Civil & Environmental Engineering Research Works

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A Novel Framework for Sustainable Traffic Safety Programs Using the Public as Sensors of Hazardous Road Information
(MDPI, 2018-10-26) Chung, Younshik; Won, Minsu
Traditionally, traffic safety improvement programs (TSIPs) have been based on the number of crashes at a specific location or their severity. However, the crash datasets used for such programs are obtained from the police and include two limitations: not all crashes are collected by the police (most minor and near-miss crashes are not reported), and the traditional process uses crash data recorded for the past two or three years (meaning most data inevitably include a time lag). To overcome these limitations, this study proposes a new approach for a TSIP based on citizen participation through an online survey that is broadcasted through social media. The method uses the public as sensors of hazardous road information, which means that information can be collected on individual experiences of minor crashes and latent risk factors, such as near misses and traffic conflicts. To demonstrate this approach, a case study was carried out in a small district in the city of Goyang, Korea, which has one of the highest usage rates of social media technologies. The proposed method and a traditional method were both assessed.
Advancing Scientific Knowledge: Ethical Issues in the Journal Publication Process
(MDPI, 2017-12-31) McCuen, Richard H.
The goal of this paper is to assess the journal publication process from value and ethical perspectives. The specific objectives are: (1) To define fundamental values relevant to scientific journal publication; (2) To identify stakeholders involved in professional journals and their value rights and responsibilities; (3) To discuss the steps of the journal publication process where ethical dilemmas arise and the potential influences of such dilemmas on the advancement of knowledge; and (4) To summarize actions that can minimize unethical practices throughout the steps of the publication process. Values such as honesty, efficiency, accountability, and fairness will be discussed. Issues related to the various stakeholders such as self-citation, plagiarism, dual publication, a lack of timeliness, and issues related to authorship will be a primary focus.
Agricultural practices influence foliar endophytic communities in coffee plants of different varieties
(Wiley, 2023-02-16) Castillo-Gonzalez, Humberto; Bloomberg, Joshua; Alvarado-Picado, Eduardo; Yarwood, Stephanie; Chaverri, Priscila
Fungal endophytes are pivotal components of a plant's microbiome, profoundly impacting its health and fitness. Yet, myriad questions remain concerning the intricate interactions between these microorganisms and their hosts, particularly in the context of agriculturally important plants such as Coffea arabica. To bridge this knowledge gap and provide a comprehensive framework, this study investigated how farming practices shape the taxonomic and functional diversity of phylloplane endophytes in coffee. Coffee plant leaves from two distinct producing regions in Costa Rica were sampled, ensuring the representation of various coffee varieties (Obatá, Catuaí, and Caturra), agricultural management methods (organic vs. conventional), sun exposure regimes (full sunlight/monoculture vs. natural shade/agroforestry), and leaf developmental stages (newly emerged asymptomatic vs. mature leaves). Fungal communities were characterized by employing both culture-dependent and independent techniques (internal transcribed spacer 2 nuclear ribosomal DNA metabarcoding). The results showed a greater diversity of endophytes in mature leaves and conventionally managed plants, with coffee variety exerting an unclear influence. The effect of sun exposure was surprisingly negligible. However, data emphasize the benefits of agroforestry and organic farming, which are linked to reduced putative pathogens and heightened levels of potentially mutualistic fungi, fostering functionally diverse communities. Despite the role that plant microbiomes might play in agricultural production, the knowledge to shape endophytic communities through breeding or management is lacking. The results from this study provide a framework to understand how both plant and agricultural practices influence endophyte diversity within coffee crops. These insights hold promise for guiding future efforts to manipulate coffee microbial communities effectively.
