A large complex water quality data set of a polluted river, the Tay Ninh River, was evaluated to identify its water quality problems, to assess spatial variation, to determine the main pollution sources, and to detect relationships between parameters. This river is highly polluted with organic substances, nutrients, and total iron. An important problem of the river is the inhibition of the nitrification. For the evaluation, different statistical techniques including cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) were applied. CA clustered 10 water quality stations into three groups corresponding to extreme, high, and moderate pollution. DA used only seven parameters to differentiate the defined clusters. The PCA resulted in four principal components. The first PC is related to conductivity, NH4-N, PO4-P, and TP and determines nutrient pollution. The second PC represents the organic pollution. The iron pollution is illustrated in the third PC having strong positive loadings for TSS and total Fe. The fourth PC explains the dependence of DO on the nitrate production. The nitrification inhibition was further investigated by PCA. The results showed a clear negative correlation between DO and NH4-N and a positive correlation between DO and NO3-N. The influence of pH on the NH4-N oxidation could not be detected by PCA because of the very low nitrification rate due to the constantly low pH of the river and because of the effect of wastewater discharge with very high NH4-N concentrations. The results are deepening the understanding of the governing water quality processes and hence to manage the river basins sustainably.

Temporal and spatial water quality data are essential to evaluate human health risks. Understanding the interlinking variations between water quality and socio-economic development is the key for integrated pollution management. In this study, we applied several multivariate approaches, including trend analysis, cluster analysis, and principal component analysis, to a 15-year dataset of water quality monitoring (1999 to 2013) in the Thi Vai estuary, Southern Vietnam. We discovered a rapid improvement for most of the considered water quality parameters (e.g., DO, NH4, and BOD) by step trend analysis, after the pollution abatement in 2008. Nevertheless, the nitrate concentration increased significantly at the upper and middle parts and decreased at the lower part of the estuary. Principal component (PC) analysis indicates that nowadays the water quality of the Thi Vai is influenced by point and diffuse pollution. The first PC represents soil erosion and stormwater loads in the catchment (TSS, PO4, and Fetotal); the second PC (DO, NO2, and NO3) determines the influence of DO on nitrification and denitrification; and the third PC (pH and NH4) determines point source pollution and dilution by seawater. Therefore, this study demonstrated the need for stricter pollution abatement strategies to restore and to manage the water quality of the Thi Vai Estuary.

We analyzed the precipitation chemistry for a maritime region in northern Germany (Schleswig–Holstein) from 1997 to 2017 in order to reveal temporal and spatial patterns and to evaluate the role of meteorological factors relative to emission reductions in Germany and Europe. Therefore, we applied several statistical methods such as time series decomposition, principal component, and redundancy analysis. We extracted two main groups: (i) a marine group (Cl, Na, Mg) that was related to natural processes like sea spray input and (ii) an anthropogenic group (Pb, Cd, As, Zn, and nitrogen species) with a terrestrial subgroup (Fe, Al, Mn), which were both related to emissions. These groups were valid for the spatial, seasonal, and annual trend data. Other elements, like Ca, K, total P, and sulfate, were influenced by natural and anthropogenic processes. The seasonal variation of ammonium deposition was caused primarily by ammonia emissions and ancillary by precipitation. Most heavy metals as well as sulfate, nitrate, and ammonium showed decreasing trends in concentrations and deposition fluxes. Only Hg did not show any trend. The decreasing depositions of sulfate and total nitrogen were correlated to emission reductions in Germany. The deposition of most heavy metals was influenced by emission reductions on European scale and meteorological factors such as wind speed and humidity. Hg did not show any correlation with the emission time series in Europe. Instead, it was correlated to the NAO index and wind, implying that global emissions and transport pathways determine the temporal development of Hg depositions. Overall, the study reveals that emission reductions positively influence regional depositions for most investigated substances. The regional spatial patterns of depositions were also influenced by local meteorological factors.