期刊名称：Journal of Environmental Health Science and Engineering
印刷版ISSN：2052-336X
出版年度：2017
卷号：15
期号：1
页码：23
DOI：10.1186/s40201-017-0287-x
语种：English
出版社：BioMed Central
摘要：The presence of nitrate is one of the factors limiting the quality of groundwater resources, particularly in arid and semi-arid climates. Therefore, the knowledge about the distribution of nitrate in groundwater and its source has an effective role in protecting health. The study aimed to optimize an interpolation method to predict the nitrate concentration and assessment of aquifer vulnerability in Qazvin plain. One hundred sixty-two deep wells in Qazvin plain aquifer were randomly selected and nitrate concentration was analyzed in four different lands including agricultural, residential, steppe and mixed-use areas. Interpolation was done by IDW, Spline, Kriging and National neighbor methods using ArcGIS software. To select the best interpolation method, errors of predicted values were determined by Mean Relative Error (RME) and Root Mean Square Error (RMSE). For analysis of potential vulnerability of aquifer to nitrate pollution due to agricultural activity and sewage leaks, hazard factors and control factors were used for identification of hazard indexes (HI) using IPNOA and IPNOC model. The results showed that in 8.82% and 18.52% of samples in agricultural and residential areas, the detected nitrate was above the acceptable level at 50 mg/L. National neighbor method with the lowest RME and Spline method with the lowest RMSE were provided the most accurate estimates of nitrates in the aquifer. The highest hazard was obtained in agricultural areas (HI = 6.11). Also, the most influential parameters on aquifer vulnerability were mineral fertilizer (HFf = 3), organic fertilizers (HFm = 3), irrigation systems (CFi = 1.04) and tillage patterns (CFap = 1.04). According to the results, National neighbor with the lowest RME was preferable than the other spatial interpolation methods for prediction of nitrate concentration in the aquifer. This method provided similar spatial distribution maps of nitrate in groundwater and that was an efficient method for assessing water quality. Hazard index as a result of agricultural activities (IPNOA) was ranged from “very low” to “low” which was in accordance with detected and predicted nitrate concentration in the aquifer. In addition he hazard of nitrate contamination from household (IPNOC) was in very low (class 2).
