Search Results (70)

This study explores the complex interplay between air pollution, the socioeconomic conditions, and the tourism density within Texas’s urban landscapes, focusing on Dallas, Houston, San Antonio, and Austin. Despite extensive research on environmental justice and urban tourism separately, few studies have integrated these fields to examine how tourism development intersects with environmental and socioeconomic disparities at a neighborhood level. This research addresses this gap by employing advanced geospatial analyses and multi-criteria decision analysis to reveal the pronounced clustering of stressed communities on urban peripheries, often removed from tourism’s economic benefits. The study uniquely quantifies the spatial mismatches between tourist hotspots and areas of environmental stress, a dimension often overlooked in the environmental justice literature. Local spatial statistics and cumulative impact analysis uncover statistically significant correlations between high poverty levels and elevated air pollution in specific locales. The results show varying patterns across cities, with Austin presenting the lowest inequality levels and San Antonio exhibiting significant disparities. This granular, neighborhood-centric approach provides novel insights into the tourism–environment–equity nexus, addressing the lack of comprehensive studies linking these factors in rapidly growing Texan metropolitan areas. The findings underscore the critical need for targeted policy interventions and neighborhood-specific approaches in diagnosing urban environmental disparities and crafting equitable urban development policies that consider tourism’s impact on local communities. Full article

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM${ }_{2.5}$ levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM${ }_{2.5}$ concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM${ }_{2.5}$ surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM${ }_{2.5}$ analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM${ }_{2.5}$ to 9 $\mathsf{\mu }$g/m${ }^3$, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM${ }_{2.5}$ distribution across the entire United States for our study period, we found that the entire nation encountered PM${ }_{2.5}$ levels that exceeded 9 $\mathsf{\mu }$g/m${ }^3$ for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 $\mathsf{\mu }$g/m${ }^3$ for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM${ }_{2.5}$ concentrations below 9 $\mathsf{\mu }$g/m${ }^3$. Full article

A user’s devices such as their phone and computer are constantly bombarded by IoT devices and associated applications seeking connection to the user’s devices. These IoT devices may or may not seek explicit user consent, thus leaving the users completely unaware the IoT device is collecting, using, and/or sharing their personal data or, only marginal informed, if the user consented to the connecting IoT device but did not read the associated privacy policies. Privacy policies are intended to inform users of what personally identifiable information (PII) data will be collected about them and the policies about how those PII data will be used and shared. This paper presents novel tools and the underlying algorithms employed by the Personal Privacy Assistant app (UTCID PPA) developed by the University of Texas at Austin Center for Identity to inform users of IoT devices seeking to connect to their devices and to notify those users of potential privacy risks posed by the respective IoT device. The assessment of these privacy risks must deal with the uncertainty associated with sharing the user’s personal data. If privacy risk (R) equals the consequences (C) of an incident (i.e., personal data exposure) multiplied by the probability (P) of those consequences occurring (C × P), then efforts to control risks must seek to reduce the possible consequences of an incident as well as reduce the uncertainty of the incident and its consequences occurring. This research classifies risk according to two parameters: expected value of the incident’s consequences and uncertainty (entropy) of those consequences. This research calculates the entropy of the privacy incident consequences by evaluating: (1) the data sharing policies governing the IoT resource and (2) the type of personal data exposed. The data sharing policies of an IoT resource are scored by the UTCID PrivacyCheck^™, which uses machine learning to read and score the IoT resource privacy policies against metrics set forth by best practices and international regulations. The UTCID Identity Ecosystem uses empirical identity theft and fraud cases to assess the entropy of privacy incident consequences involving a specific type of personal data, such as name, address, Social Security number, fingerprint, and user location. By understanding the entropy of a privacy incident posed by a given IoT resource seeking to connect to a user’s device, UTCID PPA offers actionable recommendations enhancing the user’s control over IoT connections, interactions, their personal data, and, ultimately, user-centric privacy control. Full article

