investigating air pollution and equity impacts of a

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Investigating Air Pollution and Equity Impacts of a Proposed Investigating Air Pollution and Equity Impacts of a Proposed

Transportation Improvement Program for Tampa Transportation Improvement Program for Tampa

Talha Kemal Kocak University of South Florida, [email protected]

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Investigating Air Pollution and Equity Impacts of a Proposed Transportation Improvement

Program for Tampa

by

Talha Kemal Kocak

A thesis submitted in partial fulfillment

of the requirements for the degree of

Master of Science in Public Health

with a concentration in Environmental and Occupational Health

College of Public Health

University of South Florida

Major Professor: Amy L. Stuart, Ph.D.

Thomas E. Bernard, Ph.D.

Robert L. Bertini, Ph.D.

Date of Approval:

March 22, 2019

Key Words: Health in All Policies, environmental justice, urban design, road expansion

Copyright © 2019, Talha Kemal Kocak

DEDICATION

I dedicate this thesis to my beloved wife Göze and my sweet daughter Lina. They are the source

of my love, happiness, and joy.

ACKNOWLEDGMENTS

First of all, I want to thank two people who made this study possible: Dr. Amy Stuart and

Dr. Sashikanth Gurram. Dr. Stuart followed my academic work closely during my master's

program. She helped me through every step in this journey. I had the opportunity to improve

myself with her advice. I've never come back empty-handed from her door, always learned

something. Now I see myself as a better environmental engineer and public health expert. I owe

her a big thank for these. Dr. Gurram provided a constant support on the modelling part of this

study and has been a great friend throughout my journey in the USF. I learned a lot from him.

Therefore, I see myself lucky to work with him.

I sincerely thank my committee members Dr. Thomas Bernard and Dr. Robert Bertini for

their support and feedback. Dr. Bernard was not only in my committee, but he also helped me to

plan my occupational safety courses. Thanks to him, I managed to please my financial sponsor

regarding occupational safety courses so I am glad to work with him.

I had a chance to work with special friends in our air quality group. I thank Samraksh K.

Ramesha, Naya Martin, Muhammad Faisyal Nur, Yousif Abdullah, Charlotte Haberstroh, and

Farshad Ebrahimi for their sharing, kindness and support. I thank my dear friend Scarlet Doyle

for her contribution to the inequality analysis. I also thank Dr. Nikhil Menon for his support on

the qualitative part of this study.

I am so thankful to have such a supportive family. I sincerely thank my wife, Goze

Kocak, for her unconditional love and endless support. Without her, all of this would have less

meaning. I’d like to thank my sweet daughter, Lina Kocak as well. She brought indescribable joy

to my life. I would also like to thank my mother Esra Toprak, my grandmother Aysel Alkan, my

father Kaya Koçak and my grandfather Necati Toprak. I cannot describe their sacrifices with

words. They are the people who raised me and made me the person I am today. I wish my

grandfather could see this too.

I’d like to acknowledge the U.S. Department of Transportation's University

Transportation Centers Program for providing partial funding this study. I also thank the USF

College of Public Health for their financial support for poster presentation events. Lastly, I

thank the Turkish government and my sponsor Turkish Petroleum Corporation. I had the

opportunity to study in the US because of their financial support. In addition, whenever I faced a

problem with technical documents, the Turkish Petroleum Corporation was always there to help

me to find a solution. I am so grateful to work for them.

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TABLE OF CONTENTS

List of Tables ................................................................................................................................. iii

List of Figures ................................................................................................................................ iv

Abstract ......................................................................................................................................... vii

Chapter 1: Introduction ................................................................................................................... 1

1.1. Specific Aims ............................................................................................................... 3

Chapter 2: Literature Review .......................................................................................................... 5

2.1. Introduction .................................................................................................................. 5

2.2. Health Effects of Traffic-related Air Pollution ............................................................ 6

2.3. Air pollution Exposure Disparities .............................................................................. 8

2.4. The Relationship Between Road Widening, Traffic Congestion, And Air

Pollution ...................................................................................................................... 11

2.5. NEPA Process for Transportation Projects ................................................................ 13

2.6. Applications of Health in All Policies to Transportation Programs .......................... 14

2.7. Conclusion ................................................................................................................. 17

Chapter 3: Impacts of a Metropolitan-Scale Road Expansion on Air Pollution and Equity ........ 19

3.1. Introduction ................................................................................................................ 19

3.2. Methods...................................................................................................................... 20

3.2.1. Scope ........................................................................................................... 20

3.2.2. Description of Modeling Approach ............................................................ 22

3.2.2.1. Transportation modelling. ............................................................ 24

3.2.2.2. Air pollution modelling................................................................ 26

3.2.2.3. Exposure modelling and inequality analysis................................ 27

3.3. Results and Discussion .............................................................................................. 30

3.3.1. Distributions of Vehicle and Human Activity ............................................ 30

3.1.1.1. Vehicle counts. ............................................................................. 30

3.3.1.2. Vehicle speeds. ............................................................................ 36

3.3.1.3. Human activity. ............................................................................ 38

3.3.2. Distributions of Emissions .......................................................................... 40

3.3.3. Distributions of Concentration .................................................................... 44

3.3.4. Exposure and its Social Distribution........................................................... 48

3.3.5. Inequity Analysis ........................................................................................ 54

3.4. Limitations ................................................................................................................. 58

3.5. Conclusion ................................................................................................................. 59

ii

Chapter 4: Evaluation of the Tampa Bay Next Program from a Health in All Policies

Perspective .............................................................................................................................. 60

4.1. Introduction ................................................................................................................ 60

4.2. Health in All Policies Perspective.............................................................................. 61

4.3. The Tampa Bay Next Transportation Program .......................................................... 67

4.4. Tampa Bay Next from the Health in All Policies Perspective................................... 70

4.5. Recommendations ...................................................................................................... 76

4.6. Limitations ................................................................................................................. 79

Chapter 5: Summary and Conclusions .......................................................................................... 80

References ..................................................................................................................................... 84

Appendix A: Additional Results for the Base Case and the TBNext Case .................................. 99

Appendix B: Fair Use Argument for the Figure 1.1 ................................................................... 112

iii

LIST OF TABLES

Table 3.1. TBNext’s proposed lanes and estimated toll rates, corresponding to the

roadway section. ....................................................................................................25

Table 3.2. Total vehicle counts by regions of proposed TBNext lanes during the

morning and evening peaks for both cases ........................................................... 32

Table 3.3. Annual average daily traffic at vehicle count stations and for the base case .........34

Table 3.4. Average speed by region of proposed TBNext lanes during the morning

and evening peak................................................................................................... 36

Table 3.5. Summary statistics of human activity ................................................................... 38

Table 3.6. Average percentages of daily time spent by activity type .................................... 40

Table 3.7. Total emissions (gram) by regions during the morning and evening rush

hours ...................................................................................................................... 41

Table 3.8. Summary statistics of the NOx exposure (µg/m3) distribution in

Hillsborough county by race, ethnicity, and income level .................................... 51

Table 3.9. Comperative environmental risk index by race, ethnicity, and income for

three risk levels ..................................................................................................... 55

Table 3.10. Subgroup inequity index by race, ethnicity, and income for three risk

levels ..................................................................................................................... 56

Table 3.11. Toxic demographic quatient index by race, ethnicity, and income for

three risk levels ..................................................................................................... 56

iv

LIST OF FIGURES

Figure 1.1. The Tampa Bay Next Master plan for the six segments of the planned road

expansion ................................................................................................................ 3

Figure 2.1. NOx and CO emissions (millions of tons) in the USA by source category ............ 5

Figure 3.1. The study area. Hillsborough County is the focus for transportation, air

pollution, and exposure modeling ......................................................................... 22

Figure 3.2. The transportation, air pollution, and exposure modeling framework. ................. 23

Figure 3.3. Total vehicle count by hour for an average winter day for both cases. ................ 31

Figure 3.4. Total vehicle count difference (TBnext - base cases) by hour of day. ................. 31

Figure 3.5. Difference in the spatiotemporal distribution of hourly average vehicle

counts for a typical day for Hillsborough County and St. Petersburg

between the two cases ........................................................................................... 33

Figure 3.6. Locations of traffic count stations......................................................................... 35

Figure 3.7. Comparison of annual average daily traffic data between actual counts and

the base case results. ............................................................................................. 35

Figure 3.8. Difference in the spatiotemporal distribution of hourly average vehicle

speeds for a typical day for Hillsborough County and St. Petersburg

between the two cases ........................................................................................... 37

Figure 3.9. Difference in the spatiotemporal distribution of human activity duration

densities by the block group area between the two cases ..................................... 39

Figure 3.10. Total emissions (metric ton) for each hour of an average winter day. ................. 40

Figure 3.11. Difference in the spatiotemporal distribution of NOx emissions by hour of

an average winter day between the two cases ....................................................... 43

Figure 3.12. Comparison of the diurnal cycle of hourly NOx concentrations (µg/m3)

between the base case and TBNext case. .............................................................. 44

v

Figure 3.13. Comparison of the diurnal cycle of hourly NOx concentration (µg/m3) for

2010 winter months between the base case and an air quality monitor

station. ................................................................................................................... 45

Figure 3. 14. Difference in the spatiotemporal distribution of NOx concentration

(µg/m3) by hour of an average winter day between the two cases ....................... 47

Figure 3.15. Difference in the spatiotemporal distribution of aggregated individual NOx

exposure by block groups ((µg/m3).hr/km2)) for each hour of an average

winter day between the two cases. ........................................................................ 50

Figure 3. 16. Population subgroup means with 95 % confidence intervals by race,

ethnicity, and income for the base case and the TBNext case .............................. 52

Figure 3.17. Comparison of the mean individual NOx exposure estimates of population

subgroups between the base case, Gurram et al. (2015) study, and Gurram

et al. (2019) study. ................................................................................................ 53

Figure 3.18. Cumulative distribution box plots of individual daily NOx exposure

concentration by race, ethnicity, and income level for a) the base case, b)

the TBNext case, and c) their difference .............................................................. 54

Figure 3.19. Inequality index values for the TBNext case by race, ethnicity, and income ....... 57

Figure A 1. Spatiotemporal distribution of hourly average vehicle counts for a typical

day for the base case. .......................................................................................... 100

Figure A 2. Spatiotemporal distribution of hourly average vehicle counts for a typical

day for the TBNext case. .....................................................................................101

Figure A 3. Spatiotemporal distribution of hourly average vehicle speeds for a typical

day for the base case. .......................................................................................... 102

Figure A 4. Spatiotemporal distribution of hourly average vehicle speeds for a typical

day for the TBNext case. .................................................................................... 103

Figure A 5. Spatiotemporal distribution of human activity duration densities by the

block group for a typical day for the base case. .................................................. 104

Figure A 6. Spatiotemporal distribution of human activity duration densities by the

block group for a typical day for the TBNext case. ............................................ 105

Figure A 7. Spatiotemporal distribution of NOx emissions by each hour of an average

winter day for the base case. ............................................................................... 106

vi

Figure A 8. Spatiotemporal distribution of NOx emissions by each hour of an average

winter day for the TBNext case. ......................................................................... 107

Figure A 9. Spatiotemporal distribution of NOx concentration by each hour of an

average winter day for the base case. .................................................................. 108

Figure A 10. Spatiotemporal distribution of NOx concentration by each hour of an

average winter day for the TBNext case. ............................................................ 109

Figure A 11. Spatiotemporal distribution of aggregated individual NOx exposure by

block groups by each hour of an average winter day for the base case. ............. 110

Figure A 12. Spatiotemporal distribution of aggregated individual NOx exposure by

block groups by each hour of an average winter day for the TBNext case. ....... 111

vii

ABSTRACT

Transportation infrastructure is important for human mobility and population well-being.

However, it can also have detrimental impacts on health and equity, including through increased

air pollution and its unequal social distribution. There is a need for better understanding of these

impacts and for better approaches that improve health and equity outcomes of transportation

planning programs. In this study, we are investigating the air pollution and health equity impacts

of an ongoing large-scale metropolitan transportation improvement program, Tampa Bay Next

(TBNext). Specific objectives are: 1) to characterize and quantify the air pollution levels and

population exposures resulting from the roadway expansion currently planned under TBNext,

and 2) to identify key attributes that could improve health and equity consideration in TBNext

and similar programs.

Using a multi-component modeling system that combines agent-based travel demand

simulation with air pollution dispersion estimation, we simulated population exposures to oxides

of nitrogen (NOx) resulting from two scenarios: one with the proposed TBNext lane expansions

and one without it. To elucidate potential impacts on equity, including disparities in exposure for

low-income and minority groups, the distribution of exposure among the population was

compared using three measures of inequality. Additionally, through document review, we also

performed a qualitative analysis of the TBNext program from a Health in All Policies (HiAP)

perspective.

Results from the modeling component indicate that the proposed lane scenario increased

the number of vehicles, NOx emission rates, NOx concentration, and block group NOx exposure

viii

densities in downtown Tampa and its surrounding neighborhoods during the morning and

evening rush hours. However, the proposed lanes also caused a decrease in the simulated total

emissions and the daily average NOx concentration in Hillsborough County. The average

individual-level NOx exposure also decreased, but disparities in exposure for minority and the

below-poverty population groups increased in the proposed lane scenario.

Results of the HiAP analysis suggest that health and equity should be priorities in major

policies and programs such as transportation improvement programs. Multi-sectoral

collaboration that provides benefit for all parties and stakeholders is also essential to improve the

health and equity outcomes. Furthermore, health departments and public health agencies should

be included in the transportation decision-making process. Finally, improving active

transportation modes was commonly found in HiAP case studies to promote public health and

equity in transportation planning programs.

Evaluation of TBNext transportation improvement program from the HiAP perspective

show that health consideration was not one of the priorities in the program. However, the Florida

Department of Transportation (FDOT) frequently engaged with stakeholders in community

meetings throughout the recent development process of the program. Additionally, FDOT and

local governments addressed some inequity issues as a response to public concerns.

Through assessment of a real case study, the results of this study contribute to the body of

knowledge on the air quality and equity impacts of large-scale transportation improvement

programs. Further, they suggest that air quality assessments and equity analyses should be

conducted in more detail than what the law currently requires for transportation programs.

Lastly, this study also shows that the HiAP paradigm could promote health and equity outcomes

of transportation improvement programs

1

CHAPTER 1:

INTRODUCTION

Urban areas have become a focus for human activity with the majority of the population

now residing in cities (Gillespie, Masey, Heal, Hamilton, & Beverland, 2017). This makes urban

design very important for population well-being. Urban design is the process of shaping physical

elements such as buildings, landscapes, roadways, and other infrastructures in settlements

(Montgomery,1998). Therefore, urban designs need to balance costs and benefits and provide

sustainability. Transportation planning policies, as a common type of urban design, are used to

improve population wellbeing in urban areas. However, as human activities in urban areas

increase, the complexity of transportation planning policies also increases (De Jong, Joss,

Schraven, Zhan, & Weijnen, 2015; Ramaswami, Russell, Culligan, Sharma, & Kumar, 2016).

Therefore, impacts of such policies on air pollution-related health and equity are not fully

understood.

Urban transportation policies, like all policy decisions made by the government, are

important determinants of health (Braveman, Egerter, & Williams, 2011). Transportation has the

potential to cause public health problems such as exposure to air and noise pollution, reduction

of physical activity, and traffic accidents. In addition, the complexity of the transportation

decision-making process often requires a multi-sectoral approach to provide a healthy and

sustainable city (Rudolph, Caplan, Ben-Moshe, & Dillon, 2013). Therefore, there is a need for

approaches such as Health in All Policies (HiAP) that consider health and equity in

transportation policies. HiAP is a paradigm that aims to support public health in all government

2

decisions. According to the HiAP perspective, all parties in government policymaking should

work in collaboration to maximize public health and to minimize inequities within society

(World Health Organization, 2015). However, there is a lack of studies that investigate

transportation plans from the HiAP perspective.

