Indoor Environment Management Research Articles

Application of indoor thermal energy cycle and visual communication simulation scene based on Machine vision and optical image enhancement

Traditional thermal analysis methods have limitations in complex environments, and innovative technologies are urgently needed to improve the accuracy of indoor thermal monitoring and optimization. In this paper, machine vision and optical image enhancement technology are combined to simulate the indoor heat energy cycle process to achieve more efficient heat management and visual communication, so as to improve the energy utilization efficiency and residential comfort of buildings. In this paper, a high-resolution camera is used to capture real-time images of indoor environment, image quality is enhanced by image processing algorithm, and heat distribution characteristics are extracted. The machine learning model is used to analyze the dynamic changes of the thermal energy cycle, and the visual model of the indoor thermal energy cycle is constructed by combining the fluid dynamics theory. The experimental results show that the proposed method can effectively identify and display the key areas of indoor heat circulation and their changing trends. The simulation results show that the optical enhanced images can show different temperature regions more clearly, directly reflect the heat flow situation, and have a high agreement with the actual measurement data. Therefore, the indoor thermal energy cycle simulation method based on machine vision and optical image enhancement not only improves the accuracy and real-time performance of thermal energy analysis, but also provides visual support for indoor environmental management.

Assessment of sources and health risks of heavy metals in metropolitan household dust among preschool children: The LEAPP-HIT study

The most common construction material used in Taiwan is concrete, potentially contaminated by geologic heavy metals (HMs). Younger children spend much time indoors, increasing HM exposure risks from household dust owing to their behaviors. We evaluated arsenic (As), cadmium (Cd), and lead (Pb) concentrations in fingernails among 280 preschoolers between 2017 and 2023. We also analyzed HM concentrations, including As, Cd, Pb, chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), iron (Fe), and manganese (Mn), in 90 household dust and 50 road dust samples from a residential area where children lived between 2019 and 2021 to deepen the understanding of sources and health risks of exposure to HMs from household dust. The average As, Cd, and Pb concentrations in fingernails were 0.12 ± 0.06, 0.05 ± 0.05, and 0.95 ± 0.77 μg/g, respectively. Soil parent materials, indoor construction activities, vehicle emissions, and mixed indoor combustion were the pollution sources of HMs in household dust. Higher Cr and Pb levels in household dust may pose non-carcinogenic risks to preschoolers. Addressing indoor construction and soil parent materials sources is vital for children's health. The finding of the present survey can be used for indoor environmental management to reduce the risks of HM exposure and avoid potential adverse health effects for younger children.

The Biodiversity of Anopheles and Malaria Vector Control in Indonesia: A Review

Malaria is a re-emerging vector-borne disease. Malaria is caused by Plasmodium parasites namely P. falciparum, P. malariae, P. vivax, P. ovale, and P. knowlesi. The parasite is transmitted by mosquitoes from the genus Anopheles. The genus Anopheles comprises more than 480 described species worldwide and many more are yet to be described. It is known that there are more than 100 species of Anopheles that can transmit malaria parasites to humans. The present review aims to assess studies on the biodiversity of Anopheles as a malaria vector and its control in Indonesia. The results showed that a total of 80 species of Anopheles mosquitos are reported in Indonesia, and at least 31 species of Anopheles mosquitoes have been reported as malaria vectors in Indonesia. Several efforts to decrease the number of malaria cases are the distribution and wide-scale use of Long-Lasting Insecticide Treated Bednets (LLIN), Indoor Residual Spraying (IRS) applications, larvae control, and environmental management. Vector control and entomological surveillance of malaria are one of the main domains for achieving malaria elimination. In conclusion, a combination of national policies, local-specific, habitat-specific, and community participation approaches are needed for effective malaria vector control in Indonesia.

