Ventilation System Research Articles

Uncertainty quantification: For an IAQ and energy performance assessment method for smart ventilation strategies

In new low-energy buildings or buildings after thermal refurbishment, the envelope high airtightness could have an impact on air renewal and could decrease the indoor air quality (IAQ). In this context smart-ventilation systems with variable airflows could play a role in providing better IAQ without compromising the energy performance.However, smart-ventilation strategies are quite recent, and their benefits need to be clearly quantified. This article, proposes to quantify the uncertainty of a recent multi-criteria performance assessment method, using global RBD-FAST sensitivity analysis. The impact of the pollutant emissions scenarios, model input parameters and ventilation strategies is assessed.Five ventilation systems were studied: two constant airflow, one humidity-controlled and two humidity/CO2 controlled, applied on a French low-energy house. 2500 simulations were performed to calculate 504 sensitivity indices across 12 input variables and 9 output performance indicators. The sensitivity analysis shows that occupant bio-effluent, formaldehyde and PM2.5 emissions rates are responsible for 11 %–87 % of the uncertainty for the IAQ performance indicators. The PM2.5 deposition velocity parameter is responsible for 50 % of the uncertainty on the PM2.5 indicator, which was an unknown impact. In addition, the benefits of humidity-controlled ventilation were highlighted regarding energy performance, with, in average, 20 % lower heat-loss compared to constant airflow ventilation. Moreover, smart-ventilation provides clear IAQ benefits without drastically increasing the energy demand. This work demonstrates the potential of the proposed evaluation method for ventilation performance assessment.

Enhancing hotel efficiency and environmental health with biosensors and big data analytics

The hospitality industry embraces digital technologies to enhance efficiency, guest satisfaction, and environmental sustainability. Integrating biosensors and big data analytics allows hotels to monitor operational processes while maintaining high ecological health standards. However, the impact on environmental health is not fully understood, leading to suboptimal decisions and missed opportunities. This research proposes a novel Tabu Search Drove-Extended Multi-Layer Perceptron (TSD-EMLP) to evaluate the effectiveness of digital operations in hotels through the use of biosensors and big data for predicting hotel environmental conditions. Initially, smart biosensors are deployed in key areas and heating, ventilation, and air conditioning denoted as HVAC systems in the hotel, then the water quality data, noise levels, lighting quality, waste management data, operational data, financial and operational effectiveness data are collected and transmitted to the Internet of Things (IoT) cloud for further process. Interquartile Range (IQR) utilizes the IoT cloud data to remove sensor errors and anomalous events to frame outlier data for the Wavelet Packet Transform (WPT) feature extraction process to decompose sensor data into detailed frequency bands, allowing for precise analysis of complex environmental signals, TSD-EMLP model predicts the environmental health in hotels using the decomposed data. The results demonstrate reducing energy consumption, ventilation systems, indoor environment control, and guest satisfaction, improving air quality, and adjusting environmental settings based on real-time environmental conditions through TSD-EMLP optimized settings. TSD-EMLP classification model achieved high accuracy in predicting hotel environmental health, with a low improvement in guest satisfaction metrics.

Optimizing ventilation system retrofitting: balancing time, cost, and indoor air quality with NSGA-III

Optimizing ventilation system retrofitting: balancing time, cost, and indoor air quality with NSGA-III

Experimental and simulation study of the effect of temperature and air supply on energy performance of a temperature-controlled ventilated double-layer Trombe wall

