Indoor Environmental Parameters Research Articles

Effects of thermal-acoustic interaction on airport terminal's indoor thermal comfort: A case study in cold region of China

Airport terminals are typical large-scale public buildings. Improving the indoor environment of the terminal plays an important role in increasing users' comfort status. In this study, we chose Xianyang Airport Terminal 3 and conducted field tests and questionnaire surveys to obtain indoor environmental parameters and subjective perceptual evaluations. By comparing the correlations between the two aspects, we made analyses about the interaction effects of thermal-acoustic environments on people's indoor thermal comfort. The results show that there are significant correlations between TSV and the temperature, SSV and the sound level. The neutral temperatures of MB and CB are 19 °C and 21 °C, respectively. TCV are mostly above 0, but when the temperature reaches a certain state, it would decrease rapidly. Most of the participants hasn't showed strong feelings of comfort or discomfort, which revealed a relatively high tolerance and adaptation to the indoor environment. As the summer is hot and humid, setting the indoor temperature at 24 °C would be a balance between cost and comfort. The overall sound level is loud, which decreased the TCV to a certain extent while the tendency of how thermal comfort changes remains the same. According to different characteristics of the space, various passive and active approaches were suggested to improve the thermal and acoustic environment. This study partly agrees with the previous studies and we hope to provide reference for the optimization of summer indoor environment in large-scale public buildings in similar climate and location.

An empirical study of indoor air quality in badminton stadiums in hot summer and cold winter regions of China during spring and fall seasons

The indoor air quality has a direct impact on human health. In order to obtain the current status of indoor air quality in typical sports buildings in hot summer and cold winter climate zones in China, indoor badminton courts in 10 cities in Hubei Province in this climate zone were selected as research objects for field testing of indoor environmental parameters in spring and autumn, and predict air quality parameters for non-testing times. All the tested stadiums are naturally ventilated in non-event conditions, and the average daily indoor CO2 concentration was 526.78 ppm in spring and 527.63 ppm in autumn, and the average daily PM2.5 concentration was 0.035 mg/m3 in spring and 0.024 mg/m3 in autumn, all of which met the requirements of GB/T 18883-2022, the average concentration of CO2 ≤ 1000 ppm and PM2.5 ≤ 0.05 mg/m3. The indoor CO2 concentration and PM2.5 concentration of the tested badminton halls under natural ventilation gradually increased with the accumulation of exercise time, making the indoor air quality of the badminton halls decrease, which would negatively affect the health of the people exercising in this environment.

Factor analysis and risk assessment of indoor environmental parameters and TSV in fitness centres under different zones, exercise intensities and times

Maintaining regular physical exercise is essential for maintaining good health. In China, fitness centres are thriving, and the cleanliness of the exercise environment has become increasingly important. This study investigated the thermal response and indoor air environment of fitness personnel in various areas of a fitness centre using subjective evaluation and on-site air environment measurements. The findings revealed that the indoor air environment in different exercise areas of the fitness centre varied spatially, which can lead to discomfort amongst fitness personnel during exercise. Overall, the thermal sensation in different areas of the fitness centre was found consistently too hot. Temperature and humidity have the greatest impact on the thermal sensation of individuals in different areas, while indoor air pollutants have a minimal effect. The intensity and duration of exercise can also influence the body’s heat sensation and pollutant inhalation dose. The body’s heat balance mechanism can affect subjective evaluations. This study proposes three strategies for optimizing the indoor environment of fitness centres through principal component analysis. This paper provides data and a methodological framework for the development of specifications supporting the design, operation and management of the environment in fitness centres.

Operating characteristics and evaluation of air source heat pumps in cold regions during heating season in China

The mismatch between the actual heat production of an air source heat pump (ASHP) and the instantaneous load of the building it heats is a critical cause of low energy efficiency. Research on the measured heating effect provides an effective approach to improve the energy efficiency of an ASHP. This study therefore focused on two small office buildings to explore the operating data describing the ASHP system source and load sides during the heating season. A sensitivity analysis was employed to explore the actual operational effects of the ASHP. Common problems associated with system operation were comprehensively identified, including the operation characteristics, energy efficiency coefficients, load characteristics, and energy consumption characteristics of the transmission and distribution systems. The effects of these problems were evaluated from three perspectives: integrated part load value (IPLV), system performance loss, and indoor environmental parameters. The results shows that the highest coefficient of performance (COP) of the ASHP in Building A was 3.27 while the IPLV was 2.68, operating status reached the standard. The COP value of the ASHP in Building B was 1.55 while the IPLV was 2.45. Poor value shows the low performance in operating phase, which can be further optimised by matching the building instantaneous load to the heat production of the ASHP, improves the operating effect of two buildings comprehensively.