Tampa Bay is going through a transportation improvement program called Tampa Bay

Next (TBNext). It includes several transportation development ideas such as bicycle lanes,

pedestrian facilities, bridge renovation, and freight mobility improvement. However, the addition

of both toll and general purpose lanes to existing roadways is in the most advanced planning

stage within the program. The lanes to be added start from I-275 in Pinellas County to I-4 in

Tampa and extend to the end of Hillsborough County (Figure 1.1). The project has gone through

two incarnations (the former was called Tampa Bay Express) due to controversy over

distribution of costs and benefits. The controversy emerged when the local people raised

concerns that the project would disproportionately affect the low income population and

minorities, as they will have to move to another place and their neighborhood will divide. In the

new form under the TBNext, the planned toll lanes remained the same as the initial Tampa Bay

Express project, but people’s involvement was increased with stakeholder meetings. In addition,

the northern part of the I-275 toll lanes was recently canceled due to the public concerns.

Nevertheless, the debate over the planned toll lanes continues. Hence, the TBNext provides a

good case study to investigate health and equity impacts of a transportation program from a

HiAP perspective.

3

Figure 1.1. The Tampa Bay Next Master plan for the six segments of the planned road expansion. (“TBX

Draft Master Plan”, 2015).

1.1. Specific Aims

The overall goal of this study is to improve understanding of the air pollution and equity

impacts of large-scale transportation policies. This will be achieved by investigating a real-world

case, the Tampa Bay Next Program (TBNext) in two specific objectives:

1- To characterize and quantify the air pollution impacts of the TBNext’s road widening

plans by estimating the air pollution concentration and exposure disparity impacts of the

proposed lanes.

2- To identify and understand the factors that could be leveraged to improve human health

and equity outcomes of the Tampa Bay Next plan.

This study has two chapters that address these objectives after the literature review

chapter. In the literature review chapter, we review the current state of knowledge on health

consideration through the National Environmental Protection Act and the HiAP perspective in

4

transportation programs. We also review the relationship between air pollution and road

expansion designs. In chapter 3, we investigate the impacts of TBNext’s road expansion design

on air pollution and equity, which addresses the first objective. In chapter 4, we identify factors

that may improve large-scale transportation programs in terms of health and equity by applying a

HiAP lens to the TBNext program.

Results provide data on the air pollutant emission, concentration, and exposures of a real-

case county-scale transportation project. Moreover, the current study also adds the body of

knowledge on sustainable urban design. Additionally, study results could inform the local

community and decision-makers about the potential in equality and health effects of the

transportation program.

5

CHAPTER 2:

LITERATURE REVIEW

2.1. Introduction

Traffic-related air pollution (TRAP) is a major environmental problem for both public

health and welfare. It poses many negative health outcomes including all-cause mortality,

cardiovascular morbidity, respiratory symptoms, and childhood asthma (Health Effects Institute,

2010). TRAP is a complex mix of many pollutants, including carbon monoxide (CO), oxides of

nitrogen (NOx), particulate matter (PM), black carbon, and numerous others (Health Effects

Institute, 2010), with traffic being one of the biggest sources of air pollutant emission to ambient

air. Figure 2.1 shows the total CO and NOx emissions by source sectors in the US. As seen in the

figure, mobile sources dominate emissions of these pollutants in ambient air.

Figure 2.1. NOx and CO emissions (millions of tons) in the USA by source category. (National Emissions

Inventory, 2014).

05

10152025303540

Biogenics Fire Sources MobileSources

StationarySources

2014 Total CO Emissions in millions of tons

0

2

4

6

8

Biogenics Fire Sources MobileSources

StationarySources

2014 Total NOx Emissions in millions of tons

6

Transportation designs in urban areas, on the other hand, are common approaches for

relieving traffic congestion and decreasing TRAP. However, the impacts of large-scale

transportation designs on human health, air pollution, and exposure inequities are often

overlooked. Additionally, the complexity of transportation projects may lead to negative

outcomes if a multi-sectoral approach and collaboration are not achieved (Ramaswami et al.,

2016). The Health in All Policies (HiAP) paradigm encourages a multi-sectoral approach to

promote health and equity and the World Health Organization suggests that it should be

considered in any policy implementation (World Health Organization, 2015). Use of this

perspective to examine transportation plans could improve understanding of the levers that

provide sustainable, healthy and equitable outcomes in large-scale infrastructure designs.

Examining these concepts for a real case using a HiAP paradigm will provide approaches for

future transportation programs. The Tampa Bay Next (TBNext) program for the Tampa Bay

Area provides a good case study for investigating air quality and equity impacts of a large-scale


This chapter reviews the current literature on the relationship between air pollution and

transportation design with a focus on road expansion practice. HiAP applications to the

transportation sector as well as the National Environmental Policy Act (NEPA) process for

transportation projects are reviewed because they are important parts of this study.

2.2. Health Effects of Traffic-related Air Pollution

Traffic-related air pollution (TRAP) causes many negative outcomes. The acute and

chronic health effects of air pollutants from mobile sources are frequently studied since it is a

public health concern which draws a lot of attention. Some of the health outcomes associated

7

with the traffic-related air pollution include all-cause mortality, various types of cancer, and

cardiovascular disorders.

TRAP is shown to be correlated with the increased mortality. Héroux et al. (2015)

quantitatively investigated the health impacts of air pollutants, suggesting that ozone, nitrogen

dioxide (NO2), oxides of nitrogen (NOx), and particulate matter (PM) are associated with

mortality and morbidity. It has also been found that chronic exposure to fine particles increases

the risk of natural cause mortality (Beelen et al., 2014). Moreover, Bønløkke et al. (2016) have

shown in Denmark that mortality rates have been reduced by decreasing the concentration of fine

particulate matter in the air, which supports the claim that fine particles are likely to affect

mortality rate (Hoek et al., 2013). In another study, observations made in human subjects living

in areas with high traffic intensity have shown that the exposure to NO2 and black smoke are

linked to respiratory mortality (Beelen et al., 2008). These studies show a strong association

between traffic-related air pollution and all-cause mortality.

The risk of various types of cancer may be increased with the exposure to TRAP. TRAP

is predicted to potentially cause cancer in the case of long-term exposure, and may especially

increase the risk of lung, bladder, kidney and prostate cancer (Cohen et al., 2017). Additionally,

Hamra et al. (2015) found that there may be a risk of developing lung cancer in people living in a

region close to roadways. Particularly, it has been found that fine particles of combustion origin

increase the risk of cardiopulmonary and lung cancer-related deaths (Pope, 2002). Another study

conducted in nine European countries also shows that particulate matter is associated with lung

cancer (Raaschou-Nielsen et al., 2013). Moreover, Pedersen et al. (2017) suggest that air

pollution from mobile sources may increase the risk of liver cancer. It has also been found that

the air pollution may increase the risk of developing parenchyma cancer, according to research

8

conducted in the people residing near intensive traffic roadway in Europe (Raaschou-Nielsen et

al., 2017). Therefore, there may be a correlation between long term exposure to TRAP and

increased risk of cancer.

Cardiovascular disorders and respiratory problems are another negative health outcomes

linked to exposure to TRAP. They are especially common in people who spend most of their

time in areas close to traffic (Brugge, Durant, & Rioux, 2007). For example, PM is known to be

an important air pollutant that triggers cardiovascular disorders (Özkaynak, Baxter, Dionisio, &

Burke, 2013). According to a study conducted in 202 counties of the United States, short-term

exposure to fine particles was found to increase the risk of hospital admission, due to

cardiovascular and respiratory diseases (Dominici et al., 2006). In addition, oxides of nitrogen

were found to cause damage to the respiratory system (Kampa, & Castanas, 2008). Furthermore,

NO2 was found to associated with childhood asthma (Pandey, Kumar, & Devotta, 2005).

To sum up, exposure to TRAP may pose many negative outcomes. Cardiovascular

disorders and respiratory diseases are common types of negative health effects of TRAP. There

is also a correlation between TRAP and increased mortality. Additionally, long-term exposure to

TRAP has been found to increase the risk of getting some types of cancer. Because traffic-related

air pollution is a complex mixture of many pollutants, health effects studies often use individual

pollutants as surrogates for the mixture (Health Effects Institute, 2010), with NOx as an often

used surrogate(Cheng et al., 2016; Clark et al., 2010; Gurram, Stuart, and Pinjari, 2019).

2.3. Air pollution Exposure Disparities

Inequality or inequity in air pollution exposure has become an important topic of

environmental justice studies in the United States (US). Inequality and inequity are actually not

synonyms: while inequality can be defined as the uneven distribution of benefits and burdens to

9

a sample population (Atkinson, 1970), inequity is their unfair distribution (Macinko, & Starfield,

2002). However, because the uneven distribution of air pollution among population groups is

believed to be inherently unfair, these two terms are often used interchangeably in the air

pollution literature (Mennis, & Jordan, 2005; Buzzelli & Jerrett, 2007; Stuart, Mudhasakul &

Sriwatanapongse, 2009). The current body of literature suggests that the distribution of air

pollution exposure is not equal to different segments of the society in the US and Hillsborough

County is no exception to this.

Several studies on air pollution exposure clearly suggest the unequal distribution of air

pollution burden by race/ethnicity and socioeconomic status in the US. For example, in a study

conducted in 6 cities of the US, Jones et al. (2014) found that white communities were associated

with lower exposure to PM2.5 and NOx than Hispanic communities. Similarly, Zou et al. (2014)

found that black, native American, Asians, and low-income communities were

disproportionately exposed to benzene pollution through a census tract level analysis across the

US. As for the socioeconomic status, a study conducted in North America found that the

communities that have low socioeconomic status have been found to face higher exposure to

criteria air pollutants (Hajat, Hsia, & O’Neill, 2015). Additionally, the communities with low

socioeconomic status and higher proportion of renters have been found to be exposed to higher

level of CO and NOx in Phonex, Arizona (Grineski, Bolin, & Boone, 2007). In addition,

exposure disparities may depend on the pollutant. For example, a North Carolina study suggests

that while PM2.5 exposure increases with lower socioeconomic status, communities with higher

socioeconomic status were associated with higher ozone exposure (Gray, Edwards, & Miranda.

2013). Additionally, a study in California found that while white people and high-income people

are disproportionately exposed to secondary pollutants, non-whites and lower-income people are

10

exposed to higher level of primary pollutants (Marshall, 2008). Overall, the evidence from the

literature shows that race/ethnicity and socioeconomic status are predictors of the exposure

disparities.

The study area, Hillsborough County/Florida, has been found to have similar patterns as

the rest of US in terms of the exposure disparities. For example, blacks, economically

disadvantaged groups and Hispanics were found to exposed to higher levels of NOx than the high

income and white population (Yu & Stuart, 2013). Additionally, low-income inhabitants living

in suburban areas and black people (whether living in suburban or urban area) were estimated to

be more exposed to traffic-related NOx than the other subgroups including Asians, whites,

Hispanics, and high-income people (Gurram et al., 2015). Moreover, when looking at the

geographical distribution of the Hillsborough county population, Stuart et al. (2009) found that

black, Hispanic and low-income people live in areas closer to air pollution sources and furher

from monitors than the white population (Stuart et al., 2009). In addition, Chakraborty (2009)

found that non-automobile owners, renters (home), and people living poverty level

disproportionately live in areas where highest risks of cancer and respiratory disorders from

traffic-related emissions were observed. However, disparities were also found to depend on the

pollutant (Yu & Stuart, 2017). These studies clearly suggest air pollution exposure inequity

problem in the study area.

The existing literature is well-documented with studies that show that air pollution

exposure disparities are an important environmental injustice issue in US. Regarding this,

population subgroups are disproportionately exposed to air pollutants depending on the

socioeconomic makeup and pollutant type. There are also a few studies that investigate the

disproportionate distribution of air pollution exposures to individuals in Hillsborough County as

11

discussed above. In conclusion, the growing body of evidence on air pollution addresses that

exposure inequities are an important public health problem.

2.4. The Relationship Between Road Widening, Traffic Congestion, And Air Pollution

Roadway expansion applications are commonly preferred by authorities, policymakers,

and stakeholders as solutions to traffic problems. However, the existing literature is

contradictory. Although some studies suggest that road widening projects do not help to reduce

traffic congestion, and decrease emissions, other studies have found the opposite. Before

discussing the issue below, we need to point out that traffic congestion term is broad and has

many definitions such as high number of vehicles, increased travel time, signal cycle failure,

increased travel distance and etc (Bertini, 2005). Regarding this, for the rest of this paper, we

used more specific terms instead of traffic congestion to make the traffic problem clear.

Highway widening projects are often applied to solve traffic problem in a region by

departments of transportation. This is due to the fact that highways play a major role in economic

growth (Perz et al., 2012). However, it is well documented that adding more lanes on a highway

doesn’t solve traffic problems. Duranton and Turner (2009) state that multiplying the number of

lanes also increases the total kilometers traveled by vehicles. Additionally, Milam et al. (2017)

found that adding more miles on an urban roadway by widening the road simply creates more

vehicle-miles traveled. Another example is seen on I-405 in Los Angeles; it took five years to

finish the widening construction but now the traffic flows one minute slower than before the

construction project (Barragan, 2014, October 10). Similarly, adding more lanes to Katy

Highway in Texas (it became the widest highway of the world with 26 lanes in 2012) did not

alleviate travel times. Conversely, commute times increased by 30 percent for the morning and

55 percent for the afternoon in two years (Cortright, 2015, December 16). The correlation

12

between adding extra lanes and increased number of vehicles is known as “induced demand,”

which is accepted as “the Fundamental Law of Traffic Congestion” among many scholars

(Duranton & Turner, 2009).

Road expansion projects not only increase traffic problems but they also lead to more

emissions. Williams-Derry (2007) suggested that widening highways eventually causes increased

carbon dioxide emissions from vehicles. In a case study of Accra, road capacity expansion was

found to cause more vehicles on roadways and higher emission rates (Armah, Yawson, &

Pappoe, 2010). In addition to these, in many cases, traffic volume has a positive correlation with

the emission rates. For example, in a recent study, decreasing traffic volume under two different

modeling scenarios was found to decrease emission rates substantially (Gately, Hutyra, Peterson,

& Wing, 2017). Traffic-related air pollutants including CO, NOx, NO2, CO2, and PM2.5 were

estimated to decrease up to 6.1 % with a speed adjustment scenario and up to 9.5 % with a

reduced vehicle-travel distance scenario (Gately et al., 2017). In urban China, estimations show

that when the vehicle count increases by 22 %, daily average NO2 concentration in the ambient

air also goes up by 12 % (Zhong, Cao, & Wang, 2017). Therefore, the current literature suggests

that there is a correlation between increased roadway capacity and emission rates.

While a growing body of evidence suggests that road expansion is not a good solution to

traffic problems, there are also some studies that oppose this conclusion. For example, Shamsher

and Abdullah (2015) suggest constructing elevated expressways and general purpose lanes to

decrease traffic volumes in the city of Chittagong, Bangladesh. In a study conducted in

Louisiana, it was predicted that the road widening model scenario made on 10 different streets

could reduce the total travel time and vehicle travel distance compared to its current road

network at the time (Antipova & Wilmot, 2012). Moreover, it is also argued that increasing road

13

capacity and adding toll lanes can reduce traffic volume and increase free-flow speed, if the new

network is envisaged properly (Fields, Hartgen, Moore, & Poole, 2009).

2.5. NEPA Process for Transportation Projects

The National Environmental Policy Act (NEPA) was enacted by the US Congress in

1969 to put environmental impacts of public agencies’ actions into consideration (Luther, 2003).

NEPA establishes requirements for major transportation proposals of public agencies so they

disclose their proposal’s environmental impacts to public before taking any further actions

(Council on Environmental Quality [CEQ], 2007). If a proposed action of an agency has a

significant impact on the environment, NEPA requires the agency to prepare an Environmental

Impact Assessment (EIA) to identify the potential impacts of the proposed action (Wernham,

2011). The EIA is a process that involves determining the negative environmental impacts of a

proposed project and taking necessary measures to minimize its impact on the environment

(Canter, & Larry, 1996). This process also provides opportunities for public agencies to

reevaluate their alternative approaches to terminate or minimize a proposal’s negative

environmental impacts (Luther, 2003). In addition, environmental justice analysis (EJA) is also

required in the NEPA process to mitigate disproportionate impact of the proposed action on

minorities and low income people (Bass, 1998). As a consequence, EIA and EJA under the

NEPA umbrella provide an approach that is meant to minimize negative environmental effects

and ensure social justice. However, lack of health consideration in EIA often causes the impact

of major transportation policies on public health to be overlooked (Bhatia, & Wernham, 2008).

