Effect of hydrogeological conditions and surface loads on shallow groundwater nitrate pollution in the Shaying River Basin: Based on least squares surface fitting model.

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2019
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Abstract
Nitrate pollution in groundwater has become a widespread problem worldwide, but understanding of the factors influencing groundwater nitrate pollution remains limited. Numerous studies have attributed nitrate pollution mostly to surface conditions and have neglected the role of hydrogeology. Therefore, this study used the Shaying River Basin as the study area and developed a least squares surface fitting (LSSF) model to systematically analyze the effect of hydrogeological conditions and surface pollution loads on groundwater nitrate pollution. Intrinsic vulnerability and total soil nitrogen (TSN) were used to represent hydrogeological conditions and surface pollution loads, respectively. The results showed that the concentrations of NO-N in shallow groundwater ranged from 0.002 to 256.29 mg/L (with an average of 14.38 mg/L). The concentration had an overall decreasing trend along the flow path. The water chemistry tended to change from HCO-Ca to HCO·Cl-Ca as the NO-N concentration increased. Groundwater nitrate pollution was simultaneously controlled by intrinsic vulnerability and TSN, and the LSSF model explained 83.5% of the result within a 95% confidence interval. These findings explained the phenomenon by which some areas had high surface loads but no serious groundwater nitrate pollution and some areas had nitrate pollution but no high surface loads. Nitrate accumulated in high levels in areas with a high intrinsic vulnerability due to hydrogeological conditions. TSN, which was the main source of NO-N in groundwater, came mainly from agricultural nitrogen fertilizer inputs and livestock manure. These findings provide helpful information for those tasked with managing and controlling groundwater quality.
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he2019effectwater Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors He, Baonan;He, Jiangtao;Wang, Lei;Zhang, Xiaowen;Bi, Erping;
Journal Water research
Year 2019
DOI S0043-1354(19)30652-9
URL
Keywords Keywords not found

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