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Modeling spatiotemporal bacterial variability with meteorological and watershed land-use characteristics

Bacteria are a primary contaminant in natural surface water. The instream concentration of fecal coliform, a potential indicator of pathogens, is influenced by meteorological conditions and land-use characteristics. However, the relationships between these conditions and fecal coliforms are not full...

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Published in:Water research (Oxford) 2016-09, Vol.100, p.306-315
Main Authors: Cha, YoonKyung, Park, Mi-Hyun, Lee, Sang-Hyup, Kim, Joon Ha, Cho, Kyung Hwa
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description Bacteria are a primary contaminant in natural surface water. The instream concentration of fecal coliform, a potential indicator of pathogens, is influenced by meteorological conditions and land-use characteristics. However, the relationships between these conditions and fecal coliforms are not fully understood. Furthermore, the sources of large variability in fecal coliform counts, e.g., temporal or spatial sources, remain unexplained, especially at large scales. This study proposes the use of Bayesian overdispersed Poisson models, whereby the combined effects of temperature, rainfall, and land-use characteristics on fecal coliform concentration are quantified with predictive uncertainty, and the sources of variability in fecal coliform concentration are assessed. The models were developed using 8-year weekly observations of fecal coliforms obtained from the Wachusett Reservoir watershed in Massachusetts, USA. The results highlight the importance of interactions among meteorological and land-use characteristics in controlling the instream fecal coliform concentration; the increase in fecal coliform concentration with temperature increase was more drastic when rainfall occurred. Also, the responses of fecal coliforms to temperature increases were more pronounced in forest-dominated than in urban-dominated areas. In contrast, the fecal coliform concentration increased more rapidly with rainfall increases in urban-dominated than in forest-dominated areas. The models also demonstrate that among the sources of variability, the monthly component made the most significant contribution to the variability in fecal coliform concentrations. Our results suggest that seasonally dependent processes, including surface runoff, are critical factors that regulate fecal coliform concentration in streams. [Display omitted] •Positive fecal coliforms-temperature relationships are steeper when rainfall occurs.•Positive fecal coliforms-temperature relationships are sharper in forested areas.•Positive fecal coliforms-precipitation relationships are steeper in urban areas.•Surface runoff and seasonally dependent processes control instream fecal coliforms.
doi_str_mv 10.1016/j.watres.2016.05.024
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[Display omitted] •Positive fecal coliforms-temperature relationships are steeper when rainfall occurs.•Positive fecal coliforms-temperature relationships are sharper in forested areas.•Positive fecal coliforms-precipitation relationships are steeper in urban areas.•Surface runoff and seasonally dependent processes control instream fecal coliforms.</description><identifier>ISSN: 0043-1354</identifier><identifier>EISSN: 1879-2448</identifier><identifier>DOI: 10.1016/j.watres.2016.05.024</identifier><identifier>PMID: 27208919</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Bacteria ; Bayes Theorem ; Bayesian model ; Contaminants ; Counting ; Enterobacteriaceae ; Fecal coliform ; Feces - microbiology ; Land use ; Mathematical models ; Meteorology ; Overdispersed-Poisson regression ; Pathogens ; Rainfall ; Rivers ; Temperature ; Variability ; Water Microbiology ; Watersheds</subject><ispartof>Water research (Oxford), 2016-09, Vol.100, p.306-315</ispartof><rights>2016 Elsevier Ltd</rights><rights>Copyright © 2016 Elsevier Ltd. 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subjects Bacteria
Bayes Theorem
Bayesian model
Contaminants
Counting
Enterobacteriaceae
Fecal coliform
Feces - microbiology
Land use
Mathematical models
Meteorology
Overdispersed-Poisson regression
Pathogens
Rainfall
Rivers
Temperature
Variability
Water Microbiology
Watersheds
title Modeling spatiotemporal bacterial variability with meteorological and watershed land-use characteristics
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