Air Change Rate and SARS-CoV-2 Exposure in Hospitals and Residences: A Meta-Analysis.

As SARS-CoV-2 swept across the globe, increased ventilation and implementation of air cleaning were emphasized by the US CDC and WHO as important strategies to reduce the risk of inhalation exposure to the virus. To assess whether higher ventilation and air cleaning rates lead to lower exposure risk to SARS-CoV-2, 1274 manuscripts published between April 2020 and September 2022 were screened using key words "airborne SARS-CoV-2 or "SARS-CoV-2 aerosol". Ninety-three studies involved air sampling at locations with known sources (hospitals and residences) were selected and associated data were compiled. Two metrics were used to assess exposure risk: SARS-CoV-2 concentration and SARS-CoV-2 detection rate in air samples. Locations were categorized by type (hospital or residence) and proximity to the sampling location housing the isolated/quarantined patient (primary or secondary). The results showed that hospital wards had lower airborne virus concentrations than residential isolation rooms. A negative correlation was found between airborne virus concentrations in primary-occupancy areas and air changes per hour (ACH). In hospital settings, sample positivity rates were significantly reduced in secondary-occupancy areas compared to primary-occupancy areas, but they were similar across sampling locations in residential settings. ACH and sample positivity rates were negatively correlated, though the effect was diminished when ACH values exceeded 8. While limitations associated with diverse sampling protocols exist, data considered by this meta-analysis support the notion that higher ACH may reduce exposure risks to the virus in ambient air.

An Open-Source Platform for Indoor Environment Monitoring with Participatory Comfort Sensing.

We present an open-source wireless network and data management system for collecting and storing indoor environmental measurements and perceived comfort via participatory sensing in commercial buildings. The system, called a personal comfort and indoor environment measurement (PCIEM) platform, consists of several devices placed in office occupants' work areas, a wireless network, and a remote database to store the data. Each device, called a PCFN (personal comfort feedback node), contains a touchscreen through which the occupant can provide feedback on their perceived comfort on-demand, and several sensors to collect environmental data. The platform is designed to be part of an indoor climate control system that can enable personalized comfort control in real-time. We describe the design, prototyping, and initial deployment of a small number of PCFNs in a commercial building. We also provide lessons learned from these steps. Application of the data collected from the PCFNs for modeling and real-time control will be reported in future work. We use hardware components that are commercial and off-the-shelf, and our software design is based on open-source tools that are freely and publicly available to enable repeatability.

Impact of the COVID-19 Pandemic on the U.S. Electricity Demand and Supply: An Early View From Data.

After the onset of the recent COVID-19 pandemic, a number of studies reported on possible changes in electricity consumption trends. The overall theme of these reports was that "electricity use has decreased during the pandemic, but the power grid is still reliable"-mostly due to reduced economic activity. In this paper, we analyze electricity data until the end of May 2020, examining both electricity demand and variables that can indicate stress on the power grid. We limit this study to three states in the U.S. California, Florida and New York. The results indicate that the effect of the pandemic on electricity demand is not a simple reduction, and there are noticeable differences among regions analyzed. The variables that can indicate stress on the grid (e.g., daily peak and trough of the hourly demand, demand ramp rate, demand forecast error, and net electricity interchange) also conveyed mixed messages: some indicate an increase in stress, some indicate a decrease, and some do not indicate any clear difference. A positive message is that some of the changes that were observed around the time stay-at-home orders were issued appeared to revert back by May 2020. A key challenge in ascribing any observed change to the pandemic is correcting for weather as it can be challenging to accurately define it for large geographic regions. We provide a weather-correction method, apply it to a small city-wide area in North Central Florida, and discuss the implications of the estimated changes in demand. The results indicate that a 10% (95% CI [2%, 18%]) increase in electricity demand is likely to have occurred due to COVID-19 for the city analyzed.

Prior storm experience moderates water surge perception and risk.

BACKGROUND: How accurately do people perceive extreme water speeds and how does their perception affect perceived risk? Prior research has focused on the characteristics of moving water that can reduce human stability or balance. The current research presents the first experiment on people's perceptions of risk and moving water at different speeds and depths. METHODS: Using a randomized within-person 2 (water depth: 0.45, 0.90 m) ×3 (water speed: 0.4, 0.8, 1.2 m/s) experiment, we immersed 76 people in moving water and asked them to estimate water speed and the risk they felt. RESULTS: Multilevel modeling showed that people increasingly overestimated water speeds as actual water speeds increased or as water depth increased. Water speed perceptions mediated the direct positive relationship between actual water speeds and perceptions of risk; the faster the moving water, the greater the perceived risk. Participants' prior experience with rip currents and tropical cyclones moderated the strength of the actual-perceived water speed relationship; consequently, mediation was stronger for people who had experienced no rip currents or fewer storms. CONCLUSIONS: These findings provide a clearer understanding of water speed and risk perception, which may help communicate the risks associated with anticipated floods and tropical cyclones.

Wind speed perception and risk.

BACKGROUND: How accurately do people perceive extreme wind speeds and how does that perception affect the perceived risk? Prior research on human-wind interaction has focused on comfort levels in urban settings or knock-down thresholds. No systematic experimental research has attempted to assess people's ability to estimate extreme wind speeds and perceptions of their associated risks. METHOD: We exposed 76 people to 10, 20, 30, 40, 50, and 60 mph (4.5, 8.9, 13.4, 17.9, 22.3, and 26.8 m/s) winds in randomized orders and asked them to estimate wind speed and the corresponding risk they felt. RESULTS: Multilevel modeling showed that people were accurate at lower wind speeds but overestimated wind speeds at higher levels. Wind speed perceptions mediated the direct relationship between actual wind speeds and perceptions of risk (i.e., the greater the perceived wind speed, the greater the perceived risk). The number of tropical cyclones people had experienced moderated the strength of the actual-perceived wind speed relationship; consequently, mediation was stronger for people who had experienced fewer storms. CONCLUSION: These findings provide a clearer understanding of wind and risk perception, which can aid development of public policy solutions toward communicating the severity and risks associated with natural disasters.