Indoor air最新文献

Consumer products are significant contributors to indoor volatile organic compound (VOC) emissions, with oil-based wood stains, commonly used in do-it-yourself (DIY) projects, being particularly notable sources. This study was aimed at evaluating personal exposure to VOCs during DIY application of oil-based wood stains by integrating passive sampling, dermal exposure assessment, and urinary biomonitoring. Twenty-two volunteers wore a validated expanded polytetrafluoroethylene passive sampler (ePTFE PS) during indoor painting activities in naturally ventilated residential settings. Despite open-window ventilation, benzene concentrations increased from 4.5 to 5100 μg/m³—a 1100-fold rise. Toluene, ethylbenzene, xylenes, and styrene showed similar increases ranging from 160 to 710 times. Limonene and α-pinene concentrations rose by 440% and 600%, respectively. A statistically significant correlation was observed between inhalation exposure to m-xylene and its urinary metabolites (p- and o-methylhippuric acids), with metabolite concentrations reaching up to 57.2 μg/mg creatinine. Inhalation exposure was calculated to be 630–1100 times higher than dermal exposure. These findings highlight the dominance of the inhalation route and demonstrate that passive sampling is an effective and practical approach for assessing VOC exposure from consumer products in real-world environments.

The heavy reliance on traditional biomass fuels in sub-Saharan Africa, particularly in Ethiopia, significantly contributes to deforestation, environmental degradation, and health risks associated with inefficient cooking practices. This study is aimed at examining the socioeconomic factors influencing the adoption of the “Mirt” fuel-efficient cook stove in the Bale Zone, Sinana District, Southeast Ethiopia, and its potential contribution to carbon emission reduction. Adopting a mixed methods approach, primary data were collected through a household survey and personal interviews from four selected villages, comprising a sample of 298 households. A binary logistic regression analysis was used to determine the factors influencing stove adoption. It is shown that 46.97% of the sampled households have already adopted an improved cook stove. Level of education of the household head, family size, and having a separate kitchen were positively associated and significantly associated with adoption, and male-headed households were less likely to adopt the cook stove compared to female-headed households. These results underscore the importance of education and gender relations in the adoption of technology and, more generally, the role of household infrastructure (like separate cooking compartments). It also underlines the demand for better stoves and wider dissemination to raise take-up and sustainable forest management. Ultimately, education and awareness campaigns, women’s empowerment, and improved housing infrastructure are critical for advancing the adoption of fuel-efficient cook stoves in rural Ethiopia. These types of interventions may help to reduce household use of fuelwood, lower deforestation, and reduce carbon emissions from traditional cooking methods. Policy options include promoting community-based education programs, formulating gender-sensitive outreach, and further advancing stove design considerations for sustainable energy and environmental outcomes. This research offers significant empirical insights for policymakers and development practitioners looking to enhance rural energy consumption and promote environmental sustainability in the sub-Saharan African context. Future investigations should examine supplementary sociocultural and economic variables influencing adoption to formulate more efficacious interventions.

Rationale: CPR is known to generate aerosols, increasing the risk of transmitting airborne diseases. This study evaluated aerosol exposure during manual and mechanical CPR, noting that pressure-driven mechanical CPR might exacerbate aerosol dispersion.

Objectives: The objectives were to measure aerosol exposure to health workers during manual and mechanical CPR and to evaluate the efficacy of mitigation strategies.

Methods: A high-fidelity mannequin simulated CPR at 110 compressions per minute, assessing the effectiveness of mask coverings, supraglottic airways, endotracheal tubes, HEPA filters, and evacuators in reducing aerosol dispersion.

Measurements: Aerosol concentrations were continuously measured at the mannequin’s head, trunk, and feet for 10 min.

Results: Mechanical CPR produced less aerosol than hands-on CPR. Surgical masks and N95 respirators reduced aerosol levels, while sealed airway devices with HEPA filters further minimized dispersion. HEPA evacuators provided the most significant reduction.

