A Semi-Infinite Interval-Stochastic Risk Management Model for River Water Pollution Control under Uncertainty

Jing Liu,Yurui Fan,Guohe Huang,Yongping Li

doi:10.3390/w9050351

Jing Liu, Yurui Fan + Show 2 more

Open Access

https://doi.org/10.3390/w9050351

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Journal: Water	Publication Date: May 18, 2017
Citations: 2	License type: CC BY 4.0

Affiliation: University of Regina, Xiamen University of Technology

Abstract

In this study, a semi-infinite interval-stochastic risk management (SIRM) model is developed for river water pollution control, where various policy scenarios are explored in response to economic penalties due to randomness and functional intervals. SIRM can also control the variability of the recourse cost as well as capture the notion of risk in stochastic programming. Then, the SIRM model is applied to water pollution control of the Xiangxihe watershed. Tradeoffs between risks and benefits are evaluated, indicating any change in the targeted benefit and risk level would yield varied expected benefits. Results disclose that the uncertainty of system components and risk preference of decision makers have significant effects on the watershed's production generation pattern and pollutant control schemes as well as system benefit. Decision makers with risk-aversive attitude would accept a lower system benefit (with lower production level and pollutant discharge); a policy based on risk-neutral attitude would lead to a higher system benefit (with higher production level and pollutant discharge). The findings can facilitate the decision makers in identifying desired product generation plans in association with financial risk minimization and pollution mitigation.

Highlights

The problem of water quality deterioration hinders socio-economic development and eco-environmental sustainability in many countries (e.g., China)
A semi-infinite interval-stochastic risk management (SIRM) model has been proposed proposed through integrating the concepts of financial risk management and functional interval through integrating the concepts of financial risk management and functional interval parameters parameters into a two‐stage stochastic programming (TSP) framework
The SIRM model can handle into a two-stage stochastic programming (TSP) framework

Summary

Introduction

The problem of water quality deterioration (e.g., water pollution) hinders socio-economic development and eco-environmental sustainability in many countries (e.g., China). Effective planning of water pollution control for watersheds plays an important role for national and/or regional sustainable development [2,3,4]. Decisions in water pollution control are often made on the basis of uncertain information (i.e., various uncertainties) existing in system components and their interactions [5,6]. The major sources of uncertainty in water pollution control are the random characteristics of natural processes (e.g., precipitation and climate change) and stream conditions (e.g., stream flow, water supply, and point/nonpoint source pollution), the errors in estimated modeling parameters, and the imprecision of system objectives and constraints [7]. Pollutant discharge allowances are often affected by pollutant discharge rates (which influenced by random events such as temperature and precipitation) and decision makers’ estimations; the allowable loads are not measured with

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