Document Type

Article

Disciplines

2.10 NANO-TECHNOLOGY, Food and beverages

Publication Details

SAML-25 WORKSHOP ON STATISTICAL AND MACHINE LEARNING

doi:10.21427/4xqj-ec38

Abstract

Understanding the dynamics of heavy metal accumulation in soil–plant systems is essential for addressing urban environmental risks, particularly in post-industrial and post-conflict settings. This study presents a novel application of Positive Matrix Factorization (PMF) as a core statistical tool for identifying pollution sources and modelling the transfer of trace metals in the urban environment of Dnipro, Ukraine. The PMF model was applied to a dataset combining concentrations of Cr, Cu, Zn, Pb, As, Cd, Hg, and particulate matter (PM) in air. Results were further integrated with bioaccumulation and translocation indices calculated for two indicator species—Ambrosia artemisiifolia L. and Erigeron canadensis L. The factor profiles obtained via PMF enabled the differentiation of anthropogenic, industrial, and natural sources of contamination and their respective influence on plant metal uptake. This integrative, data-driven approach advances pollution source apportionment in urban soil–plant-air systems and supports the development of targeted remediation strategies. The findings are particularly relevant in the context of post-war environmental recovery and sustainable land management in Ukrainian cities.

DOI

https://doi.org/10.21427/4xqj-ec38

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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