Abstract
We estimated fine-mode black carbon (BC) concentrations at the surface using AERONET data from five AERONET sites in Korea, representing urban, rural, and background. We first obtained the columnar BC concentrations by separating the refractive index (RI) for fine-mode aerosols from AERONET data and minimizing the difference between separated RIs and calculated RIs using a mixing rule that can represent a real aerosol mixture (Maxwell Garnett for water-insoluble components and volume average for water-soluble components). Next, we acquired the surface BC concentrations by establishing a multiple linear regression (MLR) between in-situ BC concentrations from co-located or adjacent measurement sites, and columnar BC concentrations, by linearly adding meteorological parameters, month, and land-use type as the independent variables. The columnar BC concentrations estimated from AERONET data using a mixing rule well reproduced site-specific monthly variations of the in-situ measurement data, such as increases due to heating and/or biomass burning and long-range transport associated with prevailing westerlies in the spring and winter, and decreases due to wet scavenging in the summer. The MLR model exhibited a better correlation between measured and predicted BC concentrations than those based on columnar concentrations only, with a correlation coefficient of 0.64. The performance of our MLR model for BC was comparable to that reported in previous studies on the relationship between aerosol optical depth and particulate matter concentration in Korea. This study suggests that the MLR model with properly selected parameters is useful for estimating the surface BC concentration from AERONET data during the daytime, at sites where BC monitoring is not available.
Highlights
Black carbon (BC), the strongest light-absorbing aerosol, can positively contribute to radiative forcing of climate change, semi-directly affect changes in the albedo of snow and ice, and indirectly act as cloud condensation nuclei (CCN), which can alter cloud albedo [1,2]
Because of the increasing importance of BC emissions from developing countries and forest areas and the necessity of understanding spatial and temporal variation in BC, we investigated the columnar BC concentration from five AErosol RObotic NETwork (AERONET) sites in Korea using mixing rules, which can represent aerosols in the real world, and we established an multiple linear regression (MLR) model to predict the in-situ BC concentration by considering additional relevant parameters
The total column concentration was associated with the geographical location; for example, it was highest in the urban area (Seoul) due to a large amount of local emissions, followed by the rural (Yongin and Anmyon) and background regions (Baengnyeong and Gosan)
Summary
Black carbon (BC), the strongest light-absorbing aerosol, can positively contribute to radiative forcing of climate change, semi-directly affect changes in the albedo of snow and ice, and indirectly act as cloud condensation nuclei (CCN), which can alter cloud albedo [1,2]. The seasonal R varied from 0.28 in fall to 0.54 in summer using linear regression methods (y = ax + b), and these values were much lower than those obtained from our model This difference might have been due to the use of a simple linear equation and the much wider spatial distribution of monitoring sites, which showed high regional variability in meteorological parameters and emission factors. Seo et al [46] focused on the relationship between AERONET AOD and PM10 from 10 air quality monitoring stations in Seoul, Korea They reported a much higher R value (0.68) when using an MLR model similar to our formula, but the measurement period (March to May 2012) did not cover whole seasons. On the basis of these comparisons, our model has been validated as a representative method for accurately predicting the in-situ BC concentration from AERONET sites where BC instruments are not installed, by allowing the real-time monitoring of BC during the daytime with global coverage
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