Indoor air最新文献

Despite growing concerns about both indoor air pollution and depression, research on their association remains limited. This study examined the association between exposure to indoor air pollutants and depression, using nationally representative data. We utilized 1685 participants based on the Eighth Korea National Health and Nutrition Examination Survey data (the 2020–2021 cycle). Indoor concentrations of PM_2.5, CO₂, formaldehyde (HCHO), total volatile organic compounds (TVOCs), and their components including benzene, toluene, ethylbenzene, xylene, and styrene were measured. Depression status was defined as self-reported physician-diagnosed depression during the period of residence at the time of the survey. We analyzed using multivariable logistic regression models adjusted for demographic and environmental covariates, seasonal factors, and a time trend. The prevalence of depression was 2.6%. The mean concentrations of indoor air pollutants excluding TVOC were higher in the depression group. Notably, increased exposure to PM_2.5 was significantly associated with depression (OR: 1.20, 95% CI: 1.04–1.37), as was TVOC exposure (OR: 1.08, 95% CI: 1.01–1.16). We also found the association between depression and benzene (OR: 2.32, 95% CI: 1.35–4.00), toluene (OR: 1.17, 95% CI: 1.04–1.30), and ethylbenzene (OR: 1.11, 95% CI: 1.02–1.20). Specifically, males and low-income groups were more susceptible to the association of PM_2.5, while high-income groups were more strongly associated with TVOC. Our study demonstrated differences in the association between indoor air pollutants and depression across exposure groups and types, providing important insights for public health interventions.

Previous studies on the associations between exposure to various indoor air pollutants, as well as coexposure to ambient and indoor air pollutants, and rhinitis are limited. Data from the Korea National Health and Nutrition Examination Survey (2020–2021) were analyzed (n = 1812). Ambient air pollutant concentrations were estimated using the Community Multiscale Air Quality model, while indoor air pollutant concentrations were measured in each household. Associations with the presence of rhinitis symptoms in the past 12 months (PRS) were examined using logistic regression models for individual ambient and indoor air pollutants, as well as for a composite exposure variable that combined ambient fine particulate matter (PM_2.5) and indoor total volatile organic compound (TVOC) concentrations (low–low, low–high, high–low, and high–high). Stratified analyses by household income were conducted using the same models. A doubling of ambient PM_2.5 concentrations over 1 year was associated with higher odds of PRS (odds ratio [OR] = 2.68, 95% confidence interval [CI]: 1.45, 4.78). A doubling of TVOC (OR = 1.08, 95% CI: 1.01, 1.16) and toluene (OR = 1.10, 95% CI: 1.02, 1.20) concentrations was associated with higher odds of PRS. Compared to the low–low group, other composite exposure groups had increased odds of PRS. These associations were more pronounced among individuals with lower income than among those with higher income. Ambient and indoor air pollution exposures were both individually and collectively associated with a higher risk of rhinitis symptoms among adults. Since this study used a cross-sectional design, further longitudinal studies are needed.

This study experimentally investigated the transport dynamics of exhaled aerosols, which may carry infectious pathogens, in a mixing-ventilated test room using three heated dummies as human simulators. Two dummies were seated at desks separated by a partition wall, while the third stood nearby. Each dummy acted as an infector one at a time, releasing test aerosols through a low-momentum horizontal jet simulating continuous mouth exhalation. Aerosol concentrations were monitored using 28 sensors to provide high-resolution data on aerosol spread dynamics. The mixing ventilation air change rates were 1.8 and 3.2 1/h, and additional measurements were conducted with an air cleaner in operation. CFD simulations revealed that particles from the low-momentum exhalation jet were deflected upwards by the dummy′s thermal plume and quickly mixed with supply air from a circular ceiling diffuser. The results showed that the exhaled particles reached the exposed person′s breathing zone within 20–100 s. Particle concentrations were relatively uniform throughout the room, indicating that a well-mixed approximation is suitable for estimating airborne infection transmission risk from indirect exposure in small spaces. Relative transmission risks were analysed under various conditions. While the partition wall delayed initial exposure, it had minimal impact on long-term risk. Air cleaners increased air mixing and reduced the delay between aerosol release and exposure, potentially elevating short-term risk. However, the long-term benefits of enhanced ventilation outweighed the initial increase in risk, reducing overall airborne infection transmission over extended durations.

