![]() ![]() Moreover, decreasing the distance between the ventilation systems installed location and the ceiling can drop the fraction of the suspended particles by over 35%, and the number of individuals who are subjected to becoming infected by viral particles drops from 6 to 2. The results show that reducing the output capacity by raising the concentration of suspended particles and increasing their traveled distance caused a growth in the individuals' exposure to contaminants. The main objective of this study is to investigate the effects of the window opening frequency, exhaust layouts, and the location of the air conditioner systems on the dispersion of the particles. Computational fluid dynamics based on coupled Eulerian–Lagrangian techniques are used to explore the characteristics of the airflow field in the domain. ANSYS Fluent software has been used to investigate the dispersion of the viral particles generated during a coughing event and their transport dynamics inside a safe social-distance meeting room. To predict elevated concentration events, results show that indirect classification through a regression prediction that was then compared to a threshold performed marginally better than a direct classification prediction for all pollutants except PM1.Īirborne transmission of respiratory aerosols carrying infectious viruses has generated many concerns about cross-contamination risks, particularly in indoor environments. Long-Short Term Memory was consistently the best method for predicting indoor pollutants, though the best combinations of input variables differed depending on pollutant of interest. Four different methods (Rolling Average, Random Forest, Gradient Boosting, and Long-Short Term Memory) for predicting eight indoor pollutant concentrations (carbon dioxide, nitrogen dioxide, ozone, PM 1, PM 2.5, PM 10, formaldehyde, total volatile organic compounds) are compared for their ability to predict future sensor signals in a single commercial building in California. 4) Investigate methods for predicting elevated concentration events from historical data. 3) Develop an understanding of how far into the future we can conceivably predict indoor concentrations based on low-cost airborne pollutant signals. 2) Investigate which algorithms are most useful for making these predictions. In service of this overarching goal, this work pursues four objectives: 1) Determine which low-cost airborne pollutant sensors are useful for prediction of indoor air quality variables of interest, investigating whether a few commercially available sensors held value for making such predictions. Prediction of indoor airborne pollutant concentrations can enable a smart indoor air quality control strategy that potentially reduces building energy use and improves occupant outcomes. ![]()
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