Technological Trends in Environmental Pollution Detection

Abstract

Environmental contamination is a critical global challenge affecting ecosystems, human health, and climate stability. Rapid industrialization and urbanization have increased pollution in air, water, and soil, while traditional monitoring methods based on manual sampling and laboratory analysis are insufficient to address complex and dynamic pollution patterns. This paper reviews technological advancements transforming pollution detection, including sensor technologies, artificial intelligence, remote sensing, IoT architectures, and data-driven modeling. Modern systems emphasize real-time monitoring, distributed sensing networks, and predictive analytics. Emerging sensing platforms integrate nanotechnology, biosensors, spectroscopy, and MEMS to enhance sensitivity and response time, while satellite imaging and UAVs enable large-scale environmental monitoring. Machine learning algorithms further support anomaly detection, source identification, and forecasting. The study categorizes detection technologies and proposes an integrated monitoring framework evaluated on accuracy, scalability, latency, and robustness. Findings highlight the effectiveness of hybrid systems combining physical sensors with AI-based inference models. Despite improvements in detection accuracy and cost efficiency, challenges remain in calibration stability, data heterogeneity, energy efficiency, cybersecurity, and ethical data use. Future research should focus on self-organizing sensor networks, edge intelligence, multi-modal data fusion, and sustainable monitoring infrastructures