When droughts occur, freshwater inputs to coastal wetlands can become scarce and hypersalinity can become a problem. In 2023, a severe drought negatively affected a Texas watershed known as Lake Austin that fed a large expanse of wetlands on East Matagorda Bay. To study the hypersalinity problem in these wetlands, we identified freshwater inflows and mapped vegetation changes over time. We found that from 1943 to 2023, the upper portion of the Lake Austin watershed lost freshwater wetlands to agricultural conversion, and ranged from fresh to brackish, with salinity rapidly rising to a maximum of 31 mS during the summer drought of 2023. The lower portion of the watershed gained saltwater wetlands due to sea level rise, and marshes became hypersaline (64–96 mS) during the 2023 drought, endangering its biota. But after large precipitation events, the entire Lake Austin basin rapidly freshened but then returned to its normal salinities within a week as the tides re-delivered saltwater into its basin. Given current climatic trends, we expect that freshwater inflow will continue to slightly increase for the Lake Austin watershed but also that there will be more extreme periods of episodic drought that negatively affect its wetlands. Accordingly, we assessed several potential restoration actions that would improve freshwater flow and delivery to the Lake Austin coastal wetlands. Full article

A computational fluid dynamics (CFD) model was coupled with an advanced statistical strategy combining the response surface method (RSM) and the propagation of error (PoE) approach to optimize and test the robustness of the co-firing of ammonia (NH₃) and coal in a fluidized bed reactor for coal phase-out processes. The CFD model was validated under experimental results collected from a pilot fluidized bed reactor. A 3^k full factorial design of nine computer simulations was performed using air staging and NH₃ co-firing ratio as input factors. The selected responses were NO, NH₃ and CO₂ emissions generation. The findings were that the design of experiments (DoE) method allowed for determining the best operating conditions to achieve optimal operation. The optimization process identified the best-operating conditions to reach stable operation while minimizing harmful emissions. Through the implementation of desirability function and robustness, the optimal operating conditions that set the optimized responses for single optimization showed not to always imply the most stable set of values to operate the system. Robust operating conditions showed that maximum performance was attained at high air staging levels (around 40%) and through a balanced NH₃ co-firing ratio (around 30%). The results of the combined multi-optimization process performance should provide engineers, researchers and professionals the ability to make smarter decisions in both pilot and industrial environments for emissions reduction for decarbonization in energy production processes. Full article

This study examines urban fire incidents in Austin, Texas using machine learning (Random Forest) and time series (Autoregressive integrated moving average, ARIMA) methods for predictive modeling. Based on a dataset from the City of Austin Fire Department, it addresses the effectiveness of these models in predicting fire occurrences and the influence of fire types and urban district characteristics on predictions. The findings indicate that ARIMA models generally excel in predicting most fire types, except for auto fires. Additionally, the results highlight the significant differences in model performance across urban districts, indicating an impact of local features on fire incidence prediction. The research offers insights into temporal patterns of specific fire types, which can provide useful input to urban planning and public safety strategies in rapidly developing cities. In addition, the findings also emphasize the need for tailored predictive models, based on local dynamics and the distinct nature of fire incidents. Full article

This article describes the development and public performances of La Liga de la Decencia, a new play presented as part of the 2023 New Works Festival at the University of Texas at Austin. Inspired by the cabaret scene and teatro de revista of the 1940s in Mexico City, La Liga de la Decencia combines live performance and video art to explore how hegemonic gender and social norms shaped by the emergent nationalism of postrevolutionary Mexico continue to oppress femme and queer bodies today across the US–Mexico border. Through satire, parody, and dance, La Liga de la Decencia problematizes the social, class, and gender norms as established by the cultural elite and the state. Following research-based theatre as an inquiry process, this article describes how writing and directing this play allowed for a deeper understanding of the dynamics of a historical period. By mixing facts, fiction, and critical commentary, La Liga de la Decencia investigates history through embodiment. Full article

This study explores the socioeconomic disparities observed in the early adoption of Electric Vehicles (EVs) in the United States. A multiagent deep reinforcement learning-based policy simulator was developed to address the disparities. The model, tested using data from Austin, Texas, indicates that neighborhoods with higher incomes and a predominantly White demographic are leading in EV adoption. To help low-income communities keep pace, we introduced tiered subsidies and incrementally increased their amounts. In our environment, with the reward and policy design implemented, the adoption gap began to narrow when the incentive was equivalent to an increase in promotion from 20% to 30%. Our study’s framework provides a new means for testing policy scenarios to promote equitable EV adoption. We encourage future studies to extend our foundational study by adding specifications. Full article

