Ecological Risk Assessment and Quantitative Consequence Analysis

Ecological risk actually refers to two separate things. First, risk to the environment as a result of human activity. Contaminated sites are an example. Second, risk to the biota—flora, fauna, and people—as a result of environmental hazards. Geophysical risk arising from natural hazards is an example. Risk is a combination of likelihoods and consequences. This article examines methods used to quantify the consequences. At the general level, such methods are linked to the methods used to quantify the likelihoods and thus to quantify the risks. It is possible to use the existing frameworks of risk management, health risk assessment, and ecological risk analysis to develop a risk management framework that is suitable for ecological risk assessment. The framework consists of the following steps:

1. Determine concernsby using risk assessment techniques for various scenarios.

2. Identify the consequences by systematically identifying hazards.

3. Undertake calculations by using relevant models.

4. Evaluate certainties, uncertainties, and probabilities involved in the calculations of the vulnerability and of the exposure.

5. Compare with criteriato assess the need for further action.

6. Determine and act on options to control, mitigate, and adapt to the risk.

7. Communicatethe results to those who need to know.

     

Using a Network Model to Assess Risk of Forest Pest Spread via Recreational Travel

Long-distance dispersal pathways, which frequently relate to human activities, facilitate the spread of alien species. One pathway of concern in North America is the possible spread of forest pests in firewood carried by visitors to campgrounds or recreational facilities. We present a network model depicting the movement of campers and, by extension, potentially infested firewood. We constructed the model from US National Recreation Reservation Service data documenting more than seven million visitor reservations (including visitors from Canada) at campgrounds nationwide. This bi-directional model can be used to identify likely origin and destination locations for a camper-transported pest. To support broad-scale decision making, we used the model to generate summary maps for 48 US states and seven Canadian provinces that depict the most likely origins of campers traveling from outside the target state or province. The maps generally showed one of two basic spatial patterns of out-of-state (or out-of-province) origin risk. In the eastern United States, the riskiest out-of-state origin locations were usually found in a localized region restricted to portions of adjacent states. In the western United States, the riskiest out-of-state origin locations were typically associated with major urban areas located far from the state of interest. A few states and the Canadian provinces showed characteristics of both patterns. These model outputs can guide deployment of resources for surveillance, firewood inspections, or other activities. Significantly, the contrasting map patterns indicate that no single response strategy is appropriate for all states and provinces. If most out-of-state campers are traveling from distant areas, it may be effective to deploy resources at key points along major roads (e.g., interstate highways), since these locations could effectively represent bottlenecks of camper movement. If most campers are from nearby areas, they may have many feasible travel routes, so a more widely distributed deployment may be necessary.     

Review of Information Resources to Support Human Exposure Assessment Models

Efforts to model human exposures to chemicals are growing more sophisticated and encompass increasingly complex exposure scenarios. The scope of such analyses has increased, growing from assessments of single exposure pathways to complex evaluations of aggregate or cumulative chemical exposures occurring within a variety of settings and scenarios. In addition, quantitative modeling techniques have evolved from simple deterministic analyses using single point estimates for each necessary input parameter to more detailed probabilistic analyses that can accommodate distributions of input parameters and assessment results. As part of an overall effort to guide development of a comprehensive framework for modeling human exposures to chemicals, available information resources needed to derive input parameters for human exposure assessment models were compiled and critically reviewed. Ongoing research in the area of exposure assessment parameters was also identified. The results of these efforts are summarized and other relevant information that will be needed to apply the available data in a comprehensive exposure model is discussed. Critical data gaps in the available information are also identified. Exposure assessment modeling and associated research would benefit from the collection of additional data as well as by enhancing the accessibility of existing and evolving information resources.     

A Comprehensive Quantitative Assessment of Bird Extinction Risk in Brazil

In an effort to avoid species loss, scientists have focused their efforts on the mechanisms making some species more prone to extinction than others. However, species show different responses to threats given their evolutionary history, behavior, and intrinsic biological features. We used bird biological features and external threats to (1) understand the multiple pathways driving Brazilian bird species to extinction, (2) to investigate if and how extinction risk is geographically structured, and (3) to quantify how much diversity is currently represented inside protected areas. We modeled the extinction risk of 1557 birds using classification trees and evaluated the relative contribution of each biological feature and external threat in predicting extinction risk. We also quantified the proportion of species and their geographic range currently protected by the network of Brazilian protected areas. The optimal classification tree showed different pathways to bird extinction. Habitat conversion was the most important predictor driving extinction risk though other variables, such as geographic range size, type of habitat, hunting or trapping and trophic guild, were also relevant in our models. Species under higher extinction risk were concentrated mainly in the Cerrado Biodiversity Hotspot and were not quite represented inside protected areas, neither in richness nor range. Predictive models could assist conservation actions, and this study could contribute by highlighting the importance of natural history and ecology in these actions.     

