@article{
 title = {Spatial variability of fine particle mass, components, and source contributions during the regional air pollution study in St. Louis.},
 type = {article},
 year = {2005},
 identifiers = {[object Object]},
 keywords = {Air Pollutants,Air Pollutants: analysis,Air Pollutants: chemistry,Cities,Humans,Missouri,Particle Size,Statistics as Topic,Sulfur,Sulfur: analysis,Sulfur: chemistry,Time Factors,Urban Health},
 pages = {4172-9},
 volume = {39},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/15984797},
 month = {6},
 day = {1},
 id = {9492b999-1d47-3292-92d1-4fa3673beb31},
 created = {2014-05-31T04:12:17.000Z},
 file_attached = {true},
 profile_id = {9edae5ec-3a23-3830-8934-2c27bef6ccbe},
 group_id = {63e349d6-2c70-3938-9e67-2f6483f6cbab},
 last_modified = {2014-11-19T06:01:30.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 abstract = {Community time-series epidemiology typically uses either 24-hour integrated particulate matter (PM) concentrations averaged across several monitors in a city or data obtained at a central monitoring site to relate PM concentrations to human health effects. If the day-to-day variations in 24-hour integrated concentrations differ substantially across an urban area (i.e., daily measurements at monitors at different locations are not highly correlated), then there is a significant potential for exposure misclassification in community time-series epidemiology. If the annual average concentration differs across an urban area, then there is a potential for exposure misclassification in epidemiologic studies that use annual averages (or multi-year averages) as an index of exposure across different cities. The spatial variability in PM2.5 (particulate matter < or = 2.5 microm in aerodynamic diameter), its elemental components, and the contributions from each source category at 10 monitoring sites in St. Louis, Missouri were characterized using the ambient PM2.5 compositional data set of the Regional Air Pollution Study (RAPS) based on the Regional Air Monitoring System (RAMS) conducted between 1975 and 1977. Positive matrix factorization (PMF) was applied to each ambient PM2.5 compositional data set to estimate the contributions from the source categories. The spatial distributions of components and source contributions to PM2.5 at the 10 sites were characterized using Pearson correlation coefficients and coefficients of divergence. Sulfur and PM2.5 are highly correlated elements between all of the site pairs Although the secondary sulfate is the most highly correlated and shows the smallest spatial variability, there is a factor of 1.7 difference in secondary sulfate contributions between the highest and lowest site on average. Motor vehicles represent the next most highly correlated source component. However, there is a factor of 3.6 difference in motor vehicle contributions between the highest and lowest sites. The contributions from point source categories are much more variable. For example, the contributions from incinerators show a difference of a factor of 12.5 between the sites with the lowest and highest contributions. This study demonstrates that the spatial distributions of elemental components of PM2.5 and contributions from source categories can be highly heterogeneous within a given airshed and thus, there is the potential for exposure misclassification when a limited number of ambient PM monitors are used to represent population-average ambient exposures.},
 bibtype = {article},
 author = {Kim, Eugene and Hopke, Philip K and Pinto, Joseph P and Wilson, William E},
 journal = {Environmental science & technology},
 number = {11}
}

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If the day-to-day variations in 24-hour integrated concentrations differ substantially across an urban area (i.e., daily measurements at monitors at different locations are not highly correlated), then there is a significant potential for exposure misclassification in community time-series epidemiology. If the annual average concentration differs across an urban area, then there is a potential for exposure misclassification in epidemiologic studies that use annual averages (or multi-year averages) as an index of exposure across different cities. The spatial variability in PM2.5 (particulate matter < or = 2.5 microm in aerodynamic diameter), its elemental components, and the contributions from each source category at 10 monitoring sites in St. Louis, Missouri were characterized using the ambient PM2.5 compositional data set of the Regional Air Pollution Study (RAPS) based on the Regional Air Monitoring System (RAMS) conducted between 1975 and 1977. Positive matrix factorization (PMF) was applied to each ambient PM2.5 compositional data set to estimate the contributions from the source categories. The spatial distributions of components and source contributions to PM2.5 at the 10 sites were characterized using Pearson correlation coefficients and coefficients of divergence. Sulfur and PM2.5 are highly correlated elements between all of the site pairs Although the secondary sulfate is the most highly correlated and shows the smallest spatial variability, there is a factor of 1.7 difference in secondary sulfate contributions between the highest and lowest site on average. Motor vehicles represent the next most highly correlated source component. However, there is a factor of 3.6 difference in motor vehicle contributions between the highest and lowest sites. The contributions from point source categories are much more variable. For example, the contributions from incinerators show a difference of a factor of 12.5 between the sites with the lowest and highest contributions. This study demonstrates that the spatial distributions of elemental components of PM2.5 and contributions from source categories can be highly heterogeneous within a given airshed and thus, there is the potential for exposure misclassification when a limited number of ambient PM monitors are used to represent population-average ambient exposures.","bibtype":"article","author":"Kim, Eugene and Hopke, Philip K and Pinto, Joseph P and Wilson, William E","journal":"Environmental science & technology","number":"11","bibtex":"@article{\n title = {Spatial variability of fine particle mass, components, and source contributions during the regional air pollution study in St. Louis.},\n type = {article},\n year = {2005},\n identifiers = {[object Object]},\n keywords = {Air Pollutants,Air Pollutants: analysis,Air Pollutants: chemistry,Cities,Humans,Missouri,Particle Size,Statistics as Topic,Sulfur,Sulfur: analysis,Sulfur: chemistry,Time Factors,Urban Health},\n pages = {4172-9},\n volume = {39},\n websites = {http://www.ncbi.nlm.nih.gov/pubmed/15984797},\n month = {6},\n day = {1},\n id = {9492b999-1d47-3292-92d1-4fa3673beb31},\n created = {2014-05-31T04:12:17.000Z},\n file_attached = {true},\n profile_id = {9edae5ec-3a23-3830-8934-2c27bef6ccbe},\n group_id = {63e349d6-2c70-3938-9e67-2f6483f6cbab},\n last_modified = {2014-11-19T06:01:30.000Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n abstract = {Community time-series epidemiology typically uses either 24-hour integrated particulate matter (PM) concentrations averaged across several monitors in a city or data obtained at a central monitoring site to relate PM concentrations to human health effects. 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Positive matrix factorization (PMF) was applied to each ambient PM2.5 compositional data set to estimate the contributions from the source categories. The spatial distributions of components and source contributions to PM2.5 at the 10 sites were characterized using Pearson correlation coefficients and coefficients of divergence. Sulfur and PM2.5 are highly correlated elements between all of the site pairs Although the secondary sulfate is the most highly correlated and shows the smallest spatial variability, there is a factor of 1.7 difference in secondary sulfate contributions between the highest and lowest site on average. Motor vehicles represent the next most highly correlated source component. However, there is a factor of 3.6 difference in motor vehicle contributions between the highest and lowest sites. The contributions from point source categories are much more variable. 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