Rational Polynomial Functions for Modeling E. coli and Bromide Breakthrough

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dc.contributor.author Meek, David
dc.contributor.author Hoang, Chi
dc.contributor.author Kanwar, Rameshwar
dc.contributor.author Malone, Robert
dc.contributor.author Kanwar, Rameshwar
dc.contributor.author Fox, Garey
dc.contributor.author Guzman, Jorge
dc.contributor.author Shipitalo, Martin
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-02-17T05:44:58.000
dc.date.accessioned 2020-06-29T22:41:46Z
dc.date.available 2020-06-29T22:41:46Z
dc.date.issued 2012-01-01
dc.description.abstract <p>Fecal bacteria peak concentrations and breakthrough times as affected by preferential flow to subsurface (tile) drainage systems following irrigation or rainfall are important when assessing the risk of contamination. Process-based, convective-dispersive modeling of microbial transport through preferential flow has been conducted. Likewise, regression modeling has been used to study solute transport (e.g., nitrate) under agricultural systems and can have advantages over process-based modeling, such as fewer or easier to determine parameters and easier determination of confidence intervals. However, empirical models (e.g., regression) have only rarely been used to investigate microbial transport. In addition, the selection of time response curves to empirically model simple, right skewed, single breakthrough events from field or laboratory data is generally an arbitrary choice and often considers only conventional distribution-shaped response curves, such as lognormal distributions. In this study, we evaluate four rational polynomial functions for modeling bromide and E. coli data from a single breakthrough event from a tile-drained field near Nashua, Iowa. Bromide and liquid swine manure were applied to the plot immediately prior to 42 mm of overhead sprinkler irrigation. E. coli and bromide concentrations were determined in subsurface drainage water samples collected for the next 24 h. Nonlinear iteratively re-weighted least squares regression procedures were used to model the breakthrough data. The maximum event value, time of occurrence, and event total were estimated from the parameters for each model. Selection of the best model was based on multiple performance criteria. A simple rational polynomial with a linear factor in the numerator and quadratic form in the denominator was the overall best choice for E. coli (R² = 0.92). A related fractional order form also known as the Gunary model was the best choice for bromide (R² = 0.93). In comparison, the more commonly assumed lognormal distribution described only 78% of the variation in E. coli and 68% of the variation in bromide, with a weighted mean square error 3.0 to 4.6 times larger than each selected rational polynomial model. In this experiment, the chosen models clearly tracked E. coli and bromide distribution better than the lognormal model.</p>
dc.description.comments <p>This article was published in Transactions of the ASABE Vol. 55(5): 1821-1826. DOI: <a href="https://doi.org/10.13031/2013.42365" target="_blank">10.13031/2013.42365</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_pubs/676/
dc.identifier.articleid 1946
dc.identifier.contextkey 7834335
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/676
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/1467
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_pubs/676/2012_Meek_RationalPolynomial.pdf|||Sat Jan 15 01:27:58 UTC 2022
dc.source.uri 10.13031/2013.42365
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords Breakthrough curves
dc.subject.keywords Contaminant transport
dc.subject.keywords Empirical modeling
dc.subject.keywords Microbial transport
dc.subject.keywords Subsurface drainage
dc.title Rational Polynomial Functions for Modeling E. coli and Bromide Breakthrough
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 5210e67e-b8da-4e17-be3f-843a09381196
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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