Indoor air最新文献

Studies investigating the correlation between particulate matter (PM) concentrations measured by a light scattering (LS) device and environmental factors are crucial to identify LS values with significant errors. Herein, the relationship between PM_2.5 obtained through beta attenuation monitoring (BAM) and LS was examined with respect to seven environmental factors. Machine learning (ML) and general statistical methods were employed to reveal complex relationships. Data from five cities were initially analyzed to understand the association between BAM measurements and environmental factors. Our findings confirmed that wind direction (WD) had a strong nonlinear impact on short-term measurements, whereas temperature and local pressure had similar effects on long-term PM_2.5 measurements. Subsequently, a method was developed using general statistical techniques to establish an environment wherein LS could maintain a relatively high accuracy level. Furthermore, ML techniques were employed to determine that LS was more affected (by 8.2%) by the changes in WD compared with BAM, emphasizing the importance of designing devices capable of responding to WD. Finally, LS was calibrated using four ML algorithms, and through a quantitative evaluation of coefficient of determination, mean absolute error, and root mean square error values, AdaBoost was identified as an effective algorithm for correcting LS measurements. With this understanding of the correlation between PM_2.5 and environmental factors, along with an efficient correction method, its widespread adoption in future research concerning real-time PM measurement is anticipated.

Current models to determine the risk of airborne disease infection are typically based on a backward quantification of observed infections, leading to uncertainties, e.g., due to the lack of knowledge whether the index person was a superspreader. In contrast, the present work presents a forward infection risk model that calculates the inhaled dose of infectious virus based on the virus emission rate of an emitter and a prediction of Lagrangian particle trajectories using CFD, taking both the residence time of individual particles and the biodegradation rate into account. The estimation of the dose-response is then based on data from human challenge studies. Considering the available data for SARS-CoV-2 from the literature, it is shown that the model can be used to estimate the risk of infection with SARS-CoV-2 in the cabin of a Do728 single-aisle aircraft. However, the virus emission rate during normal breathing varies between different studies and also by about two orders of magnitude within one and the same study. A sensitivity analysis shows that the uncertainty in the input parameters leads to uncertainty in the prediction of the infection risk, which is between 0 and 12 infections among 70 passengers. This highlights the importance and challenges in terms of superspreaders for risk prediction, which are difficult to capture using standard backward calculations. Further, biological inactivation was found to have no significant impact on the risk of infection for SARS-CoV-2 in the considered aircraft cabin.

We estimated the inhaled and deposited dose in humans using the International Commission on Radiological Protection (ICRP) and multiple-path particle dosimetry (MPPD) models following exposure to humidifier disinfectant containing polyhexamethylene guanidine (PHMG). The disinfectant has caused at least 1,810 deaths, with an odds ratio of lung injury of 47.3 (95% confidence interval: 6.1–369.7), because of its application in Korea. In this study, the Oxy product, which is regarded as the causative agent of most lung diseases, was sprayed into a cleanroom at normal (6.5 ppm in solution) and worst case (65 ppm in solution) dilutions; the airborne aerosol was monitored with direct reading instruments. Areas of deposition were divided into the head airway, tracheobronchial, and alveolar regions. Four dose scenarios were considered in this study: adults and children in both daily average and sleep conditions. Most PHMG aerosols were smaller than PM1 (96%). Number-based concentration analysis showed that <100 nm nanoparticles comprised 81% and 69% of the aerosol when the 6.5 and 65 ppm solutions were used, respectively. In all scenarios, the number-based deposited dose increased in the order of alveolar, tracheobronchial, and head airway regions; the mass-based deposited dose increased in the order of the head airway, alveolar, and tracheobronchial regions. The deposited dose per unit body weight was higher in children than in adults in terms of both number- and mass-based concentrations. When the humidifier was sprayed, the highest number-based concentration was found at a particle size of 15.4 nm; the highest deposition fraction or dose by PM1 was observed in the pulmonary and head airways in both models.

The association between secondhand smoke exposure (SHSE) and obstructive sleep apnea (OSA) in general adults remains to be explored and therefore is investigated based on the representative National Health and Nutrition Examination Survey (NHANES) in this study. SHSE was assessed by self-reporting of passive exposure to burning cigarette in an indoor area (home, restaurant or bar, etc.), and OSA was defined by self-reporting OSA-related symptoms and frequency. A survey-weighted regression model and stratified analyses were used to estimate the association between SHSE and odds of OSA. The study involved 9,991 participants who had never smoked, representing a weighted number of 449.9 million adults ranging from 20 to 80 years old in the noninstitutionalized U. S population. There was a strong association between several kinds of SHSEs and OSA that compared with participants staying indoors without exposure to secondhand smoke (SHS), the odds of OSA was 1.2 times higher for those with SHSE at home (adjusted odds ratio (AOR) = 1.225, 95% CI: 1.009, 1.484), 1.4 times higher for those with SHSE in car (AOR = 1.404, 95% CI: 1.219, 1.616), and 1.3 times higher for those with e-cigarette SHSE (AOR = 1.302, 95% CI: 1.087, 1.557). Participants with simultaneous exposure to more different SHSs were 36% (one to three kinds of SHSEs (AOR = 1.368, 95% CI: 1.219, 1.534)) and 44% (above four kinds of SHSEs (AOR = 1.444, 95% CI: 1.034, 2.004)) more likely to have OSA, respectively. In general, general adults with SHSE in separate indoor areas, especially those with simultaneous exposure to different SHSs, had higher OSA risk. Identifying causality and health consequences of the association requires future longitudinal studies.

