E Buringh | PubMed Health

PubMed1995

Dose-effect models for ozone exposure: tool for quantitative risk estimation.

Short-term ozone exposure causes lung function decrements, increased airway reactivity, airway inflammation, increased respiratory symptoms and hospital admissions. Exposure to long-term elevated ozone levels seems to be associated with reduced lung function (aging), increase of respiratory symptoms, exacerbation of asthma, and airway cell and tissue changes. Health risk caused by exposure to ozone has been evaluated mainly in a qualitative way by comparing ozone air quality data with health-based guidelines or standards. A preliminary approach to quantifying health risk from short-term exposure to oxidant air pollution has been taken by expert judgement, describing known or expected effects at specific levels of ozone. For quantitative assessment of the health impact of distinct ozone exposure conditions (acute, repeated daily, chronic) specific exposure-dose-response models are being developed which can be linked to human exposure data. Exposure-(dose-)response models using data from epidemiological, human-clinical and animal toxicity studies are presented.

Air Pollution↗

PubMed1991

Exposure variability in the workplace: its implications for the assessment of compliance.

Day-to-day variations of occupational exposures have important implications for the industrial hygienist trying to assess compliance with an occupational exposure limit. As only a limited number of samples are taken during an observation period, extrapolations are required to estimate exposures over the unsampled period. Compliance may be evaluated using estimates of the geometric mean (GM) and the geometric standard deviation (GSD) to calculate a confidence interval around the mean exposure and compare this interval to a limit value, assuming a lognormal distribution of exposures over time. These confidence intervals are very sensitive to the estimate of GSD. Hence, the questions of when to sample and how many samples to take for a reliable assessment of exposure variability (GSD) are the focus of this paper. Analyses of simulated exposure-time series and 420 data sets of personal exposures with three or more measurements obtained from actual workplaces demonstrate that the small number of samples usually collected during surveys leads to biased estimates of the variance of the exposure distribution. There is a high likelihood of an underestimate of variance, which rapidly increases if 8-hr time-weighted average samples are collected on consecutive days or within a week. The results indicate that in 80% of the within-week exposure-time series, the estimated GSD may be too low, even up to a factor of 2. Evidence is presented that autocorrelation is a likely explanation for the bias observed.(ABSTRACT TRUNCATED AT 250 WORDS)

Environmental Monitoring↗