To study the energy performance of the temperature-controlled ventilated double-layer Trombe wall system (TCVD-TW) during winter and summer seasons, experiments and simulations were conducted in an experimental room in Qingdao where the system was installed. The experimental results demonstrate that the rooms equipped with this system exhibit favourable temperature performance in both winter and summer, with a positive impact on indoor humidity during the summer. The simulation results show that the performance of TCVD-TW is mainly affected by the supply air temperature, air volume flow rate, and indoor cooling and heating setpoints. For winter operating mode, the optimal supply air temperature setting should be 1°C higher than the heating setpoint. When the supply air temperature was set to 19℃(with a heating setpoint of 18 °C) and 21 °C (with a heating setpoint of 20 °C), the energy efficiency increased by 35.96 % and 32.38 %, respectively. When the supply air volume flow rate was set to 234 m3/h, the energy efficiency increased by 35.09 % (with a heating setpoint of 18 °C) and 30.27 % (with a heating setpoint of 20 °C), respectively. For summer operating mode, the optimal supply air temperature setting should be 1 °C lower than the cooling setpoint. When the supply air temperature was set to 25 °C (with a cooling setpoint of 26 °C), the energy efficiency improved by 33.62 %. When the supply air volume flow rate was set to 156 m3/h, the energy efficiency increased by 31.18 %. The experimental and simulation results of this study provide data support for the application of TCVD-TW.

Integrated optimization of the building envelope and the HVAC system in office building retrofitting

Increasing the energy efficiency of existing buildings is the major strategy to reach carbon neutral targets, given that the building sector is a major emitter. In particular, one major part of building energy usage is the building heating, ventilation and air-cooling system, which are highly depending on the thermal performance of building envelope and increase the challenge of selecting optimal packages of energy efficiency measures. This paper thereby presents a method for optimizing the building envelope and heating, ventilation and air-cooling system. The simulation-based NSGA-III approach developed in this paper is based on coupling the dynamic simulation models created in EnergyPlus software with the non-dominated sorting genetic algorithm, as the engine that performs an optimization process. A large domain of energy efficiency packages combined with heating, ventilation and air-cooling systems and building envelope are investigated to minimize energy consumption, retrofit cost and thermal discomfort in indoor environments, which including ideal loads air system, four pipe fan coil system and variable refrigerant flow system, insulation materials and insulation thickness for external walls and roof, different types of external windows, and window-wall ratio. An office building in Chongqing was chosen as a case study to demonstrate the practical implementation of the proposed method. The results show that the four pipe fan coil system was superior in the diversity and performance of the Pareto front. Then, the optimal packages were generated by a synthetic method, which have 58.57 kWh/m2 of energy consumption, 30.76 CNY/m2 of retrofit cost and 40.67 % of the percentage of thermal discomfort hours. The results also indicate that the yearly energy savings rates are 17.30 %, 23.05 % and 21.10 % separately for ideal loads air system, four pipe fan coil system and variable refrigerant flow system after performing optimization. These results highlight the importance and necessity of performing an optimization procedure for existing buildings to select the optimal retrofit measures.

Experimental comparison of aerosol transmission in displacement ventilation and mixing ventilation in a meeting scenario

The performance of displacement ventilation (DV) and mixing ventilation (MV) in aerosol contamination control was compared. The considered contamination was an aerosol emitted from a person in meeting scenarios of two and four persons. The experiments were carried out in a full-scale room measuring 4.4 m × 5.2 m × 2.9 m. Particle counting was carried out at 41 measurement points in the room to assess the concentration field of particles in the room and in the inhalation zone. The influence of the supply airflow rate and the number of activated thermal mannequins on the contaminant removal effectiveness were examined. The main conclusion is that the DV system was superior to the MV system in reducing contamination. An enhanced contaminant removal effectiveness of displacement ventilation was observed for all supply air flow rates and increased with higher airflow rates. At lower airflow rates, contamination in the inhalation zone was reduced by 40% compared to mixing ventilation, while at higher flow rates, the reduction surpassed 50%. This study highlights the advantages of displacement ventilation in terms of contamination control, infection prevention and energy efficiency, emphasising the importance of ventilation system selection for indoor environments, particularly those where airborne transmission risks are prevalent.

