Andy Hart

Development of a framework based on an ecosystem services approach for deriving specific protection goals for environmental risk assessment of pesticides

General protection goals for the environmental risk assessment (ERA) of plant protection products are stated in European legislation but specific protection goals (SPGs) are often not precisely defined. These are however crucial for designing appropriate risk assessment schemes. The process followed by the Panel on Plant Protection Products and their Residues (PPR) of the European Food Safety Authority (EFSA) as well as examples of resulting SPGs obtained so far for environmental risk assessment (ERA) of pesticides is presented. The ecosystem services approach was used as an overarching concept for the development of SPGs, which will likely facilitate communication with stakeholders in general and risk managers in particular. It is proposed to develop SPG options for 7 key drivers for ecosystem services (microbes, algae, non target plants (aquatic and terrestrial), aquatic invertebrates, terrestrial non target arthropods including honeybees, terrestrial non-arthropod invertebrates, and vertebrates), covering the ecosystem services that could potentially be affected by the use of pesticides. These SPGs need to be defined in 6 dimensions: biological entity, attribute, magnitude, temporal and geographical scale of the effect, and the degree of certainty that the specified level of effect will not be exceeded. In general, to ensure ecosystem services, taxa representative for the key drivers identified need to be protected at the population level. However, for some vertebrates and species that have a protection status in legislation, protection may be at the individual level. To protect the provisioning and supporting services provided by microbes it may be sufficient to protect them at the functional group level. To protect biodiversity impacts need to be assessed at least at the scale of the watershed/landscape.

Fish, contaminants and human health: Quantifying and weighing benefits and risks

This paper describes a quantitative risk-benefit assessment of fish consumption. We compare the net health effect expressed in DALYs of two scenarios. The reference scenario is the current fish intake of the Dutch population, which is less than what is recommended by the health authorities. The alternative scenario describes the health effects if the population consumes 200g of fish per week, which is close to the recommendation. All health effects due to fish consumption for which there is convincing evidence are incorporated in the assessment. The QALIBRA software (www.qalibra.eu) is used to simulate the two scenarios. The results show there is a net benefit for the population if it consumes 200g of fish each week.

Probabilistic risk assessment for snails, slugs, and endangered honeycreepers in diphacinone rodenticide baited areas on Hawaii, USA

Three probabilistic models were developed for characterizing the risk of mortality and subacute coagulopathy to Poouli, an endangered nontarget avian species, in broadcast diphacinone-baited areas on Hawaii, USA. For single-day exposure, the risk of Poouli mortality approaches 0. For 5-d exposure, the mean probability of mortality increased to 3% for adult and 8% for juvenile Poouli populations. For Poouli that consume snails containing diphacinone residues for 14 d, the model predicted increased levels of coagulopathy for 0.42 and 11% of adult and juvenile Poouli populations, respectively. Worst-case deterministic risk characterizations predicted acceptable levels of risk for nonthreatened or endangered species such as northern bobwhite quail and mallards. Also, no acute toxicity was noted for snails and slugs that feed on diphacinone baits.

Fish consumption during child bearing age: a quantitative risk-benefit analysis on neurodevelopment.

The fish ingredient N3-docosahexaenoic acid 22:6 n-3 (DHA) stimulates brain development. On the other hand methylmercury (MeHg) in fish disturbs the developing central nervous system. In this Context the IQ score in children is considered as an aggregate measure of in utero brain development. To determine the effect of DHA exposure on prenatal neurodevelopment the maternal DHA intake during pregnancy was compared with its epidemiologically observed effect on the IQ score of children. For MeHg the maternal intake was converted into its accumulation in the maternal body. The maternal body burden then was compared with its epidemiologically observed relationship with the IQ score. Taking the MeHg and DHA content of 33 fish species the net effect of these compounds on the IQ score was quantified. For most fish species the adverse effect of MeHg on the IQ score exceeded the beneficial effect of DHA. In the case of long-living predators a negative effect up to 10 points on the IQ score was found. The results of this study indicate that food interventions aiming at the beneficial effects of fish consumption should focus on fish species with a high DHA content, while avoiding fish species with a high MeHg content.

Application of uncertainty analysis in assessing dietary exposure.

Conventional approaches for assessing dietary exposure to contaminants and additives in food are deterministic, using point estimates for consumption and contamination. In reality, both consumption and contamination are variable. Furthermore our knowledge of them is uncertain, e.g. due to measurement uncertainty. Conventional approaches attempt to allow for this by using worst-case assumptions or safety factors, but these are often subjective and may result either in overestimation or underestimation of the true range of exposures. Probabilistic approaches take account of variability and uncertainty by using distributions rather than point estimates for consumption and contamination. The outputs are distributions for exposure, which provide a more complete and balanced description of risk for the decision-maker. These approaches also facilitate the use of sensitivity analysis to identify those factors that impact most on exposure, and to identify areas of uncertainty where additional data will improve exposure estimates. This paper reviews examples of the application of these methods to the assessment of dietary exposure to food contaminants, including dioxins in seafood, where it was found that the greatest uncertainties relate to toxicity rather than exposure. Further work required to implement probabilistic approaches for dietary exposure assessment is discussed.

