Assessing the Risk of Engineered Nanomaterials in the Environment: Modeling Fate, Exposure, and Bioaccumulation

Engineered nanomaterials (ENMs) are a relatively new class of material for which the risks of negative environmental impacts are still being determined. A comprehensive assessment of the environmental risks of ENMs entering the environment is essential, in part due to the continued increase in ENM production and release to the environment. The technical difficulty in measuring ENM fate and toxicity in complex and dynamic environmental media necessitates the use of mathematical models. In this research, the environmental risks of ENMs are assessed through: (i) the collection and analysis of emerging information on significant fate and transport processes; (ii) development of an ENM-specific fate and transport model to predict the accumulation of ENMs and their exposure to organisms in the environment; (iii) development of a statistical model to predict the distribution of species toxicity from specific ENMs in freshwater; and (iv) development of a bioaccumulation model to predict the long-term accumulation of ENMs through a food chain. The NanoFate model, which was developed as part of the research described in this paper, is used to predict the temporal variability in fate across a broad range of complex environmental media at various spatial scales using both traditional fate and transport processes such as advection, deposition, and erosion, but also using ENM-specific processes and transformations such as heteroaggregation, sedimentation, and dissolution. A case study on San Francisco is then used to explore how fate and accumulation may vary among 4 different metallic ENMs, n-CeO2, n-CuO, n-TiO2, and n-ZnO, because the rates of fate processes and the toxicity are known to vary among these four ENMs. Chapter 1 specifically explores how these processes and toxicities vary among different types of ENMs. Chapter 2 explores how species sensitivities vary between different ENMs within a freshwater ecosystem. A species sensitivity distribution (SSD) is a cumulative probability distribution of a chemical’s toxicity measurements obtained from single-species bioassays that can be used to estimate the ecotoxicological impacts of that ENM. The SSD results indicate that size, formulation, and the presence of a coating can alter toxicity, and therefore the corresponding range of toxic concentrations. Chapter 3 describes the development of the NanoFate model and explores the implications of the San Francisco case study. By investigating both the range in rate processes and release scenarios, ENM fate was found to vary by multiple orders of magnitude among different environmental media and that even with an improved understanding of ENM fate, predictions of environmental concentrations are still very uncertain. We compare the predicted environmental concentrations for San Francisco Bay across many different release scenarios with the results of the SSDs and found that while CuO, TiO2, and ZnO are likely to exceed No Observed Effect Concentrations (NOEC) in freshwater, this is not the case for soils. The worst-case scenario, where the predicted concentrations would exceed lethal concentrations (LC50), was not found in any scenario explored within the case study. Chapter 4 explores the range in bioaccumulation that could result from the NanoFate predictions for a freshwater ecosystem. A toxicokinetics model, using as much species-specific and ENM-specific uptake, biotransformation, and elimination rates as were available for CuO, TiO2, and ZnO is used to predict the likelihood of bioconcentration and biomagnification through a simple food chain. Though bioconcentration was found for most species, biomagnification was not predicted to be significant with increasing trophic levels. Uncertainty analysis indicates that these results may vary by as much as two orders of magnitude. A parameter sensitivity analysis highlights key biological and environmental parameters that can be used to focus future research. While further developments will improve these predictions as our understanding of ENM fate and toxicity progresses, current understanding indicates that risk is likely low for most ENMs at predicted environmental concentrations though there is some concern that under high and localized release scenarios, toxic impacts will occur

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Species sensitivity distributions for engineered nanomaterials.

Engineered nanomaterials (ENMs) are a relatively new strain of materials for which little is understood about their impacts. A species sensitivity distribution (SSDs) is a cumulative probability distribution of a chemical's toxicity measurements obtained from single-species bioassays of various species that can be used to estimate the ecotoxicological impacts of a chemical. The recent increase in the availability of acute toxicity data for ENMs enabled the construction of 10 ENM-specific SSDs, with which we analyzed (1) the range of toxic concentrations, (2) whether ENMs cause greater hazard to an ecosystem than the ionic or bulk form, and (3) the key parameters that affect variability in toxicity. The resulting estimates for hazardous concentrations at which 5% of species will be harmed ranged from <1 ug/L for PVP-coated n-Ag to >3.5 mg/L for CNTs. The results indicated that size, formulation, and the presence of a coating can alter toxicity, and thereby corresponding SSDs. Few statistical differences were observed between SSDs of an ENM and its ionic counterpart. However, we did find a significant correlation between the solubility of ENMs and corresponding SSD. Uncertainty in SSD values can be reduced through greater consideration of ENM characteristics and physiochemical transformations in the environment

