Indoor air quality impacts of residential mechanical ventilation system retrofits in existing homes in Chicago, IL

Cleaning technologies integrated in duct flows for the inactivation of pathogenic microorganisms in indoor environments: A critical review of recent innovations and future challenges

Pathogenic microorganisms are a major concern in indoor environments, particularly in sensitive facilities such as hospitals, due to their potential to cause nosocomial infections. This study evaluates the concentration of airborne bacteria and fungi in the University Hospital Complex of Albacete (Spain), comparing the results with recent literature. Staphylococcus is identified as the most prevalent bacterial genus with a percentage distribution of 35%, while Aspergillus represents the dominant fungal genus at 34%. The lack of high Technology Readiness Levels (TRL 6, TRL 7) for effective indoor air purification requires research efforts to bridge this knowledge gap. A screening of disinfection technologies for pathogenic airborne microorganisms such as bacteria and fungi is conducted. The integration of filtration, irradiation or and (electro)chemical gas treatment systems in duct flows is discussed to enhance the design of the air-conditioning systems for indoor air purification. Concerns over microbial growth have led to recent studies on coating commercial fibrous air filters with antimicrobial particles (silver nanoparticles, iron oxide nanowires) and polymeric materials (polyaniline, polyvinylidene fluoride). Promising alternatives to traditional short-wave UV-C energy for disinfection include LED and Far-UVC irradiation systems. Additionally, research explores the use of TiO2 and TiO2 doped with metals (Ag, Cu, Pt) in filters with photocatalytic properties, enabling the utilization of visible or solar light. Hybrid photocatalysis, combining TiO2 with polymers, carbon nanomaterials, or MXene nanomaterials, enhances the photocatalytic process. Chemical treatment systems such as aerosolization of biocidal agents (benzalkonium chloride, hydrogen peroxide, chlorine dioxide or ozone) with their possible combination with other technologies such as adsorption, filtration or photocatalysis, are also tested for gas disinfection. However, the limited number of studies on the use of electrochemical technology poses a challenge for further investigation into gas-phase oxidant generation, without the formation of harmful by-products, to raise its TRL for effectively inactivating airborne microorganisms in indoor environments.

Energy efficient ventilation and indoor air quality in the context of COVID-19 - A systematic review

New COVID-19 ventilation guidelines have resulted in higher energy consumption to maintain indoor air quality (IAQ), and energy efficiency has become a secondary concern. Despite the significance of the studies conducted on COVID-19 ventilation requirements, a comprehensive investigation of the associated energy challenges has not been discussed. This study aims to present a critical systematic review of the Coronavirus viral spreading risk mitigation through ventilation systems (VS) and its relation to energy use. COVID-19 heating, ventilation and air conditioning (HVAC)-related countermeasures proposed by industry professionals have been reviewed and their influence on operating VS and energy consumption have also been discussed. A critical review analysis was then conducted on publications from 2020 to 2022. Four research questions (RQs) have been selected for this review concerning i) maturity of the existing literature, ii) building types and occupancy profile, iii) ventilation types and effective control strategies and iv) challenges and related causes. The results reveal that employing HVAC auxiliary equipment is mostly effective and increased fresh air supply is the most significant challenge associated with increased energy consumption due to maintaining IAQ. Future studies should focus on novel approaches toward solving the apparently conflicting objectives of minimizing energy consumption and maximizing IAQ. Also, effective ventilation control strategies should be assessed in various buildings with different occupancy densities. The implications of this study can be useful for future development of this topic not only to enhance the energy efficiency of the VS but also to enable more resiliency and health in buildings.

Impacts of residential indoor air quality and environmental risk factors on adult asthma-related health outcomes in Chicago, IL

BACKGROUND

Residential environments are known to contribute to asthma.

OBJECTIVE

To examine the joint impacts of exposures to residential indoor and outdoor air pollutants and housing risk factors on adult asthma-related health outcomes.

METHODS

We analyzed >1-year of data from 53 participants from 41 homes in the pre-intervention period of the Breathe Easy Project prior to ventilation and filtration retrofits. Health outcomes included surveys of asthma control, health-related quality of life, stress, and healthcare utilizations. Environmental assessments included quarterly measurements of indoor and outdoor pollutants (e.g., HCHO, CO, CO2, NO2, O3, and PM), home walk-throughs, and surveys of environmental risk factors. Indoor pollutant concentrations were also matched with surveys of time spent at home to estimate indoor pollutant exposures.