Assessing the performance of polyphosphate accumulating organisms in a full-scale side-stream enhanced biological phosphorous removal
(Wiley, 2024-01-11) Aghilinasrollahabadi, Khashayar; Ghandehari, Shahrzad Saffari; Kjellerup, Birthe Veno; Nguyen, Caroline; Saavedra, Yerman; Li, Guangbin
Phosphorous (P) removal in wastewater treatment is essential to prevent eutrophication in water bodies. Side-stream enhanced biological phosphorous removal (S2EBPR) is utilized to improve biological P removal by recirculating internal streams within a side-stream reactor to generate biodegradable carbon (C) for polyphosphate accumulating organisms (PAOs). In this study, a full-scale S2EBPR system in a water resource recovery facility (WRRF) was evaluated for 5 months. Batch experiments revealed a strong positive correlation (r = 0.91) between temperature and C consumption rate (3.56–8.18 mg-COD/g-VSS/h) in the system, with temperature ranging from 14°C to 18°C. The anaerobic P-release to COD-uptake ratio decreased from 0.93 to 0.25 mg-P/mg-COD as the temperature increased, suggesting competition between PAOs and other C-consumers, such as heterotrophic microorganisms, to uptake bioavailable C. Microbial community analysis did not show a strong relationship between abundance and activity of PAO in the tested WRRF. An assessment of the economic feasibility was performed to compare the costs and benefits of a full scale WRRF with and without implementation of the S2EBPR technology. The results showed the higher capital costs required for S2EBPR were estimated to be compensated after 5 and 11 years of operation, respectively, compared to chemical precipitation and conventional EBPR. The results from this study can assist in the decision-making process for upgrading a conventional EBPR or chemical P removal process to S2EBPR.
Biobjective optimization for railway alignment fine-grained designs with parallel existing railways
(Wiley, 2024-01-09) Gao, Yan; Zhang, Tianlong; Zhu, Caiyiyi; Yang, Shusheng; Schonfeld, Paul; Zou, Kai; Zhang, Jialing; Zhu, Ying; Wang, Ping; He, Qing
Urban high-speed railway construction is complex due to limited land resources, high population density, and potential construction risks, especially when new tracks are parallelly aligned to operational railways. Addressing a gap in current literature on fine optimization of manual alignment in such scenarios, this paper introduces a biobjective approximate fine-grained optimization model for railway alignments (BA-FORA). Utilizing an approximate dynamic programming (ADP) method, BA-FORA effectively searches the feasible region to approach a global optimum, overcoming the dimensionality challenges inherent in standard dynamic programming (DP). This paper presents a biobjective optimization framework that takes into account both construction cost and construction risk adjacent to existing operating railways (CRAEOR), offering a method for the fine-grained design of new railways adjacent to existing railways. Finally, the proposed BA-FORA framework is applied to practical cases, demonstrating its superior optimization performance. The findings indicate that the BA-FORA model can autonomously investigate and enhance railway alignment. It generates cost-effective and low-risk solutions exceeding manual efforts, ensuring alignment constraint compliance.
Comparison of Vertical Surface Deformation Estimates Derived from Space-based Gravimetry, Ground-based GPS, and Model-based Hydrologic Loading over Snow-dominated Watersheds in the United States
(2020-07) Yin, Gaohong; Forman, Barton Allen
The data archived here includes the NASA Catchment Land Surface Model output of monthly TWS anomalies (after removing the long-term mean) used in the investigation of vertical displacement comparison for the Great Basin and Upper Colorado basins in the paper “Comparison of Vertical Surface Deformation Estimates Derived from Space-based Gravimetry, Ground-based GPS, and Model-based Hydrologic Loading over Snow-dominated Watersheds in the United States” for publication in the Journal of Geophysical Research – Solid Earth.
Compositional Approach to Distributed System Behavior Modeling and Formal Validation of Infrastructure Operations with Finite State Automata: Application to Viewpoint-Driven Verification of Functionality in Waterways
(MDPI, 2018-01-12) Austin, Mark A.; Johnson, John
Now that modern infrastructure systems are moving toward an increased use of automation in their day-to-day operations, there is an emerging need for new approaches to the formal analysis and validation of system functionality with respect to correctness of operations. This paper describes a compositional approach to the multi-level behavior modeling and formal validation of large-scale distributed system operations with hierarchies and networks of finite state automata. To avoid the well-known state explosion problem, we develop a new procedure for viewpoint-action-process traceability, thereby allowing parts of a behavior model not relevant to a specific decision to be removed from consideration. Key features of the methodology are illustrated through the development of behavior models and validation procedures for polite conversation between two individuals, and lockset- and system-level concerns for ships traversing a large-scale waterway system.
COVID-19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages
(Wiley, 2022-11-02) Sun, Qianqian; Zhou, Weiyi; Kabiri, Aliakbar; Darzi, Aref; Hu, Shonghua; Younes, Hannah; Zhang, Lei
Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real-world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real-world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay-at-home percentage, miles traveled per person, trip rate, work trip rate, and non-work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: “best performance,” “effort,” and “consistency.” We found that for all 18 indicators, high-income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low-income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real-world data and offers compelling perspectives for future discussions.
