In this study, the concentration of fluoride and the associated health risks for infants, children, and adults were analyzed and compared for three drinking water sources in Yancheng City, Jiangsu Province, China. To analyze the relationship between the water quality parameters of pH, fluoride (F⁻), sulfate (SO₄²⁻), chloride (Cl⁻), total dissolved solids (TDS), total alkalinity (TAlk), sodium (Na⁺), and potassium (K⁺), statistical analyses including correlation analysis, R-mode cluster analysis and factor analysis were performed based on monthly data from the year 2010 to 2015. The results indicated: (1) Fluoride concentrations in the drinking water sources ranged from 0.38 to 1.00 mg L⁻¹ (mean = 0.57 mg L⁻¹) following the order of Tongyu River > Yanlong Lake > Mangshe River; (2) fluoride concentrations in 22.93% of the collected samples were lower than 0.5 mg L⁻¹, which has the risk of tooth cavities, especially for the Mangshe River; (3) the fluoride exposure levels of infants were higher than children and adults, and 3.2% of the fluoride exposure levels of infants were higher than the recommended toxicity reference value of 122 μg kg⁻¹ d⁻¹ as referenced by Health Canada, which might cause dental fluorosis issues; (4) the physico-chemical characteristics are classified the into four groups reflecting F⁻- TAlk, Na⁺-K⁺, SO₄²⁻-Cl⁻, and pH-TDS, respectively, indicating that fluoride solubility in drinking water is TAlk dependent, which is also verified by R-mode cluster analysis and factor analysis. The results obtained supply useful information for the health department in Yancheng City, encouraging them to pay more attention to fluoride concentration and TAlk in drinking water sources.

Eutrophication has become one of the most serious problems threatening the lakes/reservoirs in China over 50 years. Evaluation of eutrophication is a multi-criteria decision-making process with uncertainties. In this study, a cloud matter element (CME) model was developed in order to evaluate eutrophication level objectively and scientifically, which incorporated the randomness and fuzziness of eutrophication evaluation process. The elements belonging to each eutrophication level in the CME model were determined by means of certainty degrees through repeated simulations of cloud model with reasonable parameters of expectation E_x, entropy E_n, and hyper-entropy H_e. The weights of evaluation indicators were decided by a combination of entropy technology and analytic hierarchy process method. The neartudes of water samples to each eutrophication level of lakes/reservoirs in the CME model were generated and the eutrophication levels were determined by maximum neartude principal. The proposed CME model was applied to evaluate eutrophication levels of 24 typical lakes/reservoirs in China. The results of the CME model were compared with those of comprehensive index method, matter element model, fuzzy matter element model, and cloud model. Most of the results obtained by the CME model were consistent with the results obtained by other methods, which proved the CME model is an effective tool to evaluate eutrophication.

Evaluating the eutrophication level of lakes with a single method alone is challenging since uncertain, fuzzy, and complex processes exist in eutrophication evaluations. The parameters selected for assessing eutrophication include chlorophyII-a, chemical oxygen demand, total phosphorus, total nitrogen, and clarity. Firstly, to deal with the uncertainties and fuzziness of data, triangular fuzzy numbers (TFN) were applied to describe the fuzziness of parameters. Secondly, to assess the eutrophication grade of lakes comprehensively, an improved fuzzy matter element (FME) approach was incorporated with TFNs with weights determined by combination of entropy method and analytic hierarchy process (AHP). In addition, the Monte Carlo (MC) approach was applied to easily simulate the arithmetic operations of eutrophication evaluation. The hybrid model of TFN, FME, and MC method is termed as the TFN–MC–FME model, which can provide more valuable information for decision makers. The developed model was applied to assess the eutrophication levels of 24 typical lakes in China. The evaluation indicators were expressed by TFNs input into the FME model to evaluate eutrophication grade. The results of MC simulation supplied quantitative information of possible intervals, the corresponding probabilities, as well as the comprehensive eutrophication levels. The eutrophication grades obtained for most lakes were identical to the results of the other three methods, which proved the correctness of the model. The presented methodology can be employed to process the data uncertainties and fuzziness by stochastically simulating their distribution characteristics, and obtain a better understanding of eutrophication levels. Moreover, the proposed model can also describe the trend of eutrophication development in lakes, and provide more valuable information for lake management authorities.