The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications.

With the widespread prevalence of mobile devices, ecological momentary assessment (EMA) can be combined with geospatial data acquired through geographic techniques like global positioning system (GPS) and geographic information system. This technique enables the consideration of individuals' health and behavior outcomes of momentary exposures in spatial contexts, mostly referred to as "geographic ecological momentary assessment" or "geographically explicit EMA" (GEMA). However, the definition, scope, methods, and applications of GEMA remain unclear and unconsolidated. To fill this research gap, we conducted a systematic review to synthesize the methodological insights, identify common research interests and applications, and furnish recommendations for future GEMA studies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines to systematically search peer-reviewed studies from six electronic databases in 2022. Screening and eligibility were conducted following inclusion criteria. The risk of bias assessment was performed, and narrative synthesis was presented for all studies. From the initial search of 957 publications, we identified 47 articles included in the review. In public health, GEMA was utilized to measure various outcomes, such as psychological health, physical and physiological health, substance use, social behavior, and physical activity. GEMA serves multiple research purposes: 1) enabling location-based EMA sampling, 2) quantifying participants' mobility patterns, 3) deriving exposure variables, 4) describing spatial patterns of outcome variables, and 5) performing data linkage or triangulation. GEMA has advanced traditional EMA sampling strategies and enabled location-based sampling by detecting location changes and specified geofences. Furthermore, advances in mobile technology have prompted considerations of additional sensor-based data in GEMA. Our results highlight the efficacy and feasibility of GEMA in public health research. Finally, we discuss sampling strategy, data privacy and confidentiality, measurement validity, mobile applications and technologies, and GPS accuracy and missing data in the context of current and future public health research that uses GEMA.

Mental health among U.S. College students: implications of COVID-19 and roles of institutional and individual characteristics.

Previous research on college campus environments, student mental health, and COVID-19 has primarily focused on individual-level factors, with limited attention to the broader institutional characteristics. Objective and Methods: Using the national survey data from the American College Health Association, this study examines the influence of both individual-level and institutional-level characteristics on college students' stress, psychological distress, and psychological well-being, before and during COVID-19. Results: (1) COVID-19 significantly impacted students' mental health; (2) institutional-level factors, such as school size, locale, region, and religiously affiliation, were significant predictors of mental health outcomes; and (3) individual-level variables, including gender, age, race/ethnicity, relationship status, moderated the relationship between COVID-19 and mental health. Conclusion: This study suggests the need to consider various institutional contexts in future efforts to understand predictors of mental health conditions and resilience.

What is the spatiotemporal pattern of benzene concentration spread over susceptible area surrounding the Hartman Park community, Houston, Texas?

The Hartman Park community in Houston, Texas-USA, is in a highly polluted area which poses significant risks to its predominantly Hispanic and lower-income residents. Surrounded by dense clustering of industrial facilities compounds health and safety hazards, exacerbating environmental and social inequalities. Such conditions emphasize the urgent need for environmental measures that focus on investigating ambient air quality. This study estimated benzene, one of the most reported pollutants in Hartman Park, using machine learning-based approaches. Benzene data was collected in residential areas in the neighborhood and analyzed using a combination of five machine-learning algorithms (i.e., XGBR, GBR, LGBMR, CBR, RFR) through a newly developed ensemble learning model. Evaluations on model robustness, overfitting tests, 10-fold cross-validation, internal and stratified validation were performed. We found that the ensemble model depicted about 98.7% spatial variability of benzene (Adj. R2 =0.987). Through rigorous validations, stability of model performance was confirmed. Several predictors that contribute to benzene were identified, including temperature, developed intensity areas, leaking petroleum storage tank, and traffic-related factors. Analyzing spatial patterns, we found high benzene spread over areas near industrial zones as well as in residential areas. Overall, our study area was exposed to high benzene levels and requires extra attention from relevant authorities.

Exploring the association between campus environment of higher education and student health: A systematic review of findings and measures.

