Электронный каталог

Статья

Badawy, W.M.
Multivariate and Machine Learning-Based Assessment of Soil Elemental Composition and Pollution Analysis / W.M.Badawy, F.I.El-Agawany, M.G.Blokhin, [a.o.]. – Text : electronic // Environments. – 2025. – Vol. 12, No. 8. – P. 289. – URL: https://doi.org/10.3390/environments12080289. – Bibliogr.: 61.

The present study provides a comprehensive characterization of soil elemental composition in the Nile Delta, Egypt. The soil samples were analyzed using Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), highly appropriative for the major element determination and Inductively Coupled Plasma Mass Spectrometry (ICP–MS), outstanding for the trace element analysis. A total of 55 elements were measured across 53 soil samples. A variety of statistical and analytical techniques, including both descriptive and inferential methods, were employed to assess the elemental composition of the soil. Bivariate and multivariate statistical analyses, discriminative ternary diagrams, ratio biplots, and unsupervised machine learning algorithms—such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Hierarchical Agglomerative Clustering (HAC)—were utilized to explore the geochemical similarities between elements in the soil. The application of t-SNE for soil geochemistry is still emerging and is characterized by the fact that it preserves the local distribution of elements and reveals non-linear relationships in geochemical research compared to PCA. Geochemical background levels were estimated using Bayesian inference, and the impact of outliers was analyzed. Pollution indices were subsequently calculated to assess potential contamination. The findings suggest that the studied areas do not exhibit significant pollution. Variations in background levels were primarily attributed to the presence of outliers. The clustering results from PCA and t-SNE were consistent in terms of accuracy and the number of identified groups. Four distinct groups were identified, with soil samples in each group sharing similar geochemical properties. While PCA is effective for linear data, t-SNE proved more suitable for nonlinear dimensionality reduction. These results provide valuable baseline data for future research on the studied areas and for evaluating their environmental situation.



ОИЯИ = ОИЯИ (JINR)2025
Спец.(статьи,препринты) = С 44 г - Физико-химические методы анализа элементов. Анализ с помощью ядерных методов
Спец.(статьи,препринты) = 28.08 - Экология$
Спец.(статьи,препринты) = С 350 - Приложения методов ядерной физики в смежных областях
Спец.(статьи,препринты) = Ц 849 - Искусственный интеллект. Теория и практика
Бюллетени = 43/025