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Occupant behaviour simulation frameworks can employ synthetic populations to characterize occupancy and behavioural patterns in buildings based on observed demographic data at a certain geographical location. For buildings, very few synthetic occupant populations have been generated. This paper uses a Bayesian Networks (BN) structural learning approach to synthesize populations of occupants in a multi-family housing case study. Two additional cases of office occupants and senior housing residents are considered as a cross-case comparison. We draw upon the extended version of drivers-needs-actions-systems (DNAS) framework to guide the selection of variables and data imputation. Our results show that the BN approach is powerful in learning the structure of data sets. The synthetic data sets successfully match the joint distributions of the underlying combined data sets. Experiments on the multi-family housing particularly show better performance than the office and senior housing cases.

A desire to shift from fossil fuels to non-carbon-emitting energy sources has become an imperative supported by national, state, and local policies. In the U.S., a diverse array of policies at multiple levels of government have helped the solar industry achieve exponential growth. These advancements, however, are mediated by local implementation processes, which might be expected to have a large impact in the context of U.S. federalism. This paper investigates the effects of two countervailing forces – policy incentives and implementation disincentives – on residential solar adoption in New Jersey. The New Jersey case study includes two complementary analyses designed to illuminate policy incentives and implementation disincentives, respectively. The first is an interrupted time-series analysis that evaluates the effects of federal and statewide renewable energy policies on residential photovoltaic (PV) growth. The second is a set of semi-structured in-depth interviews with solar industry experts, providing implementation insights from the solar industry and indirectly portraying residential customers’ experiences. Results confirm that market-based instruments at the state-level play a crucial role in increasing the relative financial advantage of these systems and, thus, the attraction of residential solar PV to adopters. In addition, the behaviours of future adopters and the valuation of this technology as an investment in the housing market will impact the spread of residential PV systems in the future. Unfortunately, case study findings also confirm that an absence of standardized solar application procedures and outdated interconnection standards is a significant drag on the adoption rate. This highlights a need for policymakers to place greater emphasis on local implementation pathways in solar policy design. Options to achieve this include more robust ex-ante coordination among state and local levels of government and with industry to standardize implementation processes. While U.S. federalism may at times be constraining, our results suggest that even minimal levels of ex-ante coordination can lead to implementation gains that will have a large impact on solar diffusion outcomes. Key policy insightsFederal and state-level financial incentives significantly impact the residential photovoltaic solar adoption rate.The absence of streamlined interconnection application procedures and outdated interconnection standards for the grid limit the residential PV adoption in New Jersey.Our findings underline the importance of paying equal attention to both policy design and to technical standards to underpin implementation processes, especially in the decentralized context of U.S. federalism. Federal and state-level financial incentives significantly impact the residential photovoltaic solar adoption rate. The absence of streamlined interconnection application procedures and outdated interconnection standards for the grid limit the residential PV adoption in New Jersey. Our findings underline the importance of paying equal attention to both policy design and to technical standards to underpin implementation processes, especially in the decentralized context of U.S. federalism.

Bioaerosol concentrations in residential buildings located in the Northeastern US have not been widely studied. Here, in 2011-2015, we studied the presence and seasonal variability of culturable fungi and bacteria in three multi-family apartment buildings and correlated the bioaerosol concentrations with building ventilation system types and environmental parameters. A total of 409 indoor and 86 outdoor samples were taken. Eighty-five percent of investigated apartments had indoor-outdoor (I/O) ratios of culturable fungi below 1, suggesting minimal indoor sources of fungi. In contrast, 56% of the apartments had I/O ratios for culturable bacteria above 1, indicating the prominence of indoor sources of bacteria. Culturable fungi I/O ratios in apartments serviced by central heating, ventilation, and air-conditioning (HVAC) system were lower than those in apartments with window AC. The type of ventilation system did not have a significant effect on the presence of indoor culturable bacteria. A significant positive association was determined between indoor dew point (DP) levels and indoor culturable fungi (P

