Occupant behavior and indoor particulate concentrations in daycare centers

'Occupant behavior' is the primary mechanism determining indoor particulate concentrations. Various indoor human activities generate particulate matter. Human-building interactions, such as window opening behavior, change the number of outdoor particulate matter introduces to the building. 'Daycare center' where young children spend considerable time has an occupant schedule distinguished from other types of buildings. In the study, we analyzed the effects of occupant behavior on indoor particle concentrations in daycare centers by on-site monitoring. The measurements were performed in four daycare centers located in Gyeonggi-do, South Korea. Optical particle counters(OPS, model 3330, TSI Inc., Shoreview, MN, USA) were used for particulate concentration monitoring. The source strengths of particles resuspended by each human activity were calculated, and their contributions to indoor particle concentrations were evaluated. Further, characteristics of human-building interactions and their corresponding impacts on indoor air quality were also analyzed. Results showed that particle resuspension was greater when occupants were awake (mean, 41.0 particles·min^-1) than when they were asleep (mean, 9.2 particles·min^-1), and the contribution of occupant status was also higher when awake (37-70% vs. 8-18%) for particles sized (0.3-10.0 μm). Analyzing five detailed human activities, vacuuming (9.8·10⁷ particles·min^-1) emitted the highest amount of particulate matter per person, followed by physical activity (4.8·10⁷ particles·min^-1), sedentary activity (1.9·10⁷ particles· min^-1), meals (1.9·10⁷ particles·min^-1), and nap time (8.1·10⁶ particles·min^-1). The study suggests that vacuuming should be avoided while children are occupied. This research also shows that children could be exposed to high daily average indoor particulate concentration (up to 1217 particles·cm^-3) when windows were opened for an extended period of time while poor outdoor air quality. These results indicate that indoor air quality can be severely degraded by opening windows without considering the level of outdoor particle concentration.

The impact of the COVID-19 on households' hourly electricity consumption in Canada

The spread of the COVID-19 pandemic caused a tremendous impact on our societies, including changes in household energy consumption. Using measured electricity use data from 500 homes in Ottawa, Canada, this study applies changepoint analysis, descriptive statistics, k-means clustering, and the corresponding change of electricity utility bills before and after COVID-19. Our analysis indicates that the average household daily electricity consumption increased by about 12% in 2020 relative to 2019, about one-third was due to warmer temperatures, with much of the rest due to the temperature-independent loads (e.g., lighting and appliances). Additionally, the highest five peak loads corresponding to post-COVID are significantly higher (15-20%) than peaks that occurred pre-COVID. The lockdown's impact on household electricity use is not consistent, and there are noticeable differences among different months, seasons, and day types. Two clusters of household electricity use patterns emerged, with about one-third showing significant increases during the pandemic and the remainder showing only minor changes. On the other hand, in the summer, all customers' electricity use profile patterns after the pandemic resemble the pattern before the pandemic. Yet, there is a significant increase (from 16.3 to 29.1%) in daily demand after COVID-19. Finally, the average increase in the utility bill post-COVID would be 9.71% if TOU rates were used instead of the flat rate that was implemented as a subsidy to consumers.

Datasets for occupancy profiles in apartment-style student housing for occupant behavior studies and application in building energy simulation

Building energy simulation (BES) tools fail to capture diversity among occupants' consumption behaviors by using simple and generic occupancy and load profiles, causing uncertainties in simulation predictions. Thus, generating actual occupancy profiles can lead to more accurate and reliable BES predictions. In this article, occupancy profiles for apartment-style student housing are presented from high-resolution monitored occupancy data. A geo-fencing app was designed and installed on the cellphones of 41 volunteer students living in student housing buildings on Clarkson University's campus. Occupants' entering and exiting activities were recorded every minute from February 4 to May 10, 2018. Recorded events were sorted out for each individual by the date and time of day considering 1 for 'entered' events and 0 for 'exited' events to show the probability of presence at each time of day. Accounting for excluded days (234 days with errors and uncertainties), 1,096 daily occupancy observations were retained in the dataset. Two methods were used to analyze the dataset and derive weekday and weekend occupancy schedules. A simple averaging method and K-means clustering techniques were performed [1]. This article provides the input datasets that were used for analysis as well as the outputs of both methods. Occupancy schedules are presented separately for each day of a week, weekdays, and weekend days. To show differences in students' occupancy patterns, occupancy schedules in 7 clusters for weekdays and 3 clusters for weekend days are provided. These datasets can be beneficial for modelers and researchers for either using provided occupancy schedules in BES tools or understanding occupant behaviors in student housing.