Quantitative Microbial Risk Assessment Models for Consumption of Raw Vegetables Irrigated with Reclaimed Water

Quantitative microbial risk assessment models for estimating the annual risk of enteric virus infection associated with consuming raw vegetables that have been overhead irrigated with nondisinfected secondary treated reclaimed water were constructed. We ran models for several different scenarios of crop type, viral concentration in effluent, and time since last irrigation event. The mean annual risk of infection was always less for cucumber than for broccoli, cabbage, or lettuce. Across the various crops, effluent qualities, and viral decay rates considered, the annual risk of infection ranged from 10⁻³ to 10⁻¹ when reclaimed-water irrigation ceased 1 day before harvest and from 10⁻⁹ to 10⁻³ when it ceased 2 weeks before harvest. Two previously published decay coefficients were used to describe the die-off of viruses in the environment. For all combinations of crop type and effluent quality, application of the more aggressive decay coefficient led to annual risks of infection that satisfied the commonly propounded benchmark of ≤10⁻⁴, i.e., one infection or less per 10,000 people per year, providing that 14 days had elapsed since irrigation with reclaimed water. Conversely, this benchmark was not attained for any combination of crop and water quality when this withholding period was 1 day. The lower decay rate conferred markedly less protection, with broccoli and cucumber being the only crops satisfying the 10⁻⁴ standard for all water qualities after a 14-day withholding period. Sensitivity analyses on the models revealed that in nearly all cases, variation in the amount of produce consumed had the most significant effect on the total uncertainty surrounding the estimate of annual infection risk. The models presented cover what would generally be considered to be worst-case scenarios: overhead irrigation and consumption of vegetables raw. Practices such as subsurface, furrow, or drip irrigation and postharvest washing/disinfection and food preparation could substantially lower risks and need to be considered in future models, particularly for developed nations where these extra risk reduction measures are more common.     

Caramel Color in Soft Drinks and Exposure to 4-Methylimidazole: A Quantitative Risk Assessment

Caramel color is added to many widely-consumed beverages as a colorant. Consumers of these beverages can be exposed to 4-methylimidazole (4-MEI), a potential carcinogen formed during its manufacture. California’s Proposition 65 law requires that beverages containing 4-MEI concentrations corresponding to exposures that pose excess cancer risks > 1 case per 100,000 exposed persons (29 μg 4-MEI/day) carry warning labels. Using ultrahigh-performance liquid chromatography-tandem mass spectrometry, we assessed 4-MEI concentrations in 12 beverages purchased in California and a geographically distant metropolitan area (New York) in which warning labels are not required. In addition, we characterized beverage consumption by age and race/ethnicity (using weighted means calculated from logistic regressions) and assessed 4-MEI exposure and resulting cancer risks and US population cancer burdens attributable to beverage consumption. Data on beverage consumption were obtained from the National Health and Nutrition Examination Survey, dose-response data for 4-MEI were obtained from the California Environmental Protection Agency Office of Environmental Health Hazards Assessment, and data on population characteristics were obtained from the U.S. Census Bureau. Of the 12 beverages, Malta Goya had the highest 4-MEI concentration (915.8 to 963.3μg/L), lifetime average daily dose (LADD - 8.04x10^-3 mg/kgBW-day), lifetime excess cancer risk (1.93x10^-4) and burden (5,011 cancer cases in the U.S. population over 70 years); Coca-Cola had the lowest value of each (4-MEI: 9.5 to 11.7μg/L; LADD: 1.01x10^-4 mg/kgBW-day; risk: 1.92x10^-6; and burden: 76 cases). 4-MEI concentrations varied considerably by soda and state/area of purchase, but were generally consistent across lots of the same beverage purchased in the same state/area. Routine consumption of certain beverages can result in 4-MEI exposures > 29 μg/day. State regulatory standards appear to have been effective in reducing exposure to carcinogens in some beverages. Federal regulation of 4-MEI in caramel color may be appropriate.     

