Scoping review of indoor air quality indexes: Characterization and applications

peer reviewedBetter understanding of indoor air quality (IAQ) and parameters affecting it, can improve the management of indoor environment quality (IEQ), reduce health risks, and enhance the occupant's well-being. The energy divisions, health and economy sectors are highly correlated to the concept of IAQ in terms of air ventilation, public health, and productivity. We performed a worldwide scoping review in accordance with the PRISMA extension (PRISMA-ScR) on IAQ indexes with different definitions and indicators, for various aims and applications (from indoor climate to indoor pollutants; for different indoor environments and ventilation setups). Correspondingly, IAQ-related issues were reviewed, including health effects, energy efficiency, and economic impacts. Information on different IAQ indexes was obtained from 110 studies from 23 countries. The use and type of ventilation systems as well as the duration and location of studies are reviewed. Also, the variability of the studied parameters in the literature were investigated. Finally, a novel detailed scoping classification based on different approaches for IAQ index development is presented for the first time. The “objective” approach has become prevalent over the “subjective” approach, in design, development, and application of IAQ indexes. In addition, consideration of mechanical and natural ventilation was observed in 57 and 18% of the studies, respectively. This scoping review can aid as a first step, to better understand different expressions of IAQ by mean of an index, and their applications for future research and developments.OCCuPANt13. Climate actio

IMPACTS OF CLIMATE CHANGE ON THE INDOOR AIR OF RESIDENTIAL BUILDINGS IN BELGIUM

Technical Research PresentationsOccupan

Exploring the Indoor Air Quality in the context of changing climate in a naturally ventilated residential Building using CONTAM

peer reviewedIndoor Air Quality (IAQ) of residential buildings is a crucial field of study as it aims important subjects: providing healthy indoor air to inhabitants, avoiding high pollutants concentration and impacts of extreme heat events along with climate change and global warming concerns. In order to set up a performance-based method, a critical question has to be answered: What are the relevant IAQ performance parameters that are mostly affected in the context of climate change? To address this, a measurement campaign was carried out in summer of 2021 at south of Belgium. Indoor temperature, relative humidity, CO, Particulate Matters (PM2.5, PM10), Volatile Organic Compounds (VOCs), NO, NO2 and O3 concentrations were measured with fabricated monitoring devices of low-cost sensors, as well as the corresponding outdoor values. In the first step of this study, IAQ parameters are computed using designed model with CONTAM software for the test house. In the next step, the validation of the developed model is investigated

Historical and future weather data for dynamic building simulations in Belgium using the regional climate model MAR: typical and extreme meteorological year and heatwaves

peer reviewedAbstract. Increasing temperatures due to global warming will influence building, heating, and cooling practices. Therefore, this data set aims to provide formatted and adapted meteorological data for specific users who work in building design, architecture, building energy management systems, modelling renewable energy conversion systems, or others interested in this kind of projected weather data. These meteorological data are produced from the regional climate model MAR (Modèle Atmosphérique Régional in French) simulations. This regional model, adapted and validated over Belgium, is forced firstly, by the ERA5 reanalysis, which represents the closest climate to reality and secondly, by three Earth system models (ESMs) from the Sixth Coupled Model Intercomparison Project database, namely, BCC-CSM2-MR, MPI-ESM.1.2, and MIROC6. The main advantage of using the MAR model is that the generated weather data have a high resolution (hourly data and 5 km) and are spatially and temporally homogeneous. The generated weather data follow two protocols. On the one hand, the Typical Meteorological Year (TMY) and eXtreme Meteorological Year (XMY) files are generated largely inspired by the method proposed by the standard ISO15927-4, allowing the reconstruction of typical and extreme years, while keeping a plausible variability of the meteorological data. On the other hand, the heatwave event (HWE) meteorological data are generated according to a method used to detect the heatwave events and to classify them according to three criteria of the heatwave (the most intense, the longest duration, and the highest temperature). All generated weather data are freely available on the open online repository Zenodo (https://doi.org/10.5281/zenodo.5606983, Doutreloup and Fettweis, 2021) and these data are produced within the framework of the research project OCCuPANt (https://www.occupant.uliege.be/ (last access: 24 June 2022)​​​​​​​ – ULiège).ARC OCCuPANt7. Affordable and clean energy11. Sustainable cities and communities13. Climate actio

Thermodynamic assessment and multi-objective optimization of performance of irreversible Dual-Miller cycle

In this study, a new series of assessments and evaluations of the Dual-Miller cycle is performed. Furthermore, the specified output power and the thermal performance associated with the engine are determined. Besides, multi-objective optimization of thermal efficiency, ecological coefficient of performance (ECOP) and ecological function (Eun) by means of NSGA-II technique and thermodynamic analysis are presented. The Pareto optimal frontier obtaining the best optimum solution is identified by fuzzy Bellman-Zadeh, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved. For the results of the condition that (n k) the best point has been LINMAP and TOPSIS answer. The thermal efficiency for this point has been 0.5385. Also, ECOP and Eun have been 1.6875 and 279.7315, respectively. Furthermore, the errors are examined through comparison of the average and maximum errors of the two scenarios

