The study investigates the ability of selected tree species to absorb heavy metals (Pb, Ni, Zn) from polluted soils. Seedlings of Adansonia digitata (P), Jatropha curcas (P), and Hildegardia barteri (P) were transplanted into polythene pots with soils from a dumpsite (T), highway (T), industrial area (T), and farmland (T), forming a 3x4 factorial experiment replicated five times in a Completely Randomized Block Design. Pre-sowing analysis showed T and T had the highest Pb and Zn concentrations, T had the highest Ni, and T had the lowest heavy metal concentrations. After 12 weeks, heavy metal concentrations decreased in all soils. P concentrated metals in the root, P in the shoot, and P in various plant parts, with significant differences between species. P was identified as an effective phytoextractor for Pb and Zn (TF > 1), and P for Ni. All species showed potential for phytostabilization. The study concludes that these species are viable options for phytoremediation of heavy metals in contaminated soils.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/15226514.2024.2404169 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Efficient diagnosis of diabetes mellitus using an improved ensemble method.
Sci Rep
January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
View Article and Find Full Text PDFRubber intercropping with arboreal and herbaceous species alleviated the global warming potential through the reduction of soil greenhouse gas emissions.
Sci Rep
January 2025
College of Ecology and Environment, Hainan University, Haikou, 570228, China.
Agroforestry systems are known to enhance soil health and climate resilience, but their impact on greenhouse gas (GHG) emissions in rubber-based agroforestry systems across diverse configurations is not fully understood. Here, six representative rubber-based agroforestry systems (encompassing rubber trees intercropped with arboreal, shrub, and herbaceous species) were selected based on a preliminary investigation, including Hevea brasiliensis intercropping with Alpinia oxyphylla (AOM), Alpinia katsumadai (AKH), Coffea arabica (CAA), Theobroma cacao (TCA), Cinnamomum cassia (CCA), and Pandanus amaryllifolius (PAR), and a rubber monoculture as control (RM). Soil physicochemical properties, enzyme activities, and GHG emission characteristics were determined at 0-20 cm soil depth.
View Article and Find Full Text PDFMachine-learning based risk prediction of outcomes in patients hospitalised with COVID-19 in Australia: the AUS-COVID score.
J Am Med Inform Assoc
January 2025
Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia.
Objective: We aimed to develop a highly interpretable and effective, machine-learning based risk prediction algorithm to predict in-hospital mortality, intubation and adverse cardiovascular events in patients hospitalised with COVID-19 in Australia (AUS-COVID Score).
Materials And Methods: This prospective study across 21 hospitals included 1714 consecutive patients aged ≥ 18 in their index hospitalization with COVID-19. The dataset was separated into training (80%) and test sets (20%).
Statistical models versus machine learning approach for competing risks in proctological surgery.
Updates Surg
January 2025
Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
Clinical risk prediction models are ubiquitous in many surgical domains. The traditional approach to develop these models involves the use of regression analysis. Machine learning algorithms are gaining in popularity as an alternative approach for prediction and classification problems.
View Article and Find Full Text PDFWhat Kills Mature Street and Park Trees in Cities? Systematic Quantitative Review of Published Case Studies.
Environ Manage
January 2025
School of Environment and Science, Griffith University, 170 Kessels Road, Nathan, 4111, Australia.
Street and park trees often endure harsher conditions, including increased temperatures and drier soil and air, than those found in urban or natural forests. These conditions can lead to shorter lifespans and a greater vulnerability to dieback. This literature review aimed to identify confirmed causes of street and park tree dieback in urban areas from around the world.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!