Indoor air最新文献

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.

Buildings need practical ways to monitor indoor air quality (IAQ) beyond aggregate TVOC readings. We show that low-cost commercial VOC sensors, coupled with machine learning, can recover compound-specific information from plant-emitted terpenes, enabling practical, real-time bioindication in buildings. In an office testbed, we exposed sensors to 16 terpenes and trained random forest, support vector machine, and XGBoost models on time series features. The models detected “any terpene versus background” at 97%–100% accuracy, identified “plants versus background” at ~100%, and discriminated among individual compounds with accuracies up to 96%. Feature importance emphasized temporal dynamics (e.g., autocorrelation lags and entropy measures) rather than static peaks, highlighting the value of sequence information for commodity hardware. Complementary experiments with living basil plants showed reproducible VOC profiles and stress-induced bursts of ~70–100 ppb, confirming in situ feasibility. A placement analysis across 13 locations indicated that the HVAC return-air duct provides the most actionable, room-integrated signal for deployment, balancing accuracy and coverage. Together, these results establish a pathway from TVOC to compound-aware IAQ using sensors already common in smart buildings, with immediate applications to exposure triage and demand-controlled ventilation, and a foundation for plant-integrated environmental monitoring.

This study investigates windcatchers as sustainable ventilation solutions in architecture, addressing energy efficiency and reduced fossil fuel dependence. Traditional windcatchers, passive cooling devices, have been enhanced with sealed doors and windows to overcome earlier limitations. Focusing on dry climates, this research evaluates the performance of windcatchers in maintaining indoor comfort. Using computational fluid dynamics (CFD) through Comsol Multiphysics, factors like output velocity, pressure differential, and mass flow rate are assessed, with Hail City selected for testing. The study also examines the impact of inlet orientation on airflow dynamics, temperature, and humidity distribution within a building, comparing two cases: lateral inlet (Case 1) and top-side inlet (Case 2). Results show that Case 2 achieves higher velocities, particularly at the exit, where speeds are 3.5 times greater than in Case 1. Temperature distributions vary, with Case 1 demonstrating lower exit temperatures and Case 2 exhibiting reduced inlet temperatures. Humidity rises with inlet speed in both cases, more notably in Case 1. These findings highlight the importance of inlet orientation in enhancing airflow efficiency and optimizing environmental conditions, offering valuable insights for architects and engineers aiming to integrate sustainable design elements for improved indoor comfort and energy savings.

This study presents a comprehensive bibliometric review of research on ventilation and air quality in swimming pool facilities from 2001 to 2025. Based on literature retrieved from Web of Science, Scopus, and PubMed, this study employs the CiteSpace visualization tool to analyze and shows that research has evolved from basic assessments of air exchange to multidimensional concerns including disinfection by-products (DBPs), pollutant exposure, thermal comfort, and energy efficiency. Despite these advances, significant gaps remain: most studies prioritize energy performance over detailed pollutant control, long-term high-resolution monitoring data are scarce, and ventilation standards often lack explicit criteria for swimmer health. Geographically, Canada, China, and the United States have led contributions in this field. The findings demonstrate a thematic transition from experience-based to precision control, supported by tools such as CFD, TRNSYS, and model predictive control, and signal growing potential for artificial intelligence and data-driven facility management. This review identifies core themes and technical trajectories, offering theoretical insight and decision-making support for the design and operation of low-carbon, health-oriented swimming pool environments.

A major objective of home energy efficiency upgrades is to reduce uncontrolled ventilation in order to reduce heat loss. Such reduction also affects concentrations of indoor pollutants by reducing the ingress of pollutants from the outdoor air and the egress of pollutants generated inside the home, which may lead to increased levels of indoor pollutants. In the absence of routine monitoring of the effect of home energy efficiency measures on air exchange, we develop a simple model of the relationship between indoor radon concentrations and dwelling air exchange and, for the first time, use population radon survey data for the United Kingdom to infer the impact of home energy efficiency improvements on home air infiltration. The model suggests that concentrations of radon rise steeply for quite modest reductions in air exchange, especially at low air exchange levels. We estimate that an increase in indoor radon of 10 Bq/m³ implies a mean reduction in air change rate of 0.22 air changes/h (ach), with 10th, 50th and 90th centiles of 0.03, 0.15 and 0.51 ach, respectively. Applying the model to observed data on current radon levels in UK homes with different levels of energy efficiency suggests a range of effects depending on the initial and final energy efficiency characteristics of the dwelling. For example, the maximum difference in indoor radon, between a typical UK home without any energy efficiency measures and one with wall insulation, glazing and loft insulation, is around 35 Bq/m³, which according to our model would correspond to a reduction in air change rate of 0.79 ach. The results suggest that current home energy efficiency measures may be associated with an appreciable decrease in air exchange in UK homes and that retrofit design principles may need to be re-evaluated to avoid embedding unintended adverse consequences for indoor air quality and health.

This study is aimed at comparing the presence of BTEX (benzene, toluene, ethylbenzene, and o-xylene and m,p-xylene) and certain aldehydes (acetaldehyde and formaldehyde) in the residential environments of ALS patients (cases) and healthy individuals (controls). Moreover, a comparative analysis of indoor air pollutants in urban and rural areas to determine differences based on environment and housing type was performed, and the relationship between the presence of benzene and toluene in indoor air and the levels of their metabolites in the urine of the occupants was assessed. To this end, a cross-sectional, case-control, observational study was designed. Forty-three locations (13 cases, 28 controls, and 2 positive controls) and two negative controls were analyzed. Passive samplers were used to measure BTEX (Tenax) and aldehydes (Radiello). t,t-Muconic acid and hippuric acid levels (metabolites of benzene and toluene, respectively) were quantified in urine using liquid chromatography. The indoor air comparative analysis between cases and controls showed no statistically significant differences. Higher formaldehyde levels were found in urban indoor environments compared to rural ones (p < 0.001), based on the measurements from the 28 control subjects (analyzed separately as the case patients experienced changes in their daily routines due to their illness). Formaldehyde levels were also higher in apartment buildings than in detached houses (p = 0.007). Finally, in the analysis of biological samples, no association was found between the levels of benzene and toluene in the air and the levels of t,t-muconic acid and hippuric acid in urine.

Automotive technicians are exposed to lead during maintenance operations, and the associated risks remain unexplored. This study utilized the ghost wipe as a sampling medium to collect particulates on the wall surfaces from 12 automobile repair workshops. The measured lead deposition ranged from 15.2 ± 0.06 to 416.9 ± 83.13  μg/m². A linear correlation was observed between lead deposition and the number of repaired automotives. A lead deposition–air model was developed to estimate lead concentrations within automotive workshops. Three additional workshops were investigated, and the measured lead depositions of them are similar to the model′s prediction. Furthermore, the lead emission factor (C_c: 0.0625 μg/m³/car) was evaluated, and a risk model was developed based on C_c, employment duration, and annual number of repaired automotives. It was verified that wearing protective masks during maintenance operations and maintaining an acceptable air exchange rate can effectively reduce lead exposure. In summary, our study introduces the lead deposition–air model as a novel method for assessing occupational exposure in the context of particulate matter contamination.