The strong connection between environment and public health has been well-documented

(Giles-Corti et al., 2016; Frank, & Engelke, 2005; Srinivasan, O’fallon, & Dearry, 2003).

Concerns about the environmental damage caused by major federal projects triggered the

14

establishment of NEPA of 1969. First of all, NEPA emphasizes the need to protect the

environment as well as the promotion of human health and welfare (Congress, 1982).

Subsequently, public agencies use EIA as a tool to comply with NEPA requirements. In addition,

EIA is seen as a good opportunity to evaluate potential health impacts of a proposed plan but

decision-makers usually don't take this opportunity because the evaluation of a program's impact

on public health is not required by law (Bhatia, & Wernham, 2008). Regarding this, there are

only a few studies that take into account public health in EIA. For example, San Francisco

successfully integrated health consideration in the EIA of land use to promote public health

(Bhatia, 2007). Moreover, in Alaska, health analysis was conducted in EIA to assess potential

health effects of a proposed oil and gas leasing project (Wernham, 2009). Outside of the US,

Australia (Harris, & Spickett, 2011) and Canada (Peterson, & Kosatsky, 2016) also implemented

advanced health analysis within the scope of EIA. Therefore, these examples show that health

consideration can successfully be embedded in EIA and this practice promotes public health.

Even though public health and welfare promotion is one of the requirement in the NEPA

process (Congress, 1982), the transportation projects that put health consideration within EIA are

scarce. In addition, the American Association of State Highway and Transportation Officials’

(AASHTO) handbook for NEPA practitioners and transportation planners does not even touch

on the public health concerns (AASHTO, 2008). On the contrary, transportation is a health

determinant (Jackson, Dannenberg, & Frumkin, 2013) and should be evaluated from a health

perspective (Caplan et al., 2017).

2.6. Applications of Health in All Policies to Transportation Programs

The Health in All Policies (HiAP) approach has been implemented in several

transportation programs across the United States. There is a direct link between transportation

15

systems, land use and human health (Frank, 2000). This connection necessitates that health and

equity consideration should be embedded in transportation designs and policies as a primary

objective. This can be achieved by applying the HiAP paradigm to the transportation sector.

Regarding this, a few case studies have pioneered to use HiAP principles in transportation

programs to promote health and equity.

One of the most important case studies that implemented a HiAP approach is the

California HiAP Task Force that was established to provide social and environmental equities,

reduce chronic diseases, and control climate change (Polsky et al., 2015). The task force

followed an approach that creates multi-sectoral corporation to accomplish health and equity

promotion in transportation planning (Rudolph et al., 2013). One of the initiatives of the Task

Force was the improvement of active transportation in school facilities by applying the HiAP

approach (Caplan et al., 2017). In addition, a multi-sectoral working group established by the

Task Force that includes transportation, land use, and air pollution agencies worked

collaboratively to decrease air pollution exposure disparities (Wernham, & Teutsch, 2015).

These efforts led to an outcome in the transportation sector that California Department of

Transportation integrated health and equity objectives in their management plan (Brown, Kelly,

Dougherty, & Ajise, 2015).

In another case study, Massachusetts initiated the Health Impact Assessment (HIA)

requirement in large-scale transportation designs in 2009. HIA is a practice that is used to

analyze a policy or program through a health lens to identify potential negative health effects and

inform decision makers during the planning stage (Dannenberg et al., 2014). Upon the

emergence of the concept of HiAP, which provides a broader look at health and equity

consideration than HIA, HIA became one of the tools under the HiAP umbrella. The World

16

Health Organization (2015) states that HIA is a key factor that shows health is at the center of a

policy. In addition, HIA provides opportunities to promote health and equity in transportation

programs, thus it became a requirement for transportation programs in Massachusetts in 2009

(National Association of County & City Health Officials [NACCHO], 2017). In order to

implement HIA, transportation, public health, and environmental experts in Massachusetts

started to work cooperatively in the early stage of transportation programs (Association of State

and Territorial Health Officials, 2013). As a result, applications of HIA to transportation

planning like in Massachusetts suggested that transportation is a very important determinant of

health and equity (Dannenberg et al., 2014).

Minnesota Department of Transportation and the Department of Health worked together

to implement several transportation strategies that promote health and equity. Some of these

strategies include encouraging active transportation use, improving bicycle and pedestrian

infrastructure, designing complete streets, and implementation of HIA in transportation programs

(American Public Health Association, 2019). There are also other HiAP implementation

examples that promote active transportation and complete streets such as in Seattle/King County,

Boston, and Nashville/Tennessee (Wernham & Teutsch, 2015). Additionally, the Nashville Area

Metropolitan Planning Organization developed an active transportation funding policy to design

programs that promote mass transit, biking, and walking (Center for Training and Research

Translation, University of North Carolina at Chapel Hill, 2012). Moreover, beside active

transportation improvement, San Francisco’s health, equity, and sustainability program

implemented a collaborative action to reduce indoor air pollution near major roadways and

minimize pedestrian injuries in transportation designs (Wernham & Teutsch, 2015).

17

Consequently, these HiAP approaches improved understanding of the connection between health

and transportation.

The above case studies are examples of HiAP implementation in the transportation

sector. Highlights of the efforts that have been made regarding this include promoting active

transportation (biking, walking, public transportation, and wheeling), construction of complete

streets, and conducting HIA in transportation designs. According to the case studies as well as

the WHO (2015), to assemble a team that can apply HiAP principles, multi-sectoral cooperation

including the public health sector is required. In conclusion, the case studies provide many

benefits to promote health and equity. These benefits include raising awareness of the

relationship between transportation and health, informing decision-makers about the potential

health impacts of transportation designs, and enforcing local management to include health and

equity objectives in their transportation development plans.

2.7. Conclusion

Transportation programs that consist of mainly road expansion plans are common

practices in urban areas. However, their impact on public health, air quality, and equity can be

mixed. To address this issue, major transportation proposals are required to be evaluated with the

Environmental Impact Assessment (EIA) under the National Environmental Policy Act (NEPA).

Nonetheless, EIA lacks health consideration and additional efforts are required to put health at

the center of a transportation program. This can be achieved by applying Health in All Policies

Perspective (HiAP) to transportation programs. Regarding this, some case studies have applied a

HiAP approach to the transportation sector, but they have largely focused on improving active

transportation modes. In other words, the impacts of road expansion and interstate improvement

programs have not been evaluated through the HiAP perspective. Besides, the impact of county-

18

scale road expansion programs on air quality and exposure disparities is not well-understood.

Although a growing body of literature proposes a positive correlation between road expansion

and increased number of vehicles which eventually leads to more air pollution, some studies

suggest that a well-planned road expansion plan might help to reduce traffic volume and

emission rates. To fill these gaps and improve understanding of the impacts of county-scale

transportation programs on public health, air quality, and equity, detailed air pollution and

exposure modeling, as well as the HiAP perspective, are required.

19

CHAPTER 3:

IMPACTS OF A METROPOLITAN-SCALE ROAD EXPANSION DESIGN ON AIR

POLLUTION AND EQUITY

3.1. Introduction

Transportation has a strong influence on public health and equity (Braveman et al., 2011).

Similarly, decisions in the transportation sector affect air quality and social equity. Regarding

these, poorly analyzed decisions or policies may lead to increased air pollution exposure that

causes cardiovascular disorders and respiratory diseases (Dominici et al., 2006). Furthermore,

they may also have negative impacts on exposure disparities in terms of air pollution (Gurram et

al., 2015). Therefore, transportation decisions such as road expansion programs are required to

be analyzed in detail to provide more understanding of their impacts on air quality and equity.

County-scale road expansion designs are commonly applied to ease human mobility, but

their air pollution and equity impacts are not well understood. Studies that investigate the

relationship between a road expansion program and air pollution have largely been limited to the

air quality impacts of roadway widening (Armah, Yawson, & Pappoe, 2010; Williams-Derry,

2007) or road tunnels (Bellasio 1997; Cowie et al., 2012). Moreover, many studies didn’t

investigate the impacts of road expansion on population exposures. There are also few studies

use comprehensive transportation models to improve traffic flow and air quality (Antipova &

Wilmot, 2012; Shamsher & Abdullah, 2015). These studies either use a travel demand model

(Antipova & Wilmot, 2012), a statistical approach (Shamsher & Abdullah, 2015), a qualitative

observation (Armah et al., 2010), or an emission model to investigate impacts of road expansion

20

scenarios. Nevertheless, those studies are contradictory about whether road expansion improves

air quality or make it worse. In other words, some studies suggest that road expansion increases

air pollution and vehicle count, while others state the opposite.

There are several studies on inequity impact of exposure to traffic-related air pollution

(TRAP). Studies point out that minorities and low income people are disproportionately exposed

to TRAP (Grineski et al., 2007; Yu & Stuart, 2013; Hajat, et al., 2015; Gurram et al., 2015).

However, except for a few studies (Yu & Stuart, 2017; Gurram, Stuart, & Pinjari, 2019), there is

a lack of study that investigates the changes in air pollution exposure inequalities caused by a


The Tampa Bay Next (TBNext) transportation improvement program provides a good

case study to investigate a large-scale transportation design’s impact on TRAP and exposure

disparities. TBNext primarily consists of road expansion with both toll and general purpose

lanes, although other improvements ideas such as new bicycle and pedestrian lanes, transit plans,

and freight mobility are also in early stage of planning. In this study, we focus on the road

expansion part because it is in the most advanced stages of implementation. In addition, there is

concept plans and fact sheets regarding road expansion plans. Our aim is to improve

understanding of air quality and equity impact of a county-scale road expansion design by

applying transportation, air pollution, and exposure models.

3.2. Methods

3.2.1. Scope

The study area for this study is Hillsborough County of Florida shown in figure 3.1.

Hillsborough County had a population of 1,229,179 as of 2010 according to the US Census

Bureau (2010). The population distribution for white, black, Asian people, and the others is

21

respectively 74.5 %, 17.8 %, 4.3 %. and 3.4 % in 2010. The study area had also 28.6 % Hispanic

or Latino people (US Census Bureau, 2010). The area is located in the west of Florida State in a

position on the Tampa Bay that feeds the Gulf of Mexico. There are three interstates serving in

the area, Interstate-275 (I-275), Interstate 4 (I-4), and Interstate 75 (I-75). I-275 provides a

connection from St. Petersburg to Wesley Chapel, the north of Tampa Bay. Before reaching

Wesley Chapel, I-275 connects I-4 in Downtown Tampa. Major commuting from downtown

Tampa to Plant City is provided by I-4. The third interstate, I-75, serves as a south-north passage

between Sun City Center and Wesley Chapel. Major interstate modifications in Tampa Bay Next

(TBNext) design are planned on the I-275 and the I-4. This attribute, along with the diverse mix

of the population in the study area, makes Hillsborough County a good fit for investigating

impacts of a large-scale transportation design on traffic-related air pollution and exposure

disparities.

In addition to Hillsborough County's roadway network, we also estimated link-specific

emissions in St. Petersburg's roadway network due to the fact that the west part of I-275 is

included in TBNext transportation improvement program. Although, the study area is

Hillsborough County for air pollution concentration and exposure modelling, transportation

modelling covers Citrus, Hernando, Pasco, Hillsborough, and Pinellas Counties (Figure 3.1). In

this way, better emission, concentration, and exposure estimates can be achieved because the

travels between counties are simulated (Gurram et al., 2019). However, air pollution and

exposure modeling results are only analyzed for Hillsborough County.

22

Figure 3.1. The study area. Hillsborough County is the focus for transportation, air pollution, and

exposure modeling. Citrus, Hernando, Pasco, and Pinellas Counties are also included in transportation

modeling domain. Study area within Florida State is seen on the right picture.

The focus pollutant of the study is oxides of nitrogen (NOx). NOx consists of NO and

NO2. NOx along with the volatile organic compounds plays a significant role in the formation of

tropospheric ozone (O3). NOx also causes the formation of acid rain in the atmosphere. NO2 is

identified as criteria air pollutants by the United States federal law, the Clean Air Act (CAA).

NOx is also often used as a surrogate for the complex mixture of traffic-related air pollution

(TRAP) in studies of health effects (Cheng et al., 2016; Clark et al., 2010; Health Effects

Institute, 2010). Hence, we focus here on NOx as both an important individual pollutant category

and to represent the mix of pollutants from traffic.

3.2.2. Description of Modeling Approach

The modeling framework used here is that developed by Gurram et al. (2019). It consists

of transportation, air pollution, and exposure modeling (Figure 3.2). In the transportation

23

modeling, individual’s spatiotemporal locations as well as the traffic volume of roadway links

and average vehicle speeds are estimated. The volume and speeds are used as one of the inputs in

air pollution modeling in which emission rates of roadway links and air pollution concentration,

with 500 m resolution throughout the study area, are estimated. In the exposure modeling part,

spatiotemporal locations of individuals are matched with the spatiotemporal air pollution

concentration to estimate people’s exposures. Lastly, inequality analysis is conducted for

population subgroup exposures to identify exposure disparities.

Figure 3.2. The transportation, air pollution, and exposure modeling framework. This framework is based

on work by Gurram et al., 2019.

There are two scenarios in this study. We call the first “the base case scenario”. It has the

2010 roadway network of the study area with 2010 toll prices. We called the second “the

TBNext scenario”. For TBNext scenario, we updated the roadway network with toll and general

purpose lanes from TBNext as well as the toll prices estimation. We conducted transportation, air

pollution, and exposure modeling for both scenarios separately. We then compared results from

24

the two scenarios to quantify the potential impacts of TBNext’s roadway expansion part on

traffic volume, emission rates, air pollution concentration, and exposure inequalities.

3.2.2.1. Transportation modelling. Individual’s spatiotemporal locations and hourly

link-specific volumes (vehicle counts) and average speeds during the average winter day were

estimated by the Multi-Agent Transport Simulation (MATSim). MATSim is a traffic simulation

program that iterates to optimize the travel demand of every agent (Horni, Nagel, & Axhausen,

2016). The travel demand input for MATSIM from the previous study (Gurram et al., 2019) was

also used here. It contains individual travel activity for each individual, and transit vehicle plans

obtained from the Person Day Activity and Travel Simulator (DaySim). DaySim is an individual

travel activity and pattern simulation model that estimates individual’s daily activity by

maximizing their utility. The other inputs used to run MATSim include network and road pricing

files. The study region’s 2010 roadway network from Gurram et al.’s (2019) parameters were

also used as the basis of this work. It contains length, capacity, free-flow speed, number of lanes,

travel modes, and toll data for every link. In the base case scenario, we updated this network with

the inclusion of the road pricing scheme as well as other travel modes (transit bus, cycling,

walking, ride, and school bus) rather than restricting to the car mode like in Gurram et al. (2019).

Transit bus input were obtained from Gurram (2017) who used an activity-based model to

generate transit routes, stops, and vehicle distribution throughout Hillsborough County. For the

TBnext scenario, roadway links were updated with the planned toll and general purpose lanes by

using the TBNext concept plans and fact sheets (“Interstate Modernization”, n.d). These plans

include adding toll lanes to I-275 from St. Petersburg to downtown Tampa, Howard Franklin

Bridge, and I-4 and modifying Westshore and downtown Tampa interchanges with general

purpose lanes (Table 3.1 and Figure 1.1).

25

Table 3.1. TBNext’s proposed lanes and estimated toll rates, corresponding to the roadway section.

