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.

本研究调查了 Koprivnica-Križevci 县（克罗地亚西北部）公共供水井中地下水污染的风险。从 2008 年到 2017 年，对所有地下水样品的五个物理化学参数进行了监测，以识别井之间的主要差异，评估时间变化并了解降雨改变地下水污染负荷的能力。多变量判别分析显示，基于分析的参数，六个采样孔之间存在统计学显着差异（Wilks λ：0.001；F  = 26.2；p < 0.0000)。主成分分析揭示了两个显着因素，其中因素 1 解释了 32.8% 的方差（表明地下水水质主要受硝酸盐控​​制）和因素 2 占总方差的 16.2%（对应于 KMnO ₄ /氧化性和较小程度的pH）。时间序列数据显示出不同的趋势，硝酸盐浓度增加，而 pH 值和 KMnO ₄下降，而电导率和氯化物水平保持稳定。尽管降雨可以通过含水层中的稀释过程影响地下水污染负荷，但由此产生的物理化学参数波动会因降雨事件和当地地形的变化以及气候变化而变得复杂。因此，使用人工神经网络或类似方法的机器学习算法来预测未来地下水水质的污染是很重要的。多元统计技术可用于验证由人为因素和与降雨相关的自然过程引起的时间和空间变化。由此确定的地下水质量风险将为进一步保护地下水提供基础，