Embedded information of aerosol type, hygroscopicity and scattering enhancement factor revealed by the relationship between PM2.5 and aerosol optical depth

Kuo-En Chang; Ta-Chih Hsiao; Si-Chee Tsay; Tang-Huang Lin; Stephen M Griffith; Chian-Yi Liu; Charles C-K Chou

doi:10.1016/j.scitotenv.2023.161471

Embedded information of aerosol type, hygroscopicity and scattering enhancement factor revealed by the relationship between PM_2.5 and aerosol optical depth

Sci Total Environ. 2023 Apr 1:867:161471. doi: 10.1016/j.scitotenv.2023.161471. Epub 2023 Jan 10.

Authors

Kuo-En Chang¹, Ta-Chih Hsiao², Si-Chee Tsay³, Tang-Huang Lin⁴, Stephen M Griffith⁵, Chian-Yi Liu⁶, Charles C-K Chou⁶

Affiliations

¹ Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan; Research Centre for Environmental Changes, Academia Sinica, Taipei, Taiwan.
² Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan; Research Centre for Environmental Changes, Academia Sinica, Taipei, Taiwan. Electronic address: tchsiao@ntu.edu.tw.
³ NASA Goddard Space Flight Center, Greenbelt, MD, USA.
⁴ Center for Space and Remote Sensing Research, National Central University, Taoyuan, Taiwan.
⁵ Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan.
⁶ Research Centre for Environmental Changes, Academia Sinica, Taipei, Taiwan.

PMID: 36634778
DOI: 10.1016/j.scitotenv.2023.161471

Abstract

Satellite aerosol optical depth (AOD) provides an alternative way to depict the spatial distribution of near-surface PM_2.5. In this study, a mathematical formulation of how PM_2.5 is related to AOD is presented. When simplified to a linear equation, a functional dependence of the slope on the aerosol type, scattering enhancement factor f(RH), and boundary layer height is revealed, while the influence of the vertical aerosol profile is embedded in the intercept. Specifically, we focus on the effects of aerosol properties and employ a new aerosol index (Normalized Gradient Aerosol Index, NGAI) for classifying aerosol subtypes. The combination of AOD difference at shorter wavelengths over longer-wavelength AOD from AERONET data could distinguish and subclassify aerosol types previously indistinguishable by AE (i.e., urban-industrial pollution, U/I, and biomass burning, BB). AOD-PM_2.5 regressions are performed on these aerosol subtypes at various relative humidity (RH) levels. The results suggest that BB aerosols are nearly hydrophobic until the RH exceeds 80 %, while the AOD-PM_2.5 regressions for U/I depend on RH levels. Moreover, the scattering enhancement factor f(RH) can be calculated by taking the ratio of intercepts between dry and humidity conditions, which is proposed and tested for the first time in this study. Our results show an f(RH ≥ 80 %) of ∼2.6 for U/I-dominated aerosols, whereas the value is not over 1.5 for BB aerosols. The f(RH) can be further used to derive the optical hygroscopicity parameter (κ_sca), demonstrating that the NGAI can be used to exploit differences in aerosol hygroscopicity and improve the AOD-PM_2.5 relationship.

Keywords: Aerosol Robotic Network (AERONET); Aerosol subtype; Fine particulate matter (PM(2.5)); Optical hygroscopic growth factor.