Adequate indoor air quality in nursing homes: An unmet medical need.

A small but growing body of literature indicates that concentrations of indoor particulate and gaseous pollutants in long-term care facilities (i.e., skilled nursing facilities) for older adults, hereafter referred to nursing homes, often exceed those recorded in nearby, comparable outdoor environments. Unlike the outdoors, indoor air quality (IAQ) in nursing homes is not regulated by legislation and is seldom monitored. To that end, residents of nursing homes commonly spend the vast majority of their time indoors where they are exposed to indoor air pollutants for long periods of time. Given that many nursing home residents, especially those of advanced age, are more susceptible to the effects of air pollutants, even at low concentrations, this prolonged exposure may adversely affect their health, well-being, quality of life and increase medical expenditures due to frequent, unscheduled acute care visits and hospitalizations. We propose an action plan for assessing IAQ in nursing homes, understanding the impacts of IAQ on adverse health outcomes of nursing home residents, and addressing vulnerabilities in these facilities to safeguard health, well-being, and quality of life of nursing home residents and minimizing unscheduled acute care visits and hospitalizations. We propose that IAQ should be regularly monitored in nursing homes to proactively identify and address vulnerabilities in these facilities and that resources should be provided for remedial interventions to improve IAQ in nursing homes, including but not limited to source control, improving ventilation and filtration, and deploying air cleaners where appropriate. This proactive approach may pave the way for establishing enforceable standards for indoor air quality in nursing homes that will promote health, well-being, and quality of life of nursing home residents and reduce medical expenditures.

Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns.

Urban areas contribute substantially to human exposure to ambient air pollution. Numerous statistical prediction models have been used to estimate ambient concentrations of fine particulate matter (PM2.5) and other pollutants in urban environments, with some incorporating machine learning (ML) algorithms to improve predictive power. However, many ML approaches for predicting ambient pollutant concentrations to date have used principal component analysis (PCA) with traditional regression algorithms to explore linear correlations between variables and to reduce the dimensionality of the data. Moreover, while most urban air quality prediction models have traditionally incorporated explanatory variables such as meteorological, land use, transportation/mobility, and/or co-pollutant factors, recent research has shown that local emissions from building infrastructure may also be useful factors to consider in estimating urban pollutant concentrations. Here we propose an enhanced ML approach for predicting urban ambient PM2.5 concentrations that hybridizes cascade and PCA methods to reduce the dimensionality of the data-space and explore nonlinear effects between variables. We test the approach using different durations of time series air quality datasets of hourly PM2.5 concentrations from three air quality monitoring sites in different urban neighborhoods in Chicago, IL to explore the influence of dynamic human-related factors, including mobility (i.e., traffic) and building occupancy patterns, on model performance. We test 9 state-of-the-art ML algorithms to find the most effective algorithm for modeling intraurban PM2.5 variations and we explore the relative importance of all sets of factors on intraurban air quality model performance. Results demonstrate that Gaussian-kernel support vector regression (SVR) was the most effective ML algorithm tested, improving accuracy by 118% compared to a traditional multiple linear regression (MLR) approach. Incorporating the enhanced approach with SVR algorithm increased model performance up to 18.4% for yearlong and 98.7% for month-long hourly datasets, respectively. Incorporating assumptions for human occupancy patterns in dominant building typologies resulted in improvements in model performance by between 4% and 37%. Combined, these innovations can be used to improve the performance and accuracy of urban air quality prediction models compared to conventional approaches.

Ventilation and laboratory confirmed acute respiratory infection (ARI) rates in college residence halls in College Park, Maryland.

