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

Rostami, AA; Karimi, V; Khatibi, R; Pradhan, B

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

Rostami, AA Karimi, V Khatibi, R Pradhan, B

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Journal of environmental management, 2020, 270, pp. 110843
Issue Date:: 2020-09

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Field	Value	Language
dc.contributor.author	Rostami, AA
dc.contributor.author	Karimi, V
dc.contributor.author	Khatibi, R
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2020-10-31T01:04:32Z
dc.date.available	2020-05-24
dc.date.available	2020-10-31T01:04:32Z
dc.date.issued	2020-09
dc.identifier.citation	Journal of environmental management, 2020, 270, pp. 110843
dc.identifier.issn	0301-4797
dc.identifier.issn	1095-8630
dc.identifier.uri	http://hdl.handle.net/10453/143633
dc.description.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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier BV
dc.relation.ispartof	Journal of environmental management
dc.relation.isbasedon	10.1016/j.jenvman.2020.110843
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Environmental Sciences
dc.subject.mesh	Nitrates
dc.subject.mesh	Water Pollutants, Chemical
dc.subject.mesh	Seasons
dc.subject.mesh	Environmental Monitoring
dc.subject.mesh	Groundwater
dc.subject.mesh	Environmental Monitoring
dc.subject.mesh	Groundwater
dc.subject.mesh	Nitrates
dc.subject.mesh	Seasons
dc.subject.mesh	Water Pollutants, Chemical
dc.title	An investigation into seasonal variations of groundwater nitrate by spatial modelling strategies at two levels by kriging and co-kriging models.
dc.type	Journal Article
utslib.citation.volume	270
utslib.location.activity	England
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-31T01:04:18Z
pubs.publication-status	Published
pubs.volume	270

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.

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http://hdl.handle.net/10453/143633