A.B. Hamida
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Acoustical preferences and needs of students
Methods and indicators to assess the acoustical quality of study places
University students are self-directed learners who dedicate considerable time to studying in study places. Research on indoor environmental quality (IEQ) highlights the adverse effects of prolonged indoor exposure to environmental stressors, including noise. Acoustical quality can significantly influence students’ health and comfort. To evaluate the acoustical quality of study places, three groups of indicators can be considered: occupant-related, dose-related, and building-related indicators. Given that students have different acoustical and psychosocial preferences for study places, it is crucial to consider occupant-related indicators. However, existing acoustical guidelines for study places and educational buildings primarily focus on dose-related and building-related indicators, while occupant-related indicators have been overlooked. Therefore, the main research question of this dissertation was posed:
How to assess the acoustical quality of study places?
This question was answered through several research methods. First, a literature review identified indicators and methods used to study students’ acoustical preferences and needs. Then, ‘MyStudyPlace’ questionnaire was completed by university students who were clustered based on their IEQ and psychosocial preferences, resulting in nine profiles. Subsequently, students were re-clustered based on acoustical and selected psychosocial preferences, resulting in five profiles. To further explore these profiles, 23 home study places were visited, incorporating interviews, building inspections, and sound pressure level measurements. After that, 15 of these students participated in sound exposure lab experiments, which involved bodily responses, audiometric tests, and perceptual assessments. Furthermore, an indoor soundscape approach using semi-structured interviews with the 23 students examined their sound environment experiences of their home study places. This dissertation offers future research a set of suggested methods and indicators to assess the acoustical quality of study places. ...
How to assess the acoustical quality of study places?
This question was answered through several research methods. First, a literature review identified indicators and methods used to study students’ acoustical preferences and needs. Then, ‘MyStudyPlace’ questionnaire was completed by university students who were clustered based on their IEQ and psychosocial preferences, resulting in nine profiles. Subsequently, students were re-clustered based on acoustical and selected psychosocial preferences, resulting in five profiles. To further explore these profiles, 23 home study places were visited, incorporating interviews, building inspections, and sound pressure level measurements. After that, 15 of these students participated in sound exposure lab experiments, which involved bodily responses, audiometric tests, and perceptual assessments. Furthermore, an indoor soundscape approach using semi-structured interviews with the 23 students examined their sound environment experiences of their home study places. This dissertation offers future research a set of suggested methods and indicators to assess the acoustical quality of study places. ...
University students are self-directed learners who dedicate considerable time to studying in study places. Research on indoor environmental quality (IEQ) highlights the adverse effects of prolonged indoor exposure to environmental stressors, including noise. Acoustical quality can significantly influence students’ health and comfort. To evaluate the acoustical quality of study places, three groups of indicators can be considered: occupant-related, dose-related, and building-related indicators. Given that students have different acoustical and psychosocial preferences for study places, it is crucial to consider occupant-related indicators. However, existing acoustical guidelines for study places and educational buildings primarily focus on dose-related and building-related indicators, while occupant-related indicators have been overlooked. Therefore, the main research question of this dissertation was posed:
How to assess the acoustical quality of study places?
This question was answered through several research methods. First, a literature review identified indicators and methods used to study students’ acoustical preferences and needs. Then, ‘MyStudyPlace’ questionnaire was completed by university students who were clustered based on their IEQ and psychosocial preferences, resulting in nine profiles. Subsequently, students were re-clustered based on acoustical and selected psychosocial preferences, resulting in five profiles. To further explore these profiles, 23 home study places were visited, incorporating interviews, building inspections, and sound pressure level measurements. After that, 15 of these students participated in sound exposure lab experiments, which involved bodily responses, audiometric tests, and perceptual assessments. Furthermore, an indoor soundscape approach using semi-structured interviews with the 23 students examined their sound environment experiences of their home study places. This dissertation offers future research a set of suggested methods and indicators to assess the acoustical quality of study places.
How to assess the acoustical quality of study places?
