High-resolution aerosol liquid water content in the contiguous United States using machine learning

Aerosol liquid water content (ALWC) plays an important role in climate and public health by influencing aerosol formation, chemical composition, and toxicity. However, ALWC remains sparsely measured and poorly constrained across space and time, despite its large variability. In this study, we derived a high-resolution (1 km × 1 km, daily) ALWC dataset for the contiguous US from 2000 to 2019. The dataset was generated by training machine learning (ML) models on outputs from a chemical transport model (GEOS-Chem) to capture the thermodynamic relationships between ALWC and relevant predictors, then applying these relationships to high-resolution, biased-corrected input datasets. Compared with GEOS-Chem simulations, the ML-based dataset better captures daily variations and spatial heterogeneity in ALWC. The predicted ALWC levels are highest in the Midwest US and lowest in the Western US, largely driven by regional differences in PM2.5 concentration, chemical composition, temperature, and relative humidity. Over the study period, ALWC declined significantly across most regions, driven primarily by the reduction in sulfate. We further demonstrate that ALWC provides a physically meaningful constraint for interpreting variability in water-soluble iron, a health-relevant fraction of aerosol metals, highlighting the potential value of this dataset for future studies of aerosol toxicity and epidemiological exposure.