Data-Driven Spatial Analysis of Airborne Particle Contamination in Industrial Environments Using RSM

This study focuses on modelling the spatial dependence of airborne particle contamination using Response Surface Methodology (RSM), with consideration of its implications for technical cleanliness and employee health. The analysis is based on two measurement campaigns conducted in an industrial production hall, where particle concentrations were recorded across multiple size fractions using a TROTEC PC220 device. The results demonstrate that RSM effectively captures nonlinear relationships and spatial gradients, enabling the identification of local extrema and contamination hotspots. Statistical analysis confirmed a significant influence of spatial coordinates on particle concentration across all fractions, with finer particles exhibiting stronger spatial dependence, consistent with aerosol behaviour in indoor environments. Quadratic model terms revealed stable hotspot regions persisting even after corrective measures, indicating persistent contamination sources or structural factors. Residual analysis suggested additional unmodeled local sources or transport mechanisms. Based on the integration of RSM and multi-fraction analysis, a mechanistic contamination model (source–transport–receptor framework with deposition processes) is proposed, linking particle behaviour with surface contamination and potential human exposure. The approach enables data-driven, localised contamination control and supports optimisation of technical cleanliness and occupational health conditions.