Data fusion in buildings: Synthesis of high-resolution IEQ and occupant tracking data

https://doi.org/10.1016/j.scitotenv.2021.146047Get rights and content

Highlights

  • Synthesis of passively collected high resolution Indoor Environmental Quality and location data in an office environment

  • Estimates continuous IEQ exposure levels of occupants based on their spatiotemporal coordinates.

  • Longitudinal tracking of individual space usage is feasible and may inform post-occupancy evaluations of built environments

Abstract

Indoor Environmental Quality (IEQ) reflects a building's performance in relation to the health, comfort and wellbeing of its occupants. Conventional IEQ measurement strategies fail to capture spatial or temporal variations in IEQ. Recent technological developments in IEQ monitoring and occupant location tracking provide opportunities to monitor IEQ across the entire floorplate of a building and to develop deeper insights into individuals' IEQ exposure.

The aims of this study were 1) to establish the feasibility of synthesising continuous IEQ exposure based on high-resolution real-time location data, and 2) to investigate individuals' exposure to the indoor climate by mapping occupant location relative to IEQ. IEQ was measured continuously using 12 autonomous desk-mounted devices spread out across the office floorplate. A Real Time Location System (RTLS) tracked occupant location continuously over one month, with 47 location sensors across the research site (1220 m2) and 45 tags attached to occupants' staff-ID access cards. We estimated IEQ spatial distributions across the entire floorplate using cubic splines and fused these with occupants' high-resolution spatiotemporal coordinates. We confirm that it is possible to infer continuous exposure to IEQ conditions from diverse data sources. In this case, we identified distinct IEQ zones across the floorplate that were reflected in the exposure profiles for individual occupants. While there were several limitations to the study, future research could expand this data fusion framework to investigate IEQ relative to occupant health and wellbeing by including occupant-reported data. The framework could also be useful for future investigations aimed at simultaneously optimising building energy and occupant comfort.

Introduction

Indoor environmental quality (IEQ) reflects a building's performance in relation to the health and wellbeing of its occupants, and comprises parameters such as indoor air quality, thermal comfort, lighting and acoustics (Kim and de Dear, 2012). IEQ can influence employees' workplace satisfaction (Al horr et al., 2016; Kim and de Dear, 2012; MacNaughton et al., 2016), health and wellbeing (Agha-Hossein et al., 2013; Sadick and Issa, 2017). For example, office IEQ has been associated with improvements in mental wellbeing (Dreyer et al., 2018), cognitive function (Abbasi et al., 2019; Allen et al., 2016; MacNaughton et al., 2017), perceived productivity (Gupta et al., 2018; Singh et al., 2010) and reduced absenteeism (Lamb and Kwok, 2016; Singh et al., 2010; Zhang et al., 2019). Conversely, poor IEQ can lead to adverse physiological health impacts (Wai and Willem, 2011) as well as increased tiredness and distractibility, decreased mood and motivation (Lamb and Kwok, 2016; MacNaughton et al., 2016). Improvements in office IEQ can deliver large health and wellbeing benefits for employees and, in turn, financial benefits for organisations (Fisk et al., 2011). IEQ has been a major focus of building sustainability rating tools such as BREEAM, LEED and Greenstar since their inception (Building Research Establishment Ltd., 2021; Green Building Council of Australia, 2021; United States Green Building Council, 2021). The focus on IEQ, particularly in the commercial building sector, has further intensified with the release of tools such as WELL and Fitwel which are specifically aimed at promoting the health and wellbeing of building occupants (Center for Active Design, 2021; International WELL Building Institute, 2021). The current COVID-19 pandemic has also drawn attention to issues such as fresh air ventilation rate and efficiency, and system design to minimise risk of exposure to airborne infectious aerosols within buildings (Dietz et al., 2020; Kenarkoohi et al., 2020).

However, maintaining office IEQ by implementing strategies such as increasing ventilation rates, constraining occupant thermal comfort within relatively narrow performance bands and providing adequate lighting levels, requires a considerable amount of energy which entails large costs for organisations and contributes unsustainable volumes of greenhouse gas emissions (Urge-Vorsatz et al., 2013). Indeed, current estimates attribute 19% of global greenhouse gas emissions to buildings (Pachauri et al., 2014). Many organisations and researchers are working to reduce these effects through schemes and tools such as LEED and similar building sustainability rating tools (MacNaughton et al., 2018). However, there is still a need for further investigations to balance optimal IEQ for occupant wellbeing with reduced building energy use.

Despite the importance of IEQ, current measurement tools are often inadequate as they are based on crude instrumentation and unrepresentative sampling strategies. For example, sensors in building management systems (BMS) have been proposed for IEQ evaluations (Hunn et al., 2012; Wang et al., 2019) but the sensors in these systems are rarely calibrated, even at commissioning stage, and so are unfit for IEQ evaluation purposes. These sensors are also typically positioned on the perimeter of floorplate, well away from where occupants and their workstations are actually located. Other instrumental strategies proposed for IEQ performance evaluation sample at a single time-point in a limited number of locations only (Parkinson et al., 2019a, Parkinson et al., 2019b). Such evaluations cannot adequately represent spatial and temporal variations in IEQ that may exist throughout a building (Ortiz Perez et al., 2018; Ramos et al., 2015). Therefore, the IEQ data are likely to represent a limited area of the office space rather than the total indoor environmental conditions experienced by all of a building's occupants (Parkinson et al., 2019a, Parkinson et al., 2019b). Recent developments in sensor technology have led to the emergence of low-cost, continuous IEQ monitoring systems designed for commercial office environments. These systems are able to measure a range of IEQ parameters including air temperature, relative humidity, air speed, sound pressure level, illuminance, carbon dioxide and particular matter, at regular intervals (i.e. ≥ 5 min) across multiple days (Coulby et al., 2020; Parkinson et al., 2015). Although typically less accurate than laboratory based equipment, the ability to position these sensors across multiple areas of a floorplate can allow them to better characterise the occupants' experience of long-term building performance compared with spot measurements and conventional wall-mounted thermostat sensors (Parkinson et al., 2019a).