Performance Analysis of Galvanized Structures for an Earth-Air Heat Exchanger System

This study presents an investigation on Earth-Air Heat Exchangers (EAHEs), a sustainable and cost-effective alternative for improving thermal conditions in environments. The EAHE consists of one or more ducts buried at a certain depth, and a ventilation system, which allows air to flow through the ducts and exchange heat with the soil. The soil, being warmer than the ambient air during cold periods and cooler during hot periods, facilitates this heat exchange. The research aims to evaluate and compare the potential of the soil and EAHE in a system where a galvanized material, shaped like an ellipse, is attached around the duct. Given the high thermal conductivity of galvanization, this material helps enhance the thermal potential of the soil. Two tests were conducted by altering the horizontal and vertical dimensions of the ellipse while keeping the area constant. The results obtained with the galvanized structure were compared with those obtained without the use of this material. Moreover, the authors compared two different geometries of the structures: a circular one, which had been previously tested, and an ellipsoidal one. Additionally, the thermal potentials of the soil and the system improved as the horizontal length of the ellipse decreased.

Regression prediction of critical exhaust volumetric flow rate in tunnel fire with two-point extraction ventilation based on neural network method

In this study, a Genetic Algorithm-Backpropagation Neural Network (GA-BPNN) model was developed to predict critical exhaust volumetric flow rate in tunnel fires with two-point extraction ventilation system. Seven influencing factors served as inputs for the model, while the critical exhaust volumetric flow rate was the output. Experimental data from two reduced-scale tunnels were utilized to both train and validate the GA-BPNN model. The results demonstrate a satisfactory prediction performance, with key metrics such as Mean Absolute Percentage Error (MAPE), Relation Coefficient (R2), and Root Mean Square Error (RMSE) achieving values of 0.0384, 0.9989, and 3.5258, respectively. Importantly, the model maintains a maximum Absolute Percentage Error (APE) below 10 %. In contrast, the traditional BPNN model exhibited an APE of approximately 18.9 %. Moreover, the predictive results of the GA-BPNN model were compared with those of a previously semi-empirical formula. It was observed that the semi-empirical predictions exhibited APE exceeding 20 % for certain data points. This further underscores the superiority of the GA-BPNN model in predicting critical exhaust volumetric flow rates in tunnel fires with two-point ventilation system. These findings affirm the feasibility and effectiveness of GA-BPNN regression model as a reliable method for predicting critical exhaust volumetric flow rates under multiple factors.

User-centric approach to optimizing thermal comfort in university classrooms: Utilizing computer vision and Q-XGBoost reinforcement learning

The ’one-size-fits-all’ setting of heating, ventilation, and air conditioning (HVAC) systems in university classrooms not only compromises occupant comfort but also leads to potential energy wastage. This study proposes a user-centered approach to optimize the thermal environment in university classrooms by integrating computer vision and Q-XGBoost learning. Computer vision facilitates non-intrusive and real-time sensing of thermal comfort in dynamic university classroom environments. Simultaneously, the Q-XGBoost model identifies optimal operations for HVAC systems by learning from the interactions between occupants and their environment, ensuring a balance between comfort and energy efficiency. These innovative methodologies are harmoniously integrated into a smart air conditioning control box (SACC-Box), achieving intelligent operation of HVAC systems in the real world. Subsequently, a controlled study was conducted to test the user-centered approach’s performance in improving thermal comfort and energy efficiency. The results reveal that classrooms equipped with the SACC-Box significantly improve occupant satisfaction with thermal comfort by approximately 13.5 % while concurrently achieving up to a 5 % reduction in energy consumption compared to classrooms operating with traditional built-in smart control systems. This investigation offers a viable solution to balance energy efficiency and user comfort in university classrooms, underlining the transformative potential of integrating intelligent technologies that inspire a greener, more adaptable, and user-centric futures in higher education establishments.

The association of solid fuel use in households for cooking with elevated blood pressure among reproductive-aged married women in Bangladesh.

