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

High ambient relative humidity (RH) poses a substantial challenge to the accuracy of low-cost optical sensors used for measuring the fine particulate matter (PM_2.5) concentration. In this study, we developed a novel, practical, and feasible framework for mechanistically correcting low-cost PM_2.5 sensor measurements under high-humidity conditions by quantitatively separating aerosol liquid water mass (ALW) using the widely available EPA Chemical Speciation Network (CSN) data set, after accounting for the necessary optical calibration procedures that affect sensor performance at elevated RH. We introduced two key correction processes for a low-cost 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 ALW to estimate dry-equivalent PM_2.5 mass concentrations under high RH conditions. The corrected PM_2.5 data exhibit 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 colocated 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.