Addressing the impact of COVID-19 lockdown on energy use in municipal buildings: A case study in Florianópolis, Brazil

COVID-19 has spread quickly to several countries following the initial outbreak of the disease. As a consequence, several measures have been taken to mitigate the virus spread worldwide. In the city of Florianópolis, in southern Brazil, a strict lockdown was implemented on 16 March 2020. Although commercial activities were allowed to resume 21 April, a complete lockdown of municipal public buildings (e.g., administrative buildings and schools) lasted up to 5 August 2020. Reports in the literature emphasize the influence of occupant presence and actions on energy use in buildings. Therefore, the objective of this study was to assess the impact of the COVID-19 lockdown on the electric energy use of municipal buildings in Florianópolis. A large database with monthly electric energy use data was provided by the City Hall and analyzed. Firstly, the consumer units were grouped into three categories: systems, services and buildings. This revealed that buildings were directly affected by the lockdown measures, but systems and services were not. Therefore, an in-depth evaluation of health centers, administrative buildings, elementary schools and nursery schools was conducted and mean electric energy reductions of 11.1 %, 38.6 %, 50.3 %, and 50.4 %, respectively, were observed. Although it may initially seem unexpected, municipal health centers had a small electric energy use reduction, because they were not directly responsible for COVID-19 treatment, as patients were forwarded to specific facilities. Walkthroughs and energy audits were performed in an administrative building, an elementary school, and a nursery school, to gain a deeper understanding of the consumption trends. It was observed that municipal buildings present a basal energy use intensity even when the buildings are unoccupied. Energy audits verified that stand-by loads and vital loads, such as lighting for safety and computer servers, play a key role in this share of energy use.

Heating behavior using household air-conditioners during the COVID-19 lockdown in Wuhan: An exploratory and comparative study

Wuhan is located in China's hot summer and cold winter (HSCW) zone, where the average temperature of the city from January to February 2020 is only 6.6 °C. This study aimed to explore and compare the air conditioner (AC) heating behavior of Wuhan residents before and after the COVID-19 lockdown. The date of commencement of the Wuhan lockdown (January 23, 2020) was considered the demarcation point to divide the AC monitoring data from the Internet of Things cloud platform into two groups; before and after Wuhan lockdown. Statistical methods were applied to analyze AC heating behavior of Wuhan residents from a total of 378 air conditioners during these two periods. The daily AC usage rate and average daily AC usage duration following the lockdown had a stronger correlation with daily outdoor temperature than that before the lockdown. AC heating behavior continued to demonstrate a part-time intermittent operation during the lockdown period, despite residents staying at home for a longer period. Trigger temperatures for occupants to turn on or adjust their AC during the lockdown period were overall 1-2 °C higher than before the lockdown. The AC heating demand in the HSCW zone has been increasing in recent years. These research results inform research on household energy demand and thermal comfort in China's HSCW zone, and provide a reference on the household behavioral changes in the occupants in the context of a lockdown as a result of the global COVID-19 pandemic.

Impacts of the COVID-19 lockdown on energy consumption in a Canadian social housing building

The COVID-19 pandemic hit societies in full force in 2020 and compelled people all around the world to change their lifestyle. The time spent at home significantly surged during the pandemic and this change in occupancy can have a direct impact on building energy consumption. COVID-19 lockdowns also accelerated the transition towards telework, a trend that many expect to last. Changes in energy consumption under lockdown is thus a valuable asset to forecast how energy could be consumed in buildings in the future. Here, we aim to quantify the impacts of the COVID-19 lockdown on the energy consumption (electricity, hot water and space heating) in residential buildings by answering these two questions: (i) Did the lockdown lead to changes in total energy consumption?, and (ii) Did the lockdown lead to changes in consumption patterns (i.e. time of the day at which energy is consumed)? To do so, we compared the energy consumption measured in a 40-dwelling social housing building located in Quebec City (Canada) during four months of lockdown to those of the months that preceded the lockdown. It is found that consumption patterns for electricity and hot water changed for the first two months of the lockdown, when the most intensive lockdown measures were applied. Overall consumption slightly increased for these two energy expenditures, but the more important change was that consumption occurred throughout the day instead of being concentrated in the evening as observed before the lockdown. Results shed light on the impact of lockdown on energy bills for consumers and on how energy utilities might be solicited during this kind of episode.

Assessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California

The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires-by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviors by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires.