Modelling reducing uncertainty

If it is already known at the pilot stage in which plant the final operation will be situated, solvent recycling, waste treatment, and utility supply processes should be modelled according to that specific plant. This reduces uncertainty in the scale-up from pilot to operation. 

Restrictions which may exist for the choice of a commercial reactor need not be imposed at the development stage. In some cases, a reactor of one type may be best for acquiring data in model characterisation, whereas a reactor of another type might be more suitable for full-scale production. (The cautions expressed in Sect. 4 must be taken into account.) Continuous flow back-mixed reactors can be very useful for kinetic studies because the absence of concentration gradients can reduce uncertainties in concentration measurements. When these reactors have attained a steady state, many of the problems associated with stiffness (see above) can be avoided. 

Linkoff I, Burmistrov D, Kandlikar M, Schell WR. 1999. Reducing uncertainty in the radionuclide transport modeling for the Chernobyl forests using Bayesian updating. In Linkov I, Schell WR, editors. Contaminated forests. Dordrecht (DE) Kluwer, p 143-150. 

A Bayesian hierarchical modeling framework was used to evaluate the effects data for each species and test endpoint (Figure 7.4). Hierarchical models reduce the effect of incomplete data sets, small numbers of tests, inconsistent information on effects among species, and other issues that lend uncertainty to the risk characterization results. 

Also special care should be taken to reduce uncertainties on emission data and measurements. The validation of an aerosol model requires the analysis of the aerosol chemical composition for the main particulate species (ammonium, sulphate, nitrate and secondary organic aerosol). To find data to perform this kind of more complete evaluation is not always easy. The same applies to emissions data. The lack of detailed information regarding the chemical composition of aerosols obliges modellers to use previously defined aerosols components distributions, which are found in the literature. Present knowledge in emission processes is yet lacunal, especially concerning suspension and resuspension of deposited particles [37]. 

Nowadays, highly sophisticated modelling approaches are available, which allow assessing PM at high spatial and temporal resolution, as needed for human exposure estimation. Thus no new models need to be developed (models predicting transport and transformation of aerosols in the atmosphere are available). Instead, methods need to be devised which are able to reduce uncertainty of modelled outputs. The respective results made available for a certain use allows understanding if answers to specific user questions can or cannot be supplied reliably. 

To improve the representation of cryospheric processes in models to reduce uncertainties in simulations of climate and predictions of climate change. 

Sensitivity analysis can be used to identify and prioritize key sources of uncertainty or variability. Knowledge of key sources of uncertainty and their relative importance to the assessment end-point is useful in determining whether additional data collection or research would be useful in an attempt to reduce uncertainty. If uncertainty can be reduced in an important model input, then the corresponding uncertainty in the model output would also be reduced. Knowledge of key sources of controllable variability, their relative importance and critical limits is useful in developing risk management options. 

Again, this approach can be applied to substances that are either rich or poor in ecotoxicity data. The prerequisites are knowledge of the soil parameter values influencing bioavailability of the substance in the individual ecotoxicity tests and applicability of the model for the species that is tested. This approach further reduces uncertainty and increases the ecological relevance of the SQS. 

Errors can be introduced through experimental design or the procedures used for measurement and sampling. Such errors can be reduced by adherence to good laboratory practices and adherence to established experimental protocols. Errors also can be introduced during simulation model development. Uncertainty in the development and use of models can be reduced through sensitivity analyses, comparison with similar models, and field validation. 

Hoffman and Hammonds 1994). In addition, standard data distributions have been proposed for a variety of exposure variables, such as age-specific distributions for soil ingestion rates, inhalation rates, body weights, skin surface area, tap water and fish consumption, residential occupancy and occupational tenure, and soil-on-skin adherence (Finley et al. 1994). It should also be pointed out that these techniques can be combined with other advanced risk assessment methods (i.e., PBPK modeling) to further reduce uncertainty in exposure estimates (Cronin et al. 1995 Simon 1997 Nestorov 1999, 2003). 

Pleil, J. D., Kim, D., Prah, J. D., and Rappaport, S. M. (2007). Exposure reconstruction for reducing uncertainty in risk assessment Example using MTBE biomarkers and a simple pharmacokinetic model. Biomarkers 12, 331-348. 

Overall it is concluded that population modeling is of potential use, particularly to refine the risk to terrestrial vertebrates where the risk is very often not sufficiently addressed by refinements focusing only on exposure. Experiments with vertebrates should be reduced to a minimum because of animal welfare considerations. Hence, further testing to reduce uncertainty related to differences in species sensitivity should be avoided as a standard risk refinement option. Field studies investigating effects on population level are cost-intensive, and population modeling may provide a more cost-effective alternative. 

Level 2 In the second level (Level 2), physico-chemical speciation models are introduced in order to correct the toxicity data for chemical availability. Indeed, NOEC and/or ECm values that are used in the effects assessment are generally generated in test media with varying physico-chemical characteristics (e.g. pH, hardness, DOC) known to alter metal availability and toxicity. In case metal concentrations are reported and appropriate speciation models (e.g. WHAM, MINTEQA2, etc.) and relevant input data (i.e. main physico-chemical parameters driving the availability of a metal such as pH, DOC, etc.) are available, NOEC and/or ECm values should be expressed on the basis of the metal species of concern in order to reduce uncertainty. Eor regulatory compliance purposes, the dissolved exposure concentrations should also be translated at the same level of availability (expressed in the same units) as the effects assessment. 

Since ground-water chemistry investigations are under way at a great number of sites for a large number of purposes it is important that one consult the scientific literature critically. In this way, one can gain confidence in various techniques and decisions which reduce uncertainty and improve the reliability of the inputs to chemical modeling. 

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