Sensory-relevant organic pollutants and exposure-oriented odor assessment during waste treatment processes

Waste treatment processes emit complex mixtures of volatile organic compounds (VOCs) that frequently cause odor nuisance, public complaints, and growing concerns regarding human exposure and well-being. Persistent exposure to odors has been widely associated with psychological stress, annoyance, and a reduced quality of life, making odor pollution an increasingly relevant public health issue. However, conventional concentration-based indicators, such as total VOCs (TVOC), often fail to represent odor perception and exposure relevance, indicating that sensory response is dominated by a limited number of odor-active compounds rather than by overall chemical abundance.

In this study, VOC emission characteristics during typical waste treatment processes were systematically investigated to identify sensory-relevant organic pollutants and evaluate their implications for exposure-oriented assessment. VOC compositions were characterized using 2D gas chromatography–mass spectrometry (GC×GC–MS), enabling comprehensive profiling of both abundant compounds and low-concentration species with high odor activity. Odor concentration was determined by dynamic olfactometry, providing an independent sensory reference to confirm key odorants and to define control-relevant compounds based on their sensory contribution.

The results demonstrate that odor perception was governed by a small subset of sensory-active VOCs, mainly aldehydes and mercaptans, whose concentrations were relatively low but whose sensory impacts were disproportionately high. This reveals a pronounced mismatch between chemical abundance and sensory relevance, highlighting the limitations of concentration-based metrics for exposure characterization of organic air pollutants. Based on sensory evaluation, priority odor-active compounds were identified, offering a robust basis for targeted control strategies during waste treatment operations.

Furthermore, electronic nose measurements were applied to explore rapid, sensor-based prediction of odor concentration. Multivariate models linking electronic nose responses to olfactometric odor concentration showed good predictive performance, indicating that electronic noses can effectively capture sensory-relevant emission dynamics and support real-time exposure-oriented monitoring.

Overall, this study demonstrates that integrating chemical characterization, sensory assessment, and sensor-based prediction provides a more exposure-relevant framework for evaluating organic air pollutants from waste treatment processes, with implications for health-oriented air pollution assessment.