Numerous studies have investigated the relationship between the neighborhood environments and residents' health. However, other important settings, such as university campuses, have received little attention. This paper conducted a systematic review and synthesized existing empirical works examining the association between the university/college campuses built and natural environments and students' health. Following the PRISMA guidelines, we searched nine databases using keywords related to higher-education campuses and health-related outcomes. A total of 19 articles were identified, including fifteen cross-sectional studies, three experimental studies, and one longitudinal study. The majority of the studies were conducted in Asian countries and published in the past five years. The findings indicate that active transportation infrastructure, such as increased road intersections and better walkability, were found to be positively associated with students' physical activity. The natural environments, including perceived naturalness, blue space, and greenness was shown to support student's mental health and quality of life. Specifically, blue space was found to be the most preferred place for mental restoration, and scattered trees demonstrated a supportive effect in reducing depression symptoms. Even just viewing virtual trees had a restorative effect and feel less anxiety. Additionally, during the summer, tree shadows were identified as the most important factors for enhancing thermal comfort. This review emphasizes the crucial role of campus environments in promoting college students' health. Future longitudinal studies and investigations using multiple campuses would provide a more comprehensive understanding of this relationship. Such endeavors can contribute to the development of evidence-based strategies for designing and planning healthy campus environments that optimize students' well-being.

The spatial relationship between long-term vacant housing and non-communicable diseases in U.S. shrinking and growing metropolitan areas.

The rising prevalence of non-communicable diseases (NCDs) has led to increased attention on understanding how built environments affect NCD risks. However, there's a significant gap in the literature regarding the relationship between housing vacancy duration and NCDs in metropolitan areas with varying development rates. Our research addresses this gap by examining the association between housing vacancy duration and NCDs across all U.S. metropolitan areas, considering growing, shrinking, and fluctuating counties. We used a Multiscale Geographically Weighted Regression (MGWR) model to analyze this relationship, finding that longer-term vacant housing (over 3 years) is more positively associated with NCDs compared to short-term vacancies. We also discovered that this association is non-uniform across metropolitan counties, except for cancer and stroke outcomes. Shrinking counties in the Northeast are particularly affected, emphasizing the need for targeted public health interventions in these areas. This study underscores the importance of revitalizing vacant homes, especially those vacant for over 3 years, in both shrinking and growing regions to improve public health. Policymakers should adopt tailored strategies, engage public health experts, and invest in healthcare infrastructure to effectively address the health risks linked to vacant housing.

Using neighborhood characteristics to predict vacancy types: Comparing multi-scale conditions surrounding existing vacant lots.

Vacant and abandoned land can be public eyesores that can potentially result in neighborhood distress in the long term. In some cases, the contextual conditions of a neighborhood have been shown to have more of a negative effect on communities than the vacant property itself. Maximum opportunities to actually reuse vacant and abandoned land is known to primarily exist in cases where the surrounding area has locational benefits or when local economic conditions are hopeful. This study examines and compares neighborhood socioeconomic characteristics around vacant lots in Minneapolis, Minnesota, USA, to identify spatial heterogeneity within vacancy types and neighborhood characteristics. Specifically, we examine 1) if the socioeconomic characteristics of a neighborhood can predict existing vacant lots and 2) what neighborhood characteristics are associated with certain vacant lot types. Three logistic regressions were tested with different buffers around each vacant lot, and a total of eighteen regressions were performed to capture the effects on six vacancy types. Results suggest that there are various types of vacancies interacting differently at the neighborhood scale, and that a large-scale neighborhood context matters when predicting vacancy types. The results also indicate three salient points. First, minority populations are a strong predictor of residential and commercial vacancies. Second, high-income areas tend to predict vacancies with potential investment opportunities or vacancies as a part of an existing park or recreational system. Third, vacant properties designated for institutional land uses tend to be found in lower-income areas, yet, not necessarily in areas with high minority populations. Managing and repurposing vacant and abandoned land should be handled more progressively with a better understanding of the socioeconomic characteristics of neighborhoods. Further, examining vacancy types by community can be a way to diagnose potential neighborhood risks associated with vacant and abandoned land.

Twenty years of research on shrinking cities: a focus on keywords and authors.

Most scholarly attention to vanishing cities is fairly recent so, to guide future research, a comprehensive evaluation of prior findings is required. This study is a network analysis of 333 publications authored in English, published over the last two decades. The findings are as follows: (1) shrinking city research has increased significantly since 2016; (2) the key themes are planning, decline, depopulation, policy, regeneration, vacant land, green infrastructure, and case studies such as Detroit; and (3) major academic groups have not yet collaborated effectively on the subject.