Low-cost data-driven estimation of indoor occupancy based on carbon dioxide (CO2) concentration: A multi-scenario case study

Occupancy levels significantly influence HVAC system operation, making accurate occupancy prediction essential for the advancement of Occupant-Centered HVAC control. This study aims to develop a simple and effective occupant prediction model in buildings using low-cost indoor environmental sensors and artificial intelligence technology. In-situ measurements were taken in two university classrooms in South Korea over a three-month period, collecting data on indoor and outdoor temperature, humidity, and CO2 levels. Five machine learning algorithms, including Linear Regression (LR), Random Forest (RF), Gradient Boosting Regression (GBR), Multi-Layer Perceptron (MLP), and Long Short-Term Memory neural networks (LSTM), were applied to compare models of indoor occupancy. The results demonstrate that, among the five machine learning models evaluated, the LSTM model outperforms the others, achieving an RMSE of 3.43. This result indicates a close match between predicted and actual indoor occupancy based on CO2 concentration. The integration of a multivariate multi-step input method further enhances its accuracy, making it suitable for a variety of real-world scenarios in indoor occupancy prediction. This study reveals that using processed data as input sources leads to improved prediction performance for indoor occupant states. Importantly, this work does not infringe on biometric information, such as human image privacy, and relies on minimal measurement data. Furthermore, it not only emphasizes the model's feasibility and practicality in predicting indoor occupancy but also its potential in HVAC system automation, building energy conservation, and indoor environmental management. This study offers guidance and support for the advancement of smart cities and intelligent buildings in the future.

Design and implementation of an indoor environment management system using a deep reinforcement learning approach

The Indoor Household Environment (IHE) has caught the attention of industry and academia due to the substantial amount of time that families spend indoors. Domestic air pollution imposes significant burdens on communities, although both indoor and outdoor sources of air pollution contribute to outdoor pollution. This article presents a precise approach for an Intelligent Home Energy Management System (IHEMS) that integrates Deep Reinforcement Learning (DRL) and Demand Response (DR) techniques. IHEMS employs Markov decision processes to handle problems and concerns efficiently. A novel approach has been developed to enhance the efficiency of deep reinforcement learning control, utilizing a double deep Q network and a restart strategy during a priority event. Unlike the HEMS technique, which uses a predictive control model, the optimal strategy resulted in a 20% decrease in monthly power consumption.

Real-time indoor thermal comfort prediction in campus buildings driven by deep learning algorithms

Thermal comfort prediction is vital in achieving a good indoor environment and efficient energy management. However, thermal comfort is strongly nonlinear and dynamically changing over time, making it difficult to predict thermal comfort accurately. Two real-time thermal comfort prediction models on multi-time scales are proposed based on deep learning algorithms. The indirect prediction model for thermal comfort integrates the bidirectional long and short-term memory network to predict real-time environmental parameters. It subsequently combines the thermal comfort calculation method with the predicted environmental parameters to obtain the predicted thermal comfort value. The indirect prediction model for thermal comfort combines the bidirectional long and short-term memory network to realize real-time prediction of environmental parameters and then calculates thermal comfort by the thermal comfort calculation method to realize indirect thermal comfort prediction. The other is the direct prediction model of thermal comfort, which is realized using the bidirectional long short-term memory network for real-time thermal comfort prediction. The two real-time prediction models are compared and analyzed for 10-min, 30-min, and 60-min scales. Experimental results show that the prediction accuracy of the indirect prediction model is better than the direct prediction model, but the robustness of the indirect prediction model is lower than the direct prediction model. In addition, the accuracy of the thermal comfort prediction model on the small time scale is higher than that on the large time scale, in which the RMSE and MAE reached 0.0551 and 0.0543 (10-min scale), respectively. Therefore, the proposed thermal comfort prediction models can be integrated into the control model of heating, ventilation and air conditioning systems, providing a reference for optimizing indoor thermal comfort.

