Tier 2 deterministic uncertainty analysis

Deterministic sensitivity analysis performed during modelling of population exposures to ambient fine particulate matter by using high (H), medium (M) and low (L) values during the analysis of the impact of uncertainties associated with key inputs and parameters on model predictions (e.g. time spent outdoors [H = 95%, M = 80%, L = 50%], residential building infiltration fractions [H = 0.7, M = 0.5, L = 0.2], deposition rates [H = 0.4, M = 0.3, L = 0.1]). 

Deterministic sensitivity analysis conducted using SOP algorithms for estimating children s 

---

An uncertainty analysis gives the assessor the opportunity to re-evaluate the scenario, model approaches and parameters of the analysis and to consider their influence in the overall analysis. The practical impact of uncertainty analysis is illustrated within the annexed case-studies, which also clarify how uncertainty analyses follow a systematic methodology, based on a tiered approach, and consider all possible sources of uncertainty. The first step in uncertainty analysis consists of a screening, followed by a qualitative analysis and two levels of quantitative analysis, using deterministic and probabilistic data. The assessor should be aware that an uncertainty analysis cannot answer all the questions, which, moreover, may lead to new questions. 

Uncertainties assessed at Tier 2 (deterministic) generate alternative point estimates for exposure and may be communicated in various ways, depending on the particular methods used for sensitivity analysis. As a minimum, this should identify which sources of uncertainty have been treated at Tier 2, state and justify the alternative quantitative estimates used for each one (e.g. minimum, maximum and most likely values), present exposure estimates for those combinations of alternative estimates that are considered plausible and state and justify any combinations of estimates that are considered implausible. In addition, it will be useful (especially if upper estimates exceed levels of concern) to show which of the quantified uncertainties have most influence on the outcome. 

---