Indoor air最新文献

Previous studies have evaluated the effectiveness of air filters in mitigating the symptoms of allergic rhinitis (AR). However, these studies have yielded inconsistent results. This systematic review and meta-analysis was conducted to assess the effectiveness of air filters for patients with AR. For this, we comprehensively searched the PubMed, Embase, and Cochrane Library databases to identify relevant articles. The results are presented in terms of standardized mean difference (SMD) and 95% confidence intervals (CI) values with the fixed-effects model (FEM) and random-effects model (REM). Eight randomized controlled trials were included in our meta-analysis. Of these, three had a parallel design and five had a crossover design. Regarding clinical outcomes, pooled analyses performed using patients’ nighttime and daytime symptom scores revealed SMD values of −0.21 (95% CI: −0.35 to −0.07 (FEM) and −0.35 to −0.08 (REM)) and −0.16 (95% CI: −0.30 to −0.03 (both FEM and REM)), respectively. However, no significant changes were noted in the SMD values when assessing medication use, quality of life (QoL), or peak expiratory flow rate (PEFR). In conclusion, air filters may help alleviate symptoms associated with AR; however, their effects on medication use, QoL, and PEFR appear to be limited. This systemic review and meta-analysis is registered with CRD42022380560.

Buildings are major consumers of energy, accounting for a significant proportion of total energy use worldwide. This substantial energy consumption not only leads to increased operational costs but also contributes to environmental concerns such as greenhouse gas emissions. In the United States, building energy consumption accounts for about 40% of total energy use, highlighting the importance of efficient energy management. Therefore, accurate prediction of energy usage in buildings is crucial. However, accurate prediction of building energy consumption remains a challenge due to the intricate interaction of indoor and outdoor variables. This study introduces the Partitioned Hierarchical Multitask Regression (PHMR), an innovative model integrating recursive partition regression (RPR) with multitask learning (hierML). PHMR adeptly predicts building energy consumption by integrating both indoor factors, such as building design and operational variables, and outdoor environmental influences. Rigorous simulation studies illustrate PHMR’s efficacy. It outperforms traditional single-predictor regression models, achieving a 32.88% to 41.80% higher prediction accuracy, especially in scenarios with limited training data. This highlights PHMR’s robustness and adaptability. The practical application of PHMR in managing a modular house’s Heating, Ventilation, and Air Conditioning (HVAC) system in Spain resulted in a 37% improvement in prediction accuracy. This significant efficiency gain is evidenced by a high Pearson correlation coefficient (0.8) between PHMR’s predictions and actual energy consumption. PHMR not only offers precise predictions for energy consumption but also facilitates operational cost reductions, thereby enhancing sustainability in building energy management. Its application in a real-world setting demonstrates the model’s potential as a valuable tool for architects, engineers, and facility managers in designing and maintaining energy-efficient buildings. The model’s integration of comprehensive data analysis with domain-specific knowledge positions it as a crucial asset in advancing sustainable energy practices in the building sector.

The primary aim of this study is to explore the utility of machine learning algorithms for predicting personal PM_2.5 exposures of elderly participants and to evaluate the effect of individual variables on model performance. Personal PM_2.5 was measured on five consecutive days across seasons in 66 retired adults in Beijing (BJ) and Nanjing (NJ), China. The potential predictors were extracted from routine monitoring data (ambient PM_2.5 concentrations and meteorological factors), basic questionnaires (personal and household characteristics), and time-activity diary (TAD). Prediction models were developed based on either traditional multiple linear regression (MLR) or five advanced machine learning methods. Our results revealed that personal PM_2.5 exposures were well predicted by both MLR and machine learning models with predictors extracted from routine monitoring data, which was indicated by the high nested cross-validation (CV) R² ranging from 0.76 to 0.88. The addition of predictors from either the questionnaire or TAD did not improve predictive accuracy for all algorithms. The ambient PM_2.5 concentrations were the most important predictor. Overall, the random forest, support vector machine, and extreme gradient boosting algorithms outperformed the reference MLR method. Compared with the traditional MLR approach, the CV R² of the RF model increased up to 7% (from 0.82 ± 0.13 to 0.88 ± 0.10), while the RMSE reduced up to 18% (from 19.8 ± 5.4 to 16.3 ± 4.5) in BJ.

