Water quality modeling for load reduction under uncertainty: A Bayesian approach

• Published in: Water research (Oxford) 2008-07, Vol.42 (13), p.3305-3314
• Main Authors: Liu, Yong, Yang, Pingjian, Hu, Cheng, Guo, Huaicheng
• Format: Article
• Language: English
• Subjects: ammonium nitrogen; Applied sciences; Bayes Theorem; Bayesian approach; Bayesian theory; biochemical oxygen demand; Exact sciences and technology; Hun-Taizi River; Load estimation; Markov chain Monte Carlo (MCMC); Models, Biological; organic matter; Other industrial wastes. Sewage sludge; pollutants; Pollution; pollution load; Quaternary Ammonium Compounds - analysis; rivers; Rivers - chemistry; simulation models; spatial variation; Uncertainty; velocity; Wastes; water flow; water pollution; water quality; Water quality modeling; Water Supply - analysis; Water treatment and pollution
• ISSN: 0043-1354; 1879-2448
• DOI: 10.1016/j.watres.2008.04.007

A Bayesian approach was applied to river water quality modeling (WQM) for load and parameter estimation. A distributed-source model (DSM) was used as the basic model to support load reduction and effective water quality management in the Hun-Taizi River system, northeastern China. Water quality was surveyed at 18 sites weekly from 1995 to 2004; biological oxygen demand (BOD) and ammonia (NH 4 +) were selected as WQM variables. The first-order decay rate ( k i ) and load ( L i ) of the 16 river segments were estimated using the Bayesian approach. The maximum pollutant loading ( L m) of NH 4 + and BOD for each river segment was determined based on DSM and the estimated parameters of k i . The results showed that for most river segments, the historical loading was beyond the L m threshold; thus, reduction for organic matter and nitrogen is necessary to meet water quality goals. Then the effects of inflow pollutant concentration ( C i− 1) and water velocity ( v i ) on water quality standard compliance were used to demonstrate how the proposed model can be applied to water quality management. The results enable decision makers to decide load reductions and allocations among river segments under different C i− 1 and v i scenarios.