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.

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.

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.

EvtSNN: Event-driven SNN simulator optimized by population and pre-filtering.

Recently, spiking neural networks (SNNs) have been widely studied by researchers due to their biological interpretability and potential application of low power consumption. However, the traditional clock-driven simulators have the problem that the accuracy is limited by the time-step and the lateral inhibition failure. To address this issue, we introduce EvtSNN (Event SNN), a faster SNN event-driven simulator inspired by EDHA (Event-Driven High Accuracy). Two innovations are proposed to accelerate the calculation of event-driven neurons. Firstly, the intermediate results can be reused in population computing without repeated calculations. Secondly, unnecessary peak calculations will be skipped according to a condition. In the MNIST classification task, EvtSNN took 56 s to complete one epoch of unsupervised training and achieved 89.56% accuracy, while EDHA takes 642 s. In the benchmark experiments, the simulation speed of EvtSNN is 2.9-14.0 times that of EDHA under different network scales.

Combining Co$ting Nature and Suitability Modeling to Identify High Flood Risk Areas in Need of Nature-Based Services.

Coastal areas are often subject to the severe consequences of flooding from intense storms or hurricanes. Increases in coastal development have amplified both flooding intensity and negative impacts for coastal communities. Reductions in pervious land cover and replacement with impervious ones have reduced the amount of ecosystem services. This research examines the services provided by nature-based solutions by applying outputs from Co$ting Nature models into suitability models to quantify ecosystem services along the Texas Coast. Results show that only around 13% of the Houston-Galveston coastal area has relatively high NBS, and nearly 1 4 of the area shows relatively low NBS. The majority of the areas lie in the middle, which, due to increases in development, are at particular risk for becoming areas offering low NBS in the future if not treated. Such vulnerability assessment informs future implementation strategies for NBS in coastal communities to protect people and property from flooding.

GROWTH AND SHRINKAGE PRE AND POST TSUNAMI IN FUKUSHIMA PREFECTURE, JAPAN.

Depopulation is a severe problem in many urban areas globally. Massive population migrations can occur due to relocation after natural disasters and significantly change the demographic composition of regions and cities. The 2011 Great Tsunami in Japan resulted in a combined total of deaths and missing persons of more than 24,500. Post-tsunami recovery efforts resulted in widespread population relocation of high-risk communities into lower-risk areas. Using the Fukushima Prefecture in Japan as the study area, a region characterized by several depopulating cities both pre and post-tsunami, this research examines how the population relocation efforts have either exacerbated or assisted in lessening the effects of urban shrinkage and decline after the earthquake and tsunami of 2011. The results show that 30 municipalities have seen population and economic growth since 2011, and 12 municipalities are underdoing trends toward decline within Fukushima. Negatively affected cities tend to have larger populations than positively affected cities. Most of the small towns and villages closer to the inundation area are fall into the category of negatively affected areas. Moreover, the population increases in many post-disaster cities are primarily due to significant increases in elderly populations with minimal young persons that will inevitably decline in the next decade. By determining the effects of their relocation efforts, the government can better develop targeted strategies that good for the prosperity and development of the Fukushima Prefecture.

The Living Green Infrastructure Lab: Advancing Interdisciplinary Teaching and Experiential Learning in Landscape Architecture Pedagogy.

Demonstrating and experimenting interdisciplinary teaching and experiential learning, faculty and students across three colleges (Agriculture and Life Sciences, Architecture and Engineering), and 4 departments (Landscape Architecture and Urban Planning, Horticultural Sciences, and Civil, Biological and Agricultural Engineering) designed, implemented, and are monitoring effects of a rain garden. This collaboration presents a model for multi-scalar, interdisciplinary studio instruction involving a project conducted by over 200 undergraduate and graduate students across allied fields. Landscape Architecture students provided designs, construction details, and performance monitoring of the site as well as developed a large-scaled campus master plan. Horticultural Sciences students propagated and produced the plants. Civil engineers assisted with constructed infrastructure design and water quality/quantity assessment. Professional landscape architects, urban planners, horticulturalists, engineers and campus facilities maintenance personnel evaluated student work. This paper specifies lessons learned from the application of a program that sought to educate and train students in LID alternatives to traditional stormwater management through hands-on outdoor classroom activities. While opportunities for interdisciplinary networking, knowledge of the landscape construction process, and the ability to utilize scientific rationale for design decision making all increased, challenges included coordination efforts across disciplines, overcoming unknown nomenclature specific to each field, delays due to unforeseen circumstances, and budgetary increased as a result of maintenance issues. However, Collaboration between multidisciplinary professionals enabled students to experience the professional design process and have a deeper understanding of the positive impacts of green infrastructure through interdisciplinary experiential learning.