Roadway Section Proposed Lanes Estimated TBNext

Scenario Toll Rates South of Gandy Boulevard to 4th

Street North

1 toll lane in each direction $ 1.0 in each direction

US 19 to west of I-275

2 toll lanes in each direction $ 0.5 in each direction

Bayside Bridge to west of I-275


Howard Frankland Bridge 2 toll lanes in each direction

4 general purpose lanes from west to

east

$ 1.0 in each direction

West section of Westshore

interchange

3 toll lanes from west to east

2 toll lanes from east to west

6 general purpose lanes in each

direction


North section of Westshore

interchange

3 toll lanes in each direction

6 general purpose lanes from north to

south

7 general purpose lanes from south to

north


East section of Westshore

interchange



direction


Westshore to Downtown Corridor


West section of downtown

interchange


4 general purpose lanes from west to

east

6 general purpose lanes from east to

west


North section of downtown

interchange


direction

East section of downtown

interchange



direction


I-4 from downtown interchange to

the Polk Parkway in Polk County


26

In order to create the toll pricing file for TBNext scenario, the toll prices were scaled by

the actual toll rates of 2010 (present in the 2010 network file). Particularly, we used the 2025

dynamic pricing estimates of Veteran expressway/downtown corridor for morning peak from

FDOT (“What are express lanes?”, n.d) and actual Veteran expressway toll rate from 2010

roadway network to define a scale factor. As a result, we multiplied 2025 toll estimates with the

scaling factor (2/3) to estimate toll prices for TBNext scenario (Table 3.1). Lastly, we assumed

road pricing is static because of the modeling restrictions, although planned TBNext toll lanes

could be dynamic.

We ran the MATsim with the specifications described above for both scenarios. Upon

completing MATsim runs, we obtained link-specific vehicle counts and average speeds data to

use in the emission model as input. We also obtained individual travel activity data for exposure

modeling from MATsim output.

3.2.2.2. Air pollution modelling. Air pollution modeling part involves estimating

emission rates and air pollution concentrations. Link-specific emission rates (mass/distance)

were estimated by using the Motor Vehicle Emission Simulator 2014a (MOVES) (Koupal et al.,

2003). To set up MOVES, link specific vehicle volumes and speeds, Hillsborough County’s

2010 vehicle age and fuel type distribution data as well as 2010 meteorology data were used. The

link specific vehicle volumes and speeds inputs were provided by MATSim simulation that

generates a diurnal cycle of hourly vehicle counts and average speeds for each roadway link for a

typical weekday. MOVES default database was used for the vehicle age and fuel type

distribution data. Similarly, for the meteorological data, the diurnal cycle (temperature and

relative humidity for each hour) of an average winter day (winter months are January, February,

March, November, and December) were obtained from Gurram et al. (2019). The reason why we

27

used an average winter day is that NOx concentrations in the winter season are usually higher

than the NOx concentration in the rest of the year (Shi & Harrison, 1997). After MOVES runs

finished, we obtained the link-specific emission rates later used in pollution concentration model.

A line source air dispersion model (RLINE) was applied to estimate oxides of nitrogen

concentrations throughout Hillsborough. RLINE is a dispersion model by Environmental

Protection Agency for estimating air pollution concentration from a line source (Snyder et al.,

2013). RLINE uses a Gaussian-plume analytical solution to estimate concentration at receptor

locations (Snyder et al., 2013; Venkatram et al., 2013). RLINE requires four input files to

simulate dispersion (Snyder, & Heist, 2013). The first input file is used to set up program run

options. The other three files include source, receptor, and meteorological input files. Gurram et

al’s (2019) parameters were used to populate program run options. However, link-specific

mobile source emission rates obtained from the MOVES run were used to generate source input.

The receptor points cover Hillsborough County with a 500-meter resolution. Moreover, the same

meteorological input data for the winter months used by Gurram et al. (2019) were used here.

Lastly, we obtained hourly NOx concentrations from RLINE results to use as an input of

exposure estimates.

3.2.2.3. Exposure modelling and inequality analysis. In order to estimate individual

exposures, we combined the spatiotemporal locations of individuals obtained from MATSim

simulation with RLINE’s output, spatiotemporal pollutant concentrations using an in-house

exposure estimator (Gurram et al., 2019). This estimator calculates the daily exposure

concentration of each individual as CA = (ΣcσΔtσ)/T, where CA represents the daily exposure

concentration of each individual, while cσ and Δtσ are the pollutant concentration and time spent

at a spatiotemporal location, respectively. T is the duration of total exposure (24 hours here).

28

The pollutant concentration (cσ) with 500 m resolution throughout Hillsborough County and the

time spent at a spatiotemporal location (Δtσ) with 1-minute resolution on typical weekday was

obtained from the RLINE and MATsim runs, respectively.

After estimating the daily exposure concentration of each individual, we calculated

summary statistics (minimum, 25th percentile, mean, median, 75th percentile, and maximum) of

exposure concentration for the population and the subgroup level. Subgroup populations were

categorized by race (white, black, Asian, and others), ethnicity (Hispanic and not Hispanic), and

household income level (above $75,000, above poverty to below $75,000, and below poverty), as

described by Gurram et al. (2015). Subsequently, three comparative measures were used to

compare quantitatively the subgroup exposures: the comparative environmental risk index

(Harner, Warner, Pierce, & Huber, 2002), the toxic demographic quotient index (Harner et al,

2002), and the subgroup inequity index (Stuart, Mudhasakul, & Sriwatanapongse, 2009). The

comparative environmental risk index (CERI) is used to compare an at-risk population within a

subgroup to at-risk population not of that subgroup to identify whether the at-risk population

subgroup is more exposed to a hazard than the rest (Harner et al, 2002). Actually, CERI is

similar to the relative risk of exposure for a subgroup. CERI can be formulated as (Harner et al,

2002):

CERI = (𝑎𝑡-𝑟𝑖𝑠𝑘 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑢𝑏𝑔𝑟𝑜𝑢𝑝 𝐴)/(𝑡𝑜𝑡𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑢𝑏𝑔𝑟𝑜𝑢𝑝 𝐴)

(𝑎𝑡-𝑟𝑖𝑠𝑘 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑛𝑜𝑛-𝐴)/(𝑡𝑜𝑡𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑛𝑜𝑛-𝐴) (Eq. 3.1)

Where population A can be a population subgroup of any type such as that defined by

race, ethnicity, or income level. The at-risk population is the population above certain risk (or

exposure) level, such as that residing within buffer zone around a point pollution source (Harner

et al, 2002). In a similar way, the toxic demographic quotient index (TDQI) compares a

29

subgroup’s at-risk population to the same subgroup’s not-at-risk population to quantify exposure

disparities. It can be defined as (Harner et al., 2002):

TDQI= (𝑎𝑡-𝑟𝑖𝑠𝑘 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑢𝑏𝑔𝑟𝑜𝑢𝑝 𝐴)/(𝑡𝑜𝑡𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑎𝑡-𝑟𝑖𝑠𝑘)

(𝑛𝑜𝑡-𝑎𝑡-𝑟𝑖𝑠𝑘 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑢𝑏𝑔𝑟𝑜𝑢𝑝 𝐴)/(𝑡𝑜𝑡𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑛𝑜𝑡-𝑎𝑡-𝑟𝑖𝑠𝑘) (Eq. 3.2)

Where the not-at-risk population is the population below the specified risk level. The

interpretation of CERI and TDQI is similar: if a subgroup’s index is greater than 1, they are at

higher risk. If it is less than one, it indicates they are at lower risk. Lastly, as the third

comparative index, we use the subgroup inequity index (SII) to compare subgroup exposures

(Stuart et al., 2009; Yu & Stuart, 2013; Yu & Stuart, 2016). It can be defined as (e.g. Stuart et al.,

2009);

SII=log10 ((𝑎𝑡-𝑟𝑖𝑠𝑘 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑢𝑏𝑔𝑟𝑜𝑢𝑝 𝐴)/(𝑡𝑜𝑡𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑎𝑡-𝑟𝑖𝑠𝑘 )

(𝑡𝑜𝑡𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑢𝑏𝑔𝑟𝑜𝑢𝑝 𝐴)/(𝑡𝑜𝑡𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛)) (Eq. 3.3)

Here the logarithmic transformation leads to positive values indicating a

disproportionately high exposure and negative values indicating disproportionately low

exposure. For the analysis here, the at-risk level population was defined as individuals whose

exposure is above a threshold exposure concentration. Individuals whose exposure is below the

threshold level were considered not-at-risk population for the TDQI calculations. We conducted

inequity analysis in three different threshold levels as the 85th, 90th, and 95th percentile of

individual exposure concentration since they are the upper end exposures.

These three comparative indices actually serve for the same purpose: to identify and

quantify exposure disparities by race, ethnicity and income. Here, we applied three different

indices instead of one to observe whether the outcomes of all three indices are similar.

30

3.3. Results and Discussion

Results of the study are presented here. First, link-specific vehicle count and average

speeds, as well as the human activity data, are given by transportation modeling components. As

for the air pollution modeling components, links specific NOx emission rates and NOx

concentration with a 500 m2 resolution are provided. Subsequently, individual subgroup

population exposures by race, ethnicity, and income level are given. Lastly, we provide the

exposure inequity measures.

3.3.1. Distributions of Vehicle and Human Activity

3.1.1.1. Vehicle counts. The diurnal distribution of the total vehicle count in the study

area for average winter day resulting from the simulations is given in figure 3.3. Total vehicle

count was higher between 7 – 9 am in the morning and 4 – 8 pm in the evening than rest of the

day for both the base and TBNext cases. This diurnal pattern is similar to that found by Gurram

et al. (2019). The highest vehicle count for a single link (7349) was observed at 6 pm between

Westshore and downtown corridor for the base case. However, the highest vehicle count for a

single link (7463) was on Howard Frankland Bridge at 6 pm for the TBNext case. This could be

due to the proposed toll lanes on the bridge. Moreover, during the peak hours from 5 to 9 am,

from 4 to 7 pm, and from 11 to midnight, TBNext case had higher vehicle counts than the base

case, but the base case had higher counts for rest of the day (figure 3.4).

Table 3.2 shows the total vehicle count in six regions where with TBNext lane expansion

(see figure 1.1). The difference in vehicle counts between cases (TBNext – Base case) is also

provided in the table. Total vehicle counts were higher in TBNext case than in the base case

except for the I-275 St. Petersburg region which had 15% less vehicles at 8 am for the TBNext

case. Most substantially, the vehicle count for the Westshore to downtown corridor increased by

31

37% in the TBNext case. The increase in vehicle count in TBNext case may be due to the

redistribution of vehicles for these roads.

Figure 3.3. Total vehicle count by hour for an average winter day for both cases.

Figure 3.4. Total vehicle count difference (TBnext - base cases) by hour of day.

0

2000000

4000000

6000000

8000000

10000000

120000001

:00

AM

2:0

0 A

M

3:0

0 A

M

4:0

0 A

M

5:0

0 A

M

6:0

0 A

M

7:0

0 A

M

8:0

0 A

M

9:0

0 A

M

10

:00

AM

11

:00

AM

12

:00

PM

1:0

0 P

M

2:0

0 P

M

3:0

0 P

M

4:0

0 P

M

5:0

0 P

M

6:0

0 P

M

7:0

0 P

M

8:0

0 P

M

9:0

0 P

M

10

:00

PM

11

:00

PM

12

:00

AM

Tota

l Veh

icle

Co

un

t

Hours

Base Case TBNext Case

-250000 -200000 -150000 -100000 -50000 0 50000 100000 150000 200000

1

3

5

7

9

11

13

15

17

19

21

23

Hours of the Day Tampa Bay Next CaseBase Case

32

Table 3.2 Total vehicle counts by region of proposed TBNext lanes during the morning and evening

peaks for both cases. See figure 1.1 for location of these regions.

Region Base Case

Total Vehicle Count

TBNext Case

Total Vehicle Count

Difference

(TBNext – Base cases)

Vehicle Count

8 am 6 pm 8 am 6 pm 8 am 6 pm

Gateway

express

52901 55513 44521 57499 -8380 1986

Howard

Franklin

Bridge

34404 34623 40151 39464 5747 4841

Westshore

interchange

36438 33901 54230 49409 17792 15508

Westshore to

downtown

corridor

91281 98201 124205 123023 32924 24822

downtown

interchange

59418 64343 67156 71305 7738 6962

I-4

expansion

72409 74448 80532 81819 8123 7371

The difference in spatial distribution of link-specific vehicle counts between the TBNext

case and the base case for hours of a typical day is given in figure 3.5 (See figure A 1 and figure

A 2 in Appendices A for the spatiotemporal distribution of hourly average vehicle counts for a

typical day for the base case and the TBNext case, respectively). During the morning rush hours

(from 6 am to 8 am), the number of vehicles on the north part of I-275, downtown interchange,

and Howard Franklin bridge were higher in the TBNext case compared in the base case.

Moreover, these roadways also had more vehicles at 6 pm in the TBNext case compared in the

base case. Specifically, Howard Franklin bridge had substantially higher vehicle counts in the

TBNext case than in the base case from 7 am to 9 am. However, the I-4 corridor from downtown

to Plant City had significantly higher vehicle count in the base case than in the TBNext case

33

during the day. Lastly, the base case had slightly higher number of vehicles on other major

roadways than the TBNext case in the rest of the day.

Figure 3.5. Difference in the spatiotemporal distribution of hourly average vehicle counts for a typical day

for Hillsborough County and St. Petersburg between the two cases. Positive values indicate higher vehicle

count in the TBNext case and negative values indicate higher vehicle count in the base case.

34

A comparison for the base case annual average daily traffic (AADT) estimates with the

actual AADT data (“Traffic Counts”, n.d) from randomly chosen 10 different traffic count

stations for 2010 is shown in table 3.3. Counting station locations are shown in figure 3.6. Our

AADT estimates were between 2% and 12% lower than the actual AADT (figure 3.7). This was

expected because we didn’t simulate freight mobility. We only included passenger vehicles and

buses as motor vehicle-based travel modes. Therefore, our estimates were slightly lower than the

actual counts.

Table 3.3. Annual average daily traffic at vehicle count stations and for the base case.

Station

IDs

Location AADT (Count

Stations)

AADT (Model

Estimates) 1

I-275: Sr 93/I-275, East Of 7th Ave 188000 166057

2

I-4: East of Sr 45/Nebraska Ave. 163500 127109

3

I-4: East Of 40th Street 154500 137797

4

I-275: North of Sr 400/I-4 147500 132877

5

I-275: east of memorial highway 134000 118641

6

I-75: North of Sr 60 131613 116946

7

Hillsborough Ave.: W. Of Dale Mabry high way 76000 73927

8

Fowler Ave: 50th St to 52nd ST 59000 51990

9

Selmon Expressway: East of Sr 45/21 st 42000 40709

10 Gunn highway: east of Citrus park 33964 31254

To sum up, total daily average vehicle number decreased in the TBNext case but during

the morning and evening peaks, the proposed lanes (see figure 1.1) had higher vehicle counts in

the TBNext case than in the base case except for the gateway express section.

35

Figure 3.6. Locations of traffic count stations. Numbers are station IDs used for table 3.4 and figure 3.7.

Figure 3.7. Comparison of annual average daily traffic data between actual counts and the base case

results.

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 6 7 8 9 10

An

nu

al A

vera

ge D

aily

Tra

ffic

x 1

00

00

Vehicle Count Station IDs

AADT (Count Stations) AADT (Model Estimates)

36

3.3.1.2. Vehicle speeds. Table 3.4 shows the link-specific average speeds in six regions

where with TBNext lane expansion (see figure 1.1). Average free-flow speeds in the regions

increased in TBNext case except for the I-275 St. Petersburg region and the Howard Franklin

bridge. Additional lanes could be the reason for that.

Table 3.4. Average speed (miles/hour) by region of proposed TBNext lanes during the morning and

evening peak. See figure 1.1 for location of these regions.

Region Base Case

Average Speed

(miles/hour)

TBNext Case

Average Speed

(miles/hour)

Difference

(TBNext – Base cases)

(miles/hour)

8 am 6 pm 8 am 6 pm 8 am 6 pm Gateway

express

47.9 47.9 47.8 47.7 -0.10 -0.17

Howard

Franklin

Bridge

49.8 49.8 49.8 49.8 -0.02 -0.01

Westshore

interchange

39.8 39.0 47.6 47.7 7.84 8.78

Westshore to

downtown

corridor

42.1 40.1 46.4 48.1 4.27 7.93

downtown

interchange

47.6 47.9 48.0 47.9 0.36 0.03

I-4

expansion

52.0 52.3 52.7 52.7 0.67 0.39

The difference in vehicle speeds is shown in figure 3.8 (See figure A 3 and figure A 4 in

Appendices A for the spatiotemporal distribution of hourly average vehicle speeds for a typical

day for the base case and the TBNext case, respectively). The average vehicle speeds were

slightly higher in the TBNext case than in the base case during the morning and evening rush

hours on Westshore and downtown interchanges. In addition, some links in the Northern region

of Tampa had significantly higher speeds in the TBNext case than in the base case. There wasn’t

37

any substantial difference from 1 am to 5am between cases. Other than these, most speed

difference was less than 0.3 miles/hour during the day on the rest of the roadway network.

