Challenges and Research Needs for Risk Assessment of Pesticides for Registration in Africa

Risk assessment is necessary for registration and risk management of new pesticides. The aim of this article is to discuss challenges that risk assessors in Africa face when conducting risk assessment of pesticides. Risk assessment requires toxicity assessment, environmental fate studies, and the use of models for occupational, dietary, residential, and environmental exposure assessments. Toxicity studies are very costly with the result that toxicity data used to register pesticides in Africa are often sourced from northern hemisphere countries. Assessors also often use exposure modeling results from the northern hemisphere. This is not an ideal approach as occupational exposure is influenced by agricultural practices, climatic conditions, and other factors. Furthermore, residential exposure models require time-location-activity information, exposure factors, and toxicokinetic rate constants for particular pesticides. Dietary exposure assessment needs accurate and comprehensive local food consumption data. Authorities in African countries should therefore generate the required data, despite these being very costly and tedious. Authorities should also provide guidance on the type of models and standard scenarios for estimating predicted environmental concentrations in various environmental compartments. It is recommended that higher educational institutions in Africa should incorporate risk assessment in general and pesticide toxicity and exposure models in particular in their curricula.     

Ecological Risk Assessment and Regulation for Genetically-Modified Ornamental Plants

Classic plant breeding has increased the beauty and utility of ornamental plants, but biotechnology can offer completely new traits for plants used in homes and gardens. The creation of blue petal color in carnations and roses are examples where biotechnology has created novelty that conventional hybridization cannot match. However, all innovations have benefits and risks, and future commercialization of transgenic ornamental plants raises complex questions about potential negative impacts to managed landscapes and natural ecosystems. Predictive ecological risk assessment is a process that uses current knowledge to estimate future environmental harms or benefits arising from direct or indirect exposure to a genetically-modified (GM) plant, its genes, or gene products. This article considers GM ornamental plants in the context of current ecological risk assessment principles, research results, and current regulatory frameworks. The use of ecological risk assessment by government agencies to support decision-making is reviewed in the context of ornamental plants. Government risk assessments have usually emphasized the potential for pollen-mediated gene flow, weediness in managed areas, invasion of natural areas, and direct harm to nontarget organisms. Some of the major challenges for predictive risk assessment include characterizing gene flow over time and space, plant fitness in changing environments, and impacts to nontarget organisms, communities and ecosystems. The lack of baseline information about the ecology and biodiversity of urban areas, gardens, and natural ecosystems limits the ability to predict potential hazards, identify exposure pathways, and design hypothesis-driven research. The legacy of introduced ornamental plants as invasive species generates special concern about future invasions, especially for GM plants that exhibit increased stress tolerance or adaptability. While ecological risk assessments are a valuable tool and have helped harmonize regulation of GM plants, they do not define the acceptable level of risk or uncertainty. That responsibility belongs to regulators, stakeholders and citizens.     

The 3MRA Risk Assessment Framework—A Flexible Approach for Performing Multimedia,Multipathway, and Multireceptor Risk Assessments Under Uncertainty

A flexible framework for conducting nationwide multimedia, multipathway and multireceptor risk assessments (3MRA) under uncertainty was developed to estimate protective chemical concentration limits in a source area. The framework consists of two components: risk assessment and uncertainty analysis. The risk component utilizes linked source, fate/transport, exposure and risk assessment models to estimate the risk exposures for the receptors of concern. Both human and ecological receptors are included in the risk assessment framework. The flexibility of the framework is based on its ability to address problems varying in spatial scales from site-specific to regional and even national levels; and its ability to accommodate varying types of source, fate/transport, exposure and risk assessment models. The uncertainty component of the 3MRA framework is based on a two-stage Monte Carlo methodology. It allows the calculation of uncertainty in risk estimates, and the incorporation of the effects of uncertainty on the determination of regulatory concentration limits as a function of variability and uncertainty in input data, as well as potential errors in fate and transport and risk and exposure models. The framework can be adapted to handle a wide range of multimedia risk assessment problems. Two examples are presented to illustrate its use, and to demonstrate how regulatory decisions can be structured to incorporate the uncertainty in risk estimates.     

