The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

Municipal solid waste landfills are major contributors to anthropogenic emissions of methane (CH₄), which is the major component of natural gas, a potent greenhouse gas, and a precursor for the formation of tropospheric ozone. The development of sensitive, selective, and fast-response instrumentation allows the deployment of mobile measurement platforms for CH₄ measurements at landfills. The objectives of this study are to use mobile monitoring to measure ambient levels of CH₄ at eight large operating landfills in southeast Michigan, USA; to characterize diurnal, daily and spatial variation in CH₄ levels; and to demonstrate the influence of meteorological factors. Elevated CH₄ levels were typically found along the downwind side or corner of the landfill. Levels peaked in the morning, reaching 38 ppm, and dropped to near-baseline levels during midday. Repeat visits showed that concentrations were highly variable. Some variation was attributable to the landfill size, but both mechanistically-based dilution-type models and multivariate models identified that wind speed, boundary layer height, barometric pressure changes, and landfill temperature were key determinants of CH₄ levels. Collectively, these four factors explained most (r² = 0.89) of the variation in the maximum CH₄ levels at the landfill visited most frequently. The study demonstrates the ability to assess spatial and temporal variation in CH₄ levels at landfills using mobile monitoring along perimeter roads. Such monitoring can identify the location of leaks and the best locations for long-term emission monitoring using fixed site monitors.

Airport taxiway operations refer to the safe and efficient movement of aircraft while they are on the ground. It is now evident that as aviation systems become progressively crowded, mitigation of unnecessary congestion, accidents and costly delays will require new approaches to the scheduling and control of airport taxiway operations. This paper proposes a combined semantic model and reasoning, and real-time planning approach to decision making for safe and efficient airport taxiway operations. A key challenge in this work is finding ways to coordinate the real-time planning with the semantic model knowledge and reasoning capability to produce a system that has good performance, and can dynamically adapt to unexpected events. We exercise the proposed methodology on a planning problem that requires the coordination of multiple planes as they move from the terminal building to the same departure runway.

This paper presents a framework for the semiautomated development of textual requirements for the building construction industry, a domain in which quality of textual requirements has a direct bearing on the avoidance of unnecessary project losses and failures. The proposed framework is novel in three respects. First, it employs a combination of natural language processing (NLP) and template matching to restrict the range of ways in which textual requirements can be stated. Second, knowledge-based models work hand-in-hand with the NLP and templates to help designers fill in the details of a model that might be missing. Third, we envision the proposed approach being part of a multidomain semantic modeling and reasoning framework that includes the building construction domain and its connections to processes for project management and governance. To demonstrate the potential of the proposed approach to the practical development and construction of building systems, we developed a prototype software tool—the framework for linking ontology objects and textual requirements (FLOOR)—and procedures for the validation of individual textual requirements and groups of textual requirements related to construction tasks. This paper presents a new methodology and a prototype software framework for the semiautomated development and validation of textual requirements for the building construction industry. The work is novel in three respects: (1) it combines a natural language processing tool with a sentence-and-template matching technique to limit the ways in which textual requirements can be stated; (2) it employs a multidomain semantic modeling and reasoning framework to provide coverage in the range of textual requirements that can be handled; and (3) it employs a multidomain semantic modeling and reasoning framework that can work with individual requirements and groups of textual requirements. During the early phases of project development, these three factors work together to improve the quality of textual requirements, which in turn leads to improved levels of communication in construction management and avoidance of unnecessary project delays and failures.

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.