Key issues for the development and application of the species sensitivity distribution (SSD) model for ecological risk assessment

The species sensitivity distribution (SSD) model is one of the most commonly used methods for ecological risk assessment based on the potentially affected fraction (PAF) of and the combined PAF (msPAF) as quantitative indicators. There are usually four steps for the development of SSD models and their applications: (1) obtain the toxicity data of the pollutants; (2) fit the SSD curves; (3) calculate the potentially affected fractions (PAFs) of the individual pollutants for the ecological risk assessment of an individual pollutant; and (4) calculate the accumulated multi-substance potentially affected fractions (msPAFs) for the joint ecological risk assessment of multiple pollutants. Among the above mentioned four steps, the first two steps are paramount. In the present study, the following six key issues are discussed: (1) how to select the appropriate species, (2) how to preprocess the toxicity data collected from the ecotoxicity database, (3) how to transform the acute toxicity data into chronic data, (4) how to best fit the toxicity data, (5) how to calculate the msPAF of multiple pollutants, and (6) how to determine the uncertainty of the SSD model”. In response to these questions, several principles were proposed to select appropriate species; three data processing methods, including the geometric mean, weight assigning and using all raw data without processing, were compared to determine the appropriate method for the DDT (dichloro diphenyl trichloroethane) toxicity data preprocessing. The method of acute to chronic ratio (ACR) and binary correlation analysis were contrasted using the zinc toxicity data for the transformation of the acute toxicity data into chronic data. The Burr III, Loglogistic and Lognormal models were compared to determine the best fit model using the DDT toxicity data for invertebrates. The concentration addition or response addition were discussed to calculate msPAF according to the toxic model of action (TMoA). The uncertainties of the SSD models for five heavy metals and for eight polycyclic aromatic hydrocarbons (PAHs) were performed. The comparison of the coefficients of variation (CVs) for the toxicity data and exposure levels in Lake Chaohu for eight polycyclic aromatic hydrocarbons (PAHs) were also presented to demonstrate the uncertainties of the ecological risks assessed by the SSD model based on 5000 Monte Carlo simulations.