Analyzing the significant environmental factors on the spatial and temporal distribution of water quality utilizing multivariate statistical techniques: a case study in the Balihe Lake, China.

Abstract

The assessment of surface water quality is significant to the management of aquatic ecosystem. In this research, in Balihe Lake which is an agricultural watershed lake, 11 environmental parameters (pH, water temperature, water depth, turbidity, DO, COD, TN, NH4+-N, NO3--N, TP, Chl-a) are monitored at 45 sampling sites in four seasons (winter of 2016, spring, summer, and autumn of 2017). With these monitoring data, two kinds of multivariate statistical methods including cluster analysis (CA) and principal component analysis (PCA) are applied to evaluate the spatial and temporal characteristics of the surface water quality. The results reveal that the spatial clusters (less, moderately, and highly polluted sections) of 45 sampling sites classified by the CA method are exactly consistent with the geographical distribution of these sampling sites, which rely on water quality meliorating downstream. From the perspective of time scale, the correlations between environmental parameters generated by the PCA method reveal that the main factors affecting the surface water quality are different in the four seasons. For the whole study period, which is a longer time scale rather than season, the main factors are also different to that of any season. Large time scale may weaken the effect and potential risk of nutrients on water quality, and it is therefore reasonable to select seasonal scale for the study of water quality in an agricultural watershed by using PCA. The results of this research may demonstrate significance to the identification of the main pollution factors and water quality assessment in freshwater lake with multivariate statistical methods.