Artificial neural networks (ANNs) are a valid alternative predictive method to the traditional statistical techniques currently used in many research fields where a massive amount of data is challenging to manage. In environmental analysis, ANNs can analyze pollution sources in large areas, estimating difficult and expensive to detect contaminants from other easily measurable pollutants, especially for screening procedures. In this study, organic micropollutants have been predicted from heavy metals concentration using ANNs. Sampling was performed in an agricultural field where organic and inorganic contaminants concentrations are beyond the legal limits. A critical problem of a neural network design is to select its parametric topology, which can prejudice the reliability of the model. Therefore, it is very important to assess the performance of ANNs when applying different types of parameters of the net. In this work, based on Taguchi L12 orthogonal array, turning experiments were conducted to identify the best parametric set of an ANNs design, considering different combinations of sample number, scaling, training rate, activation functions, number of hidden layers, and epochs. The composite desirability value for the multi-response variables has been obtained through the desirability function analysis (DFA). The parameters’ optimum levels have been identified using this methodology.
Per- and poly-fluoroalkyl substances (PFAS) and Total Chromium (Cr) in surface waters are polluting factors that can directly or indirectly affect human and animal health. These are conjunctly used in several manufacturing activities such as electroplating, stainless steel welding, leather tanning and the production of electronic components. In this paper we present a study based on the use of the Principal Component Analysis (PCA) statistical technique to explore emission sources of PFAS and Cr in surface water samples taken from a polluted area in Northern Italy. It has been found that the discriminant factor is the link between PFBS and Cr. It is due to electroplating, and stainless steel welding activities. No other links were found between Cr and other PFAS. This information is important both for risk assessment and forensic activities.
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