Evaluating the impact of proximity to reported toxic release facilities and flood events on chronic health outcomes in the city of Galena Park, Texas.

Evidence has conclusively revealed that environmental justice communities experience poor environmental conditions compared to more affluent majority communities. However, there has been little research evaluating the health impacts of immediate proximity to industrial pursuits and flood events on a population compared to others living within the same community who are only marginally removed from these locations. This cross-sectional study (N = 130) utilized three approaches to assess health outcomes (1) the 12 item Short Form Health Survey, which creates a general physical component score, (2) self-reported noncancerous chronic conditions, and (3) self-reported diagnosis of twelve different cancers. Three risk levels were spatially created using a 5-scale ordinal score for each residential parcel based on the corresponding flood probability level and proximity to facilities which report to the United States Environmental Protection Agencies Toxic Release Inventory. Analysis revealed that general physical health scores were significantly lower (P-value < 0.001) in the medium and high-risk locations, Similarly chronic conditions witnessed a non-significant twofold increased risk in the highest-risk locations compared to the lowest (POR 1.91; 95 % CI 0.82-4.39) and a non-significant increased risk of cancer diagnosis (POR 1.51; 95 % CI 0.38-5.99). This research underscores the importance of place and health outcomes even within relatively geographically compact communities. Public health and urban planning interventions and designs should take into account fine grain approaches to respond to community needs while still being mindful of limited resources.

Quality of Stormwater Infrastructure Systems in Vulnerable Communities: Three Case Studies from Texas.

A properly functioning local stormwater drainage system is essential for mitigating flood risks. This study evaluates the quality of roadside drainage channels in three underserved communities in Texas: the Sunnyside neighborhood in Houston (Harris County), a neighborhood in the City of Rockport (Aransas County), and the Hoehn colonia (Hidalgo County). These communities have a history of flooding, are highly socially vulnerable, and rely on roadside ditches as their principal stormwater drainage system for runoff control. Mobile lidar (Light Detection and Ranging) measurements were collected for 6.09 miles of roadside channels in these communities. The raw lidar measurements were processed to evaluate drainage conditions based on the channel's geometric properties, hydraulic capacity, and level of service. The assessment results are linked to a Geographic Information System (GIS) tool for enhanced visualization. Finally, the paper provides insights regarding the quality of stormwater infrastructure in the study communities and discusses their practical implications.

Application of Ion Mobility Spectrometry-Mass Spectrometry for Compositional Characterization and Fingerprinting of a Library of Diverse Crude Oil Samples.

Exposure characterization of crude oils, especially in time-sensitive circumstances such as spills and disasters, is a well-known analytical chemistry challenge. Gas chromatography-mass spectrometry is commonly used for "fingerprinting" and origin tracing in oil spills; however, this method is both time-consuming and lacks the resolving power to separate co-eluting compounds. Recent advances in methodologies to analyze petroleum substances using high-resolution analytical techniques have demonstrated both improved resolving power and higher throughput. One such method, ion mobility spectrometry-mass spectrometry (IMS-MS), is especially promising because it is both rapid and high-throughput, with the ability to discern among highly homologous hydrocarbon molecules. Previous applications of IMS-MS to crude oil analyses included a limited number of samples and did not provide detailed characterization of chemical constituents. We analyzed a diverse library of 195 crude oil samples using IMS-MS and applied a computational workflow to assign molecular formulas to individual features. The oils were from 12 groups based on geographical and geological origins: non-US (1 group), US onshore (3), and US Gulf of Mexico offshore (8). We hypothesized that information acquired through IMS-MS data would provide a more confident grouping and yield additional fingerprint information. Chemical composition data from IMS-MS was used for unsupervised hierarchical clustering, as well as machine learning-based supervised analysis to predict geographic and source rock categories for each sample; the latter also yielded several novel prospective biomarkers for fingerprinting of crude oils. We found that IMS-MS data have complementary advantages for fingerprinting and characterization of diverse crude oils and that proposed polycyclic aromatic hydrocarbon biomarkers can be used for rapid exposure characterization. Environ Toxicol Chem 2023;42:2336-2349. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Calculating the Environmental Impacts of Low-Impact Development Using Long-Term Hydrologic Impact Assessment: A Review of Model Applications.

