article · Hygiene and Environmental Health Advances
Artificial intelligence offers significant improvements for environmental monitoring by enabling accurate disaster forecasts, pollution source detection, and comprehensive tracking of air and water quality. Conventional monitoring methods face challenges that AI systems can address through the integration of diverse data sources, real-time pollution tracking, and precise air quality forecasting. These capabilities facilitate timely interventions and support sustainable development within smart cities. However, wide adoption is constrained by a shortage of specialised AI experts in the environmental domain, alongside serious concerns regarding data access, control, and privacy. These obstacles are particularly acute in areas with developing technological infrastructure. Overcoming these barriers requires active data governance by governments. Ongoing advances in computing power, data collection methods, and algorithms are expected to enhance the accuracy and efficiency of future pollution management systems.
Environmental pollution directly affects public health, urban living standards, and disaster readiness. Understanding how artificial intelligence can modernise traditional monitoring allows communities and municipal authorities to react swiftly to contamination and natural hazards. Identifying constraints like data privacy and skills shortages also highlights where policy support and investment are needed to protect fragile ecosystems.
The abstract points to applications in real-time pollution tracking, disaster forecasting, and smart city planning. Potential users include municipal planners, environmental agencies, and urban managers. As a broad review, the work reflects conceptual and early-stage synthesis rather than a direct commercial product, noting that real-world deployment remains constrained by data governance issues, infrastructure limitations, and a shortage of specialised AI personnel.
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• AI-driven pollution detection enhances environmental protection. • Real-time monitoring facilitates prompt interventions for pollution prevention. • Accurate air quality forecasting aids in planning pollution-reducing activities. • AI's role in smart cities fosters sustainable urban development. • AI algorithms integrate diverse data sources for pollution detection. The application of Artificial Intelligence (AI) in environmental monitoring offers accurate disaster forecasts, pollution source detection, and comprehensive air and water quality monitoring. This article provides an overview of the value of environmental monitoring, the challenges of conventional methods, and potential AI-based solutions. Several significant AI applications in environmental monitoring are highlighted, showcasing their contributions to effective environmental management. AI technologies enhance environmental monitoring by enabling better understanding, prediction, and mitigation of environmental risks. However, realizing the full potential of AI faces hurdles such as a shortage of specialized AI experts in the environmental sector and challenges related to data access, control, and privacy. These issues are more pronounced in regions with developing technological infrastructure. The paper advocates for proactive data governance measures by governments to protect sensitive information. Despite these challenges, the future of AI in environmental monitoring remains promising, with advancements in AI algorithms, data collection techniques, and computing power expected to further improve accuracy and efficiency in pollution monitoring and management.
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DOI: 10.1016/j.heha.2024.100114
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