This question was answered through several research methods. First, a literature review identified indicators and methods used to study students’ acoustical preferences and needs. Then, ‘MyStudyPlace’ questionnaire was completed by university students who were clustered based on their IEQ and psychosocial preferences, resulting in nine profiles. Subsequently, students were re-clustered based on acoustical and selected psychosocial preferences, resulting in five profiles. To further explore these profiles, 23 home study places were visited, incorporating interviews, building inspections, and sound pressure level measurements. After that, 15 of these students participated in sound exposure lab experiments, which involved bodily responses, audiometric tests, and perceptual assessments. Furthermore, an indoor soundscape approach using semi-structured interviews with the 23 students examined their sound environment experiences of their home study places. This dissertation offers future research a set of suggested methods and indicators to assess the acoustical quality of study places.
During perception with our senses interactions of different environmental stressors (olfactory, auditory, visual and thermal stimuli) at brain level might occur. To test these cross-modal effects, a three-way factorial design was applied. In total, 60 students across six groups were each exposed to three randomized combinations of different environmental conditions: three sound conditions, three lighting conditions, and two ventilation modes, while sitting in a semi-lab environment. Heart rate and respiration rate were monitored using a smart watch; acceptability and experience were collected through a questionnaire to assess subjects' comfort perception. Results showed no statistical differences between the two ventilation modes and no effect of light type on the physiological indicators. A trend towards an interaction effect was found for sound∗light on the acceptability of odour (p=0.076) and the perceived level of sound (p=0.055). For future studies, it is therefore important to first identify physiological indicators that can be affected by all the independent factors studied.
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During perception with our senses interactions of different environmental stressors (olfactory, auditory, visual and thermal stimuli) at brain level might occur. To test these cross-modal effects, a three-way factorial design was applied. In total, 60 students across six groups were each exposed to three randomized combinations of different environmental conditions: three sound conditions, three lighting conditions, and two ventilation modes, while sitting in a semi-lab environment. Heart rate and respiration rate were monitored using a smart watch; acceptability and experience were collected through a questionnaire to assess subjects' comfort perception. Results showed no statistical differences between the two ventilation modes and no effect of light type on the physiological indicators. A trend towards an interaction effect was found for sound∗light on the acceptability of odour (p=0.076) and the perceived level of sound (p=0.055). For future studies, it is therefore important to first identify physiological indicators that can be affected by all the independent factors studied.
How different sounds affect bodily responses and the perception of odour, light and temperature
A pilot study on interaction effects within IEQ domains
During perception with our senses, interactions of different environmental stressors at brain level might occur. Previous studies have shown cross-modal effects between sound and odour. To test these effects of different sounds and levels of sounds on the perception of sound, temperature, odour, and light, as well as a number of physiological indicators, sixteen students were exposed to four different sounds (two indoor: mechanical ventilation & people talking; two outdoor: quiet rural area & city centre area) and two different sound pressure levels per sound, while sitting in a semi-lab environment. Bodily responses were sampled with wearable devices. Heart rate and breathing rate were monitored using a smart watch; EEG measurements were performed to assess their attention and mental relaxation levels; Acceptability and experience were assessed through a questionnaire to assess their comfort perception. Additionally, each student took a hearing test. The outcome showed when the traffic sound level increased, the students perceived the air as more smelly and less acceptable. The other sounds did not show any cross-modal effect. Moreover, heart rate and breathing rate significantly differed during the different tests, confirming that these two indicators can help to explain the physiological effect of noise as a stressor.
...
During perception with our senses, interactions of different environmental stressors at brain level might occur. Previous studies have shown cross-modal effects between sound and odour. To test these effects of different sounds and levels of sounds on the perception of sound, temperature, odour, and light, as well as a number of physiological indicators, sixteen students were exposed to four different sounds (two indoor: mechanical ventilation & people talking; two outdoor: quiet rural area & city centre area) and two different sound pressure levels per sound, while sitting in a semi-lab environment. Bodily responses were sampled with wearable devices. Heart rate and breathing rate were monitored using a smart watch; EEG measurements were performed to assess their attention and mental relaxation levels; Acceptability and experience were assessed through a questionnaire to assess their comfort perception. Additionally, each student took a hearing test. The outcome showed when the traffic sound level increased, the students perceived the air as more smelly and less acceptable. The other sounds did not show any cross-modal effect. Moreover, heart rate and breathing rate significantly differed during the different tests, confirming that these two indicators can help to explain the physiological effect of noise as a stressor.
