Indoor Environmental Control Research Articles

Hepatoprotective Effects of Taraxacum officinale Root Extract on Permethrin-induced Liver Toxicity in Adult Mice

Background: Globally, permethrin is used as an insecticide for pest control in indoor environments and in agriculture to enhance food production by eradicating undesirable insects and controlling disease vectors. 
 Objective: The present study investigated the protective effects of Taraxacum officinale (dandelion) on permethrin-induced liver injury in mice. 
 Methods: Adult mice were divided into four groups. The first group was the negative control group, whereas the second group was the positive control group that received dandelion through the diet at 2% (corresponding to a dose of 5 g/kg bw). The third group received permethrin (96 mg/kg bw) by gavage, whereas the fourth group received permethrin and a diet enriched with dandelion (cotreatment). All mice were sacrificed after 14 days of treatment.
 Results: Biomarkers of liver toxicity (AST, ALT, ALP, and LDH activities and bilirubin level) increased following permethrin treatment. Permethrin induced oxidative stress, which was indicated by an increase in MDA and GSH levels as well as GPx activity and a decrease in SOD activity. Permethrin treatment caused histological alterations in the liver, whereas co-treatment with dandelion reduced liver injury. Our results revealed that alterations of biochemical parameters and liver histological profile in mice following permethrin exposure were reversed towards normalization by the treatment with dandelion roots extract. 
 Conclusion: The protective effect of this plant might be due to its antioxidant capacity.

Experimental Investigation of Green Façade Components for Industrial and Storage Buildings

This paper deals with vertical greenery applied on the external side of lightweight walls for industrial and storage buildings. The greenery is expected to have some positive impacts on the building performance, in particular reduction of building energy consumption and peak cooling power. The achievable benefits are however dependent on local climatic conditions, hygro-thermal properties of the building enclosure, the requirements for the indoor environment and on the system of greenery applied on the walls. The side-by-side real-scale experiment involving three different test walls was built in two test windows with dimensions of 3.0 x 3.2 m. A climatic test room with controlled indoor environment is located behind test windows. Two reference walls present current standard technical solutions constructed as metal frame filled with thermal insulation with metal sheets on the exterior side, either in direct contact with core wall or with ventilated air gap. The third wall replaces the external metal sheets by the system of baskets filled by soil substrate and greenery with integrated irrigation pipes. The experiment was built in June 2019 and is extensively monitored since beginning of July 2019. Temperatures, relative humidity, heat fluxes, moisture content of soil substrate, inflow and outflow of irrigation water, and environmental boundary conditions are continuously recorded. Results of the first year of monitoring are presented and discussed in the paper. The measured results showed reduction of daily peak values of heat flux on the internal surface during hot summer days and evaluated power of evaporative cooling.

An Artificial Neural Network based Indoor Environment Control System

Purpose: Various control methods based on specific conditions of building spaces have been studied to improve the performance of system’s operation and users’ comfort. The methods used in this research examine an improved control strategy for higher control precision not to increase system’s energy consumption, and lower energy consumption not to decrease its thermal comfort level. Method: This research proposes optimized supply air conditions to satisfy the indoor setting values by controlling the amount of supply air and its temperature, and investigates the auxiliary performance of an adaptive controller. Result: For maintaining thermal comfort levels, it is confirmed that an artificial neural network based controller is about 13% more efficient than two different controllers, but, for energy performance, it consumes more energy by about 12% to maintain indoor thermal comfort.

Forecasting office indoor CO2 concentration using machine learning with a one-year dataset

Modern buildings are expected to fulfill energy efficiency regulations while providing a healthy and comfortable living environment for their occupants. Demand-controlled heating, ventilation, and air conditioning can improve energy efficiency and well-being, especially if changes in indoor environmental factors can be forecast reliably enough. As a first step, this study afforded a baseline for CO2 forecasting evaluation by providing for the public use a comprehensive dataset covering a full year. Secondly, we studied the applicability of four machine learning methods, Ridge regression, Decision Tree, Random Forest, and Multilayer Perceptron, for modeling the future concentration of CO2 in indoors. We evaluated their prediction accuracy within different forecasting and history window time frames and the impact of multiple sensor modalities. All models performed better than the baseline method of predicting the last observed value, and the Decision Tree was found to be almost as accurate as the computationally heavier Random Forest model. When the future forecasting window was longer than a minute, the optimal sensor modalities included occupant activity data from passive infrared sensors in addition to CO2 concentration. Our findings suggest that machine learning can be applied in multimodal time-series data to find a simple, accurate, and resource-efficient forecasting model for proactive control of indoor environments.

