ArchABM: An agent-based simulator of human interaction with the built environment. CO2 and viral load analysis for indoor air quality

Martı́nez, Iñigo; Bruse, Jan L.; Florez-Tapia, Ane M.; Viles, Elisabeth; Olaizola, Igor G.

doi:10.1016/j.buildenv.2021.108495

Cited by 17 publications

(11 citation statements)

References 37 publications

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“…Although analyzing the contagion of a disease requires more complex models that take into account the transport of exhaled droplets, the level of CO 2 can be a practical measure in classrooms to reduce the risk of contagion since high levels of CO 2 are an indication of agglomerations. Recent publications report that the concentration of CO 2 in classrooms can help reduce the risk of transmission of contagious diseases, such as SARS-CoV-2, since low levels of CO 2 indicate clean air [ 2 , 44 , 45 , 46 , 47 ].…”

Section: Resultsmentioning

confidence: 99%

Computational Study of Thermal Comfort and Reduction of CO2 Levels inside a Classroom

Ovando-Chacon

León

Ovando-Chacón

et al. 2022

IJERPH

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Due to the current COVID-19 pandemic, guaranteeing thermal comfort and low CO2 levels in classrooms through efficient ventilation has become vitally important. This study presents three-dimensional simulations based on computational fluid dynamics of airflow inside an air-conditioned classroom located in Veracruz, Mexico. The analysis included various positions of an air extractor, Reynolds numbers up to 3.5 × 104, four different concentrations of pollutant sources, and three different times of the day. The simulations produced velocity, air temperature, and CO2 concentrations fields, and we calculated average air temperatures, average CO2 concentrations, and overall ventilation effectiveness. Our results revealed an optimal extractor position and Reynolds number conducive to thermal comfort and low CO2 levels due to an adequate ventilation configuration. At high pollutant concentrations, it is necessary to reduce the number of students in the classroom to achieve safe CO2 levels.

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Section: Resultsmentioning

confidence: 99%

Computational Study of Thermal Comfort and Reduction of CO2 Levels inside a Classroom

Ovando-Chacon

León

Ovando-Chacón

et al. 2022

IJERPH

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“…Although the ventilation type should also be considered, it has been recommended to keep carbon dioxide levels below 700 to 800 ppm. Martinez et al [56] developed a novel agent-based simulator named "ArchABM". ArchABM is intended to aid in the design of new buildings or the adaptation of existing ones by calculating sufficient room sizes, ventilation characteristics, and assessing the effect of policies while accounting for IAQ as a result of complex human-building interaction patterns.…”

Section: Introductionmentioning

confidence: 99%

Transmission Probability of SARS-CoV-2 in Office Environment Using Artificial Neural Network

Kapoor

Kumar

et al. 2022

IEEE Access

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In this paper, curve-fitting and an artificial neural network (ANN) model were developed to predict R-Event. Expected number of new infections that arise in any event occurring over a total time in any space is termed as R-Event. Real-time data for the office environment was gathered in the spring of 2022 in a naturally ventilated office room in Roorkee, India, under composite climatic conditions. To ascertain the merit of the proposed ANN and curve-fitting models, the performances of the ANN approach were compared against the curve fitting model regarding conventional statistical indicators, i.e., correlation coefficient, root mean square error, mean absolute error, Nash-Sutcliffe efficiency index, mean absolute percentage error, and a20-index. Eleven input parameters namely indoor temperature (T In ), indoor relative humidity (RH In ), area of opening (A O ), number of occupants (O), area per person (A P ), volume per person (V P ), CO 2 concentration (CO 2 ), air quality index (AQI), outer wind speed (W S ), outdoor temperature (T Out ), outdoor humidity (RH Out ) were used in this study to predict the R-Event value as an output. The primary goal of this research is to establish the link between CO 2 concentration and R-Event value; eventually providing a model for prediction purposes. In this case study, the correlation coefficient of the ANN model and curve-fitting model were 0.9992 and 0.9557, respectively. It shows the ANN model's higher accuracy than the curvefitting model in R-Event prediction. Results indicate the proposed ANN prediction performance (R=0.9992, RMSE=0.0018708, MAE=0.0006675, MAPE=0.8643816, NS=0.9984365, and a20-index=0.9984300) is reliable and highly accurate to predict the R-event for offices.

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“…It has been suggested that CO 2 levels be kept between 700 and 800 ppm, however the method of ventilation should also be taken into account. “ArchABM” is a brand-new agent-based simulator that Martinez et al [ 32 ] created. By estimating ventilation characteristics and adequate room sizes, as well as evaluating the impacts of policies while accounting for IAQ as a consequence of complex building–human interaction patterns, the system is made to help with either the creation of brand new structures or the modification of already existing ones.…”

Section: Introductionmentioning

confidence: 99%

“…There is less research on the relationship between indoor CO 2 levels and viral transmission [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. However, as CO 2 may be utilized scientifically as a proxy for indoor viral transmission, similarly the indoor CO 2 concentration can be used to predict the probable number of infected people as well.…”

Section: Introductionmentioning

confidence: 99%

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

Kapoor

Kumar

et al. 2022

IJERPH

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The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

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ArchABM: An agent-based simulator of human interaction with the built environment. CO2 and viral load analysis for indoor air quality

Cited by 17 publications

References 37 publications

Computational Study of Thermal Comfort and Reduction of CO2 Levels inside a Classroom

Computational Study of Thermal Comfort and Reduction of CO2 Levels inside a Classroom

Transmission Probability of SARS-CoV-2 in Office Environment Using Artificial Neural Network

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

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