Probability-based nitrate contamination map of groundwater in Kinmen
Journal
Environmental Monitoring and Assessment
Journal Volume
185
Journal Issue
12
Pages
10147-10156
Date Issued
2013
Author(s)
Abstract
Groundwater supplies over 50 % of drinking water in Kinmen. Approximately 16.8 % of groundwater samples in Kinmen exceed the drinking water quality standard (DWQS) of NO3 --N (10 mg/L). The residents drinking high nitrate-polluted groundwater pose a potential risk to health. To formulate effective water quality management plan and assure a safe drinking water in Kinmen, the detailed spatial distribution of nitrate-N in groundwater is a prerequisite. The aim of this study is to develop an efficient scheme for evaluating spatial distribution of nitrate-N in residential well water using logistic regression (LR) model. A probability-based nitrate-N contamination map in Kinmen is constructed. The LR model predicted the binary occurrence probability of groundwater nitrate-N concentrations exceeding DWQS by simple measurement variables as independent variables, including sampling season, soil type, water table depth, pH, EC, DO, and Eh. The analyzed results reveal that three statistically significant explanatory variables, soil type, pH, and EC, are selected for the forward stepwise LR analysis. The total ratio of correct classification reaches 92.7 %. The highest probability of nitrate-N contamination map presents in the central zone, indicating that groundwater in the central zone should not be used for drinking purposes. Furthermore, a handy EC-pH-probability curve of nitrate-N exceeding the threshold of DWQS was developed. This curve can be used for preliminary screening of nitrate-N contamination in Kinmen groundwater. This study recommended that the local agency should implement the best management practice strategies to control nonpoint nitrogen sources and carry out a systematic monitoring of groundwater quality in residential wells of the high nitrate-N contamination zones. © 2013 Springer Science+Business Media Dordrecht.
Subjects
Groundwater quality; Logistic regression; Nitrate contamination map; Zonal management
SDGs
Other Subjects
Best management practices; Ground-water qualities; Logistic regression models; Logistic regressions; Nitrate contamination; Occurrence probability; Water quality management plan; Zonal managements; Contamination; Groundwater; Management science; Nitrates; Probability; Radioactive materials; Regression analysis; Spatial distribution; Water conservation; Water management; Water quality; Water supply; Groundwater pollution; dissolved oxygen; drinking water; ground water; nitrate; best management practice; concentration (composition); drinking water; groundwater pollution; logistics; map; nitrate; nitrogen; probability; regression analysis; spatial distribution; water management; water quality; well water; article; audiovisual equipment; controlled study; electric conductivity; electric potential; environmental monitoring; geographic names; Kinmen; pH; probability; risk assessment; screening; season; soil; water contamination; water quality; water supply; water table; water temperature; Agriculture; China; Environmental Monitoring; Groundwater; Nitrates; Probability; Regression Analysis; Water Pollutants, Chemical; Water Pollution, Chemical; Water Quality; Quemoy; Taiwan
Type
journal article