Demographics and risk of isolation due to sea level rise in the United States
(Springer Nature, 2023-11-30) Best, Kelsea; He, Qian; Reilly, Allison C.; Niemeier, Deb A.; Anderson, Mitchell; Logan, Tom
Within coastal communities, sea level rise (SLR) will result in widespread intermittent flooding and long-term inundation. Inundation effects will be evident, but isolation that arises from the loss of accessibility to critical services due to inundation of transportation networks may be less obvious. We examine who is most at risk of isolation due to SLR, which can inform community adaptation plans and help ensure that existing social vulnerabilities are not exacerbated. Combining socio-demographic data with an isolation metric, we identify social and economic disparities in risk of isolation under different SLR scenarios (1-10 ft) for the coastal U.S. We show that Black and Hispanic populations face a disproportionate risk of isolation at intermediate levels of SLR (4 ft and greater). Further, census tracts with higher rates of renters and older adults consistently face higher risk of isolation. These insights point to significant inequity in the burdens associated with SLR.
Development of a Fatigue Life Assessment Model for Pairing Fatigue Damage Prognoses with Bridge Management Systems
(IntechOpen, 2018-12-18) Saad, Timothy; Fu, Chung C.; Zhao, Gengwen; Xu, Chaoran
Fatigue damage is one of the primary safety concerns for steel bridges reaching the end of their design life. Currently, US federal requirements mandate regular inspection of steel bridges for fatigue cracks; however, these inspections rely on visual inspection, which is subjective to the inspector’s physically inherent limitations. Structural health monitoring (SHM) can be implemented on bridges to collect data between inspection intervals and gather supplementary information on the bridges’ response to loads. Combining SHM with finite element analyses, this paper integrates two analysis methods to assess fatigue damage in the crack initiation and crack propagation periods of fatigue life. The crack initiation period is evaluated using S-N curves, a process that is currently used by the FHWA and AASHTO to assess fatigue damage. The crack propagation period is evaluated with linear elastic fracture mechanic-based finite element models, which have been widely used to predict steady-state crack growth behavior. Ultimately, the presented approach will determine the fatigue damage prognoses of steel bridge elements and damage prognoses are integrated with current condition state classifications used in bridge management systems. A case study is presented to demonstrate how this approach can be used to assess fatigue damage on an existing steel bridge.
Does federal flood hazard mitigation assistance affect community rating system participation?
(Wiley, 2022-09-13) Frimpong, Eugene; Reilly, Allison C.; Niemeier, Deb
With the inexorable march of climate change, increased flooding is inevitable. Understanding the feedback between federal flood mitigation policies and the ways in which local governments build flood resilience is a significant gap in the literature. In particular, the effect that federal flood mitigation grants have on the intensity of local flood mitigation is nonexistent. This work measures flood risk mitigation by using the level of participation in FEMA's Community Rating System (CRS). Communities that participate in the CRS and undertake mitigation are awarded points; more points imply a higher level of participation. Since its inception in 1990, CRS communities have received considerably more federal pre-disaster flood mitigation grants compared to non-CRS communities. This study assesses the effect of federal pre-disaster flood mitigation grants on the level of participation in the CRS program. We use data on Hazard Mitigation Assistance programs and CRS participation data between 2010 and 2015. We link these data to flood risk and socioeconomic information. Our results indicate (i) federal pre-disaster flood mitigation grants do not appear to significantly influence the level of CRS participation, (ii) the effect of flood risk and socioeconomic factors on the level of CRS participation are mixed, and (iii) the current level of CRS participation is influenced by the previous level of CRS participation, which is not tied to federal pre-disaster flood mitigation grant. These findings add to the growing discussions on the drivers and barriers of local flood risk mitigation.
Elimination of Bloodstream Infections Associated with Candida albicans Biofilm in Intravascular Catheters
(MDPI, 2015-06-29) Akbari, Freshta; Kjellerup, Birthe Veno
Intravascular catheters are among the most commonly inserted medical devices and they are known to cause a large number of catheter related bloodstream infections (BSIs). Biofilms are associated with many chronic infections due to the aggregation of microorganisms. One of these organisms is the fungus Candida albicans. It has shown to be one of the leading causes of catheter-related BSIs. The presence of biofilm on intravascular catheters provide increased tolerance against antimicrobial treatments, thus alternative treatment strategies are sought. Traditionally, many strategies, such as application of combined antimicrobials, addition of antifungals, and removal of catheters, have been practiced, but they were not successful in eradicating BSIs. Since these fungal infections can result in significant morbidity, mortality, and increased healthcare cost, other promising preventive strategies, including antimicrobial lock therapy, chelating agents, alcohol, and biofilm disruptors, have been applied. In this review, current success and failure of these new approaches, and a comparison with the previous strategies are discussed in order to understand which preventative treatment is the most effective in controlling the catheter-related BSIs.
