Improved comprehensive ecological risk assessment method and sensitivity analysis of polycyclic aromatic hydrocarbons (PAHs).

Based on the existing comprehensive ecological risk assessment methods of PAHs, this paper proposed an improved hierarchical Archimedean copula integral assessment (HACIA) model with the optimization in the model selection mechanism and accelerating the calculation speed, and according to which performed the sensitivity analysis of the integrated risk relative to the underlying grouped risk probability. Taihu Lake in China and the Bay of Santander in Spain were taken as study areas, whose samples were obtained and extracted concentrations of 16 priority polycyclic aromatic hydrocarbons (PAHs). After briefly analyzing their concentration characteristics and source, their comprehensive ecological risks were evaluated by the improve HACIA model and their sensitivity was also analyzed. The results proved that, for Taihu Lake, pyrogenic sources occupied the dominance, especially grass, coal and wood combustion, while the risk proportion of 5-rings PAHs was the lowest indeed based on the improved HAICA model. For the Bay of Santander, source apportionment indicated both petrogenic and pyrogenic sources, mainly from vehicle emissions including gasoline and diesel engines, and 4-ring PAHs were urgently needed to be managed. However, the sensitivity analysis results of two study areas showed that the most effective control target for reducing integral risk has no obvious relationship with the maximum grouped risk. And a clear linear relationship between the maximum sensitivity range and the logarithm of the initial overall risk only presented in one of study areas, which required further research to clarify. In brief, the improved HACIA model is helpful to evaluate the comprehensive ecological risk of 16 PAHs, and formulate risk management strategies based on grouped risk assessment and sensitivity analysis, with the former points out the admonitory risk and the latter helps to find the most effective mitigation measures.