Sustainable Urban Water Environment

Sustainable Urban Water Environment

Climate, Pollution and Adaptation

Ashantha Goonetilleke, Tan Yigitcanlar, Godwin A. Ayoko and Prasanna Egodawatta

This multi-disciplinary book provides practical solutions for safeguarding the sustainability of the urban water environment. Firstly, the importance of the urban water environment is highlighted and the major problems urban water bodies face and strategies to safeguard the water environment are explored. Secondly, the diversity of pollutants entering the water environment through stormwater runoff are discussed and modelling approaches for factoring in climate change and future urban and transport scenarios are proposed. Thirdly, by linking the concepts of sustainable urban ecosystems and sustainable urban and transport development, capabilities of two urban sustainability assessment models are demonstrated.

Chapter 5: Source contributions of pollutants

Ashantha Goonetilleke, Tan Yigitcanlar, Godwin A. Ayoko and Prasanna Egodawatta

Subjects: economics and finance, environmental economics, environment, climate change, environmental economics, water, urban and regional studies, urban studies


As highlighted in Chapter 4, water quality monitoring is a significant exercise that is commonly undertaken to assess compliance with quality guidelines, evaluate the effectiveness of management strategies, obtain baseline data, examine spatial and temporal information on specific pollutants, or estimate the contribution of various pollution sources to a water body. However, two major issues are associated with water quality monitoring. First, the process of sampling and data acquisition can be time consuming and resource intensive. Second, large quantities of data, consisting of several measurements used to assess multiple water quality influencing variables are usually obtained. On one hand, the first issue can be addressed through the identification of simple surrogate parameters that can be monitored in place of other expensive water quality indicators. On the other hand, the second issue is tackled by employing pattern recognition techniques, which enable few parameters to reproduce the underlying structure of the data with limited loss of vital information contained in the original data-set. Consequently, both of these issues have been resolved in many instances through recourse to multivariate modelling or multivariate data analysis (MDA) techniques. The commonly used techniques are described in the ensuing sections of this chapter.

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