Literature DB >> 32721304

An investigation into seasonal variations of groundwater nitrate by spatial modelling strategies at two levels by kriging and co-kriging models.

Ali Asghar Rostami1, Vahid Karimi2, Rahman Khatibi3, Biswajeet Pradhan4.   

Abstract

Nitrate pollution of groundwater through spatial models is investigated in this paper by using a sample of nitrate values at monitoring wells using the data from four seasons of a year, in which data are sparse. Two spatial modelling strategies are formulated at two levels, in which Strategy 1 comprises: three variations of kriging-based models (ordinary kriging, simple kriging and universal kriging), which are constructed at Level 1 to predict nitrate concentrations; and a Multiple Co-Kriging (MCoK) model is used at Level 2 to enhance the accuracy of the predictions. Strategy 2 is also at two levels but employs Indicator Kriging (IK) at Level 1 as a probabilistic spatial model to predict areas at risk of exceeding two thresholds of 37.5 mg/L and 50 mg/L of nitrate concentration, and Multiple Co-Indicator Kriging (MCoIK) at Level 2 for a better accuracy. The improvements at Level 2 for both strategies are remarkable and hence they are used to gain an insight into inherent problems. The results of a study delineate areas with excessive nitrate concentrations, which are in the vicinity of urban areas and hence reflect poor planning practices since the 1990s. The results further reveal the patterns on sensitivities to seasonal variations driven by aquifer recharge and strong dilution processes in spring times; and on the role of pumpage impacting aquifers giving rise to possible hotspots of nitrate concentrations.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Groundwater contamination; Inclusive multiple model (IMM); Interpolation; Nitrate; Spatial models

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Year:  2020        PMID: 32721304     DOI: 10.1016/j.jenvman.2020.110843

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  1 in total

1.  A new mixture copula model for spatially correlated multiple variables with an environmental application.

Authors:  Mohomed Abraj; You-Gan Wang; M Helen Thompson
Journal:  Sci Rep       Date:  2022-08-16       Impact factor: 4.996

  1 in total

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