Understanding university students’ perceptions of classroom environment: A synergistic approach integrating grounded theory (GT) and analytic hierarchy process (AHP)

University classrooms play a crucial role in students' educational experience, as they spend a significant amount of time in these spaces. Understanding students' perceptions of the classroom environment is essential for improving classroom design and management. Adopting a user-centric approach, a novel research design combining both qualitative and quantitative research methods is employed to develop and validate an evaluation framework for classroom environment evaluation. Specifically, this study utilises grounded theory (GT) methodology to identify and prioritises the indoor environment parameters that students consider important. A classroom environment performance evaluation framework for the classrooms within a Hong Kong university was then developed based on the qualitative findings using the Analytic Hierarchy Process (AHP) method. The framework was then used to examine students' perceived importance of the university classroom performance among different sub-groups. The findings reveal that “facilities and services” emerged as the most critical parameter, emphasizing the importance of effective post-occupancy management. Intriguingly, there are minor differences in importance rankings between genders and education levels, with males emphasizing “indoor air quality” and females prioritizing “thermal comfort”. Moreover, distinct priorities are observed between various classroom types; students in lecture theatres focus on “seating plans” and “temperature”, while those in general classrooms prioritise “seating layout” and “fire safety”. These findings emphasise the need for effective post-occupancy management, while also highlight nuanced preferences based on gender, education levels, and classroom types, thereby offer crucial insights for designers to tailor indoor environments that better align with diverse student needs and preferences. Furthermore, a human-built environment interaction model is developed to summarise the research findings and provide insights for future research directions. This comprehensive approach enhances understanding of students’ preferences and experiences in university classrooms.

European residential ventilation: Investigating the impact on health and energy demand

The fundamental goal of building ventilation is to provide indoor conditions conducive to occupant health with low energy use. However, given the dynamic interplay of indoor and outdoor environmental conditions, there is limited knowledge on benchmarking human health and energy performance in relation to different ventilation systems. We coupled outdoor air pollution and meteorological data from 9 European cities with building energy simulations to investigate the effect of residential ventilation on indoor air quality, Disability-Adjusted Life Years (DALYs), and energy demand. Examining five representative ventilation systems, each operating under three distinct ventilative cooling (VC) scenarios, our findings reveal that PM2.5 accounted for the majority of DALYs (∼94 %). Filtering outdoor air in accordance with the standards reduced DALYs on average by ∼37 %. In the presence of filtration, VC based on indoor-outdoor temperatures increased DALYs by ∼10 %. However, a more comprehensive VC control based on both indoor-outdoor temperatures and outdoor air pollution did not increase DALYs. Overall, demand-controlled mechanical ventilation with energy recovery, outdoor air filtration, and VC resulted in the lowest energy demand and DALYs. Our results suggest that considering both indoor and outdoor environmental parameters can improve the performance of residential ventilation in terms of health and energy.

