Indoor air最新文献

Background. Airborne SARS-CoV-2 plays a prominent role in COVID-19 transmission. Numerous studies have sampled air from patient rooms, but airborne spread to other hospital areas such as corridors is less investigated. Methods. Size-fractionated aerosol particles were collected weekly, with 12 hours of sampling time daily, in corridors at two infectious disease wards in southern Sweden between March 2020 and May 2021. Samples were analysed with real-time reverse transcription polymerase chain reaction (RT-qPCR) for detection of SARS-CoV-2 RNA. Indoor temperature, relative humidity, and CO₂ concentration were monitored during the sampling period. Results. 20 of the 784 collected samples contained SARS-CoV-2 RNA, although in low concentrations. Positive air samples were found in sizes between 0.14 and 8.1 μm, but none >8.1 μm. 45% were found in submicron particles. No clear seasonal pattern was observed among the positive samples. There was no significant difference in the positivity rate of the samples between the two wards. Conclusions. SARS-CoV-2 was only detected in 2.6% of the aerosol samples, which indicates that the spread of airborne virus from patient rooms to the corridor was limited.

A well-ventilated room is essential to reduce the risk of airborne transmission. As such, the scientific community sets minimum limits on ventilation with the idea that increased ventilation reduces pathogen concentration and thus reduces the risk of transmission. In contrast, the upper limit on ventilation is usually determined by human comfort and the need to reduce energy consumption. While average pathogen concentration decreases with increased ventilation, local concentration depends on multiple factors and may not follow the same trend, especially within short exposure times over large separation distances. Here, we show through experiments and high-fidelity simulations the existence of a worst-case ventilation where local pathogen concentration increases near the receiving host. This occurs during the type of meetings that were recommended during the pandemic (and in some cases solely authorized) with reduced occupancy adhering to social distancing and short exposure times below 20 minutes. We maintain that for cases of high occupancy and long exposure time, increased ventilation remains necessary.

Household air pollution from solid cooking fuel use influences multiple health outcomes, but its association with body pain remains poorly understood. This was a longitudinal study of 8880 adults who participated in the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018. Household cooking fuels were extracted from the baseline household questionnaire. Transitions in cooking fuels from 2011 to 2018 were also identified. Body pain status was reported in the three waves of surveys conducted in 2011, 2015, and 2018. The associations between cooking fuel type, fuel transition, and pain site number were examined using generalized estimating equations. Among the 8880 participants, 41.4% (n = 3680) primarily used clean fuels for cooking, and 58.6% (n = 5200) used solid ones at baseline. Cooking with solid fuels was associated with more pain sites (incidence rate ratio (IRR): 1.14; 95% confidence interval (CI): 1.08 to 1.21), but a slower rate of pain sites increases from 2011 to 2018 (IRR = 0.78; 95% CI: 0.71 to 0.86, for 2018 × solid fuels). Compared with those who persistently used clean fuels for cooking, the number of pain sites increased by 10% in participants who transiting from using solid to clean fuels (IRR = 1.10; 95% CI: 1.04 to 1.18), by 21% in those transiting from cooking with clean to solid fuels (IRR = 1.21: 95% CI: 1.08 to 1.35) and by 25% among those persistent using solid fuels for cooking (IRR = 1.25; 95% CI: 1.18 to 1.34). Our findings provided new evidence linking using solid fuels for cooking with more pain sites, but a slower rate of pain sites increases. Public health efforts should focus on fuel transition and take measures to help clean fuels spread.

As worldwide evidence shows that the predominant transmission route of SARS-CoV-2 and other respiratory pathogens is airborne, the need for suitable methods for the sampling of bioparticles directly from the air is more urgent than ever. The present paper describes the development of a method for the collection of biological aerosols, using a preexisting cyclonic impinger, the Coriolis μ, combined with a lysis buffer and subsequent qPCR analysis of the generated samples in lab. Four phases of method development are described: exploratory, validation, blank tests, and application. The application phase consisted of a field experiment in which the method was simultaneously applied at two daycare facilities. The method achieved a good level of accuracy and reliability in detecting different types of infectious agents in the air, with a global uncertainty of 19.6%. Furthermore, our method allows the simultaneous detection of 26 different respiratory pathogens in air samples, it is relatively simple, and the equipment is easy to use. Additionally, the time to collect a representative sample is short compared to other methods. The method does not cause significant disturbance to those present in the sampled rooms, and it is safe for operators and flexible, meaning it can be used in virtually any environment regardless of use, size, or occupancy. Further research is being developed to allow quantitative analysis of the collected samples and to test the methods’ ability to assess the viability of the microorganisms collected in the sample.

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.