Water Quality Assessment and Analysis of Spatial Patterns and Temporal Trends. (1st August 2013)

• Record Type: Journal Article
• Authors: Gazzaz, Nabeel M.; Yusoff, Mohd Kamil; Juahir, Hafizan; Ramli, Mohammad Firuz; Aris, Ahmad Zaharin
• Is Part Of: Water environment research. Volume 85:Number 8(2013)
• Page Start: 751
• Page End: 767
• Publication Date: 2013-08-01
• Subjects: Kinta River -- water quality -- archived data -- statistical analysis -- screening and pretreatment -- spatial patterns -- temporal trends -- linear regression -- artificial neural network; Water quality management -- Periodicals; Water -- Purification -- Periodicals; Water -- Pollution -- Periodicals; Water -- Pollution; Water -- Purification; Water quality management; Sewage; Water Pollution; Periodicals; Electronic journals; Periodicals; 628.16
• DOI: 10.2175/106143013X13596524516347
• Languages: English
• ISSNs: 1061-4303

Abstract

ABSTRACT: : This study investigated relationships of a water quality index (WQI) with multiple water quality variables (WQVs), explored variability in water quality over time and space, and established linear and non‐linear models predictive of WQI from raw WQVs. Data were processed using Spearman's rank correlation analysis, multiple linear regression, and artificial neural network modeling. Correlation analysis indicated that from a temporal perspective, the WQI, temperature, and zinc, arsenic, chemical oxygen demand, sodium, and dissolved oxygen concentrations increased, whereas turbidity and suspended solids, total solids, nitrate nitrogen (NO3 ‐N), and biochemical oxygen demand concentrations decreased with year. From a spatial perspective, an increase with distance of the sampling station from the headwater was exhibited by 10 WQVs: magnesium, calcium, dissolved solids, electrical conductivity, temperature, NO3 ‐N, arsenic, chloride, potassium, and sodium. At the same time, the WQI; Escherichia coli bacteria counts; and suspended solids, total solids, and dissolved oxygen concentrations decreased with distance from the headwater. Lastly, regression and artificial neural network models with high prediction powers (81.2% and 91.4%, respectively) were developed and are discussed.