Statistical modelling of groundwater contamination monitoring data: a comparison of spatial and spatiotemporal methods

McLean, M.I. , Evers, L. , Bowman, A.W. , Bonte, M. and Jones, W.R. (2019) Statistical modelling of groundwater contamination monitoring data: a comparison of spatial and spatiotemporal methods. Science of the Total Environment, 652, pp. 1339-1346. (doi:10.1016/j.scitotenv.2018.10.231) (PMID:30586819)

McLean, M.I. , Evers, L. , Bowman, A.W. , Bonte, M. and Jones, W.R. (2019) Statistical modelling of groundwater contamination monitoring data: a comparison of spatial and spatiotemporal methods. Science of the Total Environment, 652, pp. 1339-1346. (doi:10.1016/j.scitotenv.2018.10.231) (PMID:30586819)

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Abstract

Field monitoring of groundwater contamination plumes is an important component of managing risks for downgradient receptors and remedial strategies that rely on monitored natural attenuation. Collection of groundwater quality data can however take a considerable effort and be associated with high cost. Here, we investigated the relative merits of analyzing groundwater quality data using spatial compared to spatiotemporal statistical modelling and assessed the accuracy of both methods and implications for data collection requirements. The aim of this was to determine whether the quantity of data collected can be reduced, while retaining the same level of estimation accuracy, by analyzing groundwater contamination data using a spatiotemporal model which “borrows strength” across time, rather than a spatial model for individual sampling events. To capture the variability encountered under field conditions, we used three hypothetical groundwater contamination plumes with increasing complexity, and site data for a large groundwater gasoline additive plume. The results show that spatiotemporal methods can increase efficiency markedly so that, in comparison with repeated spatial analysis, spatiotemporal methods can achieve the same level of performance but with smaller sample sizes.

Item Type:Articles (Other)
Additional Information:This work was part funded by Shell Global Solutions (UK) Ltd.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jones, Dr Wayne and Bowman, Professor Adrian and Evers, Dr Ludger and Low, Dr Marnie
Authors: McLean, M.I., Evers, L., Bowman, A.W., Bonte, M., and Jones, W.R.
Subjects:H Social Sciences > HA Statistics
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Science of the Total Environment
Publisher:Elsevier
ISSN:0048-9697
ISSN (Online):1879-1026
Published Online:22 October 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Science of the Total Environment 652: 1339-1346
Publisher Policy:Reproduced under a Creative Commons License

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