Applications of Raman spectroscopy technology in the detection of new pollutants in coastal zone

In recent years, emerging pollutants in coastal regions—such as microplastics, persistent organic pollutants (POPs), antibiotics, and endocrine-disrupting chemicals (EDCs)—have garnered significant attention due to their complex chemical properties, high bioaccumulation potential, and threats to ecosystems and human health. These pollutants are persistent, highly toxic, and resistant to degradation, thereby posing considerable risks to marine ecosystems. To address these challenges, a variety of advanced Raman techniques—including surface-enhanced Raman scattering (SERS), stimulated Raman scattering (SRS), Raman microspectroscopy (RMS), and confocal Raman spectroscopy (CRS)—have demonstrated exceptional potential for pollutant analysis and detection. This review systematically summarizes the technical characteristics and recent advancements of these Raman techniques for the analysis and detection of emerging pollutants in coastal areas. It elucidates the application of these techniques in the qualitative and quantitative analysis of the distribution characteristics of microplastics, POPs, antibiotics, EDCs, and emerging composite pollutants in near-coastal water bodies, sediments, and biological samples. Additionally, the progress of Raman-based multimodal sensing technologies in enhancing the detection of emerging coastal pollutants is discussed. While all show advantages such as high sensitivity, molecular fingerprint specificity, resistance to water interference, and non-destructiveness, different Raman techniques exhibit unique advantages for specific applications. For instance, SERS significantly amplifies signal intensity through the surface enhancement effect, making it particularly suitable for detecting ultra-trace pollutants such as microplastics and antibiotics in complex water samples. With its rapid imaging capabilities, SRS facilitates the swift screening of antibiotics and endocrine disruptors in biological samples, greatly enhancing detection efficiency and reducing sample preparation time. RMS provides precise data on the distribution and morphology of pollutants at a microscopic scale, rendering it an ideal method for studying pollutant accumulation and migration in sediment samples. CRS enables high-resolution three-dimensional imaging, revealing the spatial distribution of pollutants within complex matrices such as water bodies and biological tissues, thereby offering direct evidence for investigating the migration and interaction mechanisms of pollutants. Moreover, the development of portable Raman equipment, in conjunction with efficient sample pretreatment techniques like solid-phase extraction, has further enhanced pollutant detection performance in complex matrices, paving the way for rapid on-site monitoring of coastal pollutants. Despite significant advancements in Raman spectroscopy technologies, challenges remain in practical applications, including slow spectral acquisition, low detection throughput, limited high-throughput data processing, automated analysis capabilities, and fluorescence background interference from complex matrices. Future breakthroughs may be achieved by employing line imaging or wide-field imaging technologies, larger scanning stages, or stitching techniques to mitigate limitations in Raman imaging regarding scanning time, area, and depth. Furthermore, the rapid advancement of artificial intelligence and machine learning is anticipated to enhance Raman spectroscopy′s capabilities in data processing throughput, efficiency, and automated analysis. The integration of emerging algorithms such as convolutional neural networks, support vector machines, and extreme gradient boosting, along with deep learning techniques for spectral denoising, feature extraction, and pattern recognition, is expected to facilitate automated analysis of large datasets, thereby improving the accuracy and efficiency of pollutant detection in complex environments. To address fluorescence interference in complicated samples, advances in three areas are required: light source adjustment, enhancement mechanism design, and signal algorithm optimization.