Combining PM2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures.
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| Title: | Combining PM2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures. |
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| Authors: | Kim, Sun-Young1,2 puha0@snu.ac.kr, Sheppard, Lianne1,3, Larson, Timothy V.4, Kaufman, Joel D.1,5,6, Vedal, Sverre1 |
| Source: | Environmental Health Perspectives. Jul2015, Vol. 123 Issue 7, p651-658. 8p. 2 Charts, 3 Graphs, 1 Map. |
| Subject Terms: | *Particulate matter, Statistical models, Statistical correlation, Medical cooperation, Research, Research funding, Time series analysis, Data analysis, Content mining, Descriptive statistics |
| Geographic Terms: | United States |
| Abstract: | BACKGROUND: Regulatory monitoring data have been the exposure data resource most commonly applied to studies of the association between long-term PM2.5 components and health. However, data collected for regulatory purposes may not be compatible with epidemiological studies. OBJECTIVES: We studied three important features of the PM2.5 component monitoring data to determine whether it would be appropriate to combine all available data from multiple sources for developing spatiotemporal prediction models in the National Particle Component and Toxicity (NPACT) study. METHODS: The NPACT monitoring data were collected in an extensive monitoring campaign targeting cohort participant residences. The regulatory monitoring data were obtained from the Chemical Speciation Network (CSN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE). We performed exploratory analyses to examine features that could affect our approach to combining data: comprehensiveness of spatial coverage, comparability of analysis methods, and consistency in sampling protocols. In addition, we considered the viability of developing spatiotemporal prediction models given a) all available data, b) NPACT data only, and c) NPACT data with temporal trends estimated from other pollutants. RESULTS: The number of CSN/IMPROVE monitors was limited in all study areas. The different laboratory analysis methods and sampling protocols resulted in incompatible measurements between networks. Given these features we determined that it was preferable to develop our spatiotemporal models using only the NPACT data and under simplifying assumptions. CONCLUSIONS: Investigators conducting epidemiological studies of long-term PM2.5 components need to be mindful of the features of the monitoring data and incorporate this understanding into the design of their monitoring campaigns and the development of their exposure prediction models. [ABSTRACT FROM AUTHOR] |
| Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences 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.) | |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 103609884 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Combining PM2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kim%2C+Sun-Young%22">Kim, Sun-Young</searchLink><relatesTo>1,2</relatesTo><i> puha0@snu.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Sheppard%2C+Lianne%22">Sheppard, Lianne</searchLink><relatesTo>1,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Larson%2C+Timothy+V%2E%22">Larson, Timothy V.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Kaufman%2C+Joel+D%2E%22">Kaufman, Joel D.</searchLink><relatesTo>1,5,6</relatesTo><br /><searchLink fieldCode="AR" term="%22Vedal%2C+Sverre%22">Vedal, Sverre</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Health+Perspectives%22">Environmental Health Perspectives</searchLink>. Jul2015, Vol. 123 Issue 7, p651-658. 8p. 2 Charts, 3 Graphs, 1 Map. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Particulate+matter%22">Particulate matter</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+cooperation%22">Medical cooperation</searchLink><br /><searchLink fieldCode="DE" term="%22Research%22">Research</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Content+mining%22">Content mining</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: BACKGROUND: Regulatory monitoring data have been the exposure data resource most commonly applied to studies of the association between long-term PM2.5 components and health. However, data collected for regulatory purposes may not be compatible with epidemiological studies. OBJECTIVES: We studied three important features of the PM2.5 component monitoring data to determine whether it would be appropriate to combine all available data from multiple sources for developing spatiotemporal prediction models in the National Particle Component and Toxicity (NPACT) study. METHODS: The NPACT monitoring data were collected in an extensive monitoring campaign targeting cohort participant residences. The regulatory monitoring data were obtained from the Chemical Speciation Network (CSN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE). We performed exploratory analyses to examine features that could affect our approach to combining data: comprehensiveness of spatial coverage, comparability of analysis methods, and consistency in sampling protocols. In addition, we considered the viability of developing spatiotemporal prediction models given a) all available data, b) NPACT data only, and c) NPACT data with temporal trends estimated from other pollutants. RESULTS: The number of CSN/IMPROVE monitors was limited in all study areas. The different laboratory analysis methods and sampling protocols resulted in incompatible measurements between networks. Given these features we determined that it was preferable to develop our spatiotemporal models using only the NPACT data and under simplifying assumptions. CONCLUSIONS: Investigators conducting epidemiological studies of long-term PM2.5 components need to be mindful of the features of the monitoring data and incorporate this understanding into the design of their monitoring campaigns and the development of their exposure prediction models. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=103609884 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1289/ehp.1307744 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 651 Subjects: – SubjectFull: Particulate matter Type: general – SubjectFull: Statistical models Type: general – SubjectFull: Statistical correlation Type: general – SubjectFull: Medical cooperation Type: general – SubjectFull: Research Type: general – SubjectFull: Research funding Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Content mining Type: general – SubjectFull: Descriptive statistics Type: general – SubjectFull: United States Type: general Titles: – TitleFull: Combining PM2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kim, Sun-Young – PersonEntity: Name: NameFull: Sheppard, Lianne – PersonEntity: Name: NameFull: Larson, Timothy V. – PersonEntity: Name: NameFull: Kaufman, Joel D. – PersonEntity: Name: NameFull: Vedal, Sverre IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 00916765 Numbering: – Type: volume Value: 123 – Type: issue Value: 7 Titles: – TitleFull: Environmental Health Perspectives Type: main |
| ResultId | 1 |
