Nilufar Marufi

The contamination of water due to heavy metals (HMs) is a big concern for humankind; particularly in developing countries. This research is a systematic review, conducted by searching google scholar, Web of Science, Science Direct, PubMed, Springer, and Scopus databases for related published papers from 2010 to July 2021, resulting in including 40 articles. Among the analyzed HMs in the presented review, the average content of Cr, Pb, Ba, Al, As, Zn, and Cd exceeded the permissible limits suggested by the World Health Organization (WHO) and 1,053 Iranian standards. Also, the rank order of Hazard Quotient (HQ) of HMs was defined as Cd>As>Cr>Pb>Li for children which means Cd has the highest non-carcinogenic risk and Li has the least. This verifies to the current order As>Cr>Pb>Fe=Zn=Cu>Cd for adults. The corresponded values of HQ and Hazard Index (HI) in most cities and villages were investigated and the results indicated a lower than 1 value, which means consumers are not at non-carcinogenic risk (HQ). Carcinogenic risk (CR) of As in the adult and children consumers in most of the samples (58.82% of samples for both groups) were investigated too, and it was more than>1.00E-04 value, which determines that consumers are at significant CR.

Most agricultural products are exposed to pesticides. Organochlorine (OCPs) insecticides have been banned for years due to their persistence in the environment and lipophilic properties. On the other hand, some carcinogenic organophosphates are used in high amounts. Therefore, this systematic review was performed with the keywords; pesticide, carcinogenic, carcinogen, residue, contamination, pollution, and food to determine the type of food and pesticide. 663 manuscripts were found by searching in databases. After initial screening and quality assessment of full text, 26 manuscripts were selected. In this study, by reviewing selected manuscripts, about 13 pesticides were associated with carcinogenic effects. These pesticides were Chlorothalonil, Glyphosate, Tetrachlorvinphos, Parathion, Malathion, Diazinon, heptachlor, Hexachlorobenzene, aldrin, dieldrin, DDT, chlordane, Lindane. Most of these pesticides were organochlorine. The organochlorine pesticides are primarily detected in foods of animal origin. In some studies, the amount of carcinogenic organochlorine was higher than the permissible levels. From the carcinogenic herbicide, Glyphosate. An important finding of this systematic review is that carcinogenic organochlorines are still a threat to cancer incidence.

The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines the strengths of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) into a single framework. By doing so, it allows for quicker learning and adaptable interpretation capabilities, which are useful for modeling complex patterns and identifying nonlinear relationships. One significant challenge in assessing water quality is the difficulty and time-consuming nature of determining the various factors that impact it. Given this situation, predicting heavy metal levels in groundwater resources, both urban and rural, is essential. This paper investigates two methods, ANFIS-FCM and ANFIS-SUB, to determine their effectiveness in modeling Cadmium (Cd) in groundwater resources. ‏The parameters to be considered are: dissolved solids (TDS), electroconductivity (EC), turbidity (TU), and pH were assumed to be the independent variables. A total of 51 sampling location were used with in the groundwater resource were used to develop the fuzzy models. For evaluating the performance of ANFIS-FCM and ANFIS-SUB models, three different performance criteria including the correlation coefficient, root mean square error, and sum square error were used for comparing the model outputs with actual outputs‏.‏ Based on the obtained results from scatter plots of actual and predicted value by ANFIS-SUB and ANFIS- FCM models, the determination coefficient (R2) value for total data, test and train sets is equal to 0.978, 0.982, 0.993 and to 0.983, 0.999 and 0.998 respectively. This result proved the Cd predictions of the implemented ANFIS-FCM model was significantly close to the measured all experimental data with R2 of 0.983. The performance of the implemented ANFIS-FCM model was compared with the ANFIS-SUB model and it is found that the ANFIS-FCM provided slightly higher accuracy than the ANFIS-SUB model. Also, the results obtained from the comparison between the predicted and the actual data indicated that the ANFIS-FCM and ANFIS-SUB have a strong potential in estimating the heavy metals in the groundwater with a high degree of accuracy.