Benchmark of plant-based VOCs control effect for indoor air quality: Green wall case in smith campus at Harvard University

The emission of volatile organic compounds (VOCs) from interior materials can significantly impact people's health and daily activities, necessitating effective management. In the construction of the interior built environment, plants serve as a suitable means to enhance air quality. They not only function as ecological living materials for air purification and VOCs removal but are also valued for their aesthetic appeal. However, often the emphasis in designing green infrastructure is placed more on the aesthetics of planting design rather than considering plants' ecological properties. This research examines the ability of 12 plant species used in the interior green wall design at Harvard University's Smith Center as a case study to decompose and absorb VOCs through experimental studies. By integrating ecological properties and key factors such as spatial and budget constraints into the design process, this research explores the potential of using an algorithmic model to select plant species capable of reducing interior VOC pollution in green wall design. The significance of this study lies in its contribution to indoor environmental health and environmental management practices through providing a potential plant selection model and suggesting a relevant workflow for interior planting design with the goal of controlling VOC emissions. By leveraging the knowledge gained from experiments on the VOC removal abilities of selected plant species, this study offers a valuable resource for practitioners seeking to create innovative indoor air cleaning and decontamination technologies.

Environmental Benefits of Green Buildings with BIM Technology

Abstract Under accelerated urbanisation and rapid social and economic development, environmental pollution has become an important content of the construction industry. Therefore, the environmental benefit analysis of green buildings is of great significance to the effective use of energy and resources and to achieve the goal of protecting resources, reducing pollution and improving the ecological environment. The results show that the measured results of incremental environmental costs of the office building in Taiyuan City in land saving, energy saving, water saving, material saving, indoor environmental quality and operation management are decreased by 13.56 %, 11.02 %, 25.17 %, 14.43 %, and 15.25 %, respectively; incremental environmental benefits in land saving, energy saving, water saving, material saving, indoor environmental quality and operation management are increased by 23.15 %, 10.37 %, 19.30 %, 18.25 %, and 22.53 %, respectively. Those suggest that the BIM technology applied to the complete life cycle cost analysis of green buildings can test, measure and guide all the steps of building design, construction and maintenance and largely meet the criteria of resource-saving and energy-saving. The results of this paper provide a reference for further research on the environmental benefit analysis of green buildings based on BIM technology.

Research on Evaluation of Eco-Metro Station Levels Based on Hierarchical Analysis Models

With the help of hierarchical analysis and fuzzy comprehensive evaluation theory, this paper innovatively constructs a hierarchical evaluation model of underground metro station ecological building projects from six aspects, namely, land saving, material saving, indoor environment, water saving, energy saving and operation management, and combines with the premise of spatial specificity and complexity of underground metro stations, so as to provide a theoretical basis for the development of ecological buildings in the construction of underground metro stations. The article selects the ecological building of underground station in Qingdao, China to carry out comprehensive evaluation and validation. The results of the study show that the evaluation model based on hierarchical analysis can effectively quantify the ecological grades of metro station projects.

고령자의 인식과 필요를 고려한 스마트 홈 연구

Korean society is rapidly entering an aging society. The elderly population in Korea is expected to increase to 20.3% in 2025. Therefore, in order to cope with an aging society, research on smart home services from the perspective of users is necessary. Therefore, this study aims to develop various user personas focusing on the characteristics, perception, and necessity of smart homes of the elderly, and to develop a smart home service model suitable for the elderly based on scenario analysis. This study is conducted as a research method of literature research, case study, survey, persona development, and scenario verification analysis. Through prior research and case analysis of smart home services, smart home service items are classified into safety services, indoor environment management services, health services, culture and convenience services, and service support contents are set according to living space. Through the survey, two scenarios were developed: the preliminary elderly and the elderly. Through this, the service model was verified by setting two types of personas, and the model was developed through the selected items. As a result of this study, a smart home service model suitable for various elderly people was selected and presented in 6 spaces, 4 service types, and 18 items.