Pesticide Risk Perceptions, Knowledge, and Attitudes of Operators, Workers, and Residents: A Review of the Literature

ABSTRACT The literature on the risk perceptions, knowledge levels, and attitudes of operators, workers, and residents in relation to non-dietary exposure to agricultural pesticides is reviewed. No literature was identified in relation to bystander exposure. Research has primarily been conducted on participants in developing countries and migrant workers in the United States. For operators and workers, illiteracy, poverty, and a perception that exposure to pesticides is an inevitable part of their work results in limited adoption of safety precautions while using and storing pesticides. As a result, risk communication activities aimed at operator and workers need to take account of the wider socioeconomic and cultural conditions in which workers and operators are working and living. There is less research focused on residents’ and bystanders’ perceptions, attitudes, and behaviors. The lack of European data in general, and residents’ and bystanders’ data in particular, represents a knowledge gap that is pertinent to emerging EU legislation requiring residents’ and bystanders’ inclusion in pesticide risk assessment. This review provides a comprehensive overview that can assist policy-makers, and risk communicators in the development of targeted training and awareness-raising material for operators, workers, bystanders, and residents. Areas for future research are suggested.

New approaches to uncertainty analysis for use in aggregate and cumulative risk assessment of pesticides

Risk assessments for human exposures to plant protection products (PPPs) have traditionally focussed on single routes of exposure and single compounds. Extensions to estimate aggregate (multi-source) and cumulative (multi-compound) exposure from PPPs present many new challenges and additional uncertainties that should be addressed as part of risk analysis and decision-making. A general approach is outlined for identifying and classifying the relevant uncertainties and variabilities. The implementation of uncertainty analysis within the MCRA software, developed as part of the EU-funded ACROPOLIS project to address some of these uncertainties, is demonstrated. An example is presented for dietary and non-dietary exposures to the triazole class of compounds. This demonstrates the chaining of models, linking variability and uncertainty generated from an external model for bystander exposure with variability and uncertainty in MCRA dietary exposure assessments. A new method is also presented for combining pesticide usage survey information with limited residue monitoring data, to address non-detect uncertainty. The results show that incorporating usage information reduces uncertainty in parameters of the residue distribution but that in this case quantifying uncertainty is not a priority, at least for UK grown crops. A general discussion of alternative approaches to treat uncertainty, either quantitatively or qualitatively, is included.

On the application of loss functions in determining assessment factors for ecological risk.

Assessment factors have been proposed as a means to extrapolate from data on the concentrations hazardous to a small sample of species to the concentration hazardous to p% of the species in a given community (HCp). Aldenberg and Jaworska [2000. Uncertainty of the hazardous concentration and fraction affected for normal species sensitivity distributions. Ecotoxicol. Environ. Saf. 46, 1-18] proposed estimators that prescribed universal assessment factors which made use of distributional assumptions associated with species sensitivity distributions. In this paper we maintain those assumptions but introduce loss functions which punish over- and under-estimation. Furthermore, the final loss function is parameterised such that conservatism can be asymmetrically and non-linearly controlled which enables one to better represent the reality of risk assessment scenarios. We describe the loss functions and derive Bayes rules for each. We demonstrate the method by producing a table of universal factors that are independent of the substance being assessed and which can be combined with the toxicity data in order to estimate the HC5. Finally, through an example we illustrate the potential strength of the newly proposed estimators which rationally accounts for the costs of under- and over-estimation to choose an estimator; as opposed to arbitrarily choosing a one-sided lower confidence limit.

A European model and case studies for aggregate exposure assessment of pesticides

Exposures to plant protection products (PPPs) are assessed using risk analysis methods to protect public health. Traditionally, single sources, such as food or individual occupational sources, have been addressed. In reality, individuals can be exposed simultaneously to multiple sources. Improved regulation therefore requires the development of new tools for estimating the population distribution of exposures aggregated within an individual. A new aggregate model is described, which allows individual users to include as much, or as little, information as is available or relevant for their particular scenario. Depending on the inputs provided by the user, the outputs can range from simple deterministic values through to probabilistic analyses including characterisations of variability and uncertainty. Exposures can be calculated for multiple compounds, routes and sources of exposure. The aggregate model links to the cumulative dietary exposure model developed in parallel and is implemented in the web-based software tool MCRA. Case studies are presented to illustrate the potential of this model, with inputs drawn from existing European data sources and models. These cover exposures to UK arable spray operators, Italian vineyard spray operators, Netherlands users of a consumer spray and UK bystanders/residents. The model could also be adapted to handle non-PPP compounds.

Bayesian modeling of measurement errors and pesticide concentration in dietary risk assessments.

We propose new models for dealing with various sources of variability and uncertainty that influence risk assessments for dietary exposure. The uncertain or random variables involved can interact in complex ways, and the focus is on methodology for integrating their effects and on assessing the relative importance of including different uncertainty model components in the calculation of dietary exposures to contaminants, such as pesticide residues. The combined effect is reflected in the final inferences about the population of residues and subsequent exposure assessments. In particular, we show how measurement uncertainty can have a significant impact on results and discuss novel statistical options for modeling this uncertainty. The effect of measurement error is often ignored, perhaps due to the laboratory process conforming to the relevant international standards, for example, or is treated in an ad hoc way. These issues are common to many dietary risk analysis problems, and the methods could be applied to any food and chemical of interest. An example is presented using data on carbendazim in apples and consumption surveys of toddlers.