Figure 3.8. Difference in the spatiotemporal distribution of hourly average vehicle speeds for a typical

day for Hillsborough County and St. Petersburg between the two cases. Positive values indicate higher

speeds in the TBNext case and negative values indicate higher speeds in the base case.

38

3.3.1.3. Human activity. Summary statistics of human activity (as person-hr/km2) are

given in table 3.5 for both cases. The mean and median durations of human activity were 863

and 654 for the base case compared to 863 and 656 for the TBNext case, respectively. The

TBNext case was slightly higher activity duration densities for the 25th percentile, median, 75th

percentile, and maximum than the base case.

Table 3.5. Summary statistics of human activity. (as person-hr/km2).

Sample

size

Min.

25th

percentile

Median Mean 75th

percentile

Max.

Base case 316,272 0.14 247 654 863 1229 16218

TBNext Case 316,272 0.14 248 656 863 1231 16242

The difference in spatial distribution of normalized human activity duration densities by

the block group area (as person-hours/km2) between the two cases (TBNext case – base case) is

given in figure 3.9 (See figure A 5 and figure A 6 in Appendices A for the spatiotemporal

distribution of human activity duration densities by the block group for a typical day for the base

case and the TBNext case, respectively). During the rush hours (7 am to 9 am and 6 pm to 8 pm),

duration densities were considerably higher in University of South Florida (USF), Westshore

area, downtown Tampa, North Tampa, and Seminole heights, in the TBNext Case than in the

base case. However, the difference in other regions in the rest of the day wasn’t substantial.

Simulated human activity summary for both cases is provided in table 3.6 along with the

National Human Activity Pattern Survey (NHAPS) conducted by Klepeis et al. (2001) for the

entire United States. The simulated individuals spend about 67 % of their time at home, which is

similar to the amount of NHAPS. However, average time spent in travel was lower for both cases

than NHAPS. This could be because this study only simulates Hillsborough County, in which

has a large elderly population, compared to the entire US (Gurram et al., 2015). As for the other

39

activities (including work, school, meal, and other indoor/outdoor activities), simulated case

values were slightly higher than the NHAPS.

Figure 3.9. Difference in the spatiotemporal distribution of human activity duration densities by the block

group area between the two cases. Red means higher duration densities in the TBNext case and blue

means higher duration densities in the base case.

40

Table 3.6. Average percentages of daily time spent by activity type.

Activity Type NHAPS (Percentage) Base Case

(Percentage)

TBNext Case

(Percentage) Home 67 67.0 67.0

Travel 5.7 3.72 3.61

Others

24 25.3 25.3

To sum up, TBNext case had higher vehicle counts and human activity densities in majority of

business centers and on major highways than the base case during the morning and evening rush hours.

However, average speeds were observed to increase in the TBNext case on these areas during the typical

day. The base case, on the other hand, had slightly higher vehicle count than the TBNext case during the

day except for the rush hours.

3.3.2. Distributions of Emissions

Total emissions for each hour for an average winter day are shown in figure 3.10. The

TBNext case had higher emissions between 6 and 8 am than the base case, this is likely because

of the increased number of vehicles. However, the base case had higher emissions than TBNext

case for rest of the day consistent with vehicle activity.

Figure 3.10. Total emissions (metric ton) for each hour of an average winter day.

41

A comparison of the TBnext and base case total emission for regions is given in table 3.7.

the TBNext case had higher emissions than the base case in all the regions shown in figure 1.1

except for the Gateway express. Furthermore, the highest increase (20 %) was observed on

Westshore interchange during the evening rush hours. The total emissions in other regions

showed an increase between 2% and 10% for the TBNext case compared to the base case.

However, total emissions decreased 11%, 13%, and 9% for daily total, morning rush hours, and

evening rush hours respectively on the Gateway express for TBNext case.

Table 3.7. Total emissions (gram) by region during the morning and evening rush hours.

Domain Base Case TBNext Case Diference

(TBNext case – Base case) Daily Morning

peak

(7 – 9

am)

Evening

peak

(4 – 7

pm)

Daily Morning

peak

Evening

peak

Daily Morning

peak

Evening

peak

Gateway

express

162,851 32,374 50,105 144,509 27,844 45,445 -18,342 -4,530 -4,660

Howard

Franklin

Bridge

595,532 134,999 18,5247 609,128 141,785 198,254 13,596 6,786 13,007

Westshore

interchange

147,692 32,330 44,723 161,984 35,093 53,793 14,292 2,763 9,070

Westshore

to

downtown

corridor

334,932 71,455 102,348 350,724 77,578 108,698 15,792 6,123 6,350

downtown

interchange

181,088 35,352 56,243 187,911 37,719 60,350 6,823 2,367 4,107

I-4

expansion

724,901 160,552 229,681 728,667 157,964 235,469 -18,342 -4,530 -4,660

Spatiotemporal distribution of emissions showed a similar pattern with the Gurram et al.

(2019) study that emissions were higher during the morning and evening peaks than the rest of

the day for both the base and TBNext cases. However, the highest link-specific emission was

42

13364 grams on the Bayside bridge at 6 pm in the base case. This is greater than Gurram et al.’s

highest emission (9800 grams on the north part of I-275 at 8 am). This was expected because we

included additional sources of emissions including transit buses, ride shares, and school buses. In

addition, toll lanes inclusion in the current study could be the reason of redistribution of

emissions.

The difference in spatiotemporal distribution of emissions between the TBNext case and

the base case (TBNext case – Base case) for each hour of an average winter day is shown in

figure 3.11 (See figure A 7 and figure A 8 in Appendices A for the spatiotemporal distribution of

NOx emissions by each hour of an average winter day for the base case and the TBnext case,

respectively). Emissions on north part of I-275 and Howard Franklin bridge from 5 to 8 am and

from 5 to 7 in the TBNext case were higher than in the base case. Especially, majority of the

roadway network, except for the I-4, had higher emissions at 6 am in the TBNext case than in the

base case. However, the base case emissions were higher on major roadways at 9 am (Note that

total emission at 9 am dropped significantly for TBNext case because MOVES did not produce

emissions for 95 links in that particular hour). Emissions for the rest of the day were higher on I-

275, I-4, and Howard Franklin bridge in the base case than in the TBNext case.

The results of emission estimation indicate that proposed lanes caused a decrease in total

emissions in the study area (Hillsborough county and St. Petersburg) in general. However, major

business centers including downtown and Westshore area, as well as the major highways I-275

and I-75, experienced higher emission rates during the rush hours for the TBNext case. However,

throughout the day, I-4 had lower emissions in the base case than in the TBNext case.

43

Figure 3.11. Difference in the spatiotemporal distribution of NOx emissions by hour of an average winter

day between the two cases. Red means higher in the TBNext case and blue means higher in the base case.

44

3.3.3. Distributions of Concentration

The domain average NOx concentrations were approximately 8.7 and 8.6 μg/m2 for base

and TBNext cases respectively. These averages are higher than the averages for the same domain

in Gurram et al.’s (2019) study (4.7 μg/m2) but lower than the averages in Yu and Stuart’s (2013)

study (12 μg/m2). This was expected because we included additional travel modes including

transit buses, ride sharing, and school buses compared the Gurram et al. (2019) study so our

concentration predictions are higher than Gurram et al.’s (2019). As for the Yu and Stuart (2013)

study, they didn’t just include the on-road mobile sources, they also included stationary point

sources as pollution sources in their study. In addition, their modeling year (2002) is also

different too. Therefore, their NOx concentration estimation is higher than the current study’s

estimation.

Figure 3.12. Comparison of the diurnal cycle of hourly NOx concentrations (µg/m3) between the base case

and TBNext case.

Figure 3.12 compares the diurnal cycle of hourly domain NOx concentration between the

TBnext case and the base case. During the morning rush hours (5 to 8 am) the TBNext case had

45

higher concentration than the base case but it was the opposite from 6 to 8 pm. The TBnext case

also had slightly higher NOx concentration from 4 to 5 pm and from 11 to 12 pm than the base

case. However, the base case had higher NOx concentration than the TBNext case for the rest of

the day.

Diurnal cycle of modeled hourly NOx concentration for the base case is compared with

the air quality monitor station on Gandy Boulevard (Air Quality System site id: 120571065) in

figure 3.13. Estimated concentration followed a similar diurnal pattern with the observed

concentrations. However, similar to Gurram et al.’s (2019) study, the hourly mean of estimated

concentrations was higher during the rush hours but lower during the rest of the day than the

observed concentrations.

Figure 3.13. Comparison of the diurnal cycle of hourly NOx concentration (µg/m3) for 2010 winter months

between the base case and an air quality monitor station.

46

Figure 3.14 shows the difference between the TBNext case and the base case for the

spatiotemporal distribution of NOx concentration by each hour of an average winter day (See

figure A 9 and figure A 10 in Appendices A for the spatiotemporal distribution of NOx

concentration by each hour of an average winter day for the base case and the TBnext case,

respectively). From 5 to 6 am, the TBNext case had substantially higher concentrations on I-

275, Veteran expressway, Selmon expressway, downtown and Westshore interchanges than the

base case. In addition, I-275 had higher NOx concentration from 5 to 7 pm in the TBNext case

than in the base case. However, the difference in the concentrations between the two cases

started to decrease from 6 to 8 am and the concentrations in the urban core in the base case

became substantially higher from 8 to 9 am than in the TBNext case. The high concentrations

differences between 8 and 9 am may be due to the links that were lost in MOVES run. Moreover,

the concentrations from 8 to 10 pm in downtown Tampa, Westshore area, and USF were higher

in the base case than in TBNext case. Finally, there was no substantial difference in

concentration between the two cases in the other areas and the rest of the day.

In summary, the domain average NOx concentration in the TBNext case was slightly

lower than in the base case but during the rush hours (from 5 to 8 and from 5 to 7 pm), USF area,

downtown Tampa, and Westshore area had higher concentration in the TBNext case than in the

base case. Similarly, these regions had higher emissions from 8 to 9 am and from 7 to 10 pm in

the base case than in the TBNext case. Other than these, differences in NOx concentration were

small between the two cases.

47

Figure 3. 14. Difference in the spatiotemporal distribution of NOx concentration (µg/m3) by hour of an

average winter day between the two cases. Red means higher in the TBNext case and blue means higher

in the base case.

48

3.3.4. Exposure and its Social Distribution

The difference in the spatiotemporal distribution of aggregated individual NOx exposure

by block groups for hours of an average winter day between the base case and the TBNext case

is given in figure 3.15 (See figure A 11 and figure A 12 in Appendices A for the spatiotemporal

distribution of aggregated individual NOx exposure by block groups by each hour of an average

winter day for the base case and the TBNext case, respectively). The base case had higher

exposure densities in south Tampa, Westshore area, downtown Tampa, and Carolwood area than

the TBNext case from 12 to 3 am. However, starting from 3 am, the exposure densities in the

TBNext case became higher than the exposure densities in the base case in the Tampa urban core

until 8 am. Especially, between 5 and 6 am, the exposure densities in the TBNext case were

slightly higher than the exposure densities in the base case throughout the Hillsborough County.

Contrarily, between 8 and 9 am, exposure densities in the base case were higher than the

exposure densities in the TBNext case throughout the Hillsborough County except for downtown

interchange and USF area. This was expected because spatiotemporal distribution of NOx

concentration also showed similar pattern for these hours. During the evening rush hours (from 5

to 8 pm), Westshore area interchange, USF area, and Temple Terrace had higher exposure

densities in the TBnext case than in the base case. Moreover, TBNext case also had higher

exposure densities than the base case in south Tampa, Westshore area, downtown Tampa, USF

area, Carolwood area, and Brandon between 11 and 12 pm. The highest difference of exposure

densities between the two cases was seen in downtown Tampa at 6 pm that the exposure density

in the base case was higher than the exposure density in the TBNext case by 170 %. However,

the highest exposure density difference where TBNext is higher than the base case was in

49

downtown Tampa interchange that the exposure density in the TBNext case was higher than the

exposure density in the base case by 24 %.

50

Figure 3.15. Difference in the spatiotemporal distribution of aggregated individual NOx exposure by

block groups ((µg/m3).hr/km2)) for each hour of an average winter day between the two cases. Red means

higher in the TBNext case and blue means higher in the base case.

Summary statistics of distribution of NOx exposure estimate are provided in table 3.8 for

both cases. The base case had 18.3 and 16.6 µg/m3 in the mean and median individual NOx

exposure, respectively. For the TBNext case, the mean and median individual NOx exposure

were 18.1 and 16.5 µg/m3, respectively. Based on the t-test, there is a statistically significant

difference in the population exposure means between the base case and TBNext case at the 95 %

confidence level (p-value = 2.2e-16). Furthermore, the exposure range was from 0.46 to 241

µg/m3 for the base case and from 0.45 to 227 µg/m3 for the TBNext, respectively.

The spatiotemporal distribution of aggregated individual NOx exposure by block groups

(see figure A 11 and A 12 for the base case and the TBNext case, respectively) showed similar

pattern for both cases: The Westshore area, downtown Tampa, Brandon, and north part of I-275

experienced high exposure densities from 5 to 8 am and from 5 to 8 pm. In addition, the urban

core had higher exposure densities than the other regions throughout the day. Lastly, rural areas

had the lowest exposure densities throughout the day.

Figure 3.16 shows subgroup means (µg/m3) with 95 % confidence intervals by race,

ethnicity, and income for the base case and the TBNext case. According to the results, there was

a statistically significant difference in the group exposure means between the base case and the

TBNext case for white, black, Hispanic, not-Hispanic, middle income, and high-income people.

However, the exposure mean difference between the two cases for Asian and below poverty

people was not statistically significant. In addition, group exposure means for all groups were

lower in the TBNext case than in the base case.

51

Table 3.8. Summary statistics of the NOx exposure (µg/m3) distribution in Hillsborough county by race, ethnicity, and income level.

Base Case TBNext Case Sample

Size (n)

Max 75th

percentile

Mean Median 25th

percentile

Min Sample

Size (n)

Max 75th

percentile

Mean Median 25th

percentile

Min

Population

1,048,575 241 22.1 18.3 16.6 11.7 0.45 1,048,575 227 21.8 18.1 16.5 11.6 0.46

Race

White

795,280 241 22.0 18.1 16.5 11.5 0.45 795,306 227 21.6 17.9 16.3 11.4 0.46

Black

163,538 240 22.7 19.2 17.4 12.4 1.59 162,908 227 22.3 19.0 17.2 12.3 1.60

Asian

27,129 233 22.4 18.5 16.9 11.7 2.32 27,815 222 22.0 18.3 16.7 11.6 3.30

Others

62,628 232 22.2 18.4 16.9 11.8 1.41 62,546 214 21.8 18.2 16.7 11.7 1.57

Ethnicity

Hispanic

218,993 237 22.5 18.8 17.1 12.0 1.41 218,994 223 22.1 18.5 16.9 11.9 1.22

Not

Hispanic

829,529 240 22.0 18.2 16.5 11.6 0.45 829,528 228 21.7 18.0 16.3 11.5 0.46

Income

Below

poverty

104,660 240 23.5 20.6 18.2 13.1 1.41 104,644 227 23.1 20.4 18.0 13.0 1.46

Middle

income

546,961 241 22.6 18.7 17.1 12.1 0.45 546,967 227 22.1 18.4 16.9 12.0 0.46

High

Income

396,954 234 21.2 17.2 15.6 10.9 1.40 396,964 223 20.9 17.0 15.4 10.8 1.33

52

Figure 3. 16. Population subgroup means with 95 % confidence intervals by race, ethnicity, and income

for the base case and the TBNext case. Black dots represent exposure means and blue lines show

confidence intervals.

The mean individual NOx exposure in the study area for the base case was estimated as

18.3 µg/m3. This is somewhat higher than Gurram et al.’s (2019) estimate (10.2 µg/m3) but much

closer the mean exposure value (17 µg/m3) in Gurram et al. (2015) study that used more

comprehensive modeling sources to estimate individual NOx exposure in Hillsborough county.