QUANTITATIVE APPROACHES IN ARCHITECTURAL DESIGN FOR USER-ORIENTED INDOOR ENVIRONMENTAL QUALITY

ABSTRACT Understanding indoor environmental quality (IEQ) and integrating indoor data into architectural design is a critical component of green building design for building occupants’ comfort and health. With a growing emphasis on creating healthy and comfortable indoor environments in green buildings, indoor data in architectural design has increasingly incorporated quantitative methods to analyze and optimize indoor environmental quality parameters. Data-driven design in architecture has been used as a decision-making approach to the green building design process; it has implications for the building occupants to voluntarily enhance their indoor environments and comfort. A higher degree of personal control for indoor environmental quality (IEQ) leads to higher satisfaction, energy savings, and productivity in work environments. IEQ data access related to thermal, visual, and acoustic comforts and indoor air enables occupants to acquire ownership of indoor controls, contributing to their comfort and task productivity. Undergraduate architecture students carried out a series of data-driven designs for IEQ monitoring and design integrations by conducting a field study, data collection, data analysis, and visualization, and linking findings to architectural proposals. Participating groups were requested to i) theoretically understand IEQ data parameters for four primary indoor environments, ii) experimentally conduct a field survey of target spaces, iii) numerically predict IEQ conditions by using diverse simulation tools and measurement devices, iv) graphically visualize IEQ findings, and v) finally integrate findings into the architectural design as a solutional proposal. The findings highlight that the potential of quantitative approaches in architectural design enhances students’ understanding of environmental contributors and design solutions for occupants’ comfort and health.

Enhancing real-time nonintrusive occupancy estimation in buildings via knowledge fusion network

Real-time nonintrusive occupancy estimation can maximize the use of existing sensors to infer occupant information in buildings with the advantages of fewer privacy concerns and fewer extra device costs. Recently, many deep learning architectures have proven effective in estimating occupancy directly from raw sensor data. However, some handcrafted features manually extracted from statistical and temporal domains might convey additional information for occupancy estimation. In this study, a novel knowledge fusion network for nonintrusive occupancy estimation is proposed to integrate knowledge from two streams, i.e. automatic knowledge stream from a deep learning architecture and handcrafted knowledge stream from manual feature engineering. Moreover, four different fusion modules are investigated to optimize the design of the fusion network. To verify the effectiveness of the proposed network, experiments are conducted in a dataset from the ASHRAE Global Occupant Behavior Database, which is collected from an office space with records of indoor environment parameters, occupant-building interactions, and contextual information. The results demonstrate the superiority of the proposed fusion network, which outperforms five representative algorithms. Furthermore, the ablation study underscores the benefits of knowledge fusion and occupant-building interaction information, showing that the proposed fusion network can enhance the occupancy estimation accuracy by 3.47 % to 9.24 %.

Performance Comparison of Indoor Humidity Modeling: A Novel Hybrid Model vs. the Simplified Calculation Model by EN ISO 13788

Ventilation is essential for maintaining indoor air quality and human comfort, particularly in buildings with mechanical ventilation systems. However, modeling the complex relationship between ventilation rate and measurable indoor environmental parameters, such as humidity, poses significant challenges. Current methods for modeling this relationship are often inefficient. For example, data-driven models frequently require sufficient information on ventilation, which may not be available for constant air volume (CAV) systems. On the other hand, simulation programs can be time-consuming and require expert engagement. In this study, we present two simplified calculation models, including a hybrid model developed by the authors, and a well-known model introduced by EN ISO 13788. We compare their performance in evaluating the impact of ventilation rate on indoor humidity. The hybrid model uses a novel analytical method to upgrade a single-input regression model (which considers only outdoor humidity as input) to enable the evaluation of indoor humidity based on both outdoor humidity and ventilation rate. We used the commercial TRNSYS program to model two scenarios: increasing the reference ventilation rate and decreasing the reference ventilation rate. The results showed that both models matched TRNSYS simulations with satisfactory accuracies, but the hybrid model demonstrated superior performance to the EN ISO 13788 model in both scenarios. The hybrid model’s higher accuracy and hybrid features make it better suited for big data analysis and field studies.

Associations between house dust mite concentrations in bedroom dust in Northern China and childhood asthma, rhinitis and eczema

House dust mite (HDM) allergen is a risk factor for children's health. There are few studies of associations of house dust mites with childhood asthma, wheeze, rhinitis and eczema in northern China. During 2013–2016, we performed a nested case-control study involving 410 children from 399 homes. The concentration of Dermatophagoides farina class I allergen (Der f 1) was measured in dust samples from mattresses in children's bedroom by enzyme-linked immunosorbent assay (ELISA). The indoor environmental parameters such as pets keeping and environmental tobacco smoke (ETS) were collected by a questionnaire. Adjusted odds ratios (AOR) of Der f 1 to children's allergic symptoms was estimated by logistic regression. A non-linear trend was found on the association between asthma and HDM exposure, and the risk of wheezing rose at low levels of Der f 1 exposure and the risk attenuated thereafter. A dose-response trend with the prevalence of rhinitis and eczema among children was observed with the increase of concentration of house dust mite allergen Der f 1 in bed mattress. When concentration of Der f 1 was above 4500 ng/g, the adjusted odds ratios of rhinitis current (AOR = 2.40), eczema ever (AOR = 12.23), eczema current (AOR = 2.52) and diagnosed eczema (AOR = 6.41) reached significance (p < 0.05).