Role of Ecological Modeling in Risk Assessment

Ecological models are useful tools for evaluating the ecological significance of observed or predicted effects of toxic chemicals on individual organisms. Current risk estimation approaches using hazard quotients for individual-level endpoints have limited utility for assessing risks at the population, ecosystem, and landscape levels, which are the most relevant indicators for environmental management. In this paper, we define different types of ecological models, summarize their input and output variables, and present examples of the role of some recommended models in chemical risk assessments. A variety of population and ecosystem models have been applied successfully to evaluate ecological risks, including population viability of endangered species, habitat fragmentation, and toxic chemical issues. In particular, population models are widely available, and their value in predicting dynamics of natural populations has been demonstrated. Although data are often limited on vital rates and doseresponse functions needed for ecological modeling, accurate prediction of ecological effects may not be needed for all assessments. Often, a comparative assessment of risk (e.g., relative to baseline or reference) is of primary interest. Ecological modeling is currently a valuable approach for addressing many chemical risk assessment issues, including screening-level evaluations.     

Integrated Risk Assessment — Results from an International Workshop

The World Health Organization's International Programme on Chemical Safety and international partners have developed a framework for integrated assessment of human health and ecological risks and four case studies. An international workshop was convened to consider how ecological and health risk assessments might be integrated, the benefits of and obstacles to integration, and the research and mechanisms needed to facilitate implementation of integrated risk assessment. Using the case studies, workshop participants identified a number of opportunities to integrate the assessment process. Improved assessment quality, efficiency, and predictive capability were considered to be principal benefits of integration. Obstacles to acceptance and implementation of integrated risk assessment included the disciplinary and organizational barriers between ecological and health disciplines. A variety of mechanisms were offered to overcome these obstacles. Research recommendations included harmonization of exposure characterization and surveillance methods and models, development of common risk endpoints across taxa, improved understanding of mechanisms of effect at multiple scales of biological organization, and development of methods to facilitate comparison of risks among endpoints.     

Statistical models for low dose exposure

Extrapolation of health risks from high to low doses has received a considerable amount of attention in carcinogenic risk assessment over decades. Fitting statistical dose-response models to experimental data collected at high doses and use of the fitted model for estimating effects at low doses lead to quite different risk predictions. Dissatisfaction with this procedure was formulated both by toxicologists who saw a deficit of biological knowledge in the models as well as by risk modelers who saw the need of mechanistically-based stochastic modeling. This contribution summarizes the present status of low dose modeling and the determination of the shape of dose-response curves. We will address the controversial issues of the appropriateness of threshold models, the estimation of no observed adverse effect levels (NOAEL), and their relevance for low dose modeling. We will distinguish between quantal dose-response models for tumor incidence and models of the more informative age/time dependent tumor incidence. The multistage model and the two-stage model of clonal expansion are considered as dose-response models accounting for biological mechanisms. Problems of the identifiability of mechanisms are addressed, the relation between administered dose and effective target dose is illustrated by examples, and the recently proposed Benchmark Dose concept for risk assessment is presented with its consequences for mechanistic modeling and statistical estimation.     

Risk analysis and management decisions for weed biological control agents: Ecological theory and modeling results

Biologically based control methods offer many advantages for the control of invasive plant species; however, these methods are not without risks to native species. Thus, there is a need for more effective and efficient methods of risk analysis for biological control agents. We show how the process of ecological risk assessment established by the United States’ Environmental Protection Agency may be adapted to improve assessment of the risks of proposed biological control agents. We discuss the risks posed by weed biological control agents, and present a simple individual-based model of herbivorous insect movement and oviposition on two species of host plant, a target invasive plant species and a non-target native species, in simulated landscapes. The model shows that risks of non-target impacts may be influenced by the details of the movement behavior of biological control agents in heterogeneous landscapes. The specific details of insect movement that appear to be relevant are readily measured in field trials and the general modeling approach is readily adapted to real landscapes. Current biological control risk assessments typically emphasize effects analysis at the expense of exposure analysis; the modeling approach presented here provides a simple and feasible way to incorporate exposure analyses. We conclude that models such as ours should be given serious consideration as part of a comprehensive strategy of risk assessment for proposed weed biological control agents.     

Risk analysis and management decisions for weed biological control agents: Ecological theory and modeling results

Biologically based control methods offer many advantages for the control of invasive plant species; however, these methods are not without risks to native species. Thus, there is a need for more effective and efficient methods of risk analysis for biological control agents. We show how the process of ecological risk assessment established by the United States’ Environmental Protection Agency may be adapted to improve assessment of the risks of proposed biological control agents. We discuss the risks posed by weed biological control agents, and present a simple individual-based model of herbivorous insect movement and oviposition on two species of host plant, a target invasive plant species and a non-target native species, in simulated landscapes. The model shows that risks of non-target impacts may be influenced by the details of the movement behavior of biological control agents in heterogeneous landscapes. The specific details of insect movement that appear to be relevant are readily measured in field trials and the general modeling approach is readily adapted to real landscapes. Current biological control risk assessments typically emphasize effects analysis at the expense of exposure analysis; the modeling approach presented here provides a simple and feasible way to incorporate exposure analyses. We conclude that models such as ours should be given serious consideration as part of a comprehensive strategy of risk assessment for proposed weed biological control agents.     

