Field study on endogenous perfluoroalkyl acid release and their spatiotemporal distribution processes induced by inland navigation.

Dense populations and industries in regions with developed inland waterways have caused the significant discharge of perfluoroalkyl acids (PFAAs) into surrounding waterways. Despite being the dominant energy input in the waterways, the impact of ship navigation on endogenous PFAA release is unclear. In this study, a field experiment was carried out in the Wangyu River (Taihu Basin, China) to investigate the spatiotemporal distribution processes of PFAAs in the water column after passage of ships with different tonnages, speeds, and draughts. The results showed that the PFAA contents did not decrease continuously with time but increased with a lag after the passing ship triggered a transient massive dissolution of PFAAs into the overlying water. In addition, PFAA contents in suspended particulate matter (SPM) exhibited a fluctuating downward trends after their peak at the moment of ship passage. Vertically, the PFAA concentrations among the layers of overlying water were relatively homogeneous, whereas SPM exhibited substantial heterogeneity in its distribution and adsorption of PFAAs. Moreover, the differences in jet scouring velocity (u), disturbance duration (t), and draught (h) of ships resulted in large variability in PFAA contents in the water column. Variance partitioning analysis further quantified the effects of u, t, and h on total PFAAs in the water column, with individual contributions of 53 %, 12 %, and 6 %, respectively. Furthermore, the release of endogenous PFAAs induced by ship passage involved rapid and slow processes, the former determining the overall PFAA release and the latter affecting PFAA concentration recovery in the water column. The findings provide in-situ observational data on spatiotemporal variations of PFAAs in multiphase media following ship passage, enhancing our understanding of endogenous pollution in inland waterways.

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods.

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018-2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.