Heat exposure for urban populations has become more prevalent as the temperature and duration of heat waves in cities increase. Occupational exposure to heat is a major concern for personal health, and excessive heat exposure can cause devastating outcomes. While occupational heat exposure studies have traditionally focused on environmental temperature, work intensity, and clothing, little is known about the daily exposure profile of workers, including their daily travel and working patterns. This study developed a novel measure of exposure and reprieve dynamics, the moving average hourly exposure (MAHE) to balance short-duration but high-exposure events and capture the inability to reprieve from exposure events. MAHE was assessed by combining an activity-based travel model (ABM) and the Occupational Requirement Survey to simulate urban workers' total daily heat exposure. The simulation considers daily travel, work schedules, and outdoor working frequency. The simulation was conducted for 1 million workers in Phoenix, Arizona, using Mean Radiant Temperature (MRT). The results show that 53% to 89% of workers in Phoenix's construction, agriculture, transportation, raw material extraction, and entertainment industries will likely experience MAHE over 38°C for at least an hour. These industries also have up to 34% of the laborers exposed to over 7 hours of continuous 38°C and above MAHE exposure. The location of the most intense heat exposure was identified near the downtown and central business districts, significantly different from the home locations of the workers in suburban and rural areas. Formulating the MAHE balances heat risk events with cooling benefits and aids in identifying individuals with prolonged high heat exposure.

Abstract:

Cascading failures across a network propagate localized issues to more broad and potentially unexpected failures in the network. In power networks, where load must be delivered in real-time by a generation source, network layout is an important part of cascading failure analysis. In lieu of real power network data protected for security reasons, we can use synthetic networks for academic purposes in developing a validating methodology. A contingency analysis technique is used to identify cascading failures, and this involves randomly selecting initial failure points in the network and observing how current violations propagate across the network. This process is repeated many times to understand the breadth of potential failures that may occur, and the observed trends in failure propagation are analyzed and compared to generate recommendations to prevent and adapt to failure. Emphasis is placed on power transmission networks where failures can be more catastrophic.