Low-impact development (LID) is a planning and design strategy that addresses water quality and quantity while providing co-benefits in the urban and suburban landscape. The Long-Term Hydrologic Impact Assessment (L-THIA) model estimates runoff and pollutant loadings using simple inputs of land use, soil type, and climatic data for the watershed-scale analysis of average annual runoff based on curve number analysis. Using Scopus, Web of Science, and Google Scholar, we screened 303 articles that included the search term "L-THIA", identifying 47 where L-THIA was used as the primary research method. After review, articles were categorized on the basis of the primary purpose of the use of L-THIA, including site screening, future scenarios and long-term impacts, site planning and design, economic impacts, model verification and calibration, and broader applications including policy development or flood mitigation. A growing body of research documents the use of L-THIA models across landscapes in applications such as the simulations of pollutant loadings for land use change scenarios and the evaluation of designs and cost-effectiveness. While the existing literature demonstrates that L-THIA models are a useful tool, future directions should include more innovative applications such as intentional community engagement and a focus on equity, climate change impacts, and the return on investment and performance of LID practices to address gaps in knowledge.

COVID-19 and campus users: A longitudinal and place-based study of university mobilities in Texas.

The COVID-19 pandemic has disrupted people's daily routines, including travel behaviors, social interactions, and work-related activities. However, the potential impacts of COVID-19 on the use of campus locations in higher education such as libraries, food courts, sports facilities, and other destinations are still unknown. Focusing on three largest universities in Texas (Texas A&M university, the University of Texas at Austin, and Texas Tech University), this study compares changes in campus destination visitations between pre and post COVID-19 outbreak (2019 Fall and 2021 Fall semesters, respectively) using the mobility data from SafeGraph. It also examines the potential moderation effects of walkable distance (i.e. 1 km) and greenery (i.e. NDVI value). The results presented the significant effects of COVID-19 on decreasing visitations to various campus places. The visitation decreased more significantly for people living within 1 km (defined as a walkable distance) of campus and for the food, eating, and drinking places and the sports, recreation, and sightseeing places. This finding suggests that those living near campus (mostly students) decreased their reliance on campus destinations, especially for eating/drinking and recreation purposes. The level of greeneries at/around campus destinations did not moderate campus visitations after COVID-19. Policy implications on campus health and urban planning were discussed.

Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda.

Urban digital twins (UDTs) have been identified as a potential technology to achieve digital transformative positive urban change through landscape architecture and urban planning. However, how this new technology will influence community resilience and adaptation planning is currently unclear. This article: (1) offers a scoping review of existing studies constructing UDTs, (2) identifies challenges and opportunities of UDT technologies for community adaptation planning, and (3) develops a conceptual framework of UDTs for community infrastructure resilience. This article highlights the need for integrating multi-agent interactions, artificial intelligence, and coupled natural-physical-social systems into a human-centered UDTs framework to improve community infrastructure resilience.

Simulating the Spatial Impacts of a Coastal Barrier in Galveston Island, Texas: A Three-Dimensional Urban Modeling Approach.

Due to its vulnerability to hurricanes, Galveston Island, TX, USA, is exploring the implementation of a coastal surge barrier (also referred to as the "Ike Dike") for protection from severe flood events. This research evaluates the predicted effects that the coastal spine will have across four different storm scenarios, including a Hurricane Ike scenario and 10-year, 100-year, and 500-year storm events with and without a 2.4ft. sea level rise (SLR). To achieve this, we develop a 1:1 ratio, 3-dimensional urban model and ran real-time flood projections using ADCIRC model data with and without the coastal barrier in place. Findings show that inundated area and property damages due to flooding will both significantly decrease if the coastal spine is implemented, with a 36% decrease in the inundated area and $4 billion less in property damage across all storm scenarios, on average. When including SLR, the amount of protection of the Ike Dike diminishes due to flooding from the bay side of the island. While the Ike Dike does appear to offer substantial protection from flooding in the short term, integrating the coastal barrier with other non-structural mechanisms would facilitate more long-term protection when considering SLR.

