Exploring River Water Quality Management: Development of a Copula-Based Optimization Model

Abstract

China, as the largest developing country in the world, is seeking effective approaches to improve river water quality. However, existing policies cannot adequately balance economic growth and water quality protection. In addition, climate change increases the variability of water quality conditions and thus introduces additional uncertainties into river water quality management. To address these challenges, this study proposes a copula-based water quality optimization model (CWQO) for river water quality management, aiming to achieve adaptive improvements in river water quality. The proposed CWQO can tackle water quality management problems in which complex uncertainties in pollutant concentrations are modeled through copula-based joint chance-constrained programming method integrated with a water pollution diffusion model. Through the CWQO, decision makers can generate pollutant discharge plans while accounting for the impacts of climate change on river water quality. A hypothetical case study is used to demonstrate the applicability of the proposed model. The behaviors of factories under different management policy scenarios are examined. The results indicate that joint wastewater treatment among factories within the same industry can effectively reduce total treatment costs and improve the efficiency of river water quality management.

Keywords: copula, joint chance-constrained programming, water quality management, water pollution diffusion model, stochastic uncertainty, optimization