Indoor air quality of daycare centers in Seoul, Korea

Hwang, Sung Ho; Seo, Sung Chul; Yoo, Young; Kim, Ki Yeon; Choung, Ji Tae; Park, Wha Me

doi:10.1016/j.buildenv.2017.07.042

Cited by 33 publications

(18 citation statements)

References 29 publications

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“…Despite extensive research that shows a correlation between indoor air pollution and aggravation of asthma [4]- [6], providing a personalized risk assessment in real-time based on indoor air quality is still at an infant state. In [7], [8], a static relationship between the indoor air quality and the asthma attack is shown. However, these cannot be used for real-time assessment.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Asthma Risk Prediction Using IoT and Smartphone Applications

et al. 2021

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In this paper, we present an asthma risk prediction tool based on machine learning (ML). The entire tool is implemented on a smartphone as a mobile-health (m-health) application using the resources of Internet-of-Things (IoT). Peak Expiratory Flow Rates (PEFR) are commonly measured using external instruments such as peak flow meters and are well known asthama risk predictors. In this work, we find a correlation between the particulate matter (PM) found indoors and the outside weather with the PEFR. The PEFR results are classified into three categories such as 'Green' (Safe), 'Yellow' (Moderate Risk) and 'Red' (High Risk) conditions in comparison to the best peak flow value obtained by each individual. Convolutional neural network (CNN) architecture is used to map the relationship between the indoor PM and weather data to the PEFR values. The proposed method is compared with the state-of-the-art deep neural network (DNN) based techniques in terms of the root mean square and mean absolute error accuracy measures. These performance measures are better for the proposed method than other methods discussed in the literature. The entire setup is implemented on a smartphone as an app. An IoT system including a Raspberry Pi is used to collect the input data. This assistive tool can be a cost-effective tool for predicting the risk of asthma attacks.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Asthma Risk Prediction Using IoT and Smartphone Applications

et al. 2021

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“…The researchers concluded that the evaluated indoor air pollution might present adverse effects to children, where the most problematic were PM 2.5 and formaldehyde levels. Study in 25 daycare centres in Seoul [11] evaluated biological (mould and bacteria) and chemical pollutants (formaldehyde, CO 2 , CO, and total volatile organic compounds). The concentrations were associated with building age and environmental factors such as ventilation time.…”

Section: Deteriorated Indoor Environmental Quality As a Collateral Damentioning

confidence: 99%

Deteriorated Indoor Environmental Quality as a Collateral Damage of Present Day Extensive Renovations

Dovjak¹,

Slobodnik²,

Krainer³

2018

SV-JME

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Optimal indoor environmental quality (IEQ) is especially important in every living and working environment with present vulnerable population groups. Especially problematic are educational institutions, where prolonged exposure time of users additionally increases health risks. The present study is focused on the problem of deteriorated IEQ in renovated kindergarten. The problem was critically assessed from the aspects of indoor air quality (IAQ) and energy use. A combination of simulations of the selected IAQ parameters and building energy use was performed for five sets of scenarios, where required and recommended design ventilation rates variated according to Slovenian legislation. Characteristics of actual kindergarten in central Slovenia, renovated in 2016, were used for simulations. Concentrations of CO 2 and formaldehyde were calculated in two model playrooms with CONTAM 3.2, whereas building energy use was calculated for two thermal zones of playrooms with Energy Plus 8.0.0. If ventilation in playrooms was designed according to the minimal permissible value (air changes per hour) ACH 0.5, CO 2 concentrations exceeded the national maximum permissible level by 2.5 and 3 times, and formaldehyde concentrations was close to the value recommended by World Health Organisation (WHO) and exceeded the level recommended by National Institute for Occupational Safety and Health (NIOSH-CDC) by 4.6 and 4.5 times. All required and recommended design ventilation rates resulted in exceeded values of CO 2 above recommendation for category I of IAQ, except the design ventilation rate 55 m 3 /h per person. In-line with public health protection measures, relevant information is an aid for recommendation definitions for policies and strategies towards healthier indoor environments as well as for raising awareness about current design practice.

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“…IAQ can affect occupants' health through exposure to viruses, fine particles, etc. Particularly, for children's health, IAQ is important in childcare and daycare centers [5]. In addition, efficient energy operation and control will reduce energy use and cost as well as greenhouse gas emissions, while maintaining targeted thermal comfort and IAQ levels.…”

Section: Introductionmentioning

confidence: 99%

Detailed Analysis of Thermal Comfort and Indoor Air Quality Using Real-Time Multiple Environmental Monitoring Data for a Childcare Center

Song

2021

Energies

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Thermal comfort, indoor air quality (IAQ), and energy use are closely related, even though these have different aspects with respect to building performance. We analyzed thermal comfort and IAQ using real-time multiple environmental data, which include indoor air temperature, relative humidity, carbon dioxide (CO2), and particulate matter (e.g., PM10 and PM2.5), as well as electricity use from an energy recovery ventilation (ERV) system for a childcare center. Thermal comfort frequency and time-series analyses were conducted in detail to thoroughly observe real-time thermal comfort and IAQ conditions with and without ERV operation, and to identify energy savings opportunities during occupied and unoccupied hours. The results show that the highest CO2 and PM10 concentrations were reduced by 51.4% and 29.5%, respectively, during the occupied hours when the ERV system was operating. However, it was also identified that comfort frequencies occurred during unoccupied hours and discomfort frequencies during occupied hours. By analyzing and communicating the three different types of real-time monitoring data, it is concluded that the ERV system should be controlled by considering not only IAQ (e.g., CO2 and PM2.5) but also thermal comfort and energy use to enhance indoor environmental quality and save energy based on real-time multiple monitoring data.

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Indoor air quality of daycare centers in Seoul, Korea

Cited by 33 publications

References 29 publications

Machine Learning-Based Asthma Risk Prediction Using IoT and Smartphone Applications

Machine Learning-Based Asthma Risk Prediction Using IoT and Smartphone Applications

Deteriorated Indoor Environmental Quality as a Collateral Damage of Present Day Extensive Renovations

Detailed Analysis of Thermal Comfort and Indoor Air Quality Using Real-Time Multiple Environmental Monitoring Data for a Childcare Center

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