A Quantitative Assessment of Risk for the Importation of Camels into New Zealand

Introduced species, particularly mammals, have caused significant damage to New Zealand's environment. In 1996 specific legislation was put in place to manage intentional introductions. In 2008 approval was sought to import 16 camels (Camelus dromedarius) into New Zealand for the purpose of establishing a trekking company. This article provides a summary of the quantitative assessment undertaken on the risks of importing camels. It was found that camels posed a low risk to public health but had a serious risk of establishing a wild population in New Zealand. The risk of camels becoming a pest was assessed as moderate. Overall, although the assessment ranked camels as a serious threat to New Zealand, this likely reflects the overly conservative nature of the assessment tool.     

Applying Mental Models to Qualitative Risk Assessment at the Tar Creek Superfund Site

Re-suspension of heavy metal residuals generated by former mining activities conducted at the Tar Creek Superfund site may result in exposures of nearby residents. Currently, remediation and mitigation activities include removal of yard soil containing high concentrations of heavy metals, removal of selected chat piles, and providing assistance for families with young children. Although these are important activities to reduce potential risks to Tar Creek residents, a qualitative assessment of the site that uses mental models identifies other potential contaminants of concern, transport, and exposure pathways that may require further investigation. Mental models structure judgments about hazards and risk by visually representing a contaminated site through the use of influence diagrams. A mental model was applied to the Mayer Ranch area of the Tar Creek Superfund site in Ottawa County, Oklahoma, to support a qualitative assessment of the health risks from heavy metals exposure. Documents were reviewed to develop influence diagrams illustrating the processes, interactions, and potential exposures that could subject a hypothetical individual recreating at Mayer Ranch to various health risks. The diagrams were modified to reflect expert judgments. The mental model includes four influence diagrams: source term, fate, and transport processes that may influence human health risks due to exposure to heavy metals; natural wetland processes that could remove heavy metals; net alkaline constructed wetland processes that could remove heavy metals; and potential future impacts from the removal of sediment and land development that could increase risk. Policies, regulations, and technologies that could most reduce the probability of exposures can be determined and evaluated by focusing on the environmental transport pathways and contaminants that pose the greatest potential risks and determining the difference between the risks. Also evident are the knowledge gaps with respect to the processes within wetlands.     

Ethical Issues in the Use of Data from Testing of Human Subjects to Support Risk Assessment

The public understands and supports the ethical use of human subjects in medical research, recognizing the unique role for this type of study in the development of new drugs and therapeutic strategies for treatment of disease. The use of data from human subjects can also be of value in understanding the circumstances under which individuals exposed to chemicals in the food supply, in the workplace, or in the environment might experience toxicity, i.e., in support of risk assessment. However, questions have been raised as to whether this latter type of research is ethical, or can be performed in an ethical manner. Under what circumstances is it acceptable to intentionally expose human subjects to potentially toxic agents? This is an extremely important issue for the risk assessment community to address, because it affects in a fundamental way the types of information that will be available to conduct human health risk assessments. Four papers in this issue offer viewpoints on the value of human data, the circumstances under which human subjects might be exposed to toxic chemicals for research purposes, the ethical problems associated with this research, and the role of human vs. animal data in the development of toxicity values for human health risk assessment     

Liposome-Mediated Delivery of Radionuclides to Tumor Models for Cancer Radiotherapy: A Quantitative Analysis

Abstract

Towards the design of therapeutically effective liposome-radionuclide conjugates, the predominant focus should rest with the ability of such modalities to efficiently target tumor sites and thus selectively deliver cytotoxic levels of radiation doses. For this reason analytic dosimetric calculations were carried out to quantitatively examine the critical physical parameters for the potential clinical application of radionuclide-liposome conjugates in internal radiotherapy. The radiodosimetric model employed followed the mathematical formalism of the MIRD (Medical Internal Radiation Dose Committee) scheme. Analytic pharmacokinetic functions for a variety of liposome constructs coupled with the radiation properties of three of the most promising particle emitting radionuclides: Cu-67, Re-188, At-211 and the most widely used in the clinic 1-131, were used as input information to the model developed. Results are presented in the form of radiation absorbed doses and tumor-to-normal-tissue radiation ratios. It is concluded that liposome-mediated radionuclide tumor targeting for radiotherapy is certainly promising, and critically dependent on the optimal matching between radionuclide half-life and the time range when the tumor-to-(critical)organ liposome accumulation ratios become maximal. Liposome-mediated chemotherapy (drug targeting) is also comparatively discussed demonstrating the predominant importance of “timing factors” in the case of radiotherapeutic (radionuclide targeting) applications.     

