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.
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References
Aelion, C. M., & Conte, B. C. (2004). Susceptibility of residential wells to VOC and nitrate contamination. Environmental Science and Technology, 38(6), 1648–1653.
Carbo, L. I., Flores, M. C., & Herrero, M. A. (2009). Well site conditions associated with nitrate contamination in a multilayer semiconfined aquifer of Buenos Aires, Argentina. Environmental Geology, 57, 1489–1500.
Chen, P. Y., Wang, M. K., Yang, D. S., & Chang, S. S. (2004). Kaolin minerals from Chinmen Island (Quemoy). Clays and Clay Minerals, 52(1), 130–137.
Clark, W. A., & Hosking, P. L. (1986). Statistical methods for geographers. New York: John Wiley & Sons.
Eckhardt, D. A. V., & Stackelberg, P. E. (1995). Relationship of ground-water quality to land use on Long Island, New York. Ground Water, 33(6), 1019–1033.
Gardner, K. K., & Vogel, R. M. (2005). Predicting ground water nitrate concentration from land use. Ground Water, 43(3), 343–352.
Hill, J., Hossain, F., & Sivakumar, B. (2008). Is correlation dimension a reliable proxy for the number of dominant influencing variables for modeling risk of arsenic contamination in groundwater? Stochastic Environmental Research and Risk Assessment, 22, 47–55.
Hu, K., Huang, Y. F., Li, H., Li, B. G., Chen, D. L., & White, R. E. (2005). Spatial variability of shallow groundwater level, electrical conductivity and nitrate concentration, and risk assessment of nitrate contamination in north China plain. Environmental International, 31, 896–903.
Jang, C. S., Chen, S. K., & Kuo, Y. M. (2012). Establishing an irrigation management plan of sustainable groundwater based on spatial variability of water quality and quantity. Journal of Hydrology, 414, 201–210.
Knobeloch, L., Salna, B., Hogan, A., Postle, J., & Anderson, H. (2000). Blue babies and nitrate-contaminated well water. Environmental Health Perspectives, 108(7), 675–678.
Lee, J. J., Jang, C. S., Liu, C. W., Liang, C. P., & Wang, S. W. (2009). Determining the probability of arsenic in groundwater using a parsimonious model. Environmental Science and Technology, 43(17), 6662–6668.
Lee, J. J., Liu, C. W., Jang, C. S., & Liang, C. P. (2008). Zonal management of multipurpose use of water from arsenic-affected aquifers by using a multivariable indicator kriging approach. Journal of Hydrology, 359, 260–273.
Lemeshow, S., Teres, D., Avrunin, J. S., & Pastides, H. (1988). Predicting the outcome of intensive care unit patients. Journal of the American Statistical Association, 83, 348–356.
Liu, A., Ming, J., & Ankumah, R. O. (2005). Nitrate contamination in private wells in rural Alabama, United States. Science of the Total Environment, 346, 112–120.
Liu, C. W., Lin, C. N., Jang, C. S., Chen, C. P., Chang, J. F., Fan, C. C., et al. (2006). Sustainable groundwater management in Kinmen Island. Hydrological Processes, 20, 4363–4372.
Liu, C. W., Lin, C. N., Jang, C. S., Ling, M. P., & Tsai, J. W. (2010). Assessing nitrate contamination and its potential health risk to Kinmen residents. Environmental Geochemistry and Health, 33, 503–514.
Liu, C. W., Jang, C. S., Chen, C. P., Lin, C. N., & Lou, K. L. (2008). Characterization of groundwater quality in Kinmen Island using multivariate analysis and geochemical modeling. Hydrological Processes, 22, 376–383.
Malaguerra, F., Albrechtsen, H. J., Thorling, L., & Binning, P. J. (2012). Pesticides in water supply wells in Zealand, Denmark : a statistical analysis. Science of the Total Environment, 414, 433–444.
Militino, A. F., Ugarte, M. D., & Ibanez, B. (2008). Longitudinal analysis of spatially correlated data. Stochastic Environmental Research and Risk Assessment, 22, S49–S57.
Mineral Resources Survey (1962). Drilling report of kinmen soil, annual report. Mineral Resources Survey: Taipei, pp. 55–75 (in Chinese).
Nolan, B. T., Ruddy, B. C., Hitt, K. J., & Helsel, D. R. (1997). Risk of nitrate in ground waters of the United States—A national perspective. Environmental Science and Technology, 31(8), 2229–2236.
Papatheodorous, G., Lambrakis, N., & Panagopoulos, G. (2007). Application of multivariate statistical procedures to the hydrochemical study of a coastal aquifer : an example from Grete, Greece. Hydrological Processes, 21, 1482–1495.
Rechang Consulting Company. (2002). Setup groundwater observation well in Kinmen. Kinmen: Kinmen County Government (in Chinese).
Twarakavi, N. K. C., & Kaluarachchi, J. J. (2005). Aquifer vulnerability assessment to heavy metals using ordinal logistic regression. Ground Water, 43(2), 200–214.
Ward, M. H., Kifory, B. A., Weyer, P. J., Anderson, K. E., Folsom, A. R., & Cerhan, J. R. (2010). Nitrate intake and the risk of thyroid cancer and thyroid disease. Epidemiology, 21(3), 389–395.
Weyer, P. J., Cerhan, J. R., Kross, B. C., Hallberg, G. R., Kantamneni, J., Breuer, G., et al. (2001). Municipal drinking water nitrate level and cancer risk in older women: the Iowa Women’s Health Study. Epidemiology, 12(3), 327–338.
Wu, D. D. (2011). Introduction to the special SERRA issue on “Risks, Uncertainties, and the Environment”. Stochastic Environmental Research and Risk Assessment, 25, 301–304.
Yang, C. Y., Cheng, M. F., Tsai, S. S., & Hsieh, Y. L. (1998). Calcium, magnesium, and nitrate in drinking water and gastric cancer mortality. Japanese Journal of Cancer Research, 89, 124–130.
Acknowledgment
The authors would like to thank the Soil and Groundwater Remediation Fund Management Board of the Environmental Protection Administration (EPA) in Taiwan and Environmental Protection Bureau (EPB) of Kinmen County for the data provided for Kinmen Island.
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Liu, CW., Wang, YB. & Jang, CS. Probability-based nitrate contamination map of groundwater in Kinmen. Environ Monit Assess 185, 10147–10156 (2013). https://doi.org/10.1007/s10661-013-3319-8
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DOI: https://doi.org/10.1007/s10661-013-3319-8