Bangladesh is experiencing a rapid increase in hypertension prevalence, particularly in socio-economically disadvantaged communities. The higher use of solid fuel in these communities could be one of the significant factors contributing to this trend, but evidence supporting this hypothesis is limited in Bangladesh. Therefore, this study aims to investigate the associations of household solid fuel use and its exposure level with systolic and diastolic blood pressure (DBP) and hypertension. We analysed 7,320 women's data from 2017/18 Bangladesh Demographic and Health Survey. We considered three outcome variables: (i) systolic blood pressure (BP) (continuous response), (ii) DBP (continuous response), and (iii) hypertension status (yes, no). Our primary exposures of interest were fuel type (clean vs solid) and the potential level of household air pollution exposure through solid fuel use (unexposed, moderately exposed, and highly exposed). We used a multilevel mixed-effects Poisson regression model with robust variance to determine association between exposure and outcome variables while adjusting for confounders. Of the total respondents analysed, approximately 82% used solid fuel for cooking. The age-standardised prevalence of hypertension was 28%. Respondents using solid fuel were found to be 1.44 times (95% confidence interval [CI], 1.04-1.89) more likely to develop hypertension compared to clean fuel users. Compared to women using clean fuel, the likelihood of hypertension was found to be 1.61 times (95% CI, 1.07-2.20) higher among the moderately exposed group and 1.80 times (95% CI, 1.27-2.32) higher among the highly exposed group. Similar associations were reported for systolic and DBP. The use of solid fuel increases the risk of becoming hypertensive and elevates systolic and DBP. Policies and programmes are necessary to increase awareness of the adverse effects of solid fuel use on health, including hypertension. Efforts should be made to reduce solid fuel use and ensure proper ventilation systems in households where solid fuel is used.

INVESTIGATION OF THE INFLUENCE OF CHOOSING THE TYPE OF FACADE SYSTEM USING ABC ANALYSIS ON THE COST OF THE LIFE CYCLE OF THE BUILDING OBJECT

The article is devoted to a comprehensive study of the influence of the choice of the type of facade systems on the total cost of the life cycle of the construction object, with a special emphasis on the application of ABC-analysis as a tool for determining the optimal option for arranging the facade, taking into account economic and quality aspects. The main purpose of the article is to justify the feasibility of using ABC analysis to select the type of facade systems and assess its impact on the costs associated with the life cycle of the building. The article provides a detailed analysis of various types of facade systems from the point of view of their fastening, materials, design and method implementation. Such systems as ventilated and non-ventilated facades, hinged ventilated facade systems, facades with cladding made of natural stone, composite materials and other modern materials are considered. Special attention is paid to the study of technological characteristics, durability and maintenance costs of various types of facade systems. The application of ABC analysis allows classifying facade systems according to their impact on the cost and quality of construction. In this context, facade systems are divided into three categories: A - the most important and expensive, B - medium importance and expensive, C - less important and with minimal expenses. This approach allows builders and designers to focus their attention on the most critical elements that affect the overall cost and efficiency of construction.As a result of the study, it becomes possible to make an informed decision regarding the choice of the optimal facade system, which best meets the specific needs of the project and contributes to the optimization of the cost of the life cycle of the building. In particular, the selection of appropriate materials and technologies allows to reduce construction costs, ensure durability and reduce maintenance and repair costs in the future. In addition, the results of the study can become the basis for the development of practical recommendations for professionals in the construction industry, including builders, designers and architects. This makes it possible to create more efficient, economical and ecologically sustainable construction objects. Also, the article emphasizes the importance of creating specialist training schools and conditions for the successful implementation of life cycle assessment technologies in the construction industry.

Assessing Building Energy Savings and the Greenhouse Gas Mitigation Potential of Green Roofs in Shanghai Using a GIS-Based Approach

