Emerging Analytical Techniques for Detection of Environmental Pollutants: A Review

Environmental pollution is one of the most challenging issues for the sustainability of our planet, human health, and ecosystem integrity. Increasing evidence of the release of organic, inorganic, and emerging contaminants necessitates measures for more accurate, rapid, and inexpensive techniques for contamination detection. Although traditional analytical methodologies yield reliable results, they are often hampered by time-consuming sample preparation, high operational costs, and low applicability in the field. Recent developments in analytical science have provided a host of innovative technologies that can overcome these limitations. This review provides a thorough examination of emerging analytical techniques for the detection of pollutants in the environment, including nanomaterial-based sensors, surface-enhanced Raman spectroscopy (SERS), laser-induced breakdown spectroscopy (LIBS), and mass spectrometry-coupled hybrid systems GC–GC-MS and LC–MS/MS. Portable applications have also been developed by integrating microfluidic and lab-on-a-chip devices for convenient real-time analysis. Finally, the inclusion of machine learning (ML) and artificial intelligence (AI) algorithms has dramatically changed the data interpretation process to include predictive modeling and automated environmental monitoring. There is also an emphasis on environmentally friendly and sustainable analytical techniques, including miniaturized, solvent-free, and bioanalytical procedures, that conform to green chemistry guidelines. Despite these achievements, there are significant obstacles in terms of standardization, analytical performance, and scalability. The objectives of this review are to discuss the present advancements, address the identified limitations, and offer future research strategies on smart, integrated, and sustainable analytical methodologies for the rapid detection and management of environmental contaminants.

کلیدواژه ها:

Environmental Pollutants ، analytical techniques ، nanomaterial-based sensors ، Spectroscopy ، microfluidics ، Machine Learning