Personal CO2 cloud: laboratory measurements of metabolic CO2 inhalation zone concentration and dispersion in a typical office desk setting

Passive and low-energy strategies to improve sleep thermal comfort and energy resilience during heat waves and cold snaps

Sleep is a pillar of human health and wellbeing. In high- and middle-income countries, there is a great reliance on heating, ventilation, and air conditioning systems (HVAC) to control the interior thermal environment in the bedroom. However, these systems are expensive to buy, maintain, and operate while being energy and environmentally intensive-problems that may increase due to climate change. Easily-accessible passive and low-energy strategies, such as fans and electrical heated blankets, address these challenges but their comparative effectiveness for providing comfort in sleep environments has not been studied. We used a thermal manikin to experimentally show that many passive and low-energy strategies are highly effective in supplementing or replacing HVAC systems during sleep. Using passive strategies in combination with low-energy strategies that elevate air movement like ceiling or pedestal fans enhances the cooling effect by three times compared to using fans alone. We extrapolated our experimental findings to estimate heating and cooling effects in two historical case studies: the 2015 Pakistan heat wave and the 2021 Texas power crisis. Passive and low-energy strategies reduced sleep-time heat or cold exposure by 69-91%. The low-energy strategies we tested require one to two orders of magnitude less energy than HVAC systems, and the passive strategies require no energy input. These strategies can also help reduce peak load surges and total energy demand in extreme temperature events. This reduces the need for utility load shedding, which can put individuals at risk of hazardous heat or cold exposure. Our results may serve as a starting point for evidence-based public health guidelines on how individuals can sleep better during heat waves and cold snaps without relying on HVAC.

Physiology or Psychology: What Drives Human Emissions of Carbon Dioxide and Ammonia?

Humans are the primary sources of CO2 and NH3 indoors. Their emission rates may be influenced by human physiological and psychological status. This study investigated the impact of physiological and psychological engagements on the human emissions of CO2 and NH3. In a climate chamber, we measured CO2 and NH3 emissions from participants performing physical activities (walking and running at metabolic rates of 2.5 and 5 met, respectively) and psychological stimuli (meditation and cognitive tasks). Participants' physiological responses were recorded, including the skin temperature, electrodermal activity (EDA), and heart rate, and then analyzed for their relationship with CO2 and NH3 emissions. The results showed that physiological engagement considerably elevated per-person CO2 emission rates from 19.6 (seated) to 46.9 (2.5 met) and 115.4 L/h (5 met) and NH3 emission rates from 2.7 to 5.1 and 8.3 mg/h, respectively. CO2 emissions reduced when participants stopped running, whereas NH3 emissions continued to increase owing to their distinct emission mechanisms. Psychological engagement did not significantly alter participants' emissions of CO2 and NH3. Regression analysis revealed that CO2 emissions were predominantly correlated with heart rate, whereas NH3 emissions were mainly associated with skin temperature and EDA. These findings contribute to a deeper understanding of human metabolic emissions of CO2 and NH3.

Human personal air pollution clouds in a naturally ventilated office during the COVID-19 pandemic

Personal cloud, termed as the difference in air pollutant concentrations between breathing zone and room sites, represents the bias in approximating personal inhalation exposure that is linked to accuracy of health risk assessment. This study performed a two-week field experiment in a naturally ventilated office during the COVID-19 pandemic to assess occupants' exposure to common air pollutants and to determine factors contributing to the personal cloud effect. During occupied periods, indoor average concentrations of endotoxin (0.09 EU/m³), TVOC (231 μg/m³), CO₂ (630 ppm), and PM₁₀ (14 μg/m³) were below the recommended limits, except for formaldehyde (58 μg/m³). Personal exposure concentrations, however, were significantly different from, and mostly higher than, concentrations measured at room stationary sampling sites. Although three participants shared the same office, their personal air pollution clouds were mutually distinct. The mean personal cloud magnitude ranged within 0-0.05 EU/m³, 35-192 μg/m³, 32-120 ppm, and 4-9 μg/m³ for endotoxin, TVOC, CO₂, and PM₁₀, respectively, and was independent from room concentrations. The use of hand sanitizer was strongly associated with an elevated personal cloud of endotoxin and alcohol-based VOCs. Reduced occupancy density in the office resulted in more pronounced personal CO₂ clouds. The representativeness of room stationary sampling for capturing dynamic personal exposures was as low as 28% and 5% for CO₂ and PM₁₀, respectively. The findings of our study highlight the necessity of considering the personal cloud effect when assessing personal exposure in offices.

Building performance simulations can inform IoT privacy leaks in buildings

As IoT devices become cheaper, smaller, and more ubiquitously deployed, they can reveal more information than their intended design and threaten user privacy. Indoor Environmental Quality (IEQ) sensors previously installed for energy savings and indoor health monitoring have emerged as an avenue to infer sensitive occupant information. For example, light sensors are a known conduit for inspecting room occupancy status with motion-sensitive lights. Light signals can also infer sensitive data such as occupant identity and digital screen information. To limit sensor overreach, we explore the selection of sensor placements as a methodology. Specifically, in this proof-of-concept exploration, we demonstrate the potential of physics-based simulation models to quantify the minimal number of positions necessary to capture sensitive inferences. We show how a single well-placed sensor can be sufficient in specific building contexts to holistically capture its environmental states and how additional well-placed sensors can contribute to more granular inferences. We contribute a device-agnostic and building-adaptive workflow to respectfully capture inferable occupant activity and elaborate on the implications of incorporating building simulations into sensing schemes in the real world.

