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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.
Summary
A new high-resolution dataset reveals aerosol liquid water content (ALWC) trends across the US. ALWC significantly declined, primarily due to sulfate reduction, impacting air quality and health studies.
Area of Science:
- Atmospheric Chemistry and Physics
- Environmental Science
- Climate Science
Background:
- Aerosol liquid water content (ALWC) is crucial for climate and public health, affecting aerosol properties and toxicity.
- ALWC is highly variable but sparsely measured and poorly constrained, limiting our understanding.
Purpose of the Study:
- To develop a high-resolution (1km x 1km, daily) ALWC dataset for the contiguous US from 2000-2019.
- To analyze spatial and temporal trends in ALWC and identify key drivers.
- To assess the utility of ALWC data for understanding health-relevant aerosol components.
Main Methods:
- Trained machine learning (ML) models on GEOS-Chem chemical transport model outputs.
- Captured thermodynamic relationships between ALWC and predictors.
- Applied ML models to high-resolution, bias-corrected input datasets to generate the ALWC dataset.
Main Results:
- The ML-based ALWC dataset demonstrates improved daily variation and spatial heterogeneity compared to GEOS-Chem simulations.
- Highest ALWC levels were observed in the Midwest US, lowest in the Western US, influenced by PM2.5, chemistry, temperature, and humidity.
- Significant ALWC decline observed across most regions, mainly driven by sulfate reduction.
Conclusions:
- The derived ALWC dataset offers a valuable resource for climate and public health research.
- ALWC provides a meaningful constraint for interpreting water-soluble iron variability, relevant to aerosol toxicity.
- This dataset can enhance future studies on aerosol toxicity and epidemiological exposure assessment.
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