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Environ Sci Pollut Res Int. 2015 Nov;22(22):17540-9. doi: 10.1007/s11356-015-4751-9. Epub 2015 Jul 5.

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.

Author information

1
Department of Environment and Health, School of Public Health, Shandong University, Jinan, 250012, China.
2
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
3
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.
4
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.
5
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.

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.

KEYWORDS:

Antibiotic residue; Bayesian network; Fluoroquinolone antibiotic; Intensive vegetable cultivation area

PMID:
26141975
DOI:
10.1007/s11356-015-4751-9
[Indexed for MEDLINE]

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