Lee CM, Choi H, Kim Y, Kim M, Kim H, Hamm SY. Characterizing land use effect on shallow groundwater contamination by using self-organizing map and buffer zone.
THE SCIENCE OF THE TOTAL ENVIRONMENT 2021;
800:149632. [PMID:
34426351 DOI:
10.1016/j.scitotenv.2021.149632]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/22/2021] [Accepted: 08/09/2021] [Indexed: 06/13/2023]
Abstract
Nitrate-nitrogen (NO3-N) contamination in groundwater is a major problem of drinking and domestic waters in rural areas. This study revealed the influence of land use type on shallow alluvial groundwaters in a typical rural area in South Korea by applying a self-organizing map (SOM), principal component analysis (PCA), and hierarchical cluster analysis (HCA). The uncertainty of spatial information on land use was improved by using a buffer zone of the average influence radius of 32.65 m surrounding wells. Two major land-use types, forests (44.9%) and rice fields (28.8%), occupied a total of 73.7% of the rural area. The higher concentrations of NO3-N in public facilities and livestock areas were demonstrated to directly recharge groundwater pollutants. NO3-N contamination in rice paddies, which also contained chlorine (Cl) and sulfate (SO4), was assessed according to the nutrients and residual salt in the soil. In addition, different NO3-N concentrations for the same land use indicate various biochemical reactions and NO3-N recharge types into the groundwater system. The shallow groundwaters in the study area were classified into three clusters according to their chemical constituents and land-use properties, especially NO3-N concentration, including pH, Cl, and SO4, using a SOM, PCA, and HCA. Unlike existing studies, we applied a buffer zone based on the Cooper-Jacob equation to obtain an improved SOM model prediction accuracy approximately 10% greater than that using the original dataset.
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