Indoor Environments最新文献

Indoor air stability is a condition under which the indoor temperature gradients can affect the trajectories of constant breathing flows. However, it remains unclear how indoor air stability affects the airborne contaminants from violent respiratory events such as coughing. Here, we report a study of the dispersion of airborne droplets from coughing under different indoor air stability conditions. The distributions of droplets from coughing processes under stable and unstable conditions were compared. The results revealed that the stable conditions confined the coughed droplets to the breathing zone of the droplet host, whereas the unstable conditions intensified the dispersion of droplets and reduced their local concentration. The dimensionless vertical distance of the droplet cloud under the stable condition was 4 times longer than that of the unstable condition at t = 15 s. In addition, we observed that high ventilation rate caused an intense mixing of the indoor air, thus weakening the effect of indoor air stability on the propagation of droplet cloud. Personal exposure of the stable condition was 6.5 times larger than that of the unstable condition at a ventilation rate of 2.1 ACH, while it decreased to lower than half of that of the unstable condition at 7.1 ACH and 12.3 ACH. Personal exposure to the indoor environment should be assessed by simultaneously considering the indoor air stability conditions and ventilation rates. This study has provided insights into the transmission of cough droplets in indoor environments and has practical significance for preventing the spread of contagious diseases.

The quality of the classroom environment, including ventilation, air quality and thermal conditions, has an important impact on children’s health and academic achievement. The use of portable HEPA filter air cleaners is widely suggested as a strategy to mitigate exposure to particulate matter and airborne viruses. However, there is a need to quantify the relative benefits of such devices including the impacts on energy use. We present a simple coupled dynamic thermal and air quality model and apply it to naturally ventilated classrooms, representative of modern and Victorian era construction. We consider the addition of HEPA filters with, and without, reduced opening of windows, and explore concentrations of carbon dioxide (CO₂), particulate matter PM_2.5, airborne viral RNA, classroom temperature and energy use. Results indicate the addition of HEPA filters was predicted to reduce PM_2.5 by 40–60 % and viral RNA by 30–50 % depending on the classroom design and window opening behaviour. The energy cost of running HEPA filters is likely to be only 1 %–2 % of the classroom heating costs. In scenarios when HEPA filters were on and window opening was reduced (to account for the additional clean air delivery rate of the filters), the heating cost was predicted to be reduced by as much as − 13 %, and these maximum reductions grew to − 46 % in wintertime simulations. In these scenarios the HEPA filters result in a notable reduction in PM_2.5 and viral RNA, but the CO₂ concentration is significantly higher. The model provides a mechanism for exploring the relative impact of ventilation and air cleaning strategies on both exposures and energy costs, enabling an understanding of where trade-offs lie.

Odor guide values (OGV) are used when a person is exposed to an odor in a room and complains about odor annoyance. OGV are derived from odor detection thresholds (ODT) measured under standard environmental conditions. The study question was whether uncomfortable environmental conditions alter odor perception and should therefore be considered when applying OGV. ODT for n-butanol were determined with an olfactometer and in room air. Twenty healthy, non-smoking volunteers (10 women/10 men, 19–50 years) were selected and trained compliant with the European Standard for Olfactometry EN 13725:2022. Standard conditions were an average temperature between 22 °C and 23 °C, warm light (2800 Kelvin), quiet fan noise (45 dB), 415 ppm carbon dioxide (CO₂), and relative humidity between 34 % and 42 %. On each test day, one of five conditions was examined: high temperature (26°C), cold light (6500 Kelvin), traffic noise (70 dB with peaks up to 85 dB), 1000 ppm CO₂ and 4000 ppm CO₂. Comparability of log-transformed ODT values was assessed by Bland-Altman plot analysis. None of the five conditions systematically affected ODT, either when measured with an olfactometer or in room air. Compared to standard conditions (Limits of Agreement (± LoA) 1.03), the variability of ODT was higher at high temperature (± LoA 1.70) and traffic noise (± LoA 1.45), but not at cold light (± LoA 1.02), 1000 ppm CO₂ (± LoA 0.87) or 4000 ppm CO₂ (± LoA 0.93). The results show that OGV can be used in uncomfortable environmental conditions. However, because ODT were lower or higher in a few subjects at high temperatures and traffic noise, individual occupant’s perception of temperature and noise should always be considered when applying the OGV concept. Furthermore, the results confirm that the experimental determination of ODT should be performed under controlled and standardized environmental conditions.