Figure 3.17 compares the mean individual NOx exposure estimates of population subgroups

between the base case, Gurram et al. (2015) study, and Gurram et al. (2019) study. The base case

had slightly higher exposure estimates than the Gurram et al. (2019) that only includes exposure

estimates from passenger vehicles. This was expected because the inclusion of bus, school bus,

and ride travel modes increased the vehicle count, emission rates, and NOx concentration, and the

53

inclusion of cycling and walking travel modes increased the personal exposures in the study area.

However, the mean of subgroup exposures in the base case was consistent with Gurram et al.

(2015) study.

Figure 3.17. Comparison of the mean individual NOx exposure estimates of population subgroups

between the base case, Gurram et al. (2015) study, and Gurram et al. (2019) study.

In summary, the mean and median exposure concentrations in the base case were higher

than the mean and median exposure concentrations in the TBNext case for all population

subgroups, respectively. However, the exposure densities in Tampa urban core were higher in the

TBNext case than in the base case during the morning and evening rush hours but yet total

exposure densities in the Hillsborough county was 1.1 % higher in the base case than in the

TBNext case.

0

5

10

15

20

25

White Black Hispanic Below poverty Middle income High income

NO

xEx

po

sure

Co

nce

ntr

atio

n (

µg/

m3)

Population Subgroups

Base Case Gurram et al. (2015) Gurram et al. (2019)

54

3.3.5. Inequity Analysis

As seen in table 3.8, for both cases, in the race category, black people were

disproportionately exposed to higher NOx exposure concentrations on average compared to

Asians and white people. In addition, white people had the lowest average NOx exposure. For the

ethnicity category, Hispanic people were disproportionately exposed to NOx concentrations

compared to non-Hispanic people for both cases, on average. Both cases also had a similar

pattern for the income category: the below poverty subgroup had the highest NOx exposure

concentration, followed by middle income and high-income people, respectively (figure 3.18).

Figure 3.18. Cumulative distribution box plots of individual daily NOx exposure concentration by race,

ethnicity, and income level for a) the base case, b) the TBNext case, and c) their difference. Positive

values mean the TBNext case is higher, negative values mean the base case is higher for c.

Table 3.9, 3.10, and 3.11 show the results of comparative environmental risk index

(CERI), subgroup inequity index (SII), and toxic demographic quotient index (TDQI),

respectively, in three risk levels (85th, 90th, and 95th percentiles of individual exposure) for both

cases. According to the results of the CERI, black people had the highest NOx exposure burden

55

among other races. While they were followed by Asians, white people had the lowest exposure

among all races. Moreover, Hispanic people were disproportionately exposed to NOx compared

to not-Hispanic population. Lastly, by income level, people living below poverty had the highest

NOx exposure, whereas high-income population had the lowest NOx exposure. These patterns did

not change for the SII and TDQI. In addition, this NOx exposure order of population subgroups

from highest to lowest was the same for both cases as well as the three risk levels. However,

when the risk level increased from 85th through 95th percentile, the CERI, SII, and TDQI of white

people, not-Hispanic population, and high income people decreased for both cases. In other

words, their exposures to NOx even got lower. Contrarily, the indices of the black people,

Hispanics, below poverty population, and middle income people increased with the increase of

risk percentile levels for both cases. For the Asians, their CERI, SII, and TDQI increased from

85th to 90th percentiles but decreased from 90th to 95th percentiles for both cases.

Table 3.9. Comparative environmental risk index by race, ethnicity, and income for three risk levels. The

risk levels are 85th, 90th, and 95th percentiles of individual exposure.

Comparative Environmental Risk Index

85th percentile 90th percentile 95th percentile

Base

Case

TBNext

Case

Base Case TBNext

Case

Base Case TBNext

Case Race

White 0.914 0.913 0.901 0.899 0.866 0.866

Black 1.112 1.114 1.131 1.135 1.188 1.188

Asian 1.054 1.061 1.073 1.082 1.050 1.046

Others 1.019 1.014 1.078 1.012 1.035 1.034

Ethnicity

Hispanic 1.077 1.074 1.079 1.079 1.102 1.099

Not Hispanic 0.928 0.931 0.927 0.927 0.907 0.910

Income

Below poverty 1.256 1.264 1.296 1.312 1.504 1.522

Middle income 1.093 1.094 1.098 1.102 1.117 1.116

High Income 0.823 0.820 0.807 0.799 0.731 0.726

56

Table 3.10. Subgroup inequity index by race, ethnicity, and income for three risk levels. The risk levels

are 85th, 90th, and 95th percentiles of individual exposure.

Subgroup Inequity Index


Base Case TBNext

Case

Base Case TBNext

Case

Base Case TBNext

Case Race

White -0.010 -0.010 -0.011 -0.012 -0.016 -0.016

Black 0.039 0.039 0.045 0.046 0.062 0.062

Asian 0.022 0.025 0.030 0.034 0.021 0.019

Others 0.008 0.006 0.007 0.005 0.014 0.016

Ethnicity

Hispanic 0.025 0.025 0.026 0.026 0.033 0.032

Not Hispanic -0.007 -0.007 -0.007 -0.007 -0.009 -0.009

Income

Below poverty 0.088 0.091 0.100 0.105 0.156 0.160

Middle income 0.018 0.0181 0.019 0.020 0.022 0.022

High Income

-0.055 -0.055 -0.060 -0.063 -0.090 -0.091

Table 3.11. Toxic demographic quotient index by race, ethnicity, and income for three risk levels. The

risk levels are 85th, 90th, and 95th percentiles of individual exposure.

Toxic Demographic Quotient Index


Base Case TBNext

Case

Base Case TBNext

Case

Base Case TBNext

Case Race

White 0.974 0.974 0.971 0.971 0.962 0.962

Black 1.111 1.113 1.122 1.126 1.164 1.164

Asian 1.063 1.070 1.079 1.090 1.052 1.048

Others 1.021 1.016 1.019 1.012 1.034 1.039

Ethnicity

Hispanic 1.069 1.068 1.071 1.068 1.084 1.081

Not Hispanic 0.982 0.982 0.981 0.982 0.978 0.979

Income

Below poverty 1.275 1.284 1.296 1.312 1.465 1.482

Middle income 1.050 1.051 1.050

1.052 1.056 1.055

High Income

0.864 0.862 0.858 0.852 0.806 0.802

57

Figure 3.19. Inequality index values for the TBNext case by race, ethnicity, and income. Positive

numbers in the parenthesis indicate that index value increased in the TBNext compared the base case.

CERI is the comparative environmental risk index, TDQI is the toxic demographic quotient index, and SII

is the subgroup inequity index.

58

As discussed in section 3.3.4, average exposures for subgroups were slightly lower in the

TBnext case than in the base case. While this is true, the results of comparative inequality

measures indicated that exposure disparities by race, and income are increased in the TBNext

case. This is clearly seen in figure 3.19 shows the CERI, SII, and TDQI index values for the

TBNext case. For example, CERI, SII, and TDQI of black people increased (they became more

disproportionately exposed) in the TBNext case compared the base case for any percentile risk

level, but all three indices of white people decreased (they became more disproportionately

unexposed) in the TBNext case compared the base case. Thus, index difference between black

and white people increased in the TBNext case compared the base case. Similar observations

were also seen between below poverty and high income populations, between middle income and

high income populations. However, exposure disparities between Hispanic and not-Hispanic

people decreased in the TBNext case for all three inequality measures. Therefore, exposure

disparities by race and income level increased, while the disparities decreased by ethnicity in the

TBNext case.

3.4. Limitations

This study is limited in several ways. For a start, we only estimated NOx concentrations

from the on-road mobile sources, so the NOx concentration could be underestimated. In addition,

the timeframe of the modeling system applied in this study is 2010. However, the road expansion

process of the Tampa Bay Next won’t finish before 2020. Furthermore, individuals’ exposure in

indoor spaces was not considered in this study so that people’s actual exposure levels could be

higher or lower than our findings. In addition, all toll lanes in the transportation modeling part

were assumed to be statically priced in the current study, although some of the proposed toll

lanes could have dynamic pricing option. Finally, the road expansion part of the TBNext

59

program is an ever-changing process. For example, there was a plan to add toll lanes on the north

part of I-275 but canceled, instead, adding toll lanes on I-75 came to the fore (Winer, 2018).

Therefore, the findings from this study may not represent the final form of the TBNext program.

3.5. Conclusion

In this study, we applied a modeling framework developed by Gurram at al. (2019) that

incorporates transportation, air pollution, and exposure models to investigate air quality and

equity impacts of a large-scale road expansion project as a part of TBNext. The overall results

indicated that widened roads cause an increase in emission rates and TRAP concentration on

major roadways during the morning and evening rush hours. However, the total emission and the

daily average NOx concentration decreased in the Hillsborough county for the TBNext case. In

addition, even though individual NOx exposures decreased on average due to the program,

minorities and low-income people were still disproportionately exposed to NOx. As for the

inequality indices, they showed similar results about the exposure disparities: the disparities by

race and income increased because of the road expansion design. However, the road expansion

design caused a decrease in exposure disparities by ethnicity. This outcome suggests the need

for inequality index analysis for such studies because they are capable of determining exposure

disparities between population subgroups even though average exposures are decreased. Lastly,

these three indices gave a similar outcome about exposure disparities by population subgroups.

60

CHAPTER 4:

EVALUATION OF THE TAMPA BAY NEXT PROGRAM FROM A HEALTH IN ALL

POLICIES PERSPECTIVE

4.1. Introduction

Transportation infrastructure as a part of the built environment is a social determinant of

health (Frumkin, 2005). Improvements in transportation infrastructure are important for human

mobility and population wellbeing (Ribeiro et al., 2007). However, they can also lead to

detrimental impacts on health and equity (Litman, & Burwell, 2006). In addition, health and

equity aspects are often narrowly considered in the development of transportation improvement

programs (Wernham, 2011). Health in All Policies (HiAP) is a perspective that aims to promote

health and equity in major policies or programs, including transportation designs (World Health

Organization [WHO], 2015). Therefore, using the HiAP approach in transportation programs

may improve health and equity considerations.

In this chapter, we aim to identify key concepts and strategies that may boost public

health and equity in major transportation projects. In order to achieve this aim, the Tampa Bay

Next (TBNext) program and its historical development process are reviewed based on the HiAP

key attributes. TBNext is a transportation improvement program that is aimed at modernizing

Tampa Bay’s transportation infrastructure. It includes a study of a variety of solutions, from

interstate roadway and bridge expansion to bicycle and pedestrian facilities (“What is TBNext?”,

n.d.). The development process of the program has been controversial due to the concern that toll

61

lanes could lead to environmental injustice within the society (“TBX Toll Lanes”, 2015). These

aspects of TBNext suggest it is a good case study to investigate the health and equity impacts of

a transportation program. In the final section, recommendations that may improve TBNext and

large-scale transportation programs in terms of health and equity are given. In addition, we have

also made recommendations about the aspects of the HiAP perspective to be applied to

transportation programs.

4.2. Health in All Policies Perspective

Health in all policies (HiAP) is a paradigm that considers health as a priority in any

government decision to improve the health and equity of a population (WHO, 2015). This

approach proposes a multi-sectoral partnership to minimize the potential impacts that could harm

public health and equity. The importance of this multi-sectoral partnership for health was first

emphasized in the declaration of Alma-Ata (WHO, 1978). In the following years, it has been

published in many statements that government policies have a potential to affect public health

(Rudolph, Caplan, Ben-Moshe, & Dillon, 2013., 2013). However, the concept of HiAP paradigm

as a unique perspective was first used by the Finnish government in 2006. Particularly, the

Finnish government published guidance called “HiAP: prospects and potentials” to improve

public health and equity consideration in all government policies (Ståhl et al., 2006). Prior to

this, health impact assessment (HIA) was an important tool that was used to inform decision-

makers about the potential health and equity impacts of a project or policy (Delany et al., 2014).

However, HIA is considered a limited approach for identifying opportunities to improve health

and equity during the policy implementation process (Government of South Australia’s

Department of Health, 2010). This is mainly due to the fact that HIA is applied at the project

level rather than the policy level (Koivusalo, 2010). The social environment, on the other hand,

62

should be examined in a wider range than HIA to promote public health and equity because it

consists of components that are in a complex relationship with each other (Leppo et al., 2013).

For this reason, there is a need for the HiAP approach, which also includes analyzing tools such

as HIA and the health lens (Lawless et al., 2012) for major government decisions.

The main goal of the HiAP approach is to promote public health and to minimize

inequities among population subgroups (Rudolph et al., 2013). HiAP case studies around the

globe have provided several key attributes that can help to achieve this goal. These attributes

include ‘health at the core’, ‘equity’, ‘multi-sectoral approach’, ‘win-win’, ‘create structural

process change’, and ‘political commitment’. The rest of this chapter describes these attributes

along with the case studies of HiAP.

The ‘Health at core’ attribute says that health should be at the center of a policy (Ollila,

2011). Promoting health should be the main objective regardless of what the policy is (WHO,

2015). This strategy encourages decision-makers to analyze a policy through a health lens

(Rudolph et al., 2013). One example of ‘health at the core’ is seen in California. The California

Department of Transportation (Caltrans) defined health and equity goals for its transportation

designs (Brown et al., 2015). Particularly, Caltrans is planning to provide a safe transportation

system for everyone including cyclists, pedestrians, and road construction workers by providing

education and multimedia campaigns about safe driving and cycling. Moreover, the department

is also planning to increase zero-emission and low-emission vehicles in the transportation system

to decrease both greenhouse gases and criteria air pollutants (Brown et al., 2015).

‘Equity’ is another core attribute that can be seen in any HiAP application. The

persistence of inequalities in health and environment makes the 'equity' attribute an inseparable

part of HiAP approach (Baum & Laris, 2010; Brownell, 2003). The ‘equity’ attribute states that

63

the benefits and burdens of a policy or program should be divided among individuals fairly and

equally regardless of their socioeconomic, racial, and ethnic differences (Dahlgren & Whitehead,

1991). As an example of ‘equity’ attribute, Australia conducted many implementations including

fair financing, equal access to essential services, and gender equality in work to provide an equal

distribution of money, resource, and power to all segments of society (Kickbusch & Buckett,

2010). In addition, many countries including Argentina, Chile, Brazil, New Zealand, and Canada

implement a social determinants of health approach to tackle health inequities (Marmot, & Allen,

2013). The social determinants of health approach means focusing on the improvement of the

condition in which people live and interact with each other or the environment throughout the

course of life (Marmot, Allen, Bell, Bloomer, & Goldblatt, 2012). As seen in the examples, the

‘equity’ attribute along with the ‘health at core’ are the most basic HiAP attributes.

The ‘multi-sectoral approach’ is another fundamental attribute in HiAP. Ståhl et al (2006)

states that sectors that are involved in a policy should work in cooperation with the health sector

and each other. Additionally, HiAP promotes integration and collaboration across sectors and

other non-government stakeholders (Freiler et al., 2013). For example, transportation, public

health, and environmental agencies worked cooperatively to implement active transportation

plans in Massachusetts (Association of State and Territorial Health Officials, 2013). Similarly,

Caltrans implements a health-centered transportation management plan that encourages

leadership, strategic partnership, and multi-sectoral collaboration to identify transportation

options that are accessible and suitable for everyone (Brown et al., 2015).

The ‘win-win’ attribute describes policy collaboration in which all partners benefit in a

decision (Ståhl et al., 2006). This attribute is employed to create mutual interests for all parties

involved in a policy (Ollila, 2011). The ‘win-win’ attribute also encourages government and

64

private agencies to be involved in HiAP implementation because they have also their sectoral

benefits along with the health and equity benefits to committing to the policy process (Molnar et

al., 2016). Moreover, while addressing health consideration, the 'win-win' attribute does not

derogate the primary objectives of the sectors involved in a policy (Freiler et al., 2013). This is

particularly important to engage multiple sectors in policy implementation and can be achieved

through multi-sectoral cooperation (Ollila, 2011). As an example of the ‘win-win’ attribute,

reduction of violence promotes public transportation and has co-benefits for other sectors such as

air quality, law enforcement, and housing agencies (Rudolf et al., 2013). In another example,

Kahlmeier et al. (2010) states that promoting cycling reduces the mortality rate and also provides

economic savings due to the reduction of mortality rate.

