[Improving accumulation-mode fraction based on spectral aerosol optical depth in Beijing]

Area of Science:

• Atmospheric Science
• Aerosol Physics
• Remote Sensing

Context:

• Aerosol optical properties are critical for climate and air quality research.
• Accurate estimation of aerosol size distribution is essential for understanding aerosol impacts.
• Aerosol Robotic Network (AERONET) data provides valuable ground-based aerosol information.

Purpose:

• To develop and validate a parameterization method for correcting the fine-mode Angstrom index.
• To improve the accuracy of the accumulation-mode fraction (AMF) using aerosol optical depth spectral deconvolution.
• To establish a method for correcting extreme values of the fine-mode Angstrom index.

Summary:

• A novel parameterization method was developed to correct the fine-mode Angstrom index using aerosol optical depth and volume distribution data from AERONET in Beijing.
• Coupling this method with spectral deconvolution improved the accuracy of the accumulation-mode fraction, reducing mean deviation by 38.89%.
• Sensitivity tests confirmed that the corrected fine-mode Angstrom index and AMF are influenced by aerosol normalized volume distribution, with established constraints for extreme values.

Impact:

• Improved accuracy in estimating anthropogenic aerosol optical thickness and direct radiative forcing.
• Enhanced environmental quality assessment through more precise aerosol characterization.
• Provides a more robust approach for utilizing AERONET data in aerosol research.