Indoor heat and air pollution pose concurrent threats to human health and wellbeing, and their effects are more pronounced for vulnerable individuals. This study investigates exposures to summertime indoor overheating and airborne particulate matter (PM2.5) experienced by low-income seniors and explores the potential of natural ventilation on maintaining good indoor thermal conditions and air quality (IAQ). Environmental and behavioural monitoring and a series of interviews were conducted during summer 2017 in 24 senior apartments on three public housing sites in NJ, USA (1930s’ low-rise, 1960s’ high-rise and LEED-certified 2010s’ mid-rise). All sites had high exposures to overheating and PM2.5 concentrations during heat waves and on regular summer days, but with substantial between-site and between-apartment variability. Overheating was higher in the 30s’ low-rise site, while pollutant levels were higher in the 60s’ high-rise. Mixed linear models indicated a thermal and air quality trade-off with window opening (WO), especially in some ‘smoking’ units from the older sites, but also improved both thermal and PM2.5 concentration conditions in 20% of the apartments. Findings suggest that with warmer future summers, greater focus is needed on the interdependencies among (1) thermal and IAQ outcomes and (2) technological and behavioural dimensions of efforts to improve comfort for vulnerable occupants.

Understanding and improving how humans adapt to climate change are priorities in our research community, and coastal settlements are good places to study adaptation. Severe storm events and sea‐level rise are threatening coastal communities with increasing levels of flood damage. Because ownership of coastal assets is distributed among many private and public actors, both individual property owners and public officials must take adaptive actions. This paper introduces an integrated agent‐based and hedonic pricing modeling system to simulate coastal real estate market performance under non‐equilibrium conditions that reflect the effects of storm events. The modeling system, which is used for policy analysis, is calibrated to conditions in two towns in Monmouth County, New Jersey, USA, which were badly damaged by Hurricane Sandy in 2012. The key findings are that (a) coastal real estate markets capitalize flood risk into property values but this discount diminishes rapidly as time passes between storm events, and (b) there is a distinct equity versus efficiency tradeoff in designing public policies to reduce the cost to society of coastal flooding. Stringent regulation of building practices reduces flood damage but drives away poorer home buyers and owners, whereas informational and incentive‐based policies are fairer but less effective. Hands‐off, market‐based retreat from risky areas is socially costly but allows less wealthy people to remain at the shore, albeit in vulnerable situations. Managed retreat should emphasize improved recreational access to coastal amenities while discouraging people from living there.

In this article we argue that “smart” work spaces are created through access to wireless networks and mobile cloud computing collaboration software. Yet the power relations embedded in these overla...

Indoor air quality (IAQ) is determined by indoor and outdoor sources and conditions, building characteristics, and occupant behavior. In the field study context where the researcher lacks full control of observational conditions, it is difficult to compare and integrate these determinants because they require such different types and sources of data. This pilot-level project investigated the potential to overcome these limitations by integrating traditional IAQ measurement techniques with questionnaires and analysis of building deficiencies using 3D infrared thermography imaging in two residential multi-apartment buildings. Of the building deficiencies detected by the 3D thermography, missing insulation (MI) correlated best with the IAQ measurements and questionnaire data. Apartments missing more than 5% of insulation in their exterior wall (n = 6) had a significantly higher number concentration of ultrafine airborne particles (diameter

Many governments, businesses, and institutions are committing to net zero greenhouse gas emissions by 2050, a goal and process known as deep decarbonization. Achieving this goal in the United States requires a national, economy-wide transformation in energy production and use in five sectors: electricity, transportation, industry, land-based carbon sinks, and buildings. All of these sectors interact with planning for the built environment and land use, so planning scholars and practitioners have many opportunities to engage policymakers working on national-level decarbonization strategies. This article analyzes the consequences of deep decarbonization for the future speed, scale, scope, role, and relevance of planning.

Exposure to bioaerosols has been implicated in adverse respiratory symptoms, infectious diseases, and bioterrorism. Although these particles have been measured within residential and occupational settings in multiple studies, the deposition of bioaerosol particles within the human respiratory system has been only minimally explored. This paper uses real-world environmental measurement data of total fungal spores using Air-o-Cell cassettes in 16 different apartments and residents' physiological data in those apartments to predict respiratory deposition of the spores. The airborne spore concentrations were measured during the spring, summer, and fall. The respiratory deposition of five most prevalent spore genera-Ascospores, Aspergillus, Basidiospores, Cladosporium, and Myxomycetes-was predicted using three empirical models: the Multiple Path Particle Dosimetry model, using both the Yeh and age-specific versions, and the Bioaerosol Adaptation of the International Committee on Radiological Protection's Lung deposition model. The predicted total deposited number of spores was highest for Ascospores and Cladosporium. While the majority of spores deposit were in the extrathoracic region, there is a significant deposition for both Aspergillus and Cladosporium in the alveolar region, potentially leading to the development of aspergillosis or allergic asthma. Although the dose-response relationship is unknown, the estimate of the actual spore deposition could be the first step in determining such a relationship.

