Cluster analysis is also useful to handle large water quality datasets and identify the dominant mechanisms and factors which control the groundwater chemistry (Kouping et al. 2006).
The first pilot trading will begin at the end of 2012, and again will allow those involved to use any credits against expected obligations in the future.If that project takes off it could become the world's largest water-quality trading programme, spanning as many as eight states and allowing trading between 46 power plants, thousands of wastewater facilities and about 230,000 farmers.
This study presents usefulness of multivariate statistical techniques for evaluation and interpretation of large complex water quality data sets and apportionment of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources.
Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality.
Therefore, DA allowed a reduction in the dimensionality of the large data set and indicated a few significant parameters responsible for large variations in water quality that could reduce the number of sampling parameters.
Large cities face water quality and quantity problems due to increasing population and improper disposal of solid and liquid wastes.
In other words, WQI summarizes large amounts of water quality data into simple terms (e.g., excellent, good, bad, etc).
WQI is a mathematical tool which can transform large amount of water quality data into a single number which represents the water quality level.
In the 1990s, after a large-scale biomanipulation, water quality improved and harmful blooms of cyanobacteria decreased, but in the 2000s, the blooms appeared again (Keto et al. 2005).
The applicability of SWQIA model needs to be explored and validated in the context of a larger set of water quality parameters and project scenarios at a greater spatial scale.
