A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments |
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| Affiliation: | 1. School of Municipal and Environment Engineering, Harbin Institute of Technology, Harbin 150090, China;2. School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales, Australia;3. Department of Land, Air and Water Resources, University of California, Davis, USA;4. Department of Land Resources & Environmental Sciences, Montana State University, Bozeman, USA;1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Future Earth Research Institute, Beijing Normal University, Beijing/Zhuhai 100875/519087, China;2. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA;3. State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China;4. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;5. College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;6. Key Laboratory of Regional Climate-Environment Research for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;1. Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China;2. Institute of Water Resources and Environment, Jilin University, Changchun 130021, China;3. College of Construction Engineering, Jilin University, Changchun 130026, China;1. GEAMA (Environmental and Water Engineering Group), E.T.S Caminos, Universidade de A Coruña, Campus Elviña s/n, 15071 A Coruña, Spain;2. Department of Civil and Natural Resources Engineering, University of Canterbury, Private Bag, 4800, Christchurch, New Zealand |
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| Abstract: | 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. |
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| Keywords: | Water quality modeling Seasonal water quality management Multi-pollution resources Bayesian Markov chain Monte Carlo (MCMC) |
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