Soil, air, and water pollution from mining and industrial activities: Sources of pollution, environmental impacts, and prevention and control methods

Mining activities, as essential components of industrial development, play a crucial role in the economy. However, they often entail significant environmental consequences, notably soil contamination by heavy metals. This article aims to comprehensively review the issue of soil pollution with heavy metals resulting from mining operations, focusing specifically on the ions of arsenic, cadmium, lead, mercury, and zinc. It begins by examining the sources and impacts of this pollution on soil, water, vegetation, wildlife, and human health. Various methods for monitoring and assessing soil contamination with these heavy metal ions are then discussed, including sampling, chemical analysis, remote sensing, and modeling techniques. Finally, a range of management and control strategies for addressing this form of pollution are presented, encompassing biological, chemical, and physical approaches, alongside innovative and sustainable practices within the mining sector. In conclusion, soil contamination with heavy metal ions from mining activities poses a significant environmental challenge, necessitating the implementation of diverse monitoring, assessment, and mitigation measures to safeguard human health and ecosystem integrity. Future research should prioritize developing innovative technologies for mitigating and remediating contamination from lead, zinc, cadmium, mercury, and arsenic, leveraging artificial intelligence and machine learning for predictive modeling and risk assessment, and promoting sustainable and environmentally friendly mining practices. • Emphasizing the need for further research on the long-term health effects of exposure to lead, zinc, cadmium, mercury, and arsenic on vulnerable groups such as children and pregnant women. • Highlighting the importance of enhancing environmental standards and regulations in mines to reduce pollution resulting from mining activities. • Underlining the necessity of utilizing artificial intelligence and machine learning for predictive modeling and assessment of environmental pollution risks, as well as promoting sustainable mining practices. • Stressing the importance of conducting additional research on control and remediation methods for soil pollution with heavy metals to select effective approaches suitable for diverse environmental and economic conditions. • Highlighting the significance of transparency and disclosure of environmental performance data to promote accountability and progress towards sustainability goals in environmental management.