An integrated optimization method for river water quality management and risk analysis in a rural system

Area of Science:

• Environmental Science
• Water Resource Management
• Risk Analysis

Background:

• Rural river systems face significant uncertainties in components and interrelationships affecting water quality.
• Effective water quality management requires methods that account for probabilistic and interval-valued inputs.
• Decision-makers' risk attitudes influence policy outcomes and system reliability.

Purpose of the Study:

• To develop and apply an interval-stochastic-based risk analysis (RSRA) method for rural river water quality management.
• To analyze the impact of uncertainties and risk attitudes on pollutant discharge and system benefits.
• To identify effective pollution control strategies for the Heshui River Basin.

Main Methods:

• Developed an interval-stochastic-based risk analysis (RSRA) method.
• Incorporated probability distributions and interval values for inputs like pollutant discharge.
• Applied the RSRA method to the Heshui River Basin, analyzing chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and soil loss.

Main Results:

• Uncertainties and risk attitudes significantly impact pollutant discharge and system benefits.
• Higher risk aversion leads to reduced system benefits but increased system reliability.
• Agriculture is the primary source of soil loss, TN, and TP; livestock husbandry dominates COD discharge; fishery presents a high pollution load with low net benefit.

Conclusions:

• The RSRA method effectively supports risk management and policy analysis in water quality control.
• Targeted abatement actions are recommended for agricultural (paddy, dry farms) and livestock (poultry) sectors.
• Cessation of fishery activities may be beneficial due to high pollution contribution and low economic return, balancing development and sustainability.