Reducing the dimension of water quality parameters in source water: An assessment through multivariate analysis on the data from 441 supply systems

In this research, multivariate statistical analysis was performed on twenty water quality parameters (WQP) collected on tri-monthly basis (four times/year) from 441 drinking water sources in Newfoundland and Labrador (NL), Canada for 18 years (1999–2016). The WQP included alkalinity (Alk), color (Co...
Ausführliche Beschreibung

In this research, multivariate statistical analysis was performed on twenty water quality parameters (WQP) collected on tri-monthly basis (four times/year) from 441 drinking water sources in Newfoundland and Labrador (NL), Canada for 18 years (1999–2016). The WQP included alkalinity (Alk), color (Col), conductivity (Cond), hardness (Hard), pH, total dissolved solids (TDS), turbidity (Turb), bromide (Br), calcium (Ca), chloride (Cl), fluoride (F), potassium (K), sodium (Na), sulfate (SO4), dissolved organic carbon (DOC), ammonia (NH3), nitrate (NO3), Kjeldahl nitrogen (N), total phosphorus (P) and magnesium (Mg). The assessment was conducted on surface water (SWS) and groundwater (GWS) sources separately. In SWS and GWS, number of samples analyzed for each WQP were in the ranges of 3434–6057 and 1915–1919 respectively. Averages of DOC and pH showed increasing trends (SWS: DOC = 0.0722 mg/L/year; pH = 0.0375 units/year; GWS: DOC = 0.0491 mg/L/year; pH = 0.0441 units/year) while the other WQP showed variable characteristics, which could increase treatment cost and deteriorate tap water quality. Strong correlations were observed for Ca-Hard (r = 0.97–0.98), TDS-Cond (r = 0.91–0.99) and Na–Cl (r = 0.87–0.96). In SWS, Alk had stronger correlations with Cond, Hard, pH, TDS, Ca and Mg (r = 0.62–0.94) than GWS (r = 0.56–0.63). Principal Component Analysis revealed separate clusters for DOC-Col, Na–Cl, TDS-Cond, Ca-Alk and Mg-Hard, indicating that these WQP moved together. In SWS and GWS, six principal components were significant (eigenvalue ≥ 1.0), and explained 74.8% and 72.9% of overall variances respectively. In Factor Analysis, six varifactors explained 73.4% and 70.5% of total variances in SWS and GWS respectively. For SWS and GWS, eleven and ten WQP, respectively explained these variances, indicating 45% and 50% data reduction respectively. The findings can assist in controlling water quality through monitoring reduced number of WQP, which is likely to minimize the monitoring cost. Ausführliche Beschreibung