Understanding students' preferences of their study place, in particular acoustical and psychosocial preferences, is important to students' health and comfort. This study aimed to identify clusters of students with similar acoustical and psychosocial preferences, and to identify reasons for certain preferences of students in each cluster. A mixed-methods approach was applied, consisting of a questionnaire, which was completed by 451 bachelor students, and a field study conducted with 23 students from the same sample. The questionnaire data included among others acoustical and psychosocial preferences scores, while the field study data comprised interview transcripts, building checklists, and sound pressure level measurements. The questionnaire data were analysed using TwoStep cluster analysis to identify clusters of students based on their acoustical and psychosocial preferences. This produced five clusters of students that significantly differed in 14 variables, including preferences and perception of indoor environmental quality (e.g., noise from outside). Then, the field study data were analysed and categorised based on the five clusters of the students. The outcome explained the aspects associated with the acoustical preferences of students in each cluster. Building-related indicators such as the location of the building were found as an aspect that could affect the student's acoustical preferences. This study provides insight into the profiles of students based on their acoustical and psychosocial preferences, which are important for their health and comfort at their study places.
...
Understanding students' preferences of their study place, in particular acoustical and psychosocial preferences, is important to students' health and comfort. This study aimed to identify clusters of students with similar acoustical and psychosocial preferences, and to identify reasons for certain preferences of students in each cluster. A mixed-methods approach was applied, consisting of a questionnaire, which was completed by 451 bachelor students, and a field study conducted with 23 students from the same sample. The questionnaire data included among others acoustical and psychosocial preferences scores, while the field study data comprised interview transcripts, building checklists, and sound pressure level measurements. The questionnaire data were analysed using TwoStep cluster analysis to identify clusters of students based on their acoustical and psychosocial preferences. This produced five clusters of students that significantly differed in 14 variables, including preferences and perception of indoor environmental quality (e.g., noise from outside). Then, the field study data were analysed and categorised based on the five clusters of the students. The outcome explained the aspects associated with the acoustical preferences of students in each cluster. Building-related indicators such as the location of the building were found as an aspect that could affect the student's acoustical preferences. This study provides insight into the profiles of students based on their acoustical and psychosocial preferences, which are important for their health and comfort at their study places.
University students spend a considerable time at study places. The acoustical quality of these study places is one of the indoor environmental qualities (IEQ) that can have an impact on student’s health, comfort, and performance. The indoor soundscape approach has been introduced to better understand occupants’ sound perception and experience of sounds in relation to the environment. This study aims to explore the indoor soundscapes of home study places of these students by conducting semi-structured interviews with 23 university students with different profiles. For qualitative analysis, open coding was used. Sub-categories, based on the codes, and categories were created and assigned to the soundscape themes that are defined in ISO 12913-1. An affinity diagram consisting of the themes, categories, and sub-categories was initially developed. Then, it was validated through two workshops with participants. The results showed that the interpretation of the sound environment, responses, and outcomes differed among the students. In a previous study, 451 students were clustered in 5 clusters with similar acoustical preferences (profiles). Therefore, it is recommended to consider making the indoor soundscape approach applicable for different profiles of occupants.
...
University students spend a considerable time at study places. The acoustical quality of these study places is one of the indoor environmental qualities (IEQ) that can have an impact on student’s health, comfort, and performance. The indoor soundscape approach has been introduced to better understand occupants’ sound perception and experience of sounds in relation to the environment. This study aims to explore the indoor soundscapes of home study places of these students by conducting semi-structured interviews with 23 university students with different profiles. For qualitative analysis, open coding was used. Sub-categories, based on the codes, and categories were created and assigned to the soundscape themes that are defined in ISO 12913-1. An affinity diagram consisting of the themes, categories, and sub-categories was initially developed. Then, it was validated through two workshops with participants. The results showed that the interpretation of the sound environment, responses, and outcomes differed among the students. In a previous study, 451 students were clustered in 5 clusters with similar acoustical preferences (profiles). Therefore, it is recommended to consider making the indoor soundscape approach applicable for different profiles of occupants.
