A case study comparing static and spatially explicit ecological exposure analysis methods

Spatially explicit analysis of metal transfer to biota: influence of soil contamination and landscape

Concepts and developments for a new field in ecotoxicology, referred to as “landscape ecotoxicology,” were proposed in the 1990s; however, to date, few studies have been developed in this emergent field. In fact, there is a strong interest in developing this area, both for renewing the concepts and tools used in ecotoxicology as well as for responding to practical issues, such as risk assessment. The aim of this study was to investigate the spatial heterogeneity of metal bioaccumulation in animals in order to identify the role of spatially explicit factors, such as landscape as well as total and extractable metal concentrations in soils. Over a smelter-impacted area, we studied the accumulation of trace metals (TMs: Cd, Pb and Zn) in invertebrates (the grove snail Cepaea sp and the glass snail Oxychilus draparnaudi) and vertebrates (the bank vole Myodes glareolus and the greater white-toothed shrew Crocidura russula). Total and CaCl₂-extractable concentrations of TMs were measured in soils from woody patches where the animals were captured. TM concentrations in animals exhibited a high spatial heterogeneity. They increased with soil pollution and were better explained by total rather than CaCl₂-extractable TM concentrations, except in Cepaea sp. TM levels in animals and their variations along the pollution gradient were modulated by the landscape, and this influence was species and metal specific. Median soil metal concentrations (predicted by universal kriging) were calculated in buffers of increasing size and were related to bioaccumulation. The spatial scale at which TM concentrations in animals and soils showed the strongest correlations varied between metals, species and landscapes. The potential underlying mechanisms of landscape influence (community functioning, behaviour, etc.) are discussed. Present results highlight the need for the further development of landscape ecotoxicology and multi-scale approaches, which would enhance our understanding of pollutant transfer and effects in ecosystems.

Accounting for pH heterogeneity and variability in modelling human health risks from cadmium in contaminated land

The authors have previously published a methodology which combines quantitative probabilistic human health risk assessment and spatial statistical methods (geostatistics) to produce an assessment, incorporating uncertainty, of risks to human health from exposure to contaminated land. The model assumes a constant soil to plant concentration factor (CF(veg)) when calculating intake of contaminants. This model is modified here to enhance its use in a situation where CF(veg) varies according to soil pH, as is the case for cadmium. The original methodology uses sequential indicator simulation (SIS) to map soil concentration estimates for one contaminant across a site. A real, age-stratified population is mapped across the contaminated area, and intake of soil contaminants by individuals is calculated probabilistically using an adaptation of the Contaminated Land Exposure Assessment (CLEA) model. The proposed improvement involves not only the geostatistical estimation of the contaminant concentration, but also that of soil pH, which in turn leads to a variable CF(veg) estimate which influences the human intake results. The results presented demonstrate that taking pH into account can influence the outcome of the risk assessment greatly. It is proposed that a similar adaptation could be used for other combinations of soil variables which influence CF(veg).

A spatial risk assessment methodology to support the remediation of contaminated land

When soil and groundwater contaminations occur over large areas, remediation measures should be spatially prioritized on the basis of the risk posed to human health and in compliance with technological and budget constraints. Within this scope, the application of human health risk assessment algorithms in a spatially resolved environment raises a number of methodological and technical complexities. In this paper, a methodology is proposed and applied in a case study to support the entire formulation process of remediation plans, encompassing hazard assessment, exposure assessment, risk characterisation, uncertainty assessment and allocation of risk reduction measures. In the hazard assessment, it supports the selection of Contaminants of Concern (CoC) with regard to both their average concentrations and peak concentrations, i.e. hot spots. In the exposure assessment, it provides a zoning of the site based on the geostatistical mapping of contaminant. In the risk characterisation, it generates vector maps of Risk Factors on the basis of the risk posed by multiple substances and allows the interrogation of most relevant CoC and exposure pathways for each zone of the site. It also supports the Monte Carlo based probabilistic estimation of the Risk Factors and generates maps of the associated uncertainty. In the risk reduction phase, it supports the formulation of remediation plans based on the stepwise spatial allocation of remediation interventions and the on-time simulation of risk reduction performances. The application of this methodology is fully supported by an easy-to-use and customized Geographical Information System and does not require high expertise for interpretation. The proposed methodology is the core module of a Decision Support System (DSS) that was implemented in the DESYRE software aimed at supporting the risk-based remediation of megasites.