A State of Art Review on Methodologies of Occupancy Estimating in Buildings from 2011 to 2021

Occupancy information is important to building facility managers in terms of building energy efficiency, indoor environmental quality, comfort conditions, and safety management of buildings. When combing the distribution characteristics of the literature, it is found that the field of estimating occupancy counts is a very active area. Researchers from various countries have undertaken extensive explorations to obtain more research results. In this survey, the commonly used occupancy measurement systems and algorithms are described. Through the analysis and research of different occupancy measurement systems and algorithms, their advantages, disadvantages, and limitations are summarized, so that researchers can use them selectively. As for how to choose the method of estimating occupancy counts, suggestions are given in terms of the range of people, accuracy, cost, and privacy. There are still many pressing issues relating to high-density crowd occupancy counting, complex environmental impact, and system robustness. According to the current research progress and technology development trend, the possible future research directions are pointed out. The innovation of this review is the quantitative analysis of the selection of occupancy measurement systems for different ranges of people, and the occupancy counting accuracy situation of different measurement systems and algorithms. It provides more informed opinions on the selection of practical applications. It can be used by other researchers as a starting point for their research and/or project work.

Developing a Green Building Assessment Tool for Ethiopia

Sustainable/green building rating systems are utilized by both developed and developing countries based on their local context. This paper aims to develop an assessment tool that considers the triple bottom line sustainability of buildings. In Ethiopia, buildings of various types and purposes are constructed at an alarming rate with inadequate resources and wasteful uses, so this tool is in urgent need. Developing such a tool is highly appreciated due to the diversified and complicated ecological and socio-economic issues in the building construction sector. This research has developed new green building assessment categories and criteria depending upon consensus reached with 93 experienced experts working on the construction sectors. This research reviewed a total of 10 widely and repeatedly used tools that were critically studied, for instance, Leadership in Energy and Environmental Design, Building Research Establishment Environmental Assessment Method, Comprehensive Assessment System for Building Environmental Efficiency, Deutsche Gesellschaft für Nachhaltiges Bauen, Sustainable Building Tool, and so on. The Analytic Hierarchy Process technique was applied for weighting and prioritizing after selecting these assessment categories and criteria. The outcomes of the research with the relative priority values were materials and resources (18.66%), sustainable sites and ecology (16.92%), energy efficiency (16.78%), indoor environmental quality (12.60%), economic aspects (10.41%), management (10.30%), water efficiency (8.06%) and location and transportation (6.27%). Thus the proposed sustainable building assessment tool that best suits Ethiopian settings was developed.

Pollutant Emissions in Livestock Buildings: Influence of Indoor Environment, Rearing Systems, And Manure Management

The issue of air pollutants from livestock buildings is prevalent in the literature. Because they and their emissions impact both animal production and livestock building users as well as the outdoor environment. This paper aims to compile and review data available in the scientific literature on the types of pollutants for a better understanding of their generation form, their distribution according to the kind of animal, and the main factors affecting their generation and concentration, i.e., the rearing system, the indoor microclimate, and the manure management. The elevated generation of pollutants in animal buildings is tied to the dense occupancy in this industrial activity. The indoor air quality is defined according to the type of livestock in animal housing, considering its welfare needs, and the types and concentrations of pollutants generated as a function of the family of animal and the management used in production. The main gases generated are CH4 , CO2 , H2 S, NH3 , N2 O, in addition to particulate matter and airborne microorganisms such as fungi and bacteria that very negatively affect the health of animals and users of the animal buildings. Furthermore, knowledge about the main contaminants generated, the form of generation, their origin, their concentrations, and their distribution throughout the shed is essential to achieve a permanent and adequate indoor air quality and, with that, a high-quality product that will lead to high production yield without neglecting animal welfare.

