About this course

The course provides students with in‑depth knowledge and practical experience in experimental methodology and modeling within radioecology, as well as an understanding of the importance of interaction between researchers who develop models and those who generate experimental data.

The lectures cover a range of radioecological models and methods for determining the speciation of radionuclides, dosimetry (including wildlife dosimetry), biological effects (including radiosensitivity and the impact of multiple stressors), advanced analytical techniques for exposure characterization (nano‑ and micro‑analytical methods) and source identification (isotopic ratios as "fingerprints"), as well as nuclear safety and mitigation actions related to nuclear accidents.

The laboratory exercises include a dynamic aquatic microcosm experiment where radioactive tracers, sediments, water, and biota are used to study speciation, mobility, and biological uptake over time.

Learning outcome

Knowledge: The student is expected to acquire in‑depth insight into radioecology, with particular emphasis on process understanding, experimental methods, and the development and application of models.

Skills: The student should be able to plan, carry out, and report on both radioecological experiments and modeling studies. The student should also be able to explain the principles of a stepwise development of radioecological models, as well as apply several of the radioecological modeling tools covered in the course, including assessing the uncertainties in model estimates.

General competence: The students are to hold a competence that enable them to contribute to national nuclear emergency preparedness and decommissioning activities by using modeling and experimental approaches to obtain—or advise on obtaining—information that supports consequence and risk assessments related to radioactive contamination.

  • Learning activities

    Lectures, exercises with model software, laboratory assignments with lab report.
  • Teaching support

    Lectures, literature (books, reports and scientific articles), mentoring.

    Case studies included in some lectures.

  • Syllabus

    Information about the syllabus will be provided in Canvas.
  • Prerequisites

    RAD210, RAD320
  • Assessment method

    Portfolio assessment consisting of lab reports.

    Grading: Passed/Not passed.

  • About use of AI

    Oral exam: K1 - No use of AI

    Mandatory activity: K3 - Full use of AI.

    The use of AI is permitted, but it must comply with the Guidelines for Use of Artificial Intelligence (AI) at NMBU.

    Descriptions of AI-category codes.

  • Examiner scheme

    Portfolio-based assessment will be performed by the internal examiner.
  • Mandatory activity

    The first lecture, case studies, and laboratory exercises are mandatory.
  • Notes

    Students who want to take the course must apply for admission in Studentweb no later than December 1st. After that, the places in the course will be distributed.

    If there are few students, the course may either be postponed by one year or offered with an alternative teaching arrangement.

  • Teaching hours

    Lectures: 21 hours (7 hr per week).

    Laboratory exercises: 25 hr

    Case studies 2 hrs

  • Preferential right

    M-RAMI
  • Reduction of credits

    3 ECTS reduced against KJM351
  • Admission requirements

    Science