Adams Rackes

In commercial buildings, ventilation, or air exchange between an indoor environment and the outdoors, is necessary for controlling contaminants emitted by indoor sources such as occupants, cleaning and personal care products, and building materials. In offices, increased ventilation has also been shown to significantly increase worker productivity and reduce sick leave. At the same time, increasing ventilation introduces more outdoor air pollutants, including ones with known public health consequences like particulate matter and ozone. Furthermore, ventilation accounts for about one-fourth of U.S. commercial heating, ventilation, and air-conditioning (HVAC) energy use and changes can have significant effects on building energy consumption. This research project aims to quantify, compare, and optimally or nearly optimally balance these multiple impacts for office buildings, while remaining alert to the fact that outcomes differ significantly by building, operating conditions, and user preference. The project had three objectives. The first was to use Monte Carlo analysis over a wide range of climates and office building characteristics to evaluate combinations of mature existing technologies including demand-controlled ventilation (DCV), economizing, supply air temperature reset, and increased ventilation rate (VR). Some combinations were 'win-win,' reducing HVAC energy consumption by 12-27% while increasing work performance by 0.5% and eliminating 5 hours of absenteeism per year. Annually, such strategies could save U.S. $1.25 billion in energy costs and generate $28-55 billion in total net benefits. The second objective was to develop an outcome-based ventilation (OBV) decision-making framework, using a loss function to combine scientific knowledge, uncertainty, and parameters to express user preferences. The OBV framework confirmed that human-related outcomes are much more valuable than energy use. For example, we evaluated an intervention that increased the VR by ~10 L/s/occ on a dataset representing the office sector. With "best estimate" user parameters, the average loss impact of every other outcome was greater than the one related to HVAC energy costs-by a factor of 47 for work performance, 25 for excess absence, 3.9 for particle exposure, and 1.1 for ozone exposure. Even the most ventilation-adverse user preferences still produced VRs that were very often as high as 30 L/s/occ and only rarely lower than 15 L/s/occ. The third objective was to use optimization with the OBV framework to minimize loss over a daylong horizon and take advantage of weather, pollution, occupancy, and other transient dynamics. An optimal control problem was formulated, then translated to a nonlinear optimization problem, and solved by interior point methods. Results showed that, contrary to our hypothesis, numerically optimizing ventilation control for a single day did not provide substantial Pareto improvements over existing control methods. In fact, a strategy with economizer and DCV was very close to Pareto optimal on most days. Neither time-of-use pricing nor any factor in a sensitivity analysis revealed opportunities in which optimizing ventilation within each day of the year saved more than 5% of annual HVAC energy costs. In concluding, we used the insights of this research to outline a procedure for next-generation ventilation that takes advantage of opportunities to optimize over an annual horizon and adjust for the influential climate and building parameters identified by sensitivity analysis. For daily control, it would employ existing successful technology components, like DCV and economizer controls, that we have shown to be capable of significant energy savings and, on a daily timescale, nearly optimal. These methods would be embedded in and guided by a more conscious annual strategy that includes an initial preference elicitation step and an offline annual optimization to intelligently allocate ventilation resources across the year. Such an approach could help make ventilation more effective and reliable, and allow users to make informed decisions about ventilation tradeoffs and understand their consequences.

This article presents an outcome‐based ventilation (OBV) framework, which combines competing ventilation impacts into a monetized loss function ($/occ/h) used to inform ventilation rate decisions. The OBV framework, developed for U.S. offices, considers six outcomes of increasing ventilation: profitable outcomes realized from improvements in occupant work performance and sick‐leave absenteeism; health outcomes from occupant exposure to outdoor fine particles and ozone; and energy outcomes from electricity and natural gas usage. We used the literature to set low, medium, and high reference values for OBV loss function parameters, and evaluated the framework and outcome‐based ventilation rates using a simulated U.S. office stock dataset and a case study in New York City. With parameters for all outcomes set at medium values derived from literature‐based central estimates, higher ventilation rates’ profitable benefits dominated negative health and energy impacts, and the OBV framework suggested ventilation should be ≥ 45 L/s/occ, much higher than the baseline ~8.5 L/s/occ rate prescribed by ASHRAE 62.1. Only when combining very low parameter estimates for profitable impacts with very high ones for health and energy impacts were all outcomes on the same order. Even then, however, outcome‐based ventilation rates were often twice the baseline rate or more.

High-performance buildings will increasingly use sensors to control ventilation and manage indoor air quality, but there is little knowledge regarding placement and accuracy limitations. The authors simulated and assessed two approaches for locating from 1 up to 10 hypothetical sensors for measuring carbon dioxide and the sum of volatile organic compounds in typical offices. The first network approach was intended to best measure spatial variability; the second simply sited each additional sensor in the mechanical system's return duct and averaged readings. Each approach was compared at three levels of sensor accuracy in four different office cases, within a Monte Carlo analysis that varied hundreds of relevant parameters. Results indicated that concentrations in areas served by a common mechanical system, given similar per-area supply flows and at least some air recirculation, were reasonably homogeneous. The standard deviation of concentrations among locations was almost always <20% of the mean concentration, and variability was generally overwhelmed by sensor error when building grade sensors were modeled. One implication was that capturing spatial distribution is likely to be less important than improving sensor accuracy, and without better quality sensors, attempting to do so can actually degrade network accuracy.

We used existing data to develop distributions of time-averaged air exchange rates (AER), whole-building 'effective' emission rates of volatile organic compounds (VOC), and other variables for use in Monte Carlo analyses of U.S. offices. With these, we explored whether long-term VOC emission rates were related to the AER over the sector, as has been observed in the short term for some VOCs in single buildings. We fit and compared two statistical models to the data. In the independent emissions model (IEM), emissions were unaffected by other variables, while in the dependent emissions model (DEM), emissions responded to the AER via coupling through a conceptual boundary layer between the air and a lumped emission source. For 20 of 46 VOCs, the DEM was preferable to the IEM and emission rates, though variable, were higher in buildings with higher AERs. Most oxygenated VOCs and some alkanes were well fit by the DEM, while nearly all aromatics and halocarbons were independent. Trends by vapor pressure suggested multiple mechanisms could be involved. The factors of temperature, relative humidity, and building age were almost never associated with effective emission rates. Our findings suggest that effective emissions in real commercial buildings will be difficult to predict from deterministic experiments or models.

How building stakeholders (e.g. owners, tenants, operators, and designers) understand impacts of Indoor Air Quality (IAQ) and associated energy costs is unknown. We surveyed 112 stakeholders across the United States to ascertain their perceptions of their current IAQ and estimates of benefits and costs of, as well as willingness to pay for, IAQ improvements. Respondents' perceived IAQ scores correlated with the use of high‐efficiency filters but not with any other IAQ‐improving technologies. We elicited their estimates of the impacts of a ventilation–filtration upgrade (VFU), that is, doubling the ventilation rate from 20 to 40 cfm/person (9.5 to 19 l/s/person) and upgrading from a minimum efficiency reporting value 6 to 11 filter, and compared responses to estimates derived from IAQ literature and energy modeling. Minorities of respondents thought the VFU would positively impact productivity (45%), absenteeism (23%), or health (39%). Respondents' annual VFU cost estimates (mean = $257, s.d. = $496, median = $75 per person) were much higher than ours (always <$32 per person), and the only yearly cost a plurality of respondents said they would pay for the VFU was $15 per person. Respondents holding green building credentials were not more likely to affirm the IAQ benefits of the VFU and were less likely to be willing to pay for it.