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Open Access Publications from the University of California

Open Access Policy Deposits

This series is automatically populated with publications deposited by UC Berkeley Department of Architecture researchers in accordance with the University of California’s open access policies. For more information see Open Access Policy Deposits and the UC Publication Management System.

Cover page of Assessment and mitigation of personal exposure to particulate air pollution in cities: An exploratory study

Assessment and mitigation of personal exposure to particulate air pollution in cities: An exploratory study


Assessment of integrated personal exposure (PE) to airborne particulate matter (PM) across diverse microenvironments (MEs) over 24 hours under different exposure scenarios is necessary to identify appropriate strategies to improve urban air quality and mitigate the health effects of PM. We carried out a collaborative study in a densely populated city-state (Singapore) to assess the integrated PE to fine particles (PM2.5), ultrafine particles (UFPs) and black carbon (BC) across diverse indoor and outdoor urban MEs, estimate related health risks and make suitable recommendations for healthy living in cities. Two volunteers with different lifestyles participated in the study by tracking their PE to particulate air pollution and the time-activity patterns over 24 hours using portable PM monitoring devices and recording their whereabouts using GPS coordinates. Home, transport and recreation (i.e., food court) MEs represented pollution hotspots of PM2.5 (21.0 μg/m3), BC (3.4 μg/m3) and UFP (33.0 × 103 #/cm3), respectively. Among the different modes of transport used by the participants (walking, cycling, e-scooter, mass rapid transport (MRT), bus, car and taxi), the air pollutants had elevated concentrations while commuting by public transport (bus and MRT) as well as during active modes of transport (walking and cycling). Air-conditioned cars and taxis, equipped with air filtration systems, represented the lowest PE. The health risk assessment revealed that there are potential carcinogenic risks associated with the long-term exposure to elevated levels of PM2.5-bound toxic trace elements. These risks can be mitigated with the introduction of low-carbon and active modes of transport in place of internal combustion engines and the use of indoor air pollution exposure mitigation devices.

Cover page of Ivy: Bringing a Weighted-Mesh Representation to Bear on Generative Architectural Design Applications

Ivy: Bringing a Weighted-Mesh Representation to Bear on Generative Architectural Design Applications


Mesh segmentation has become an important and well-researched topic in computational geometry in recent years (Agathos et al 2008) . As a result, a number of new approaches have been developed that have led to innovations in a diverse set of problems in computer graphics (CG) (Shamir 2008). Specifically, a range of effective methods for the division of a mesh have recently been proposed, including by K-means (Shlafman et al. 2002), graph cuts (Golovinskiy and Funkhouser 2008; Katz and Tal 2003 ), hierarchical clustering (Garland et al 2001; Gelfand and Guibas 2004 ; Golovinskiy and Funkhouser 2008 ), primitive fitting (Athene et al 2006), random walks(Lai et al), core extraction(Katz et al), tubular multi-scale analysis(Mortara et al. 2004), spectral clustering(Liu and Zhang 2004), and critical point analysis(Lin et al 2007), all of which depend upon a weighted graph representation, typically the dual of the given mesh (Shamir 2008). While these approaches have been proven effective within the narrowly-defined domains of application for which they have been developed (Chen 2009), they have not been brought to bear on wider classes of problems in fields outside of CG, specifically on problems relevant to generative architectural design (GAD). Given the widespread use of meshes and the utility of segmentation in GAD, by surveying the relevant and recently matured approaches to mesh segmentation in CG that share a common representation of the mesh dual, this paper identifies and takes steps to address a heretofore unrealized transfer of technology that would resolve a missed opportunity for both subject areas. Meshes are often employed by architectural designers for purposes that are distinct from and present a unique set of requirements in relation to similar applications that have enjoyed more focused study in computer science. This paper presents a survey of similar applications, including thin-sheet fabrication(Mitani and Suzuki 2004), rendering optimization(Garland et al 2001), 3d mesh compression(Taubin et al 1998), morphing (Shapira et al 2008) and mesh simplification(Kalvin and Taylor 1996), and distinguish the requirements of these applications from those presented by GAD, including non-refinement in advance of fabrication (such that the mesh geometry remain unaltered), the constraining of mesh geometry to planar-quad faces, and the ability to address a diversity of mesh features (such as creased edges or patterns) that may or may not be preserved. Following this survey of existing approaches and unmet needs, the authors assert that if a generalized framework for working with graph representations of meshes is developed, allowing for the interactive adjustment of edge weights, then the recent developments in mesh segmentation may be better brought to bear on GAD problems. This paper presents recent work toward the development of just such a framework, implemented as a plug-in for the visual programming environment Grasshopper.