Strategies to protect building occupants from the risk of acute respiratory infection (ARI) need to consider ventilation for its ability to dilute and remove indoor bioaerosols. Prior studies have described an association of increased self-reported colds and influenza-like symptoms with low ventilation but have not combined rigorous characterization of ventilation with assessment of laboratory confirmed infections. We report a study designed to fill this gap. We followed laboratory confirmed ARI rates and measured CO2 concentrations for four months during the winter-spring of 2018 in two campus residence halls: (1) a high ventilation building (HVB) with a dedicated outdoor air system that supplies 100% of outside air to each dormitory room, and (2) a low ventilation building (LVB) that relies on infiltration as ventilation. We enrolled 11 volunteers for a total of 522 person-days in the HVB and 109 volunteers for 6069 person-days in the LVB, and tested upper-respiratory swabs from symptomatic cases and their close contacts for the presence of 44 pathogens using a molecular assay. We observed one ARI case in the HVB (0.70/person-year) and 47 in the LVB (2.83/person-year). Simultaneously, 154 CO2 sensors distributed primarily in the dormitory rooms collected 668,390 useful data points from over 1 million recorded data points. Average and standard deviation of CO2 concentrations were 1230 ppm and 408 ppm in the HVB, and 1492 ppm and 837 ppm in the LVB, respectively. Importantly, this study developed and calibrated multi-zone models for the HVB with 229 zones and 983 airflow paths, and for the LVB with 529 zones and 1836 airflow paths by using a subset of CO2 data for model calibration. The models were used to calculate ventilation rates in the two buildings and potential for viral aerosol migration between rooms in the LVB. With doors and windows closed, the average ventilation rate was 12 L/s in the HVB dormitory rooms and 4 L/s in the LVB dormitory rooms. As a result, residents had on average 6.6 L/(s person) of outside air in the HVB and 2.3 L/(s person) in the LVB. LVB rooms located at the leeward side of the building had smaller average ventilation rates, as well as a somewhat higher ARI incidence rate and average CO2 concentrations when compared to those values in the rooms located at the windward side of the building. Average ventilation rates in twenty LVB dormitory rooms increased from 2.3 L/s to 7.5 L/s by opening windows, 3.6 L/s by opening doors, and 8.8 L/s by opening both windows and doors. Therefore, opening both windows and doors in the LVB dormitory rooms can increase ventilation rates to the levels comparable to those in the HVB. But it can also have a negative effect on thermal comfort due to low outdoor temperatures. Simulation results identified an aerobiologic pathway from a room occupied by an index case of influenza A to a room occupied by a possible secondary case.

Elemental: An Open-Source Wireless Hardware and Software Platform for Building Energy and Indoor Environmental Monitoring and Control.

This work demonstrates an open-source hardware and software platform for monitoring the performance of buildings, called Elemental, that is designed to provide data on indoor environmental quality, energy usage, HVAC operation, and other factors to its users. It combines: (i) custom printed circuit boards (PCBs) with RFM69 frequency shift keying (FSK) radio frequency (RF) transceivers for wireless sensors, control nodes, and USB gateway, (ii) a Raspberry Pi 3B with custom firmware acting as either a centralized or distributed backhaul, and (iii) a custom dockerized application for the backend called Brood that serves as the director software managing message brokering via Message Queuing Telemetry Transport (MQTT) protocol using VerneMQ, database storage using InfluxDB, and data visualization using Grafana. The platform is built around the idea of a private, secure, and open technology for the built environment. Among its many applications, the platform allows occupants to investigate anomalies in energy usage, environmental quality, and thermal performance via a comprehensive dashboard with rich querying capabilities. It also includes multiple frontends to view and analyze building activity data, which can be used directly in building controls or to provide recommendations on how to increase operational efficiency or improve operating conditions. Here, we demonstrate three distinct applications of the Elemental platform, including: (1) deployment in a research lab for long-term data collection and automated analysis, (2) use as a full-home energy and environmental monitoring solution, and (3) fault and anomaly detection and diagnostics of individual building systems at the zone-level. Through these applications we demonstrate that the platform allows easy and virtually unlimited datalogging, monitoring, and analysis of real-time sensor data with low setup costs. Low-power sensor nodes placed in abundance in a building can also provide precise and immediate fault-detection, allowing for tuning equipment for more efficient operation and faster maintenance during the lifetime of the building.

