Eutrophication Assessment Based on the Cloud Matter Element Model

Area of Science:

• Environmental Science
• Water Quality Assessment
• Eutrophication Studies

Background:

• Eutrophication poses a significant threat to China's lakes and reservoirs, impacting ecosystems and water resources.
• Evaluating eutrophication involves complex, multi-criteria decision-making processes often affected by inherent uncertainties.

Purpose of the Study:

• To develop and validate a novel Cloud Matter Element (CME) model for objective and scientific eutrophication level evaluation.
• To address the randomness and fuzziness inherent in the eutrophication assessment process.

Main Methods:

• Developed a CME model integrating randomness and fuzziness using cloud theory.
• Determined element certainty degrees via cloud model simulations with expectation (E), entropy (E), and hyper-entropy (H).
• Combined entropy technology and analytic hierarchy process for indicator weight determination.

Main Results:

• The CME model successfully evaluated eutrophication levels for 24 typical Chinese lakes/reservoirs.
• Results demonstrated consistency with comprehensive index, matter element, fuzzy matter element, and cloud models.
• The model effectively determined eutrophication levels based on maximum neartude principal.

Conclusions:

• The proposed CME model is a scientifically sound and effective tool for evaluating lake and reservoir eutrophication.
• The CME model offers an objective approach to managing water quality and addressing eutrophication challenges.