As the use of engineered nanomaterials becomes more prevalent, the likelihood of unintended exposure to these materials also increases. Given the current scarcity of experimental data regarding fate, transport, and bioavailability, determining potential environmental exposure to these materials requires an in depth analysis of modeling techniques that can be used in both the near- and long-term. Here, we provide a critical review of traditional and emerging exposure modeling approaches to highlight the challenges that scientists and decision-makers face when developing environmental exposure and risk assessments for nanomaterials. We find that accounting for nanospecific properties, overcoming data gaps, realizing model limitations, and handling uncertainty are key to developing informative and reliable environmental exposure and risk assessments for engineered nanomaterials. We find methods suited to recognizing and addressing significant uncertainty to be most appropriate for near-term environmental exposure modeling, given the current state of information and the current insufficiency of established deterministic models to address environmental exposure to engineered nanomaterials.

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Escherichia coli (E.coli) is a widely used indicator of fecal contamination in water bodies. External contact and subsequent ingestion of bacteria coming from fecal contamination can lead to harmful health effects. Since E.coli data are sometimes limited, the objective of this study is to use secondary information in the form of turbidity to improve the assessment of E.coli at un-monitored locations. We obtained all E.coli and turbidity monitoring data available from existing monitoring networks for the 2000 -2006 time period for the Raritan River Basin, New Jersey. Using collocated measurements we developed a predictive model of E.coli from turbidity data. Using this model, soft data are constructed for E.coli given turbidity measurements at 739 space/time locations where only turbidity was measured. Finally, the Bayesian Maximum Entropy (BME) method of modern space/ time geostatistics was used for the data integration of monitored and predicted E.coli data to produce maps showing E.coli concentration estimated daily across the river basin. The addition of soft data in conjunction with the use of river distances reduced estimation error by about 30%. Furthermore, based on these maps, up to 35% of river miles in the Raritan Basin had a probability of E.coli impairment greater than 90% on the most polluted day of the study period.

Rhesus monkey rhadinovirus (RRV) is a gamma-2-herpesvirus that is closely related to Kaposi's sarcoma-associated herpesvirus (KSHV/HHV-8). Lack of an efficient culture system to grow high titers of virus, and the lack of an in vivo animal model system, has hampered the study of KSHV replication and pathogenesis. RRV is capable of replicating to high titers on fibroblasts, thus facilitating the construction of recombinant rhadinoviruses. In addition, the ability to experimentally infect naïve rhesus macaques with RRV makes it an excellent model system to study gamma-herpesvirus replication. Our study describes, for the first time, the construction of a GFP-expressing RRV recombinant virus using a traditional homologous recombination strategy. We have also developed two new methods for determining viral titers of RRV including a traditional viral plaque assay and a quantitative real-time PCR assay. We have compared the replication of wild-type RRV with that of the RRV-GFP recombinant virus in one-step growth curves. We have also measured the sensitivity of RRV to a small panel of antiviral drugs. The development of both the recombination strategy and the viral quantitation assays for RRV will lay the foundation for future studies to evaluate the contribution of individual genes to viral replication both in vitro and in vivo.

The Newport River Estuary (NPRE) is a high priority shellfish harvesting area in eastern North Carolina (NC) that is impaired due to fecal contamination, specifically exceeding recommended levels for fecal coliforms. A hydrologic-driven mean trend model was developed, as a function of antecedent rainfall, in the NPRE to predict levels of E. coli (EC, measured as a proxy for fecal coliforms). This mean trend model was integrated in a Bayesian Maximum Entropy (BME) framework to produce informative Space/Time (S/T) maps depicting fecal contamination across the NPRE during winter and summer months. These maps showed that during dry winter months, corresponding to the oyster harvesting season in NC (October 1 st to March 30 th ), predicted EC concentrations were below the shellfish harvesting standard (14 MPN per 100 ml). However, after substantial rainfall 3.81 cm (1.5 inches), the NPRE did not appear to meet this requirement. Warmer months resulted in the predicted EC concentrations exceeding the threshold for the NPRE. Predicted ENT concentrations were generally below the recreational water quality threshold (104 MPN per 100 ml), except for warmer months after substantial rainfall. Once established, this combined approach produces near real-time visual information on which to base water quality management decisions.