Indoor monitoring and long-term survey to identify the risks of Energy Poverty: the case of social housing in Northern Italy

Low-income families often live in poorly heated houses belonging to social housing programs. Tenants’ wellbeing and health in social housing is typically threatened by problems associated with energy inefficiency and poor Indoor Environmental Quality (IEQ). These conditions are among the symptoms of a social issue known as Energy Poverty (EP), which occurs when residents face difficulty in paying energy bills. In 2020 about 2.1 million households (8% of the total) were suffering from energy poverty in Italy. The risk of EP is mainly favored by three factors: the low income of the family, the high final price of energy and the poor technical construction characteristics of the building which can lead to poor IEQ. Four indicators have been proposed by the European Commission and some others developed in the scientific literature, but often neither the thresholds to assess the status of EP nor the methodology to collect data have been defined. This study aims to assess some of the recommended European indicators on a case study. The risks of EP have been investigated in social housing located in Northern Italy by means of an integrated methodology based on a site inspection, a survey, and the continuous monitoring of the indoor environmental parameters during the winter season. The proposed method allowed detecting the presence of EP in 5 dwellings out of 8.

Investigation of Indoor Thermal Environment and Heat-Using Behavior for Heat-Metering Households in Northern China

Heat-using behavior has a major impact on heating energy in heat-metering systems, and therefore, a better understanding can assist in behavior guidance to reduce energy. The objective of this paper was to investigate heat-using behavior, including adjusting heating end valves and operating windows, and identify the main factors affecting the heat-using behavior of households in heat-metering modes. Thirty households were measured and surveyed. The factors influencing heat-using behavior, including outdoor and indoor environmental parameters and time of day, were analyzed. The results are the following: (1) The indoor temperature for heat-metering households was relatively high, up to 24–25 °C (95% confidence interval). (2) The heat-using behavior of households has a lack of rationality: a low proportion of households with adjusted heating end valves, high indoor temperature settings, and more frequent window openings. Improving indoor comfort is the main reason for households to adjust heating end valves, accounting for 79% (95% confidence interval, CI: 71–87%). “Thermostat control valve does not work” is the main reason for households without adjustment, accounting for 63% (95% confidence interval, CI: 53–72%). (3) Time of day and indoor temperature affect active households’ willingness to adjust heating end valves. Time of day, indoor temperature, and outdoor temperature have impacts on opening windows during heating periods.

Feasibility Analysis of Nearly Zero-Energy Building Design Oriented to the Optimization of Thermal Performance Parameters

The effective control and reduction of building energy consumption are major global focuses. The building sector is responsible for over 40% of all direct and indirect CO2 emissions. Nearly zero-energy buildings have been the subject of aims and regulations from several developed nations. An office building located in the severe cold region of China was chosen for this case study. The building was equipped with multiple NZEB technologies. Building indoor environment parameters and energy efficiency indexes were used as performance targets, and a performance-based design approach was used to optimize building design parameters. Thermal performance of the building envelope, airtightness, energy demand, and indoor thermal environment were tested according to different evaluation criteria. The total energy demand was as low as 53.93 KWh/(m2·a), and this can be attributed to the exceptional insulation of the building. In this test, the indoor thermal environment comfort was satisfactory. This study can be used as a reference for the design and evaluation of low-carbon buildings and low-energy buildings.

