Download PDF Microbiomes of the Built Environment September 15, 2022 Volume 52 Issue 3 The covid-19 pandemic suddenly directed awareness to potential health impacts of the built environment of everyday living – schools, dwellings, offices, public buildings, and other spaces. This issue explores the “microbiome” of the built environment in the postpandemic reality in terms of ventilation performance, filtration, understanding and quantification of transmission risk, protection of “benign” microbes, and the important role of equity, among others. Toward Integrating Numeric Disease Transmission Risk in Energy Codes and Ventilation Standards Tuesday, September 20, 2022 Author: Hooman Parhizkar, Alen Mahic, and Kevin G. Van Den Wymelenberg Innovative technologies, practices, and control systems are needed to balance risk of disease transmission and operational energy use. The covid-19 pandemic has dramatically increased public awareness of airborne disease transmission and the importance of mitigation strategies to remove virus-laden particles from indoor air. Improved ventilation, filtration, and humidification have been confirmed as effective methods to mitigate inhalation dose of the SARS-CoV-2 virus (Morris et al. 2021; Parhizkar et al. 2022), and indoor air strategies are prioritized by the White House (2022) and recommended as one of the layered approaches in CDC guidance.[1] Existing Ventilation Standards and Energy Codes Current ventilation standards for common buildings such as offices and schools were established to primarily address occupancy comfort, odors, and irritation and to manage many indoor and outdoor-sourced contaminants—not to manage airborne disease transmission risk (ASHRAE 2019; Parhizkar et al. 2021). There is no direct construct for quantitatively limiting airborne disease transmission risk or monitoring indoor microbial communities in ventilation standards for common buildings. Current energy codes were intended to reduce energy consumption while maintaining minimum requirements for function, human comfort, and fresh air. Some prescriptive energy codes require demand control ventilation strategies to reduce fresh air intake during periods of low occupant density, but less attention has been given to allowances for above minimum ventilation during health risk events such as disease transmission and wildfire. In fact, there is no construct in prescriptive energy codes for managing variability of health risks, whether the exposures are indoor- or outdoor-sourced, but performance-based energy codes do provide a pathway for intermittent “safe mode” operations. There is a need for both ventilation standards and energy codes that allow buildings to dynamically address the variability in acute human health risks during “high-risk” (or “risk-on”) periods while managing energy use differently than during “low-risk” (or “risk-off”) periods. There is also a need for greater coordination between energy and air quality standards. It is not possible to prescribe health risk mitigation measures that work equally well for every building all the time, nor is it appropriate to constantly apply all risk mitigation strategies. At a minimum, it is important to manage acute health risks associated with periodic events (such as wildfire smoke or airborne disease surges) while limiting chronic health exposures and factors exacerbated by fossil fuel–associated climate change. A Risk-Energy Decision Support Platform The risk of airborne disease transmission depends on several building and occupant-related factors that are subject to rapid change. Properly managing both human health priorities (in this case, airborne disease transmission) and operational energy use concurrently requires an integrated framework that considers key variables of human activity, heating, ventilation, and air conditioning (HVAC) operations, and the associated energy and health risk outcomes. In this paper we introduce a decision-support platform that articulates the relationships between human activity, key indoor air (HVAC system) parameters, the resultant annual operating energy use, and associated probabilistic risk of airborne disease transmission. With the goal of increasing dialogue among people developing energy and air quality operations and standards, we demonstrate the application of this platform by evaluating both the infection risk to occupants and annual operational energy use of a reference small office building (~500 m3). The analysis presented does not contemplate system implications associated with a real-world building retro-fit or simple control modifications; rather, it describes the implications of design decisions and operating parameters using a simulated example. Platform Parameters SafeAirSpaces (https://safeairspaces.com/) is a single-zone aerosol risk estimation platform (described in Parhizkar et al. 2021) for evaluating the risk of covid-19 transmission with several input parameters related to indoor air and occupant behavior. There is a need for greater coordination between energy and air quality standards. The model calculates the concentration of inhaled and deposited dose in susceptible individuals’ respiratory system according to the sick persons’ (i.e., index-emitters) expiratory/respiratory activity, use of face masks, and indoor