Search

Assessing aerosol mixing states, which primarily depend on aerosol chemical compositions, is indispensable to estimate direct and indirect effects of aerosols. The limitations of the direct measurements of aerosol chemical composition and mixing states necessitate modeling approaches to infer the aerosol mixing states. The Optical Properties of Aerosols and Clouds (OPAC) model has been extensively utilized to construct optically equivalent aerosol chemical compositions from measured aerosol optical properties using Mie inversion. However, the representation of real atmospheric aerosol mixing scenarios in OPAC has perennially been challenged by the exclusive assumption of external mixing. A Python successor to the aerosol module of the OPAC model is developed, named AeroMix, with novel capabilities to (1) model externally and core–shell mixed aerosols, (2) simulate optical properties of aerosol mixtures constituted by any number of aerosol components, and (3) define aerosol composition and relative humidity in up to six vertical layers. Designed as a versatile open-source aerosol optical model framework, AeroMix is tailored for sophisticated inversion algorithms aimed at modeling aerosol mixing states and also their physical and chemical properties. AeroMix's performance is demonstrated by modeling the probable aerosol mixing states over Kanpur (urban) and the Bay of Bengal (marine) in south Asia. The modeled mixing states are consistent with independent measurements using a single-particle soot photometer (SP2) and transmission electron microscopy (TEM), substantiating the potential capability of AeroMix to model complex aerosol mixing scenarios involving multiple internally mixed components in diverse environments. This work contributes a valuable tool for modeling aerosol mixing states to assess their impact on cloud-nucleating properties and radiation budget. [ABSTRACT FROM AUTHOR]

Assessing aerosol mixing states, which mainly depend on aerosol chemical compositions is indispensable to estimate aerosol direct and indirect effects. While the limitations in the measurements of aerosol chemical composition and mixing states persist globally, the Optical Properties of Aerosols and Clouds (OPAC) model has been widely used to construct optically equivalent aerosol chemical compositions from measured aerosol optical properties using Mie inversion. However, the representation of real atmospheric aerosol mixing scenarios in OPAC has perennially been challenged by the exclusive assumption of external mixing. A Python successor to the aerosol module of the OPAC model is developed, named 'AeroMix,' with novel capabilities to 1) model externally and core-shell mixed aerosols, 2) simulate optical properties of aerosol mixtures constituted by any number of aerosol components, 3) and define aerosol composition and relative humidity in up to 6 vertical layers. Designed as a versatile open-source aerosol optical model framework, AeroMix is tailored for sophisticated inversion algorithms aimed at modeling aerosol mixing states and also their physical and chemical properties. AeroMix's performance is demonstrated by modeling the probable aerosol mixing states over Kanpur (urban), India, and the Bay of Bengal (marine). The modeled mixing states are consistent with independent measurements using single-particle soot photometer (SP2) and transmission electron microscopy (TEM), substantiating the potential capability of AeroMix to model complex aerosol mixing scenarios involving multiple internally mixed components in diverse environments. This work contributes a valuable tool for modeling aerosol mixing states to assess their impact on cloud nucleating properties and radiation budget. [ABSTRACT FROM AUTHOR]

In this study, temporal variations of surface ozone (O3) were investigated at tropical urban site of Hyderabad during the year 2009. O3, oxides of nitrogen (NOx = NO + NO2), black carbon (BC), and meteorological parameters were continuously monitored at the established air monitoring station. Results revealed the production of surface O3 from NO2 through photochemical oxidation. Averaged datasets illustrated the variations in ground-level concentrations of these air pollutants along different time scales. Maximum mean concentrations of O3 (56.75 ppbv) and NOx (8.9 ppbv) were observed in summer. Diurnal-seasonal changes in surface O3 and NOx concentrations were explicated with complex atmospheric chemistry, boundary layer dynamics, and local meteorology. In addition, nocturnal chemistry of NOx played a decisive role in the formation of O3 during day time. Mean BC mass concentration in winter (10.92 µg m−3) was high during morning hours. Heterogeneous chemistry of BC on O3 destruction and NOx formation was elucidated. Apart from these local observations, long-range transport of trace gases and BC aerosols were evidenced from air mass back trajectories. Further, statistical modeling was performed to predict O3 using multi-linear regression method, which resulted in 91% of the overall variance. [ABSTRACT FROM AUTHOR]