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Combining PM2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures.

Kim SY, Sheppard L, Larson TV, Kaufman JD, Vedal S - Environ. Health Perspect. (2015)

Bottom Line: 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.The number of CSN/IMPROVE monitors was limited in all study areas.Given these features we determined that it was preferable to develop our spatiotemporal models using only the NPACT data and under simplifying assumptions.

View Article: PubMed Central - PubMed

Affiliation: Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.

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.

No MeSH data available.


Related in: MedlinePlus

Scatter plots of log-transformed every-3rd-day measurements of EC (μg/m3) between CSN and IMPROVE at four co-located sites in Los Angeles, Chicago, Baltimore, and New York from January 2000 through July 2007.
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f2: Scatter plots of log-transformed every-3rd-day measurements of EC (μg/m3) between CSN and IMPROVE at four co-located sites in Los Angeles, Chicago, Baltimore, and New York from January 2000 through July 2007.

Mentions: Different filter analysis protocols. Although Figure 2 shows that at four co-located sites there was moderate to high agreement between protocols (correlation coefficients = 0.79–0.91), these are not consistently and sufficiently high to conclude that the data are exchangeable in some city areas for daily average measurements of EC collected from the CSN versus IMPROVE networks before the method change in May 2007. See Supplemental Material, Figure S3, for a comparison of 24-hr average measurements of EC between the NIOSH TOT and IMPROVE_A TOR filter analysis methods for the 2-month period of overlap at one CSN site in each MESA city region. In Chicago and New York, the two methods had obvious systematic differences indicated by best-fit lines with negative intercepts, even though they were highly correlated; correlation coefficients were 0.94 and 0.97, attributable partly to the large variability between measurements in these cities. In contrast, the other cities displayed weaker systematic differences and had moderate correlations (0.71–0.84).


Combining PM2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures.

Kim SY, Sheppard L, Larson TV, Kaufman JD, Vedal S - Environ. Health Perspect. (2015)

Scatter plots of log-transformed every-3rd-day measurements of EC (μg/m3) between CSN and IMPROVE at four co-located sites in Los Angeles, Chicago, Baltimore, and New York from January 2000 through July 2007.
© Copyright Policy - public-domain
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4492258&req=5

f2: Scatter plots of log-transformed every-3rd-day measurements of EC (μg/m3) between CSN and IMPROVE at four co-located sites in Los Angeles, Chicago, Baltimore, and New York from January 2000 through July 2007.
Mentions: Different filter analysis protocols. Although Figure 2 shows that at four co-located sites there was moderate to high agreement between protocols (correlation coefficients = 0.79–0.91), these are not consistently and sufficiently high to conclude that the data are exchangeable in some city areas for daily average measurements of EC collected from the CSN versus IMPROVE networks before the method change in May 2007. See Supplemental Material, Figure S3, for a comparison of 24-hr average measurements of EC between the NIOSH TOT and IMPROVE_A TOR filter analysis methods for the 2-month period of overlap at one CSN site in each MESA city region. In Chicago and New York, the two methods had obvious systematic differences indicated by best-fit lines with negative intercepts, even though they were highly correlated; correlation coefficients were 0.94 and 0.97, attributable partly to the large variability between measurements in these cities. In contrast, the other cities displayed weaker systematic differences and had moderate correlations (0.71–0.84).

Bottom Line: 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.The number of CSN/IMPROVE monitors was limited in all study areas.Given these features we determined that it was preferable to develop our spatiotemporal models using only the NPACT data and under simplifying assumptions.

View Article: PubMed Central - PubMed

Affiliation: Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.

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.

No MeSH data available.


Related in: MedlinePlus