Bayesian Updating of Model-Based Risk Estimates Using Imperfect Public Health Surveillance Data

Model-based estimation of the human health risks resulting from exposure to environmental contaminants can be an important tool for structuring public health policy. Due to uncertainties in the modeling process, the outcomes of these assessments are usually probabilistic representations of a range of possible risks. In some cases, health surveillance data are available for the assessment population over all or a subset of the risk projection period and this additional information can be used to augment the model-based estimates. We use a Bayesian approach to update model-based estimates of health risks based on available health outcome data. Updated uncertainty distributions for risk estimates are derived using Monte Carlo sampling, which allows flexibility to model realistic situations including measurement error in the observable outcomes. We illustrate the approach by using imperfect public health surveillance data on lung cancer deaths to update model-based lung cancer mortality risk estimates in a population exposed to ionizing radiation from a uranium processing facility.     

Hierarchical Activity Model for Risk‐Based Decision Making

For the practical implementation of the assessment of environmental impact, actual procedures and data requirements should be clarified so that industrial decision makers understand them. Researchers should consider local risks related to processes and environmental impact throughout the life cycle of products simultaneously to supervise these adverse effects appropriately. Life cycle assessment (LCA) is a useful tool for quantifying the potential impact associated with a product life cycle. Risk assessment (RA) is a widely used tool for identifying chemical risks in a specific situation. In this study, we integrate LCA and RA for risk‐based decision making by devising a hierarchical activity model using the type‐zero method of integrated definition language (IDEF0). The IDEF0 activity modeling language has been applied to connect activities with information flows. Process generation, evaluation, and decision making are logically defined and visualized in the activity model with the required information. The activities, information flows, and their acquisitions are revealed, with a focus on which data should be collected by on‐site engineers. A case study is conducted on designing a metal cleaning process reducing chemical risks due to the use of a cleansing agent. LCA and RA are executed and applied effectively on the basis of integrated objective settings and interpretation. The proposed activity model can be used as a foundation to incorporate such assessments into actual business models.     

The Use of Epidemiology in Risk Assessment: Challenges and Opportunities

The assessment of risk from environmental and occupational exposures incorporates and synthesizes data from a variety of scientific disciplines including toxicology and epidemiology. Epidemiological data have offered valuable contributions to the identification of human health hazards, estimation of human exposures, quantification of the exposure–response relation, and characterization of risks to specific target populations including sensitive populations. As with any scientific discipline, there are some uncertainties inherent in these data; however, the best human health risk assessments utilize all available information, characterizing strengths and limitations as appropriate. Human health risk assessors evaluating environmental and occupational exposures have raised concerns about the validity of using epidemiological data for risk assessment due to actual or perceived study limitations. This article highlights three concerns commonly raised during the development of human health risk assessments of environmental and occupational exposures: (a) error in the measurement of exposure, (b) potential confounding, and (c) the interpretation of non-linear or non-monotonic exposure–response data. These issues are often the content of scientific disagreement and debate among the human health risk assessment community, and we explore how these concerns may be contextualized, addressed, and often ameliorated.     

Genotoxicity risk assessment: a proposed classification strategy

Recent advances in genetic toxicity (mutagenicity) testing methods and in approaches to performing risk assessment are prompting a renewed effort to harmonize genotoxicity risk assessment across the world. The US Environmental Protection Agency (EPA) first published Guidelines for Mutagenicity Risk Assessment in 1986 that focused mainly on transmissible germ cell genetic risk. Somatic cell genetic risk has also been a risk consideration, usually in support of carcinogenicity assessments. EPA and other international regulatory bodies have published mutagenicity testing requirements for agents (pesticides, pharmaceuticals, etc.) to generate data for use in genotoxicity risk assessments. The scheme that follows provides a proposed harmonization approach in which genotoxicity assessments are fully developed within the risk assessment paradigm used by EPA, and sets out a process that integrates newer thinking in testing battery design with the risk assessment process. A classification strategy for agents based on inherent genotoxicity, dose-responses observed in the data, and an exposure analysis is proposed. The classification leads to an initial level of concern for genotoxic risk to humans. A total risk characterization is performed using all relevant toxicity data and a comprehensive exposure evaluation in association with the genotoxicity data. The result of this characterization is ultimately used to generate a final level of concern for genotoxic risk to humans. The final level of concern and characterized genotoxicity risk assessment are communicated to decision makers for possible regulatory action(s) and to the public.     

