The growing concern over particulate air pollution and risks it poses to human health have dramatically increased scientific interest in low cost particulate matter sensors (LCPMS). LCPMS have the potential to provide a solution to the spatial, temporal, and financial issues facing air quality monitoring today. However, the operating principle behind these sensors is prone to inaccuracies under different environmental conditions, especially relative humidity (RH). Using existing models that describe cloud condensation nuclei (CCN) activity, I derive a correction factor for the LCPMS readings to account for the influence of RH. This model was tested against an existing correction method, using data collected at the University of Minnesota Particle Technology Lab in an environmental testing chamber, using a Sensirion SPS30 as the LCPMS. While the models did not improve the overall accuracy of the data against a reference DustTrak DRX, the model did succeed in improving the linearity of the data with respect to increasing RH.

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