Harvesting water from air using adsorption material – Prototype and experimental results

Drinking water resources have always been limited in the gulf region of the Middle East and other desert regions around the world. In attempt to provide viable supplement, a device that harvests clean drinking water from air is designed, built and tested. The operation of the device is based on harvesting water naturally from air using adsorption materials. The prototype of this device consists of sorbent (silica gel is used in this study) exposed to radiant flux, water sorbent unit, condenser and reflector. Experimental studies of production of fresh water from air in controlled indoor environment have been carried out using the prototype. Several experimental tests were conducted under the conditions of 22 °C ambient temperature, a range of relative humidity (RH) from 30 to 60%, a range of silica gel thickness from 25 to 35 mm, surface area to volume ratio from 0.29 to 0.4 and radiant heat flux range from 509 to 556 W/m2. The prototype was able to produce up to 159 g of water per 1 kg of silica gel in a 12 h cycle when exposed to 556 W/m2 radiant flux. In terms of per one day (24 h), the harvester can produce 800 mL of water with an overall efficiency of 50% for 25 mm silica layer thickness. Increasing the relative humidity speeds up the adsorption cycle and increases the water capture, release and collection rates. The system can be improved by adding multiple layers of sorbent stacked on top of each other and by using sorbents with improved adsorption and desorption properties.

On the feasibility of affordable high-fidelity CFD simulations for indoor environment design and control

Computational fluid dynamics (CFD) is a reliable tool for indoor environmental applications. However, accurate CFD simulations require large computational resources, whereas significant cost reduction can lead to unreliable results. The high cost prevents CFD from becoming the primary tool for indoor environmental simulations. Nonetheless, the growth in computational power and advances in numerical algorithms provide an opportunity to use accurate and yet affordable CFD. The objective of this study is to analyze the feasibility of fast, affordable, and high-fidelity CFD simulations for indoor environment design and control using ordinary office computers. We analyze two representative test cases, which imitate common indoor airflow configurations, on a wide range of different turbulence models and discretizations methods, to meet the requirements for the computational cost, run-time, and accuracy. We consider statistically steady-state simulations for indoor environment design and transient simulations for control. Among studied turbulence models, the no-model and large-eddy simulation with staggered discretizations show the best performance. We conclude that high-fidelity CFD simulations on office computers are too slow to be used as a primary tool for indoor environment design and control. Taking into account different laws of computer growth prediction, we estimate the feasibility of high-fidelity CFD on office computers for these applications for the next decades.

The impact of providing frequency regulation service to power grids on indoor environment control and dedicated test signals for buildings

Heating, ventilation and air-conditioning systems (HVAC), at demand side, have been regarded increasingly as promising candidates to provide frequency regulation service to smart power grids. To assess the performance of the frequency regulation service provided by the demand side, dedicated frequency regulation test signals have been proposed that are relatively demanding and critical to power grids considering the quality of the service provided by the demand side. However, other practical signals might be demanding and critical to buildings at the demand side considering the impact of the service on indoor environment control. In this study, a set of criteria is proposed to assess the demanding level of frequency regulation signals to power grids and buildings at demand side, respectively. The impacts of providing frequency regulation service (to power grids) on indoor environment control are quantified when HVAC systems are following practical signals with different demanding levels to buildings. The results show that indoor air temperature can have a relatively large offset when HVAC systems are following frequency regulation signals demanding to buildings. In addition, the indoor air temperature offset will increase when regulation capacity provided increases. Two dedicated test signals for buildings are therefore recommended to verify the environment control performance of buildings when providing frequency regulation service to power grids.

Investigation of the relationships among temperature, illuminance and sound level, typical physiological parameters and human perceptions

Human perceptions are stimulated simultaneously by multiple environmental factors, such as temperature, illuminance, and sound level. Meanwhile, the human body regulates physiological parameters to maintain balance with changes in the indoor environment. This means that human perceptions can be expressed as a function of typical physiological parameters that vary with the ambient environmental conditions. Therefore, a series of human subject experiments were conducted under 144 controlled indoor environment conditions. Human perception and five physiological parameters, i.e., systolic blood pressure, diastolic blood pressure, heart rate, and skin temperatures at the wrist and forehead, under stimulation by different combinations of temperature, illuminance, and sound level, were measured to determine whether any physiological parameters are affected by environmental factors, and if so, then which parameters. The experiments were also conducted to identify which physiological parameter generated the greatest amount physiological information that could be used to represent the human perception. The study revealed that physiological parameters were affected to varying degrees by multiple environmental factors and had different degrees of correlation with human comfort votes. Moreover, the measured skin temperature at the wrist (STwrist) varied linearly with the air temperature (t) and exhibited an S-shaped relationship with thermal comfort vote (TC). In addition, the quantitative relationships between t and STwrist and between TC and STwrist were established. By combining these two quantitative relationships, the temperature ranges and acceptance rates under different thermal comfort vote ranges were obtained, which coincide with the results of previous research and satisfy the requirements of ASHRAE Standard 55–2010.

