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Single stream recyclables collection (residents set out all recyclables in one container without sorting them further) has become the dominant means of curbside recycling in the United States. Proponents claim it motivates residents to set out more materials for recovery because it is more convenient than other collection strategies; others claim it may also lead to poorer quality materials sent to market. Here we report on a conversion on Long Island (New York) from dual stream collection (paper and container recyclables collected in separate passes) to single stream collection. No other major change was made to the waste program, unlike other documented switches from single stream to dual stream, which means changes we document appear to be due solely to the collection processes change. Waste composition studies before and after the conversion added important information. We found significant (25%) increases in set outs for recycling, but also more inclusion of non-recyclable items in the recycling bins. Residents increased their separation rates of targeted recyclable materials, but because waste composition changed from 2012 to 2014, the increased separation rates resulted in about the same amount of recyclable materials being set aside. However, we estimate that the residual waste stream contained much less recyclable material concurrent with the change to single stream recycling, primarily due to the changes to the waste stream composition and size. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]