In industrial settings, practical techniques for measurement and characterisation of emissions and air pollutants for long periods of time are lacking today. Currently, measurement of air pollution in work environments for risk assessment is performed through personal monitoring at most a few days per year. We aim to develop a method for the systematic sampling of particulates (dust) with significantly higher temporal and spatial resolution than the traditionally used techniques by combining stationary (wireless sensor network based) and mobile (robot-based) measurement nodes.
The mobile robot creates environmental maps of particle concentration, airflow, gas concentration, temperature and humidity, thereby creating a high resolution spatial model of these variables. The stationary nodes provide data over long periods with high temporal resolution. The combination of both types of data result in high resolution spatiotemporal models that are validated against traditional measurements of air pollution in the work environment. Preliminary results support that the data is useful for surveillance and to evaluate the before/after situations in which the elimination of technical measures are taken.
We evaluated the proposed sampling system with data collected in an industrial foundry hall during several measurement campaigns. Preliminary results suggest that the created models can be used as a basis for companies in the planning of the daily work and can also widen the perspective of the technical measures needed to reduce worker’s exposure to particulate matter. In addition, the air flow and distribution patterns can enable the investigation of cause and effect with changes in the working environment (e.g. adjustments in the ventilation system). Furthermore, we intend to determine different exposure measures for longer periods of time to be used in epidemiological studies.
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