Estimation and variation analysis of secondary inorganic aerosols across the Greater Bay Area in 2005 and 2015

Abstract

As the concentrations of primary components of fine particulate matter (PM_2.5) have substantially decreased, the contribution of secondary inorganic aerosols to PM_2.5 pollution has become more prominent. Therefore, understanding the variations in and characteristics of secondary inorganic aerosols is vital to further reducing PM_2.5 concentrations in the future. In this study, an ensemble back-propagation neural network model was built by combining 3D numerical models, observation data, and machine learning methods, to estimate the concentrations of secondary inorganic aerosols (SO^2-₄, NO^-₃, and NH⁺₄) across the Greater Bay Area (GBA) in 2005 and 2015. The ensemble model provided a better estimation than the 3D numerical air quality model, with higher correlation coefficients (approximately 0.85) and lower root mean square errors. The model revealed that the concentrations of the SO^2-₄, NO^-₃, and NH⁺₄ decreased by 1.91, 0.20, and 0.49 μg/m³, respectively, from 2005 to 2015. To investigate the oxidation and acidy of sulfate, the sulfur oxidation ratio (SOR), degree of sulfate neutralization (DSN), and particle neutralization ratio (PNR) were calculated and analyzed for 2005 and 2015 across the GBA region. The SOR slightly increased in summer, but decreased in other seasons in 2015, indicating the overall weaker sulfate chemical formation due to sulfur emission control measures. The increasing DSN and PNR indicated that more sulfate was neutralized due to reduced sulfur emission and increased ammonia availability. Our study suggests that more effort is needed to control ammonia emission to further reduce the concentrations of SO^2-₄, NO^-₃, and NH⁺₄ across the GBA region in the future.

Keywords: Greater Bay area; Machine learning; Secondary inorganic aerosol; Spatial-temporal variation.