Quantitative Consideration of Ecosystem Characteristics in an Ecological Risk Assessment: A Case Study

Development of assessment endpoints and conceptual models aids ecological risk assessors in identifying measurable attributes that will allow quantification and prediction of risk. Measures of exposure and effect are explicitly considered, usually quantitatively, in nearly every ecological risk assessment, while measures of ecosystem characteristics are generally addressed only implicitly, if at all. Yet these characteristics influence both the behavior and location of assessment endpoint entities and the spatial and temporal distribution of stressors. This case study illustrates use of a regression partitioning model to quantify the influence of ecosystem characteristics (e.g., land use patterns, nutrient concentrations) on the concentration of a chemical stressor (atrazine) in surface waters of a large river basin. The model partitioned the basin into five land use groups ranging from High Forested to Very High Agriculture. Literature-derived chronic effects data were used with a joint-probability model to characterize atrazine risk to an aquatic assessment entity in each of these land use subgroups. Atrazine concentrations and risk directly correlated with the intensity of agricultural land use. This permits risk management to focus on agricultural areas within the basin; a focus that would not have been possible without explicitly considering ecosystem characteristics.     

Quantitative Assessment of Thermodynamic Constraints on the Solution Space of Genome-Scale Metabolic Models

Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations.     

Evaluation of Software for Propagating Uncertainty Through Risk Assessment Models

Quantitative uncertainty analysis has become a common component of risk assessments. In risk assessment models, the most robust method for propagating uncertainty is Monte Carlo simulation. Many software packages available today offer Monte Carlo capabilities while requiring minimal learning time, computational time, and/or computer memory. This paper presents an evalu ation of six software packages in the context of risk assessment: Crystal Ball, @Risk, Analytica, Stella II, PRISM, and Susa-PC. Crystal Ball and @Risk are spreadsheet based programs; Analytica and Stella II are multi-level, influence diagram based programs designed for the construction of complex models; PRISM and Susa-PC are both public-domain programs designed for incorpo rating uncertainty and sensitivity into any model written in Fortran. Each software package was evaluated on the basis of five criteria, with each criterion having several sub-criteria. A ‘User Preferences Table’ was also developed for an additional comparison of the software packages. The evaluations were based on nine weeks of experimentation with the software packages including use of the associated user manuals and test of the software through the use of example problems. The results of these evaluations indicate that Stella II has the most extensive modeling capabilities and can handle linear differential equations. Crystal Ball has the best input scheme for entering uncertain parameters and the best reference materials. @Risk offers a slightly better standard output scheme and requires a little less learning time. Susa-PC has the most options for detailed statistical analysis of the results, such as multiple options for a sensitivity analysis and sophisticated options for inputting correlations. Analytica is a versatile, menu- and graphics-driven package, while PRISM is a more specialized and less user friendly program. When choosing between software packages for uncertainty and sensitivity analysis, the choice largely depends on the specifics of the problem being modeled. However, for risk assessment problems that can be implemented on a spreadsheet, Crystal Ball is recommended because it offers the best input options, a good output scheme, adequate uncertainty and sensitivity analysis, superior reference materials, and an intuitive spreadsheet basis while requiring very little memory.     

Quantitative Assessment of the Association Between MS Gene Polymorphism and Colorectal Cancer Risk

Accumulating genetic association studies have investigated the risk of colorectal cancer (CRC) in relation to MS gene polymorphism with uncertain conclusions. In the current study, we sought to assess the association between MS gene and CRC. We performed an updated meta-analysis including 18 case-control studies with a total of 10, 303 CRC patients and 15, 389 CRC-free controls to estimate the strength of the association using odds ratios with the corresponding 95 % confidence intervals. Overall, no CRC risk associated with the genotypes of MS gene polymorphism was indicated in our meta-analysis. Similarly, the stratified analysis according to ethnicity and control source did not show any evident association either. The results of our updated meta-analysis suggest that MS gene polymorphism may not serve as a biomarker for the CRC risk. Future large-scale and well-designed studies are required to clarify the association identified in the present meta-analysis.