A radiative cooling structural material.

Reducing human reliance on energy-inefficient cooling methods such as air conditioning would have a large impact on the global energy landscape. By a process of complete delignification and densification of wood, we developed a structural material with a mechanical strength of 404.3 megapascals, more than eight times that of natural wood. The cellulose nanofibers in our engineered material backscatter solar radiation and emit strongly in mid-infrared wavelengths, resulting in continuous subambient cooling during both day and night. We model the potential impact of our cooling wood and find energy savings between 20 and 60%, which is most pronounced in hot and dry climates.

Studying airflow structures in periodic cylindrical hills of human tracheal cartilaginous rings.

The objective of this study is to assess tracheobronchial flow features with the cartilaginous rings during a light exercising. Tracheobronchial is part of human's body airway system that carries oxygen-rich air to human's lungs as well as takes carbon dioxide out of the human's lungs. Consequently, evaluation of the flow structures in tracheobronchial is important to support diagnosis of tracheal disorders. Computational Fluid Dynamics (CFD) allows evaluating effectiveness of tracheal cartilage rings in human body under different configurations. This study utilizes Large Eddy Simulation (LES) to model an anatomically-based human large conducting airway model with and without cartilaginous rings at the breathing conditions at Reynolds number of 5,176 in trachea region. It is observed that small recirculating areas shaped between rings cavities. While these recirculating areas are decaying, similar to periodic 2D-hills, the cartilaginous rings contribute to the construction of a vortical flow structure in the main flow. The separated vortically-shaped zone creates a wake in the flow and passes inside of the next ring cavity and disturb its boundary layer. At last, the small recirculation flow impinges onto tracheal wall. The outcome of this impinge flow is a latitudinal rotating flow perpendicular to the main flow in a cavity between the two cartilaginous rings crest which appear and disappear within a hundredth of a second. Kelvin-Helmholtz instability is observed in trachea caused by shear flow created behind of interaction between these flow structures near to tracheal wavy wall and main flow. A comparison of the results between a smooth wall model named simplified model and a rough wall model named modified model shows that these structures do not exist in simplified model, which is common in modeling tracheobronchial flow. This study proposes to consider macro surface roughness to account for the separating and rotating instantaneous flow structures. Finally, solving trachea airflow with its cartilages can become one of major issues in measuring the validity and capability of solving flow in developing types of sub-grid scale models as a turbulence studies benchmark.

Microbial Exchange via Fomites and Implications for Human Health.

PURPOSE OF REVIEW: Fomites are inanimate objects that become colonized with microbes and serve as potential intermediaries for transmission to/from humans. This review summarizes recent literature on fomite contamination and microbial survival in the built environment, transmission between fomites and humans, and implications for human health. RECENT FINDINGS: Applications of molecular sequencing techniques to analyze microbial samples have increased our understanding of the microbial diversity that exists in the built environment. This growing body of research has established that microbial communities on surfaces include substantial diversity, with considerable dynamics. While many microbial taxa likely die or lay dormant, some organisms survive, including those that are potentially beneficial, benign, or pathogenic. Surface characteristics also influence microbial survival and rates of transfer to and from humans. Recent research has combined experimental data, mechanistic modeling, and epidemiological approaches to shed light on the likely contributors to microbial exchange between fomites and humans and their contributions to adverse (and even potentially beneficial) human health outcomes. SUMMARY: In addition to concerns for fomite transmission of potential pathogens, new analytical tools have uncovered other microbial matters that can be transmitted indirectly via fomites, including entire microbial communities and antibiotic-resistant bacteria. Mathematical models and epidemiological approaches can provide insight on human health implications. However, both are subject to limitations associated with study design, and there is a need to better understand appropriate input model parameters. Fomites remain an important mechanism of transmission of many microbes, along with direct contact and short- and long-range aerosols.