Improving Low-Cost Optical PM Sensor Accuracy in Humid Conditions via Aerosol Liquid Water Estimation Using U.S. EPA CSN Data

YUHANG GUO, Alexandra Catena, Janie Schwab, Nga Lee Ng, Amanda Teora, Oliver Rattigan, Violet Harder, Yasi Hassanzadeh, James Schwab, Jie Zhang, Atmospheric Sciences Research Center, University at Albany

     Abstract Number: 106
     Working Group: Instrumentation and Methods

Abstract
High ambient relative humidity (RH) poses a substantial challenge to the accuracy of low-cost optical sensors used for measuring fine particulate matter (PM_2.5) concentration. In this study, we use integrated data from the Chemical Speciation Network (CSN) to evaluate two key correction processes for an optical PM_2.5 measurement system comprising a nephelometer and an optical particle counter: (1) optical calibration grounded in Mie theory to account for variations in sensor performance driven by aerosol size distribution, refractive index, and hygroscopic growth, and (2) determination of aerosol liquid water (ALW) to estimate dry-equivalent PM_2.5 mass concentrations under high RH conditions. The corrected PM_2.5 data exhibits strong agreement with EPA reference measurements, affirming the robustness of the proposed correction framework. Furthermore, the quantification of ALW offers valuable insights for advancing aqueous-phase aerosol chemistry and secondary aerosol formation studies. For regions without co-located CSN data, we provide practical guidance for applying these correction methods using surrogate information. Overall, the methodologies developed in this work are expected to significantly enhance the accuracy and applicability of low-cost optical PM_2.5 sensors in humid environments.