This study was conducted to grasp the groundwater quality characteristics in Gyeongnam and provide basic data for policy making on efficient groundwater management. It analyzed the water quality characteristics according to depth and geological features of background groundwater quality exclusive monitoring network, and the water quality characteristics according to quater(season) and pollution source of pollution exclusive monitoring network based on the data of the groundwater quality exclusive monitoring network in Gyeongnam for five years(2013-2017).
Analysis results according to depth indicated that as the depth became deeper the average value of pH and EC increased, and the average value of water temperature and DO, ORP, arsenic, total coliforms, and turbidity decreased. The sum of the mean concentrations of positive and negative ions increased as the depth grew. The Na-Cl water type that dictates the effect of artificial pollution appeared only when shallow. The total unfit rate according to depth was lower as the depth was deeper.
The analysis results according to geological features showed that the sum of the positive and negative ions mean concentrations was the highest in clastic sedimentary rock and the lowest in metamorphic rock. The water type of Na-Cl appeared only in clastic sedimentary rock. The total unfit rate according to geological features was the highest in metamorphic rocks, followed by clastic sedimentary rock and unconsolidated sediments, and finally intrusive igneous rock with the lowest.
The analysis results according to quarter results showed that the average concentration of the cations did not differs so much from quarter to quarter. Respectively HCO3- and NO3- were the highest and lowest in the fourth quarter and judged to be a result of denitrification. The sum of the average concentration of positive and negative ions was the largest in the fourth quarter and the smallest in the second quarter. The type of water according to quarter wasn’t so different. Total unfit rate according to quarter was the largest in the third quarter, followed by the second, fourth and first quarter.
The analysis results according to pollution source showed that the concentration of Na+, K+, Mg2+, and HCO3- was the highest in the industrial complex area, the concentration of Ca2+, SO42 was the highest in the sewage treatment equipment and the concentration of Cl-, NO3- and NO3-N was the largest in the abandoned mine area. The sum of the mean concentration of positive and negative ion was largest in industrial complex area, followed by sewage treatment equipment and abandoned mine area, and Na-Cl water type was the largest in industrial complex area. Total unfit rate according to pollution source was largest in sewage treatment equipment followed by industrial complex area and the abandoned mine area.
The correlation coefficient between EC positive and negative ions for the background water quality monitoring network was high. The correlation coefficient between nitric nitrogen and nitrate ion was 1. HCO3- and Ca2+, Mg2+ showed high correlation coefficient of 0.7 and above. For the pollution monitoring system, there was no significant difference from the background water quality measurement network. But Artificial pollution was expected to be greater because of the relatively high correlation coefficient between EC and Na+, Ca2+.
Results of ANOVA according to depth showed that pH, DO, ORP, Na+, Ca2+, Mg2+, NO3-N, arsenic, and turbidity differed statistically at significant levels 0.01. The p-value of T and HCO3- was less than significant levels 0.05. Results of ANOVA according to geological features and pollution source showed that measurements of 16 items(field measurement items(5), cations(4), anions(5), arsenic, and turbidity) excluding total coliforms differed statistically at 99% confidence levels. Results of ANOVA according to quarter showed that T and pH differed statistically at significant levels 1%, 5% respectively. The p-value for the remaining items was greater than the significant levels of 0.05.
These results can be used as basis data for management of water quality and prevention of groundwater pollution in Gyeongnam in the future.