Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.
Identifieur interne : 000065 ( PubMed/Corpus ); précédent : 000064; suivant : 000066Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.
Auteurs : Xuewen Li ; Yunfeng Xie ; Lianfa Li ; Xunfeng Yang ; Ning Wang ; Jinfeng WangSource :
- Environmental science and pollution research international [ 1614-7499 ] ; 2015.
English descriptors
- KwdEn :
- Anti-Bacterial Agents (analysis), Bayes Theorem, China, Crop Production (methods), Drug Residues (analysis), Environmental Monitoring (methods), Environmental Monitoring (statistics & numerical data), Fluoroquinolones (analysis), Manure (analysis), Models, Theoretical, Organic Agriculture (methods), Probability, Soil (chemistry), Soil (standards), Soil Pollutants (analysis), Vegetables (growth & development).
- MESH :
- chemical , analysis : Anti-Bacterial Agents, Fluoroquinolones, Manure, Soil Pollutants.
- chemical , chemistry : Soil.
- chemical , standards : Soil.
- geographic : China.
- analysis : Drug Residues.
- growth & development : Vegetables.
- methods : Crop Production, Environmental Monitoring, Organic Agriculture.
- statistics & numerical data : Environmental Monitoring.
- Bayes Theorem, Models, Theoretical, Probability.
Abstract
Prediction of antibiotic pollution and its consequences is difficult, due to the uncertainties and complexities associated with multiple related factors. This article employed domain knowledge and spatial data to construct a Bayesian network (BN) model to assess fluoroquinolone antibiotic (FQs) pollution in the soil of an intensive vegetable cultivation area. The results show: (1) The relationships between FQs pollution and contributory factors: Three factors (cultivation methods, crop rotations, and chicken manure types) were consistently identified as predictors in the topological structures of three FQs, indicating their importance in FQs pollution; deduced with domain knowledge, the cultivation methods are determined by the crop rotations, which require different nutrients (derived from the manure) according to different plant biomass. (2) The performance of BN model: The integrative robust Bayesian network model achieved the highest detection probability (pd) of high-risk and receiver operating characteristic (ROC) area, since it incorporates domain knowledge and model uncertainty. Our encouraging findings have implications for the use of BN as a robust approach to assessment of FQs pollution and for informing decisions on appropriate remedial measures.
DOI: 10.1007/s11356-015-4751-9
PubMed: 26141975
Links to Exploration step
pubmed:26141975Le document en format XML
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Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun Rd, Anwai, Beijing, 100101, China. lspatial@gmail.com.</nlm:affiliation></affiliation></author><author><name sortKey="Yang, Xunfeng" sort="Yang, Xunfeng" uniqKey="Yang X" first="Xunfeng" last="Yang">Xunfeng Yang</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun Rd, Anwai, Beijing, 100101, China.</nlm:affiliation></affiliation></author><author><name sortKey="Wang, Ning" sort="Wang, Ning" uniqKey="Wang N" first="Ning" last="Wang">Ning Wang</name><affiliation><nlm:affiliation>Department of Environment and Health, School of Public Health, Shandong University, Jinan, 250012, China.</nlm:affiliation></affiliation></author><author><name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun Rd, Anwai, Beijing, 100101, China. wangjf@Lreis.ac.cn.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2015">2015</date><idno type="RBID">pubmed:26141975</idno><idno type="pmid">26141975</idno><idno type="doi">10.1007/s11356-015-4751-9</idno><idno type="wicri:Area/PubMed/Corpus">000065</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000065</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.</title><author><name sortKey="Li, Xuewen" sort="Li, Xuewen" uniqKey="Li X" first="Xuewen" last="Li">Xuewen Li</name><affiliation><nlm:affiliation>Department of Environment and Health, School of Public Health, Shandong University, Jinan, 250012, China.</nlm:affiliation></affiliation></author><author><name sortKey="Xie, Yunfeng" sort="Xie, Yunfeng" uniqKey="Xie Y" first="Yunfeng" last="Xie">Yunfeng Xie</name><affiliation><nlm:affiliation>State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.</nlm:affiliation></affiliation></author><author><name sortKey="Li, Lianfa" sort="Li, Lianfa" uniqKey="Li L" first="Lianfa" last="Li">Lianfa Li</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun Rd, Anwai, Beijing, 100101, China. lspatial@gmail.com.</nlm:affiliation></affiliation></author><author><name sortKey="Yang, Xunfeng" sort="Yang, Xunfeng" uniqKey="Yang X" first="Xunfeng" last="Yang">Xunfeng Yang</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun Rd, Anwai, Beijing, 100101, China.</nlm:affiliation></affiliation></author><author><name sortKey="Wang, Ning" sort="Wang, Ning" uniqKey="Wang N" first="Ning" last="Wang">Ning Wang</name><affiliation><nlm:affiliation>Department of Environment and Health, School of Public Health, Shandong University, Jinan, 250012, China.</nlm:affiliation></affiliation></author><author><name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun Rd, Anwai, Beijing, 100101, China. wangjf@Lreis.ac.cn.