In order to implement the HiAP approach effectively, health and equity should be

considered in the early stage of a policy development rather than a post-decision process

(Rudolph et al., 2013). In other words, health and equity should be embedded in all policies’

structure from the early stage of the decision-making process (Rudolph et al., 2013). Moreover,

health and equity consideration should remain and become permanent throughout the process

(WHO, 2015). This permanence provides opportunities of procedural change that supports the

HiAP approach and promotes public health (Ollila, 2011). This attribute is called ‘create

structural or process change’. An example of this attribute is seen in Massachusetts. The local

government enacted a law in 2009 that HIA is required for major transportation programs to

inform decision-makers about the potential health impacts of the programs (NACCHO, 2017).

Lastly, developing and implementing a policy is a complex process that can take a long

time. In order to maintain sustainability throughout the policy development process, ‘political

commitment’ is required. Political commitment is a driving force that provides sustainability

65

throughout the policy process (Ståhl et al., 2006). This commitment can be ensured by

effectively announcing the practices that put health at the front and establishing inter-sectoral

alliances (WHO, 2015). The evidence of political commitment in policy includes the presence of

the leaders who advocate public health and equity in bureaucracy as well as the presence of the

laws and regulations that support the HiAP approach in major government policies (Fox,

Balarajan, Cheng, & Reich, 2014). In order to emphasize the importance of political commitment

in the HiAP approach, Howard and Gunther (2012) concluded in their HiAP literature review

that political commitment is a prerequisite for implementing HiAP in a policy successfully.

The above attributes are the essential components of HiAP approach. However,

depending on the policy or practice, more strategies can be used to implement HiAP. The

examples include but are not limited to damage limitation (Ollila, 2011), stakeholder engagement

(Rudolph et al., 2013), and capacity building (Freiler et al., 2013). Regardless of which strategies

are used, the ultimate aim of HiAP approach is to promote health and equity.

Several HiAP case studies suggest health and equity improvement strategies in policy

implementations. For example, The Safe Routes to School Local Policy Guide recommends

several strategies such as construction of complete streets, reducing speed limits around schools,

HIA, sales taxes to improve active transportation opportunities, and bicycle and pedestrian safety

education to promote public health (Cowan, Hubsmith, & Ping, 2011). In addition, Rudolf et al.

(2013) suggest inclusion of health and equity metrics in Regional Transportation Plans to track

traffic-related air pollution and chronic diseases as well as to mitigate greenhouse gasses.

Moreover, the American Public Health Association (n.d.) recommends several strategies to

promote health and equity, including providing safety in public transit use, giving education

about health benefits of active transportation, providing affordable access to active transportation

66

modes for everyone, and improving connectivity that supports safe biking, walking, and public

transportation between neighborhoods and business centers. In addition, the Welsh Government

used HIA to identify potential impacts of a road construction project on health equity (Wismar,

& Ernst, 2010). As a conclusion of the HIA, the government informed stakeholders about the

negative impacts of the road construction project on minorities and low-income people so the

construction never happened (Wismar, & Ernst, 2010). As an another example, South Australia

implements the HiAP approach across many sectors including transport, justice, education,

environment & natural resources, correctional services, planning & infrastructure, and primary

industries with various types of initiatives (Baum et al., 2017). Some of these initiatives include

improving road safety, providing educational services about parental engagement, implementing

transit-oriented developments, encouraging active transportation, and providing resources for the

health and wellbeing of international students (Baum et al., 2017).

The above-mentioned case studies have demonstrated all HiAP key attributes without

discarding any of them because the attributes are interconnected with each other. For instance, in

order to display the ‘win-win’ attribute, a well-organized multi-sectoral collaboration is needed

(Molnar at al., 2016). Similarly, the ‘multi-sectoral approach’ and ‘stakeholder engagement’

were found to facilitate maintaining health and equity (Barr, Pedersen, Pennock, & Rootman,

2008). For example, in a Finland case study, the Coronary Heart Disease Committee was

established to reduce coronary heart disease rate (Melkas, 2013). The committee included

representatives across different sectors to address the problem. The Ministry of Finance was

responsible for reducing taxes on vegetable oil and low-fat milk, the Ministry of Agriculture and

Forestry agreed to promote production of foods that are made of low-fat milk and edible oil, the

Ministry of Education took responsibility for education to students about healthy diet, and lastly,

67

the Ministry of Trade and Industry was responsible for improving labeling on food products

(Melkas, 2013). The ‘health at core’, ‘multi-sectoral approach’, ‘stakeholder engagement’, and

‘win-win’ attributes are explicitly seen in this case study. The ‘create structural or process

change’ attribute is also implied because different sectors involved in the committee agreed to

update their management plan in favor of health promotion. This case study illustrates explicitly

that all attributes are necessary components for implementing the HiAP approach.

4.3. The Tampa Bay Next Transportation Program

Tampa Bay Next (TBnext) is a program aimed at improving the transportation

infrastructure of Tampa Bay as introduced in section 4.1. Projects of the program in the most

advanced stage involve the extension of existing roadways with both toll and general purpose

lanes. Road extension plans start from I-275 in Pinellas County with the addition of toll lanes.

There are also design plans to connect Bayside Bridge to I-275 in the area. The toll lanes go from

there to I-4 through Tampa and extend until Plant City. General lane addition to Westshore and

downtown interchanges are also on the table. Other projects of the program include improving

bicycle and pedestrian infrastructure, implementing complete streets, improving freight mobility,

and supporting the local community for improving the transit system but no detailed plans are

available. Although there is a diversity in proposed projects, the road extension part of TBNext

gets considerable attention from local people and causes controversy. The rest of this section

summarizes this controversy along with the historical development of TBNext.

Interstate modernization started in the Tampa Bay with the approval of the Tampa

Interstate Study (TIS) in 1989 (Greiner, Inc., 1989). TIS included modifications of I-275, I4 and

the crosstown Expressway (now-called the Selmon Express way). The final environmental

impact statement (EIS) for the TIS was approved in 1996 (Greiner, Inc., 1996). In the EIS report,

68

equity analysis showed that the relocation process would impact 1014 family residences and 159

businesses, affecting predominantly low-income and minority groups (Greiner, Inc., 1996). From

the air quality perspective, the EIS report estimated that the carbon monoxide (CO) concentration

in the TIS project would be lower than the CO concentration in the no-action alternative. No

public health sector experts were involved in the preparation of the EIS for the 1996 TIS project.

Experts involved in the preparation of the EIS included environmental scientists, civil engineers,

archeologists, transportation experts, landscape architects, one ecologist, one air quality

specialist, and one water resources expert.

TBNext was originally announced as Tampa Bay Express (TBX) back in 2015. After the

announcement, a volunteer organization called Sunshine Citizens arranged a public meeting to

discuss their concerns with local people (“TBX Toll Lanes”, 2015). The toll lanes immediately

caused concerns for several reasons. First of all, the destruction of some houses along the

planned toll lanes will oblige people living in the area to sell their home (“Understanding Tampa

Bay”, 2015). People living in these homes will have to change their living area. The Florida

Department of Transportation (FDOT) has already begun buying properties in the area. This

would change the general structure of the neighborhoods and expose some residents to increased

traffic-related pollution (air, noise, and visual).

Another controversial point of the project is that the toll transition price will change

dynamically. This means that if there is heavy traffic, the money to be charged per mile will be

higher. In order to meet this amount of payment, people have to be above a certain level of

income, but not every segment of the society has an income at that level (“Understanding Tampa

Bay”, 2015). In addition, the use of toll lanes also poses a problem for low-income community.

The people who live near the proposed lanes will carry the environmental burden but they won’t

69

be able to benefit from the toll lanes, while wealthier people will have benefits from the toll lanes

(Johnston, 2016). The other concerns raised by locals include limited access to three employment

centers (downtown, Westshore, and University of South Florida), economic loss due to the cost

of highway expansion, and environmental damage (“TBX Toll Lanes”, 2015).

In 2016, Tampa City Council opposed the interstate expansion part of TBX because the

neighborhood around the north part of I-275 was expected to be affected disproportionately from

the construction work (Danielson, 2016). Later, similar inequity concerns were expressed that

minorities and low income people will be the most affected from relocation process due to the

roadway expansion (Johnston, 2016). In April, 2017, FDOT officials announced that TBX’s toll

lane addition plan would be reevaluated due to the public concerns (Johnston, 2017b). Then in

the May, TBX’s name was changed as TBNext but reevaluation of toll lanes was ongoing

(Johnston, 2017a).

Subsequently, FDOT announced the cancelation of toll lanes on the north part of I-275

due to the public feedback in May, 2018 (Winer, 2018). However, the planned express lanes for

the Gateway Connector in Pinellas, the Howard Frankland Bridge, I-275’ Westshore to

downtown corridor, and I-4 and the Selmon Expressway Connector are still in the works

(Newborn, 2017). In addition, the idea of adding toll lanes on the I-75 has emerged (Winer,

2018). During the whole process, FDOT conducted several public outreach events and

community workshops to engage with stakeholders; these are discussed in the next section in

detail. The reevaluation process is planned to come to an end for the road expansion in 2019.

Afterwards, FDOT will start construction of the proposed lanes if they decide to go with toll

lanes.

70

4.4. Tampa Bay Next from the Health in All Policies Perspective

TBNext is a major transportation program that has a potential impact on public health.

On the other hand, HiAP is a broad concept providing an approach that promotes public health

and equity in large-scale policies and programs such as TBNext. Regarding these, looking at the

TBNext from the HiAP perspective may help to identify leverages that improve health and

equity outcomes. Therefore, in this section, we reviewed publicly available TBNext documents

with the consideration of HiAP attributes. Particularly, the historical development process of the

program was reviewed by using the local newspaper articles as well as the community meetings

documents. We also attended a few community meetings. Lastly, government-based technical

documents related to interstate modernization, bicycle/pedestrian lane improvement, complete

streets program, freight mobility, and transit improvement were reviewed.

During the development process of TBNext, there have been some actions taken by the

FDOT to manage the planning process as discussed in section 4.3.1. However, none of these

actions demonstrated the ‘health-at core’ attribute which puts public health as the first priority.

‘Equity, on the other hand, came into consideration by the local government and the FDOT as a

response of public concerns. For example, the Tampa council opposed the road expansion part to

protect environmental justice (Danielson, 2016). Moreover, FDOT canceled the north part of I-

275 toll lanes due to the inequity concerns of the local people (Winer, 2018). As for ‘the multi-

sectoral approach’ attribute, there appears to be little collaboration between FDOT and any

public health agency related to TBNext. Nevertheless, several experts from different sectors

including transportation, environment, engineering, and law has been working collaboratively to

prepare Environmental Impact Assessments (“Project Coordination”, 2017). This is mainly due

to the fact that the Environmental Impact Assessment (EIA) is required by National

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Environmental Policy Act (NEPA), for major transportation programs (Wernham, 2011).

Similarly, there wasn't any evidence that suggests ‘political commitment’ with the emphasis on

public health and equity consideration. Finally, a structure that would benefit all parties

including the health sector (‘win-win’ attribute) was not provided. There were also no

opportunities to change the process in favor of health because the health and equity goals were

not embedded in the program in the first place.

The FDOT conducted community workshops to engage with stakeholders (“You talk”,

n.d.). The purpose of community workshops was to inform the community about the new

developments in the program and to take their feedback. In addition, the concept designs of the

program were presented by the FDOT representatives, and alternative plans were discussed. An

interactive public engagement was provided in the community workshops, and local people’s

opinions and concerns about the program were listed. In the first community workshop, general

aspects of Tampa Bay’s needs in terms of transportation were discussed without giving detail

about the concept plans of the TBNext (“Tampa Bay Next Community Working Groups [CWG]

– Regional Event”, 2017). It is important to note that this first meeting was held after the

announcement of the TBNext as a replacement of TBX in May, 2017. The next three regional

workshops had the same presentation and discussion contents. These regions include North and

West Hillsborough (“North and West Hillsborough Community Working Group”, 2017), Pasco

and Hernando Counties (“Pasco and Hernando Counties CWG”, 2017), and East and South

Hillsborough County and Polk County (“East and South Hillsborough County and Polk County

CWG”, 2017). In these meetings, the FDOT representatives mainly focused on the road

expansion concept plans but also touched on emerging transit technologies. In addition, they

briefly mentioned road safety and traffic accident reduction strategies. The next workshop was

72

held in downtown Tampa, and introduced different options for the downtown Tampa interchange

improvement program (“Downtown Tampa Urban Core Area CWG”, 2018). In the Westshore

community workshop, even though the main emphasis was on the interchange improvement and

express lanes, the FDOT also promised to support local agencies to improve the transit system in

Hillsborough County (“Westshore/West Tampa CWG”, 2018). Moreover, in the Westshore

workshop, the FDOT announced that they are preparing a document to evaluate the sociocultural

effects of the TBNext. The impacts that were planned to be evaluated included social, economic,

relocation, land use, and aesthetic (“Westshore/West Tampa CWG”, 2018). In the Pinellas

County workshop, transit-oriented development plans as well as the express lanes were discussed

(“Pinellas Community Open House”, 2018). At the end of every workshops, participants’

feedback were taken with a comment form. In general, participants were not pleased with the

addition of toll lanes and wanted to see alternative transit approaches that can reduce motor

vehicle dependency (“TBNext CWG – Regional Event”, 2017; “Downtown Tampa Urban Core

Area CWG”, 2018). In conclusion, stakeholder engagement was achieved with these meetings

and equity consideration came forward in some degree as a response to local people’s feedback.

However, public health consideration was only implied under the sociocultural effects evaluation

in the EIA.

TBNext is a multimodal transportation program that has several design plans as

mentioned in section 4.1. The road expansion with both toll and general lanes is significant part

of the TBNext program. The expansion of I-275 and I-4 as a part of the TBNext program was

introduced in the TBX master plan draft. In the draft, the express lanes to be added on I-275, I-4,

and I-75 are described and evaluated in terms of traffic volume and safety (“TBX Draft Master

Plan”, 2015). However, there is a lack of public health and equity considerations in the draft.

73

Subsequently, under the TBX name, the FDOT prepared the Regional Concept for

Transportation Operations (RCTO) report that aims to provide a connection and collaboration

between stakeholders and decision makers involved in the express lanes planning (“TBX

Regional Concept”, 2017). Nevertheless, neither the health department nor any public health

agencies are not listed in the stakeholder list of the RCTO. After the TBNext was launched in

2017, the FDOT prepared a coordination plan report whose purpose is to facilitate multi-sectoral

collaboration and stakeholder engagement in the preparation of supplemental environmental

impact statement (SEIS) for the interstate modernization part (“Project Coordination”, 2017).

There is a big emphasis on the potential impacts of the interstate modernization program on the

environment, health, and equity in the report (“Project Coordination”, 2017). First of all, the

FDOT promises to evaluate how minorities and low-income people will be affected by the

program. Moreover, the FDOT plans to address the program's physical effects such as air

pollution, noise, and aesthetics. Lastly, the coordination report evaluates the impact of the

program on natural sources and historical buildings (“Project Coordination”, 2017; “Urban

Design Guidelines”, 2017). However, according to the coordination report (“Project

Coordination”, 2017), nether the health department nor any public health agencies will not be

included in the SEIS process. In other words, public health and equity will be evaluated by

experts from outside of the health sector. There are also other technical reports such as “the

traffic and revenue report” (“Tampa Bay Express Planning”, 2017) that estimates toll revenue

under different scenarios and “the purpose and need report draft” (“Tampa Interstate Study”,

2017) that tries to explain why Tampa Bay needs tolled express lanes by pointing to regional

problems such as population growth, increasing traffic volume, and road traffic injuries. The

purpose and need report draft also mentions that high occupancy vehicle/transit lanes options

74

will be evaluated in the SEIS in 2019. These reports briefly address the safety concerns but the

public health and equity consideration is missing again.