Examining Occupant-Comfort Responses to Indoor Humidity Ratio in Conventional and Vernacular Dwellings: A Rural Indian Case Study

Optimum indoor humidity is often associated with comfort and overall well-being. Occupant comfort is often evaluated with a focus on “thermal comfort” using the PMV (predicted mean vote), PDD (predicted percentage of dissatisfied), and adaptive thermal comfort models. Humidity-determined comfort parameters, like skin and respiratory comfort, are well acknowledged in the scientific community, but strangely not considered for indoor comfort computations. This study proposes a new computational approach for describing and evaluating humidity-related skin comfort in buildings using skin temperature, evaporative loss, and skin wettedness as critical parameters. The Development and validation of the computational model was demonstrated through a case study in a rural Indian context. The case study involves real-time monitoring of indoor environmental parameters and humidity-determined occupant comfort votes recorded through a novel aggregated humidity comfort vote method. The simulation results were compared with the community comfort/health survey. It was observed that, even at neutral skin temperatures, an increase in skin wettedness increases the thermal sensation vote. Clothing varies according to gender, community, and personal preferences, influencing physiological parameters which determine comfort. The acceptable humidity ratio was found to be in the range of 17.4 to 22.6 g-wv/kg-da for Indian participants. Including humidity-related comfort parameters in building simulation tools would aid in selecting building materials for improved indoor comfort.

Assessment of health hazards of greenhouse workers considering UV exposure and thermal comfort

Greenhouse (GH) indoor environments are usually manipulated to enhance plant growth and yield, but those environments might not be favorable for GH workers. This study aimed to investigate the health hazards of greenhouse workers from UV radiation exposure and analyze the heat stress and thermal comfort in air-conditioned greenhouses. Two GHs with different covering materials (glass greenhouse-GGH and polycarbonate greenhouse-PCGH) were selected for this study. The UV index of GGH varied from moderate to high (3 to 8) based on the season and deployment of the shade screen, whereas PCGH was always UV risk-free below and above the shade screen. Heat stress was evaluated in terms of wet bulb globe temperature (WBGT) and thermal comfort with the predicted mean vote (PMV) and predicted percent dissatisfied (PPD). The value of WBGT in GGH varied between 17.67 °C and 26.54 °C and between 18.25 °C and 25.97 °C in PCGH. The PMV values ranged from -2.57 to 1.15 for GGH and from -2.24 to 1.42 for PCGH, depending on airspeed, metabolic rate, and indoor conditions. The PPD values ranged between 5.0 to 94.6 % for GGH and 5.0 to 75.0 % for PCGH. As the optimal target range of the indoor environmental parameters (temperature and relative humidity) were maintained in the modern GHs throughout the year, the potential for heat stress and thermal discomfort was not severe. However, as a precaution, avoiding heavy activities around noon is recommended, even in perfectly conditioned greenhouses, which could be above the danger level in low-tech greenhouses.

Assessment of automated shading systems’ utilization and environmental performance: An experimental study

This paper presents an experimental study conducted in a full-scale test cell “btga-box” from July until September 2020 at Wuppertal University, Germany. This study focused on evaluating occupant interaction and satisfaction with automated shading systems in office environments, and the underlying thermal and visual conditions under different scenarios to optimize automated shading design and operation for improving occupant comfort and energy efficiency. Six different scenarios were evaluated using several performance metrics. Twenty-eight participants of varying ages, gender, and ethnicity took part in the experiments. After each scenario, the participants were asked to complete a web-based questionnaire to report their behaviour, their perceived thermal and visual comfort, satisfaction, and preferences concerning the performance of automated shading controls. Concurrently, indoor environmental parameters, weather data, and system and user-triggered actions were recorded. The key findings of this study suggest that a robust shading system (i.e., few override actions) can be achieved by: deploying a multi-objective control strategy with an intermediate position, an acceptable range of irradiance thresholds, and a decent level of other adaptive behavioural control options (i.e., window opening, fan usage, and light control). Providing active cooling or ventilation systems (i.e., operable windows, exhaust ventilation, and ceiling fan) can improve user satisfaction with the indoor environment, including shade operation. Using a small window size combined with a high irradiance shade-lowering threshold decreased the user-raising actions by 49% compared to the low threshold. However, energy implications should be considered for further studies since user satisfaction with blind control should be balanced with energy efficiency.

