Book Review : P. Leinster, E. Mitchell A Review of Indoor Air Quality and Its Impact on the Health and Well-Being of Office Workers A Report to the Commission of the European Communities No. EUR 14029 EN

This study discusses the Indoor Air Quality (IAQ) in two refurbished private schools in Shah Alam, Selangor. The level of IAQ may affect the comfort, health and well-being of the occupants of the building. Lack of monitoring IAQ in a school may affect the academic performance of the children. The objectives of the research are to observe the ventilation system used in the selected school and the comfort of the occupants; to measure the IAQ; and finally to provide an improvement plan for better air quality. The result shows that the IAQ level of both schools was average, so both were classified as safe for occupation. It is anticipated that this study will benefit the school owners in making sure that their school buildings are conducive to teaching and learning.

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Strengthening Taiwan’s Green Building Certification System from Aspects of Productivity and Energy Costs to Provide a Healthier Workplace

Atmosphere ◽

10.3390/atmos13010118 ◽

2022 ◽

Vol 13 (1) ◽

pp. 118

Author(s):

Ruey-Lung Hwang ◽

Wei-An Chen ◽

Yu-Teng Weng

Keyword(s):

Air Quality ◽

Indoor Air Quality ◽

Indoor Air ◽

Green Building ◽

Well Being ◽

Green Buildings ◽

Personnel Cost ◽

Labor Costs ◽

Certification System ◽

Health And Well Being

This study estimates the relationship between poor indoor environmental quality (IEQ) and the increasing labor costs in green buildings in Taiwan. Specifically, poor performance of IEQ including HVAC, lighting, and indoor air quality, influences the health and well-being of occupants and leads to worse productivity, ultimately causing increased personnel cost. In Taiwan’s green building certification (GBC) system, the energy-savings category is mandatory while the IEQ category is only optional. It means that certified building cases may not reach the expected level in IEQ. Thus, this study reviews the thermal environment, indoor air quality (IAQ), and illumination performances of IEQ-certified and non-IEQ-certified buildings in 20 green buildings. Building energy and IEQ simulations were conducted to analyze the relationships between indoor comfort, energy cost, and personnel cost in green buildings. The results show that IEQ-certified green buildings averagely perform better than non-IEQ-certified ones in the aspects of IEQ and building costs. Besides, 3 of 13 non-IEQ-certified green buildings undertake extremely high additional expenditure for the poor IEQ. The results correspond to some previous findings that green-certified buildings do not necessarily guarantee good building performance. This study further inspects the pros and cons of Taiwan’s GBC system and proposes recommendations against its insufficient IEQ evaluation category. As the trade-off of energy-saving benefits with health and well-being in green buildings has always been a concern, this study aims to stimulate more quantitative research and promote a more comprehensive green building certification system in Taiwan.

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Indoor air quality prediction systems for smart environments: A systematic review

Journal of Ambient Intelligence and Smart Environments ◽

10.3233/ais-200574 ◽

2020 ◽

Vol 12 (5) ◽

pp. 433-453

Author(s):

Jagriti Saini ◽

Maitreyee Dutta ◽

Gonçalo Marques

Keyword(s):

Public Health ◽

Air Quality ◽

Indoor Air Quality ◽

Indoor Air ◽

Well Being ◽

Smart Environments ◽

Research Activities ◽

Prediction Systems ◽

The Impact ◽

Health And Well Being

Air quality is a critical matter of concern in terms of the impact on public health and well-being. Although the consequences of poor air quality are more severe in developing countries, they also have a critical impact in developed countries. Healthcare costs due to air pollution reach $150 billion in the USA, whereas particulate matter causes 412,000 premature deaths in Europe, every year. According to the Environmental Protection Agency (EPA), indoor air pollutant levels can be up to 100 times higher in comparison to outdoor air quality. Indoor air quality (IAQ) is in the top five environmental risks to global health and well-being. The research community explored the scope of artificial intelligence (AI) in the past years to deal with this problem. The IAQ prediction systems contribute to smart environments where advanced sensing technologies can create healthy living conditions for building occupants. This paper reviews the applications and potential of AI for the prediction of IAQ to enhance building environment and public health. The results show that most of the studies analyzed incorporate neural networks-based models and the preferred evaluation metrics are RMSE, R 2 score and error rate. Furthermore, 66.6% of the studies include CO2 sensors for IAQ assessment. Temperature and humidity parameters are also included in 90.47% and 85.71% of the proposed methods, respectively. This study also presents some limitations of the current research activities associated with the evaluation of the impact of different pollutants based on different geographical conditions and living environments. Moreover, the use of reliable and calibrated sensor networks for real-time data collection is also a significant challenge.