Previous studies have shown that sound influences students both physiologically and perceptually. However, most of these studies focussed on the effects of sounds at group-level, ignoring individual differences. Therefore, we investigated which indicators can be used to identify differences in bodily responses and perceptual assessments of each individual when exposed to four different sounds. First, based on an audiometric test, the hearing acuity of 15 students (from five different profiles based on their acoustical preferences and needs) was measured. Then, two sound exposure experiments were conducted in the SenseLab: direct sound exposure using earbuds in a laboratory setting, and indirect sound exposure with speakers in a real room setting. During each experiment, the attention level (AL), mental relaxation level (MRL), heart rate (HR), and respiration rate (RR) were measured with wearable devices, and students made perceptual assessments of each condition. The percentage of change normalised the four bodily response measurements among students. Based on correlation analysis and t-tests, bodily responses, and perceptual assessments across experiments were compared, at group-level and individual-level. Six students, who suffered from mild hearing loss in low-frequency sounds, showed bodily responses such as increased HR during exposure to low-frequency sound conditions. Perceptual assessments of different sound types during both lab experiments substantiated the acoustical preferences of the students from the five profiles. Bodily responses showed no strong nor significant correlations with perceptual assessments during the direct sound exposure experiments. Differences in bodily responses and perceptual assessments between the two experiments and between group-level and individual-level were observed in AL. It is concluded that hearing acuity and type of sound (sound frequencies) are key indicators for identifying differences in bodily responses (such as HR and RR) and perceptual assessment. For future research, it is crucial to consider incorporating audiometric tests, bodily responses such as HR and RR, and perceptual assessments in this type of investigations.
...
Previous studies have shown that sound influences students both physiologically and perceptually. However, most of these studies focussed on the effects of sounds at group-level, ignoring individual differences. Therefore, we investigated which indicators can be used to identify differences in bodily responses and perceptual assessments of each individual when exposed to four different sounds. First, based on an audiometric test, the hearing acuity of 15 students (from five different profiles based on their acoustical preferences and needs) was measured. Then, two sound exposure experiments were conducted in the SenseLab: direct sound exposure using earbuds in a laboratory setting, and indirect sound exposure with speakers in a real room setting. During each experiment, the attention level (AL), mental relaxation level (MRL), heart rate (HR), and respiration rate (RR) were measured with wearable devices, and students made perceptual assessments of each condition. The percentage of change normalised the four bodily response measurements among students. Based on correlation analysis and t-tests, bodily responses, and perceptual assessments across experiments were compared, at group-level and individual-level. Six students, who suffered from mild hearing loss in low-frequency sounds, showed bodily responses such as increased HR during exposure to low-frequency sound conditions. Perceptual assessments of different sound types during both lab experiments substantiated the acoustical preferences of the students from the five profiles. Bodily responses showed no strong nor significant correlations with perceptual assessments during the direct sound exposure experiments. Differences in bodily responses and perceptual assessments between the two experiments and between group-level and individual-level were observed in AL. It is concluded that hearing acuity and type of sound (sound frequencies) are key indicators for identifying differences in bodily responses (such as HR and RR) and perceptual assessment. For future research, it is crucial to consider incorporating audiometric tests, bodily responses such as HR and RR, and perceptual assessments in this type of investigations.