Conclusion: In conclusion, MCPR reduces aerosol production compared to HCPR by avoiding personnel rotation and creating horizontal airflow instead of upward airflow. Aerosol mitigation is effectively enhanced through the use of properly fitted masks and well-sealed SGAs or ETs equipped with HEPA filters. Notably, HEPA evacuators are highly effective in minimizing aerosol dispersion. Implementing MCPR, wearing N95 masks, ensuring a proper mask fit for face coverings, transitioning early to ETs, and using HEPA evacuators are critical measures for enhancing health worker safety during CPR.

The long periods of time people often spend inside their automobiles expose them to chemical substances and bioaerosols, such as molds, dust mites, pollens, and pet dander. This study evaluated the health impact of remaining inside a vehicle by combining an impinger that efficiently collects air quality in water from a small space with a bioassay using cultured macrophages. The impinger container was curved to minimize the splashing of water caused by air inflow, and a spray nozzle was attached to its tip to efficiently mix water and air. The water collected to evaluate the interior air quality as well as the reference material CRM28 showed increased expression levels of tumor necrosis factor α (TNFα) and cytochrome P450 1A1 (CYP1A1) in U937 macrophages, demonstrating the utility of impinger collection and subsequent bioassay in evaluating air quality. The water sample collected from an old vehicle induced higher expression of TNFα, CYP1A1, and heme oxygenase-1 than that from a relatively new vehicle. This effect is considered to partially depend on the presence of mold in the vehicular interior space. The method proposed in this study integrating impinger collection with bioassay is suitable for investigating the effects of combined exposure in small spaces such as vehicle interiors.

Infrared radiation equipment, as a typical personal comfort system (PCS) unit, could effectively improve human thermal sensation vote (TSV) in cold environments. It could also be positioned diversely and controlled independently to meet personalized thermal requirements. However, the heat transfer principle of infrared radiation differs from thermal convection and conduction. Most parts of the human body were covered by clothes in winter; thus, the thermal responses differed from those uncovered. As a result, studies on the thermal responses and thermal sensitivity of different parts with clothing to local radiation were limited. An experiment was conducted at the indoor air temperature of 18°C in winter, during which five body parts were stimulated by infrared radiation at the intensity of 4 and 20 W/m². Subsequently, the indoor environment and physiological parameters were measured. Meanwhile, the local and overall thermal responses of the human body were collected with questionnaires. Furthermore, the calculation method based on thigh sweating rate was determined as an approach to assess the sensitivity of each body part to infrared radiation more precisely. In terms of this method, the chest was more sensitive to infrared radiation than limbs, and the intensity of the infrared radiation had greater impacts on chest sensitivity while fewer effects on limbs. These findings could help improve the reliability in predicting and evaluating human thermal responses to infrared radiation environments and also provided references for the applications of infrared radiation PCS devices.

Mold is found in most indoor environments and is of great concern due to adverse health effects and infrastructure damage it can cause. One key aspect of this growing problem is early detection and localization of mold contamination so appropriate measures can be implemented. In this study, a combination of solid-phase microextraction (SPME), semiquantitative gas chromatography–mass spectroscopy (GC-MS) analysis, and principal component analysis (PCA) of 14 select microbial volatile organic compounds (MVOCs) was used to determine if volatile organic profiling could be used to differentiate between molds grown on various building materials. Briefly, SPME fibers with PDMS/DVB coatings were employed to collect and generate volatile organic profiles of target MVOCs emitted by Aspergillus versicolor and Penicillium chrysogenum when grown on two common building materials. The volatile compound extraction and identification method revealed that P. chrysogenum grown on gypsum and A. versicolor grown on pine produced unique MVOC profiles from one another, which indicated species and substrate differentiation could be made based on the volatile organic profiles. Additionally, the production of dimethyl disulfide (DMDS) and geosmin was found to be specific to P. chrysogenum and A. versicolor, respectively, and therefore could serve as potential biomarkers for screening for the presence of each species. This study suggests profiling select MVOCs is viable for detecting specific hazardous molds when the substrate is known and a streamlined workflow for indoor mold monitoring: initial broad-spectrum GC-MS screening for fungal presence → selected MVOC profiling for species identification → molecular verification of hazardous species.