On the Procedure of Draught Rate Assessment in Indoor Spaces

The objective of the paper is to demonstrate the importance of the unsteady Computational Fluid Dynamics (CFD) simulations and long-term measurements for the reliable assessment of thermal comfort indoors, for proper categorization of the indoor thermal environment and for identifying the reasons for complaints due to draught discomfort. Numerical simulations and experimental measurements were applied in combination to study ventilation in a field laboratory, a university classroom with a controlled indoor environment. Strong unsteadiness of the airflow was registered both in the unsteady RANS results and the real-scale long-term velocity data measured with thermo anemometer. Low-frequency high-amplitude velocity fluctuations observed lead to substantial time variation of the draught rate. In case of categorization of a thermal environment, the point measurements or steady-state RANS computations would lead to wrong conclusions as well as they cannot be used for identification of the reasons for people’s complaints due to draught discomfort if strong unsteadiness of the airflow exists. It is demonstrated that the length of the time interval for draught rate (DR) assessment may not be universal if low-frequency high-amplitude pulsations are present in the room airflow.

Examining growth rates of Scopulariopsis brevicaulis on decomposing porcine remains

The purpose of this study was to examine growth rates for the filamentous fungus Scopulariopsis brevicaulis on decomposing porcine remains in relation to three controlled temperatures. This study was performed by inoculating porcine tissue samples retrieved from the same source. These samples were placed into three controlled environments to examine changes over a month. This study was intended to develop a photographic and quantifiable chronology of fungal growth that can be used as a reference by investigative personnel interested in utilizing mycological evidence in death investigations. Providing this novel information to the inadequately studied discipline of forensic mycology is intended to help establish the credibility of this field. These efforts will aid in interpreting post-mortem intervals for cadavers experiencing fungal growth in controlled indoor environments.

Sparse Robot Swarms: Moving Swarms to Real-World Applications.

Robot swarms are groups of robots that each act autonomously based on only local perception and coordination with neighboring robots. While current swarm implementations can be large in size (e.g., 1,000 robots), they are typically constrained to working in highly controlled indoor environments. Moreover, a common property of swarms is the underlying assumption that the robots act in close proximity of each other (e.g., 10 body lengths apart), and typically employ uninterrupted, situated, close-range communication for coordination. Many real world applications, including environmental monitoring and precision agriculture, however, require scalable groups of robots to act jointly over large distances (e.g., 1,000 body lengths), rendering the use of dense swarms impractical. Using a dense swarm for such applications would be invasive to the environment and unrealistic in terms of mission deployment, maintenance and post-mission recovery. To address this problem, we propose the sparse swarm concept, and illustrate its use in the context of four application scenarios. For one scenario, which requires a group of rovers to traverse, and monitor, a forest environment, we identify the challenges involved at all levels in developing a sparse swarm—from the hardware platform to communication-constrained coordination algorithms—and discuss potential solutions. We outline open questions of theoretical and practical nature, which we hope will bring the concept of sparse swarms to fruition.

Sensor deployment strategy using cluster analysis of Fuzzy C-Means Algorithm: Towards online control of indoor environment’s safety and health

Sensor monitoring plays a very important role in indoor environment control and energy efficiency. However, it is always a challenging task when discussing the methods and strategy for sensor deployment, considering its numbers and locations. Thus, this work aims to provide a systematic methodology of sensor deployment for efficient indoor environment monitoring by using clustering method of Fuzzy C-means (FCM) algorithm. An experimental chamber was considered for ventilation basis. Low-dimensional Linear Ventilation Models (LLVM) were applied to generate a series of low-dimensional pollutant concentrations, representing the potential hypothetic monitoring data. FCM algorithm was then adopted to cluster these data as well as corresponding grid points classified by three zones, i.e., main flow, main diffusion and well-mixed zones. Then the cluster centers of each zone were determined representing as the locations of sensor deployment. Lastly, taking the cluster centers as the hypothetic sensor locations and corresponding concentrations as the input for LLVM-based ANN (Artificial Neural Network), the predicted volume-averaged CO2 concentrations agree well with the simulated ones directly from low-dimensional CFD results, with the maximal error of 6.5 %. This work will be further facilitating the realization of artificial intelligent ventilation system and efficient indoor environment control.

