Source identification of nitrate contamination of groundwater in Yellow River Irrigation Districts using stable isotopes and Bayesian model
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Abstract
Nitrate (NO3-) pollution in groundwater has become a serious environmental problem across the world. It is very important to determine the sources of nitrogen contamination in order to prevent and control NO3- pollution in groundwater. This is because the intake of polluted water can increase health risk of methemoglobinemia and cancer in both aquatic lives and humans. There has been an increasing trend in NO3- pollution in groundwater in the Lower Yellow River Irrigation Districts. Once groundwater is polluted by NO3-, recovery efforts can be very daunting. The effective control and management of NO3- pollution require accurate identification of the actual sources of pollution. In this paper, the sources of NO3- in groundwater in the Lower Yellow River Irrigation District (Panzhuang Irrigation District) were identified using stable isotopes (δ15N and δ18O) and the Bayesian model. The results showed that the range of NO3- concentrations in groundwater in the study area was 0.1-197.0 mg·L-1, with a mean of 34.2 mg·L-1. About 10% of the groundwater samples had NO3- concentration in excess of the maximal standard of nitrate level in drinking water in China (90 mg·L-1). Samples were divided into three depths, including 0-30 m (shallow layer), 30-60 m (middle layer) and >60 m (deep layer). The average NO3- concentrations in shallow groundwater layer, middle layer and deep layer were 25.9 mg·L-1, 39.7 mg·L-1 and 20.1 mg·L-1, respectively. There were high NO3- concentrations in groundwater across Ningjin County, Wucheng County, Pingyuan County and Yucheng City. The composition of δ15N was in the range of 0.72‰-23.93‰, with an average of 11.62‰. That of δ18O was 0.49‰-22.50‰, with an average of 8.46‰. The values of δ15N and δ18O indicated that NO3- in groundwater in the study area mainly originated from chemical fertilizers, manure and sewage. The contributions of the four sources of NO3- (precipitation, chemical fertilizer, soil, manure and sewage) were quantified and estimated using the Bayesian model. The results showed that manure and sewage contributed the most to the overall NO3- level, with a mean NO3- contribution ratio of 56.2%. Chemical fertilizer was the second contributor, with a mean NO3- contribution ratio of 19.3%. The mean NO3- contribution ratio of precipitation and soil was 6.2% and 12.3%, respectively. After identification of NO3- pollution levels and sources, measures were required to reduce NO3- pollution in groundwater. Based on this study, the necessary measures included the construction of sewage pipeline and improving the utilization rate of chemical fertilizers in order to reduce NO3- pollution and improve water quality.
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