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Among many parameters characterizing atmospheric aerosols, aerosol mass extinction efficiency (MEE) is important for understanding the optical properties of aerosols. MEE is expressed as a function of the refractive indices (i.e., composition) and size distributions of aerosol particles. Aerosol MEE is often considered as a size-independent constant that depends only on the chemical composition of aerosol particles. The famous Malm's reconstruction equation and subsequent revised methods express the extinction coefficient as a function of aerosol mass concentration and MEE. However, the used constant MEE does not take into account the effect of the size distribution of polydispersed chemical composition. Thus, a simplified expression of size-dependent MEE is required for accurate and conventional calculations of the aerosol extinction coefficient and also other optical properties. In this study, a simple parameterization of MEE of polydispersed aerosol particles was developed. The geometric volume–mean diameters of up to 10 µm with lognormal size distributions and varying geometric standard deviations were used to represent the sizes of various aerosol particles (i.e., ammonium sulfate and nitrate, elemental carbon, and sea salt). Integrating representations of separate small mode and large mode particles using a harmonic mean-type approximation generated the flexible and convenient parameterizations of MEE that can be readily used to process in situ observations and adopted in large-scale numerical models. The calculated MEE and the simple forcing efficiency using the method developed in this study showed high correlations with those calculated using the Mie theory without losing accuracy. [ABSTRACT FROM AUTHOR]

To improve PM 2.5 predictions in Northeast Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM 2.5 observations. In contrast to the conventional method of BEC estimation that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. By taking the emission uncertainty into account, we found that the standard deviations in the BEC were significantly increased. The standard deviations became around three times larger than those in the conventional method at the surface. The impacts of the new BEC were then tested for the prediction of surface PM 2.5 over Northeast Asia using the Community Multiscale Air Quality (CMAQ) model initialized by three-dimensional variational method (3D-VAR). The surface PM 2.5 data measured at 154 sites in South Korea and 1535 sites in China were assimilated every 6 h during the campaign period of the Korea-United States Air Quality Study (KORUS-AQ) (1 May–14 June 2016). The data assimilation with the new BEC showed better agreement with the surface PM 2.5 observations than with the BEC from the conventional method. Our method was also more consistent with the observations in 24-h PM 2.5 predictions than the conventional method (specifically, with a ∼44% reduction of negative biases). We concluded that increased standard deviations, together with updated horizontal and vertical length scales in the new BEC, improved the data assimilation and short-term predictions of the surface PM 2.5. This paper also suggests several research efforts to further improve the BEC for better short-term PM 2.5 predictions in Northeast Asia. [Display omitted] • A new background error covariance (BEC) for PM 2.5 predictions was developed. • The new BEC considers the uncertainties in anthropogenic emissions. • Two different emission inventories were used to account for the uncertainty. • 24-h PM 2.5 predictions were improved with ∼44% fewer negative biases. [ABSTRACT FROM AUTHOR]

Owing to insufficient detailed in-field observations for explaining and quantifying the wet-scavenging coefficients, especially those below-cloud (Λ below), prediction of accurate aerosol concentrations and/or behaviors in the atmosphere remains challenging. As a pioneering effort, this study focused on establishing an empirical equation for size-resolved Λ below , which represent the characteristics in Korea, using long-term aerosol number size distribution and meteorological variable measurements at two background sites in Korea (Baengnyeong and Jeju). The median Λ below values derived from the total number concentrations were 8.06 × 10−6 s−1 for Baengnyeong and 1.04 × 10−5 s−1 for Jeju, which are similar to or slightly lower than the Λ below reported by previous in-situ studies. These may be caused by differences in the precipitation rates, precipitation drop sizes, aerosol chemical/hygroscopic properties, and aerosol size distributions at the measurement sites. Although the derived parameterization can cover a wide range (10 nm to 10 μm), the derived equation for the fourth-degree polynomial shows a high correlation coefficient of 0.81, indicating that the measured and fitted Λ below converge within a narrow range. Moreover, the variation in Λ below is relatively constant for aerosol diameters larger than ∼1 μm, similar to those derived from theoretical equations rather than empirical equations in previous studies. This study is thus expected to contribute to improving the below-cloud scavenging module implemented in a chemical transport model by covering a wide range of aerosol diameters to avoid overestimation by extrapolation of Λ below. [Display omitted] • Aerosol number size distributions are measured at two background sites in Korea. • Empirical equation is developed for below-cloud scavenging coefficient (Λ below). • A lower Λ below is obtained from the newly derived equation than extant equations. • Λ below for d p > ∼1 μm shows constant variation, similar to theoretical eqs. • A wide range of aerosol diameters is covered for Λ below by avoiding extrapolation. [ABSTRACT FROM AUTHOR]

Hygroscopic growth of aerosols plays an important role in the characterization of atmospheric aerosols. Physico-chemical and optical properties of aerosols are dependent on relative humidity (RH) as well as on their size and composition. In this study, scattering enhancement factors, f(RH), for polydispersed aerosols were approximated. f(RH) of ammonium sulfate and ammonium nitrate (AS) and NaCl aerosols (ss) were considered under the assumption of externally mixed aerosols. f(RH) was calculated using the Mie theory for polydispersed aerosols at a given RH and corresponding water uptake (denoted as f Mie (RH)). f(RH) was approximated as a quadratic function of RH (denoted as f app (RH)). The obtained approximated f(RH) or f app (RH)) values were compared with the values based on the Mie theory and showed a reasonable agreement between them. To the best of our knowledge, this is the first study that parametrizes the size dependency of f(RH) for log normally distributed aerosols. • Hygroscopic scattering enhancement factor f(RH) for polydispersed aerosols are approximated. • f(RH) for polydispersed aerosols was approximated as a simple formula of quadratic function of RH. • The obtained results were compared with the original results that showed good agreement. • The obtained formula estimates f(RH) and related optical properties, such as aerosol extinction coefficient with confidence. [ABSTRACT FROM AUTHOR]