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Indoor Air Quality Monitoring Systems for Enhanced Living Environments: A Review toward Sustainable Smart Cities

Sustainability ◽

10.3390/su12104024 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4024 ◽

Cited By ~ 1

Author(s):

Gonçalo Marques ◽

Jagriti Saini ◽

Maitreyee Dutta ◽

Pradeep Kumar Singh ◽

Wei-Chiang Hong

Keyword(s):

Air Quality ◽

Internet Of Things ◽

Indoor Air Quality ◽

Indoor Air ◽

Smart Cities ◽

Well Being ◽

Quality Monitoring ◽

Monitoring Systems ◽

Air Quality Monitoring ◽

Health And Well Being

Smart cities follow different strategies to face public health challenges associated with socio-economic objectives. Buildings play a crucial role in smart cities and are closely related to people’s health. Moreover, they are equally essential to meet sustainable objectives. People spend most of their time indoors. Therefore, indoor air quality has a critical impact on health and well-being. With the increasing population of elders, ambient-assisted living systems are required to promote occupational health and well-being. Furthermore, living environments must incorporate monitoring systems to detect unfavorable indoor quality scenarios in useful time. This paper reviews the current state of the art on indoor air quality monitoring systems based on Internet of Things and wireless sensor networks in the last five years (2014–2019). This document focuses on the architecture, microcontrollers, connectivity, and sensors used by these systems. The main contribution is to synthesize the existing body of knowledge and identify common threads and gaps that open up new significant and challenging future research directions. The results show that 57% of the indoor air quality monitoring systems are based on Arduino, 53% of the systems use Internet of Things, and WSN architectures represent 33%. The CO2 and PM monitoring sensors are the most monitored parameters in the analyzed literature, corresponding to 67% and 29%, respectively.

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Quality of life control by selected methods of air exchange in a typical apartment building

10.20944/preprints202103.0362.v1 ◽

2021 ◽

Author(s):

Iveta Bullová ◽

Peter Kapalo ◽

Dušan Katunský

Keyword(s):

Air Quality ◽

Indoor Air Quality ◽

Indoor Air ◽

Allergic Diseases ◽

Well Being ◽

Indoor Climate ◽

Apartment Building ◽

Change Rate ◽

Air Change Rate ◽

Negative Effect

Air change rate is an important parameter for quantification of ventilation heat losses and also affects the indoor climate of buildings. Indoor air quality is significantly associated with ventilation. If air change isn't sufficient, trapped allergens, pollutants and irritants can degrade the indoor air quality and affect the well-being of a building's occupants. Many studies on ventilation and health have concluded that lower air change rates can have a negative effect on people’s health and low ventilation may result in an increase in allergic diseases. Quantification of air change rate is complicated, since it is affected by a number of parameters, of which the one of the most variable is the air-wind flow. This study aims to determination and comparison of values of the air change rate in two methods - by quantifying of aerodynamic coefficient Cp = Cpe - Cpi – so called aerodynamic quantification of the building and the methodology based on experimental measurements of carbon dioxide in the selected reference room in apartment building.

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Indoor air quality prediction using optimizers: A comparative study

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200259 ◽

2020 ◽

Vol 39 (5) ◽

pp. 7053-7069

Author(s):

Jagriti Saini ◽

Maitreyee Dutta ◽

Gonçalo Marques

Keyword(s):

Air Quality ◽

Comparative Study ◽

Indoor Air Quality ◽

Mean Square Error ◽

Indoor Air ◽

Well Being ◽

Percentage Error ◽

Cow Dung ◽

Mean Square ◽

Human Beings

Indoor air pollution (IAP) has become a serious concern for developing countries around the world. As human beings spend most of their time indoors, pollution exposure causes a significant impact on their health and well-being. Long term exposure to particulate matter (PM) leads to the risk of chronic health issues such as respiratory disease, lung cancer, cardiovascular disease. In India, around 200 million people use fuel for cooking and heating needs; out of which 0.4% use biogas; 0.1% electricity; 1.5% lignite, coal or charcoal; 2.9% kerosene; 8.9% cow dung cake; 28.6% liquified petroleum gas and 49% use firewood. Almost 70% of the Indian population lives in rural areas, and 80% of those households rely on biomass fuels for routine needs. With 1.3 million deaths per year, poor air quality is the second largest killer in India. Forecasting of indoor air quality (IAQ) can guide building occupants to take prompt actions for ventilation and management on useful time. This paper proposes prediction of IAQ using Keras optimizers and compares their prediction performance. The model is trained using real-time data collected from a cafeteria in the Chandigarh city using IoT sensor network. The main contribution of this paper is to provide a comparative study on the implementation of seven Keras Optimizers for IAQ prediction. The results show that SGD optimizer outperforms other optimizers to ensure adequate and reliable predictions with mean square error = 0.19, mean absolute error = 0.34, root mean square error = 0.43, R2 score = 0.999555, mean absolute percentage error = 1.21665%, and accuracy = 98.87%.

