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.

A multidimension cloud model-based approach for water quality assessment.

Lakes are vitally important, because they perform a multitude of functions, such as water supply, recreation, fishing, and habitat. However, eutrophication limits the ability of lakes to perform these functions. In order to reduce eutrophication, the first step is its evaluation. The process of evaluation entails randomness and fuzziness which must therefore be incorporated. This study proposes an eutrophication evaluation method, named Multidimension Normal Cloud Model (MNCM). The model regards each evaluation factor as a one-dimension attribute of MNCM, chooses reasonable parameters and determines the weights of evaluation factors by entropy. Thus, all factors of MNCM belonging to each eutrophication level are generated and the final eutrophication level is determined by the certainty degree. MNCM is then used to evaluate eutrophication of 12 typical lakes and reservoirs in China and its results are compared with those of the reference method, one-dimension normal cloud model, related weighted nutrition state index method, scoring method, and fuzzy comprehensive evaluation method. Results of MNCM are found to be consistent with the actual water status; hence, MNCM can be an effective evaluation tool. With respect to the former one-dimension normal cloud model, parameters of MNCM are improved without increasing its complexity. MNCM can directly determine the eutrophication level according to the degree of certainty and can determine the final degree of eutrophication; thus, it is more consistent with the complexity of water eutrophication evaluation.

A cloud model-based approach for water quality assessment.

Water quality assessment entails essentially a multi-criteria decision-making process accounting for qualitative and quantitative uncertainties and their transformation. Considering uncertainties of randomness and fuzziness in water quality evaluation, a cloud model-based assessment approach is proposed. The cognitive cloud model, derived from information science, can realize the transformation between qualitative concept and quantitative data, based on probability and statistics and fuzzy set theory. When applying the cloud model to practical assessment, three technical issues are considered before the development of a complete cloud model-based approach: (1) bilateral boundary formula with nonlinear boundary regression for parameter estimation, (2) hybrid entropy-analytic hierarchy process technique for calculation of weights, and (3) mean of repeated simulations for determining the degree of final certainty. The cloud model-based approach is tested by evaluating the eutrophication status of 12 typical lakes and reservoirs in China and comparing with other four methods, which are Scoring Index method, Variable Fuzzy Sets method, Hybrid Fuzzy and Optimal model, and Neural Networks method. The proposed approach yields information concerning membership for each water quality status which leads to the final status. The approach is found to be representative of other alternative methods and accurate.