Water Quality Management

The abatement of fresh water and groundwater resources, increasing population as well as industrial demands render the protection and preservation of these resources all the more important. Water quality is a term applied to indicate the suitability or unsuitability of water for various uses. Each type of water uses needs certain physical, chemical and biological characteristic while various uses have some common characteristic. Water quality management is the management of water quality of the physical, chemical and biological characteristic of water; therefore, management and regulatory agencies can use to evaluate alternatives and make necessary decisions. In this chapter, after defining a few water quality terms and a brief review of the significance of water quality management, the framework of the book was described.

Due to financial constraints and improper selection of water quality stations considering the objective of water uses, water quality monitoring network design is an efficient method to manage water quality. The most crucial part is to find appropriate locations for monitoring stations. In the past, most of water quality selection stations were subjective and the designs on the network had some human error. However, now there are several mathematical methods using experimental data for assessment of existing monitoring stations or designing new network such as Sanders method, multiple criteria decision making (MCDM) and dynamic programming approach (DPA) that developed by researchers. In the following chapter, after reviewing the historical background of developing and application of the methods, the theory of these methods was described in details. Finally, the application of the Sanders Method to design number of water quality monitoring stations in the Kārūn River which located in the south west of Iran was studied.

Having a lot of data for different water quality parameters in surface water without proper interpretation are not useful for water quality management. Due to the extent of water quality parameters, water quality indices (WQI) could be used as a point scale for interpretation of these parameters. WQI is the essential prerequisite of water quality management. Since 1978, much effort have been done to present techniques to summarize water quality data to a defined numeric digit which describes the degree of water quality. In this chapter, at the first step, the historical background of WQI was reviewed. Afterward, National Sanitation Foundation’s Water Quality Index (NSFWQI) method and British Colombia water quality index (BCWQI) Method that are used frequently, described in details. Finally, the application of NSFWQI in the Kārūn River and Sefīd-Rūd River, which located on the south-west and north of Iran, were described. In addition the WQI of the Sefīd-Rūd River was investigated by BCWQI.

When in surface water such as rivers, ponds and lakes detailed characteristic are not available, especially in developing countries, application of deterministic model are not applicable. In this regards, stochastic modeling are applied for estimating the future value of water quality parameters. There has been much effort in developing this technique for solving other engineering matters. Time series modeling as a stochastic model is trying to make probabilistic statements about the relation between system components and their future values and is used frequently in water quality management. In this chapter, after a preliminary explaining historical background of the method and introducing of time series modeling, the various methods such as Box-Jenkins methodology including stationary and non-stationary models and seasonal and non-seasonal models, exponential smoothing methods and Winter’s method, was stated and described in detail. Finally the application of time series modeling on Latian Dam, which located in the southeastern part of Tehran province in Iran, was discussed.

Often in water quality management, understanding the relationship between input and output data might be a complicated process. In this situation Data Driven Models using information and collected data (input data) find out the relationship between inputs and outputs. In this regard, Artificial Neural Network (ANN) is one of the Data Driven Models which has recently been applied as a tool for modeling complicated processes. In this chapter, after reviewing the developing process of ANN in water quality management, the theory of the ANN is mentioned in detail for both static and dynamic methods. Data preparation, learning rate and model efficiency including selection of number of neurons in hiding layer which has a minimum error in learning rate and network efficiency is described in detail. At the end step, as a case study water quality of Zaribar Lake located in the Northwestern part of Iran, using Multilayer Perceptron (MLP) neural network method are described.

Water authority organizations are interested in information the existing situation, seasonal variations and expectations of the future situation of the water quality parameters of surface and groundwater. While detail information about surface water or ground water are available, deterministic models for predicting future values of water quality is more proper than stochastic models. In this regard, numerical models demonstrated an impressive capacity to support important water resource decisions. Therefore, in this chapter Ce-Quel-W2 and Qual-2K models as numerical models that are applied to simulate water quality are described in details. At the end, using Ce-Qual-W2 model the water quality of Karkheh Dam, which located in the Northwestern province of Khūzestān in Iran as a case study, is investigated. In addition the application of Qual-2K models for simulating water quality of the Kārūn River, which located in the south west of Iran, was described.