@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix bibo: <http://purl.org/ontology/bibo/> .
@prefix dbpprop: <http://dbpedia.org/property/> .
@prefix gcis: <http://data.globalchange.gov/gcis.owl#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix fabio: <http://purl.org/spar/fabio/> .
@prefix cito: <http://purl.org/spar/cito/> .
@prefix biro: <http://purl.org/spar/biro/> .

<http://52.38.26.42:8080/article/10.1021/es803183f>   
   dcterms:identifier "10.1021/es803183f";
   dcterms:title "Space/time analysis of fecal pollution and rainfall in an eastern North Carolina estuary"^^xsd:string;
   dcterms:isPartOf <http://52.38.26.42:8080/journal/environmental-science-technology>;
   bibo:volume "43";
   bibo:pages "3728-3735";
   dbpprop:pubYear "2009"^^xsd:gYear;
   dcterms:description "The Newport River Estuary (NPRE) is a high-priority shellfish harvesting area in eastern North Carolina that is impaired due to fecal contamination, specifically exceeding recommended levels for fecal coliforms. A hydrologic-driven mean trend model was developed, as a function of antecedent rainfall, in the NPRE to predict levels of Escherichia coli (EC, measured as a proxy for fecal coliforms). This mean trend model was integrated in a Bayesian Maximum Entropy (BME) framework to produce informative space/time (S/T) maps depicting fecal contamination across the NPRE during winter and summer months. These maps showed that during dry winter months, corresponding to the oyster harvesting season in North Carolina (October 1[?]March 30), predicted EC concentrations were below the shellfish harvesting standard (14 MPN/100 mL). However, after substantial rainfall of 3.81 cm (1.5 in.), the NPRE did not appear to meet this requirement. Warmer months resulted in the predicted EC concentrations exceeding the threshold for the NPRE. Predicted ENT concentrations were generally below the recreational water quality threshold (104 MPN/100 mL), except for warmer months after substantial rainfall. Once established, this combined approach produces near real-time visual information on which to base water quality management decisions. "^^xsd:string;
   bibo:doi "10.1021/es803183f";

   a gcis:AcademicArticle, fabio:Article .

## Contributors:


## This article is cited by the following entities in GCIS:

<http://52.38.26.42:8080/article/10.1021/es803183f>
   cito:isCitedBy <http://52.38.26.42:8080/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses>;
   biro:isReferencedBy <http://52.38.26.42:8080/reference/21d0b329-9227-4294-8841-20b97219ab1e>.

<http://52.38.26.42:8080/article/10.1021/es803183f>
   cito:isCitedBy <http://52.38.26.42:8080/report/usgcrp-climate-human-health-assessment-2016>;
   biro:isReferencedBy <http://52.38.26.42:8080/reference/21d0b329-9227-4294-8841-20b97219ab1e>.