Aerosol Optical Properties and Contribution to Differentiate Haze and Haze-Free Weather in Wuhan City

Abstract

Haze is an atmospheric phenomenon in which different types of particulates obscure the sky, and hence affect almost all human activities. Over a couple of recent decades, China has witnessed increasingly worse air quality as well as atmospheric haziness in its cities. There are various haze contributing factors including the rapid industrialization, excessive biomass burning, and an increase in the number of vehicles. This study proposes a methodology based on the aerosols scattering and absorption properties, to predict the likelihood of an episode of hazy days. This case study employs the aerosol optical properties data from integrated nephelometer and aethalometer sensors from December 2009 to September 2014 over Wuhan. The role and contribution of each aerosol optical parameter (e.g., aerosol scattering and absorption coefficients, single scattering albedo, scattering, and absorption Ångström exponents, backscatter ratio, and asymmetry factor) in distinguishing haze and haze-free conditions has been quantitatively determined based on a machine learning approach. Each aerosol optical parameter was classified independently by the support vector machine (SVM) algorithm, and the aerosol scattering (85.37%) and absorption (74.53%) coefficients were found to be primary potential indicators. Through the Kolmogorov-Smirnov test and traditional statistical analysis, the aerosol scattering and absorption coefficients were then verified as important indicators in distinguishing haze and haze-free days. Finally, through a probability density diagram and frequency histogram, we propose a simple quantitative standard to distinguish between haze and haze-free conditions based on the aerosol scattering coefficient and absorption coefficient in Wuhan City. The accuracy of the standard was determined to be 81.49% after testing, which indicates an accurate result. An error in aerosol optical properties may lead to an error in the calculation of aerosol radiative forcing, the earth’s energy budget, and climate prediction. Therefore, understanding of the aerosol properties during haze-free and haze-days will help policymakers to make new policies to control urban pollution and their effects on human health.

Keywords

• aerosols
• atmospheric scattering
• absorption
• atmospheric haze
• machine learning