title: An integrated optimization method for river water quality management and risk analysis in a rural system.
abstract: In this study, an interval-stochastic-based risk analysis (RSRA) method is developed for supporting river water quality management in a rural system under uncertainty (i.e., uncertainties exist in a number of system components as well as their interrelationships). The RSRA method is effective in risk management and policy analysis, particularly when the inputs (such as allowable pollutant discharge and pollutant discharge rate) are expressed as probability distributions and interval values. Moreover, decision-makers' attitudes towards system risk can be reflected using a restricted resource measure by controlling the variability of the recourse cost. The RSRA method is then applied to a real case of water quality management in the Heshui River Basin (a rural area of China), where chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and soil loss are selected as major indicators to identify the water pollution control strategies. Results reveal that uncertainties and risk attitudes have significant effects on both pollutant discharge and system benefit. A high risk measure level can lead to a reduced system benefit; however, this reduction also corresponds to raised system reliability. Results also disclose that (a) agriculture is the dominant contributor to soil loss, TN, and TP loads, and abatement actions should be mainly carried out for paddy and dry farms; (b) livestock husbandry is the main COD discharger, and abatement measures should be mainly conducted for poultry farm; (c) fishery accounts for a high percentage of TN, TP, and COD discharges but a has low percentage of overall net benefit, and it may be beneficial to cease fishery activities in the basin. The findings can facilitate the local authority in identifying desired pollution control strategies with the tradeoff between socioeconomic development and environmental sustainability.
authors: MOE Key Laboratory of Regional Energy Systems Optimization, Sino-Canada Resources and Environmental Research Academy, North China Electric Power University, Beijing, 102206, China. zyljing@126.com.
authors: J
authors: J
authors: Liu
authors: Sino-Canada Resources and Environmental Research Academy, North China Electric Power University, Beijing, 102206, Canada. yongping.li@iseis.org.
authors: Y P
authors: YP
authors: Li
authors: Sino-Canada Resources and Environmental Research Academy, North China Electric Power University, Beijing, 102206, Canada. gordon.huang@uregina.ca.
authors: G H
authors: GH
authors: Huang
authors: MOE Key Laboratory of Regional Energy Systems Optimization, Sino-Canada Resources and Environmental Research Academy, North China Electric Power University, Beijing, 102206, China. zxt1231@sina.com.
authors: X T
authors: XT
authors: Zeng
authors: Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, M5S 1A4, Canada. shuang.nie@mail.utoronto.ca.
authors: S
authors: S
authors: Nie
mesh_terms: D057919:Biological Oxygen Demand Analysis; D002681:China; D002947:Cities; D004784:Environmental Monitoring / Q000379:methods*; D009584:Nitrogen / Q000032:analysis; D010758:Phosphorus / Q000032:analysis; D015203:Reproducibility of Results; D045483:Rivers / Q000737:chemistry*; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014876:Water Pollution / Q000032:analysis / Q000517:prevention & control*; D060753:Water Quality
chemical_list: D014874:Water Pollutants, Chemical; D010758:Phosphorus; D009584:Nitrogen
keywords: Pollutant mitigation; Risk management; Rural area; Stochastic with recourse; Uncertainty; Water quality