Natural and manmade flows of matter form complex metal associations in the body of residents living in certain territories, which leads to functional disorders in their bodies and the depletion of adaptive reserves. It is possible to assess the distribution of metals in the body only taking into account its biogeochemical localization. The question arises about the methodological approach to the determination of regional reference values of the concentrations of metals in biosubstrates of residents of different territories, to which this study was devoted. A designed and trained neural network was used, reflecting the relationship between the concentrations of metals in consumed drinking water and biosubstrates of the body, taking into account the physiological characteristics of the tested group of children and adolescents, based on the regional reference values obtained. Neural network regression methods allowed the calculation of nonlinear dependences of indicators of the state of the internal environment of an organism with external factors, and localized reference values determined in such calculations the indicators of the base state, being guided by the intensity of external factors, which should be assessed. The results of this study are intended for patient-oriented diagnosis and the treatment of eco-conditioned microelementosis in individual locations.

High metal concentrations in the body’s biological substrates often result from a persistent, cumulative impact of adverse environmental conditions. This article considers the quantitative composition of human biological substrates as an indicator of the state of urban ecosystem components. Assessing the accumulation of metals in the body by directly measuring their concentrations in biological substrates is a multi-step analytical procedure. Here, a quick-and-easy method for determining metal concentrations in biological substrates based on a neural network algorithm was introduced. A complex neural network model was developed to enable the determination of metal inputs from the air and food-water system without the need for invasive sampling of biomaterials or too difficult processing and analysis of the samples obtained. The model also proved to be feasible in solving the inverse problems associated with the determination of metal thresholds in various components of urban ecosystems.

This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term and long-term intake from anthropogenic sources. Direct assessment of metal retention in the human body is virtually impossible due to the many internal mechanisms that ensure the kinetics of metals and the wide variety of organs, tissues, cellular structures, and secretions that ensure their functional redistribution, transport, and cumulation. We have developed an intelligent multi-neural network model capable of calculating the content of metals in the human body based on data on their environmental content. The model is two interconnected neural networks trained on actual measurement data. Since metals enter the body from the environment, the predictors of the model are metal content in drinking water and soil. In this case, water characterizes the short-term impact on the organism, and drinking water, combined with metal contents in soil, is a depository medium that accumulates metals from anthropogenic sources—the long-term impact. In addition, human physiological characteristics are taken into account in the calculations. Each period of exposure is taken into account by its neural network. Two variants of the model are proposed: open loop, where the calculation is performed by each neural network separately, and closed loop, where neural networks work together. The model built in this way was trained and tested on the data of real laboratory studies of 242 people living in different districts of Kazan. As a result, the accuracy of the neural network block for calculating long-term impact was 90% and higher, and the accuracy of the block for calculating short-term impact was 92% and higher. The closed double-loop model showed an accuracy of at least 96%. Conclusions: Our proposed method of assessing and quantifying metal accumulation in the body has high accuracy and reliability. It does not require expensive laboratory tests and allows quantifying the body’s metal accumulation content based on readily available information. The calculation results can be used as a tool for clinical diagnostics and operational and planned management to reduce the levels of polymetallic contamination in urban areas.