CONSTRAINED GA PI SLIDING MODE CONTROL OF INDOOR CLIMATE COUPLED MIMO GREENHOUSE MODEL

High accuracy trajectory tracking with constrained inputs is challenging topic in greenhouse indoor environment control. This is due to nonlinearities and inputs/outputs coupling present in physical model of greenhouse. The objective of this study is about the problem of identification and control of a Multi-Input Multi-Output (MIMO) greenhouse process. Proportional Integral (PI) and sliding mode controllers (SMC) are used in conjunction so that estimated outputs tracks desired trajectories with good performance as near as possible in spite of coupled dynamics and outside disturbances climate. To reflect the practical aspect, the constraints of the process and control elements were taken into account .The results demonstrate the feasibility of PISMC control method. Simulation has been done in the environment of Matlab and Simulink; it shows that combined controllers are capable to manage successfully the microclimate of greenhouse.

Assessing Indoor Environmental Control Practices by Race/Ethnicity Among Children With Asthma in 14 US States and Puerto Rico, 2013-2014.

IntroductionIn the United States, children in Puerto Rico and non-Hispanic black children in the mainland US have a higher burden of asthma than non-Hispanic white children in the mainland US. We examined indoor environmental control (IEC) practices that reduce asthma triggers, by race/ethnicity among children in the mainland US and Puerto Rico. MethodsWe used 2013 and 2014 data from the Behavioral Risk Factor Surveillance System Asthma Call-back Survey Child Questionnaire from 14 states and Puerto Rico to measure the association between race/ethnicity and IEC practices, adjusting for sociodemographic covariates, among children identified as ever receiving an asthma diagnosis. Racial/ethnic groups were compared in 14 US states using aggregated data. Separate analyses compared IEC practices for children diagnosed with asthma in Puerto Rico with children of all races/ethnicities diagnosed with asthma in 14 states.ResultsAmong households in 14 US states that had a child with asthma, non-Hispanic black children were more likely than non-Hispanic white children to use an air purifier (36.8% vs 25.2%; adjusted odds ratio [aOR] = 2.0; 95% confidence interval [CI], 1.3–3.2) and avoid pets in the bedroom (87.9% vs 58.3%; aOR = 4.5; 95% CI, 2.3–8.8). Children in Puerto Rico were more likely than children in 14 states to use dust mite–impermeable pillow covers (53.7% vs 36.4%; aOR = 3.6; 95% CI, 1.8–7.1) and mattress encasements (60.3% vs 30.3%; aOR = 2.4; 95% CI, 1.2–4.8).ConclusionIEC practices such as using air purifiers, pillow covers, mattress encasements, and avoiding pets in the bedroom vary by race/ethnicity among children with asthma. These findings show that vulnerable populations are using IEC practices, but asthma prevention and control measures should continue to be assessed.

Stress Recovery of Campus Street Trees as Visual Stimuli on Graduate Students in Autumn.

Human stress recovery response to landscapes is under discussion in Chinese settings. The present study aimed to clarify the stress recovery effects of campus street trees on graduate students in autumn. A total of 150 participants (23.75 ± 1.01 years old) completed the Trier Social Stress Test (TSST) and were then randomly assigned to view one of five virtual environments, including the street trees Sophora japonica, Ginkgo biloba, Platanus acerifolia, Koelreuteria paniculata, and the indoor environment (control). Physiological responses were measured by R-R interval and electroencephalography (EEG). Psychological responses were examined through the state version of the State-Trait Anxiety Inventory (STAI-S) and the Perceived Restorativeness Scale (PRS). Results showed that R-R intervals significantly increased while viewing all street trees. Both alpha and beta brainwave activities while viewing S. japonica and G. biloba were remarkably higher than those while viewing P. acerifolia and K. paniculata. The STAI-S scores significantly decreased, and the positive PRS scores were registered after viewing street trees. We concluded that a brief virtual visual experience of campus street trees in autumn has stress recovery effects on graduate students, and the different levels of stress recovery are associated with different types of street trees.