Improving indoor air quality (IAQ) in hospital environments protects vulnerable patients and healthcare professionals from airborne pollutants and pathogens. This study integrates three interconnected components: a comprehensive analysis of hospital IAQ, an evaluation of a nanosilver/chitosan–titanium dioxide (nano-Ag/CS-TiO₂) filter, and an assessment of real-time monitoring using a cloud-based platform. The nano-Ag/CS-TiO₂ filter demonstrated enhanced efficiency in reducing a broad spectrum of pollutants, including particulate matter (PM_2.5 and PM₁₀), carbon monoxide, carbon dioxide, volatile organic compounds (VOCs), and microbial aerosols. Measurements were conducted across different hospital zones and timeframes to reflect typical hospital operations and assess the adaptability of the proposed solutions. The study further addresses the long-term use of nano-Ag/CS-TiO₂ and underscores its advantages over existing filtration methods. Cloud-based monitoring provides real-time data, allowing for timely intervention for IAQ, particularly in high-risk areas like negative pressure isolation rooms. The finding in a negative pressure isolation room demonstrated significant improvement in IAQ postinstallation of the nano-Ag/CS-TiO₂ filtration system. The integration of nano-Ag/CS-TiO₂ filtration and real-time monitoring supports compliance with World Health Organization (WHO) IAQ standards. These findings highlight the potential for broader application in various healthcare settings, such as outpatient clinics and emergency departments. The study also ensures accuracy in pollutant detection, addressing the potential for false positives and negatives in cloud-based monitoring. This study highlights that a nano-Ag/CS-TiO₂ filtration system can enhance IAQ in hospital settings by effectively removing air pollutants and inactivating airborne pathogens. This approach facilitates the development of evidence-based strategies for promoting healthier indoor environments in hospitals.

Infants are particularly vulnerable to indoor air pollution due to their developing respiratory systems and prolonged time spent indoors. This study proposes a dynamic indoor air quality (IAQ) prediction model for daycare centers using automated machine learning (Auto ML) and monthly relearning. The model integrates real-time and historical data to address variability caused by occupant behavior, ventilation, and environmental conditions. A total of 446,611 observations were collected over 16 months from a two-story daycare center in South Korea, measuring CO₂, PM_2.5, PM₁₀, and TVOCs every 10 min. Among tested algorithms, ensemble learning methods (e.g., VotingEnsemble and XGBoost) showed superior performance. The model achieved predictive accuracies of 80%–89% for CO₂, 77%–98% for PM_2.5, 78%–97% for PM₁₀, and 70%–99% for TVOCs. Compared to prior studies focused on controlled environments or single-variable input, this model leverages diverse indoor–outdoor variables and continuous data accumulation, enabling real-time IAQ management. The approach is scalable to other sensitive facilities such as schools and healthcare centers. These findings demonstrate the potential of AI-based prediction frameworks for enhancing IAQ control strategies and protecting vulnerable populations.

The COVID-19 pandemic highlighted the importance of effective air disinfection technologies to mitigate the spread of airborne pathogens. In-duct ultraviolet germicidal irradiation (UVGI) systems may be a viable solution. System performance should be validated using biodosimetry, as per several existing standards. These tests yield the kill rates of a surrogate organism and its reduction equivalent dose (RED), with the intent that the RED be extrapolated to a predicted kill rate of a target pathogen of interest, such as SARS-CoV-2. However, this extrapolation requires adjustments to account for potential bias between the surrogate RED and the target RED (called the RED bias). Overlooking this mismatch can lead to inaccurate claims of the actual inactivation performance against the target. This study uses computational fluid dynamics modeling to analyze the UV dose distribution and resulting RED bias in in-duct UVGI systems. The results showed that, when MS2, a UV-resistant organism, is used as a surrogate to predict SARS-CoV-2 inactivation efficiency, the RED bias ranged from 1.14 to 1.46 within the studied cases, suggesting that the SARS-CoV-2 log inactivation can be overestimated by as much as 46%. This study also explores the combined variable (CV) approach as a more accurate method for predicting pathogen inactivation, offering an alternative to the RED bias approach. Both the RED bias approach and the CV approach were effective in improving the accuracy of performance predictions. This study underscores the need for the industry to incorporate considerations of the RED bias phenomenon in the future development of performance evaluation guidance to avoid overestimation of the treatment performance and safeguard public health.