Eric Money

Modeling approaches for characterizing and evaluating environmental exposure to engineered nanomaterials in support of risk-based decision making.

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.

Rhesus monkey rhadinovirus (RRV): construction of a RRV-GFP recombinant virus and development of assays to assess viral replication.

Rhesus monkey rhadinovirus (RRV) is a gamma-2-herpesvirus that is closely related to Kaposis 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.

Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey.

Understanding surface water quality is a critical step towards protecting human health and ecological stability. Because of resource deficiencies and the large number of river miles needing assessment, there is a need for a methodology that can accurately depict river water quality where data do not exist. The objective of this research is to implement a methodology that incorporates a river metric into the space/time analysis of dissolved oxygen data for two impaired river basins. An efficient algorithm is developed to calculate river distances within the BMElib statistical package for space/time geostatistics. We find that using a river distance in a space/time context leads to an appreciable 10% reduction in the overall estimation error, and results in maps of DO that are more realistic than those obtained using a Euclidean distance. As a result river distance is used in the subsequent non-attainment assessment of DO for two impaired river basins in New Jersey.

Geostatistical space/time estimation of water quality along the Raritan River Basin in New Jersey

The assessment of the river water quality across space and time is a considerable public health concern and it is an important issue for the efficient management of our natural water resources. The state of New Jersey is mandated by the federal Clean Water Act to assess water quality along all streams and rivers in the state, which is critical to designate use attainment and to direct total maximum daily load (TMDL) development. However due to budget and scientific limitations less than 30% of the states non-tidal stream miles have been assessed. Therefore there is a need to develop a method that can use the partial monitoring information available to estimate water quality along the unmonitored network of streams and rivers. However the high natural variability of water quality over space and time, the limited number of water samples, and the varying levels of measurement errors between samples introduce major sources of uncertainty in the estimation of water quality along rivers and over time. In this work we present the Bayesian Maximum Entropy (BME) framework to rigorously process information about the space/time variability of water quality in its aquatic environment, the uncertainty and scarcity of the monitoring data, and the relevant flow and transport governing laws, in order to obtain statistical estimate of water quality at unmonitored reaches. We implement the BME method for a case study involving the estimation of phosphate along the Raritan river basin from 1990 to 2002, and we find through cross validation that the BME space/time analysis is a substantial improvement over a purely spatial analysis.