CHAARM: A model to predict uncertainties in indoor pollutant concentrations, ventilation and infiltration rates, and associated energy demand in Chilean houses

Evaluation of housing stock indoor air quality models: A review of data requirements and model performance

“…In general, uncertainties in housing stock modelling are of three sources: (1) the heterogeneity within a building stock (e.g., a large range of building characteristics), (2) the first-order or aleatoric uncertainties where different simulation outputs are probable given the same building, and (3) the second-order or epistemic uncertainties where input parameters can take different values in light of new data or knowledge [52]. Increasingly, to quantify the uncertainties in indoor pollutant concentration levels, ventilation, and infiltration [86].…”

“…Chilean Housing Archetype AiR quality Model (CHAARM) was developed to predict uncertainties in indoor pollutant concentrations, ventilation, infiltration rates and associated energy demand in the heating season, including the sensitivity of the model outputs to the inputs [86]. The model was based on the previously identified set of archetypes to represent the national Chilean housing stock [61].…”

“…archetypes) required to represent the entire housing stock with reduced model resolution (e.g. the CHAARM [86]). Furthermore, hybrid models can account for both linear and nonlinear systems by constructing metamodels (e.g.…”

“…In fact, both white-and grey-box models can be computationally expensive when a large number of archetypes or metamodels are involved. However, some of these models managed computing efficiency by: (1) using only single-zone models to represent the entire dwelling stock such as REIAQM [97] (but with reduced prediction accuracy for both airflow and contaminant concentrations, see section 4.1); (2) running the simulations on a high performance parallel computing platform as in ENG-A [85] and ENG-B [96]; and (3) reducing the number of archetypes to a statistically acceptable level while acknowledging the loss in model resolution as in the CHAARM [86].…”

A CFD-based framework to assess airborne infection risk in buildings

“…The main source of uncertainty lies where the behaviour of ballistic drops and aerosol particles is affected by environmental constraints in which tests are conducted. Another source of uncertainty is the measure of the RNA-copies contained in virions found in drops and droplets [ 8 , 9 ]. This mainly depends on the evaporation rate, the sensitivity of sensors, as well as the availability of swab tests in the respiratory tract, which for example are very limited in asymptomatic individuals [ 10 ].…”