As another part of the TBNext program, there is also a plan to construct complete streets

(“What is Complete”, n.d.). A complete street provides an infrastructure that is safe and

convenient for all users including cyclist, pedestrians, mass transit riders, and drivers (LaPlante,

& McCann, 2008). In 2014, the FDOT announced they would integrate "complete streets policy"

into their management plan (“Complete Streets”, 2014). Regarding this, a complete streets

implementation plan was prepared and the TBNext aims to follow this plan (“Complete Street

Implementation Plan”, 2015). In general, one of the primary goals of the implementation plan

was to improve public health by promoting active transportation, reducing the rates of chronic

diseases, and protecting air quality. In addition, social equity in terms of accessing the

transportation benefits and exposure equity for environmental pollutants are also considered in

the plan. Moreover, the stakeholders from the Florida Department of Health and local public

health departments are planned to be involved in the implementation process. Therefore, it is

clearly seen that health and equity consideration is prioritized in the complete streets

implementation plan.

The transit and bike/pedestrian infrastructure improvements are another parts of the

TBNext program. Florida Department of Transportation (FDOT) has been engaged to provide

technical and financial support to improve mass transit modes under the TBNext design (“Transit

Mode Primer”, n.d; “Westshore/West Tampa CWG”, 2018). Moreover, FDOT has proposed to

expand bicycle and pedestrian lanes and to improve cycling and walking safety (“Bicycle /

Pedestrian”, n.d.). Regarding these, the TBNext program plans to apply “the Florida pedestrian

and bicycle strategic safety plan” to accommodate cyclist and pedestrian safety. The plan aims to

75

establish a multi-sectoral collaboration and find resources to reduce bicycle and pedestrian

injuries (“Florida Pedestrian and Bicycle Strategic Safety Plan”, 2013). The Florida Department

of Health is also included in this collaboration and held responsible for promoting active

transportation modes. However, there is a lack of equity consideration in terms of access the

bicycle and pedestrian infrastructure in this safety plan. In addition, the bicycle and pedestrian

improvement part of the TBNext is still in the planning stage but the “Florida pedestrian and

bicycle strategic safety plan” is a 5-year plan and expired as of 2018. Therefore, technical

documents about the bicycle and pedestrian program need to be updated with the addition of

equity consideration.

Lastly, the TBNext also plans to improve freight mobility with the guidance of the

Tampa Bay regional strategic freight plan (“Freight Mobility”, n.d.). The Tampa Bay regional

strategic freight plan (TBRSFP) includes strategies to manage regional freight mobility which

supports economic growth and responds to the needs of the region in terms of trucks mobility

and accessibility (“TBRSFP”, 2012). The plan largely focuses on the impacts of the freight

mobility strategies on economic growth but also briefly mentions about some precautions such as

limiting the number of hours that a truck driver operates in a given period of time and limiting

the load weight to protect public health and safety. However, health and equity consideration in

the plan (“TBRSFP”, 2012) is limited to these precautions.

As a final point, promoting active transportation modes such as cycling, walking, and

using public transportation is seen as a good HiAP practice (Caplan et al., 2017). However, the

program heavily focuses on interstate modernization plan which includes both toll and general

purpose lanes expansion. Regarding this, it has been well documented that the roadway

expansion practice may not fix traffic problems and may cause more air pollution (Armah,

76

Yawson, & Pappoe, 2010; Duranton & Turner, 2009; Gately et al., 2017). Additionally, none of

the case studies that applied HiAP for transportation policies followed a roadway expansion

strategy. Furthermore, public health and equity considerations are overlooked in the technical

documents so far, as of March, 2019. However, the SEIS that evaluates the impacts of the

program on land use, social environment, mobility, equity, and air quality is still in progress and

planned to be available in the summer 2019. SEIS is seen as a good opportunity to evaluate the

impacts of large-scale programs on health and equity and inform decision makers (Bhatia, &

Wernham, 2008). In addition, based on the project coordination report (“Project Coordination”,

2017), the SEIS may help promoting public health and equity consideration.

Given these points, there is health and equity consideration in TBNext design but they

have not been priorities. Looking at the historical development of TBNext design from the HiAP

perspective, a few important steps FDOT and local government have taken can be seen to

improve health and equity, including regular engagement with the stakeholders and opposition of

the road expansion plan from the legislative body of the local government. Additionally,

reevaluation of the toll scenario and cancelation of the north part of I-275 toll lanes show that

local people’s opinion matters in some degree. As for the technical parts of the program,

improving active transportation modes may be the best way to promote public health and equity

(Caplan et al., 2017; Rudolph et al., 2013), while the road expansion projects may be a less

healthy solution (Duranton & Turner, 2009; Milam et al., 2017).

4.5. Recommendations

The TBNext transportation program has a potential to impact health and equity so it

should also be assessed from a health-centered perspective. The HiAP approach provides

guidance to promote health and equity in any policies and programs. Throughout this chapter,

77

key attributes in HiAP are defined and the TBNext program is investigated against the HiAP

guidance. Regarding these, case-specific recommendations are given for the TBNext program to

improve in terms of health and equity aspects. In addition, general recommendations of HiAP

approach for transportation planning as well as improvement ideas for the HiAP paradigm that is

applied for the transportation sector are also provided below.

First of all, TBNext relies heavily on the roadway expansion part but transportation

programs that focus on motor vehicle dependency may increase traffic congestion and air

pollution (Giles-Corti et al., 2016; Duranton & Turner, 2009; Williams-Derry, 2007). On the

other hand, improving bicycle and pedestrian infrastructure and promoting walking, biking, and

the use of mass transit has been proven to promote health and equity (Caplan et al., 2017;

American Public Health Association, 2019; Wernham & Teutsch, 2015). Therefore, in order to

promote public health and minimize inequities, TBNext should focus more on the improvement

of active transportation than highway expansion. Second, FDOT should provide an infrastructure

that supports multi-sectoral cooperation with agencies including, especially the public health

sector (WHO, 2015). Additionally, the multi-sectoral approach should be used to conduct public

health analysis such as health impact assessment and health lens analysis (NACCHO, 2017).

Besides, there should be education or presentations that create an awareness of the importance of

active transportation use for the public in community meetings instead of presenting different toll

lane scenarios (American Public Health Association, 2019). Lastly, maybe as the most crucial

point of the program, environmental justice should be assessed in detail long before the

implementation process. The project coordination and public involvement report (2017) states

that the FDOT has already acquired several properties along the proposed lanes. However, there

isn't any publicly accessible equity assessment of the program and the SEIS is still pending.

78

In general, the example case studies discussed in the section 4.2 and the literature review

chapter suggest that promoting active transportation is at the center of healthy transportation

planning. Regarding this, a transportation policy should implement strategies such as putting

speed limits in sensitive areas, constructing complete streets, tax reductions on transit vehicles,

improving safety in streets and public transits, and increasing green spaces to promote cycling,

walking, and the use of public transportation (Cowan et al., 2011). Furthermore, the local health

department and public health agencies should be included in the multi-sectoral cooperation of

transportation programs. This is particularly important because health experts bring expertise in

terms of public health and equity improvement. Lastly, HIA should be implemented in

transportation programs to inform decision makers about the potential impacts of the programs

on health and equity.

There are also some areas where HiAP can improve its approach to transportation

planning. First and foremost, there is no approved HiAP guidance that recommends strategies to

improve health and equity in transportation planning. By this, we mean guidance prepared by

several experts, from multiple sectors including the health sector, who work collaboratively to

collect data and come up with applicable strategies in the transportation sector to promote public

health and equity. All the case studies of HiAP in transportation planning developed their own

methods to apply HiAP principles. However, in order to disseminate the HiAP approach to more

urban planners, a transportation-specific HiAP guidance is needed. Second, the HiAP case

studies do not evaluate interstate modifications and road widening designs, instead they focus to

improve active transportation modes. Although improving active transportation modes is very

important to promote health and equity, interstate modifications and road widening designs

should also be assessed from the HiAP perspective because they are the most common types of

79

transportation development. Finally, there should be a data collection system for HiAP

applications in transportation planning so that the strategies to promote health and equity can be

improved by processing the data.

On the whole, the HiAP principles and key attributes inform urban designers and decision

makers about the factors that can improve health and equity in transportation programs such as

TBNext. To begin with, the TBNext’s weakest point is that it heavily focuses on a motor vehicle-

dependent transportation design. However, the HiAP case studies recommend active

transportation improvement. In addition, the HiAP perspective also can be improved to include

more comprehensive and detailed guidance for transportation planning. Moreover, the interstate

projects should not be ignored in the HiAP approach as if they don’t exist. It is likely that

roadways will remain important for some automobile travel, particularly between cities or

counties.

4.6. Limitations

This study is limited because of the scope of the reviewing. First of all, the SEIS of the

program is still work in progress by FDOT so that it wasn't reviewed. In addition, interviews

with stakeholders including local people, decision-makers, and project planners were not

conducted in this study, whereas it could provide more insight about the health and equity

consideration of the program.

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CHAPTER 5:

SUMMARY AND CONCLUSION

County-scale transportation programs are linked to public health and equity. However,

potential factors that may improve transportation programs in terms of health and equity are not

well-understood. Although environmental impact assessment under the National Environmental

Policy Act (NEPA) is a beneficial tool to limit environmental damage in major transportation

programs, it is criticized to have lack of health consideration. Moreover, the impacts of large-

scale transportation programs on health and equity are often overlooked because the health

assessment tools including health impact assessment (HIA) are only limited to a single project

and miss the interactions of the multiple components of a large-scale program. Therefore, more

comprehensive approaches such as Health in All Policies (HiAP) are required to promote public

health and equity in such programs. In addition, the current literature has contradictions that

some studies suggest that road expansion designs as a major part of transportation programs may

increase air pollution, while others claim the opposite. Regarding these, our quantitative analysis

provides important insight about the impacts of a real world case’s proposed lanes on air

pollution and exposure disparities.

In order to improve understanding of the impacts of large scale transportation programs

on air quality and air pollution equity, we investigated the Tampa Bay Next (TBNext)

transportation program. Particularly, a multi-component modeling approach (Gurram et al.,

2019) was used to quantitatively compare scenarios based on the TBNext plans. Specifically, the

first scenario included a base case transportation network without the proposed lanes of the

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TBNext transportation program and the second scenario included the proposed lanes. Regarding

this, travel behavior of individuals was simulated in the transportation modeling component to

estimate hypothetical individual travel activity, link-specific vehicle counts, and average speeds.

In the air pollution modeling component, we estimated link-specific NOx emissions from on-road

mobile sources and NOx concentration in Hillsborough County with 500 m resolution. Lastly, we

analyzed individual and group NOx exposures and inequity measures to explore exposure

disparities. We found that the proposed lanes as a part of TBNext transportation improvement

program increased the vehicle count, NOx emission rates, and NOx concentration during rush

hours in the Tampa urban core. However, overall spatiotemporal average NOx concentration in

Hillsborough County was found to decrease. Population NOx exposure concentrations also

decreased but exposure disparities by race and income increased due to the new lanes.

Contrarily, exposure disparities between the Hispanic population and not Hispanic population

decreased because of the road expansion design. This study suggests that there is a need for more

focused air quality and equity assessment for major transportation programs. In addition,

applying different inequity indices were found to solidify findings of exposure disparities.

Looking at the TBNext transportation improvement program from the HiAP point of

view provided an opportunity to investigate factors that promote public health and equity.

Regarding this, first, we conducted a literature review on HiAP to identify key attributes that

facilitate HiAP implementation. Second, we reviewed publicly available TBNext resources

including local newspaper articles, stakeholder meeting documents, and technical reports. Lastly,

we provided recommendations based on our findings to improve health and equity considerations

in the TBNext and in similar transportation programs. Our literature review on HiAP suggests

that health and equity consideration should be at the core of any policy. Furthermore, a

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collaboration that involves multiple sectors is required to create a healthy policy that benefits all

parties including the health sector. Considering the TBNext transportation improvement program

from this health perspective, our analysis suggests that impacts of the program on public health

haven’t receive substantial attention, but there are some efforts to respond to local people's

inequity concerns. As for the recommendations, one of the most applied strategies in the HiAP

case studies is to improve active transportation modes. Therefore, the TBNext and similar

programs should consider focusing more on improving active transportation instead of relying

heavily on road expansion design. Moreover, such programs should also get the health sector

involved in its decision-making process. As for the improvement of HiAP approach for the

transportation policies, there should be a sector-specific HiAP guidance for transportation

programs. That would allow urban planners to follow guidance to apply HiAP principles in their

programs. The overall results point out that the transportation sector and the health sector along

with the other sectors should work in collaboration to promote public health and equity.

In order to provide more insight into the impacts of large-scale transportation policies on

air quality, public health, and equity, further studies are needed. For example, our exposure

analysis investigated overall exposures for Hillsborough County residents. For further analysis,

exposure and disparities could be investigated specifically near the proposed lanes or other

proposed changes. Furthermore, a coordination and collaboration between scholars and

department of transportation officials could provide a better analysis of air pollution, exposures,

and disparities in this type of study. Moreover, investigation of TBNext from the HiAP

perspective in this study was conducted by using newspaper articles and publicly available

TBNext documents. In future studies, interviews with stakeholders should be done to make

83

further analysis. Lastly, more case studies similar to the current one should be conducted to

contribute the body of knowledge and gain more data on this topic.

In sum, the HiAP approach has the potential to put public health and equity consideration

forward in the TBNext and similar transportation programs. The original TBNext (as Tampa Bay

Express) was canceled in 2017, 2 years after its launch, because it only included tolled express

lanes that caused public concerns. If public health and equity considerations were included in the

TBNext from the beginning, there likely wouldn’t have only been tolled express lanes in the

program. Hence, 2 years of work could potentially have been saved allowing the program to

advance more quickly than it is now. Furthermore, HiAP is not only beneficial for improving

health and equity, but also may be good for the economy as well. For example, health care costs

may decrease with the promotion of public health and equity. Therefore, a HiAP approach may

be useful for improving health, equity, and economic concerns in transportation programs

including TBnext and beyond.

84

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APPENDIX A:

ADDITIONAL RESULTS FOR THE BASE CASE AND THE TAMPA BAY NEXT CASE

Spatiotemporal distribution of vehicle counts, average speeds, human activity densities,

oxides of nitrogen (NOx) emission rates, NOx concentration, and individual NOx exposure for the

base case and the Tampa Bay Next case (TBNext) case are presented here.

100

Figure A 1. Spatiotemporal distribution of hourly average vehicle counts for a typical day for the base

case.

101

Figure A 2. Spatiotemporal distribution of hourly average vehicle counts for a typical day for the TBNext

case.

102

Figure A 3. Spatiotemporal distribution of hourly average vehicle speeds for a typical day for the base

case.

103

Figure A 4. Spatiotemporal distribution of hourly average vehicle speeds for a typical day for the TBNext

case.

104

Figure A 5. Spatiotemporal distribution of human activity duration densities by the block group for a

typical day for the base case.

105

Figure A 6. Spatiotemporal distribution of human activity duration densities by the block group for a

typical day for the TBNext case.

106

Figure A 7. Spatiotemporal distribution of NOx emissions by each hour of an average winter day for the

base case.

107

Figure A 8. Spatiotemporal distribution of NOx emissions by each hour of an average winter day for the

TBNext case.

108

Figure A 9. Spatiotemporal distribution of NOx concentration by each hour of an average winter day for

the base case.

109

Figure A 10. Spatiotemporal distribution of NOx concentration by each hour of an average winter day for

the TBNext case.

110

Figure A 11. Spatiotemporal distribution of aggregated individual NOx exposure by block groups by each

hour of an average winter day for the base case.

111

Figure A 12. Spatiotemporal distribution of aggregated individual NOx exposure by block groups by each

hour of an average winter day for the TBNext case.

112

APPENDIX B:

FAIR USE ARGUMENT FOR THE FIGURE 1.1

Figure 1.1 was generated by Google earth. Proper attribution for the figure 1.1 is

provided in compliance with the Google attribution guidelines at

https://www.google.com/permissions/geoguidelines/attr-guide/. Specifically, the figure has the

Google name and the data source names on it, which are the requirements for the proper

attribution. Therefore, this use of the figure is allowed according to the Google attribution

guidelines.

https://www.google.com/permissions/geoguidelines/attr-guide/