air parameters, and it references a relevant dose-response relationship to estimate likelihood of disease transmission (Parhizkar et al. 2021). A dose-response relationship “can be regarded as the probability that a single individual exposed to the average dose of d will have the effect” (i.e., acquire infection)(Haas 2021, p. 706). The dose response estimated by SafeAirSpaces corresponds well with a recent human challenge study (Killingley et al. 2022) that established the so-called “infectious dose” required to produce infection in about 50 percent of human volunteers. The parametric approach in our study used the SafeAirSpaces model and developed a more comprehensive platform in which inputs required for calculating the risk of disease transmission are derived from energy and comfort simulation software. We relied on published calculations, including the single-zone well-mixed aerosol dynamics assumption, but updated the risk model with a humidification viral decay parameter (Dabisch et al. 2021). We excluded any consideration of recirculated air for risk estimates. The decision-support framework is visually presented in figure 1. To demonstrate the utility of this platform, we studied a benchmark commercial reference building developed by Pacific Northwest National Laboratory (PNNL) that supported energy and ventilation calculations (-Winiarski et al. 2006, 2007). We imported an IDF (input data -format) file (EnergyPlus v. 8.0) into Honeybee v. 1.4 plug-in and Ladybug tools in Grasshopper v. 1.0.0007 coding language in which the SafeAirSpaces aerosol risk estimation platform was developed. The PNNL reference model is a single-story small office (511 m2) located in climate zone 4C (Salem, OR), with a 24.4 percent window/wall ratio on the south façade and 19.8 percent for the other three orienta-tions. It has five HVAC zones spanning a central primary zone, perimeter office spaces, and an unoccupied attic zone. Heating and cooling systems comprise air-source heat pumps with a backup gas furnace. We used ASHRAE 62.1-2013 (ASHRAE 2015) minimum ventilation rates as inputs for Case 1 (baseline) to assign the minimum outdoor air requirement of each zone according to the IDF reference model (Case 1 energy use is also compliant with ASHRAE 90.1-2013). We defined three additional cases to study alternate minimum outdoor airflow rates and levels of filtration, as shown in table 1. Annual operational energy use was simulated. We calculated the average monthly covid-19 transmission risks for a typical office space with the following conditions: Floor area = 113 m2, ceiling height = 3 m, number of occupants = 7, number of index patients = 1 high emitter (as defined in Parhizkar et al. 2021), event duration = 2 hours For each case, we conducted an annual energy simulation resulting in 8760 hourly values. We calculated the risk of disease transmission using the average values of combined air exchange rates and relative humidity values during weekdays from 08:00 to 17:00 for each month of the year and applied these values to a 2-hour event duration for disease transmission risk estimation. Calculation Results and Implications Figure 2A shows the average of the combined air changes per hour (ACH) in one of the south-facing perimeter office spaces for each month. For each case, combined air exchange was calculated as the sum of infiltration and mechanical ventilation. Figure 2B shows the average relative humidity for each month, with values of 30–45 percent, dependent on weather and HVAC parameters and no humidification during the heating season. Figure 2C shows results of the SafeAirSpaces simulations of monthly average disease transmission risk for a 2-hour period during a typical workday. Although not historically characterized in these terms, current ventilation standards and alternate HVAC operating scenarios can be associated with disease transmission risk potential. For the cases studied, the indoor air -parameters produce estimated disease transmission risks of 7–25 percent. Modifying ventilation or filtration produces meaningful changes in airborne disease transmission risk. Figure 2D illustrates the associated annual energy consumption for each case, with values spanning 309–806 MJ/m2 (27–71 kBTU/f2) annually. We observed that while 9 ACH in Case 3 delivered the lowest risk of disease transmission, Case 4 with in-room HEPA filtration delivered significantly lower risk of disease transmission than Case 1 and with lower energy consumption than Case 3. These results underscore the important relationships between HVAC system definitions and modes of operation, airborne disease transmission health risks, and annual operating energy consumption. The potential to reduce disease transmission risk is substantial (a 70 percent reduction) based on mitigation strategies, but in some cases these strategies also drive wide variance (250 percent) in operational energy consumption. In addition to energy implications, increasing outside airflow through existing HVAC systems is not always possible because of comfort implications associated with limited heating/cooling capacity, condensation, and limitations in fan and duct capacity. Therefore, innovative technologies, practices, and control systems that aim to balance risk of disease transmission and operational