Linked Life Data

20570037

Source:http://linkedlifedata.com/resource/pubmed/id/20570037

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2010-7-12
pubmed:abstractText
Nonpoint source pollution is the leading cause of the U.S.'s water quality problems. One important component of nonpoint source pollution control is an understanding of what and how watershed-scale conditions influence ambient water quality. This paper investigated the use of spatial regression to evaluate the impacts of watershed characteristics on stream NO(3)NO(2)-N concentration in the Cedar River Watershed, Iowa. An Arc Hydro geodatabase was constructed to organize various datasets on the watershed. Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO(3)NO(2)-N concentration and predict NO(3)NO(2)-N concentration at unmonitored locations. Unlike the traditional ordinary least square (OLS) method, the spatial regression method incorporates the potential spatial correlation among the observations in its coefficient estimation. Study results show that NO(3)NO(2)-N observations in the Cedar River Watershed are spatially correlated, and by ignoring the spatial correlation, the OLS method tends to over-estimate the impacts of watershed characteristics on stream NO(3)NO(2)-N concentration. In conjunction with kriging, the spatial regression method not only makes better stream NO(3)NO(2)-N concentration predictions than the OLS method, but also gives estimates of the uncertainty of the predictions, which provides useful information for optimizing the design of stream monitoring network. It is a promising tool for better managing and controlling nonpoint source pollution.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1095-8630
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2010 Elsevier Ltd. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
91
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1943-51
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
GIS-based spatial regression and prediction of water quality in river networks: a case study in Iowa.
pubmed:affiliation
Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China. xiaoying2010@gmail.com
pubmed:publicationType
Journal Article