Many people spend most of their time in an indoor environment. A positive relationship exists between indoor environmental quality and the health, wellbeing, and productivity of occupants in buildings. The indoor environment is affected by pollutants, such as gases and particles. Pollutants can be removed from the indoor environment in various ways. Air-cleaning devices are commonly marketed as benefiting the removal of air pollutants and, consequently, improving indoor air quality. Depending on the type of cleaning technology, air cleaners may generate undesired and toxic byproducts. Different air filtration technologies, such as electrostatic precipitators (ESPs) have been introduced to the market. The ESP has been used in buildings because it can remove particles while only causing low pressure drops. Moreover, ESPs can be either in-duct or standalone units. This review aims to provide an overview of ESP use, methods for testing this product, the performance of existing ESPs concerning removing pollutants and their byproducts, and the existing market for ESPs.

Indoor occupancy prediction can play a vital role in the energy-efficient operation of building engineering systems and maintaining satisfactory indoor climate conditions at the lowest possible energy use by operating these systems on the basis of occupancy data. Many methods have been proposed to predict occupancy in residential buildings according to different data types, e.g., digital cameras, motion sensors, and indoor climate sensors. Among these proposed methods, those with indoor climate data as input have received significant interest due to their less intrusive and cost-effective approach. This paper proposes a deep learning method called CNN-XGBoost to predict occupancy using indoor climate data and compares the performance of the proposed method with a range of supervised and unsupervised machine learning algorithms plus artificial neural network algorithms. The comparison is performed using mean absolute error, confusion matrix, and F₁ score. Indoor climate data used in this work are CO₂, relative humidity, and temperature measured by sensors for 13 days in December 2021. We used inexpensive sensors in different rooms of a residential building with a balanced mechanical ventilation system located in northwest Copenhagen, Denmark. The proposed algorithm consists of two parts: a convolutional neural network that learns the features of the input data and a scalable end-to-end tree-boosting classifier. The result indicates that CNN-XGBoost outperforms other algorithms in predicting occupancy levels in all rooms of the test building. In this experiment, we achieved the highest accuracy in occupancy detection using inexpensive indoor climate sensors in a mechanically ventilated residential building with minimum privacy invasion.

Respiratory aerosols from breathing and talking are an important transmission route for viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Previous studies have found that particles with diameters ranging from 10 nm to 145 μm are produced from different regions in the respiratory system and especially smaller particles can remain airborne for long periods while carrying viral RNA. We present the first study in which respiratory aerosols have been simultaneously measured with carbon dioxide (CO₂) to establish the correlation between the two concentrations. CO₂ concentrations are easily available through low-cost sensors and could be used to estimate viral exposure through this correlation, whereas source-specific aerosol measurements are complicated and not possible with low-cost sensors. The increase in both respiratory aerosols and CO₂ was linear over ten minutes in a 2 ${\mathrm{m}}^3$ chamber for all participants, suggesting a strong correlation. On average, talking released more particles than breathing, with 14,600 ± 16,800 min⁻¹ (one-$\sigma$ standard deviation) and 6210 ± 5630 min⁻¹ on average, respectively, while CO₂ increased with 139 ± 33 ppm min⁻¹ during talking and 143 ± 29 ppm min⁻¹ during breathing. Assuming a typical viral load of $7\times {10}^6$ RNA copies per mL of oral fluid, ten minutes of talking and breathing are estimated to produce 1 and 16 suspended RNA copies, respectively, correlating to a CO₂ concentration of around 1800 ppm in a 2 ${\mathrm{m}}^3$ chamber. However, viral loads can vary by several orders of magnitude depending on the stage of the disease and the individual. It was therefore concluded that, by measuring CO₂ concentrations, only the number and volume concentrations of released particles can be estimated with reasonable certainty, while the number of suspended RNA copies cannot.

The accumulation of thermal energy in construction elements during daytime, and its release during a colder night period is an efficient and green way to maintain a comfortable temperature range in buildings and vehicles. One approach to achieving this goal is to store thermal energy as latent heat of the phase transition using the so-called phase-change materials (PCMs). Vegetable oils came recently into focus as cheap, widely available, and environmentally friendly PCMs. In this study, we report the thermal properties of PCMs based on tamanu and coconut oils in three configurations: pure, emulsion, and encapsulated forms. We demonstrate the encapsulation of pure coconut- and tamanu-oil emulsions, and their mixtures and mixtures with commercial PCM paraffins in fiber matrices produced by a coaxial electrospinning technique. Polycaprolactone (PCL) was used as a shell, the PCM emulsion was formed by the studied oils, and sodium dodecyl sulfate (SDS) and polyvinyl alcohol (PVA) were used as emulsifiers. The addition of commercially available paraffin RT18 into a 70/30 mixture of coconut and tamanu oil, successfully encapsulated in the core of a PCL shell, demonstrated latent heats of melting and solidification of 63.8 and 57.6 kJ/kg, respectively.