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Due to the worldwide growth in population, agricultural and industrial activities are expanding quickly, which pollutes the air, water, and soil and leads to the buildup of potentially hazardous substances in medicinal plants. The current study sought to determine the concentrations of various metals in Silybum marianum plants and soils in order to highlight the level of pollution in a Turkish Organized Industrial Zone in Dilovasi District, Kocaeli, and to investigate the effects of these heavy metals on plants. In order to determine the concentrations of elements evaluated within the scope of this study inductively coupled plasma optical emission spectroscopy was employed, due to the benefits of ICP that provides few or no organic molecules, few ionization interferences, as well as the ability to analyze many elements sequentially. Furthermore, in order to evaluate elemental analysis in terms of nutritional values, risk assessments using RDA, EDI, THQ, and HI parameters were conducted. The value of heavy metals in co-located soil samples is relatively high. However, except for Co, Mn, and Ni, it has stayed within the World Health Organization's permitted limits. The evaluation of the co-located soil samples found that the highest contents (mg kg−1) of Al, Ca, Cd, Cr, Cu, Fe, K, Mn, Na, Ni, Pb, and Zn, were recorded as 7825.10, 4365.86, 4768.59, 2.50, 30.45, 38.49, 8352.00, 17452.26, 1495.82, 1495.82, 163.53, 1452.23, 28.67, 136.55 and 125.45, respectively, belonging to Location 1. These findings are in accordance with the Industrial Status Report since the place specified as Location 1 in the current study is characterized by its pronounced industrial activity and the robust electrical plant station with an industrial facility for generating electric power. Furthermore, the hazard index, the estimated daily intake and target hazard quotient for potentially harmful components and recommended daily allowance values for mineral nutrients were computed. Therefore, even though mineral nutritional values in some areas were found to be higher than Buyukada control location, this should be ignored. Consequently, this study warns against using S. marianum plants as food or medicine collected from current regions. As a result, ongoing inspection is necessary to prevent an excessive buildup of metals in the area and other places with similar human life-related features. [ABSTRACT FROM AUTHOR]

The use of artificial neural networks (ANN) in studies with different content is increasing day by day, and its impact and acceptability are also increasing. The importance of using ANN in popular and large‐scale fields such as environmental pollution studies is known. Studies are carried out considering the need to develop ANNs, which can be an alternative to the data obtained as a result of the analyses made with instrumental devices that require high costs and trained human labor. This study, which was carried out using real data and samples showing biomonitoring properties such as soil and plants, is an example to fill the gaps in the literature. In this study, tools that can model both linear and nonlinear relationships were used. The simulation of the dynamic ecological system containing Fe, Mn, and Ni heavy metals was carried out from the point of view of artificial intelligence. It is revealed that ANN systems are supportive methods in studies of determining environmental pollution, especially in biomonitoring studies. [ABSTRACT FROM AUTHOR]

Arundo donax and Phragmites australis were examined in 4 different periods (June and October for 2 years), heavy metal and mineral element accumulations in plants were evaluated, and water-soil-plant relationships were revealed. Element distributions, bioaccumulation factors (BAF) and translocation factors (TF) in different parts of the investigated plant species were also determined. BAFs of elements calculated by using the concentration values in underground parts and sediment samples were between 1.02 and 4.96. While the highest TF was determined as 8.07 for Zn between washed leaf and stem in A. donax, the lowest TF was determined as 0.05 for Fe between stem and underground part. Corresponding highest and lowest TFs for P. australis were 11.80 for Cu between washed leaf and stem, and 0.02 for Fe between stem and underground part, respectively. The results were supported by MANOVA statistical analyzes. Additionally, the macro-micro elements and heavy metal accumulation levels in the parts of the Orontes River ecosystem were significantly higher in the fall periods compared to the spring periods. Our research revealed that the versatile accumulation properties and high accumulation ability of A. donax for Cd, Cr, and Ni and of P. australis for Cd, Co, Cu, Ni, Pb, and Zn. The Orontes River passes through the regions of three different countries with high population density and developed economies. This study presents the current state of the relationship between the element types and their concentrations in the sediments in the Orontes River ecosystem and the ecophysiological parameters of the river. In this study, the focus was on the Türkiye-Hatay region of the Orontes River, the water-soil-plant relationship in this region was revealed in detail, and valuable data were compiled for researchers who will conduct research on the river ecophysiology. [ABSTRACT FROM AUTHOR]

This research is to predict heavy metal levels in plants, particularly in Robinia pseudoacacia L., and soils using an effective artificial intelligence approach with some ecological parameters, thereby significantly eliminating common defects such as high cost and seriously tedious and time-consuming laboratory procedures. In this respect, the artificial neural network (ANN) is employed to estimate the concentrations of essential heavy metals such as Fe, Mn and Ni, depending on the Cu and Zn concentrations of plant and soil samples collected from five different locations. The derived relative errors for the constructed ANN model have been computed within the ranges 0.041–0.051, 0.017–0.025, and 0.026–0.029 for the training, testing and holdout data regarding Fe, Mn, and Ni, respectively. In addition, it has been realized that the relative errors could be diminished up to 0.007 for Fe, 0.014 for Mn and 0.022 for Ni by considering the Cu, Zn, location and plant parts as independent variables during the analysis. The results produced seem instructive and pioneering for environmentalists and scientists to design optimal study programs to leave a livable ecosystem. The levels of essential heavy metals, Fe, Mn, Ni, based on Zn and Cu in plant and soil samples have been predicted through an AI-based prediction model, a class of feedforward artificial neural networks (ANNs) with a multilayer perceptron (MLP). Thereby common drawbacks such as high cost and severely time-consuming laboratory procedures have been significantly eradicated. In the evaluation of different pollution levels at locations, it has been shown that the ANN method can overcome several disadvantages of analytical element analyzers to monitor the amounts of heavy metals such as Fe, Mn, and Ni in soil and plants. [ABSTRACT FROM AUTHOR]

The aim of this paper is to decide on heavy metal levels based on ecological parameters by effectively eliminating common disadvantages such as high cost and serious time-consuming laboratory procedures via an effective artificial intelligence approach. Therefore, this study is hinged on an artificial intelligence technique, ANN, because of its low cost and high accuracy in overcoming the mentioned limitations and obstacles in the determination process of the amounts of elements. The ANNs have thus been employed to determine essential heavy metals, such as Fe, Mn, and Zn depending on Ca, K, and Mg concentrations of soil samples obtained from different altitudes in Mount Ida. To the best knowledge of the authors, this is the first study in the literature in which altitude was considered as a parameter in the prediction of nutrient heavy metals. The computed relative errors are significantly low for each of the considered elements (Fe, Mn, and Zn); and are found to be between 1.0-4.1%, 1.0-4.2%, 1.5-7.1%, respectively, for the training, testing, and holdout data. The findings indicate that the relative errors could still be decreased further by assuming the altitude as a factor variable. [ABSTRACT FROM AUTHOR]