Ventilation and thermal conditions in secondary schools in the Netherlands
Effects of COVID-19 pandemic control and prevention measures
Journal article
(2023)
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Er Ding, D. Zhang, A.B. Hamida, C. Garcia Sanchez, Lotte Jonker, Annemarijn R. de Boer, Patricia C.J.L. Bruijning, Kimberly J. Linde, Inge M. Wouters, P.M. Bluyssen
During the COVID-19 pandemic, the importance of ventilation was widely stressed and new protocols of ventilation were implemented in school buildings worldwide. In the Netherlands, schools were recommended to keep the windows and doors open, and after a national lockdown more stringent measures such as reduction of occupancy were introduced. In this study, the actual effects of such measures on ventilation and thermal conditions were investigated in 31 classrooms of 11 Dutch secondary schools, by monitoring the indoor and outdoorCO2 concentration and air temperature, both before and after the lockdown. Ventilation rates were calculated using the steady-state method. Pre-lockdown, with an average occupancy of 17 students, in 42% of the classrooms the CO2 concentration exceeded the upper limit of the Dutch national guidelines (800 ppm above outdoors),while 13% had a ventilation rate per person (VRp) lower than the minimum requirement (6 l/s/p). Post lockdown, the indoor CO2 concentration decreased significantly while for ventilation rates significant increase was only found in VRp, mainly caused by the decrease in occupancy (average 10 students). The total ventilation rate per classrooms, mainly induced by opening windows and doors, did not change significantly. Meanwhile, according to the Dutch national guidelines, thermal conditions in the classrooms were not satisfying, both pre and post-lockdown. While opening windows and doors cannot achieve the required indoor environmental quality at all times, reducing occupancy might not be feasible for immediate implementation. Hence, more controllable and flexible ways for improving indoor air quality and thermal comfort in classrooms are needed.
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During the COVID-19 pandemic, the importance of ventilation was widely stressed and new protocols of ventilation were implemented in school buildings worldwide. In the Netherlands, schools were recommended to keep the windows and doors open, and after a national lockdown more stringent measures such as reduction of occupancy were introduced. In this study, the actual effects of such measures on ventilation and thermal conditions were investigated in 31 classrooms of 11 Dutch secondary schools, by monitoring the indoor and outdoorCO2 concentration and air temperature, both before and after the lockdown. Ventilation rates were calculated using the steady-state method. Pre-lockdown, with an average occupancy of 17 students, in 42% of the classrooms the CO2 concentration exceeded the upper limit of the Dutch national guidelines (800 ppm above outdoors),while 13% had a ventilation rate per person (VRp) lower than the minimum requirement (6 l/s/p). Post lockdown, the indoor CO2 concentration decreased significantly while for ventilation rates significant increase was only found in VRp, mainly caused by the decrease in occupancy (average 10 students). The total ventilation rate per classrooms, mainly induced by opening windows and doors, did not change significantly. Meanwhile, according to the Dutch national guidelines, thermal conditions in the classrooms were not satisfying, both pre and post-lockdown. While opening windows and doors cannot achieve the required indoor environmental quality at all times, reducing occupancy might not be feasible for immediate implementation. Hence, more controllable and flexible ways for improving indoor air quality and thermal comfort in classrooms are needed.
People are staying indoors for most of their time (on average 90%), where they are exposed to different environmental stimuli (e.g., noise, temperature) that are related to the indoor environmental quality (IEQ) factors (Bluyssen, 2020). Students in higher education spend substantial time at their study places (at home or educational buildings) for their study-related activities (Beckers et al., 2016a). Noise is one of these environmental stimuli that could affect the students’ health and comfort. It was found that noise affected students’ health (Tristan-Hernandez et al., 2017), perception (Dzhambov et al., 2021), and performance (Shu & Ma, 2019). However, previous studies mainly focused on the students’ sound environment perception in classroom settings, while few (e.g., (Ramu et al., 2021) and (Beckers et al., 2016b)) investigated their perception in their study places.
This study aims to identify the sound sources that students are exposed to at their home study places. Furthermore, this study shows to which extent students are satisfied with the sound environment of their study places. ...
This study aims to identify the sound sources that students are exposed to at their home study places. Furthermore, this study shows to which extent students are satisfied with the sound environment of their study places. ...
People are staying indoors for most of their time (on average 90%), where they are exposed to different environmental stimuli (e.g., noise, temperature) that are related to the indoor environmental quality (IEQ) factors (Bluyssen, 2020). Students in higher education spend substantial time at their study places (at home or educational buildings) for their study-related activities (Beckers et al., 2016a). Noise is one of these environmental stimuli that could affect the students’ health and comfort. It was found that noise affected students’ health (Tristan-Hernandez et al., 2017), perception (Dzhambov et al., 2021), and performance (Shu & Ma, 2019). However, previous studies mainly focused on the students’ sound environment perception in classroom settings, while few (e.g., (Ramu et al., 2021) and (Beckers et al., 2016b)) investigated their perception in their study places.