Most people in the United States have been exposed to per- and polyfluoroalkyl substances (PFAS) which have been linked to a wide array of adverse health conditions in adults and children. The consumption of contaminated drinking water is an important human exposure pathway to PFAS. Residential sources also contribute to PFAS exposure through dermal contact and ingestion of house dust, which acts as an aggregate of chemicals from sources like furnishing materials and consumer products. The U.S. Department of Housing and Urban Development (HUD) conducted the first nationwide survey of residential hazards called the American Healthy Homes Survey (AHHS) in 2005, followed by a second survey (AHHS II) in 2017. The U.S. Environmental Protection Agency (EPA) collaborated with HUD on both efforts and subsequently analyzed PFAS in household tap water and house dust collected from the same homes during the AHHS II study. This study leverages these paired samples to investigate potentially important exposure sources and pathways in the residential environment. Here we report results for paired household tap water and house dust samples from 241 homes for 13 and 16 PFAS chemicals, respectively. All 13 targeted chemicals were detected in the household tap water samples with detections ranging from 100 % for PFBS to 1 % for PFNS, and all 16 targeted chemicals were detected in the house dust samples with detections ranging from 97 % for PFOA to 9 % for PFNS. Four chemicals (PFOA, PFOS, PFHxS, and PFHpA) were measured above the limit of detection in at least 50 % of the samples in both media. All households had at least one of the targeted PFAS detected in both their tap water and house dust. Results provided evidence that geographical factors, such as proximity to ambient contamination sources, were main drivers of PFAS contamination in tap water, while PFAS contamination in house dust was driven mainly by within-home sources. Exposure estimates calculated from the measured PFAS concentrations highlight the importance of addressing potential sources of exposure to PFAS within homes in addition to ambient sources affecting communities’ drinking water, particularly to reduce children’s exposure to these chemicals.

Background

Household PM_2.5 exposures have adverse health effects, and cooking behaviors are an important source of PM_2.5 in the home. There is a need for accurate measures of cooking activity to better understand its associations with household PM_2.5 since self-reported surveys are often subject to recall bias and misreporting of cooking events.

Objective

In this study, we aimed to address limitations associated with a self-reported cooking metric, by using temperature data to estimate cooking activity.

Methods

We developed an algorithm to identify cooking events at the 5-minute level using real-time temperature data measured near the stove and in the living room, across 148 households in Chelsea and Dorchester, MA. We compared the number of cooking events identified by this algorithm with cooking events self-reported by participants in daily activity logs and survey responses, and further assessed how these metrics differed with respect to their associations with occurrence of peak PM2.5, in mixed effects logistic regression models.

Results

We found that 65 % of the cooking events identified by the algorithm were not reported by participants. Furthermore, households classified as frequent vs infrequent cooking households using the algorithm had a larger difference in indoor PM_2.5 levels, compared to households classified by self-report. In mixed effects logistic regression models for elevated household PM_2.5 levels, we observed much stronger associations between household PM_2.5 and algorithm-derived cooking activity (OR: 2.85 [95 % CI: 2.76, 2.95]) as compared to the association between household PM_2.5 and self-reported cooking activity (OR: 1.22 [95 % CI: 1.17, 1.27] for stove use and OR: 1.67 [95 % CI: 1.58, 1.76] for grill use/frying/broiling/sauteing).

Significance

Overall, the algorithm developed in this study presents a data-driven approach to collecting cooking activity data in U.S. households, that may be more indicative of actual cooking events and also more predictive of household PM_2.5 in indoor environmental models.

For the ecotoxicological assessment of a chemical substance it is important to know its partition behavior. In inanimate nature these are water, soil and air. Indoors it is gas and particle phase, settled house dust and surfaces. Due to the complex interaction of molecules with environmental compartments, their dynamics is usually modeled. However, this requires as precise information as possible about the physical and chemical properties as well as reaction pathways. Traditionally, the parameters necessary for the respective modeling are determined experimentally. However, with the increasing performance of computers, prediction tools based on structure-activity relationships and quantum mechanical calculations have become increasingly popular. The algorithms underlying the respective tools are highly specialized and mathematically sophisticated. Therefore, scientific expertise and extensive experience are needed to assess whether a specific value is reliable or not. This work provides an overview of available databases and prediction tools. It is intended to support the user in selecting accurate molecular parameters of organic substances in order to be able to make reliable statements about the partitioning of these substances in the indoor environment and about exposure of occupants.

