A DECISION SUPPORT SYSTEM FOR TROPICAL RIVER WATER QUALITY MANAGEMENT

Abstract

Water quality is closely linked to water use and directly or indirectly to the state of economic development of a country. The many ways in which water promotes the general welfare of the society are known as beneficial uses. Unregulated wastewater disposal from varied sources conflicts with beneficial use of water; hence control of quality is required to ensure that watercourses are not exploited beyond their power of self purification while maintaining the intended water use. Controlling and protecting water quality, and modifying it for a particular purpose are the major issues in water quality management. Management of water quality is governed by a legislative framework comprising of a set of rules and regulations. Water quality management in India is promulgated under the Water (Prevention and Control of Pollution) Act, 1974 so as to maintain and restore the wholesomeness of national aquatic resources. The Central and State level Pollution Control Boards have been constituted under its provision so as to coordinate the efforts of water pollution control at a national level. The Central Pollution Control Board (CPCB) has initiated (a) use based classification for river reaches and hence designated them into classes of water ranging from Class A to Class E, and (b) evolving industry specific Minimum National Standards (MINAS) indicating a level upto which an industrial discharger has to treat or control its effluent before discharging into any receiving environment under all circumstances. In the present study, the implementation of water quality management has been attempted within the existing legislative framework employing the technologically emerging field of Decision Support Systems (DSS). A derivative of Management Information Systems, a decision support system is a set of few to many interactive systems that use data and models to help decision makers analyze the problems that are unstructured or semi structured in nature. Massive data requirements, dependence on models, and the need of human element in finalizing a decision are some of the reasons that have endorsed several DSSs in the field of water quality management. As an application study, the present work centers on building a DSS for pollution impaired Hindon river system in the Uttar Pradesh state of Northern India. The Hindon river system comprises of three main rivers viz., Hindon, Kali (west) and Krishni draining a total study area of 4361.76 sq. km. The area is predominantly an agricultural area with a number of agro based industries; mainly sugar mill and paper and paperboard industries, located in the vicinity of the natural drainage. It is understood that the growing pollution status of Hindon river system has resulted in its placement in Class E of CPCB category of river reach conditions in India; which pathetically has fallen from Class D in the recent years. Considering "Holistic Field Assessment of river water quality as the basic step governing the requirement of its management, a monitoring programme was planned and sampling strategies decided to collect water quality samples for the purpose of analyzing the trend of water quality status spanning different seasons in a period of one year. A total number of 126 samples (54 regular samples, 18 biological samples and 54 Dissolve Oxygen samples) for each sampling event spreading over five months, were analyzed as per the procedures outlined in Standard Methods (APHA, 1998) for pH, temperature, Dissolved Oxygen (DO), Total Dissolved Solids (TDS), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Organic-Nitrogen (Organic-N), Ammonia-Nitrogen (Ammonia-N), Nitrates, Total Phosphorous, Orthophosphates and Total Coliforms. The water quality analysis depicted high organic loading into the river(s), very low to nil DO content, high ammonia ii concentrations and very high microbial population of Total Coliforms even during the seasons of low temperature at and downstream of discharge of point sources. An event-based sampling was also conducted during August and September months of the monsoon season. Two hourly and at times, hourly samples were collected from the gauge station at the outlet of the watershed and the samples analyzed for Nitrogen, Phosphorous and Total Organic Carbon (TOC). However, in the absence of discharge data availability corresponding to the observed stage, no meaningful deductions could be made from this analysis. The development of DSS with its essential components serves as a platform for providing the simulation and predictive capabilities and therefore, facilitates the user in generation of scenarios for evaluating the management strategies towards water pollution control in the study area. The user was visualized as a regulatory authority that is responsible for maintaining the water quality of the river bodies for intended purpose. A steady state receiving water quality model, QUAL2E was calibrated and validated with two different field data sets for the selected river lengths of 105 Km and 86 Km of Hindon and Kali river respectively. The model fulfilled the requirements of predicting the behavior of waste load discharge and resulting water quality. Good to average performance of the model as highlighted by the quantitative evaluation parameters recommended the model for inclusion as a descriptive model in the Model Management Component of DSS. A mixed integer linear programming optimization model was developed and included as a decision model. In view of the pollution concern in the study area, simple BOD-DO relationship has been utilized in formulating the optimization model based on the concept of transfer matrix. The formulated model was expected to provide the user with a choice of treatment options in that were subjected to optimality criteria, and constrained with effluent standards and user defined water quality criteria. TheData Management Component of the perceived DSS was developed as a repository of data sets for two purposes, (a) to serve as general water quality information database including field monitored data and historic data for sampling stations if any, and (b) to temporarily store the data of one model while supplying the data to another. Microsoft SQL Server 2000 was the Relational Database Management System used to create and manage the database for the above two purposes. The total size of the database was 84MB, with total number of tables as 91 including 12 static tables and 79 dynamic tables, and overall current rows equaling to 131099. The access to the above two components for the user defined criteria of management, was made possible by coding an interface with graphical metaphors and easy to communicate controls in Visual Basic Environment (Microsoft Visual Studio 6). The Graphical User Interface (GUT) as one of the three essential components of the DSS was designed keeping in view the user requirements and the need to run the application on the PC-Windows environment. Two distinct types of interfaces: water quality data and water quality management were developed so as to present an overall integrated environment. A Microsoft Open Database Connectivity (ODBC) helped interfacing the database with the Visual Basic while Visual Basic commands for file input/output access were expressed in code of statements for model interface. The developed DSS could be successfully applied for updation and modification of existing water quality data; running the system for user defined CBOD concentration for different effluents; allowing "user defined' water quality criteria of management of each of IV the two rivers: river Hindon and river Kali (west); displaying the most feasible treatment alternatives that a user can choose from; viewing the impact of decisions on the water quality profile in the form of tables and graphs; generating summary of the user activity in the form of reports highlighting the treatment option for the effluents that best described the user defined water quality criteria. The application of the developed DSS has the future possibility of incorporation of other pollution control measures such as flow augmentation and in stream treatment options for Hindon river especially.