Abstract:

Modern climate and environmental monitoring relies heavily on optical sensing technologies that measure how electromagnetic radiation interacts with the atmosphere and the Earth's surface. Many observational systems used in Earth sciences, including satellite remote sensing, LiDAR instruments, hyperspectral imaging, and atmospheric monitoring techniques, are fundamentally based on spectroscopic measurements across the ultraviolet (UV), visible (VIS), and infrared (IR) spectral domains. Despite their central role in climate science, the physical principles behind these technologies are rarely explored in school-level STEM education through direct experimentation.

This work presents a hands-on educational framework that introduces students to the fundamental principles of optical environmental sensing through practical spectroscopy activities. The approach combines low-cost DIY spectrometers that allow students to directly observe spectral phenomena with compact professional spectrometers that can record calibrated spectral measurements. By comparing these instruments, students gain insight into how scientific sensors operate and how measurement accuracy, resolution, and calibration influence environmental observations.

During the activities, students investigate emission spectra from artificial light sources and the solar spectrum. Particular attention is paid to identifying Fraunhofer absorption lines, which provide a direct link between solar radiation, atmospheric composition, and the techniques used in remote sensing and environmental monitoring. These observations allow discussions on topics such as atmospheric gases, radiative transfer, and the role of spectral analysis in climate observations.

Spectral datasets collected during the experiments are further analyzed using digital tools and machine learning techniques, including classification using a Random Forest algorithm. This introduces students to modern approaches to environmental data analysis, in which automated algorithms help identify spectral signatures and interpret complex datasets.

The activities are implemented within the framework of the EASE (Educate, Act, Sustainably Share for the Environment) initiative, which promotes interdisciplinary STEM education, teacher training, and the integration of real environmental observations into classroom learning.

By linking hands-on spectroscopy experiments with modern data analysis tools and the principles behind satellite and atmospheric sensing technologies, the proposed approach helps students understand how environmental observations are generated and interpreted. At the same time, it develops practical skills in experimental physics, data analysis, and environmental science, strengthening scientific literacy and student engagement with climate-related challenges.

Acknowledgements:
This work was supported by the Romanian Ministry of Education and Research through UEFISCDI, within the National Plan for Research, Development and Innovation (PNCDI IV), Science in Schools Programme, project no. PN-IV-P10-SS-SC-2024-0042 (STEAM-EXPERIENCE), and by the EASE (Educate, Act, Sustainably Share for the Environment) project funded by the Erasmus+ KA220-SCH – Cooperation Partnerships in School Education programme.

Keywords:

Spectroscopy Education, Optical Environmental Sensing, DIY Scientific Instruments, Atmospheric Monitoring, Machine Learning in STEM Education.