Hydrochemical characterization and quality assessment of groundwater in the hilly area of the Taihang Mountains in Henan Province, China.

Abstract

This study evaluated the quality of groundwater and its suitability for drinking and irrigation in the hilly area of the Taihang Mountains in Henan Province, China. Groundwater samples were collected from 43 unconfined and 20 confined wells and analyzed. The pollution index of groundwater (PIG) was estimated based on the physicochemical parameters, and seven indices, including the sodium adsorption ratio (SAR), sodium percentage (%Na), residual sodium carbonate (RSC), permeability index (PI), magnesium ratio (MR), Kelley's ratio (KR), and corrosivity ratio (CR), were calculated to qualify the groundwater within the research area for irrigation activities. Multivariate statistical techniques were performed to better understand the hydrochemical processes. Chemical analysis showed that the dominant cation and anion were Ca2+ and HCO3-, respectively, and the principal hydrochemical facies was Ca-Mg-HCO3. In terms of pH, total dissolved solids, Na+, Cl-, F-, and SO42-, most samples were well within the limits prescribed by Chinese standards for drinking water quality, but more than half of the unconfined samples exceeded the specified limits for total hardness and nitrate. The PIG values suggested the pollution level was insignificant for all confined water samples and 72.09% of unconfined water samples, but the PIG distribution map showed that the water in the south central part of the study area had low to moderate pollution. According to the computed values of SAR, %Na, RSC, PI, KR, and MR and the results of a salinity diagram, the results further indicated that most of the studied samples were appropriate for irrigation usage. Only the CR values rendered 41.86% of the unconfined samples and 20% of the confined samples unfit for irrigation. Hence, proper measures are needed to resolve the corrosivity problem. Factor analysis resulted in the extraction of 3 factors that explained 81% of the data variability, and the extracted factors pointed towards geogenic factors governing the groundwater quality.