Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/3207
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Scale appropriate modelling of diffuse microbial pollution from agriculture
Author(s): Oliver, David
Heathwaite, A Louise
Fish, Robert
Chadwick, Dave R
Hodgson, Chris J
Winter, Michael
Butler, Allan J
Contact Email: david.oliver@stir.ac.uk
Keywords: diffuse pollution
faecal indicator organism
modelling
scale
stakeholder
end user
uncertainty
pathogen
Water quality management
Water pollution
Environmental Microbiology
Microbial ecology
Issue Date: Jun-2009
Date Deposited: 27-Jul-2011
Citation: Oliver D, Heathwaite AL, Fish R, Chadwick DR, Hodgson CJ, Winter M & Butler AJ (2009) Scale appropriate modelling of diffuse microbial pollution from agriculture. Progress in Physical Geography, 33 (3), pp. 358-377. https://doi.org/10.1177/0309133309342647
Abstract: The prediction of microbial concentrations and loads in receiving waters is a key requirement for informing policy decisions in order to safeguard human health. However, modelling the fate and transfer dynamics of faecally-derived microorganisms at different spatial scales poses a considerable challenge to the research and policy community. The objective of this paper is to critically evaluate the complexities and associated uncertainties attributed to the development of models for assessing agriculturally derived microbial pollution of watercourses. A series of key issues with respect to scale appropriate modelling of diffuse microbial pollution from agriculture are presented and include: (i) appreciating inadequacies in baseline sampling to underpin model development; (ii) uncertainty in the magnitudes of microbial pollutants attributed to different faecal sources; (iii) continued development of the empirical evidence base in line with other agricultural pollutants; (iv) acknowledging the added-value of interdisciplinary working; and (v) beginning to account for economics in model development. It is argued that uncertainty in model predictions produces a space for meaningful scrutiny of the nature of evidence and assumptions underpinning model applications around which pathways towards more effective model development may ultimately emerge.
DOI Link: 10.1177/0309133309342647
Rights: Published in Progress in Physical Geography. Copyright © 2009 by SAGE Publications.; The final, definitive version of this article has been published in the Journal, Progress in Physical Geography, 33/3, 2009, © SAGE Publications, Inc., 2009 by SAGE Publications, Inc. at the Progress in Physical Geography page: http://ppg.sagepub.com/ on SAGE Journals Online: http://online.sagepub.com/

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