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Environmental science and pollution research international</title><idno type="eISSN">1614-7499</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Anti-Bacterial Agents (analysis)</term><term>Bayes Theorem</term><term>China</term><term>Crop Production (methods)</term><term>Drug Residues (analysis)</term><term>Environmental Monitoring (methods)</term><term>Environmental Monitoring (statistics & numerical data)</term><term>Fluoroquinolones (analysis)</term><term>Manure (analysis)</term><term>Models, Theoretical</term><term>Organic Agriculture (methods)</term><term>Probability</term><term>Soil (chemistry)</term><term>Soil (standards)</term><term>Soil Pollutants (analysis)</term><term>Vegetables (growth & development)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Anti-Bacterial Agents</term><term>Fluoroquinolones</term><term>Manure</term><term>Soil Pollutants</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Soil</term></keywords><keywords scheme="MESH" type="chemical" qualifier="standards" xml:lang="en"><term>Soil</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" qualifier="analysis" xml:lang="en"><term>Drug Residues</term></keywords><keywords scheme="MESH" qualifier="growth & development" xml:lang="en"><term>Vegetables</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Crop Production</term><term>Environmental Monitoring</term><term>Organic Agriculture</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Environmental Monitoring</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Bayes Theorem</term><term>Models, Theoretical</term><term>Probability</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Prediction of antibiotic pollution and its consequences is difficult, due to the uncertainties and complexities associated with multiple related factors. This article employed domain knowledge and spatial data to construct a Bayesian network (BN) model to assess fluoroquinolone antibiotic (FQs) pollution in the soil of an intensive vegetable cultivation area. The results show: (1) The relationships between FQs pollution and contributory factors: Three factors (cultivation methods, crop rotations, and chicken manure types) were consistently identified as predictors in the topological structures of three FQs, indicating their importance in FQs pollution; deduced with domain knowledge, the cultivation methods are determined by the crop rotations, which require different nutrients (derived from the manure) according to different plant biomass. (2) The performance of BN model: The integrative robust Bayesian network model achieved the highest detection probability (pd) of high-risk and receiver operating characteristic (ROC) area, since it incorporates domain knowledge and model uncertainty. Our encouraging findings have implications for the use of BN as a robust approach to assessment of FQs pollution and for informing decisions on appropriate remedial measures.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">26141975</PMID><DateCreated><Year>2015</Year><Month>11</Month><Day>20</Day></DateCreated><DateCompleted><Year>2016</Year><Month>07</Month><Day>12</Day></DateCompleted><DateRevised><Year>2015</Year><Month>11</Month><Day>20</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1614-7499</ISSN><JournalIssue CitedMedium="Internet"><Volume>22</Volume><Issue>22</Issue><PubDate><Year>2015</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Environmental science and pollution research international</Title><ISOAbbreviation>Environ Sci Pollut Res Int</ISOAbbreviation></Journal><ArticleTitle>Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.</ArticleTitle><Pagination><MedlinePgn>17540-9</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s11356-015-4751-9</ELocationID><Abstract><AbstractText>Prediction of antibiotic pollution and its consequences is difficult, due to the uncertainties and complexities associated with multiple related factors. This article employed domain knowledge and spatial data to construct a Bayesian network (BN) model to assess fluoroquinolone antibiotic (FQs) pollution in the soil of an intensive vegetable cultivation area. The results show: (1) The relationships between FQs pollution and contributory factors: Three factors (cultivation methods, crop rotations, and chicken manure types) were consistently identified as predictors in the topological structures of three FQs, indicating their importance in FQs pollution; deduced with domain knowledge, the cultivation methods are determined by the crop rotations, which require different nutrients (derived from the manure) according to different plant biomass. (2) The performance of BN model: The integrative robust Bayesian network model achieved the highest detection probability (pd) of high-risk and receiver operating characteristic (ROC) area, since it incorporates domain knowledge and model uncertainty. Our encouraging findings have implications for the use of BN as a robust approach to assessment of FQs pollution and for informing decisions on appropriate remedial measures.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Xuewen</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Department of Environment and Health, School of Public Health, Shandong University, Jinan, 250012, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xie</LastName><ForeName>Yunfeng</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Lianfa</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun Rd, Anwai, Beijing, 100101, China. lspatial@gmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Xunfeng</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun Rd, Anwai, Beijing, 100101, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Ning</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>Department of Environment and Health, School of Public Health, Shandong University, Jinan, 250012, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Jinfeng</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>State Key Laboratory of 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