Ensuring the continued success of a mulch biowall at a trichloroethylene-contaminated superfund site: Lessons learned
(Wiley, 2023-08-03) Ghandehari, Shahrzad Saffari; Cheng, Shih-Huai; Hapeman, Cathleen J.; Torrents, Alba; Kjellerup, Birthe Veno
Trichloroethylene (TCE) is a toxic organic compound, which can adversely affect human health. The chemical is one of the most frequently found contaminants in groundwater in the United States and around the world. A landfill in Maryland contaminated with high levels of TCE decades ago was added to the U.S. Environmental Protection Agency's National Priority List (NPL) in 1994. A biowall was installed on the site in 2013 to promote the bioremediation of TCE and subsequently of its degradation products. Six-year monitoring data indicated a steady removal of >99% groundwater TCE at the wall since installation. However, a concurrent buildup of intermediate byproducts was observed downgradient of the wall. An examination of the entire system was necessary to find the reason behind the inefficiency of the biowall. In this study, the background of the site, remediation plan, and installation were assessed. Monitoring data, including the concentration of TCE and its degradation byproducts, and geochemical and physical characteristics were evaluated to understand the conditions and challenges facing decision-makers of this project and possible options to improve biowall efficacy.
Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines
(2018-04-16) Xue, Yuan; Forman, Barton; Reichle, Rolf; Forman, Barton
To estimate snow mass across North America, multi-frequency brightness temperature (Tb) observations collected by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) from 2002 to 2011 were assimilated into the Catchment land surface model using a support vector machine (SVM) as the observation operator as part of a one-dimensional ensemble Kalman filter. The performance of the assimilation system is evaluated through comparisons against ground-based measurements and publicly-available reference SWE and snow depth products. Assimilation estimates agree better with ground-based snow depth measurements than model-only (“open loop”, or OL) estimates in approximately 82% (56 out of 62) of pixels that are colocated with at least two ground-based stations. In addition, assimilation estimates tend to agree better with all snow products over tundra snow, alpine snow, maritime snow, as well as sparsely-vegetated snow-covered pixels. Improvements in snow mass via assimilation translate into improvements in cumulative runoff estimates when compared against discharge measurements in 11 out of 13 major snow-dominated basins in Alaska. These results prove that a SVM can serve as an efficient and effective observation operator for snow mass estimation within a radiance assimilation system.
Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature Using Aquarius Observations over Non-Frozen Land across North America
(MDPI, 2020-09-22) Park, Jongmin; Forman, Barton A.; Reichle, Rolf H.; De Lannoy, Gabrielle; Tarik, Saad B.
L-band brightness temperature (𝑇𝑏) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in 𝑇𝑏 observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, 𝑇𝑏 estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily 𝑇𝑏 observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed 𝑇𝑏 were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed 𝑇𝑏 with a relatively low, domain-average bias (0.79 K (ascending) and −2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled 𝑇𝑏 due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.
Exploring the Spatiotemporal Coverage of Terrestrial Snow Mass Using a Suite of Satellite Constellation Configurations
(MDPI, 2022-01-28) Wang, Lizhao; Forman, Barton A.; Kim, Edward
Terrestrial snow is a vital freshwater resource for more than 1 billion people. Remotely-sensed snow observations can be used to retrieve snow mass or integrated into a snow model estimate; however, optimally leveraging remote sensing observations of snow is challenging. One reason is that no single sensor can accurately measure all types of snow because each type of sensor has its own unique limitations. Another reason is that remote sensing data is inherently discontinuous across time and space, and that the revisit cycle of remote sensing observations may not meet the requirements of a given snow applications. In order to quantify the feasible availability of remotely-sensed observations across space and time, this study simulates the sensor coverage for a suite of hypothetical snow sensors as a function of different orbital configurations and sensor properties. The information gleaned from this analysis coupled with a dynamic snow binary map is used to evaluate the efficiency of a single sensor (or constellation) to observe terrestrial snow on a global scale. The results show the efficacy achievable by different sensors over different snow types. The combination of different orbital and sensor configurations is explored to requirements of remote sensing missions that have 1-day, 3-day, or 30-day repeat intervals. The simulation results suggest that 1100 km, 550 km, and 200 km are the minimum required swath width for a polar-orbiting sensor to meet snow-related applications demanding a 1-day, 3-day, and 30-day repeat cycles, respectively. The results of this paper provide valuable input for the planning of a future global snow mission.
Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia
(MDPI, 2019-09-28) Kwon, Yonghwan; Forman, Barton A.; Ahmad, Jawairia A.; Kumar, Sujay V.; Yoon, Yeosang
This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.
Facility design and worker justice: COVID-19 transmission in meatpacking plants
(Wiley, 2023-06-17) Lou, Jiehong; Borjigin, Sachraa; Tang, Connie; Saadat, Yalda; Hu, Ming; Niemeier, Deb A.
Background Meatpacking plants were major sources of COVID-19 outbreaks, posing unprecedented risks to employees, family members, and local communities. The effect on food availability during outbreaks was immediate and staggering: within 2 months, the price of beef increased by almost 7% with documented evidence of significant meat shortages. Meatpacking plant designs, in general, optimize on production; this design approach constrains the ability to enhance worker respiratory protection without reducing output. Methods Using agent-based modeling, we simulate the spread of COVID-19 within a typical meatpacking plant design under varying levels of mitigation measures, including combinations of social distancing and masking interventions. Results Simulations show an average infection rate of close to 99% with no mitigation, 99% with the policies that US companies ultimately adopted, 81% infected with the combination of surgical masks and distancing policies, and 71% infected with N95 masks and distancing. Estimated infection rates were high, reflecting the duration and exertion of the processing activities and lack of fresh airflow in an enclosed space. Conclusion Our results are consistent with anecdotal findings in a recent congressional report, and are much higher than US industry has reported. Our results suggest current processing plant designs made rapid transmission of the virus during the pandemic's early days almost inevitable, and implemented worker protections during COVID-19 did not significantly affect the spread of the virus. We argue current federal policies and regulations are insufficient to ensure the health and safety of workers, creating a justice issue, and jeopardizing food availability in a future pandemic.
Fatigue Assessment of Highway Bridges under Traffic Loading Using Microscopic Traffic Simulation
(IntechOpen, 2018-11-13) Zhao, Gengwen; Fu, Chung C.; Lu, Yang; Saad, Timothy
Fatigue is a common failure mode of steel bridges induced by truck traffic. Despite the deterioration caused by environmental factors, the increasing truck traffic volume and weight pose a premier threat to steel highway bridges. Given the uncertainties of the complicated traffic loading and the complexity of the bridge structure, fatigue evaluation based on field measurements under actual traffic flow is recommended. As the quality and the quantity of the available long-term traffic monitoring data and information have been improved, methodologies have been developed to obtain more realistic vehicular live load traffic. A case study of a steel interstate highway bridge using microscopic traffic simulation is presented herein. The knowledge of actual traffic loading may reduce the uncertainty involved in the evaluation of the load-carrying capacity, estimation of the rate of deterioration, and prediction of remaining fatigue life. This chapter demonstrates a systematic approach using traffic simulation and bridge health monitoring-based fatigue assessment.
Health Impacts of the Built and Social Environments, and Travel Behavior: The Case of the Sunshine State
(MDPI, 2022-07-26) Mahmoudi, Jina; Zhang, Lei
As physical inactivity statistics for the U.S. population show an alarming trend, many health problems have been increasing among Americans in recent decades. Thus, identification of the factors that influence people’s physical activity levels and health outcomes has become ever more essential to promote public health. The built envSFironment is among the main factors that impact individuals’ health outcomes. However, little is known about the health impacts of built environment factors at large geographical scales such as those of the metropolitan area of residence. Further, the health impacts of travel behavior such as telecommuting and teleshopping remain unclear. This study uses an ecological model framework to probe the roles of travel behavior and built as well as social environments at different spatial levels in health. Instrumental variable binary probit models have been developed to examine the complex interlinks between measures of travel behavior, physical activity levels, built and social environment characteristics, and individuals’ health outcomes. Findings indicate that built and social environment factors at different spatial levels, including the metropolitan area, are correlated with individuals’ health outcomes. Additionally, the findings suggest that increased levels of telecommuting and teleshopping within communities may lead to unfavorable health outcomes. The findings shed light on the most promising policy interventions that can promote public health through modifications targeting people’s travel choices as well as the built and social environments within urban areas.

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