Environmental pollutants mainly include pesticides, heavy metals, microplastics, and harmful microorganisms, which mainly come from industrial and agricultural production activities and pose a threat to the ecological environment and human health. To effectively manage environmental pollutants, it is necessary to accurately detect and quantify their levels in the corresponding environmental media. Currently, various digital on-site real-time monitoring technologies based on sensors and probes are effective means of identifying environmental pollutants.

This study focuses on the development of digital on-site real-time monitoring technology for environmental pollutants, systematically summarizes the ecological hazards of residual chemical substances in water bodies, soils, and other environments, analyzes in detail the application status of various sensors in pollutant detection, and deeply explores the main challenges facing current technology. Research has shown that digital on-site real-time monitoring technology can instantly obtain dynamic data on environmental pollution status, providing scientific basis for pollution prevention and control decisions. The sensor technologies currently used for digital on-site real-time monitoring of environmental pollutants worldwide mainly include colorimetric sensors, optical sensors, potential sensors, electrochemical sensors, fluorescence sensors, current sensors, conductivity sensors, impedance sensors, electrochemical luminescence sensors, biosensors, and microbial bioelectronic sensors. Research suggests that although sensor technology still faces technical bottlenecks such as insufficient sensitivity, high cost, and limited real-time monitoring capabilities in pollutant detection, as well as issues such as susceptibility to environmental interference and complex maintenance, through technological innovation and interdisciplinary cooperation, especially deep integration with advanced technologies such as artificial intelligence, sensor technology has broad application prospects in the field of environmental monitoring and is expected to achieve intelligent upgrades in the future.

This study was supported by major projects such as the Agricultural Biotechnology Breeding Major Project (2023ZD04062), the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences, and the Special Fund for Modern Agricultural Industry Technology System (CARS-15-21). Associate Researcher Gao Xueke from China National Cotton Research Institute, Master's degree holder Wang Lisha from Xinjiang Agricultural University, and postdoctoral fellow Elumalai from China National Cotton Research Institute are co first authors of the paper. Researcher Luo Junyu from China National Cotton Research Institute and Assistant Researcher Zhao Qing from Grassland Institute of Chinese Academy of Agricultural Sciences are co corresponding authors.