Green building: A comprehensive solution to urban heat

Many cities are facing urban heat problems, the combined effect of heatwaves under global climate change, and local warming associated with urbanization, resulting in severe environmental, economic, social, and health impacts. It is urgent to address urban heat problems. Buildings are an important cause of urban heat, while buildings are favorable spaces to deal with urban heat problems through built environment decarbonization and the implementation of cooling strategies. Green building (GB) which has been recognized as an innovative philosophy and practice in the building sector, is proposed to address urban heat challenges. However, existing studies have offered a limited understanding of GB-based urban heat mitigation and adaptation (UHMA). Therefore, this study aims to examine how GBs can contribute to UHMA. In particular, this study analyses the contribution of buildings to urban heat problems in aspects of carbon emissions and the extensive modifications towards artificial landscapes, and discusses the possible impacts caused by urban heat problems. Afterwards, this study develops the framework of GB’s responses to urban heat in global warming mitigation, local warming mitigation, and urban heat adaptation. Based on this, such responses are analyzed in aspects of site planning, outdoor environments, transportation, building design, energy efficiency, water efficiency, material efficiency, indoor environmental quality, operation management, construction, and maintenance following the whole life cycle perspective. This paper helps understand how GB techniques contribute to UHMA and provides a reference to the revision of the GB assessment system for addressing urban heat problems.

Cost Effective Indoor HVAC Energy Efficiency Monitoring based on Intelligent Decision Support System under Fermatean Fuzzy Framework

The heating, ventilation, and air conditioning (HVAC) control system is responsible for the efficient building energy system. Indoor energy consumption patterns can be monitored and reduced intelligently. Occupancy information plays a vital role to save a reasonable amount of energy. Traditional energy monitoring and control systems can be improved with the installation of the occupancy monitoring system which will consist of a network of sensors and cameras. In this research work, we propose a new and revolutionary convolutional neural network (CNN) based on real-time camera occupancy detection and recognition techniques across different sorts of sensors that provide realistic low-cost energy-saving solutions with robust graphical processing units (GPUs). This occupancy information will decide the energy behaviour inside buildings. Decision-making tools can be used to select the appropriate occupancy detection and recognition alternative for indoor environment and energy monitoring and management. In this research work, we introduce and develop the "Fermatean fuzzy prioritized weighted average and geometric operator". These aggregation operators (AOs) are a modern approach to modelling complexities in decision-making. In the end, we give an algorithm for an intelligent decision support system (IDSS) using proposed AOs to compare our CNN based method with other existing sensors techniques.

Microclimatic and Ventilation Conditions in Existing Healthcare Facilities. A Study in the Waiting Room-Testing Centre of a Florentine Historic Hospital

The pandemic COVID-19 era we are experiencing has changed our way of seeing, thinking and designing indoor and outdoor environments and, above all, plant systems and building-plant management. Energy environmental sustainability is a common fundamental target for buildings and plant systems, but health protection and prevention are the priority issues for the basis of any retrofitting and refurbishment operation. This question becomes even more complex if the building is historic and used for hospital or for healthcare facilities. In this research we propose a methodological approach based on the combination of physical-real and “virtual”, i.e. measured and simulated information. The proposed method can be a useful tool for setting up continuous monitoring systems for microclimatic and ventilation conditions, user influx/presence and behaviour, real operation (on demand) of the plants and control/regulation system adjustment. Results show the importance of drawing up useful guidelines for training health workers and people/patient subjects, aiming at conscious interaction for health and wellbeing protection, but also better indoor environment management. This is particularly important for healthcare environments such as the one studied.

Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach

Indoor environmental quality is an important issue since people spend most of their time indoors. This paper aims to develop an autonomous indoor environment management approach to ensure a healthy indoor environment with minimized energy cost via the optimal control of ventilation system and heating/cooling system in smart homes. Due to the uncertainties of weather conditions, electricity price and home occupancy, as well as the complex interaction between indoor air quality and indoor thermal environment, it is challenging to develop an efficient control strategy. To address this challenge, the indoor environment management problem is formulated as a Markov decision process, and then a deep reinforcement learning control strategy, which combines double deep Q network with prioritized experience replay mechanism, is proposed to solve the Markov decision process. The proposed approach can make adaptive control decisions based on the current observations without requiring any forecast information of system uncertainties. Control performance under different scenarios show the proposed approach has good adaptability to the variation of weather conditions, electricity prices, home occupancy patterns and indoor temperature requirements. Moreover, the proposed approach is compared with a double deep Q network-based approach and a model predictive control-based approach. Comparison results show that the proposed approach reduces the average daily energy cost by 3.51% and 8.56% in winter scenarios and 4.05%, 7.88% in summer scenarios while achieving the smallest indoor air quality and temperature violations.