Site-specific applications of probabilistic health risk assessment: review of the literature since 2000

Whether and to what extent contaminated sites harm ecologic and human health are topics of considerable interest, but also considerable uncertainty. Several federal and state agencies have approved the use of some or many aspects of probabilistic risk assessment (PRA), but its site-specific application has often been limited to high-profile sites and large projects. Nonetheless, times are changing: newly developed software tools, and recent federal and state guidance documents formalizing PRA procedures, now make PRA a readily available method of analysis for even small-scale projects. This article presents and discusses a broad review of PRA literature published since 2000.

An examination of ecological risk assessment and management practices

Ecological risk assessment has grown and evolved since the 1980s, as have new challenges (e.g. global climate change, loss of habitat and biodiversity and the effects of multiple anthropogenic chemicals on ecological systems) that need to be factored into the risk assessment processes. There is also an on-going shift from evaluating adverse health impacts on particular, often small scale, environments to undertaking more complex ecological assessments of whole populations and communities across ecologically meaningful landscapes. These trends are generating an increased demand for much more complex ecological assessments, making it increasingly clear that to achieve its potential as a management tool, methods must be developed to apply ecological risk assessment to larger and more complex scales. This paper reviews the development of the ecological risk assessment paradigm in the United States, identifies ways it is being applied and adapted in other countries, explores future research needs and practice improvements, and examines current issues that need to be considered in taking forward the scientific development of ecological risk assessment as a useful environmental management tool.

A spatially-evaluated methodology for assessing risk to a population from contaminated land

A methodology is proposed which combines quantitative probabilistic human health risk assessment and spatial statistical methods (geostatistics) to produce an assessment of risks to human health from exposure to contaminated land, in a manner which preserves the spatial distribution of risks and provides a measure of uncertainty in the assessment. Maps of soil contaminant levels, which incorporate uncertainty, are produced from sparse sample data using sequential indicator simulation. A real, age-stratified population is mapped across the contaminated area, and intake of soil contaminants by individuals is calculated probabilistically using an adaptation of the Contaminated Land Exposure Assessment (CLEA) model. An abundance of information is contained in results which can be interrogated at the population and individual level, and mapped to provide a powerful visual tool for risk managers, enabling efficient targeting of risk reduction measures to different locations.

Defining populations: a key step in identifying spatial and temporal scales

The scale at which ecological risk assessors frame their analyses can be driven by a number of considerations. In many cases, the selection of the scale is among the most important decisions within the 'problem-formulation' phase of the ecological risk characterization. The scale of a risk characterization is often defined based on ownership boundaries, ecological, or habitat boundaries and/or physical and hydrogeological boundaries (e.g., watersheds). With increasing value being placed on consideration of spatial scales in risk characterization, the need to define a scale that balances analytical and ecological realities is imperative. Defining the spatial scale based on the scale of a population offers the risk assessor the opportunity to improve the ecological relevance of the risk characterization. Arriving at a single operational definition of a population would be difficult and not appropriate because of the variability in sites, issues, and management goals; however, we can begin to review operational definitions and establish analytical frameworks by which to select the appropriate, site-specific scale within the problem-formulation phase. Defining a scale based on a combination of ecological and site ownership considerations and the inclusion of habitat quality in the assessment of scale can improve the power and relevance of the problem formulation and risk characterization. In this analysis, we assess methods for incorporating spatial considerations into risk analyses, beginning with developing an understanding of the context of a site within the larger landscape. Clearly defining the scale of an assessment including the operational definition of populations is an important step in the problem formulation. Within a site or landscape, the scale may be refined further based on the percentage of the land within the scale of the analysis that provides habitat for a given species.

A spatially and bioenergetically explicit terrestrial ecological exposure model

In typical exposure models, dose is a function of ingestion rate, which is a function of field metabolic rate and food energy availability. It is implicitly assumed that neither food energy nor ingestion rate is limited. This is unlikely to be true in the field. Poor habitat quality (expressed as limited or lacking food energy) or a physiologically limited maximum ingestion rate may collectively limit energy intake. A receptor may thus be as much at risk from lack of energy as from toxicant effects. To explore this possibility, an existing spatially explicit exposure model (SE3M) was enhanced to: 1) express 'habitat quality' in terms of gross energy available from a suite of habitat-specific food types, 2) track fulfillment of a receptor's daily energy needs as it traverses habitat patches with varying gross energy levels, 3) link intake of contaminants to food consumed to meet daily energy needs, and 4) track contaminant doses and resulting tissue residue levels as a receptor moves through habitat patches with differing levels of contamination. A feedback term through which chemical stressors affect a receptor's ability to intake and process energy was not considered at this time. The now spatially and energetically explicit exposure model, SE(4)M, provides a platform for exploring spatial and bioenergetic factors that may influence a receptor's acquisition of energy and contaminant tissue residues as it moves through space and time. An application of this model would be to provide predictions of tissue residue levels that are accessible to calibration or validation with empirical field data.