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Phytoremediation Strategies for Heavy Metal Contamination: A Review on Sustainable Approach for Environmental Restoration

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Journal of Environmental Protection 2024年第4期15卷 450-474页

作者： Mariam Salifu Matthew Abu John Murtala Abubakar ibukunoluwa abimbola bankole Nneka Damola Ajayi Olawumi Amusan Department of Chemistry and Biochemistry University of Mississippi Olemiss MS USA Department of Biochemistry Ahmadu Bello University Zaria Nigeria Department of Plant Pathology North Dakota State University Fargo ND USA Directorate of Research and Development Nigerian Institute of Leather and Science Technology (NILEST) Zaria Nigeria Department of Chemistry University of Akron Akron OH USA

Current globalization trends and important breakthroughs globally need a complete study of heavy metal contamination, its causes, its impacts on human and environmental health, and different remediation strategies. Heavy metal pollution is mostly produced by urbanization and industry, which threatens ecosystems and human health. Herein, we discuss a sustainable environmental restoration strategy employing phytoremediation for heavy metal pollution, the carcinogenic, mutagenic, and cytotoxic effects of heavy metals such as cadmium, copper, mercury, selenium, zinc, arsenic, chromium, lead, nickel, and silver, which may be fatal. Phytoremediation, which was prioritized, uses plants to remove, accumulate, and depollute pollutants. This eco-friendly method may safely collect, accumulate, and detoxify toxins using plants, making it popular. This study covers phytostabilization, phytodegradation, rhizodegradation, phytoextraction, phytovolatilization, and rhizofiltration. A phytoremediation process’s efficiency in varied environmental circumstances depends on these components’ complex interplay. This paper also introduces developing phytoremediation approaches including microbe-assisted, chemical-assisted, and organic or bio-char use. These advancements attempt to overcome conventional phytoremediation’s limitations, such as limited suitable plant species, location problems, and sluggish remediation. Current research includes machine learning techniques and computer modeling, biostimulation, genetic engineering, bioaugmentation, and hybrid remediation. These front-line solutions show that phytoremediation research is developing towards transdisciplinary efficiency enhancement. We acknowledge phytoremediation’s promise but also its drawbacks, such as site-specific variables, biomass buildup, and sluggish remediation, as well as ongoing research to address them. In conclusion, heavy metal pollution threatens the ecology and public health and must be reduced. Phytoremediation

关键词： Phytoremediation Heavy-Metal Contamination Sustainability Restoration

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Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review

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American Journal of Plant Sciences 2023年第11期14卷 1260-1295页

作者： Matthew Abu John ibukunoluwa bankole Oluwatayo Ajayi-Moses Tofunmi Ijila Timilehin Jeje Patil Lalit Department of Plant Pathology North Dakota State University Fargo ND USA Department of Genomics Phenomics and Bioinformatics North Dakota State University Fargo ND USA USDA-ARS Cereal Crops Research Unit Edward T. Schafer Agricultural Research Center Fargo ND USA Department of Agriculture Agribusiness and Environmental Sciences Texas A&M University Kingsville TX USA Department of Botany and Plant Pathology Purdue University West Lafayette IN USA Department of Plant Pathology Indian Agricultural Research Institute New Delhi India

Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.

关键词： Disease Management Detection Techniques Advanced Detection Sustainability Science-Policy Food Security

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