energy use are needed. The capital and maintenance costs associated with renovating HVAC systems and alternate operational parameters should be analyzed according to both acute and long-term impacts on human health and energy use. Adding in-room HEPA filtration to existing buildings with inadequate ventilation is shown to be an effective short-term disease transmission risk mitigation strategy, and the benefits of increasing outside air ventilation rates extend beyond disease transmission (Allen et al. 2016; Satish et al. 2012). Conclusion This paper demonstrates the potential for an integrated disease transmission risk and energy simulation platform to provide building design decision support and HVAC operations guidance. The aim is to help balance the optimization of human health outcomes with energy investments and to guide safer and more energy-efficient modes of building operations and the development of future codes and standards. Future research should explore the impacts of a variety of HVAC systems with options to evaluate risk of disease transmission and energy use in buildings with alternate fractions of recirculated air (through MERV filters[2]) and outside air. Future energy codes, ventilation standards, and HVAC system operational and maintenance guidelines would all benefit from a concurrent understanding of how key indoor air variables implicate annual energy use and airborne disease transmission health risks. Setting specific disease transmission risk thresholds is not a concept currently considered by building owners or standards development organizations, but the field has progressed to the point that such consideration is now possible—and necessary, given the severity and persistence of the covid-19 pandemic and increasingly dire climate crises. We provide a framework for advancing this dialogue. References Allen JG, MacNaughton P, Satish U, Santanam S, Vallarino J, Spengler JD. 2016. Associations of cognitive function scores with carbon dioxide, ventilation, and volatile organic compound exposures in office workers: A controlled exposure study of green and conventional office environments. Environmental Health Perspectives 124(6):805–12. ASHRAE [American Society of Heating, Refrigerating and Air-Conditioning Engineers]. 2015. Ventilation for Acceptable Indoor Air Quality. Atlanta. Dabisch P, Schuit M, Herzog A, Beck K, Wood S, Krause M, Miller D, Weaver W, Freeburger D, Hooper I, and 8 others. 2021. The influence of temperature, humidity, and-- simulated sunlight on the infectivity of SARS-CoV-2 in aerosols. Aerosol Science & Technology 55:142–53. DOE [US Department of Energy]. 2011. Commercial Reference Buildings. Washington. Haas CN. 2021. Action levels for SARS-CoV-2 in air: Preliminary approach. Risk Analysis 41(5):705–09. Killingley B, Mann AJ, Kalinova M, Boyers A, -Goonawardane N, Zhou J, Lindsell K, Hare SS, Brown J, Frise R, and 21 others. 2022. Safety, tolerability and viral kinetics during SARS-CoV-2 human challenge in young adults. Nature Medicine 28:1031–41. Morris DH, Yinda KC, Gamble A, Rossine FW, Huang Q, Bushmaker T, Fischer RJ, Matson MJ, Van Doremalen N, Vikesland PJ, and 3 others. 2021. Mechanistic theory predicts the effects of temperature and humidity on inactivation of SARS-CoV-2 and other enveloped viruses. eLife 10:e65902. NREL [National Renewable Energy Laboratory]. 2022. OpenStudio 2.0.0 Release. Operated by the Alliance for Sustainable Energy, LLC. Available online: https://www.openstudio.net/. Parhizkar H, Van Den Wymelenberg KG, Haas CN, Corsi RL. 2021. A quantitative risk estimation platform for indoor aerosol transmission of COVID-19. Risk Analysis, Oct 28. Parhizkar H, Dietz L, Olsen-Martinez A, Horve PF, Barnatan L, Northcutt D, Van Den Wymelenberg KG. 2022. Quantifying environmental mitigation of aerosol viral load in a controlled chamber with participants diagnosed with COVID-19. Clinical Infectious Diseases, ciac006. Satish U, Mendell MJ, Shekhar K, Hotchi T, Sullivan D, Streufert S, Fisk WJ. 2012. Is CO2 an indoor pollutant? Direct effects of low-to-moderate CO2 concentrations on human decision-making performance. Environmental Health Perspectives 120(12):1671–77. White House. 2022. Let’s clear the air: An OSTP discussion on COVID and clean indoor air, Mar 29. Washington. Winiarski DW, Jiang MA, Halverson MA. 2006. Review of Pre- and Post-1980 Buildings in CBECS – HVAC Equipment. Richland WA: Pacific Northwest National Laboratory. Winiarski DW, Halverson MA, Jiang W. 2007. Analysis of Building Envelope Construction in 2003 CBECS. Richland WA: Pacific Northwest National Laboratory. [1] Centers for Disease Control and Prevention, Ventilation in buildings (https://www.cdc.gov/coronavirus/2019-ncov/community/ ventila tion.html) [2] A minimum efficiency reporting value (MERV) rating measures a filter’s effectiveness in preventing the transmission of dust and other contaminants. About the Author:Hooman Parhizkar is a postdoctoral researcher, Institute for Health in the Built Environment; Alen Mahic´ is a research associate, Baker Lighting Lab; and Kevin Van Den Wymelenberg is a professor of architecture and director, Institute for Health and the Built Environment, Energy Studies in Buildings Laboratory, and Biology and the Built Environment Center, all at the University of Oregon, Eugene.