A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments

• Published in: Environmental modelling & software : with environment data news Vol. 57; pp. 216 - 226
• Main Authors: Zhao, Ying, Sharma, Ashish, Sivakumar, Bellie, Marshall, Lucy, Wang, Peng, Jiang, Jiping
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2014; Elsevier
• Subjects: ammonia; Animal and plant ecology; Animal, plant and microbial ecology; Applied ecology; Bayesian; Bayesian theory; Biological and medical sciences; branches; China; computer software; Conservation, protection and management of environment and wildlife; Ecotoxicology, biological effects of pollution; Fresh water ecosystems; Fundamental and applied biological sciences. Psychology; General aspects; General aspects. Techniques; hydrologic models; Markov chain Monte Carlo (MCMC); Methods and techniques (sampling, tagging, trapping, modelling...); Multi-pollution resources; pollutants; rivers; seasonal variation; Seasonal water quality management; Synecology; water quality; Water quality modeling; China; Water quality modeling; Multi-pollution resources; Bayesian; Seasonal water quality management; Markov chain Monte Carlo (MCMC); Water resource management; Method; Environmental management; Modeling; Freshwater environment; Markov chain; Pollution; Seasonal variation; Water quality; Pollution source; Stream; Models
• DOI: 10.1016/j.envsoft.2014.03.005
• ISSN: 1364-8152

Excessive pollutant discharge from multi-pollution resources can lead to a rise in downriver contaminant concentration in river segments. A multi-pollution source water quality model (MPSWQM) was integrated with Bayesian statistics to develop a robust method for supporting load (I) reduction and effective water quality management in the Harbin City Reach of the Songhua River system in northeastern China. The monthly water quality data observed during the period 2005–2010 was analyzed and compared, using ammonia as the study variable. The decay rate (k) was considered a key factor in the MPSWQM, and the distribution curve of k was estimated for the whole year. The distribution curves indicated small differences between the marginal distribution of k of each period and that water quality management strategies can be designed seasonally. From the curves, decision makers could pick up key posterior values of k in each month to attain the water quality goal at any specified time. Such flexibility is an effective way to improve the robustness of water quality management. For understanding the potential collinearity of k and I, a sensitivity test of k for I2i (loadings in segment 2 of the study river) was done under certain water quality goals. It indicated that the posterior distributions of I2i show seasonal variation and are sensitive to the marginal posteriors of k. Thus, the seasonal posteriors of k were selected according to the marginal distributions and used to estimate I2i in next water quality management. All kinds of pollutant sources, including polluted branches, point and non-point source, can be identified for multiple scenarios. The analysis enables decision makers to assess the influence of each loading and how best to manage water quality targets in each period. Decision makers can also visualize potential load reductions under different water quality goals. The results show that the proposed method is robust for management of multi-pollutant loadings under different water quality goals to help ensure that the water quality of river segments meets targeted goals. •An environmental modeling (EM) approach is presented for controlling pollutant discharge.•A Bayesian approach is used in the EM to manage water quality targets.•The EM is an effective way to improve the robustness of water quality management.•The potential collinearity of ammonia decay rate (k) and loading (I) is analyzed.•The best way to manage water quality targets in each period is described.