Evaluation of particle penetration factors based on indoor PM2.5 removal by an air cleaner

Assessment of indoor environmental quality and seasonal well-being of students in a combined historic technical school building in Slovakia

One of the major present challenges in the building sector is to construct sustainable and low-energy buildings with a healthy, safe, and comfortable environment. This study is designed to explore long-term impacts of indoor environmental quality (IEQ) parameters in a historic technical school building on the health and comfort of students. The main objective is to identify environmental problems in schools and to direct public policy towards the enhancement of in-service historic buildings. The collected data on five consecutive days in various seasons from five different classrooms indicate allergy in 45% and asthma in 10% of students. Environmental factors, such as temperature, draught, noise, or light, affected 51% of students' attention. Low temperature, unpleasant air, noise, and draught were found to be the most frequent concerns for students. The lowest temperature was measured during spring at 17.6 °C, the lowest humidity of 21.1% in winter, the largest CO2 amount in the air in autumn at 2041 ppm level, and the greatest total volatile organic compounds (TVOC) as 514 µg/m3. The experimental and statistical analysis results suggest the necessity of a comprehensive restoration of the building with a focus on enhancement of IEQ as well as replacement of old non-standard materials. An effective ventilation system is also necessary. The building requires major renovations to preserve its historic features while safeguarding the well-being and comfort of students and staff. Further research is needed on acoustics, lighting, and energy factors as well as the health effects of old building materials.

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction

Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.