Impact of climate change on nearly zero-energy dwelling in temperate climate: Time-integrated discomfort, HVAC energy performance, and GHG emissions

peer reviewedGlobal warming is widely recognized to affect the built environment in several ways. This paper projects the current and future climate scenarios on a nearly zero-energy dwelling in Brussels. Initially, a time-integrated discomfort assessment is carried out for the base case without any active cooling system. It is found that overheating risk will increase up to 528%, whereas the overcooling risk will decrease up to 32% by the end of the century. It is also resulted that the overheating risk will overlap the overcooling risk by 2090s under high emission scenarios. Subsequently, two commonly applied HVAC strategies are considered, including a gas-fired boiler + an air conditioner (S01) and a reversible air-to-water heat pump (S02). In general, S02 shows ∼6–13% and 15–27% less HVAC primary energy use and GHG emissions compared to S01, respectively. By conducting the sensitivity analysis, it is found that the choice of the HVAC strategy, heating set-point, and cooling set-point are among the most influential parameters determining the HVAC primary energy use. Finally, some future recommendations are provided for practice and future research.[OCCuPANt] Impacts of climate change on buildings in Belgium during summe

Exploring the Indoor Air Quality in the context of changing climate in residential buildings. Part A: developed measurement devices of low-cost sensors

peer reviewedIndoor air quality (IAQ) is influenced by several parameters and the sources of indoor air pollutants are numerous (building materials, occupant behavior, HVAC systems, Outdoor air, etc.). Utilization of low-cost sensor devices for screening the indoor air pollution has made notable interests over the recent years. These systems are easy to access, portable, low-maintenance needed, and can provide real-time and continuous screening of target contaminants. The implementation of these systems to monitor the IAQ in real-time and for long period, can support the study of indoor air pollutants trends and variations. In this paper, we present sensors performance needed for an indoor air use. For this reason, four multi-sensor devices are fabricated and developed to measure O3, CO, NO, NO2, PM2.5, PM10, as well as the temperature and humidity, in an experimental measurement campaign study and were compared with results of a validated reference analyzers with high accuracy. The results showed a sufficient correlation of the measuring devices and the reference data considering the temperature and relative humidity. By the mean of Orthogonal regression method, the calibration equations were acquired for measuring parameters to enhance the IAQ monitoring devices performances. The results were examined on the basis of threshold limit value concentrations defined by European Commission indoor exposure limit value

Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network

In this paper two artificial intelligence techniques to predict and control behavior of a 25W fabricated proton exchange membrane (PEM) fuel cell, have been investigated. These approaches are: “Parametric Neural Network (PNN)” and “Group Method of Data Handling (GMDH)” for the first time. A PNN model is developed by introducing a “p” parameter in the activation function of the neural network. PNN model with its specific tangent hyperbolic transfer function have the ability to be with different nonlinearity degrees of input data. To develop GMDH network, quadratic polynomial was utilized. To determine proper weights of GMDH network, back propagation algorithm has been used. The input layer consists of gas pressure, fuel cell temperature and input current experimental data, to predict the output voltage. The results show that both generalized Parametric and GMDH-type neural networks are reliable tools to predict the output voltage of PEM fuel cell with high coefficient of determination values of 0.96 and 0.98

Solar radiation prediction based on ICA and HGAPSO for Kuhin City, Iran

In this study, sun radiation prediction has been carried out by utilization of ICA and HGAPSO algorithms, based on some climatic parameters such as air temperature, relative humidity and wind speed. Air temperature, relative humidity and wind speed data have been measured by accurate measuring tools in Kuhin city. The application of these data networks to predict solar radiation in a similar situation have been taught to the system. The networks precision is shown by their correlation coefficient (R2) and mean square error (MSE). Due to the different climatic conditions, solar radiation is very difficult to be predicted. Thus, the trained networks have deviations; hence the networks with R2 equal to 0.7 and above were acceptable. At the end, the solar radiation prediction was carried out for some the days of two years

Connectionist intelligent model estimates of convective heat transfer coefficient of nanofluids in circular cross-sectional channels

Nanofluids are kind of fluids, which have a wide range of applications in different fields such as industry or engineering systems. The present study efforts to find accurate relationships between the convective heat transfer coefficient of the nanofluids containing the silica nanoparticles as a function of Reynolds number, Prandtl number, and mass fraction nanofluid. To that end, a number of seven different models including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), least square support vector machine (LSSVM), genetic programming (GP), principal component analysis (PCA), and committee machine intelligent system (CMIS) have been implemented according to experimental databases designed for measuring the convective heat transfer coefficient of nanofluid in circular cross-sectional channels. Results indicated the satisfactory capability of suggested models, especially CMIS model in order to estimate the convective heat transfer coefficient of nanofluid. The obtained statistical analyses such as the mean square error and R-squared (R2) for the ANFIS, ANN, SVM, LSSVM, PCA, GP, and CMIS were 380.6671 and 0.9946, 215.062 and 0.9969, 335.748 and 0.9951, 298.88 and 0.9959, 1601.336 and 0.977, 1891.861 and 0.973, and 205.366 and 0.9970 correspondingly. We expect that these suggested models can help engineers who deal with heat transfer phenomenon to have great predictive tools for estimating convective heat transfer coefficient of nanofluid