What Can Be Done to Ensure the Usefulness of Epidemiologic Data for Risk Assessment Purposes?

Epidemiologic studies can play a central role in risk assessments. They are used in all risk assessment phases: hazard identification, dose-response, and exposure assessment. Epidemiologic studies have often been the first to show that a particular environmental exposure is a hazard to health. They have numerous advantages with respect to other sources of data which are used in risk assessments, the most important being that they do not require the assumption that they are generalizable to humans. For this reason, fewer and lower uncertainty factors may be appropriate in risk characterization based on epidemiologic studies. Unfortunately, epidemiologic studies have numerous problems, the most important being that the exposures are often not precisely measured. This article presents in detail the advantages of and problems with epidemiologic studies. It discusses two approaches to ensure their usefulness, biomarkers and an ordinance which requires baseline and subsequent surveillance of possible exposures and health effects from newly sited potentially polluting facilities. Biomarkers are biochemical measures of exposure, susceptibility factors, or preclinical pathological changes. Biomarkers are a way of dealing with the problems of poor measures, differential susceptibility and lack of early measures of disease occurrence that inherent in many environmental epidemiologic studies. The advantages of biomarkers is they can provide objective information on exposure days, months or even years later and evidence of pathology perhaps years earlier. The ordinance makes possible the use of a powerful epidemiologic study design, the prospective cohort study, where confounder(s) are best measured, and exposures, pathological changes, and health effects can be detected as soon as possible.     

Are all distributions of risk with the same 95thpercentile equally acceptable?

When evaluating a probabilistic health risk assessment, say at a hazardous waste site, risk managers need a risk management policy that distinguishes an acceptable distribution of risks to individuals in a population from an unacceptable one. If a risk manager decides that the distribution of risk for the status quo is unacceptable, then a risk assessor needs a way to compute cleanup targets, i.e., the risk assessor needs a policy statement against which to estimate distributions of exposure point concentrations which, if engineered at a site, will achieve an acceptable distribution of risk. Some regulatory agencies base acceptability on whether the 95^th percentile of the risk distribution falls at or below a given value, without considering the behavior of the rest of the distribution. As regulatory agencies adopt risk management policies for use with probabilistic risk assessments, we recommend that they base their new policies on two simultaneously binding constraints‐one on an upper percentile and one on the arithmetic mean of the distribution of risk‐in addition to other non‐risk criteria.     

A Simulation-Assessment Modeling Approach for Analyzing Environmental Risks of Groundwater Contamination at Waste Landfill Sites

An integrated simulation-assessment modeling approach for analyzing environmental risks of groundwater contamination is proposed in this paper. It incorporates an analytical groundwater solute transport model, an exposure dose model, and a fuzzy risk assessment model within a general framework. The transport model is used for predicting contaminant concentrations in subsurface, and the exposure dose model is used for calculating contaminant ingestion during the exposure period under given exposure pathways. Both models are solved through the Monte Carlo simulation technique to reflect the associated uncertainties. Based on consideration of fuzzy relationships between exposure doses and cancer risks, risk levels of different exposure doses for each contaminant can be calculated to form a fuzzy relation matrix. The overall risks can then be quantified through further fuzzy synthesizing operations. Thus, probabilistic quantification of different risk levels (possibilities) can be realized. Results of the case study indicate that environmental risks at the waste landfill site can be effectively analyzed through the developed methodology. They are useful for supporting the related risk-management and remediation decisions.     