Assessing the Risk of Engineered Nanomaterials in the Environment: Development and Application of the nanoFate Model

We developed a dynamic multimedia fate and transport model (nanoFate) to predict the time-dependent accumulation of metallic engineered nanomaterials (ENMs) across environmental media. nanoFate considers a wider range of processes and environmental subcompartments than most previous models and considers ENM releases to compartments (e.g., urban, agriculture) in a manner that reflects their different patterns of use and disposal. As an example, we simulated ten years of release of nano CeO₂, CuO, TiO₂, and ZnO in the San Francisco Bay area. Results show that even soluble metal oxide ENMs may accumulate as nanoparticles in the environment in sufficient concentrations to exceed the minimum toxic threshold in freshwater and some soils, though this is more likely with high-production ENMs such as TiO₂ and ZnO. Fluctuations in weather and release scenario may lead to circumstances where predicted ENM concentrations approach acute toxic concentrations. The fate of these ENMs is to mostly remain either aggregated or dissolved in agricultural lands receiving biosolids and in freshwater or marine sediments. Comparison to previous studies indicates the importance of some key model aspects including climatic and temporal variations, how ENMs may be released into the environment, and the effect of compartment composition on predicted concentrations

Linking Exposure and Kinetic Bioaccumulation Models for Metallic Engineered Nanomaterials in Freshwater Ecosystems

We developed a model (nanoBio) to simulate long-term kinetic bioaccumulation of metallic engineered nanomaterials (ENMs) across trophic levels within a freshwater aquatic ecosystem based on current understanding of environmental and biological fate. Seven species were chosen to understand exposure pathways, accumulation through trophic levels, and the potential for biomagnification. Uptake, elimination, and dissolution of the ENM are the only processes modeled, though different routes and rates are accounted for with each species. We explored the bioaccumulation of nCuO, nTiO2, and nZnO. nanoBio estimates the potential range in average body concentration across populations. Estimated bioconcentrations ranged from 1.7 × 10–8 pg nCuO g–1 for Selenastrum capricornutum to 27 μg nTiO2 g–1 for Oncorhynchus mykiss. The highest overall biomagnification was predicted for nTiO2 within the highest trophic level species. ENM dissolution decreases total biomagnification; however, the released metal ions may still cause toxicity. nanoBio results serve to (1) highlight trophic levels at potentially higher risk of bioaccumulation; (2) temporal patterns that influence peaks in concentration; (3) processes which require more experimental data to reduce uncertainty. Based on a sensitivity analysis, the most significant parameters to the variability in estimates include uptake rates from multiple exposure routes and assimilation efficiency, which has a substantial impact on biomagnification. Better understanding of the mechanisms and processes that impact bioaccumulation through targeted laboratory testing will greatly improve the predictive accuracy of nanoBio. We should stress the conditional nature of the rate constants used in this study, because the environment, the biology, and the toxicity itself can alter these parameter values over time. The model also can be used to guide testing protocols to determine key parameter values that influence bioaccumulation

Downscaling approaches of climate change projections for watershed modeling: Review of theoretical and practical considerations

Water resources managers must increasingly consider climate change implications of, whether the concern is floods, droughts, reservoir management, or reliably supplying consumers. Hydrologic and water quality modeling of future climate scenarios requires understanding global climate models (GCMs), emission scenarios and downscaling GCM output, since GCMs generate climate predictions at a resolution too coarse for watershed modeling. Here we present theoretical considerations needed to understand the various downscaling methods. Since most watershed modelers will not be performing independent downscaling, given the resource and time requirements needed, we also present a practical workflow for selecting downscaled datasets. Even given the availability of a number of downscaled datasets, a number of decisions are needed regarding downscaling approach (statistical vs. dynamic), GCMs to consider, options, climate statistics to consider for the selection of model(s) that best predict the historical period, and the relative importance of different climate statistics. Available dynamically-downscaled datasets are more limited in GCMs and time periods considered, but the watershed modeler should consider the approach that best matches the historical observations. We critically assess the existing downscaling approaches and then provide practical considerations (which scenarios and GCMs have been downscaled? What are some of the limitations of these databases? What are the steps to selecting a downscaling approach?) Many of these practical questions have not been addressed in previous reviews. While there is no “best approach” that will work for every watershed, having a systematic approach for selecting the multiple options can serve to make an informed and supportable decision