Emerging technologies for occurrence, fate, effect and remediation of organic contaminants in soil and sludge

Abstract

Sludge generated at sewage treatment plants is of environmental concern due to the large production and the presence of a high amount of organic contaminants. Agricultural situations often include organic pollutants such as herbicides, insecticides, antibiotics, polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins, and polychlorinated dibenzofurans (PCDD/Fs). They are frequently transported into the environment as waste-water effluents due to their use in farm management practices and for human health plans.

The impact of these contaminants on plant health is also a critical concern, as they can affect plant growth, nutrient uptake, and overall ecosystem stability. This study examines advanced remediation technologies, including physical, chemical, and biological methods, for their effectiveness in addressing soil contamination from sludge. Emphasis is placed on the integration of advanced techniques like phytoremediation, bioremediation, and soil washing, alongside emerging technologies such as artificial intelligence (AI) and machine learning (ML) for enhanced site characterization, contaminant detection, and optimization of remediation strategies. By enhancing the accuracy, efficiency, and sustainability of remediation efforts, these technologies hold the potential to significantly mitigate the environmental and health risks associated with organic contaminants in soil and their impacts on plants.

Keywords

Organic contaminants / Remediation technologies / Soil Health / Soil contamination / Artificial Intelligence (AI) / Machine learning (ML)

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