Six tonnes of discards and recyclables from three waste districts in a New York suburb were sorted in 2012. The districts were chosen because one had a higher recycling percentage, one had median performance, and one was a low performing district. ASTM standards were followed for the waste composition sorting. The results showed, as expected, that the waste district with the highest recycling rate appeared to have the highest separation efficiencies, leading to greater amounts of recyclable materials being source separated. The waste districts also had different overall waste generation, both in terms of the amounts of wastes generated, and their composition. The better recycling district generated less waste, but had a higher percentage of recyclables in the waste stream. Therefore, in some sense, its waste stream was enriched in recyclables. Thus, although the residents of that district recovered materials at a higher rate, they also left large amounts of recyclables in their discardsas did the residents of the other districts. In fact, the districts only recycled between one quarter and less than half of all available recyclables, so that their discards were comprised of up to one third recyclable materials. This level of performance does not appear to be unique to this Town; therefore, we believe that additional recovery efforts through post-collection sorting for recyclables may be warranted.

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N.Y., for maintaining and providing production-well pumpage data.

We demonstrate the application of the Area Metric developed by Ferson et al. (2008) for multimodel validity assessment. The Area Metric quantified the degree of models' replicative validity: the degree of agreement between the observed data and the corresponding simulated outputs represented as their empirical cumulative distribution functions (ECDFs). This approach was used to rank multiple representations of a case study groundwater flow model of a landfill by their Area Metric scores. A multimodel approach allows to account for uncertainties that may either be epistemic (from lack of knowledge) or aleatory (from variability inherent in the system). The Area Metric approach enables explicit incorporation of model uncertainties, epistemic as well as aleatory, into validation assessment. The proposed approach informs understanding of the collected data and that of the model domain. It avoids model overfitting to a particular system state, and in fact is a blind assessment of the models' validity: models are not adjusted, or updated, to achieve a better numerical fit. This approach assesses the degree of models' validity, in place of the typical binary model validation/invalidation process. Collectively, this increases confidence in the model's representativeness that, in turn, reduces risk to model users.