Cover page of Automated decontamination of workspaces using UVC coupled with occupancy detection

Automated decontamination of workspaces using UVC coupled with occupancy detection


Periodic disinfection of workspaces can reduce SARS-CoV-2 transmission. In many buildings periodic disinfection is performed manually; this has several disadvantages: it is expensive, limited in the number of times it can be done over a day, and poses an increased risk to the workers performing the task. To solve these problems, we developed an automated decontamination system that uses ultraviolet C (UVC) radiation for disinfection, coupled with occupancy detection for its safe operation. UVC irradiation is a well-established technology for the deactivation of a wide range of pathogens. Our proposed system can deactivate pathogens both on surfaces and in the air. The coupling with occupancy detection ensures that occupants are never directly exposed to UVC lights and their potential harmful effects. To help the wider community, we have shared our complete work as an open-source repository, to be used under GPL v3.

Impact of Cognitive Tasks on CO2 and Isoprene Emissions from Humans.


The human body emits a wide range of chemicals, including CO2 and isoprene. To examine the impact of cognitive tasks on human emission rates of CO2 and isoprene, we conducted an across-subject, counterbalanced study in a controlled chamber involving 16 adults. The chamber replicated an office environment. In groups of four, participants engaged in 30 min each of cognitive tasks (stressed activity) and watching nature documentaries (relaxed activity). Measured biomarkers indicated higher stress levels were achieved during the stressed activity. Per-person CO2 emission rates were greater for stressed than relaxed activity (30.3 ± 2.1 vs 27.0 ± 1.7 g/h/p, p = 0.0044, mean ± standard deviation). Isoprene emission rates were also elevated under stressed versus relaxed activity (154 ± 25 μg/h/p vs 116 ± 20 μg/h/p, p = 0.041). The chamber temperature was held constant at 26.2 ± 0.49 °C; incidental variation in temperature did not explain the variance in emission rates. Isoprene emission rates increased linearly with salivary α-amylase levels (r2 = 0.6, p = 0.02). These results imply the possibility of considering cognitive tasks when determining building ventilation rates. They also present the possibility of monitoring indicators of cognitive tasks of occupants through measurement of air quality.

Balancing thermal comfort datasets: We GAN, but should we?


Thermal comfort assessment for the built environment has become more available to analysts and researchers due to the proliferation of sensors and subjective feedback methods. These data can be used for modeling comfort behavior to support design and operations towards energy efficiency and well-being. By nature, occupant subjective feedback is imbalanced as indoor conditions are designed for comfort, and responses indicating otherwise are less common. This situation creates a scenario for the machine learning workflow where class balancing as a pre-processing step might be valuable for developing predictive thermal comfort classification models with high-performance. This paper investigates the various thermal comfort dataset class balancing techniques from the literature and proposes a modified conditional Generative Adversarial Network (GAN), comfortGAN, to address this imbalance scenario. These approaches are applied to three publicly available datasets, ranging from 30 and 67 participants to a global collection of thermal comfort datasets, with 1,474; 2,067; and 66,397 data points, respectively. This work finds that a classification model trained on a balanced dataset, comprised of real and generated samples from comfortGAN, has higher performance (increase between 4% and 17% in classification accuracy) than other augmentation methods tested. However, when classes representing discomfort are merged and reduced to three, better imbalanced performance is expected, and the additional increase in performance by comfortGAN shrinks to 1 - 2%. These results illustrate that class balancing for thermal comfort modeling is beneficial using advanced techniques such as GANs, but its value is diminished in certain scenarios. A discussion is provided to assist potential users in determining which scenarios this process is useful and which method works best.

Cover page of Occupant satisfaction with the indoor environment in seven commercial buildings in Singapore

Occupant satisfaction with the indoor environment in seven commercial buildings in Singapore


