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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

Time-series plots of log-transformed (Ln) 2-week averages of silicon between every-3rd-day and every-6th-day measurements at the same four CSN sites co-located with four NPACT fixed sites in Chicago, Minneapolis–St. Paul, Baltimore, and New York from 1999 to 2009.
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f3: Time-series plots of log-transformed (Ln) 2-week averages of silicon between every-3rd-day and every-6th-day measurements at the same four CSN sites co-located with four NPACT fixed sites in Chicago, Minneapolis–St. Paul, Baltimore, and New York from 1999 to 2009.

Mentions: Different sampling protocols. Table 2 indicates numbers of CSN and IMPROVE sites by sampling schedule. Fewer than half of the CSN sites (the core CSN sites) and all the IMPROVE sites sampled PM2.5 components every third day, whereas more than half of the CSN sites (the supplemental sites) sampled every sixth day. Smoothed temporal patterns for 2-week averages of silicon based on CSN data collected at four sites co-located with NPACT fixed sites generally did not vary greatly when based on data collected every sixth day versus every third day at the same site, although a few local differences were evident (Figure 3). Correlations between 2-week average EC concentrations measured during May 2007–August 2008 at co-located NPACT fixed sites and CSN sites (using the IMPROVE_A TOR filter analysis method) in each city were relatively low (0.27–0.62) (Figure 4). In addition to NPACT measurements being generally higher than CSN measurements in all cities, there were nonsystematic differences indicated by some measurements being far from best-fit lines between the two networks. Time-series plots with smoothed temporal patterns of the same data used in Figure 4 show local differences over time (see also Supplemental Material, Figure S4). Supplemental Material, Figures S5 and S6, show that silicon measurements are more comparable than EC with higher correlation coefficients of 0.56–0.78.


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)

Time-series plots of log-transformed (Ln) 2-week averages of silicon between every-3rd-day and every-6th-day measurements at the same four CSN sites co-located with four NPACT fixed sites in Chicago, Minneapolis–St. Paul, Baltimore, and New York from 1999 to 2009.
© Copyright Policy - public-domain
Related In: Results  -  Collection

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

f3: Time-series plots of log-transformed (Ln) 2-week averages of silicon between every-3rd-day and every-6th-day measurements at the same four CSN sites co-located with four NPACT fixed sites in Chicago, Minneapolis–St. Paul, Baltimore, and New York from 1999 to 2009.
Mentions: Different sampling protocols. Table 2 indicates numbers of CSN and IMPROVE sites by sampling schedule. Fewer than half of the CSN sites (the core CSN sites) and all the IMPROVE sites sampled PM2.5 components every third day, whereas more than half of the CSN sites (the supplemental sites) sampled every sixth day. Smoothed temporal patterns for 2-week averages of silicon based on CSN data collected at four sites co-located with NPACT fixed sites generally did not vary greatly when based on data collected every sixth day versus every third day at the same site, although a few local differences were evident (Figure 3). Correlations between 2-week average EC concentrations measured during May 2007–August 2008 at co-located NPACT fixed sites and CSN sites (using the IMPROVE_A TOR filter analysis method) in each city were relatively low (0.27–0.62) (Figure 4). In addition to NPACT measurements being generally higher than CSN measurements in all cities, there were nonsystematic differences indicated by some measurements being far from best-fit lines between the two networks. Time-series plots with smoothed temporal patterns of the same data used in Figure 4 show local differences over time (see also Supplemental Material, Figure S4). Supplemental Material, Figures S5 and S6, show that silicon measurements are more comparable than EC with higher correlation coefficients of 0.56–0.78.

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