"Quantifying the precision of ecological risk: Misunderstandings and errors in the methods for assessment factors versus species sensitivity distributions"

Revisiting assessment factors for species sensitivity distributions as a function of sample size and variation in species sensitivity

To use species sensitivity distributions (SSDs) for ecological risk assessment, there are various uncertainties, which require applying assessment factors (AFs) accordingly. This study aims to quantify the uncertainty of estimating statistical distributions. Given a management goal of protecting 95% of species, the concentration that affects 5% of the species (HC5) is estimated. Since the true concentration affecting 5% of the species (population HC5) is unknown, the estimated HC5 is divided by an AF to derive the predicted no-effect concentration (PNEC), which is set as the protection goal, to compensate for the deviation in the estimated HC5 from the population HC5. Although the deviation between these two HC5 values depends on the sample size and the variation in sensitivity (standard deviation of the distribution) among species, there has been little discussion of how to quantify the degree of uncertainty. By assuming that toxicity values are a random sample from a lognormal distribution, we mathematically analyzed the SSD to derive the magnitude of AF needed to achieve a given protection goal (as an example, the protection of 95% of species with a probability of 95%). We successfully derived an equation that explicitly relates the magnitude of AF to the sample size and the variation in species sensitivity, providing a new basis to statistically determine the magnitude of AF for ecological risk assessments.

A QSAR-ICE-SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity

Alkylphenols (APs) are ubiquitous and generally present in higher residue levels in the environment. The present work focuses on the development of a set of in silico models to predict the aquatic toxicity of APs with incomplete/unknown toxicity data in aquatic environments. To achieve this, a QSAR-ICE-SSD model was constructed for aquatic organisms by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the hazardous concentrations (HCs) of selected APs. The research indicated that the keywords "alkylphenol" and "nonylphenol" were most commonly studied. The selected ICE models were robust (R2: 0.70-0.99; p-value < 0.01). All models had a high reliability cross- validation success rates (>75%), and the HC5 predicted with the QSAR-ICE-SSD model was 2-fold than that derived with measured experimental data. The HC5 values demonstrated nearly linear decreasing trend from 2-MP to 4-HTP, while the decreasing trend from 4-HTP to 4-DP became shallower, indicates that the toxicity of APs to aquatic organisms increases with the addition of alkyl carbon chain lengths. The ecological risks assessment (ERA) of APs revealed that aquatic organisms were at risk from exposure to 4-NP at most river stations (the highest risk quotient (RQ) = 1.51), with the highest relative risk associated with 2.9% of 4-NP detected in 82.9% of the sampling sites. The targeted APs posed potential ecological risks in the Yongding and Beiyun River according to the mixture ERA. The potential application of QSAR-ICE-SSD models could satisfy the immediate needs for HC5 derivations without the need for additional in vivo testing.

Development of a Bayesian network for probabilistic risk assessment of pesticides

Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modeling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterized for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterization using azoxystrobin as an example. We also demonstrate the seasonal risk calculation for the three pesticides. Integr Environ Assess Manag 2022;18:1072-1087. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).