We can infer from this that the latitude of uncertainty brought about by myriads of factors lend a propensity to appropriate the measure in accordance with one's interest or advocacy.
However, we do not preclude the validity of the concerns of the citizens. As noted by Contini, et al (1991), a risk analysis on the accidental release of ammonia conducted by teams of scientist from eleven European countries resulted in eleven risk estimates whose numerical results were dictated or dependent on many assumptions introduced during every step of the risk analysis.
In presentation as to the uses, limitation and abuses of risk assessment, risk assessments are being used as tool/proof to advance technologies as hard science using unrealistic assumptions which are kept hidden and not stated openly (Howard). This gives credence to the citizen's concern that there seem to be blind adherence or faith in assessments masked as hard science but oftentimes are based on unrealistic assumptions. So their clamor for validly tested models in assessing population exposure seems justifiable on this account.
However, requiring that exposure assessments be based only on validated models ...
n prediction simply because of the multiplicity of specific variability and hence the model can be applied only to individual data sets due to "many community-specific characteristics which may be difficult to quantify" (Environment Protection Agency [EPA], 1994).
However, as applied to environmental protection the presence or probability of scientific uncertainty cannot be used as an excuse in delaying programs for preventing hazards to environment as promulgated by the Precautionary Principle, particularly Principle 15 (Rio Declaration, 1992). It means that even if there is no scientifically established relationship of cause and effect precautionary measures need to be undertaken when there is threat to human health or the environment (Wingspread Statement)
How can it be determined if the assumptions made about the population's exposure are valid One key or possibility of approach can be gleaned from The reliability of modeled estimates of chemical concentration in the general environment depends on how well the model assumptions match reality (i.e. how realistic are the assumptions such as steady-state conditions and homogenous media properties), whether the model performance has been demonstrated under conditions similar to those of concern; and the quantity and quality of input data (Principles). This could be the basic principle in determining whether an assumption is reliable - whether they match actual or real situations or conditions. And for every area of population exposure or assessment, parameters need to be established by which an assumption can be measured as approximating reality.
However, in other, if not most, assessment of population exposure assumptions may not indeed be realistic. In such situation an assumption may be unrealistic, but