Integrating ToxPi outputs with ArcGIS Dashboards to identify neighborhood threat levels of contaminant transferal during flood events.

The convergence of flooding and environmental contamination heightens the potential for mobility and transfer of toxic substances. Spatial analytic platforms can help identify the risks of toxic substance release during flooding, but these platforms are not integrated with one another, making data sharing difficult across platforms. Using the Toxics Mobility Inventory for the State of Rhode Island, this paper presents a method which integrates Toxicological Prioritization Index outputs across multiple data visualization platforms. The workflow presented creates an accessible interface for residents and policy makers to monitor the spatial conditions related to toxic substances during floods to better target solutions.

Unseen Risk: Mapping Contamination Hazards to Enhance Risk Perception in Galena Park, Texas.

As extreme weather events have become more frequently observed in recent decades, concerns about exposure to potential flood risk have increased, especially in underserved and socially vulnerable communities. Galena Park, Texas, is a socially vulnerable community that also confronts escalated physical vulnerabilities due to existing flood risks from Buffalo Bayou and the Houston Ship Channel as well as proximity to industrial facilities that emit chemical pollution. To better understand the underlying risks that Galena Park is facing, this research assesses and visualizes the existing contamination hazards associated with the chemical facilities within Galena Park. Through this process, we (1) compute the environmental, health, and physical hazards associated with industrial facilities, (2) spatially geocode the points of contamination sources and flood exposure, and (3) increase awareness of existing risk by visualizing and distributing related information using an ArcGIS Dashboard. The results indicate that there are 169 points of location from 127 industrial facilities, and 24 points were inducing potential chemicals. In total, 126 chemicals have potential physical, health, and environmental hazards. On average, each facility has 2.4 chemicals that could cause potential hazards with a range of zero to 57 chemicals. When examining the specific physical, health, and environmental risks associated with the chemicals, on average each facility has 14.6 types of risks associated with it. This includes, on average, 9.8 types of health hazards, 1.53 physical hazards, and 2.3 environmental hazards per facility. When analyzing the spatial relationship between the chemical exposure and the current flood risk using the Dashboard, it is noticeable that most of the industrial facilities are located in the south of Galena Park, near Buffalo Bayou, where a variety of industrial facilities are clustered. Through this study, we spatially mapped the existing risks in Galena Park that are not readily available to the community and risks that are not currently tangible or visible. The utility of ArcGIS Dashboards affords the opportunity to translate massive databases into digestible knowledge that can be shared and utilized within the community. This study also takes another step toward building community resilience by providing knowledge that can be used to prepare for and respond to disasters. Visualizing unseen risks and promoting awareness can enhance risk perception when supported by scientific knowledge. Further investigation is necessary to enhance preparedness behaviors, identify proper evacuation techniques and routes, and build community networks to comprehensively promote resilience to multi-hazard circumstances.

Associations between vacant urban lands and public health outcomes in growing and shrinking cities.

Vacant urban land, although not officially designated as a green space, often exhibits a semi-wild natural state due to being left open to colonization by nature. Attention to the effects of vacant urban land on human health has increased due to both rising urban vacancy rates and non-communicable diseases (NCDs). However, relationships between many vacant land characteristics (such as vegetation coverage, size, duration, and location) and NCDs have not been comprehensively examined, especially comparing shrinking (depopulating) and growing (populating) cities. This study evaluates St. Louis, MO (shrinking), and Los Angeles, CA (growing) to explore these relationships using ordinary least squares (OLS) interaction analysis with a moderator approach. Results show that associations between vacancy rate, duration, location, and NCDs differ significantly between city types. Vegetation coverage and size are associated with specific NCDs, but there are no differences between city types. Unlike the largely dilapidated vacant lands in the shrinking city, which tend to harm public health, vacant lots in the growing city were more functional green spaces that can, in some cases, even mitigate NCDs. Interestingly, In St. Louis, the shorter the average duration of the vacant land, the greater the risk of NCDs in a shrinking city. This is because vacant land can be contagious to nearby lots if not treated, leading to more newly emerged vacant lands and reducing the average duration of vacant land. In such cases, census tracts with the lower duration of vacant lands in St. Louis tend to be areas facing persistent environmental degradation and high public health threats. Regarding location, vacant lands near industrial areas were linked to negative health outcomes in the Los Angeles (growing), while those near single-family and commercial areas posed higher risks of NCDs in the St Louis (shrinking). The findings aid decision-making for land supply regulation and regeneration as well as urban green space management to promote human health and well-being.