Indoor particle number size distribution (0.3-10 μm), equivalent black carbon (eBC), and Ångström absorption exponent (AAE) data were collected in real conditions, over a ten-month period at a research area building, in a semirural site, to characterize indoor aerosol loading. Additionally, during the campaign, emissions from four indoor sources commonly used at the site (incense, traditional cigarettes, electronic cigarettes, and heat-not-burn products) were studied during short-term experiments with the support of ultrafine particle (UFP) monitoring. Two particle low-cost sensors (PM LCS), Sensirion SPS30 (0.3-10 μm), were evaluated in the long-term campaign and during fast emission processes, to assess their accuracy and reliability. Penetration and infiltration of both fine and coarse particles from outdoor traffic, domestic heating, and dust resuspension were inferred as the main sources of indoor aerosols on a long-term basis. Moreover, long-range transported dust aerosols were found to influence indoor coarse number concentration. Among the source events, heat-not-burn (HNB) product resulted in the lowest effect on indoor air quality, whereas the highest AAE values from incense and traditional cigarettes suggest the brown carbon (BrC) production. The highest emission of UFP was caused by electronic cigarettes (e-cig), which spanned particles from the ultrafine to the coarse fractions. This was likely due to the release of metal and silicate from the coil. Analysis of number size distributions of the four experiments revealed the emission of fine particles (0.3-1 μm) and super micron particles. SPS30s performance was satisfactory in terms of accuracy, precision, and durability, indicating that these devices are suitable for monitoring indoor air quality. Additionally, the two PM LCS were able to detect all simulated fast emission sources.

Inhaling airborne droplets exhaled from an infected person is the principal mode of COVID-19 transmission. When residential energy efficiency workers conduct blower door tests in occupied residences with a COVID-19-infected occupant potentially present, there is a concern that it could put the workers at risk of infection with massive flows of air being generated by the tests. To minimize this risk, computational fluid dynamics (CFD) simulations were conducted for four prototype houses to develop guidelines for workers to follow during their service visits. The CFD simulations visualized the movements and evaluated the residence time of small particles released at certain locations under a series of scenarios representing situations that are likely to be encountered during in-home energy efficiency services. Guidelines were derived from the simulated tracks of droplets to help to increase the safety of the worker(s).

Depression and anxiety carry an important public health burden. Indoor air pollution is associated with depression and anxiety. Ventilation can reduce the concentration of indoor air pollution and improve indoor air quality. This study explored the relationship between indoor ventilation frequency and depression and anxiety in older adults using the data from the 2018 Chinese Longitudinal Healthy Longevity Survey. Compared with older people with low indoor ventilation frequency, those with high indoor ventilation frequency had 51% lower odds of depression (OR = 0.49, 95% CI: 0.43 to 0.57) and 37% lower odds of anxiety (OR = 0.63, 95% CI: 0.43 to 0.91), and those with intermediate indoor ventilation frequency had 35% lower odds of depression (OR = 0.65, 95% CI: 0.56 to 0.75) and 45% lower odds of anxiety (OR = 0.55, 95% CI: 0.37 to 0.82). The results were similar across the seasons. However, there were sex, age, lifestyle, and cooking fuel use-specific differences in these associations. The findings emphasize that high ventilation frequency may be conducive to improving mental health in older adults, especially women, the old elder, nonsmokers, nondrinkers, and those who do not exercise and cooked at home.

In the wake of the COVID-19 pandemic, prioritizing indoor air quality has emerged as a crucial measure for preventing infections. Effective ventilation is vital in mitigating airborne pathogen transmission and maintaining a healthy indoor environment by diluting and removing infectious particles from enclosed spaces. However, increasing the supply of pathogen-free air to enhance infection control can lead to a rise in energy consumption. Nevertheless, evaluating the overall efficacy of ventilation-based infection prevention strategies while considering their energy requirements has posed challenges. This scientific paper introduces the ICEE (Infection Control’s Energy Efficiency) index, a newly developed simple integrated index to assess the effectiveness of ventilation strategies in reducing infection risks while accounting for associated energy demands. The paper reviews the current understanding of ventilation strategies, their impact on infection prevention, and their corresponding energy consumption. By employing a straightforward analytical approach, this metric offers a comprehensive framework to optimize ventilation systems for both infection prevention and energy efficiency. To quantify infection risk, a simplified equation model is utilized, incorporating factors such as ventilation effectiveness and filter efficiency, in case of recirculation. Energy demand is determined using approximations and relevant values from existing literature. Reference cases are defined, distinguishing between natural and mechanically ventilated scenarios, as these reference situations influence the energy-related effects of any implemented measures. The paper outlines the methodology employed to develop the index and illustrates its applicability through exemplary measures. The proposed index yields valuable insights for the design, operation, and retrofitting of ventilation systems, enabling informed decision-making towards fostering a healthier and more sustainable built environment.

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.