Scenario Based Municipal Wastewater Estimation

This paper develops causal loop diagrams and a system dynamics model for estimation of wastewater quantity changes as a function of future socioeconomic development and the municipal water environment of the city under the influence of several key factors. Using Wuhan a city with population more than 10 million in China as a case study, the variability of Wuhan’s wastewater quantity and water environment is modeled under different development patterns by year 2030. Nine future scenarios are designed by assigning different values to those key factors, including GDP growth rate, water consumption of annual ten thousand GDP, and wastewater treatment fee. The results show that 1 GDP growth leads to an increase in municipal wastewater quantity, but an increase in wastewater treatment fee can be in favor of reducing urban water pollution, and 2 the impact of per ten thousand yuan GDP water consumption on the amount of municipal wastewater is larger in the near future, while the impact of GDP growth rate is much larger in the long term. The dynamic model has proven to be reliable for simulating the municipal wastewater changes, and it could help decision makers to make the scientific and reasonable decisions.

[1]  M. van den Berg,et al.  Demand and distributional effects of water pricing policies , 2008 .

[2]  Stefano Alvisi,et al.  A short-term, pattern-based model for water-demand forecasting , 2006 .

[3]  M. Babel,et al.  A multivariate econometric approach for domestic water demand modeling: An application to Kathmandu, Nepal , 2007 .

[4]  Sajjad Ahmad,et al.  Evaluating Municipal Water Conservation Policies Using a Dynamic Simulation Model , 2010 .

[5]  Haakon Vennemo,et al.  Environmental drag: evidence from Norway , 1999 .

[6]  Mahmut Firat,et al.  Comparative analysis of neural network techniques for predicting water consumption time series , 2010 .

[7]  Sajjad Ahmad,et al.  Evaluating the impact of demand-side management on water resources under changing climatic conditions and increasing population. , 2013, Journal of environmental management.

[8]  Rosa Duarte,et al.  Water use in the Spanish economy: an input-output approach , 2002 .

[9]  T. Panayotou.  Empirical tests and policy analysis of environmental degradation at different stages of economic development , 1993 .

[10]  Rosa Duarte,et al.  Economic growth and the evolution of water consumption in Spain: A structural decomposition analysis , 2013 .

[11]  Sheila M. Olmstead,et al.  Water Demand Under Alternative Price Structures , 2007 .

[12]  Mahdi Zarghami,et al.  System dynamics modeling for complex urban water systems: Application to the city of Tabriz, Iran , 2012 .

[13]  R. Solow.  A Contribution to the Theory of Economic Growth , 1956 .

[14]  Rodney A. Stewart,et al.  ANN-based residential water end-use demand forecasting model , 2013, Expert Syst. Appl..

[15]  Allen C. Kelley,et al.  Aggregate Population and Economic Growth Correlations: The Role of the Components of Demographic Change , 1995 .

[16]  G H Huang,et al.  A system dynamics approach for regional environmental planning and management: a study for the Lake Erhai Basin. , 2001, Journal of environmental management.

[17]  W. Nordhaus,et al.  Lethal model 2: the limits to growth revisited. , 1992, Brookings papers on economic activity.

[18]  Rogelio Oliva,et al.  Model calibration as a testing strategy for system dynamics models , 2003, Eur. J. Oper. Res..

[19]  Yaman Barlas,et al.  Formal aspects of model validity and validation in system dynamics , 1996 .

[20]  Kaiqin Xu,et al.  Analysis of water demand and water pollutant discharge using a regional input–output table: An application to the City of Chongqing, upstream of the Three Gorges Dam in China , 2006 .

[21]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 1—background and methodology , 2005 .

[22]  Lixiao Zhang,et al.  System dynamics modeling of the influence of the TN/TP concentrations in socioeconomic water on NDVI in shallow lakes , 2015 .

[23]  R. Stewart,et al.  Water security through scarcity pricing and reverse osmosis: A system dynamics approach , 2015 .

[24]  P. Hutton,et al.  The natural resource. , 1990 .

[25]  Yuzhi Cai.  Forecasting for quantile self-exciting threshold autoregressive time series models , 2010 .

[26]  S. Simonovic,et al.  Global water resources modeling with an integrated model of the social-economic-environmental system , 2011 .

[27]  Jingjing Xie,et al.  A multi-scale relevance vector regression approach for daily urban water demand forecasting , 2014 .

[28]  Lei Gao,et al.  Relationships between regional economic sectors and water use in a water-scarce area in China: A quantitative analysis , 2014 .