Cyanobacterial harmful algal proliferations (cyanoHAPs) are increasingly associated with dog and livestock deaths when benthic mats break free of their substrate and float to the surface. Fatalities have been linked to neurotoxicosis from anatoxins, potent alkaloids produced by certain genera of filamentous cyanobacteria. After numerous reports of dog illnesses and deaths at a popular recreation site on Lady Bird Lake, Austin, Texas in late summer 2019, water and floating mat samples were collected from several sites along the reservoir. Water quality parameters were measured and mat samples were maintained for algal isolation and DNA identification. Samples were also analyzed for cyanobacterial toxins using LC-MS. Dihydroanatoxin-a was detected in mat materials from two of the four sites (0.6–133 ng/g wet weight) while water samples remained toxin-free over the course of the sampling period; no other cyanobacterial toxins were detected. DNA sequencing analysis of cyanobacterial isolates yielded a total of 11 genera, including Geitlerinema, Tyconema, Pseudanabaena, and Phormidium/Microcoleus, taxa known to produce anatoxins, including dihydroanatoxin, among other cyanotoxins. Analyses indicate that low daily upriver dam discharge, higher TP and NO₃ concentrations, and day of the year were the main parameters associated with the presence of toxic floating cyanobacterial mats. Full article

In 2017, Bureau of Economic Geology (BEG) researchers at the University of Texas at Austin (UT Austin) conducted an airborne lidar survey campaign, collecting topographic and bathymetric data over Lower Laguna Madre, which is a shallow hypersaline lagoon in south Texas. Researchers acquired 60 hours of lidar data, covering an area of 1600 km² with varying environmental conditions influencing water quality and surface heights. In the southernmost parts of the lagoon, in-situ measurements were collected from a boat to quantify turbidity, water transparency, and depths. Data analysis included processing of Sentinel-2 L1C satellite imagery pixel reflectance to classify locations with intermittent turbidity. Lidar measurements were compared to sonar recordings, and results revealed height differences of 5–25 cm where the lagoon was shallower than 3.35 m. Further, researchers analyzed satellite bathymetry at relatively transparent lagoon locations, and the results produced height agreement within 13 cm. The study concluded that bathymetric efforts with airborne lidar and optical satellite imaging have practical limitations and comparable results in large and dynamic shallow coastal estuaries, where in-situ measurements and tide adjustments are essential for height comparisons. Full article

Austin, Texas emerged as one of the fastest-growing cities in the U.S. over the past decade. Urban transformation has exacerbated inequities and reduced ethnic/racial diversity among communities. This qualitative study focused on housing insecurity and other syndemic factors among Black and Latina cisgender women (BLCW). Data collection from 18 BLCW using in-depth interviews guided by syndemic theory was conducted three times over three months between 2018 and 2019. Four housing insecurity categories emerged: (a) very unstable, (b) unstable, (c) stable substandard, and (d) stable costly. Participants who experienced more stable housing, particularly more stable housing across interviews, reported fewer instances of intimate partner violence (IPV), less substance use, and a reduced risk of acquiring HIV. Results identified the importance of exploring housing insecurity with other syndemic factors among BLCW along with determining structural- and multi-level interventions to improve housing circumstances and other syndemic factors. Future research should explore these factors in other geographic locations, among other intersectional communities, and among larger sample sizes and consider using a mixed methods approach. Full article

Urban vacancies have been a concern for neighborhood distress and economic decline and have gained more recent attention as potential green infrastructure is known to benefit communities in diverse ways. To investigate this, this study looked into the relationship between land cover, natural environment, and urban vacancies in Austin, Texas. Additionally, we investigated the spatial patterns of green infrastructure and urban vacancies by different income groups to see if low income communities would potentially lack the benefits of green infrastructure. To measure green infrastructure, we used different land covers such as forests and shrublands, as well as natural environments such as tree canopies and vegetation richness, using remote sensing data. Urban vacancy information was retrieved from the USPS vacant addresses and parcel land uses. Through a series of multivariate analyses examining green infrastructure variables one by one, the study results indicate that green infrastructure interacts with residential and business vacancies differently. Additionally, low-income communities lack green infrastructure compared with the rest of the city and are exposed to more urban vacancies in their neighborhoods. Further study is required to understand the dynamics of vacancies in underserved communities and examine how existing vacant land can benefit the communities as ecological resources. Full article