This study aims to identify the sound sources that students are exposed to at their home study places. Furthermore, this study shows to which extent students are satisfied with the sound environment of their study places.
This study aims to identify the sound sources that students are exposed to at their home study places. Furthermore, this study shows to which extent students are satisfied with the sound environment of their study places.
Research has shown that students differ in their preferences of indoor environmental quality (IEQ) and psychosocial aspects of their study places. Since previous studies have mainly focused on identifying these preferences rather than investigating the different profiles of students, this study aimed at profiling students based on their IEQ and psychosocial preferences of their study places. A questionnaire was completed by 451 bachelor students of the faculty of Architecture and the Built Environment. A TwoStep cluster analysis was performed twice separately. First, to cluster the students based on their IEQ preferences, and second based on their psychosocial preferences. This resulted in three clusters under each cluster model. Then, the overlap between these two models was determined and produced nine unique profiles of students, which are: (1) the concerned perfectionist, (2) the concerned extrovert, (3) the concerned non-perfectionist, (4) the visual concerned perfectionist, (5) the visual concerned extrovert, (6) visual concerned non-perfectionist, (7) the unconcerned introvert, (8) the unconcerned extrovert, and (9) the unconcerned non-perfectionist. A number of variables was found to be significantly different among these profiles. This study’s outcome indicates that studying the overlap between IEQ and psychosocial preferences is required to understand the different possible profiles of students.
...
Research has shown that students differ in their preferences of indoor environmental quality (IEQ) and psychosocial aspects of their study places. Since previous studies have mainly focused on identifying these preferences rather than investigating the different profiles of students, this study aimed at profiling students based on their IEQ and psychosocial preferences of their study places. A questionnaire was completed by 451 bachelor students of the faculty of Architecture and the Built Environment. A TwoStep cluster analysis was performed twice separately. First, to cluster the students based on their IEQ preferences, and second based on their psychosocial preferences. This resulted in three clusters under each cluster model. Then, the overlap between these two models was determined and produced nine unique profiles of students, which are: (1) the concerned perfectionist, (2) the concerned extrovert, (3) the concerned non-perfectionist, (4) the visual concerned perfectionist, (5) the visual concerned extrovert, (6) visual concerned non-perfectionist, (7) the unconcerned introvert, (8) the unconcerned extrovert, and (9) the unconcerned non-perfectionist. A number of variables was found to be significantly different among these profiles. This study’s outcome indicates that studying the overlap between IEQ and psychosocial preferences is required to understand the different possible profiles of students.
Students are exposed to various environmental stimuli at their home study places. However, different students have different preferences in terms of indoor environmental quality (IEQ) aspects and psychosocial aspects of these places. A previous study on students' preferences of their study places resulted in nine profiles based on their IEQ and psychosocial preferences of their study places. It was found that there are profiles that were not highly concerned with sounds at their study places, while other profiles are concerned about sounds. Accordingly, this present study aims at clustering students based on their acoustical-related preferences of their study places. A questionnaire survey was completed by 451 first-year bachelor students at the Faculty of Architecture and the Built Environment at TU Delft. TwoStep cluster analysis was performed, and five unique profiles were identified. These are: 1) sound extremely concerned introvert, 2) sound unconcerned introvert, 3) sound partially concerned introvert, 4) sound concerned extrovert, and 5) sound unconcerned extrovert. The outcomes of this study showed that TwoStep cluster analysis facilitate researchers to better understand the different profiles of students based on their acousticalrelated preferences in study places.
...
