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SSD was first raised by US EPA (environmental protection agency) in early 1980s, with decades of developments, it now has been widely utilized in eco-risk assessments (Solomon et al., 1996; Steen et al., 1999). SSD models are a set of dose–response models of bio-community level, their horizontal ordinates represent the exposure concentrations and the vertical ordinates mean the affected proportion of species in the bio-community (eco-risk). The eco-risk of a particular substance under a certain concentration is calculated by statistical methods based on the eco-toxicity data (e.g. LC50 as acute toxicity data or NOEC as chronic toxicity data) of the substance. Generally, the models follow cumulative distribution curves. It means when the exposure concentration reaches a certain value, the vertical ordinate shows the proportion of the species affected by the substance under that concentration, since the exposure level exceeds their toxicity limits (van Straalen, 2002). Aside from accessing the eco-risk of a single pollutant, SSD models can also be applied to determine the overall eco-risk of multiple mixture pollutant (Solomon et al., 1996; Steen et al., 1999). Despite all the progress and improvements of SSD models so far, there are some disadvantages and limitations still. For example, the uncertainties of the risks tend to be ignored easily (Aldenberg and Jaworska, 2000; Forbes et al., 2001; Forbes and Calow, 2002). Therefore, Grist et al. (2006) utilized Bayesian theory in constructing SSD model and they proved Bayesian theory to be effective in reducing uncertainties. Furthermore, Aldenberg and Jaworska (2000) & Verdonck et al. (2000) provided practical cases for the Bayesian-SSD models.

So far, there have been little eco-risk assessment software based on SSD models, including BurrliOZ (with Burr III model and ReWeibull model) developed by Commonwealth Scientific and Industrial Research Organization (CSIRO) (Hose and Van den Brink, 2004). However, the software like BurrliOZ can only set the model parameters, and they are not able for uncertainty analysis or batch calculation. As general model building software using Microsoft Office Excel for operation interface, Oracle Crystal Ball (coupled with C Language for the construction of joint probability curve, JPC) is utilized in eco-risk assessment of Aquatic organisms by US EPA. Unlike BurrliOZ, Oracle Crystal Ball can successfully achieve uncertainty analysis and batch calculation goals. However, the user group of Crystal Ball is limited since it is a commercial product and the operating process of software is quite complex. Instead of obtaining the eco-rick of a certain exposure concentration directly, users need to go through multiple processes including model selection, parameter construction and fitting, model optimization, uncertainty simulation and eco-risk calculation. In addition, establishing models using C Language also narrows the user group, which ultimately limits the generalization of SSD models. Some developers developed several software which specialized in exposure risk assessment like Ecological Risk Assessment (ERA), but ERA only has a one-way risk calculation function, it cannot calculate exposure thresholds by the corresponding risks (Buckler et al., 2005). The QuatroPro software based on Microsoft Office Excel expanding function by Solomon et al. (2000) can successfully build joint probability curve, but its default SSD parameters are set to obey normal distribution. Therefore, there may be certain errors when calculating the uncertainties, especially when the parameters do not follow normal distribution, the uncertainties of calculated risks will be wrong.