The air-conditioning systems have become an indispensable part of our daily life for keeping the quality of life. However, to improve the thermal comfort and reduce energy consumption is crucial to use the air conditioners effectively with rapid development of artificial intelligence technology. This study explored the correlation between the response of human physiological parameters and thermal sensation voting (TSV) to evaluate the comfort level among various cold and hot stimulations. The variations of the three physiological parameters, which were body surface temperature, skin blood flow (SBF), and sweat area on the skin surface, and TSV values were all positively correlated with the stimulation amount under the stimulation of cold wind, hot wind, and heat radiation, but the relationship was not completely linear. Among the three physiological parameters, the forehead skin temperature has the closest relationship with TSV, followed by the SBF and sweat. Among three stimulations, the cold wind stimulation causes the closest relationship between TSV and forehead temperature, followed by the radiation and hot wind stimulations. Through three different machine learning models, namely, random forest (RF) model, support vector machine (SVM) model, and neural network (NN) model, the stimulation of cold wind, hot wind, and heat radiation was applied to investigate the variation of the three physiological parameters as the input of the models. Moreover, the models were evaluated and verified by TSV. The results revealed that among the three different machine learning methods, RF had the best accuracy. The established thermal comfort models can predict the real-time user’s thermal comfort feeling, so that air-conditioning equipment’s performance can be optimized to create a healthy and energy-saving comfortable environment.

The behavior of polyhexamethylene guanidine (PHMG), the causative agent of many humidifier-induced lung diseases, is not well known because of its various oligomer structures and analytical difficulties. The aim of this study was to identify different PHMG oligomer types both in solution and aerosols and to estimate the airborne concentration of oligomers during humidifier use. Three products containing PHMG as the main component were diluted to the manufacturer’s recommended concentration (6.5 ppm) or the worst-case concentration (65 ppm or 125 ppm). Samples were qualitatively and quantitatively analyzed with liquid chromatography-quadrupole time-of-flight (LC-qToF) mass spectrometry in the diluted solution and in the air at 0.5 m and 1 m. The LC-qToF data were processed using UNIFI software to characterize the PHMG structure. For all products in both the humidifier solution and air, the linear type was predominant over the branched/cyclic structure, but each product had different characteristics. The linear structure in the Oxy product, the main product of lung diseases, accounted for 90.6%, while that of the Scunder and BOC Sciences’ products accounted for 78.6% and 75.8%, respectively. The concentration of the oligomer in air for the Oxy product was estimated to be 35.89 and 390.96 μg/m³ at 6.5 and 65 ppm, respectively. Most of the oligomers in the solution were found in air at a short distance (0.5 m), with a negligible concentration beyond 1 m. Oligomers with 1–7 monomer units were identified in the humidifier solution, whereas mainly monomers, dimers, and trimers were identified in the air. The results of this study will facilitate further investigations of the mechanisms of lung disease by identifying the behaviors and forms of PHMG in the air, along with previously revealed toxicity results.

The objective of this research was to explore the impacts of heightened CO₂ concentrations on human health and wellness in an underground confined space. A total of 180 participants were subjected to CO₂ concentrations ranging from 1000 to 10000 ppm within a confined underground environment. The study assessed not only subjective perceptions and physiological responses but also cognitive performance, integrating novel measures such as emotion, skin conductance (SC), and heart rate variability (HRV). The findings demonstrated a notable variation in thermal sensation votes (TSV) and perceived air quality acceptability with the change in CO₂ concentration. A significant increase in total mood disturbance (TMD) of 1.5 units was observed at a CO₂ concentration of 8500 ppm, compared to 1000 ppm. Cognitive performance remained consistent for concentrations below 8500 ppm; however, a substantial alteration was noted at 10000 ppm. In terms of task difficulty, numerical calculations were perceived to require 0.74 units more effort than letter searches. As CO₂ concentration exceeded 7500 ppm, significant variances were noted in physiological parameters such as diastolic blood pressure (DBP), heart rate (HR), LF/HF, MF/HF ratios, PNN 50, and frequency domains of HRV (LF, MF, and HF) in comparison to the parameters at 1000 ppm. At 8500 ppm, the LF and HF parameters were found to be 780 and 452.3 units, respectively, higher than at 7000 ppm. These findings suggest that high humidity, low temperature, and elevated CO₂ concentrations collectively contribute to the significant human stress responses. This study is of interest as there are limited reported researches on the air quality in underground confined space.