ADVANCED TECHNIQUES AND SENSOR SYSTEMS FOR ENVIRONMENTAL ANALYSIS

The course is part of the advanced training within the PhD program in Environmental Sciences, providing students with theoretical and practical tools for the understanding and application of advanced analytical techniques and innovative sensor systems aimed at monitoring and characterizing various environmental matrices, such as air, water, soil, and biota. It integrates interdisciplinary skills essential for developing research strategies and interventions on complex environmental challenges. The course offers also an interdisciplinary exploration of Artificial Intelligence (AI) and Machine Learning (ML) applications within environmental sciences.

By the end of the course, students will be able to appropriately select and apply advanced analytical techniques and sensor systems for monitoring and characterizing various environmental matrices, whether simple or complex. They will be able to critically assess available technologies in relation to specific analytical goals, design suitable experimental protocols, interpret environmental data, including multidimensional datasets, and integrate these tools into interdisciplinary research activities. Participants will gain foundational knowledge of AI concepts, including supervised, unsupervised, and reinforcement learning, and understand how these technologies are transforming environmental research and practices.

In light of the diverse educational backgrounds of PhD students, the course includes an introductory module aimed at harmonizing the foundational knowledge required to engage with the advanced topics covered. This module will be structured to ensure accessibility for all participants, without being redundant for those who already possess a solid background in the relevant areas.

The course explores the advanced use of analytical techniques and sensor systems for environmental monitoring, with a specific focus on air, water, soil, and biota matrices. Topics include: principles and classification of chemical, physical, and biological sensors; optical sensor technologies; and emerging tools for the detection of persistent pollutants, microplastics, heavy metals, nanomaterials, and radioactive contaminants. Real-world applications will be discussed, including air pollution monitoring, water quality assessment, and soil contamination analysis, with focus on sampling protocols, data calibration, and validation. The course concludes with a critical overview of future challenges with a focus on AI, the integration of sensors into large-scale environmental monitoring systems, and opportunities for technology transfer to industrial and institutional contexts.

Advanced Sensor Technology. Biomedical, Environmental, and Construction Applications. Ahmed Barhoum and Zeynep Altintas. 2023. Elsevier. ISBN 978-0-323-90222-9. https://doi.org/10.1016/C2020-0-03502-4
Modern Environmental Analysis Techniques for Pollutants. Chaudhery Mustansar Hussain and Rüstem Keçili. 2020. Elsevier. ISBN 978-0-12-816934-6. https://doi.org/10.1016/C2018-0-01639-4

Learning outcomes will be assessed through the critical discussion of case studies, the preparation of an individual technical report on a real-world application of the analytical and sensor techniques covered. The student’s ability to integrate the acquired knowledge within multidisciplinary research contexts will also be evaluated.

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The final grade, regardless of whether the student is attending or non-attending, will be assigned according to the following criteria:

A. Score range 18–22
The grade will be awarded in the presence of:
- sufficient knowledge and applied understanding of the course content;
- limited ability to select and interpret environmental and instrumental data, with only partially independent judgment;
- basic communication skills, with elementary use of technical-scientific language related to analytical techniques and sensor systems.

B. Score range 23–26
The grade will be awarded in the presence of:
- fair knowledge and applied understanding of the course content;
- fair ability to collect, analyze, and interpret data, with autonomous judgment applied to environmental cases;
- appropriate communication skills, with correct use of the technical terminology relevant to the subject.

C. Score range 27–30
The grade will be awarded in the presence of:
- good or very good knowledge and solid applied understanding of the course content;
- good or very good critical analysis skills and ability to evaluate analytical and sensor methodologies in real-world environmental contexts;
- fully appropriate communication skills, with precise and relevant use of technical-scientific language.

D. 30 cum laude (with honors)
Honors will be awarded in the presence of:
- excellent mastery of the content, with fully autonomous comprehension and application skills;
- strong critical and evaluative abilities, including in complex or interdisciplinary cases;
- outstanding communication skills and command of technical-scientific language, with the ability to develop and elaborate content in a personal and in-depth manner.

The course combines lectures, thematic seminars and case study discussions. Students will have the opportunity to directly apply the analytical techniques and sensor systems discussed, using advanced detection tools. Additionally, theoretical insights will be explored through readings and scientific discussions, alongside individual and group research activities.

This subject deals with topics related to the macro-area "Natural capital and environmental quality" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development