Air Quality Atmosphere and Health最新文献

Operation room personnel are exposed to high concentrations of surgical smoke during electrosurgery and laser treatment. Surgical smoke contains viral aerosol, particulate matter, volatile organic compounds, and microorganisms. Current local exhaust ventilation control methods can be noisy, bulky, and expensive. In this study, we are the first to build a cost-effective air curtain device to remove surgical smoke. Experiments were conducted in an operating room by cutting porcine samples with electrosurgical units. An air curtain system was installed below the surgical light. We measured the particle number and mass concentrations in the breathing zone. The concentrations were recorded under four scenarios: no control, commercial smoke evacuation pencil, low-velocity air curtain, and high-velocity air curtain. Results indicate that the air curtain reduces the concentration of particulate matter and produces less noise than commercial smoke evacuation pencils. The particle number removal efficiencies for the smoke evacuation pencil, low-velocity air curtain, and high-velocity air curtain were 88.52%, 70.79%, and 91.29%, respectively. The respective PM_2.5 removal efficiencies were 90.92%, 85.38%, and 97.99%. Thus, installing an air curtains under surgical lights is a promising method for reducing surgical smoke and protecting medical personnel.

Dosimetry models for the estimation of particle deposition in the human respiratory tract (RT) in conjunction with clearance transport models are vital components to relate human exposure with internal dose in a quantitative manner. The current work highlights knowledge and modelling approaches on particle deposition and translocation in the human body in an effort to determine health risks in respect to different particle physicochemical properties and human physiology parameters. These include breathing conditions, variability of the geometry of the RT, chemical composition and size of deposits. Different dosimetry modelling approaches have been studied including empirical formulations, one-dimensional flow modelling and computational fluid dynamic methods (CFD). The importance of a realistic modelling of hygroscopicity has been also investigated. A better understanding of the relationship between health effects and inhaled particle dose may be elaborated using dosimetry and clearance modelling tools. A future required approach is to combine dosimetry models with physiologically based pharmacokinetic models (PBPK) to simulate the transport and cumulative dose of particle-bound chemical species in different organs and tissues of the human body.

Currently odor problems caused by animal feeding operation play a role in provoking environmental civil petition. The objectives of this study were to investigate the monthly distribution characteristics of 22 offensive odor compounds in swine houses, which are under regulation in Korea, and to compare their levels according to ventilation type and manure treatment mode. During the 1 year survey between July 2021 and June 2022, air samples were collected. In this study, ammonia was observed at the highest levels (the annual mean, 13.9 × 10³ ppb) among other offensive odorous compound regardless of the types of swine house and seasons, followed by fatty acids (139.9 ppb), sulfuric odorous compounds (52.3 ppb), volatile organic compounds (27.5 ppb), and trimethyl amine (24.5 ppb). Furthermore, hydrogen sulfide among sulfur compounds, methyl ethyl ketone among volatile organic compounds, and propionic acid and n-butylic acid among fatty acids were observed to the highest level of several hundreds of ppb. In particular, five aldehydes (acetaldehyde, propionaldehyde, butyraldehyde, n-valeraldehyde, and i-valeraldehyde) among the 22 offensive odor-generating compounds were not detected in any season. The levels of odorous compounds were the highest in winter (Dec.–Feb.) and lowest in summer (June–Aug.). For comparison of overall distribution regarding concentrations of odorous compounds by ventilation type and manure removal mode, relatively lower concentrations were observed in swine houses with forced ventilation or with scrapper type. Our findings indicate that annual monitoring for these odorous compounds would be necessary for establishment of control strategy. Also, installation of active ventilation, as well as the increase of removal frequency of pig manure could contribute to lower concentrations of odorous compounds in swine buildings.

