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A sewer system is a principal element of infrastructure in modern cities, accounting for massive amounts of public investments. Corrosion of manholes in the sewer system is a global issue, and millions of dollars are being spent on the maintenance, restoration, and replacement of deteriorated sewer networks. Concrete manholes in the sewer system are deteriorating due to the attack of sulfuric acid produced by microorganisms in a process termed microbial induced concrete corrosion (MICC), which reduces the lifespan of concrete sewer elements. The objective of this paper is to investigate the correlation between the gas- and liquid-phase sewer environmental factors and hydrogen sulfide concentration in the gas phase. The production, emission, and build-up of hydrogen sulfide gas in manholes is identified as a major cause of MICC in manhole shafts. The field study was conducted in more than 200 manholes in the City of Arlington (Texas, US). The data was collected every minute for 48 h to understand the trends of liquid- and gas-phase parameters such as hydrogen sulfide (H2S concentration), liquid and gas temperature, pH, DO, and relative humidity. The study also examines how gas-phase H2S concentrations vary with season; manhole design, including manholes' depth, slope, and presence of drop; and sewer flow conditions such as velocity and turbulence. Although no strong linear correlation was found between liquid-/gas-phase parameters, the manhole categories were found to play a significant role in H2S generation. The manholes with hydraulic jump generated the highest average H2S concentrations, followed by manholes with drops. High turbulence zones were observed in manholes of both categories, leading to H2S stripping from liquid to gas phase. The highest H2S concentration was recorded in summer, suggesting that higher liquid temperature resulted in increased bacterial activity, which generated greater liquid-phase sulfide. Greater Henry's law constants in summer, due to high temperatures, would have favored transfer of liquid-phase sulfide to the gas phase. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to intersection improvement planning utilizing telematics data from connected vehicles to generate performance measures for mobility, safety, and emissions. Congestion, crashes, and emissions are three major issues in urban areas, particularly at intersections, and agencies often struggle to prioritize improvement plans because of a lack of objective data. Traditional infrastructure sensors provide limited information at selected locations, but it is not feasible to deploy them at all intersections. The use of telematics data from connected vehicles provides a high granularity of information on driving events and trajectories that can be used in conjunction with vehicle emission modeling to efficiently generate performance measures for all intersections. In a case study of over 300 intersections in Arlington, Texas, the Pareto front method was used to evaluate and rank intersections based on multiple criteria. Intersections falling on the Pareto front were identified as having at least one outstanding (poor) performance measure and were required to be given priority for improvement. The results were cross-validated with historical crash reports and the judgments of city traffic engineers, demonstrating the effectiveness of the proposed framework in generating objective and reliable intersection performance measures. This approach has the potential to significantly improve intersection safety, mobility, and environmental impact, and can serve as a valuable decision-support tool for transportation agencies. [ABSTRACT FROM AUTHOR]

Cities around the world have amassed a variety of data. A main challenge lies in transforming these big data into meaningful knowledge that can inform a city's strategic decisions and enhance urban sustainability. Along with this challenge is the debate about "Wisdom of Crowds" (WOC) in the Petabyte Age. Using the City of Arlington, Texas as a case study, this research explores a hybrid approach for social inquiries with the aid of WOC and spatial learning techniques to leverage data for developing predictive models to support a city's service planning. The results indicate that there exist temporal and spatial patterns of service demands, spatial correlation between demands for code compliance and water services, as well as association with neighborhood characteristics. The findings point to opportunities for further data integration and data mining, organizational collaboration, and resource management to improve the efficiency of service provision in cities. [ABSTRACT FROM AUTHOR]

