Abstract
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.
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Data availability
Daily mean PM2.5, 8-h maximum ozone, and NO2 datasets at 1 km × 1 km resolution are publicly available from NASA Earthdata (PM2.5: https://doi.org/10.7927/g2n9-ca10; O3: https://doi.org/10.7927/5tht-jg22; NO2: https://doi.org/10.7927/rz28-p167). Annual mean aerosol composition data, including SO42−, NO3−, NH4+, and OC, are publicly available at https://doi.org/10.7927/7wj3-en73. Daily maximum and minimum temperatures are from Daymet (Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1): https://doi.org/10.3334/ORNLDAAC/2129. Daily mean RH on a 4 km × 4 km grid are from PRISM (the Parameter-elevation Relationships on Independent Slope Model, https://prism.oregonstate.edu). The 1 km daily ALWC data generated in this study for the years 2000, 2005, 2010, 2015, and 2019 are deposited in Harvard Dataverse at https://doi.org/10.7910/DVN/ADNPBD; Due to the large file size (~30GB per year), data for other years are available upon request from the corresponding author.
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Acknowledgements
Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R01AG074357 and RF1 AG079487, and the National Science Foundation Division of Atmospheric and Geospace Sciences under AGS-2307151. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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P.L. and B.Z. designed the study, B.Z. performed the ML model, generated the dataset, and wrote the initial manuscript. L.Y. performed the GEOS-Chem simulations. Y.Y., H.G., L.X., D.P., N.L.N., and R.J.W. provided observational data. Q.D., Y.W., J.W., and J.S. provide the high-resolution datasets of the ML model inputs. All the co-authors commented on data analysis and contributed to the writing of the manuscript.
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Zhang, B., Yin, L., Yang, Y. et al. High-resolution aerosol liquid water content in the contiguous United States using machine learning. npj Clim Atmos Sci (2026). https://doi.org/10.1038/s41612-026-01371-2
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DOI: https://doi.org/10.1038/s41612-026-01371-2
