We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Integrating AI with Advanced Sensor Technologies for Real‐Time Monitoring
Summary
Researchers explored how combining AI-driven machine learning with advanced sensor platforms enables real-time, large-scale microplastic monitoring across aquatic, soil, and urban environments, demonstrating that satellite-derived models paired with laboratory data can estimate microplastic concentrations accurately and identify pollution hotspots to support targeted mitigation policy.
This chapter addresses the increasing presence of microplastics in ecosystems worldwide, highlighting the pressing need for operative detection and mitigation strategies. It further explores the use of artificial intelligence (AI) in combination with precocious sensor technologies to monitor microplastics. AI-driven approaches, particularly machine learning (ML), are transforming sensor platforms into their analytical capacity and enabling the prediction of future microplastic pollution trends. These developments respond to the intrinsic challenges of monitoring and managing complex environmental systems. By integrating spatial and temporal data, researchers can create comprehensive toolkits for mapping, tracking, and analyzing microplastics across different environments such as aquatic systems, soils, and urban areas. AI integration enhances the identification of pollution hotspots and supports the design of targeted mitigation policies. Laboratory-based microplastic analyses, when combined with satellite-derived models, demonstrate the feasibility of large-scale, real-time monitoring with favorable accuracy in estimating microplastic concentrations. The synergy between AI and advanced sensor technologies bridges technological innovation with applicable applications, generating actionable insights to address microplastic pollution, strengthen environmental protection, and safeguard open health.