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- 저자 : Vedal, Sverre (검색결과 2 건)
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Kim, Sun-Young,Sheppard, Lianne,Larson, Timothy V.,Kaufman, Joel D.,Vedal, Sverre U.S. Dept. of Health, Education, and Welfare, Publ 2015 Environmental health perspectives Vol.123 No.7
<P><B>Background</B></P><P>Regulatory monitoring data have been the exposure data resource most commonly applied to studies of the association between long-term PM<SUB>2.5</SUB> components and health. However, data collected for regulatory purposes may not be compatible with epidemiological studies.</P><P><B>Objectives</B></P><P>We studied three important features of the PM<SUB>2.5</SUB> 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.</P><P><B>Methods</B></P><P>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 <I>a</I>) all available data, <I>b</I>) NPACT data only, and <I>c</I>) NPACT data with temporal trends estimated from other pollutants.</P><P><B>Results</B></P><P>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.</P><P><B>Conclusions</B></P><P>Investigators conducting epidemiological studies of long-term PM<SUB>2.5</SUB> 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.</P><P><B>Citation</B></P><P>Kim SY, Sheppard L, Larson TV, Kaufman JD, Vedal S. 2015. Combining PM<SUB>2.5</SUB> component data from multiple sources: data consistency and characteristics relevant to epidemiological analyses of predicted long-term exposures. Environ Health Perspect 123:651–658; http://dx.doi.org/10.1289/ehp.1307744</P>
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Kim, Sun-Young,Sheppard, Lianne,Bergen, Silas,Szpiro, Adam A,Sampson, Paul D,Kaufman, Joel D,Vedal, Sverre Nature America, Inc. 2016 Journal of exposure science & environmental epidem Vol.26 No.5
<P>Although cohort studies of the health effects of PM2.5 have developed exposure prediction models to represent spatial variability across participant residences, few models exist for PM2.5 components. We aimed to develop a city-specific spatio-temporal prediction approach to estimate long-term average concentrations of four PM2.5 components including sulfur, silicon, and elemental and organic carbon for the Multi-Ethnic Study of Atherosclerosis cohort, and to compare predictions to those from a national spatial model. Using 2-week average measurements from a cohort-focused monitoring campaign, the spatio-temporal model employed selected geographic covariates in a universal kriging framework with the data-driven temporal trend. Relying on long-term means of daily measurements from regulatory monitoring networks, the national spatial model employed dimension reduced predictors using universal kriging. For the spatio-temporal model, the cross-validated and temporally-adjusted R-2 was relatively higher for EC and OC, and in the Los Angeles and Baltimore areas. The cross-validated R(2)s for both models across the six areas were reasonably high for all components except silicon. Predicted long-term concentrations at participant homes from the two models were generally highly correlated across cities but poorly correlated within cities. The spatio-temporal model may be preferred for city-specific health analyses, whereas both models could be used for multi-city studies.</P>
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