Proxy methods for detection of inhalation exposure in simulated office environments

BACKGROUND

Modern health concerns related to air pollutant exposure in buildings have been exacerbated owing to several factors. Methods for assessing inhalation exposures indoors have been restricted to stationary air pollution measurements, typically assuming steady-state conditions.

OBJECTIVE

We aimed to examine the feasibility of several proxy methods for estimating inhalation exposure to CO2, PM2.5, and PM10 in simulated office environments.

METHODS

In a controlled climate chamber mimicking four different office setups, human participants performed a set of scripted sitting and standing office activities. Three proxy sensing techniques were examined: stationary indoor air quality (IAQ) monitoring, individual monitoring of physiological status by wearable wristband, human presence detection by Passive Infrared (PIR) sensors. A ground-truth of occupancy was obtained from video recordings of network cameras. The results were compared with the concurrent IAQ measurements in the breathing zone of a reference participant by means of multiple linear regression (MLR) analysis with a combination of different input parameters.

RESULTS

Segregating data onto sitting and standing activities could lead to improved accuracy of exposure estimation model for CO2 and PM by 9-60% during sitting activities, relative to combined activities. Stationary PM2.5 and PM10 monitors positioned at the ceiling-mounted ventilation exhaust in vicinity of the seated reference participant accurately estimated inhalation exposure (adjusted R² = 0.91 and R² = 0.87). Measurement at the front edge of the desk near abdomen showed a moderate accuracy (adjusted R² = 0.58) in estimating exposure to CO2. Combining different sensing techniques improved the CO2 exposure detection by twofold, whereas the improvement for PM exposure detection was small (~10%).

SIGNIFICANCE

This study contributes to broadening the knowledge of proxy methods for personal exposure estimation under dynamic occupancy profiles. The study recommendations on optimal monitor combination and placement could help stakeholders better understand spatial air pollutant gradients indoors which can ultimately improve control of IAQ.

Development of a Bayesian inference model for assessing ventilation condition based on CO2 meters in primary schools

Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO₂ meters to indicate air quality, but how to interpret the data remains unclear. Many uncertainties are also involved, including manual readings, student numbers and schedules, uncertain CO₂ generation rates, and variable indoor and ambient conditions. This study proposed a Bayesian inference approach with sensitivity analysis to understand CO₂ readings in four primary schools by identifying uncertainties and calibrating key parameters. The outdoor ventilation rate, CO₂ generation rate, and occupancy level were identified as the top sensitive parameters for indoor CO₂ levels. The occupancy schedule becomes critical when the CO₂ data are limited, whereas a 15-min measurement interval could capture dynamic CO₂ profiles well even without the occupancy information. Hourly CO₂ recording should be avoided because it failed to capture peak values and overestimated the ventilation rates. For the four primary school rooms, the calibrated ventilation rate with a 95% confidence level for fall condition is 1.96±0.31 ACH for Room #1 (165 m³ and 20 occupancies) with mechanical ventilation, and for the rest of the naturally ventilated rooms, it is 0.40±0.08 ACH for Room #2 (236 m³ and 21 occupancies), 0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room #3 (236 m³ and 19 occupancies), 0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room #4 (231 m³ and 8-9 occupancies) for three consecutive days.

Use of Low-Cost Devices for the Control and Monitoring of CO2 Concentration in Existing Buildings after the COVID Era

In the COVID-19 era, a direct relationship has been consolidated between the concentration of the pollutant carbon dioxide (CO2) and indoor disease transmission. For reducing its spread, recommendations have been established among which air renewal is a key element to improve indoor air quality (IAQ). In this study, a low-cost CO2 measurement device was designed, developed, assembled, prototyped, and openly programmed so that the IAQ can be monitored remotely. In addition, this clonic device was calibrated for correct data acquisition. In parallel, computational fluid dynamics (CFD) modeling analysis was used to study the indoor air flows to eliminate non-representative singular measurement points, providing possible locations. The results in four scenarios (cross ventilation, outdoor ventilation, indoor ventilation, and no ventilation) showed that the measurements provided by the clonic device are comparable to those obtained by laboratory instruments, with an average error of less than 3%. These data collected wirelessly for interpretation were evaluated on an Internet of Things (IoT) platform in real time or deferred. As a result, remaining lifespan of buildings can be exploited interconnecting IAQ devices with other systems (as HVAC systems) in an IoT environment. This can transform them into smart buildings, adding value to their refurbishment and modernization.

Respiratory performance of humans exposed to moderate levels of carbon dioxide

In a business as usual scenario, atmospheric carbon dioxide concentration (CO₂ ) could reach 950 parts per million (ppm) by 2100. Indoor CO₂ concentrations will rise consequently, given its dependence on atmospheric CO₂ levels. If buildings are ventilated following current standards in 2100, indoor CO₂ concentration could be over 1300 ppm, depending on specific ventilation codes. Such exposure to CO₂ could have physiological and psychological effects on building occupants. We conducted a randomized, within-subject study, examining the physiological effects on the respiratory functions of 15 persons. We examined three exposures, each 150 min long, with CO₂ of: 900 ppm (reference), 1450 ppm (decreased ventilation), and 1450 ppm (reference condition with added pure CO₂ ). We measured respiratory parameters with capnometry and forced vital capacity (FVC) tests. End-tidal CO₂ and respiration rates did not significantly differ across the three exposures. Parameters measured using FVC decreased significantly from the start to the end of exposure only at the reduced ventilation condition (p < 0.04, large effect size). Hence, poor ventilation likely affects respiratory parameters. This effect is probably not caused by increased CO₂ alone and rather by other pollutants-predominantly human bioeffluents in this work-whose concentrations increased as a result.