Theory of Ecological Risk Assessment Based on Species Sensitivity Distributions

Straalen, Nico Van

doi:10.1201/9781420032314.sec2

Cited by 27 publications

(46 citation statements)

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“…Hunt et al (2010) and others (Aldenberg et al 2002; van Straalen 2002) have identified the multiple benefits of using the JPC methodology to characterize risk, including the quantification of the risk (δ, the area under the curve) and provision of information on the type of exposure (the shape of the curve). When using the JPC to interpret ecological risk, the greater the area under the curve (AUC), the greater the ecological risk, whereas the shape of the curve allows differentiation between a high damage/low probability event and a low damage/high probability event.…”

Section: Methodsmentioning

confidence: 99%

Quantifying reduction in ecological risk in Penrhyn Estuary, Sydney, Australia, following groundwater remediation

Hunt

Birch

Warne

2011

Integr Envir Assess & Manag

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The environmental risk associated with discharge of contaminated groundwater containing a complex mixture of at least 14 volatile chlorinated hydrocarbons (VCHs) to Penrhyn Estuary, Sydney, Australia has previously been assessed. That probabilistic ecological risk assessment (ERA) was undertaken using surface water monitoring data from 2004 to 2005. Subsequently, in 2006, a groundwater remediation system was installed and commissioned to prevent further discharge of VCHs into the estuary. The present study assessed the ecological risk posed to the estuary after 2006 to evaluate the success of the remediation system. The ERA was undertaken using toxicity data derived from direct toxicity assessment (DTA) of preremediation contaminated groundwater using indigenous species, exposure data from surface water monitoring between 2007 and 2008 and the joint probability curve (JPC) methodology. The risk posed was measured in 4 zones of the entire site: source area (2), tributary (2), the inner estuary and outer estuary at high, low, and a combination of high and low tides. In the 2 source areas, risk decreased by over 2 and over 1 orders of magnitude to maximum values of less than 0.5%. In 1 estuary, risk decreased by over 1 order of magnitude, from a maximum of 36% to a maximum of 2.3%. At the other tributary and both the inner and outer estuaries, the risk decreased to less than 1%, regardless of the tide. This analysis revealed that the remediation system was very effective and that the standard level of protection required for slightly to moderately affected ecosystems (95% of species) by the Australian and New Zealand Guidelines for Fresh and Marine Water Quality was met postremediation.

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Section: Methodsmentioning

confidence: 99%

Quantifying reduction in ecological risk in Penrhyn Estuary, Sydney, Australia, following groundwater remediation

Hunt

Birch

Warne

2011

Integr Envir Assess & Manag

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“…A method to achieve this is offered by the concept of SSDs. Posthuma et al [15] state that “a SSD is a statistical distribution describing the variation among a set of species in toxicity of a certain compound or mixture.” According to Van Straalen [16], the adverse effect considered when using an SSD based on NOECs is the probability that “a species chosen randomly out of a large assemblage is exposed to an environmental concentration greater than its no‐effect level.” So far, SSDs applied in LCIA have been based on a lognormal distribution of NOEC sensitivity [7,13,14], also termed the potentially affected fraction of species (PAF) [17]. Described mathematically by Aldenberg and Slob [18] for single substances, the concept has later been extended to calculate the combined toxic effect of multiple substances [17,19].…”

Section: Methodsmentioning

confidence: 99%

Marine ecotoxic effect of pulse emissions in life cycle impact assessment

Pettersen¹,

Peters²,

Hertwich³

2006

Enviro Toxic and Chemistry

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Characterization factors for ecotoxicity in life cycle impact assessment are traditionally calculated as the product of effect and fate factors. Steady‐state multiple compartment models are used to calculate the fate factor, while effect factors are derived from species sensitivity distributions (SSDs) for multiple substances using average or marginal gradients. Others have shown that steady‐state multicompartment models can be used to calculate characterization factors if linear dose–response functions are used. Average gradients are linear dose–response functions per definition. Marginal gradients are first‐order Taylor approximations of the effect function and require marginal exposure at all points of the compartment. Instantaneous mixing, giving marginal exposure within compartments, is an implicit assumption of the multicompartment model. This paper investigates if the assumption of marginal exposure results in significant errors for the characterization factor. Ecotoxic effect of pulse emissions is simulated in a transient three‐dimensional single compartment model of the marine aquatic environment. Results show that the error in characterization factors for the Taylor approximation is less than a factor of two for multisubstance SSDs assuming concentration addition only in the aggregation of toxic effect of substances. Assuming a combination of response and concentration addition may result in a deviation of several orders of magnitude.

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“…The area under the JPC has been shown to be mathematically equivalent to the overlap of the exposure and toxicity curves [25]. The two axes of the JPC are probability and proportion of species affected.…”

Section: Risk Characterizationmentioning

confidence: 99%

Site‐specific probabilistic ecological risk assessment of a volatile chlorinated hydrocarbon‐contaminated tidal estuary

Hunt

Birch

Warne

2010

Enviro Toxic and Chemistry

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Groundwater contaminated with volatile chlorinated hydrocarbons (VCHs) was identified as discharging to Penrhyn Estuary, an intertidal embayment of Botany Bay, New South Wales, Australia. A screening-level hazard assessment of surface water in Penrhyn Estuary identified an unacceptable hazard to marine organisms posed by VCHs. Given the limitations of hazard assessments, the present study conducted a higher-tier, quantitative probabilistic risk assessment using the joint probability curve (JPC) method that accounted for variability in exposure and toxicity profiles to quantify risk (delta). Risk was assessed for 24 scenarios, including four areas of the estuary based on three exposure scenarios (low tide, high tide, and both low and high tides) and two toxicity scenarios (chronic no-observed-effect concentrations [NOEC] and 50% effect concentrations [EC50]). Risk (delta) was greater at low tide than at high tide and varied throughout the tidal cycle. Spatial distributions of risk in the estuary were similar using both NOEC and EC50 data. The exposure scenario including data combined from both tides was considered the most accurate representation of the ecological risk in the estuary. When assessing risk using data across both tides, the greatest risk was identified in the Springvale tributary (delta=25%)-closest to the source area-followed by the inner estuary (delta=4%) and the Floodvale tributary (delta=2%), with the lowest risk in the outer estuary (delta=0.1%), farthest from the source area. Going from the screening level ecological risk assessment (ERA) to the probabilistic ERA changed the risk from unacceptable to acceptable in 50% of exposure scenarios in two of the four areas within the estuary. The probabilistic ERA provided a more realistic assessment of risk than the screening-level hazard assessment.

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Theory of Ecological Risk Assessment Based on Species Sensitivity Distributions

Cited by 27 publications

References 0 publications

Quantifying reduction in ecological risk in Penrhyn Estuary, Sydney, Australia, following groundwater remediation

Quantifying reduction in ecological risk in Penrhyn Estuary, Sydney, Australia, following groundwater remediation

Marine ecotoxic effect of pulse emissions in life cycle impact assessment

Site‐specific probabilistic ecological risk assessment of a volatile chlorinated hydrocarbon‐contaminated tidal estuary

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