With aging and deterioration of underground sewer utilities, there is an urgent need for a framework to track their reliability over expected lifetime to facilitate asset management decisions like inspection, maintenance, or replacement. This study proposes multilevel, multifailure framework model for reliability evaluation of reinforced concrete sewer pipe (RCSP) considering the effect of adverse environmental factors such as chloride and sulfide induced attacks, uncertainties in different properties, seasonal temperature and humidity variations, and cracking effects. Monte-Carlo simulation with Latin hypercube sampling is chosen for stochastic analysis to reduce the computational burdens and enhance the accuracy. The application of this framework is illustrated for a case study of an RCSP that has been in service for more than 35 years. Based on results, it is shown that at serviceability stage, sulfide wall erosion is redundant relative to other serviceability issues; therefore, it cannot be used solely for serviceability evaluation of RCSP, as previously done in the literature. At the ultimate level, the effect of chloride pitting corrosion dominates and expedites failure rate. It is proven that there is a huge potential for reinforcement brittle failure under the effect of pitting corrosion, which can lead to a catastrophic collapse. [ABSTRACT FROM AUTHOR]

Cities around the world are piloting projects to evaluate the feasibility and benefits of shared autonomous vehicles (SAVs), as their large-scale implementation and integration into public transit systems have the potential to improve individuals' accessibility and transportation equity. To understand the full potential of SAVs and their likely adoption, it is important to identify how the services can be utilized most effectively and what determines the composition of the ridership. This research aims to explore the usage and adoption of SAVs, focusing on a project called RAPID (Rideshare, Automation, and Payment Integration Demonstration) that was launched in Arlington, Texas. We used real-time trip-level ridership data from the SAV platform, conducted a survey of SAV riders, and developed a study based on ordered logistic regression to estimate the determinants of ridership frequency. Data analysis of real-time ridership data revealed that spatial distribution of activities and service accessibility have crucial roles in forming the current users' travel patterns. The findings from the logistic regression demonstrated that those with higher household incomes are less likely to be frequent riders of RAPID, while those who usually walk, bike, or utilize on-demand ridesharing services are likely to use SAVs often. Users with higher levels of safety perception are also more likely to be frequent users of the service. The findings of this study will provide planners with a better understanding of SAV ridership patterns and will guide decision-makers nationwide in establishing and adopting policies that will be appropriate for future SAV implementation projects. [ABSTRACT FROM AUTHOR]

Comprehensive smoke-free policy is a strategy to prevent cardiovascular disease (CVD) at a population-level; however, evaluating their long-term outcomes is difficult. This study used an agent-based model to estimate long-term impacts of a comprehensive smoke-free policy, as it was implemented in two communities, Arlington and Mesquite, Texas. The model predicted the percentage of myocardial infarction (MI), stroke, and diabetes in the population 10 and 20 years following policy adoption. In Arlington, the percentage of the population with these conditions each decreased by approximately 0.5% over 20 years; in Mesquite, the percentage of the population with diabetes, myocardial infarction (MI), and stroke decreased by 1.1%, 0.6%, and 0.3%, respectively, after 20 years. The results were statistically significant (p < 0.001). As an evaluation strategy, agent-based modeling can help researchers and practitioners estimate the potential long-term effects of policies and garner intervention support for implementation. [ABSTRACT FROM AUTHOR]

Intermediate wheatgrass (IWG) is a new perennial dual-use crop for grain and forage with growing interest among farmers. Intercropping IWG with red clover may increase yield and nutritive value through nitrogen transfer. IWG and red clover planting timing can affect grain and forage yield, and there has not been previous research on this management practice. At two locations (Arlington and Lancaster, WI, USA) a factorial experiment was established two years with two factors: (1) IWG planting date (August through October, and April) and (2) red clover planting season (in the fall with IWG or frost seeded in the next spring). Yield data were collected for two subsequent years. Grain yield was maximized at 515 kg ha−1 and 423 kg ha−1 at Arlington and Lancaster when planted by 26 August and 13 September, respectively. Planting date influenced grain yields in the first harvest year but not in the second. Seeding red clover in the spring increased IWG and red clover biomass compared to seeding it in the fall. In Wisconsin, planting IWG by early September at the latest and planting red clover in the spring is recommended to maximize grain yield. [ABSTRACT FROM AUTHOR]