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

模型预测对许多复杂且经常耦合的输入参数很敏感。其中一些参数具有广泛的文献可接受值，因此选择合适的值并非易事。在本文中，我们使用室内详细化学模型 (INDCM) 来执行蒙特卡罗分析，其中模型输入参数值的范围广泛但真实，在 1000 次模型运行中随机变化。模型输出定义了模型性能的可能范围，并将输入参数值与预测的室内空气物质浓度直接相关。在我们的研究条件下，空气交换率或臭氧在内部材料（如油漆墙）上的沉积速度控制着臭氧、羟基和过氧自由基以及过氧乙酰硝酸盐物质的预测浓度。来自室外的紫外线透射显示最强的斯皮尔曼等级正相关系数与预测的羟基自由基 (0.92) 和有机硝酸盐 (0.95) 浓度。臭氧在涂漆墙上的沉积率与 4-氧戊醛 (-0.86) 和乙酸 (-0.83) 呈最强的负相关。减少室内紫外线传输和表面臭氧沉积率的不确定性，可将模型的臭氧和羟基自由基浓度的不确定性降低多达 70-80%。一些物种浓度显示出与各种输入参数的复杂关系。例如，最大异戊二烯浓度随空气交换率降低，但最小异戊二烯浓度在很大程度上保持不变。人类呼吸的排放确保异戊二烯始终存在于我们的模型运行中。然而，其去除率随空气交换率变化，影响臭氧和羟基自由基（均可化学去除异戊二烯）的浓度，以及通风直接去除率。最后，我们使用我们的结果来了解围绕我们的预测浓度中值的 95% 置信界限。对于羟基自由基，这些是中值的±60%。