Application of multivariate statistical methods in the assessment of water quality in selected locations in Jialing River basin in Guangyuan, China

Abstract

This study evaluated temporal and spatial variations in water quality to understand the characteristics of the Jialing River watershed in Guangyuan City, China. Data on 17 parameters obtained from seven sites from 2012 to 2015, with a total of 329 samples for each parameter, were analyzed using multivariate statistical techniques. Observation months were grouped into two periods (Period A, May–November; Period B, December–April) according to similarities in water quality characteristics through time analysis and cluster analysis (CA). Water temperature (TEMP), flow rate (Q), dissolved oxygen (DO), oils, fluoride (F) and cadmium (Cd) were the most significant parameters for discriminating between the two periods. Through a spatial analysis, the sites were classified into two groups (Groups 1 and 2). Q, total phosphorus (TP), oils, F and fecal coliform bacteria (F. coli ) were the most significant parameters for discriminating between the two groups. Results suggested that TEMP, DO and Cd as functions of time, TP and F . coli as functions of space, and Q, oils and F as functions of both time and space should be monitored closely. The main sources of water pollutant were surface runoff and industrial wastewater, of time, and wastewater from agricultural irrigation, industrial wastewater, and municipal sewage, of space.