The ongoing improvement of urban air quality urgently needs refined understanding of air pollution variation. For urban roads, due to the changeable traffic flow and complex road environments, commuters usually confront with a direct but uncertain exposure to traffic-induced air pollutants. However, the current lack of fine-grained measurements and reliable analytical methods restricts our knowledge of road air pollution risk. Therefore, we designed a bicycling experiment to collect fine-scale concentration samples of PM_2.5, PM₁₀, and black carbon (BC) in non-motorized lanes beside an expressway. Mobile measurements revealed high particle pollution at the building-intensive roadside, and the background pollution concentration removal clarified high-polluted sections. Generalized additive models demonstrated that the background pollution concentrations dominated the overall pattern of particles, and meteorological factors had significant but varied impacts on local variations of particles. Riverside winds lowered PM_2.5 and PM₁₀ levels most time, while BC was more affected by roadside greenery, distance from roadway and diesel vehicles. At the hotspots, an increase of 100 diesel vehicles per hour could increase roadside BC by about 2% per kilometer but brought no obvious increase in PM_2.5 and PM₁₀. These results confirm the availability of mobile measurements and generalized additive models in high-resolution pollution analysis, and are beneficial to countermeasures of reducing personal exposure risk of slow-moving traffic.

Mounting studies explored associations between fine particulate matter (PM_2.5) and preterm birth (PTB); however, individual and combined impacts of PM_2.5 constituents on PTB were less known. PM_2.5 and its seven constituents were assessed by V4.CH.02 product of the Dalhousie University Atmospheric Composition Analysis Group, a dataset containing combined geophysical-statistical estimates of PM_2.5 across China. Effects of PM_2.5 and its constituents on PTB and gestational age were firstly explored. Furthermore, weighted quantile sum (WQS) regression was conducted to reveal the impacts of total PM_2.5 mass and identify contributing constituents. An interquartile range (IQR) increase in PM_2.5 was associated with increased odds ratio (OR) of PTB. PM_2.5 constituents were widely associated with PTB and reduced gestational age, with different time window. The total mass of PM_2.5 (per IQR increment) in the first and the second trimester was positively associated with PTB by WQS regression (Trimester 1: OR = 1.38, 95%CI: 1.15, 1.65; Trimester 2: OR = 1.47, 95%CI: 1.21, 1.79). The most contributing factors were black carbon in the first trimester and sulphate ion in the second trimester, respectively. Especially, sea salt was identified as contributing constituent during the first trimester. The study indicated that prenatal exposure to PM_2.5 and its constituents was individually and jointly associated with PTB and reduced gestational age. Sea salt was firstly identified as a risk factor of PTB in the seaside city, which needs further exploration.

Fine particulate matter (PM2.5) is a hazardous air pollutant with an aerodynamic diameter of 2.5 μm or less, which can lead to severe health impacts such as cardiovascular disease, respiratory illnesses, and various types of cancer. Therefore, accurate forecasting of PM2.5 concentrations is crucial for public health and policy-making. However, due to the stochastic nature of PM2.5, achieving high prediction accuracy and efficiency remains a challenge. To address this challenge, this study proposes a hybrid deep learning model consisting of principal component analysis (PCA), discrete stationary wavelet transform (DSWT), and Nested LSTM (NLSTM) neural network to predict PM2.5 concentrations. The proposed model aims to leverage the strengths of each technique to achieve better accuracy and efficiency in PM2.5 forecasting. Specifically, PCA is employed as the feature extraction method to reduce the dimensionality of the data and improve computing efficiency. Additionally, DSWT is utilized to decompose the reduced-dimensional data into several sub-signals that are more regular and stable, enabling the NLSTM network to learn each sub-signal separately. Finally, the predicted values of each sub-signal are reconstructed to obtain the final PM2.5 forecast. The proposed model is validated using daily air pollutants and meteorological variables collected in Taiyuan, China, from January 1, 2016, to December 31, 2020. The long-term, medium-term, and short-term forecast results demonstrate that the proposed model achieves better accuracy and efficiency compared to existing models. Overall, the proposed hybrid deep learning model provides a promising solution for accurate and efficient forecasting of PM2.5 concentrations, and the findings of this study have important implications for public health and environmental policy.