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

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 eff...