Maintaining acceptable indoor air quality (IAQ) in kindergartens is essential for children′s health, cognitive development and staff well-being, yet it remains a persistent challenge. This study introduces an innovative dual-indicator framework for IAQ assessment that combines real-time monitoring of carbon dioxide (CO₂) and radon (Rn) with simulation-based modelling to evaluate and optimise ventilation strategies. Unlike CO₂ alone, which only indicates conditions during occupancy, Rn monitoring captures conditions before and at the start of occupancy, providing a more comprehensive assessment. Measurements were conducted for several months in two playrooms: P1, a modular steel unit with natural ventilation, and P2, a concrete structure with hybrid ventilation. During occupancy, CO₂ levels frequently exceeded health-based thresholds (405−2725 ppm, mean 1266 ± 537 ppm in P1; 405−1910 ppm, mean 865 ± 304 ppm in P2). Rn concentrations were highest before occupancy and declined gradually in the morning (2–386 Bq m⁻³, mean 99 ± 62 Bq m⁻³ in P1; 2–304 Bq m⁻³, mean 59 ± 49 Bq m⁻³ in P2), reflecting differences in airtightness and ventilation efficiency. Simulations categorised IAQ into four levels, with Category I representing optimal conditions. P2 achieved Category I or II for 59% of the time, compared to 28% in P1. Two advanced ventilation strategies were then simulated: constant air volume (CAV) and demand-controlled ventilation (DCV). Both reduced CO₂ and Rn below recommended thresholds, while DCV provided greater adaptability and achieved 17% lower ventilation heat losses than CAV. These results demonstrate the value of integrating dual-indicator monitoring with simulation tools to support data-driven, energy-efficient and health-focused ventilation strategies in early childhood environments.

In the context of climate change, the increase in frequency and intensity of heat waves, as well as air pollution, poses a challenge for environmental conditioning in buildings. This fact is of concern in schools given the special vulnerability of their occupants and the frequent energy-environmental gap of these buildings in southern Spain. The objective of this research is the assessment of different energy efficient ventilation and thermal conditioning systems in typical classrooms in warm Mediterranean climate. For this purpose, a predictive model capable of comprehensively assessing energy consumption, thermal comfort, and air quality is proposed. This model has been generated in TRNSYS and validated by analyzing discrepancies with monitoring data. The introduction of mechanical ventilation systems with heat recovery and simple mechanical ventilation supplemented with a novel evaporative cooling system, with limited implementation in schools, has been evaluated and compared. In addition, the reduction of energy consumption through the installation of a photovoltaic energy production system has been evaluated. The results show that the model is effective for the proposed comprehensive assessment including detailed analysis of indoor air quality. They also prove that the novel mechanical ventilation with evaporative cooling system achieves the best balance between thermal comfort, air quality, and energy efficiency, especially in climate change scenarios with less than 28.4% of school time in discomfort. Its integration with a photovoltaic energy installation, with an installed peak power between 0.45 and 1.35 kWp, would allow covering the ventilation and lighting energy demand, proposing itself as a viable, sustainable and replicable solution for schools in hot-dry climates.

Exposure to radon-222 (²²²Rn) and its decay products poses a significant public health risk. ²²²Rn tends to accumulate in poorly ventilated areas, underground spaces, and caves, which justifies the existence of international reference levels for its annual average indoor concentrations. This comparative study synthesizes findings, drawn from the published literature, from over 30 countries and identifies more than 30 environmental and structural factors influencing seasonal variation in ²²²Rn concentrations in both aboveground and underground buildings and environments. Five different methods for estimating annual averages using seasonal correction factors (SCFs) are critically reviewed. Due to the number and complexity of human-induced and environmental influences, seasonal fluctuations in ²²²Rn activity may be unpredictable and nonsinusoidal. The typical pattern observed in the Northern Hemisphere—elevated indoor ²²²Rn concentrations during autumn and winter—does not universally apply. Accordingly, SCFs should be locally derived and interpreted within the regional climatic and hydrogeological context. Correction factors developed for underground spaces should not be applied to aboveground buildings, and vice versa. In underground structures and caves, local air dynamics and geology can override temperature-driven ventilation effects. In aboveground structures, effective ventilation remains the key factor in reducing indoor radon levels. This study highlights the need for context-specific radon assessments and supports improved modeling and dose estimation practices.