An energy consumption prediction method for HVAC systems using energy storage based on time series shifting and deep learning

The prediction of building energy consumption plays a crucial role in responding to energy demands and achieving low-carbon control through energy saving. In this study, we focused on peak shaving and valley filling in the energy consumption of office building energy-storage HVAC systems. A time series shifting method based on the Pearson correlation coefficient was combined with a gate recurrent unit (GRU) deep learning model to predict HVAC energy consumption. Additionally, we explored the adaptability of this method to non-deep learning support vector regression (SVR) and random forest (RF) models. The results demonstrated that the Kaiser-Meyer-Olkin test values of HVAC energy consumption, meteorological parameters, and indoor environmental parameters improved by 10.8%. The GRU model exhibited a 12% reduction in the root mean square error (RMSE), and the coefficient of determination (R2) was 0.850. The RMSE of the RF model decreased to 12.24 kW·h, with a 12% increase in R2 to 0.760. The SVR model achieved an RMSE of 12.59 kW·h and an R2 of 0.793. Therefore, this method can effectively improve the prediction accuracy of models by enhancing the correlation between their input and output variables.

Energy efficiency in hospitals: comparative analysis of different HVAC configurations

ABSTRACT The characteristics of health facilities e.g. the need to operate 24 h a day, strict cleaning procedures and indoor environmental parameters made these building-type energy-intensive. The huge potential that can be exploited in terms of energy savings is evident, mainly regarding the HVAC system refurbishment. From literature emerges that there are poor systematic analyses concerning all the possible HVAC retrofit scenarios, considering solutions currently on the market or renewable sources. To fill the research gap pointed out, the present paper proposes a rigorous analysis of the possible generation subsystems available on the market that can be implemented in an HVAC retrofit for a hospital. A real case study of 88,000 m3, represented by a hospital in southern Italy, has been used. Different technologies, such as the photovoltaic system, electric heat pump, absorption heat pump driven by solar energy, cogeneration are analysed from the energy and environmental point of view, with the introduction of the Imported Energy Level Index (IELI). The best retrofit measures is the installation of an absorption heat pump driven by solar thermal collectors and cogeneration, with a primary energy saving of 20%. Considering the installation of a heat recovery unit, the saving is up to 43%.

Fast reconstruction of indoor temperature field for large-space building based on limited sensors: An experimental study

Accurately knowing the spatiotemporal distribution of indoor environmental parameters is important for indoor thermal comfort adjusting and building energy saving. Due to the significant distributed parameter characteristics of the building thermal environment, this task is difficult, especially for large-space buildings with the features of multi-input and multi-output. For a real large-space building, the reduced-dimensional model of indoor temperature field is rebuilt based on proper orthogonal decomposition (POD) and an offline-online strategy. POD technology is used to extract environmental features from steady / dynamic experimental data. Based on the solved POD modes, the optimal layout of temperature sensors is obtained by a heuristic reasoning method. By combining linear stochastic estimation (LSE) method together with the selected sensors, an offline-online strategy is proposed for rapid reconstruction and prediction of the indoor temperature fields. Digital interpolation is applied for soft sensing outside the original sampling locations. An experimental study for a large-space cafeteria with sixteen indoor air conditioning units is conducted. A total of 72 sensors are placed to collect indoor 3D temperature field data offline. Results show that using only six sensors of them, the large-space indoor temperature field could be accurately estimated online with the steady-state error of 0.35 (average RMSE) and dynamic-state errors of 0.0525 / 0.0540 (heating / cooling, average RMSE).

Predictive Modeling of Indoor Environmental Parameters for Assessing Comfort Conditions in a Kindergarten Setting.

People tend to spend the majority of their time indoors. Indoor air properties can significantly affect humans' comfort, health, and productivity. This study utilizes measurement data of indoor conditions in a kindergarten in Sofia, Bulgaria. Autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) recurrent neural network (RNN) models were developed to predict CO2 levels in the educational facility over the next hour based on 2.5 h of past data and allow for near real-time decision-making. The better-performing model, LSTM, is also used for temperature and relative humidity forecasting. Global comfort is then estimated based on threshold values for temperature, humidity, and CO2. The predicted R2 values ranged between 0.938 and 0.981 for the three parameters, while the prediction of global comfort conditions achieved a 91/100 accuracy.