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University of South Florida University of South Florida
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Graduate Theses and Dissertations Graduate School
March 2019
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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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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.
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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
transportation program.
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
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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
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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
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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
transportation program.
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
2 toll lanes in each direction $ 0.5 in each direction
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
$ 0.5 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
$ 1.0 in each direction
East section of Westshore
interchange
4 toll lanes in each direction
5 general purpose lanes in each
direction
$ 0.5 in each direction
Westshore to Downtown Corridor
2 toll lanes in each direction $ 1.0 in each direction
West section of downtown
interchange
3 toll lanes in each direction
4 general purpose lanes from west to
east
6 general purpose lanes from east to
west
$ 0.5 in each direction
North section of downtown
interchange
4 general purpose lanes in each
direction
East section of downtown
interchange
3 toll lanes in each direction
7 general purpose lanes in each
direction
$ 0.5 in each direction
I-4 from downtown interchange to
the Polk Parkway in Polk County
3 toll lanes in each direction $ 1.0 in each direction
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
85th percentile 90th percentile 95th percentile
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
85th percentile 90th percentile 95th percentile
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
71
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
81
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
82
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.
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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.