases 17 and 19

Figure 92: Fluid flow through a cross section in the rear of the bus for cases 17 and 19

Figure 93: Relationship between the particles absorbed versus particles removed for cases 17 and 19

Figure 94: Air velocity in the x-direction for cases 18 and 20

Figure 95: Air velocity in the y-direction for cases 18 and 20

Figure 96: Fluid flow through a cross section in the rear of the bus for cases 18 and 20

Figure 97: Relationship between the particles absorbed versus particles removed for cases 18 and 20

Figure 98: Air velocity in the x-direction for cases 21 and 23

Figure 99: Air velocity in the y-direction for cases 21 and 23

Figure 100: Fluid flow through a cross section in the rear of the bus for cases 21 and 23

Figure 101: Relationship between the particles absorbed versus particles removed for cases 21 and 23

Figure 102: Air velocity in the x-direction for cases 22 and 24

Figure 103: Air velocity in the y-direction for cases 22 and 24

Figure 104: Fluid flow through a cross section in the rear of the bus for cases 22 and 24

Figure 105: Relationship between the particles absorbed versus particles removed for cases 22 and 24

Figure 106: The multi-slot design compared to the long-slit design of a ventilation system

Figure 107: The positioning of the barriers located between the seats of the subway model

Figure 108: Tram with no barriers (left). Tram with barriers highlighted in blue (right)

Figure 109: Comparison of the flow trajectory of an unmodified ventilation versus a parallel flow on subway

Figure 110: Comparison of the flow trajectory of an unmodified ventilation versus a parallel flow on tram

Figure 111: Probe location for air velocity in the middle of subway and face of inlet

Figure 112: Probe location for air velocity in the middle of tram and face of inlet ventilation

Figure 113: Ventilation inlet (top) and return/exit flow (bottom)

Figure 114: Air inlet locations (left) and outlet/exit locations (right)

Figure 115: Velocity lines gathered from the subway mesh study

Figure 116: Ideal mesh count velocity line from the subway simulations

Figure 117: Inlet velocity of the subway with 5.1 million mesh cells

Figure 118: Overview of all probed velocity over different mesh sizes

Figure 119: Ideal mesh sizes to use for simulation of the subway

Figure 120. Inlet velocity of the subway with 5.1 million mesh cells

Figure 121: Starting location of an initiated cough from the mouth

Figure 122: Positioning of mannequins on the subway and tram

Figure 123: Each zone of seating 1 through 15 and the corresponding length of each zone

Figure 124: Particle trajectory results for the subway car

Figure 125: Comparison between different boundaries of the baseline, barriers, parallel and parallel/barriers

Figure 126: A map of formula dependencies in the Aerosol Transmission Estimator spreadsheet

Figure 127: Graph of the input parameters vs. their relative influence on the risk multiplier

Figure 128: Relative Risk Multiplier for each vehicle and the regression predictor

Figure 129: Communication artifact

Figure A1: Laboratory experiment set up showing location of fan, four 25g CO₂ cannisters and sensors

Figure A2: CO₂ concentrations each minute recorded by TSI AirAssure sensors #1 -#20 within lab conditions

Figure A3: CO₂ concentrations each minute recorded by TSI AirAssure sensors #1 -#10 within lab conditions

Figure A4: TSI AirAssure sensor #4 correlation to #1-#20 sensor average and #1-#10 sensor average

Figure A5: Test vehicle cabin CO₂ experiment set up with TSI AirAssure sensors #1-#20

Figure A6: CO₂ levels inside the test vehicle cabin containing a driver and passenger

Figure A7: Laboratory experiment set up showing an image of location of fan, aerosol generator, and sensors

Figure A8: PM2.5 concentrations each minute recorded by TSI AirAssure sensors #1 -#10

Figure A9: PM2.5 concentration after correction is applied to sensors #1- #10

Tables

Table 1: Bus A Test Vehicle Specifications

Table 2: Bus B Test Vehicle Specifications

Table 3: Coefficient Constraints of the non-linear CO₂ regression analysis using MatLab2023

Table 4: Stationary test matrix using CO₂ gas with conventional ventilation system

Table 5: Stationary test matrix using aerosol generator, barriers, and HEPA with conventional ventilation

Table 6: Stationary test matrix using aerosol generator, barriers, and air shower method with parallel ventilation

Table 7: Stationary test matrix using aerosol generator & new filter with conventional ventilation

Table 8: Test matrix using aerosol generator and CO₂ canisters for on-road tests

Table 9: Air exchange rate results in hr^-1 per sensor inside Bus B

Table 10: Air exchange rate results in hr^-1 per sensor inside Bus A

Table 11: Air exchange rate results in hr^-1 per sensor for on-road testing

Table 12: Particle arrival times per sensor inside Bus B with regular HVAC bus cabin for tests

Table 13: Particle arrival time per sensor with conventional ventilation system for Bus A tests

Table 14: Particle arrival time per sensor with parallel system for Bus A tests #10-18

Table 15: eACH for Bus B tests #1-3 & #9-12

Table 16: eACH for Bus A bus tests #1-9 & #22

Table 17: eACH for Bus A bus tests #10-18

Table 18: eACH for On-Road Test #2

Table 19: Turbulence parameter calculations to be used for the bus, the inlet vents, and for a cough

Table 20: Comparison of mesh size, mesh dimensions and simulation runtime

Table 21: Mesh cell counts along with simulation time and finishing conditions

Table 22: Dimensions of subway and tram

Table 23: Test Matrix

Table 24: Flowrate and Output Cross section Area

Table 25: k-є parameters of Subway

Table 26: k-є parameters of Tram

Table 27: Mesh settings used in each Subway simulation

Table 28: Results of the mesh study on the subway simulations

Table 29: Mesh settings used for each tram simulation study

Table 30: Average velocity and difference of the previous simulation on the tram

Table 31: Independent nature of input variables as it applies to risk of infection

Table 32. Processes that contribute to the removal of virus particles from the vehicle cabin

Table 33: A list of model input variables for the transit bus, light rail unit, and an airliner

Table 34: Input variable chosen for transit multivariable regression

Table 35: Yearly fuel cost from HVAC blowers

Table 36: Cost of upgrading bus with Forward Curved HVAC blowers

Table 37: Yearly cost of different filtration levels – MERV 8 and MERV 13

Table 38: Relative influence of each factor at nominal Bus A values

Table 39: Relative influence of each factor at nominal Siemens S70 Light Rail Unit values

Table 40: Relative influence of each factor at nominal Boeing 737-800 Airplane values

Table 41: Relative influence of each factor at nominal values

Table 42: Summary of the correlation parameters and their influence on the risk of infection

Table 43: Filtration improvement used for scenarios 1, 3 and 4

Table 44: Cost and Risk impact of changing from a MERV 8 to MERV 13

Table 45: Impact of both improving the filtration and setting the airflow

Table 46: Impact of increasing the speed of the blowers

Table 47: Outcomes of when a filtration improvement is made to the backward curved adjustable blowers

Table 48: Summary table of the lowest investment required for a 10% improvement in risk reduction

Table 49: Relative influence of each factor at nominal values

Table 50: Comparison of select bus filter blower options

Table 51: U.S. average cost to reduce risk through increasing vehicle frequency

Table A1: Specifications of Sensirion SCD30 Sensor

Table A2: Location of TSI AirAssure sensors for the indoor aerosol test

Table A3: ThermoKing MERV 13 filter specifications

Table A4: Synthetic MERV13 Filtration Media Specification