Species profile of volatile organic compounds emission and health risk assessment from typical indoor events in daycare centers.

Daycare centers (DCCs) play an instrumental role in early childhood development, making them a significant indoor environment for a large number of children globally. Amidst routine DCC activities, young children are exposed to a myriad of volatile organic compounds (VOCs), potentially impacting their health. Therefore, this study aims to investigate the VOC emissions during typical DCCs activities and evaluate respective health risk assessments.

Laboratory evaluation of low-cost air quality monitors and single sensors for monitoring typical indoor emission events in Dutch daycare centers.

Daycare centers (DCCs) are where infants and toddlers (0-4 years old) spend the most time besides their homes. Given their higher susceptibility to the effects of air pollutants, as compared to older children and adults, indoor air quality (IAQ) is regarded as an essential parameter to monitor in DCCs. Recent advances in IAQ monitoring technologies have enabled the deployment of low-cost air quality monitors (LCMs) and single sensors (LCSs) to continuously monitor various indoor environments, and their performance testing should also be performed in the intended indoor applications.

Identification of a dynamic system model for a building and heating system including heat pump and thermal energy storage.

Controllers employing optimal control strategies will path the way to enable flexible operations in future power grids. As buildings will increasingly act as prosumers in future power grids, optimal control of buildings' energy consumption will play a major role in providing flexible operations. Optimal controllers such as model predictive controller are able to manage buildings' operations and to optimise their energy consumption.

Machine learning algorithms applied to a prediction of personal overall thermal comfort using skin temperatures and occupants' heating behavior.

Thermal comfort modeling has been of interest in built environment research for decades. Mostly the modeling approaches focused on an average response of a large group of building occupants. Recently, the focus has been shifted towards personal comfort models that predict individuals' thermal comfort responses.

Modelling hand skin temperature in relation to body composition.

Skin temperature is a challenging parameter to predict due to the complex interaction of physical and physiological variations. Previous studies concerning the correlation of regional physiological characteristics and body composition showed that obese people have higher hand skin temperature compared to the normal weight people. To predict hand skin temperature in a different environment, a two-node hand thermophysiological model was developed and validated with published experimental data.