Towards improved models for indoor air chemistry: A Monte Carlo simulation study

Model predictions are sensitive to a number of complex and often coupled input parameters. Some these parameters have wide range acceptable values from literature therefore choosing the appropriate value is non-trivial. In this paper, we use INdoor Detailed Chemical (INDCM) perform Monte Carlo analysis, in which but realistic model parameter stochastically varied over 1000 runs. The output defines likely performance, directly correlates with predicted indoor air species concentrations. exchange rate or ozone deposition velocity onto internal materials such as painted walls, control concentrations ozone, hydroxyl peroxy radicals peroxyacetyl nitrate for our study conditions. transmission UV light outdoors showed strongest Spearman's rank positive correlation coefficients radical (0.92), organic (0.95) walls shows negative correlations 4-oxopentanal (−0.86) acetic acid (−0.83). Reducing uncertainty around indoors rates surfaces reduces by up 70–80% relationships various For instance, maximum isoprene decreased rate, minimum were largely invariant. Emissions human breath ensured that was always present However, its removal affected (which can both chemically remove isoprene), direct ventilation. Finally, used results understand 95% confidence bounds median radicals, ±60% value.