Comparative analysis of spatio-temporal exposure assessment methods for estimating odor-related responses in non-urban populations

Abstract

The assessment of air pollution exposures in epidemiological studies does not always account for spatio-temporal variability of pollutants concentrations. In the case of odor studies, a common approach is to use yearly averaged odorant exposure estimates with low spatial resolution, which may not capture the spatio-temporal variability of emissions and therefore distort the epidemiological results. This study explores the use of different exposure assessment methods for time-variant ammonia exposures with high spatial resolution, in rural communities exposed to odors from agricultural and livestock farming activities. Exposure estimations were based on monthly ammonia concentrations from emission-dispersion models. Seven time-dependent residential NH3 exposures variables were investigated: 1) Annual mean of NH3 exposures; 2) Maximum annual NH3 exposure; 3) Area under the exposure curve; 4) Peak area; 5) Peak-to-mean ratio; 6) Area above the baseline (annual mean of NH3 exposures); and 7) Maximum positive slope of the exposure curve. We developed binomial and multinomial logistic regression models for frequency of odor perception and odor annoyance responses based on each temporal exposure variable. Odor responses estimates, goodness of fit and predictive abilities derived from each model were compared. All time-dependent NH3 exposure variables, except peak-to-mean ratio, were positively associated with odor perception and odor annoyance, although the results differ considerably in terms of magnitude and precision. The best goodness of fit of the predictive binomial models was obtained when using maximum monthly NH3 exposure as exposure assessment variable, both for odor perception and annoyance. The best predictive performance for odor perception was found when annual mean was used as exposure variable (accuracy=71.82%, Cohen's Kappa=0.298) whereas odor annoyance was better predicted when using peak area (accuracy=68.07%, Cohen's Kappa=0.290). Our study highlights the importance of taking temporal variability into account when investigating odor-related responses in non-urban residential areas.