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AN EVALUATION OF INDOOR ENVIRONMENTAL QUALITY AND OCCUPANT WELL-BEING IN MANITOBA SCHOOL BUILDINGS

Journal of Green Building ◽

10.3992/1552-6100.12.1.123 ◽

2017 ◽

Vol 12 (1) ◽

pp. 123-141 ◽

Cited By ~ 1

Author(s):

Ahmed Radwan ◽

Mohamed H. Issa

Keyword(s):

Air Quality ◽

Thermal Comfort ◽

Indoor Air Quality ◽

Environmental Quality ◽

Indoor Air ◽

Well Being ◽

School Buildings ◽

Indoor Environmental Quality ◽

Middle Aged ◽

Green School

This exploratory research aims to evaluate indoor environmental quality in the classrooms of three school buildings in Southern Manitoba, Canada, and to evaluate the well-being of these schools' teachers as it pertains to their perception of their classrooms' indoor environment. The schools include a middle-aged, conventional school; a new, non-green school; and a new, green school certified using the Leadership in Energy and Environmental Design rating system. The methodology involved using a mobile instrument cart to conduct snapshot measurements of thermal comfort, indoor air quality, lighting and acoustics in classrooms and an occupant survey to evaluate teachers' long-term satisfaction with their classrooms' indoor environmental quality. The results showed that the new, green and new, non-green schools' classrooms performed better than the conventional, middle-aged school's classrooms with respect to some aspects of thermal comfort and indoor air quality only. Teachers in the new, green school and in the new, non-green school were more satisfied than teachers in the conventional, middle-aged school with their classrooms' overall indoor environmental quality, lighting quality and indoor air quality. Surprisingly, the new, green and new-non green school classrooms' performance were very comparable with the new, green school's classrooms performing statistically significantly better with respect to relative humidity. Similarly, none of the differences in teachers' satisfaction ratings between the new, green and new, non-green school were statistically significant.

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Analysis of the need of detailed modelling for the assessment of indoor air quality in residential buildings

E3S Web of Conferences ◽

10.1051/e3sconf/201911104043 ◽

2019 ◽

Vol 111 ◽

pp. 04043

Author(s):

Louis Cony-Renaud-Salis ◽

Nouamane Belhaj ◽

Olivier Ramalho ◽

Marc Abadie

Keyword(s):

Air Quality ◽

Indoor Air Quality ◽

Indoor Air ◽

Large Scale ◽

Residential Buildings ◽

Ventilation System ◽

Field Measurements ◽

Well Being ◽

Good Alternative ◽

Specific Care

Home represents an important part of the time spent indoors and is the emblematic place of a family need, e.g. well-being, comfort and safety. In France, health agencies provide information and raise the awareness of the public on health risks and on factors likely to affect the quality of indoor air. However, indoor air quality remains difficult to assess for health investigators. A solution would be to resort to field measurements, but they are expensive and hard to apply to a large-scale population when considering the numerous pollutants found indoors. Therefore, numerical simulation represents a good alternative when accurate and realistic input data are used. We already designed such a model of a dwelling prototype using a type 98 coupling procedure between CONTAM (airflow rates and pollutants concentration determination) and TRNSYS (thermal and moisture calculation). We paid a lot of attention to the details that we thought were important: dwelling multi-zonal representation, envelope airtightness, ventilation system elements (pressure driven inlet and outlet, ducts, fan characteristics), presence of furniture, people activity and location… Nevertheless, the design of this simulation requires a very specific care. This very last point naturally induces a debate: is it necessary to design the simulation to be as accurate and realistic as it actually is, or will a simpler model provide similar results? In this study, we aim to answer that question by evaluating the sensitivity of the ULR-IAQ multipollutant index, defined in a previous study, to different levels of modelling complexity.

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Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17144942 ◽

2020 ◽

Vol 17 (14) ◽

pp. 4942 ◽

Cited By ~ 3

Author(s):

Jagriti Saini ◽

Maitreyee Dutta ◽

Gonçalo Marques

Keyword(s):

Systematic Review ◽

Air Quality ◽

Internet Of Things ◽

Indoor Air Quality ◽

Indoor Air ◽

Well Being ◽

Quality Monitoring ◽

Monitoring Systems ◽

Air Quality Monitoring ◽

Research Knowledge

Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015–2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

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