RESULTS

Cross-sectional analyses using mixed-effects models indicated that lower annual average asthma control test (ACT) scores were associated (p < 0.05) with higher indoor NO2 (concentration/exposure: β = -2.42/-1.57), indoor temperature (β = -1.03 to -0.94), and mold/dampness (β = -3.09 to -2.41). In longitudinal analysis, lower ACT scores were also associated (p < 0.05) with higher indoor NO2 concentrations (β = -0.29), PM1 (concentration/exposure: β = -0.12/-0.24), PM2.5 (concentration/exposure: β = -0.12/-0.26), and PM10 (concentration/exposure: β = 10.14/-0.28). Emergency department visits were associated with poorer asthma control [incidence rate ratio (IRR) = 0.84; p < 0.001], physical health (IRR = 0.95; p < 0.05), mental health (IRR = 0.95; p < 0.05), higher I/O NO2 ratios (IRR = 1.30; p < 0.05), and higher indoor temperatures (IRR = 1.41; p < 0.05).

SIGNIFICANCE

Findings suggest that residential risk factors, including indoor air pollution (especially NO2 and particulate matter), higher indoor temperature, and mold/dampness, may contribute to poorer asthma control.

IMPACT

This study highlights the importance of residential indoor air quality and environmental risk factors for asthma control, health-related quality of life, and emergency department visits for asthma. Two timescales of mixed models suggest that exposure to indoor NO2 and particulate matter, higher indoor temperature, and mold/dampness was associated with poorer asthma control. Additionally, emergency department visits were associated with poorer asthma control and health-related quality of life, as well as higher I/O NO2 ratios and indoor temperatures. These findings deepen our understanding of the interrelationships between housing, air quality, and health, and have important implications for programs and policy.

Quantifying the dynamic characteristics of indoor air pollution using real-time sensors: Current status and future implication

People generally spend most of their time indoors, making indoor air quality be of great significance to human health. Large spatiotemporal heterogeneity of indoor air pollution can be hardly captured by conventional filter-based monitoring but real-time monitoring. Real-time monitoring is conducive to change air assessment mode from static and sparse analysis to dynamic and massive analysis, and has made remarkable strides in indoor air evaluation. In this review, the state of art, strengths, challenges, and further development of real-time sensors used in indoor air evaluation are focused on. Researches using real-time sensors for indoor air evaluation have increased rapidly since 2018, and are mainly conducted in China and the USA, with the most frequently investigated air pollutants of PM_2.5. In addition to high spatiotemporal resolution, real-time sensors for indoor air evaluation have prominent advantages in 3-dimensional monitoring, pollution peak and source identification, and short-term health effect evaluation. Huge amounts of data from real-time sensors also facilitate the modeling and prediction of indoor air pollution. However, challenges still remain in extensive deployment of real-time sensors indoors, including the selection, performance, stability, as well as calibration of sensors. In future, sensors with high performance, long-term stability, low price, and low energy consumption are welcomed. Furthermore, more target air pollutants are also expected to be detected simultaneously by real-time sensors in indoor air monitoring.

Assessment of ANN Algorithms for the Concentration Prediction of Indoor Air Pollutants in Child Daycare Centers

As the time spent by people indoors continues to significantly increase, much attention has been paid to indoor air quality. While many IAQ studies have been conducted through field measurements, the use of data-driven techniques such as machine learning has been increasingly used for the prediction of indoor air pollutants. For the present study, the concentrations of indoor air pollutants such as CO2, PM2.5, and VOCs in child daycare centers were predicted by using an artificial neural network model with three different training algorithms including Levenberg–Marquardt, Bayesian regularization, and Broyden–Fletcher–Goldfarb–Shanno quasi-Newton methods. For training and validation, data of indoor pollutants measured in child daycare facilities over a 1-month period were used. The results showed all the models produced a good performance for the prediction of indoor pollutants compared with the measured data. Among the models, the prediction by the LM model met the acceptable criteria of ASHRAE guideline 14 under all conditions. It was observed that the prediction performance decreased as the number of hidden layers increased. Moreover, the prediction performance was differed by the type of indoor pollutant. This was caused by patterns observed in the measured data. Considering the outcomes of the study, better prediction results can be obtained through the selection of suitable prediction models for time series data as well as the adjustment of training algorithms.