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. Employing a full-scale experimental setup within a well-controlled climate chamber, research was conducted into VOC emissions during three typical DCC events: arts-and-crafts (painting, gluing, modeling), cleaning, and sleeping activities tied to mattresses. The research identified 96 distinct VOCs, grouped into twelve categories, from 20 different events examined. Each event exhibited a unique VOC fingerprint, pinpointing potential source tracers. Also, significant variations in VOC emissions from different events were demonstrated. For instance, under cool & dry conditions, acrylic painting recorded high total VOC concentrations of 808 µg/m3, whereas poster painting showed only 58 µg/m3. Given these disparities, the study emphasizes the critical need for carefully selecting arts-and-crafts materials and cleaning agents in DCCs to effectively reduce VOC exposure. It suggests ventilating new mattresses before use and regular mattress check-ups to mitigate VOCs exposure during naps. Importantly, it revealed that certain events resulted in VOC levels exceeding the 10-5 cancer risk thresholds for younger children. Specifically, tetrachloroethylene and styrene from used mattresses in cool & dry conditions, ethylene oxide from new mattresses in warm & humid conditions, and styrene, during sand modeling in both conditions, were the key compounds contributing to this risk. These findings highlight the critical need for age-specific health risk assessments in DCCs. This study highlights the significance of understanding the profiles of VOC emissions from indoor events in DCCs, emphasizing potential health implications and laying a solid foundation for future investigations in this field.

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. To our knowledge, there is no study evaluating the application of LCMs/LCSs in DCCs scenarios yet. Therefore, this study is aimed to assess the response of five types of LCMs (previously not tested) and five LCSs to typical DCCs emission activities in detecting multiple IAQ parameters, i.e., particulate matter, carbon dioxide, total volatile organic compounds, temperature, and relative humidity. These LCMs/LCSs were compared to outcomes from research-grade instruments (RGIs). All the experiments were performed in a climate chamber, where three kinds of typical activities (background; arts-and-crafts; cleaning; [in a total of 32 events]) were simulated by recruited subjects at two typical indoor climatic conditions (cool and dry [20 ± 1 °C & 40 ± 10%], warm and humid [26 ± 1 °C & 70 ± 5%]). Results showed that tested LCMs had the ability to capture DCCs activities by simultaneously monitoring multiple IAQ parameters, and LCMs/LCSs revealed a strong correlation with RGIs in most events (R2 values from 0.7 to 1), but, for some events, the magnitude of responses varied widely. Sensirion SCD41, an emerging CO2 sensor built on the photoacoustic sensing principle, had a more accurate performance than all tested NDIR-based CO2 sensors/monitors. In general, the study implies that the selection of LCMs/LCSs for a specific application of interest should be based on emission characteristics and space conditions.

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. For online optimisation, model predictive controller requires a model of the energy system. The more accurate the system model represents the system dynamics, the more accurate the model predictive controller predicts the future states of the energy system while optimising its energy consumption. In this article, we present a system model that can be used in online MPC, including dynamic programming as optimisation strategy. The system model is validated using a building and heating system, including heat pump and thermal energy storage. The following bullet points summarise the main requirements for the configuration of the system model:•The system model performs fast with low computational effort in less than 1 s;•The system model can be implemented in online MPC;•The system model accurately represents the dynamic behaviour.

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. Currently, thermal comfort responses are collected from the occupants via survey. This study explored if the thermal comfort of individuals could be predicted using machine learning algorithms while relaying on the set of collected inputs from an experiment. The model was developed using experimental data including collected from a previously performed experiment in the climate chamber. Two different approaches based on the output data (thermal sensation and thermal comfort votes) and five different sets of input variables were explored. The algorithms tested were Support Vector Machine with four different Kernel functions (Linear, Quadratic, Cubic and Gaussian) and Ensemble Algorithms (Boosted trees, Bagged trees and RUSBoosted trees). The combination of occupants' heating behavior with a personal comfort system (PCS), skin temperatures, time and environmental data were used for the development of personal comfort models to predict individuals' thermal preference. The study investigated the novel combination of inputs such as the use of skin temperature and settings of the personalized heating system as parameters in predicting personal thermal comfort. The results showed that personal comfort models among all tested approaches and subjects showed the best median accuracy of 0.84 using RUSBoosted trees. Individually looking, the approach using thermal sensation output produced better prediction accuracy. On the other hand, the models based on inputs that consisted of PCS control behavior and mean and hand skin temperatures produced the best prediction accuracy when assessing all tested algorithms. The main limitation of the study is the number of test subjects, and further recommendation is to perform more experiments.

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. In addition, a sensitivity analysis was performed which showed that the variations in skin blood flow and blood temperature are most influential on hand skin temperature. The hand model was applied to simulate the hand skin temperature of the obese and normal weight subgroup in different ambient conditions. Higher skin blood flow and blood temperature were used in the simulation of obese people. The results showed a good agreement with experimental data from the literature, with the maximum difference of 0.31°C. If the difference between blood flow and blood temperature of obese and normal weight people was not taken into account, the hand skin temperature of obese people was predicted with an average deviation of 1.42°C. In conclusion, when modelling hand skin temperatures, it should be considered that regional skin temperature distribution differs in obese and normal weight people.