Understanding occupants’ satisfaction with their environment is an important step to improve indoor environmental quality (IEQ). These satisfaction data are limited to Singaporean commercial buildings. We surveyed (N = 666) occupant satisfaction with 18 IEQ parameters in seven Green Mark certified air-conditioned commercial buildings in Singapore. About 78 % of the participants expressed satisfaction with their overall workspace environment. Occupants were most satisfied with flexibility of dress code (86 % satisfaction), electrical lighting (84 %) and cleanliness (82 %), and most dissatisfied with sound privacy (42 % dissatisfaction), personal control (32 %) and temperature (30 %). We found that satisfaction with cleanliness has the highest impact to overall workspace environment satisfaction. Our results suggest achieving high occupant satisfaction for some IEQ factors is harder than others, which suggests the premise of singular satisfaction rating (e.g., 80 %) that applies to all IEQ parameters may not be reliable and representative. We determined that the major contributors to thermal dissatisfaction were insufficient air movement and overcooled workspaces. Occupants in open plan office were unhappy with the noise produced by their nearby colleagues. We also found that several IEQ variables (odors, air movement, available space, overall privacy, sound privacy and temperature) which are not statistically significant to the overall workspace satisfaction on their own, but their impacts becomes substantial when these IEQ variables are merged into larger environmental factors (i.e., Perceived Air Quality, Acoustics, Layout and Thermal). These results can support the development of an IEQ benchmarks for commercial buildings in Singapore.

Cover page of Modeling solar radiation on a human body indoors by a novel mathematical model

Modeling solar radiation on a human body indoors by a novel mathematical model


Solar radiation affects occupant comfort and building energy consumption in ways that have received relatively little attention in environmental design and energy simulation. Direct, diffuse, and reflected irradiation on the body have warming effects that can be equated to increases in the mean radiant temperature (MRT) of the occupant’s surroundings. A simplified occupant-centered model (SolarCal Model, i.e., SC Model) has recently been adopted in ASHRAE Standard 55, followed by a comprehensive simulation procedure combining detailed room- and manikin geometries using the Daylight Coefficient Model (DC Model). This paper presents an intermediate-level mathematical model (the HNU Solar Model) capable of rapid annual calculations of the MRT increases. Both the room and occupant geometries are simplified but consistent with those of the SC Model. Novel strategies of the calculation include a sky-annulus fraction, virtual body shadow, and equivalent window. Modeled results are compared with those simulated by the DC Model using Radiance software, which is assumed to be accurate. The differences in the Delta MRT by diffuse, direct, and reflected solar radiation are usually less than 1, 2, and 0.5°C between the DC and HNU Solar Models, respectively. For a given occupant position indoors, the HNU Solar Model only needs five seconds to obtain the annual Delta MRT, while the DC Model needs about seven minutes. The HNU Solar Model provides a simple and practical way to evaluate indoor environments at the room scale, to design fenestration, and to predict set-point changes in annual energy simulation of HVAC systems.

Field investigations of a smiley-face polling station for recording occupant satisfaction with indoor climate


The use of smiley-face polling stations has had a rapid growth as a means of automatically and efficiently collecting user satisfaction verdicts in airports, restrooms, museums, and retail. Their advantages are that they are low cost, efficient for both respondents and analysts, in addition to having higher response rates than other survey types. Their main disadvantage is the lack of control with who is voting, meaning both repeat voters and non-voters may lead to biased results. The aim of this study is to assess the representativeness and functioning of such publicly located satisfaction polling stations (SPSs) in an indoor climate setting, and to evaluate their potential for real-time evaluation of occupant's satisfaction with the indoor climate. We carried out continuous field tests in two office buildings for more than two months where the results of SPSs were compared with 473 survey results collected in 10 rounds during the tests. To assess how sensitive the instrument was to changing conditions, we deliberately changed temperature setpoints on selected days in one of the buildings. We found that the SPSs had a high and variable non-response bias which could result in a low accuracy for benchmarking of building indoor climate satisfaction. Results also showed a high correlation between SPS complaints and complaints recorded in the surveys for the thermal comfort aspect of indoor climate, including thermal comfort induced by temperature interventions. SPSs can provide valuable continuous recordings of the occupant's satisfaction with the indoor climate.

Cover page of Skin Temperature Sampling Period for Longitudinal Thermal Comfort Studies

Skin Temperature Sampling Period for Longitudinal Thermal Comfort Studies


There is limited scientific evidence on what is the optimal sampling period to measure skin temperature in longitudinal thermal comfort studies, and how this sampling period selection affects the results. iButtons® are among the most widely used wireless sensors in field and lab studies to measure skin temperature, since they are accurate, reliable, and cause minimal discomfort. However, their use is significantly limited by their memory capacity. We aimed to determine what is the optimal sampling period of skin temperature in studies which use iButtons®. We measured wrist skin temperature of 14 participants at 60 s intervals for a period of 1 month and wrist temperature of 5 participants at 20 s intervals for a week. Results showed that the selection of a 300 s sampling period would provide reasonably accurate results while limiting the number of times data needs to be downloaded.