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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

Locations of CSN, IMPROVE, and NPACT monitoring sites for PM2.5 components within 200 km from city centers in six MESA city areas. Each map is restricted to a smaller area including all monitoring sites than the 200-km buffer area from the city center; one to three IMPROVE sites in four cities are not shown because they are hidden behind many other sites in the city center areas or with co-located CSN sites.
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f1: Locations of CSN, IMPROVE, and NPACT monitoring sites for PM2.5 components within 200 km from city centers in six MESA city areas. Each map is restricted to a smaller area including all monitoring sites than the 200-km buffer area from the city center; one to three IMPROVE sites in four cities are not shown because they are hidden behind many other sites in the city center areas or with co-located CSN sites.

Mentions: Data compatibility between CSN, IMPROVE, and NPACT networks. Sparse coverage in urban space. There were 6–27 CSN and 1–8 IMPROVE monitoring sites within 200 km of each city center (Figure 1 and Table 2). However, MESA participant homes were clustered near the center of each area, whereas only a few CSN sites were close to the city center and most IMPROVE sites were located in rural areas away from participants. See Supplemental Material, Figure S2, for estimated smoothed temporal patterns for the CSN and IMPROVE sites in six city areas. The temporal patterns for EC at eight IMPROVE sites were different from those observed at six CSN sites in Los Angeles. There were also differences between the temporal patterns for silicon across networks, but these were less striking. In the other five city regions, the temporal patterns for EC were more or less heterogeneous depending on city, whereas those for silicon were relatively consistent in all cities.


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)

Locations of CSN, IMPROVE, and NPACT monitoring sites for PM2.5 components within 200 km from city centers in six MESA city areas. Each map is restricted to a smaller area including all monitoring sites than the 200-km buffer area from the city center; one to three IMPROVE sites in four cities are not shown because they are hidden behind many other sites in the city center areas or with co-located CSN sites.
© Copyright Policy - public-domain
Related In: Results  -  Collection

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

f1: Locations of CSN, IMPROVE, and NPACT monitoring sites for PM2.5 components within 200 km from city centers in six MESA city areas. Each map is restricted to a smaller area including all monitoring sites than the 200-km buffer area from the city center; one to three IMPROVE sites in four cities are not shown because they are hidden behind many other sites in the city center areas or with co-located CSN sites.
Mentions: Data compatibility between CSN, IMPROVE, and NPACT networks. Sparse coverage in urban space. There were 6–27 CSN and 1–8 IMPROVE monitoring sites within 200 km of each city center (Figure 1 and Table 2). However, MESA participant homes were clustered near the center of each area, whereas only a few CSN sites were close to the city center and most IMPROVE sites were located in rural areas away from participants. See Supplemental Material, Figure S2, for estimated smoothed temporal patterns for the CSN and IMPROVE sites in six city areas. The temporal patterns for EC at eight IMPROVE sites were different from those observed at six CSN sites in Los Angeles. There were also differences between the temporal patterns for silicon across networks, but these were less striking. In the other five city regions, the temporal patterns for EC were more or less heterogeneous depending on city, whereas those for silicon were relatively consistent in all cities.

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