An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions

ZM Yaseen - Chemosphere, 2021 - Elsevier
The development of computer aid models for heavy metals (HMs) simulation has been
remarkably advanced over the past two decades. Several machine learning (ML) models …

Data-driven machine learning in environmental pollution: gains and problems

X Liu, D Lu, A Zhang, Q Liu, G Jiang - Environmental science & …, 2022 - ACS Publications
The complexity and dynamics of the environment make it extremely difficult to directly predict
and trace the temporal and spatial changes in pollution. In the past decade, the …

Development and applications of GIS-based spatial analysis in environmental geochemistry in the big data era

H Xu, C Zhang - Environmental Geochemistry and Health, 2023 - Springer
The research of environmental geochemistry entered the big data era. Environmental big
data is a kind of new method and thought, which brings both opportunities and challenges to …

[HTML][HTML] Discovering hidden spatial patterns and their associations with controlling factors for potentially toxic elements in topsoil using hot spot analysis and K-means …

H Xu, P Croot, C Zhang - Environment International, 2021 - Elsevier
The understanding of sources and controlling factors of potentially toxic elements (PTEs) in
soils plays an important role in the improvement of environmental management. With the …

Manganese (Mn) removal prediction using extreme gradient model

SK Bhagat, T Tiyasha, TM Tung, RR Mostafa… - Ecotoxicology and …, 2020 - Elsevier
Abstract Exploring the Manganese (Mn) removal prediction with several independent
variables is tremendously critical and indispensable to understand the pattern of removal …

[HTML][HTML] Machine learning methods to predict cadmium (Cd) concentration in rice grain and support soil management at a regional scale

BY Huang, QX Lü, ZX Tang, Z Tang, HP Chen… - Fundamental …, 2024 - Elsevier
Rice is a major dietary source of the toxic metal cadmium (Cd). Concentration of Cd in rice
grain varies widely at the regional scale, and it is challenging to predict grain Cd …

Identification of heavy metal pollution sources and its associated risk assessment in an industrial town using the K-means clustering technique

N Khorshidi, M Parsa, DR Lentz, J Sobhanverdi - Applied Geochemistry, 2021 - Elsevier
This study intends to (i) identify the potential sources of heavy metal (HM) pollution in an
industrial town situated in the northwestern part of Iran and (ii) and assess whether the …

Co-composted Biochar Enhances Growth, Physiological, and Phytostabilization Efficiency of Brassica napus and Reduces Associated Health Risks Under Chromium …

M Naveed, B Tanvir, W Xiukang, M Brtnicky… - Frontiers in plant …, 2021 - frontiersin.org
Among heavy metals, chromium (Cr) contamination is increasing gradually due to the use of
untreated industrial effluents for irrigation purposes, thereby posing a severe threat to crop …

Comprehensive evaluation of hydro-chemical processes, suitability, health risks, and sources of groundwater contamination using compositional data analysis …

A Ullah, W Ali, S Muhammad, J Ijaz, F Amir… - Groundwater for …, 2023 - Elsevier
This study aimed to evaluate the level of contamination, health risks, hydro-chemical
processes, and sources of contaminants in the groundwater of the Nizampur basin …

Using multivariate compositional data analysis (CoDA) and clustering to establish geochemical backgrounds in stream sediments of an onshore oil deposits area. The …

D Cicchella, M Ambrosino, A Gramazio… - Journal of Geochemical …, 2022 - Elsevier
Establishing the natural background levels of chemical elements is very often extremely
complicated. This is even more true especially for the more anthropized areas, where the …