Students are exposed to various environmental stimuli at their home study places. However, different students have different preferences in terms of indoor environmental quality (IEQ) aspects and psychosocial aspects of these places. A previous study on students' preferences of their study places resulted in nine profiles based on their IEQ and psychosocial preferences of their study places. It was found that there are profiles that were not highly concerned with sounds at their study places, while other profiles are concerned about sounds. Accordingly, this present study aims at clustering students based on their acoustical-related preferences of their study places. A questionnaire survey was completed by 451 first-year bachelor students at the Faculty of Architecture and the Built Environment at TU Delft. TwoStep cluster analysis was performed, and five unique profiles were identified. These are: 1) sound extremely concerned introvert, 2) sound unconcerned introvert, 3) sound partially concerned introvert, 4) sound concerned extrovert, and 5) sound unconcerned extrovert. The outcomes of this study showed that TwoStep cluster analysis facilitate researchers to better understand the different profiles of students based on their acousticalrelated preferences in study places.
Sounds (e.g., human activity, nature, building systems) are one of the indoor environmental stimuli that may have positive and/or negative effects on students’ well-being and performance in educational buildings. Students in educational buildings have individual acoustical preferences and needs as portrayed by occupant-related indicators, for example perception. Acoustical guidelines for educational buildings are generally focused on acoustical performance in terms of dose-related (e.g., sound pressure level) and building-related indicators (e.g., sound absorbing walls), while occupant-related indicators (e.g., heart rate) are rarely mentioned. In contrast, previous studies such as indoor soundscape studies, do take into consideration occupant-related indicators, including physiological and psychological. Therefore, this study aimed at summarizing these indicators in a comprehensive overview that is essential for investigating the students’ acoustical preferences and needs in educational buildings. A literature review of relevant studies in the domain of indoor acoustics and soundscape was carried out. A number of key indicators (occupant-related, dose-related, building-related) and methods that are fundamental to be considered were identified. Only in a few studies, students’ acoustical preferences and needs were investigated by considering occupant-related indicators (both physiological and psychological). In addition, dose-related indicators of other indoor environmental quality (IEQ) factors and building-related indicators were rarely taken into account in previous studies.
...
Sounds (e.g., human activity, nature, building systems) are one of the indoor environmental stimuli that may have positive and/or negative effects on students’ well-being and performance in educational buildings. Students in educational buildings have individual acoustical preferences and needs as portrayed by occupant-related indicators, for example perception. Acoustical guidelines for educational buildings are generally focused on acoustical performance in terms of dose-related (e.g., sound pressure level) and building-related indicators (e.g., sound absorbing walls), while occupant-related indicators (e.g., heart rate) are rarely mentioned. In contrast, previous studies such as indoor soundscape studies, do take into consideration occupant-related indicators, including physiological and psychological. Therefore, this study aimed at summarizing these indicators in a comprehensive overview that is essential for investigating the students’ acoustical preferences and needs in educational buildings. A literature review of relevant studies in the domain of indoor acoustics and soundscape was carried out. A number of key indicators (occupant-related, dose-related, building-related) and methods that are fundamental to be considered were identified. Only in a few studies, students’ acoustical preferences and needs were investigated by considering occupant-related indicators (both physiological and psychological). In addition, dose-related indicators of other indoor environmental quality (IEQ) factors and building-related indicators were rarely taken into account in previous studies.
During the COVID-19 outbreak, university courses were shifted online and students spent the majority of their time inside their homes. However, staying indoors can affect students’ health due to the exposure to several environmental stressors, such as background noise, and/or inefficient ventilation, and/or insufficient lighting. Previous studies showed that the indoor environmental factors may cause health effects on students (physiological and psychological). Therefore, this research aimed at investigating the differences in students’ health and psychosocial status between before and during COVID-19. An online questionnaire survey was completed by first-year undergraduate university students in March 2019, 2020, and 2021. This questionnaire includes questions about time spent at home, psychosocial status, diseases, and home-related symptoms. The mean number of hours that students spent at home during the weekdays and on weekends were calculated, respectively. Besides, occurrence frequencies of psychosocial statuses were calculated for each year. Furthermore, a statistical analysis, including one-way ANOVA and Chi2, were performed to examine the differences between the three groups in terms of time spent at home, psychosocial statuses, diseases, and home-related symptoms. It is worthwhile to note that students spent significantly more time at home, during the COVID-19 pandemic in March 2021. Another notable result is that students’ mood and emotional states changed significantly over the three years; for example, fewer students reported to be active and inspired in 2021. Moreover, the home-related symptoms, such as headache and tiredness, significantly increased in 2021, compared with the other two years.