Accurate occupancy information helps to improve building energy efficiency and occupant comfort. Occupancy detection methods based on CO₂ sensors have received attention due to their low cost and low intrusiveness. In naturally ventilated buildings, the accuracy of CO₂-based occupancy detection is generally low in related studies due to the complex ventilation behavior and the difficulty in measuring the actual air exchange through windows. In this study, we present two novel features for occupancy detection based on the spatial distribution of the CO₂ concentration. After a quantitative analysis with Support Vector Machine (SVM) as classifier, it was found that the accuracy of occupancy state detection in naturally ventilated rooms could be improved by up to 14.8 percentage points compared to the baseline, reaching 83.2 % (F1 score 0.84) without any ventilation information. With ventilation information, the accuracy reached 87.6 % (F1 score 0.89). The performance of occupancy quantity detection was significantly improved by up to 25.3 percentage points versus baseline, reaching 56 %, with root mean square error (RMSE) of 11.44 occupants, using only CO₂-related features. Additional ventilation information further enhanced the performance to 61.8 % (RMSE 9.02 occupants). By incorporating spatial features, the model using only CO₂-related features revealed similar performance as the model containing additional ventilation information, resulting in a better low-cost occupancy detection method for naturally ventilated buildings.

Exposures to indoor air pollution from the combustion of Low-grade Fuels (LgFs) is a leading risk factor for global disease burden. Emerging evidence suggest a potential role of PM_2.5 exposures in Blood Pressure (BP) elevation. This study assessed the effects of Indoor Air Pollution (IAP) from the use of LgFs on hypertension disease burden among women in low-income settlements. We measured the kitchen concentrations and personal exposures to PM_2.5 for users of LgFs compared with users of LPG in the city of Aba, Nigeria. The study also assessed hypertension markers for 328 adult women in the population. Association between PM_2.5 exposures and BP was modelled with hierarchical multiple regression, adjusting for major covariates. The indoor PM_2.5 concentrations for users of LgFs ranged from 47.5 to 800.0 μg/m³ while that for LPG users ranged from 33.0 to 112.5 μg/m³. The mean personal exposures were 152.3 μg/m³ and 58.3 μg/m³ for users of LgFs and LPG, respectively. The age-standardized hypertension prevalence in the population was 24.4 % and 15.6 % for users of LgFs and LPG, respectively. Exposures to PM_2.5 moderately predicted SBP among users of LgFs but not among LPG users; the increase in 1 μg/m³ of PM_2.5 exposure resulted to 0.44 mmHg increase in SBP among users LgFs. Long-term exposures to IAP from the use of low-grade fuels is associated with increased SBP and greater risks of systemic hypertension. These findings reinforce the need for public policies towards improving access to, and affordability of LPG as an alternative household fuel.

It is important to quantify uncertainty in the viable genomic material encapsulated in the respiratory particles emitted by infected people so that it can be converted into an emission rate as a function of respiratory and metabolic activities and used to estimate the probability of infection for an indoor scenario. Clinical measurements of viral loads for SARS-CoV-2 made using infection surveys, Gesundheit-II samplers, and human challenge studies are evaluated and a mathematical model is derived to estimate the quantum emission rate as a function of the genomic and viable viral loads. Modelled emission rates for SARS-CoV-2 agree with clinical data above detection limits. The viral load is found to vary over at least 6 orders of magnitude because it is person and time dependent, and contingent on many other factors that are difficult to quantify. It is similarly large for other respiratory pathogens. Therefore, the genomic and viable-virion emission rates display similar heterogeneity. When emission rates are used to estimate absolute infection risk using the Wells-Riley model, the predictions are so uncertain that they cannot be used in any meaningful way to provide useful quantitative guidance for designing indoor spaces.

Indoor CO₂ levels became particularly topical during the recent COVID-19 pandemic. In this study a long-term investigation of indoor CO₂ levels in a 1970s built residential apartment in single occupancy is presented. Three NDIR CO₂ sensors were used to measure CO₂ levels over a one-year period. Mean CO₂ levels over this period were 1278 ± 504 ppm, with elevated CO₂ levels of greater than 2000 ppm not uncommon. Subsequent investigations used the single zone mass balance model and the decay of CO₂ in the absence of occupants to estimate the ventilation rates in various configurations. A mean natural ventilation rate of 0.16 ACH was estimated with all windows closed. Opening fan light windows resulted in a mean ventilation rate of 2.86 ACH whereas opening all windows increased the mean ventilation rate to 19.1 ACH. Evidence was observed of the effect of both wind speed and indoor-outdoor temperature difference on the ventilation rates. It was concluded that with all windows closed the natural infiltration rate was insufficient to maintain optimal indoor air quality even in single occupancy. Opening the fan light windows was sufficient to achieve satisfactory indoor air quality but insufficient for the effective inhibition of airborne disease transmission.