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A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution.

Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, Oron AP, Lindström J, Vedal S, Kaufman JD - Environ. Health Perspect. (2014)

Bottom Line: The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression.This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants.These successes highlight modeling advances that can be adopted more widely in modern cohort studies.

View Article: PubMed Central - PubMed

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

ABSTRACT

Background: Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time.

Objectives: We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

Methods: We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants' homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations.

Results: Prediction accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV ranged from 0.23 to 0.92.

Conclusions: This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies.

No MeSH data available.


Related in: MedlinePlus

Pollutant- and region-specific box plots of long-term averages of predictions from 1999 through early 2012 at participant residence locations. Metropolitan region abbreviations: Bal, Baltimore; Chi, Chicago; LA, Los Angeles; NY, New York; SP, St. Paul; W-S, Winston-Salem. Boxes extend from the 25th to the 75th percentile, horizontal bars represent the median, whiskers extend 1.5 times the length of the interquartile range above and below the 75th and 25th percentiles, respectively, and outliers are presented as points.
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f4: Pollutant- and region-specific box plots of long-term averages of predictions from 1999 through early 2012 at participant residence locations. Metropolitan region abbreviations: Bal, Baltimore; Chi, Chicago; LA, Los Angeles; NY, New York; SP, St. Paul; W-S, Winston-Salem. Boxes extend from the 25th to the 75th percentile, horizontal bars represent the median, whiskers extend 1.5 times the length of the interquartile range above and below the 75th and 25th percentiles, respectively, and outliers are presented as points.

Mentions: Box plots of the long-term averages of predictions at participant residences are provided in Figure 4. On average, predicted concentrations tended to be higher in New York and Los Angeles, consistent with the higher observed monitoring values in those regions. Variability in predictions is also greatest in these two cities, especially in the tails of the distributions.


A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution.

Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, Oron AP, Lindström J, Vedal S, Kaufman JD - Environ. Health Perspect. (2014)

Pollutant- and region-specific box plots of long-term averages of predictions from 1999 through early 2012 at participant residence locations. Metropolitan region abbreviations: Bal, Baltimore; Chi, Chicago; LA, Los Angeles; NY, New York; SP, St. Paul; W-S, Winston-Salem. Boxes extend from the 25th to the 75th percentile, horizontal bars represent the median, whiskers extend 1.5 times the length of the interquartile range above and below the 75th and 25th percentiles, respectively, and outliers are presented as points.
© Copyright Policy - public-domain
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4384200&req=5

f4: Pollutant- and region-specific box plots of long-term averages of predictions from 1999 through early 2012 at participant residence locations. Metropolitan region abbreviations: Bal, Baltimore; Chi, Chicago; LA, Los Angeles; NY, New York; SP, St. Paul; W-S, Winston-Salem. Boxes extend from the 25th to the 75th percentile, horizontal bars represent the median, whiskers extend 1.5 times the length of the interquartile range above and below the 75th and 25th percentiles, respectively, and outliers are presented as points.
Mentions: Box plots of the long-term averages of predictions at participant residences are provided in Figure 4. On average, predicted concentrations tended to be higher in New York and Los Angeles, consistent with the higher observed monitoring values in those regions. Variability in predictions is also greatest in these two cities, especially in the tails of the distributions.

Bottom Line: The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression.This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants.These successes highlight modeling advances that can be adopted more widely in modern cohort studies.

View Article: PubMed Central - PubMed

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

ABSTRACT

Background: Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time.

Objectives: We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

Methods: We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants' homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations.

Results: Prediction accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV ranged from 0.23 to 0.92.

Conclusions: This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies.

No MeSH data available.


Related in: MedlinePlus