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During the COVID-19 outbreak, university courses were shifted online and students spent the majority of their time inside their homes. However, staying indoors can affect students’ health due to the exposure to several environmental stressors, such as background noise, and/or inefficient ventilation, and/or insufficient lighting. Previous studies showed that the indoor environmental factors may cause health effects on students (physiological and psychological). Therefore, this research aimed at investigating the differences in students’ health and psychosocial status between before and during COVID-19. An online questionnaire survey was completed by first-year undergraduate university students in March 2019, 2020, and 2021. This questionnaire includes questions about time spent at home, psychosocial status, diseases, and home-related symptoms. The mean number of hours that students spent at home during the weekdays and on weekends were calculated, respectively. Besides, occurrence frequencies of psychosocial statuses were calculated for each year. Furthermore, a statistical analysis, including one-way ANOVA and Chi2, were performed to examine the differences between the three groups in terms of time spent at home, psychosocial statuses, diseases, and home-related symptoms. It is worthwhile to note that students spent significantly more time at home, during the COVID-19 pandemic in March 2021. Another notable result is that students’ mood and emotional states changed significantly over the three years; for example, fewer students reported to be active and inspired in 2021. Moreover, the home-related symptoms, such as headache and tiredness, significantly increased in 2021, compared with the other two years.
Interaction effects of acoustics at and between human and environmental levels
A review of the acoustics in the indoor environment
People spend around 90% of their time indoors, where they are exposed to various physical stressors, such as unpleasant sounds, odours, temperature, and lighting, which may cause annoyance and discomfort. This literature review is focused on substantial studies that emphasize noise as a physical stressor in the indoor environment. Previous studies showed that background noise has a significant impact on human health. Adding to that, several other studies showed significant cross-modal effects between noise and other environmental stressors. However, various previous studies focused on quantifying the indicators of the indoor environmental quality (IEQ) factors rather than studying the differences of each occupant on their preferences and needs. Hence, this literature review highlights studies that take into account the interaction effects of acoustics at and between human and environmental levels. This review study aimed at identifying the key indicators to be taken into account for evaluating acoustical quality.
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People spend around 90% of their time indoors, where they are exposed to various physical stressors, such as unpleasant sounds, odours, temperature, and lighting, which may cause annoyance and discomfort. This literature review is focused on substantial studies that emphasize noise as a physical stressor in the indoor environment. Previous studies showed that background noise has a significant impact on human health. Adding to that, several other studies showed significant cross-modal effects between noise and other environmental stressors. However, various previous studies focused on quantifying the indicators of the indoor environmental quality (IEQ) factors rather than studying the differences of each occupant on their preferences and needs. Hence, this literature review highlights studies that take into account the interaction effects of acoustics at and between human and environmental levels. This review study aimed at identifying the key indicators to be taken into account for evaluating acoustical quality.
Purpose: Buildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings. Design/methodology/approach: An Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed. Findings: The MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R2 value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages. Research limitations/implications: This study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle. Originality/value: By providing a holistic predictive model entitled EICAM, this study endeavours to bridge the gap between energy costs and environmental impacts in a predictive model for Saudi residential units. The novelty of this model is that it is an alternative tool that quantifies both energy cost, as well as building’s environmental impact, in one model by using a machine learning approach. Besides, EICAM predicts its outcomes more quickly than conventional tools such as DesignBuilder and is reliable for predicting accurate environmental impact costs during early design stages.
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Purpose: Buildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings. Design/methodology/approach: An Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed. Findings: The MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R2 value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages. Research limitations/implications: This study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle. Originality/value: By providing a holistic predictive model entitled EICAM, this study endeavours to bridge the gap between energy costs and environmental impacts in a predictive model for Saudi residential units. The novelty of this model is that it is an alternative tool that quantifies both energy cost, as well as building’s environmental impact, in one model by using a machine learning approach. Besides, EICAM predicts its outcomes more quickly than conventional tools such as DesignBuilder and is reliable for predicting accurate environmental impact costs during early design stages.