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

This research explores the application of Response Surface Methodology (RSM) for data-driven spatial analysis of airborne particle contamination within industrial environments. The study aims to model the spatial dependence of particle concentrations, considering its critical implications for maintaining technical cleanliness and safeguarding employee health. Utilizing data from measurement campaigns conducted in an industrial production hall, particle concentrations across various size fractions were analyzed. The methodology effectively captures nonlinear relationships and spatial gradients, facilitating the identification of local extrema and contamination hotspots. Statistical analysis confirms the significant influence of spatial coordinates on particle concentration, with finer particles demonstrating a stronger spatial dependence consistent with indoor aerosol behavior. The persistence of stable

hotspot regions, even following corrective interventions, suggests the presence of enduring contamination sources or structural factors. Residual analysis further indicates potential unmodeled local sources or transport mechanisms. The integration of RSM and multi-fraction analysis culminates in the proposal of a mechanistic contamination model, based on a source–transport–receptor framework incorporating deposition processes. This model establishes a link between airborne particle behavior, surface contamination, and potential human exposure, thereby supporting data-driven, localized contamination control strategies and optimizing technical cleanliness and occupational health protocols.

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