Parameterization of pharmaceutical emissions and removal rates for use in UK predictive exposure models: steroid estrogens as a case study.
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| Title: | Parameterization of pharmaceutical emissions and removal rates for use in UK predictive exposure models: steroid estrogens as a case study. |
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| Authors: | Heffley, J. D.1,2, Comber, S. D. W.3 seancomber@plymouth.ac.uk, Wheelera, B. W.1, Redshaw, C. H.1 c.h.redshaw@exeter.ac.uk |
| Source: | Environmental Science: Processes & Impacts. 2014, Vol. 16 Issue 11, p2571-2579. 9p. |
| Subject Terms: | *Watersheds, *Industrial wastes, Estrogen, Parameterization, Pharmaceutical industry |
| Geographic Terms: | United Kingdom |
| Abstract: | Newly available prescription data has been used along with census data to develop a localised method for predicting pharmaceutical concentrations in sewage influent and effluent for England, and applied to a case study: the steroid estrogens estrone, 17β-estradiol, and 17α-ethinylestradiol in a selected catchment. The prescription data allows calculation of the mass consumed of synthetic estrogens, while use of highly localised census data improves predictions of naturally excreted estrogens by accounting for regional variations in population demographics. This serves t wo key purposes; to increase the accuracy of predictions in general, and to call attention to the need for more accurate predictions at a localised and/or catchment level, especially in light of newly proposed regulatory measures which may in the future require removal of steroid estrogens by sewage treatment facilities. In addition, the general lack of measured sewage works data necessitated the development of a novel approach which allowed comparison of localised predictions to average national measurements of influent and effluent. Overall in the case study catchment, estrogen predictions obtained using the model described herein were with in 95% confidence intervals of measured values drawn from across the UK, with large improvements to predictions of EE2 being made compared with previous predictive methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Environmental Science: Processes & Impacts is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 100166178 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Parameterization of pharmaceutical emissions and removal rates for use in UK predictive exposure models: steroid estrogens as a case study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Heffley%2C+J%2E+D%2E%22">Heffley, J. D.</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Comber%2C+S%2E+D%2E+W%2E%22">Comber, S. D. W.</searchLink><relatesTo>3</relatesTo><i> seancomber@plymouth.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Wheelera%2C+B%2E+W%2E%22">Wheelera, B. W.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Redshaw%2C+C%2E+H%2E%22">Redshaw, C. H.</searchLink><relatesTo>1</relatesTo><i> c.h.redshaw@exeter.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Science%3A+Processes+%26+Impacts%22">Environmental Science: Processes & Impacts</searchLink>. 2014, Vol. 16 Issue 11, p2571-2579. 9p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink><br />*<searchLink fieldCode="DE" term="%22Industrial+wastes%22">Industrial wastes</searchLink><br /><searchLink fieldCode="DE" term="%22Estrogen%22">Estrogen</searchLink><br /><searchLink fieldCode="DE" term="%22Parameterization%22">Parameterization</searchLink><br /><searchLink fieldCode="DE" term="%22Pharmaceutical+industry%22">Pharmaceutical industry</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+Kingdom%22">United Kingdom</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Newly available prescription data has been used along with census data to develop a localised method for predicting pharmaceutical concentrations in sewage influent and effluent for England, and applied to a case study: the steroid estrogens estrone, 17β-estradiol, and 17α-ethinylestradiol in a selected catchment. The prescription data allows calculation of the mass consumed of synthetic estrogens, while use of highly localised census data improves predictions of naturally excreted estrogens by accounting for regional variations in population demographics. This serves t wo key purposes; to increase the accuracy of predictions in general, and to call attention to the need for more accurate predictions at a localised and/or catchment level, especially in light of newly proposed regulatory measures which may in the future require removal of steroid estrogens by sewage treatment facilities. In addition, the general lack of measured sewage works data necessitated the development of a novel approach which allowed comparison of localised predictions to average national measurements of influent and effluent. Overall in the case study catchment, estrogen predictions obtained using the model described herein were with in 95% confidence intervals of measured values drawn from across the UK, with large improvements to predictions of EE2 being made compared with previous predictive methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environmental Science: Processes & Impacts is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1039/c4em00374h Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 2571 Subjects: – SubjectFull: Watersheds Type: general – SubjectFull: Industrial wastes Type: general – SubjectFull: Estrogen Type: general – SubjectFull: Parameterization Type: general – SubjectFull: Pharmaceutical industry Type: general – SubjectFull: United Kingdom Type: general Titles: – TitleFull: Parameterization of pharmaceutical emissions and removal rates for use in UK predictive exposure models: steroid estrogens as a case study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Heffley, J. D. – PersonEntity: Name: NameFull: Comber, S. D. W. – PersonEntity: Name: NameFull: Wheelera, B. W. – PersonEntity: Name: NameFull: Redshaw, C. H. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: 2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 20507887 Numbering: – Type: volume Value: 16 – Type: issue Value: 11 Titles: – TitleFull: Environmental Science: Processes & Impacts Type: main |
| ResultId | 1 |