IEQ evaluations of workspaces also typically rely on occupant satisfaction surveys such as Post Occupancy Evaluations (Choi and Lee, 2018; Parkinson et al., 2019b). However, these surveys are potentially flawed as they depend on occupants' subjective evaluation of the building's environment (Parkinson et al., 2019b) which are prone to various types of bias (Collier and Mahoney, 1996) unrelated to building environment, such as the industrial relations climate of the workplace and Hawthorne effects. Satisfaction surveys are also typically conducted at a single point in time and fail to consider variations in occupant satisfaction over time (Parkinson et al., 2019a). Moreover, occupant surveys and Post Occupancy Evaluations require significant amounts of a large number of respondents' time, and therefore employers are often reluctant to initiate them because of productivity concerns.

Recently, researchers have recognised the potential of occupant location data to provide unbiased, long-term measurements of how occupants use a workspace (Candido et al., 2019). Occupancy sensing and counting systems such as Passive Infrared (PIR) and desk mounted sensors can detect how many people are occupying an office floor, a particular meeting room or whether a specific desk is being used or not. However, these systems lack the ability to determine whether it is the same occupant(s) and how individual occupants actually move about and use different parts of the workplace. A range of technologies, including smart phones, wearable cameras and wireless devices are now available that can provide occupant location information (Loveday et al., 2015; Mautz, 2012). Recent attempts to measure occupant location in office workplaces have used Bluetooth monitoring systems embedded in wrist and thigh-worn physical activity trackers (Clark et al., 2018) and low-resolution RFID (radio-frequency identification) systems with tags attached to lanyards worn by occupants (Spinney et al., 2015). In both cases, the resolution of the location data was low (Clark et al., 2018; Spinney et al., 2015).

Recent technological innovations are developing the ‘Internet of Things’, network-addressable devices embedded into everyday objects, allowing the measurement of previously unquantifiable events and processes in the interplay between buildings and their occupants (Plageras et al., 2018). The associated development of smart buildings containing densely distributed networks of pervasive wireless sensing devices, presents many exciting opportunities to provide insights and improvements in building energy use and occupant health and wellbeing through innovative applications (Lee and Karava, 2020; Metallidou et al., 2020). In 2019, the global value of the smart building sector was estimated to be just under USD 50 billion and is predicted to rise to over USD 125 billion by 2027 (Precedence Research, 2020). Technological advancements have also led to the production of high-resolution occupant location systems with improved accuracy that, if combined with continuous IEQ data, provide an opportunity to measure individuals' IEQ exposure continuously as they move around a building and at a much higher resolution than previously documented in the literature. Therefore, the primary aim of this study was to establish the feasibility of synthesising individuals' continuous IEQ exposure based on high-resolution, real-time location data. To support this aim, we mapped occupant location longitudinally and relative to a selected number of IEQ parameters; including, Fanger's (Olesen and Parsons, 2002) Predicted Mean Vote (PMV); sound pressure levels (SPL) and carbon dioxide levels (CO2). These estimates then served to quantify individuals' exposure to the indoor environmental conditions, as they utilised the research site.

Section snippets

Participants

The research site was situated on one floor (internal floor area = 1220 m2) of a commercial building located in Sydney's CBD (see Fig. 1). The floor was occupied by a financial service organisation and could accommodate approximately 160 employees. The office used a large open-plan office layout with a variety of fixed and adjustable height desks, meeting and ‘huddle’ spaces and a central kitchen/breakout area. Individuals were not formally allocated to desks, with teams instead being allocated

Results

The data comprised: 86,000 IEQ observations at five-minute intervals and 36.7 million RTLS observations at one-second intervals. Data were successfully recorded from ten or more (≥83.3%) SAMBA sensors on 12 days and from seven or more sensors (≥58.3%) on all days of the 31-day research period. Data from SAMBA sensors #426 and #427 were excluded as only a very limited number of observations (n = 75 and 4 respectively) were recorded for these sensors due to a data transmission error. To account

Discussion

To our knowledge, this is the first study to synthesise continuous IEQ monitoring sensor data and occupant location data at this level of accuracy and across this length of time. We have established the feasibility of estimating the continuing exposure of occupants to a range of indoor environmental quality variables through the combination of stationary IEQ sensors and real-time location tracking of occupants. Following a process of data fusion, we interpolated a range of IEQ parameters across

Conclusion

In this novel study, we used data fusion to synthesise continuous IEQ data based on stationary IEQ sensors and high-resolution occupant location data over time. The resultant data framework provided a unique opportunity to investigate how individuals were exposed to the indoor environment. While there were several limitations to the study, future research could expand and refine the current framework to investigate IEQ relative to occupant health and wellbeing, and cognitive performance by

Funding

This study was supported through a University of Sydney Commercial Development and Industry Partnerships Grant, which included financial contributions from the University of Sydney, AMP Limited and LeaseAccelerator (formerly Guardian Global Systems).

The funders of the research had no role in the study design; in the analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

CRediT authorship contribution statement

Brett Pollard: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Fabian Held: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – review & editing. Lina Engelen: Conceptualization, Funding acquisition, Methodology, Supervision, Writing –

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors wish to acknowledge and thank Joshua Kim and Marriane Makahiya from SpectData for their assistance in data analyses. We would also like to acknowledge the invaluable assistance of Tom Treffry, Chris Nunn, Charles Dalrymple-Hay, David Emerson, Anthony Fawcett and Dr. Jing Xiong.

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