An IoT-Based Smart Building Solution for Indoor Environment Management and Occupants Prediction

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939).

Problems and Implications of Shelter Planning Focusing on Habitability: A Case Study of a Temporary Disaster Shelter after the Pohang Earthquake in South Korea.

Habitability is an essential concept for shelter planning in terms of supporting victims’ right to life with dignity and recovering from what they suffered. The study aimed to identify problems and needs in shelter spaces and suggest measures to improve shelter space plans by conducting a case study in South Korea. The temporary disaster shelter in Pohang built right after the earthquake (2018) was selected as a case subject. From the literature review, a framework consisting of four concepts of habitability (safety, health, sociality, comfort) and four shelter zones (entry, residential, service, special needs zone) was developed for the in-depth interviews and analysis. The field study and in-depth interviews with victims, staff, and volunteers were conducted to collect problems and needs regarding shelter space planning. The results showed that the entry zone needed improvements in ‘protection’, ‘prevention’, ‘sanitation’, ‘accessibility’, ‘area’, and ‘privacy’. The residential zone lacked ‘area’, ‘privacy’, and ‘indoor environmental quality’. The service zone problems were mainly seen in the categories of ‘area’ and ‘privacy’. The special needs zone was less habitable in the categories of ‘protection’ and ‘area’. To appropriately respond to victims’ urgent needs, the temporary shelter planning should secure enough space beyond the legal minimum standards, provide sanitation and indoor environmental quality management, and separate spaces by function and user type.

Optical Sensing Approach to the Recognition of Different Types of Particulate Matters for Sustainable Indoor Environment Management

The indoor environment is a crucial part of the built environment where our daily time is mostly spent. It is governed not only by indoor activities, but also affected by interconnected activities such as door opening, walking and routine tasks throughout the inside and outside of buildings and houses. Pollutant control is one of the major concerns for maintaining a sustainable indoor environment, and finding the source of pollutants is a relatively hard part of that task. Pollutants are emitted from various sources, transformed by sunlight, react with vapor in ozone and are transported into cities and from country to country. Due to these reasons, there has been high demand to monitor the transportation of particulate matters and improve air quality. The monitoring of pollutants and identification of their type and concentration enables us to track and control their generation and consequently discover reliable suitable mitigation measures to control air quality at regulated levels by contaminant source removal. However, the monitoring of pollutants, especially particulate matter generation and its transportation, is still not fully operated in atmospheric air due to its open nature and meteorological factors. Even though indoor air is relatively easier to monitor and control than outdoor air in the aspect of specific volume and contaminant source, meteorological parameters still need to be considered because indoor air is not fully separated from outdoor air flow and contaminants’ transportation. In this study, an optical approach using a spectral sensor was attempted to reveal the feasibility of wavelength and chromaticity values of reflected light from specific particles. From the analysis of reflected light of various particulate matters according to different liquid additives, parameter studies were performed to investigate which experimental conditions can contribute to the enhanced selective sensing of particulate matter. Five different particulate matters such as household dust, soil, talc powder, gypsum powder and yellow pine tree pollen were utilized. White samples were selectively identified by the peak at 720 nm for talc and 433 nm and 690 nm in wavelength for gypsum under chemical additives. Other grey household dust and yellowish soil and pine tree pollen revealed a distinct chromaticity x, y coordinates shift in vector within the maximum range from (0.22, 0.19) to (0.55, 0.48). Applicable approaches to assist current particle matter sensors and improve the selective sensing were suggested.