A multi-objective optimization model integrating machine learning and time-frequency analysis for supporting nitrogen...

Area of Science:

• Environmental Science
• Water Quality Management
• Computational Modeling

Background:

• Excessive nitrogen and phosphorus (NP) discharge threatens ecosystem resilience globally.
• Addressing NP pollution requires understanding complex interactions with economic and environmental factors.

Purpose of the Study:

• To develop a multi-objective optimization model for NP pollution mitigation.
• To integrate advanced optimization, time-frequency analysis, and machine learning for water quality modeling.
• To analyze the impacts of climate change and industrial structure on NP pollution.

Main Methods:

• Developed a general modeling framework integrating optimization, time-frequency analysis, and machine learning.
• Modeled nonlinear relationships between driving forces and NP pollution variations across multiple time scales.
• Incorporated climate change and industrial structure adjustment factors into the analysis.

Main Results:

• The model effectively captures the complexity and uncertainty of water surface systems.
• Optimized industrial structure adjustments can reduce NP pollution while sustaining economic growth.
• Guangzhou case study: projected 7.10% reduction in mean NP concentrations and 52.57% decrease in extreme pollution frequencies by 2025.

Conclusions:

• The developed model offers a valuable tool for harmonizing economic development with ecological requirements.
• Quarterly industrial production structure adjustments can effectively mitigate NP pollution in transitional environments.
• The approach supports NP pollution reduction and decreases the frequency of extreme pollution events.