Robust principal component analysis in water quality index development

Some statistical procedures already available in literature are employed in developing the water quality index, WQI. The nature of complexity and interdependency that occur in physical and chemical processes of water could be easier explained if statistical approaches were applied to water quality indexing. The most popular statistical method used in developing WQI is the principal component analysis (PCA). In literature, the WQI development based on the classical PCA mostly used water quality data that have been transformed and normalized. Outliers may be considered in or eliminated from the analysis. However, the classical mean and sample covariance matrix used in classical PCA methodology is not reliable if the outliers exist in the data. Since the presence of outliers may affect the computation of the principal component, robust principal component analysis, RPCA should be used. Focusing in Langat River, the RPCA-WQI was introduced for the first time in this study to re-calculate the DOE-WQI. Results show that the RPCA-WQI is capable to capture similar distribution in the existing DOE-WQI.

Construction of a historical Water Pollution Index for the Mersey Basin

The Mersey Basin has been significantly polluted for over 200 years. However, there is a lack of quantitative historical water quality data as effective water quality monitoring and data recording only began 30-40 years ago. This paper assesses water pollution in the Mersey Basin using a Water Pollution Index constructed from social and economic data. Methodology, output and the difficulties involved with validation are discussed. With the limited data input available the index approximately reproduces historical water quality. The paper illustrates how historical studies of environmental water quality may provide valuable identification of factors responsible for pollution and a marker set for contemporary and future water quality issues in the context of the past. This is an issue of growing research interest.

Water quality index as a simple indicator of aquaculture effects on aquatic bodies

This paper proposes a water quality index (WQI) to subsidize management actions in the Medio Paranapanema Watershed in São Paulo State, Brazil, as a simple pollution indicator for aquaculture activity. Water quality of the Macuco and Queixada rivers was investigated for 2 years (from May 2003 to May 2005). The index proposed in this work is composed of three measurable environmental parameters-turbidity, total phosphorus and dissolved oxygen. Concentrations of these three variables were normalized on a scale from 0 to 100 and translated into statements of water quality (excellent, good, regular, fair and poor). The index was applied to seventeen monitoring points in the aquatic bodies described above and compared to others, one being that used by the Environmental Protection Agency of United States and proposed for the National Sanitation Foundation, other employing minimal index and the last one considering the minimum operator concept. The results show that the degradation in this watershed from aquaculture activity can be easily inferred with this index, which is more restricted than the others routinely used to infer water quality. (C) 2007 Elsevier Ltd. All rights reserved.

Development of a water quality index using a fuzzy logic: A case study for the sorocaba river

Due to growing urbanization and industrialization, the environment is suffering from pollution of rivers, degradation of soils and deteriorated air quality. Quality indices appear to be useful to evaluate the conditions of these media. The aim of this study was the development of a water quality index using a fuzzy inference system, since such an approach has proved advantageous in addressing problems that are subjective by nature or for which the data are uncertain. The methodology employed was based on this inference system, and considered the nine water quality parameters employed by CETESB (Companhia de Tecnologia de Saneamento Ambiental, São Paulo, Brazil) to evaluate water quality. After assessment of the data using the index, a comparison was made with the WQI (Water Quality Index), which is used for the monitoring of various water bodies, including in the study region. The results obtained using the index developed on the basis of fuzzy inference were found to be more useful than those derived from the method currently used by CETESB, since losses and/or omissions concerning individual parameters were minimized. © 2010 IEEE.