New Analytical Methods for the Monitoring of Environmental Pollutants
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".
Deadline for manuscript submissions: 20 August 2025 | Viewed by 91
Special Issue Editor
Interests: emerging pollutants; environmental chemistry; non-target analysis; environmental simulation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue on "New Analytical Methods for the Monitoring of Environmental Pollutants" focuses on the development and application of advanced analytical techniques for detecting, quantifying, and understanding environmental contaminants. It also highlights innovative methods in chemical analysis, spectroscopy, chromatography, biosensors, and remote sensing to address the growing challenges of monitoring pollutants in air, water, soil, and biological systems. It also explores integrated approaches combining traditional analytical techniques with modern computational tools, such as machine learning and big data analytics, to improve the accuracy and efficiency of environmental monitoring. Contributions include research on novel detection technologies for emerging contaminants, real-time monitoring systems, and methods for assessing the long-term impact of pollutants on ecosystems and public health. This Special Issue encourages interdisciplinary collaboration between environmental science, analytical chemistry, and data science to tackle complex environmental issues. By showcasing the latest advancements, this Special Issue aims to enhance the capabilities of environmental monitoring, support regulatory compliance, and promote sustainable practices for pollution management.
Dr. Christian Piechotta
Guest Editor
Manuscript Submission Information
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Keywords
- environmental monitoring
- analytical chemistry
- pollutant detection techniques
- spectroscopy in environmental science
- biosensors for pollutants
- chromatographic methods
- emerging contaminants
- remote sensing for pollution
- machine learning in environmental analysis
- sustainable pollution management
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