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Although direct comparisons with our study results are not possible because of differences in the pollutants, diabetes type, and age groups studied, other studies have also shown weak evidence of an association between diabetes and PM exposures and stronger evidence for traffic-related pollutants (represented by distance from the nearest road in our study).
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Naon et al. [13] and Amin et al. [4] observed the improvement of extraction at the evaporation phase for high flowrate application, but at the end of extraction, the difference in the pollutant removal is not significant between the high and low used flowrates.
This was accomplished by multiplying the coefficient for the linear effect of each pollutant by the difference in the pollutant-specific mixture mean and referent group mean.
These spatial differences in the two pollutants provide the opportunity to examine their independent and interactive effects.
High concentrations of PM2.5 as well as significant levels of PM10 associated with lipopolysaccharides (PM-LPS) have been registered historically in Mexico City's air, and marked regional differences in the air pollutants concentrations and composition have been reported within MCMA [ 12, 15– 19]. Figure 1 shows the trend of 24-hour average PM10 concentrations for MCMA (1995 2011).
Differences in the underlying pollutant variability likely caused some of the differences seen in temporally adjusted R CV. PM2.5 tended to exhibit less small-scale spatial variation and greater temporal variability, leading to temporally adjusted R CV that are noticeably lower than the unadjusted measures.
There was little difference in the other pollutants by proximity to traffic (see Supplemental Material, Table S1).
To test for sex differences in the association of pollutant quintile with ASD, we multiplied a continuous term for pollutant quintile (1 5) by an indicator of male sex and included this term in models with pollutant quintile, male sex, and demographic covariates.
However, the present study is unique in that there were nonasthmatic controls and there were no significant differences in the indoor air pollutant concentrations (PM2.5, PM10, O3, NO2), nor in potential sources of indoor air pollution, at present or in early life, between homes of asthmatic and control children.
The inconsistency may be attributable to chance, differences in population characteristics, misclassification of exposure (resulting from different exposure methods across studies), or inherent differences in the toxicological properties of the pollutants.
The geographic differences in the distribution of air pollutants are related to the geographic distribution of wealth and socioeconomic status [ 18, 48, 49].
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