Climate change can significantly affect building energy use and associated greenhouse gas (GHG) emissions in urban areas, as fossil fuels remain a significant energy source. Green roofs can offer multiple benefits to the urban environment, but their effects on GHG mitigation have not been fully investigated, especially under climate change. This study assessed green roofs’ contribution to GHG mitigation by saving building energy and absorbing CO2 under the present (2017–2019) and future (2049–2051) climate scenarios (SSP2-45 and SSP5-85) in Shanghai, China, at the city and township scale. A Geographic Information System (GIS)-based spatial statistical method was developed based on climate change modeling and building energy simulation. The results suggested that installing green roofs can effectively save building energy regardless of building type, yet the amount of savings can vary depending on the weather conditions within the city. The contribution analysis indicated that most saved building energy was attributed to the Heating, Ventilation, and Cooling (HVAC) system, with more energy saved under warmer climate scenarios in the future, particularly during the summer months. More energy was saved from shopping malls on an annual and monthly scale, regardless of the climate scenarios and weather zones. Finally, a case study indicated installing green roofs on all five types of buildings (office, hotel, hospital, shopping mall, apartment) of less than 50 m in height can reduce 8.28% of the CO2 emitted during the building operation stage in the entire city under the present climate scenario. The annual CO2 reduction varied with the location of townships, ranging from 2.18% to 13.78%, depending on the composition of building types and local weather conditions in Shanghai. This study offered policymakers a reference on the environmental benefits and investment values of installing green roofs in large cities.

Design and Implementation of an IoT- Based Weather Monitoring System forEnhanced Chicken Farm

The goal of the project is to address the difficulties faced by chicken farmers in properly controlling the environmental conditions of their farms through the design and implementation of an Internet of Things- based weather monitoring system. It investigates how Internet of Things (IoT) technology can be used to remotely monitor and regulate weather conditionsthat are critical to the health and productivity of chicken. The overall goals of the initiative are to maximize production efficiency, enhance animal welfare, and improve management methods on poultry farms. Chicken farms are extremely vulnerable to changes in temperature, humidity, and air quality, among other environmental factors. Unpredictable or unfavorable weather patterns can cause stress, lower egg production, and higher bird mortality rates. The goal of the project is to create an affordable, user-friendly weather monitoring system that will enable farmers to keep an eye on, control, and maintain the perfect environment for their chickens. The project's primary design focus is on placing wireless sensors all throughout the chicken farm to continuously monitor important meteorological factors. Through a user- friendly online or mobile application, farmers can obtain real-time weather data and receive alerts thanks to the sensors' connection to a central IoT network. In order to adapt the heating, cooling, and ventilation systems to variations in the weather, the system also includes automated actuators. The Internet of Things (IoT)-based weather monitoring system showed considerable advantages for chicken farmers after undergoing extensive testing and deployment.

An effective and efficient approach for smoke control in underground parking garages

Underground parking garages are enclosed spaces not directly exposed to the exterior. A smoke control system is typically required to facilitate occupants’ safe evacuation/relocation and assist in firefighting/rescue. Currently, there is a lack of guidelines for designing such systems in primary codes/standards. Only a limited number of local authorities have developed amendments to address this issue. These amendments either rely on outdated approaches, such as air change method, or simply repurpose carbon monoxide ventilation systems for smoke control without conducting additional engineering analysis and providing justification. The intent of this study is to examine the adopted approaches and explore an effective and efficient approach for garage smoke control. According to the study’s findings, it is discovered that garage floor height significantly impacts the performance of smoke control system. Additionally, structural components (beams) also influence the performance of smoke control system. Moreover, this study derives a linear equation to ascertain available safe egress time (ASET) when utilizing the carbon monoxide ventilation system or typical air change methods. Taking cost into consideration, the study concludes that for effective and cost-efficient smoke control in large underground garages, employing a carbon monoxide ventilation system is preferable to the traditional or equivalent air change method.

Adaptive strategies and thermal perceptions in intermittently used congregational Spaces: A case study

In spaces with considerable volumes that are sporadically used and typically not operating at full capacity, existing thermal comfort models face limitations. This research aims to reveal disparities between these models and survey-based comfort assessments. Comfort calculations were conducted using conventional thermal comfort models based on measured parameters and compared with survey data. Notable differences prompted the formulation of a more robust correlation, accounting for 83.4 % of the variations in Predicted Mean Votes (PMV). A regression model derived from survey data shows a high coefficient of determination (R2 = 0.84), supporting its applicability for large intermittently used spaces like mosques. Addressing limitations of existing models, this research introduces a practical and simplified approach to predicting thermal comfort, enhancing assessments in these distinctive spaces. The study also examines mosque indoor air quality dynamics. Despite sporadic ventilation, CO2 levels rose from 1500 ppm to 2500 ppm during Friday prayers, posing potential health risks. Before prayers, indoor CO2 and humidity met ASHRAE Standard 62, ISO 7730, and EN15231 values. To mitigate health concerns from elevated CO2 levels, improved ventilation systems are necessary. Comfort temperatures were only achieved during prayer periods.

