Assessing surface water quality in Hungary’s Danube basin using geochemical modeling, multivariate analysis, irrigation indices, and Monte Carlo simulation

Evaluation of water quality is crucial for managing surface water effectively, ensuring its suitability for human use, and sustaining the environment. In the lower Danube River basin, various methods were employed to assess surface water quality for irrigation, drinking, human health risk purposes and the main mechanism control the surface water chemistry. These methods included water quality indicators (WQIs), complex statistical analyses, geographic information systems (GIS), Monte Carlo simulation, and geochemical modeling. Physicochemical analyses of surface water samples revealed primarily Ca-Mg-HCO₃^- is the dominant water types. Principal component analysis (PCA), ionic ratios and piper, chloro alkaline index, Chadha, and Gibbs diagrams identified three distinct water characteristics influenced by water-rocks interaction, evaporation, ions exchange, and human activities. The geochemical modeling showed Danube River water's strong ability to dissolve gypsum, halite, and anhydrite (SI < 0) and precipitate aragonite, dolomite, and calcite with saturation index (SI) value greater than 0 along its flow path. The irrigation water quality index (IWQI = 99.6-107.6), sodium adsorption ratio (SAR = 0.37-0.68), sodium percentage (Na% = 13.7-18.7), soluble sodium percentage (SSP = 12.5-17.5), Potential Salinity (PS = 0.73-1.6), and Residual Sodium Carbonate (RSC = - 1.27-0.58) values were used, mainly indicating acceptable quality with some limitations. Danube River water was unsuitable for drinking based on WQI value (WQI = 81-104). Oral exposure of children to specific components showed a higher hazard index (HI > 1) compared to adults, indicating a 2.1 times higher overall non-carcinogenic risk hazard index. However, Monte Carlo simulation demonstrated negligible iron, manganese, and nitrate health hazards for both age groups. These findings are valuable for water quality management decisions, contributing to long-term resource sustainability.