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High-resolution aerosol liquid water content in the contiguous United States using machine learning
Bingqing Zhang1, Lifei Yin1, Yuhan Yang1
1School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA USA.
NPJ Climate and Atmospheric Science
|July 30, 2026
Summary
A new high-resolution dataset reveals aerosol liquid water content (ALWC) trends across the US. This data improves understanding of climate impacts and public health risks from aerosols.
Area of Science:
- Atmospheric Chemistry and Physics
- Environmental Science
- Data Science
Background:
- Aerosol liquid water content (ALWC) is crucial for climate and health but is poorly measured.
- Understanding ALWC variability is essential due to its influence on aerosol properties and toxicity.
Purpose of the Study:
- To create a high-resolution daily ALWC dataset for the contiguous US (2000-2019).
- To improve the spatial and temporal understanding of ALWC and its drivers.
Main Methods:
- Developed a machine learning (ML) model trained on GEOS-Chem chemical transport model outputs.
- Applied ML models to high-resolution, bias-corrected input data to derive ALWC.
- Validated the ML-based dataset against GEOS-Chem simulations.
Main Results:
- Generated a 1km x 1km daily ALWC dataset for the contiguous US.
- ML-based ALWC dataset captures daily variations and spatial heterogeneity better than GEOS-Chem.
- Highest ALWC in the Midwest US, lowest in the Western US, influenced by PM2.5, composition, temperature, and humidity.
- Significant ALWC decline observed across most regions, mainly due to sulfate reduction.
- ALWC serves as a meaningful constraint for water-soluble iron variability.
Conclusions:
- The derived ALWC dataset offers enhanced insights into aerosol behavior and impacts.
- This dataset can advance research on aerosol toxicity and epidemiological exposure.
- ALWC trends are linked to reductions in sulfate, impacting aerosol properties.
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