Appropriate landform parameters have been derived, indicating temperature and moisture distribution, exposure towards wind, snow etc.
The analysis was carried out on a sample of 1700 architectural units grouped into 22 types taking into account size, spatial distribution, exposure, building features.
Because of their skewed distribution, exposure and absenteeism were presented as median and interquartile range (IQR).
The data were too sparse to stratify the correlations according to the water distribution, exposure estimation, and sampling characteristics.
These include shared risk factors such as gender distribution, exposure to immunosuppressive drugs and possible shared genetic backgrounds of both disorders.
In the prior validation of the manual method, we compared measured and modeled PCE concentrations in the water samples according to water distribution, exposure estimation, and sampling characteristics.
Cross-sectional monitoring may be sufficient to ascertain the exposure distribution if exposures are constant over time.
Because 1-OHP exhibited non-normal distributions, exposure data were log-transformed to better approximate the assumed normality of the statistical tests.
Due to the skewed exposure distributions, exposure medians and interquartile ranges (Q25 – Q75) were calculated for total PM2.5 as well as the following metals: aluminum, chromium, copper, iron, lead, manganese, nickel, and zinc.
The exposure distribution for background exposure and activity-based exposure followed a pattern of higher exposure in those with pleural abnormalities.
HIMS is an observational study and the distribution of exposures did not occur at random.
