A multi-objective optimization model integrating machine learning and time-frequency analysis for supporting nitrogen and phosphorus pollution reduction in Guangzhou city, China.

Journal: Journal of environmental management
Published Date: May 5, 2025

Abstract

The unbridled discharge of nitrogen and phosphorus (NP) pollutants is believed to have surpassed ecosystem resilience limits for many regions, which is of great concern to research and governmental communities. In this research, a multi-objective optimization model was developed based on integrating advanced optimization, time-frequency analysis, and machine learning approaches into a general modeling framework. Nonlinear relationships among a variety of driving forces and variations of NP pollution can be effectively reflected and handled under multiple time scales, directly capturing the intricacy and uncertainty of water surface system within certain regions. At the same time, impacts of climate change and industry structure adjustment were addressed for deeply analyzing complexities of NP pollution. Results of the model can be used for harmonizing economic development with multi-dimensional ecological requirements, which can then be employed for supporting the mitigation of NP pollution and the reduction of extreme pollution frequency. The developed model was demonstrated through a real-world case study in Guangzhou of south China, a city grappling with the daunting task of reducing NP pollution while addressing economic needs. The results showed that reasonable adjustments to the industrial production structure would effectively reduce NP pollution while maintaining stable economic growth. Guangzhou would reduce mean NP concentrations by 7.10 % and decrease extreme pollution frequencies by 52.57 % in 2025. This approach provided substantial value for quarterly production structure adjustment in a transitional environment.

Authors

  • Yelin Wang
    School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China.
  • Yanpeng Cai
    State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing 100875, China; Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China; Institute for Energy, Environment, and Sustainable Communities, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada. Electronic address: yanpeng.cai@gdut.edu.cn.
  • Shunyu Zhao
    Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China.
  • Ao Wei
    Department of Cardiology, Tianjin Chest Hospital, Tianjin 300222, China.
  • Pan Zhang
    College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, People's Republic of China.
  • Hang Wan
    Research Centre of Ecology & Environment for Coastal Area and Deep Sea, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China; Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China. Electronic address: wanhang@gmlab.ac.cn.
  • Youjie Li
    Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China.

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