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 |
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| 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 Evers, Dr Ludger and Bowman, Prof Adrian 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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