Load shedding enjoys increasing popularity as a way to reduce power consumption in buildings during hours of peak demand on the electricity grid. This practice has well known cost saving and reliability benefits for the grid, and the contracts utilities sign with their “interruptible” customers often pass on substantial electricity cost savings to participants. Less well-studied are the impacts of load shedding on building occupants, hence this study investigates those impacts on occupant comfort and adaptive behaviors. It documents experience in two office buildings located near Philadelphia (USA) that vary in terms of controllability and the set of adaptive actions available to occupants. An agent-based model (ABM) framework generalizes the case-study insights in a “what-if” format to support operational decision making by building managers and tenants. The framework, implemented in EnergyPlus and NetLogo, simulates occupants that have heterogeneous thermal and lighting preferences. The simulated occupants pursue local adaptive actions such as adjusting clothing or using portable fans when central building controls are not responsive, and experience organizational constraints, including a corporate dress code and miscommunication with building managers. The model predicts occupant decisions to act fairly well but has limited ability to predict which specific adaptive actions occupants will select.

Addressing risks posed by changing climate conditions in coastal areas demands innovative strategies that intersect multiple disciplines including engineering, ecology, communication, climate science, and community planning. To be usable, it also requires engaging coastal stakeholders in the development of research questions, the assessment of implications of research for planning and policy, and the communication of research results. Yet traditional, disciplinary programs are poorly configured to train the workforce needed to assess coastal climate risk and to develop and deploy integrated strategies for increasing coastal climate resilience. This chapter discuss the successes and challenges to implementing the Coastal Climate Risk and Resilience (C2R2) Initiative, a transdisciplinary program, at a large university as well as the benefits for the student and faculty participants from evaluation and student learning outcome data.

Innovation and technological development have led to a proliferation of various low-cost air quality monitors, most of which use light scattering techniques to detect airborne particles. As these monitors are used in various environments, their testing should also be performed in diverse environments and with different particle types. This study evaluated three Foobot units and three AirVisual Nodes in two laboratory environments and in one residential apartment when measuring five types of aerosols (0.72 and 2.00 µm polystyrene latex (PSL) spheres, Arizona Road Dust (ARD), nanosilver-based surface cleaner, and particles created by a cooking event). The low-cost monitors were highly correlated with the DustTrak DRX (TSI) for most aerosol types (r > 0.78), except when measuring nanosilver spray, where the r value decreased to 0.55. For PSL spheres, Foobot overestimated DRX PM2.5 concentrations while AirVisual's readings were closer to those of DRX and aligned relatively well with the 1:1 line. Both Foobot and AirVisual underestimated PM2.5 concentrations for ARD, nanosilver spray, and particles from a cooking event. Overall, the Foobots reported higher PM2.5 concentrations than the AirVisuals, except when measuring particles created by the cooking event. The paired monitors' precision error varied between 0.15 and 0.47, depending on the aerosol type and monitor pairs. The coefficient of variation ranged from 15.2% to 21.0% for the Foobots and from 12.5% to 28.7% for the AirVisuals. Both monitors showed high accuracy and precision when measuring temperature and humidity as compared to the IAQ Meter (TSI Inc.). AirVisual showed high accuracy and precision when measuring CO2, as compared to the IAQ meter. Foobot, however, didn't report accurate CO2 concentrations. Additionally, we found the AirVisual is easier to operate, and it provided more options to read, save, and download data. Copyright © 2020 American Association for Aerosol Research

Since the introduction of the occupant behavior Drivers-Needs-Actions-Systems (DNAS) framework in 2013, researchers have used the framework or further developed it based on their case studies, which include efforts to collect new data on occupant behaviors. The effort is often costly for the relatively few new data points added. Problems emerge when the already collected data do not meet the modelers' interoperability requirements. Previous studies addressed this issue by developing more sophisticated ontologies that enable integration with other datasets and synthetic data methodologies that would meet unique research applications. This paper presents an extension of the DNAS framework for the representation of synthetic occupant data to support various applications and use cases across the building life cycle. An agent-based modeling application is one of our motivations that requires more elaborate characteristics of an occupant-agent or a group-of-agent. The extension, built upon a review of the literature, introduces new elements to the framework that fall into five categories, including socio-economic, geographical location, activities, subjective values, and individual and collective adaptive actions. On-going research includes identifying occupant datasets and developing data fusion methods to generate synthetic occupants, as well as to demonstrate its applications in agent-based modeling coupled with building performance simulation.