Improving Risk Assessment: Priorities for Epidemiologic Research

The Epidemiology Work Group at the Workshop on Future Research for Improving Risk Assessment Methods, Of Mice, Men, and Models, held August 16 to 18, 2000, at Snowmass Village, Aspen, Colorado, concluded that in order to improve the utility of epidemiologic studies for risk assessment, methodologic research is needed in the following areas: (1) aspects of epidemiologic study designs that affect doseresponse estimation; (2) alternative methods for estimating dose in human studies; and (3) refined methods for dose-response modeling for epidemiologic data. Needed research in aspects of epidemiologic study design includes recognition and control of study biases, identification of susceptible subpopulations, choice of exposure metrics, and choice of epidemiologic risk parameters. Much of this research can be done with existing data. Research needed to improve determinants of dose in human studies includes additional individual-level data (e.g., diet, co-morbidity), development of more extensive human data for physiologically based pharmacokinetic (PBPK) dose modeling, tissue registries to increase the availability of tissue for studies of exposure/dose and susceptibility biomarkers, and biomarker data to assess exposures in humans and animals. Research needed on dose-response modeling of human studies includes more widespread application of flexible statistical methods (e.g., general additive models), development of methods to compensate for epidemiologic bias in dose-response models, improved biological models using human data, and evaluation of the benchmark dose using human data. There was consensus among the Work Group that, whereas most prior risk assessments have focused on cancer, there is a growing need for applications to other health outcomes. Developmental and reproductive effects, injuries, respiratory disease, and cardiovascular disease were identified as especially high priorities for research. It was also a consensus view that epidemiologists, industrial hygienists, and other scientists focusing on human data need to play a stronger role throughout the risk assessment process. Finally, the group agreed that there was a need to improve risk communication, particularly on uncertainty inherent in risk assessments that use epidemiologic data.     

A simple framework for a complex problem? Predicting wildlife–vehicle collisions

Collisions of vehicles with wildlife kill and injure animals and are also a risk to vehicle occupants, but preventing these collisions is challenging. Surveys to identify problem areas are expensive and logistically difficult. Computer modeling has identified correlates of collisions, yet these can be difficult for managers to interpret in a way that will help them reduce collision risk. We introduce a novel method to predict collision risk by modeling hazard (presence and movement of vehicles) and exposure (animal presence) across geographic space. To estimate the hazard, we predict relative traffic volume and speed along road segments across southeastern Australia using regression models based on human demographic variables. We model exposure by predicting suitable habitat for our case study species (Eastern Grey Kangaroo Macropus giganteus) based on existing fauna survey records and geographic and climatic variables. Records of reported kangaroo–vehicle collisions are used to investigate how these factors collectively contribute to collision risk. The species occurrence (exposure) model generated plausible predictions across the study area, reducing the null deviance by 30.4%. The vehicle (hazard) models explained 54.7% variance in the traffic volume data and 58.7% in the traffic speed data. Using these as predictors of collision risk explained 23.7% of the deviance in incidence of collisions. Discrimination ability of the model was good when predicting to an independent dataset. The research demonstrates that collision risks can be modeled across geographic space with a conceptual analytical framework using existing sources of data, reducing the need for expensive or time‐consuming field data collection. The framework is novel because it disentangles natural and anthropogenic effects on the likelihood of wildlife–vehicle collisions by representing hazard and exposure with separate, tunable submodels.     

Assessing Risks of Plant-Based Pharmaceuticals: I. Human Dietary Exposure

Protein-based drugs are the fastest growing class of drugs for the treatment of disease in humans and other animals. However, the current method of producing proteins for pharmaceutical application is predicted to fall short because of population growth and demographic trends. This study characterized human dietary risks using quantitative risk assessment techniques for three pharmaceutical proteins produced in field-grown maize. The three proteins were aprotinin, gastric lipase, and Escherichia coli heat-labile enterotoxin B subunit (LT-B). The human dietary risks from the three proteins inadvertently occurring in food were evaluated using three different exposure scenarios so that potential risks could be compared. The three exposure scenarios ranged in conservatism to evaluate the range of risk between the proteins and scenarios. Risk quotients (RQs) were calculated for all three scenarios to integrate exposure and effect (toxicity). The risk assessments revealed that the most conservative scenario produced higher RQs than the other two scenarios. The dietary risks from scenario 1 for aprotinin were three orders of magnitude greater than for scenario 2, and four orders of magnitude greater than for scenario 3. This risk assessment revealed that dietary risks will vary dramatically and depend on factors such as the specific pharmaceutical protein, protein expression, and exposure scenarios. The assessment also reinforced the need for case-by-case assessments.     

Axes of Extrapolation in Risk Assessment

Extrapolation in risk assessment involves the use of data and information to estimate or predict something that has not been measured or observed. Reasons for extrapolation include that the number of combinations of environmental stressors and possible receptors is too large to characterize risks comprehensively, that direct characterization is sometimes impossible, and that the power to characterize risk in a particular situation can be enhanced by using information obtained in other similar situations. Three types of extrapolation are common in risk assessments: biological (including between taxa and across levels of biological organization), temporal, and spatial. They can be thought of conceptually as the axes of a 3-dimensional graph defining the state space of biological, temporal, and spatial scales within which extrapolations are made. Each of these types of extrapolation can introduce uncertainties into risk assessments. Such uncertainties may be reduced through synergistic research facilitated by the sharing of methods, models, and data used by human health and ecological scientists