Thermal comfort of people in a super high-rise building with central air-conditioning system in the hot-humid area of China

Built areas with centralized air conditioning systems have increased intensively in recent years in the hot-humid area of China's mainland. A longitudinal field survey was conducted in a super high-rise building with central air-conditioning system in Guangzhou for a whole year. Forty-four respondents participated in the survey; they were visited three times a week, and thermal comfort data on a large scale with sample size of 3,345 were obtained, containing indoor environmental parameters, personal factors, and subjective responses. The data analysis showed that the indoor environments varied in small operative temperature ranges of 22 °C–27 °C in summer and 19 °C–25 °C in non-summer months, with moderate relative humidity and an air speed of 0.1 m/s. The respondents’ clothing insulation changed in a narrow range of 0.55 clo to 0.60 clo in summer and in a wide range of 0.80 clo to 0.90 clo in non-summer months. The thermal neutral and acceptable operative temperatures were 27.9 °C and 22.0 °C– 29.5 °C in summer, and the thermal neutral temperature was 24.5 °C in non-summer months. A strong preference for a cooler environment was identified in summer. The PMV (Predicted Mean Vote) model was confirmed to be not applicable in, the adaptive thermal comfort theory is also applicable to people in centrally air-conditioned buildings. This study provides a better understanding of thermal comfort in centrally air-conditioned indoor environment, and it is beneficial to practices regarding the evaluation, design, and control of indoor environments for centrally air-conditioned buildings in the hot-humid area of China's mainland.

Culture, conformity, and carbon? A multi-country analysis of heating and cooling practices in office buildings

This study investigates human-building interaction in office spaces across multiple countries including Brazil, Italy, Poland, Switzerland, the United States, and Taiwan. We analyze social-psychological, contextual, and demographic factors to explain cross-country differences in adaptive thermal actions (i.e. cooling and heating behaviors) and conformity to the norms of sharing indoor environmental control features, an indicator of energy consumption. Specifically, personal adjustments such as putting on extra clothes are generally preferred over technological solutions such as adjusting thermostats in reaction to thermal discomfort. Social-psychological factors including attitudes, perceived behavioral control, injunctive norms, and perceived impact of indoor environmental quality on work productivity influence occupants’ intention to conform to the norms of sharing environmental control features. Lastly, accessibility to environmental control features, office type, gender, and age are also important factors. These findings demonstrate the roles of social-psychological and certain contextual factors in occupants’ interactions with building design as well as their behavior of sharing environmental control features, both of which significantly influence building energy consumption, and thus, broader decarbonization.

Indoor Thermal Environment Control System Based on Maximum Work Efficiency

Based on the indoor thermal environment on the influence of the office personnel work efficiency on the basis of experimental research. In this paper, a control method based on maximizing work efficiency is proposed, which aims at maximizing indoor staff’s work efficiency and rest efficiency, and takes Predicted Mean Vote (PMV) as control parameter to control indoor environment, taking into account the requirements of comfort. The operation effect of the control method based on maximum work efficiency is estimated. It is found that compared with the traditional control system based on PMV index, the system not only improves the work efficiency and rest efficiency of indoor personnel, but also is more comfortable. The indoor environment control system based on maximizing work efficiency is still in the theoretical stage. The construction and actual operation effect of the system need to be further studied.

Automated classification of indoor environmental quality control using stacked ensembles based on electroencephalograms

AbstractThis study aims to develop an automated classification of indoor air quality control using the machine learning based on an electroencephalogram (EEG) signal. Two experiments were conducted: The first aimed to select an EEG channel based on the brain wave indices when the indoor environmental quality (IEQ) changes. We then extract the brain wave indices’ feature when the subjects conduct adaptive behaviors and predict the IEQ condition control using machine learning including the stacked ensembles. The extracted features were classified using base estimators such as distributed random forest, gradient boosting machine (GBM), generalized linear models (GLMs), deep neural network, and new predicted data were retrained and predicted by metalearner (i.e., GLM). In Dimension 1, the air conditioning system and the air ventilation system, and the area under curve (AUC) of the proposed stacked ensembles trained by base estimators was the highest, 0.9038. In Dimension 2, turning on and turning off, the AUC of the GBM is the highest, 0.8384. Based on these results, the EEG signal can be used to suggest an automatic IEQ control model that can then reduce the drowsiness, and increase attention.

知的生産性を高める空間を実現する、ＡＩ・ＩｏＴ活用型知的環境制御システムの研究開発 （第２報）ヒューマンファクターとして快適性と眠気を考慮した室内環境制御の検討

知的生産性を高める空間を実現する、ＡＩ・ＩｏＴ活用型知的環境制御システムの研究開発 （第２報）ヒューマンファクターとして快適性と眠気を考慮した室内環境制御の検討