An Adaptive Toolkit for Projecting the Impact of Green Infrastructure Provisions on Stormwater Runoff and Pollutant Load-A Case Study on the City of Galena Park, Texas, USA.

The implementation of green infrastructure in retrofit projects to reduce flooding and pollution is a significant challenge in space- constrained and overly developed communities which also have complex underground utility systems. To overcome this challenge, the authors have developed an adaptive green infrastructure toolkit that can be tailored by both on-ground spatial size and underground depth of obstruction. This study aims to assess the effectiveness of this toolkit in mitigating flooding and non-point source pollutants by demonstrating the case of the city of Galena Park, Texas, USA, which has suffered from severe flooding as well as on-ground and underground space constraint issues. We first applied the toolkit to create a master plan for Galena Park and evaluated the effect of the plan by using the Delft3D-FM (Flexible Mesh) flood model alongside the Long-Term Hydrologic Impact Assessment (L-THIA) model. The results demonstrate progressive reductions in stormwater runoff and NPS pollutants across different phases. These findings highlight the toolkit's effectiveness in improving water management and pollution control, providing valuable empirical evidences for similar communities facing similar challenges.

Multi-Hazard property buyouts: Making a case for the acquisition of flood and contaminant-prone residential properties in Galena Park, TX.

The aftermath of extreme flood events can be particularly devastating for residential communities in proximity to flood-prone petrochemical facilities, as they are likely to experience multiple impacts from a single flood event. Hazard impacts could be from flood inundation to their properties, and floodwaters carrying contaminants from nearby facilities into their homes. While property acquisition or buyouts have been effectively used as a flood mitigation strategy, current buyout selection processes only factor in flood exposure, thereby ignoring other hazards such as exposure of properties to potential chemical substance transfer. In this paper, we identify properties that are eligible for flood buyouts but are also at a high risk of contaminant transferal during extreme flood events. We apply a benefit-cost analysis methodology to assess the economic viability of buyouts and proximity metrics to prioritize buyouts of contaminant-prone properties in Galena Park, Texas. Results indicate that, in selected flood-only property acquisition scenarios, cumulative avoided flood losses exceed the cost of property acquisition. However, although with lower cost-benefit values, a selection criterion that factors a combination of flood and contaminant-prone properties as buyouts results in multiple properties being removed from harm's way. Our findings emphasize the potential economic benefits of applying a multi-hazard selection criterion in dealing with flood property buyouts, especially in socially vulnerable communities with high exposure to both flooding and contaminant transfer.

Modeling the Relationships Between Historical Redlining, Urban Heat, and Heat-Related Emergency Department Visits: An Examination of 11 Texas Cities.

Place-based structural inequalities can have critical implications for the health of vulnerable populations. Historical urban policies, such as redlining, have contributed to current inequalities in exposure to intra-urban heat. However, it is unknown whether these spatial inequalities are associated with disparities in heat-related health outcomes. The aim of this study is to determine the relationships between historical redlining, intra-urban heat conditions, and heat-related emergency department visits using data from eleven Texas cities. At the zip code level, the proportion of historical redlining was determined, and heat exposure was measured using daytime and nighttime land surface temperature (LST). Heat-related inpatient and outpatient rates were calculated based on emergency department visit data that included ten categories of heat-related diseases between 2016 and 2019. Regression or spatial error/lag models revealed significant associations between higher proportions of redlined areas in the neighborhood and higher LST (Coef. = 0.0122, 95% CI = 0.0039 - 0.0205). After adjusting for indicators of social vulnerability, neighborhoods with higher proportions of redlining showed significantly elevated heat-related outpatient visit rate (Coef. = 0.0036, 95% CI = 0.0007-0.0066) and inpatient admission rate (Coef. = 0.0018, 95% CI = 0.0001-0.0035). These results highlight the role of historical discriminatory policies on the disparities of heat-related illness and suggest a need for equity-based urban heat planning and management strategies.