This paper aims to investigate the following research questions: (1) what are the hourly patterns of heat index and heat-related emergency medical service (EMS) incidents during summertime?; and (2) how do the lagged effects of heat intensity and hourly excess heat (HEH) vary by heat-related symptoms? Using the hourly weather and heat-related EMS call data in Austin-Travis County, Texas, this paper reveals the relationship between heat index patterns on an hourly basis and heat-related health issues and evaluates the immediate health effects of extreme heat events by utilizing a distributed lag non-linear model (DLNM). Delving into the heat index intensity and HEH, our findings suggest that higher heat intensity has immediate, short-term lagged effects on all causes of heat-related EMS incidents, including in cardiovascular, respiratory, neurological, and non-severe cases, while its relative risk (RR) varies by time. HEH also shows a short-term cumulative lagged effect within 5 h in all-cause, cardiovascular, and non-severe symptoms, while there are no statistically significant RRs found for respiratory and neurological cases in the short term. Our findings could be a reference for policymakers when devoting resources, developing extreme heat warning standards, and optimizing local EMS services, providing data-driven evidence for the effective deployment of ambulances. Full article

Climate change, socioeconomic development, and irrigation management are exacerbating water scarcity in many regions worldwide. However, current global-scale modeling approaches used to evaluate the impact of these factors on water resources are limited by coarse resolution and simplified representation of local socioeconomic and agricultural systems, which hinders their use for regional decision making. Here, we upgraded the irrigation water use simulation in the system dynamics and water environmental model (SyDWEM) and integrated it with the water supply stress index (WaSSI) ecosystem services model. This integrated model (SyDWEM-WaSSI) simulated local socioeconomic and agricultural systems to accurately assess future water stress associated with climate change, socioeconomic development, and agricultural management at subbasin levels. We calibrated the integrated model and applied it to assess future water stress levels in Texas from 2015 to 2050. The water stress index (WSI), defined as the ratio of water withdrawal to availability, was used to indicate different water stress levels. Our results showed that the integrated model captured changes in water demand across various sectors and the impact of climate change on water supply. Projected high water stress areas (WSI > 0.4) are expected to increase significantly by 2050, particularly in the Texas High Plains and Rolling Plains regions, where irrigation water use was projected to rise due to the impact of climate change. Metropolitan areas, including Dallas, Houston, Austin, and San Antonio, were also expected to experience increased domestic water demand, further exacerbating water stress in these areas. Our study highlights the need to incorporate socioeconomic planning into water resources management. The integrated model is a valuable tool for decisionmakers and stakeholders to evaluate the impacts of climate change, socioeconomic development, and irrigation management on water resources at the local scale. Full article

Computer simulation models are a useful tool in planning, enabling reliable yet affordable what-if scenario analysis. Many simulation models have been proposed and used for urban planning and management. Still, there are a few modeling options available for the purpose of evaluating the effects of various stormwater control measures (SCM), including LID (low-impact development) controls (green roof, rain garden, porous pavement, rainwater harvesting), upland off-line controls (sedimentation, filtration, retention–irrigation) and online controls (detention, wet pond). We explored the utility and potential of the Soil and Water Assessment Tool (SWAT) as a modeling tool for urban stormwater planning and management. This study demonstrates how the hydrologic modeling strategies of SWAT and recent enhancements could help to develop efficient measures for solving urban stormwater issues. The case studies presented in this paper focus on urban watersheds in the City of Austin (COA), TX, where rapid urbanization and population growth have put pressure on the urban stormwater system. Using the enhanced SWAT, COA developed a framework to assess the impacts on erosion, flooding, and aquatic life due to changes in runoff characteristics associated with land use changes. Five catchments in Austin were modeled to test the validity of the SWAT enhancements and the analytical framework. These case studies demonstrate the efficacy of using SWAT and the COA framework to evaluate the impacts of changes in hydrology and the effects of different regulatory schemes. Full article