This study aimed to determine the concentrations of BTEX and polycyclic aromatic hydrocarbons (PAHs; in gaseous and particulate phases) in the indoor air (truck bays, changing room, alarm point, and gym) of two selected fire stations in Poland (FSN and FSC), to compare these concentrations with the concentrations of the same compounds in the atmospheric air, and to assess the origin of air pollution at both fire stations. The measurements were conducted during the period from May to June of 2021, with data collection occurring at each measurement point for a period of 7 days, simultaneously inside and outside the building. A total of 42 samples of BTEX and 42 samples of PAHs in the gas phase, as well as 7 samples of PAHs in the solid phase, were collected at each measurement point. The mean concentrations of the sum of BTEX and PAHs were higher inside both fire stations (ƩBTEX 13.4-60.9 μg/m³; ƩPAH 250.4–715.0 ng/m³) than in the atmospheric air (ƩBTEX 3.4-13.1 μg/m³; ƩPAH 144.4–182.8 ng/m³). The order of BTEX concentrations at individual measurement points was as follows: truck bay (FSC) > gym (FSC) > alarm point (FSC) > changing room (FSN) > truck bay (FSN), while the concentrations of PAHs can be arranged in the following order: truck bay (FSC) > changing room (FSN) > alarm point (FSC) > gym (FSC) > truck bay (FSN). The findings of the diagnostic ratios and principal component analysis indicate that the primary source of indoor air pollution at the fire stations under study was identified as fuel combustion by fire vehicles and combustion equipment, as well as stored uniforms contaminated with combustion products. This research delineates the parameters for preventive measures aimed at enhancing air quality at fire stations and underscores the imperative for additional research in this domain.

Increased use of diverse consumer products generates many chemicals of potential health concerns. For monitoring personal exposure to those chemicals, passive samplers are inexpensive methods for assessing time-weighted average exposure. This review summarizes the application of passive sampling methods for assessing exposure to hazardous chemicals released from consumer products in indoor environments, discussing the principles of passive sampling, various sampler types, and their effectiveness in monitoring different classes of pollutants, including volatile organic compounds (VOCs), semi–volatile organic compounds (SVOCs), and reactive substances. Challenges associated with calibration and validation for specific chemicals are addressed. The review highlights the potential of passive sampling as a cost-effective tool for large-scale monitoring and emphasizes the need for future research to develop advanced techniques, such as miniaturized multianalyte and time-resolved samplers, to achieve comprehensive exposure assessments including transformation products and capture complex indoor air pollution dynamics.

Maintaining a healthy indoor environment is crucial for a productive and well-balanced life. This study proposes a comprehensive indoor environment index (IEI) that integrates air quality, thermal, visual, and acoustical comfort indicators using sensor data. Major indoor pollutants (CO, PM_2.5, and PM₁₀), temperature, relative humidity, noise levels, and illuminance are combined through an analytic hierarchy process to formulate the IEI. A hybrid deep learning model based on a CNN-GRU architecture is then used to forecast indoor environmental states across four categories (severe, very poor, poor, and satisfactory). ANOVA and Tukey′s HSD analysis confirmed significant differences among these categories. The model was trained on 80% of the dataset and tested on the remaining 20%, with performance evaluated using precision, recall, F1-score, and AUC-ROC. The proposed approach achieved a mean F1-score of 0.96, demonstrating high predictive accuracy and reliability. These results confirm the robustness and reliability of the proposed model. The study demonstrates its potential for supporting accurate indoor environmental quality prediction and providing a foundation for informed building management decisions.

Computational fluid dynamics (CFD) is a powerful method for predicting and optimising indoor environmental conditions due to its ability to simulate complex airflow patterns, temperature distributions and contaminant dispersion with high spatial resolution. However, the accuracy and reliability of CFD simulations depend strongly on robust verification and validation methodologies. This review critically examines how numerical models are experimentally validated in the context of indoor environmental quality (IEQ) studies, with a focus on the parameters used and methods adopted by researchers. The central objective is to understand if and how validation is performed and which variables are typically considered. The findings reveal major inconsistencies in the current literature regarding the choice of validation parameters, sensor deployment strategies and the reporting of calibration procedures, which limit reproducibility and cross-study comparability. To address these gaps, the review proposes a novel validation framework that integrates both thermal comfort and indoor air quality (IAQ) metrics into a unified approach. The review also reflects upon the advantages of advanced thermal manikins as comprehensive validation instruments capable of capturing the dynamic interaction between the human body and indoor environments, thereby enhancing the realism and applicability of CFD simulations in IEQ research. This work is, to the authors′ knowledge, the first to systematically dissect existing validation methodologies for CFD simulations of pollutant transport in indoor environments while simultaneously proposing a structured pathway forward, paving the way for standardisation efforts and the integration of more accurate, cost-effective monitoring approaches in both research and practice.