Awareness-guided incremental control optimization for chilled water system with deep learning model under cold-start scenarios

For heating, ventilation, and air conditioning (HVAC) systems, the technology of optimal control can achieve significant energy savings by resetting the control setpoint in responding to the change of user load and weather condition. However, traditional method will fail in cold-start scenarios, in which the diversity of history data is limited. Under this case, significant prediction error will occur in extrapolation space, and the optimized control strategy cannot fully achieve energy saving potential or even increase energy consumption. To solve this problem, here we propose a novel awareness-guided incremental control optimization method. Its basic idea is to explicitly consider the prediction error of energy model during the control optimization process. The deep ensemble algorithm is firstly adopted to capture the prediction error. And the optimization process will be aware of such potential error, and make a tradeoff between conservative state and exploratory state. The control optimization process will start with the known optimal setpoint (conservative state) to achieve stable energy saving, and then gradually explores other unknown candidates (exploratory state) to achieve further energy saving. To validate our method, a virtual test bed of chilled water system is developed by Modelica language to compare fixed setpoint strategy, traditional method, and proposed method. The results demonstrate that the proposed method can avoid negative energy saving ratio during the early stage of control optimization. And it can also find optimal control strategy faster. Comparing with traditional method, the awareness-guided method can achieve further 2.9 % energy saving ratio, and the overall energy saving is between 6.1% and 13.9 %.

Emergency Department Environmental Responses to COVID-19 Pandemic: A Qualitative Study.

Objective: This study aimed to document and empirically evaluate the physical environment strategies used by emergency departments (EDs) to address the challenges posed by the COVID-19 pandemic; and to develop recommendations for managing future crises. Background: Emergency departments made significant environmental modifications in responding to the COVID-19 pandemic but these modifications and the decision-making processes were seldomly studied. Methods: In this in-depth qualitative case study, a multidisciplinary research team conducted semistructured interviews with 11 professionals of various roles in environmental responses to the pandemic at a large urban ED in the U.S. Qualitative content analysis generated codes and code categories from the data as well as a conceptual framework. Design documents and photographic documentation were used to cross-check the interview data. Results: The ED faced challenges in making rapid changes with limited information and resources. Physical barriers separating patients, air filtration, airflow control, and alternative care spaces were key physical environmental strategies implemented. Among them, the physical separation of patients was perceived to be most effective, followed by air quality control measures. Interviewees recommended flexibility in building design (self-contained zones, negative pressure and air filtration in all patient rooms, pandemic mode of air ventilation system), and an all-inclusive bottom-up decision-making process. Concerns included ventilation, security, communication strategies, and workplace ergonomics. Conclusion: The physical environment constitutes an important part of ED pandemic response and the proactive preparation for future crises. Hospitals should consider the ED environment's role in pandemic response, including ventilation capability, security visibility, and functionality for staff.

AI-driven ventilation control policy proximal optimization coupled with dynamic-informed real-time model calibration for healthy and sustainable indoor PM2.5 management

Indoor air quality (IAQ) is an important factor for determining quality of life and urban sustainability. In underground subway stations, improving IAQ through ventilation systems remains challenging due to the complexity and nonstationary nature of IAQ resulting from diverse influential factors such as subway schedules, passenger volume, and outdoor air quality (OAQ). Therefore, this study aimed to develop a novel artificial intelligence (AI)-driven ventilation system for healthy and sustainable IAQ management in subway stations. First, an IAQ mechanistic model coupled with genetic algorithm (GA)-driven rolling-horizon calibration was developed from the collected IAQ big dataset, and global sensitivity analysis was then employed to identify the dominant variables in IAQ dynamics. Subsequently, proximal policy optimization (PPO), one of the reinforcement learning (RL) algorithms, was employed to control the ventilation system in both the lobby and platform areas of a subway station. The results demonstrated that the IAQ mechanistic model can capture IAQ dynamics with acceptable modeling performance, achieving around 19 % of mean absolute percentage error (MAPE). Furthermore, the PPO-driven ventilation control system can reduce energy consumption by around 22 % while maintaining IAQ at an acceptable level.

