Groundwater contamination in public water supply wells: risk assessment, evaluation of trends and impact of rainfall on groundwater quality

Applied Water Science

Magdalena Ujević Bošnjak¹  Ivan Kovač²  Višnja Oreščanin³  Jasna Nemčić-Jurec⁴  Damir Ruk⁵  Andrew Stephen Kinsela⁶ 

[1] Croatian Institute of Public Health;Faculty of Geotechnics, University of Zagreb;Faculty of Natural Sciences and Mathematics, University of Zagreb;Institute of Public Health of Koprivnica-Križevci County, Trg Tomislava Dr;Koprivnica Waters - Public Water Supply;Water Research Centar, School of Civil and Environmental Engineering, University of NewSouth Wales;

关键词: Public supply wells;    Monitoring;    Groundwater contamination;    Trends;    Rainfall;   

DOI  :  10.1007/s13201-022-01697-1

来源: DOAJ

【 摘 要 】

Abstract This study investigates the risk to contamination of groundwater in public water supply wells in the Koprivnica-Križevci county (northwest Croatia). Five physicochemical parameters were monitored in all groundwater samples from 2008 to 2017 to identify major differences between the wells, assess temporal variations and understand the capacity for rainfall to alter groundwater pollution loadings. Multivariate discriminant analysis showed statistically significant differences between the six sampled wells based on the analyzed parameters (Wilks' lambda: 0.001; F = 26.2; p < 0.0000). Principal component analysis revealed two significant factors, including factor 1 which explained 32.8% of the variance (suggesting that the quality of the groundwater was mainly controlled by nitrate) and factor 2, accounting for 16.2% of the total variance (which corresponded to KMnO4/oxidizability and to a lesser extent, pH). The time series data showed disparate trends, with nitrate concentrations increasing, whereas pH and KMnO4 decreased, while electrical conductivity and chloride levels remained stable. Although rainfall can impact groundwater pollution loadings through dilution processes in aquifers, the resulting fluctuations in physicochemical parameters are complicated by variations in rainfall events and local topography, as well as from climate change. Therefore, it is important to predict the contamination of groundwater quality in the future using machine learning algorithms using artificial neural network or similar methods. Multivariate statistical techniques are useful in verifying temporal and spatial variations caused by anthropogenic factors and natural processes linked to rainfall. The resulting identified risks to groundwater quality would provide the basis for further groundwater protection, particularly for decisions regarding permitted land use in recharge zones.

【 授权许可】

Unknown