Optimization Strategy for Thermal Comfort in Railway Stations above Ground Level in Beijing

Urban rail transit, a convenient and fast public transportation mode with rapid construction and development, occupies fewer land resources and accommodates large passenger volumes. However, thermal comfort should be given more attention. Stations above ground level experience poor thermal comfort on the platforms, especially in hot summers. This study combines field research with a simulation analysis to propose a strategy for improving thermal comfort on above-ground urban rail transit platforms. This study analyzed the effects of the skylight opening rate, side window opening rate, design of transparent maintenance structure shading, and the platform profile shape on the thermal comfort of above-ground stations using field research, comparative experiments, and a simulation analysis with the PHOENICS (Command Prompt) software. The results indicate that adding longitudinal sunshade louvers to the skylight of the station platform is a cost-effective method to reduce the average temperature and PMV value, thereby improving thermal comfort. Increasing the skylight opening rate can result in a temperature rise. Adjusting the opening rate of the side windows to 20% and adding sun-shading louvers can also significantly enhance the station’s thermal comfort. Taking Wudaokou Station on Beijing Line 13 as an example, the simultaneous installation of additional longitudinal skylight shading and side window shading and increasing the side window opening rate to 20% on the platform resulted in a 2.6 °C decrease in the average temperature, a 4.7% increase in the average wind speed, and a 0.62 decrease in the PMV value, significantly enhancing thermal comfort for passengers. This study confirms that optimizing shading and ventilation systems can significantly reduce the platform temperature and improve passengers’ thermal comfort. This study provides theoretical support and innovative methods for optimizing thermal environments in similar environments.

Development and method validation of a sampling technique for a reproducible detection of synthetic cannabinoids in exhaled breath using an in vitro pig lung model.

Alternative matrices, especially exhaled breath (EB), have gained increasing attention for a few years. To interpret toxicological findings, knowledge on the toxicokinetic (TK) properties of a substance in EB is indispensable. Whilst such data are already accessible for various drugs (e.g. Δ9-tetrahydrocannabinol), they are still not available for new psychoactive substances, particularly synthetic cannabinoids (SCs). As SCs raise a high public health concern, the aim of this study was to assess these data in future TK studies in pigs. For this purpose, an in vitro sampling technique of EB was initially developed, being prospectively applied to anesthetized and ventilated pigs for the detection of SCs in a controlled and reproducible manner as exemplified by cumyl-5F-P7AICA. Furthermore, a method for the qualitative and quantitative detection of cumyl-5F-P7AICA in EB using glass fiber filters (GFF) was established und fully validated. Therefore, cumyl-5F-P7AICA (0.5 mg/mL in ethanol abs.) was initially nebulized using a ventilation machine and a breathing tube, as they are also used in surgeries. The aerosol was delivered into a simulated pig lung. To collect EB, a pump was connected to that part of the breathing tube, that contains EB (expiratory limb), and sampling was performed repeatedly (n=6) for 15 min (2 L EB/min) each using GFF. For extraction of the substance, the GFF were macerated with acetone and the remaining experimental components were rinsed with ethanol. After sample preparation, the extracts were analyzed by LC-MS/MS. In the complete experimental setup, about 40% of the initially nebulized cumyl-5F-P7AICA dose was found with 3.6 ± 1.3% being detected in the GFF. Regarding the comparably high loss of substance, the open ventilation system and a conceivable adsorption of the SC in the ventilator have to be considered. However, the herein introduced approach is promising to determine the TK properties of cumyl-5F-P7AICA in EB.