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- ItemModifications in aerosol physical, optical and radiative properties during heavy aerosol events over Dushanbe, Central Asia(Amsterdam [u.a.] : Elsevier, 2021) Rupakheti, Dipesh; Rupakheti, Maheswar; Yin, Xiufeng; Hofer, Julian; Rai, Mukesh; Hu, Yuling; Abdullaev, Sabur F.; Kang, ShichangThe location of Central Asia, almost at the center of the global dust belt region, makes it susceptible for dust events. The studies on atmospheric impact of dust over the region are very limited despite the large area occupied by the region and its proximity to the mountain regions (Tianshan, Hindu Kush-Karakoram-Himalayas, and Tibetan Plateau). In this study, we analyse and explain the modification in aerosols’ physical, optical and radiative properties during various levels of aerosol loading observed over Central Asia utilizing the data collected during 2010–2018 at the AERONET station in Dushanbe, Tajikistan. Aerosol episodes were classified as strong anthropogenic, strong dust and extreme dust. The mean aerosol optical depth (AOD) during these three types of events was observed a factor of ~3, 3.5 and 6.6, respectively, higher than the mean AOD for the period 2010–2018. The corresponding mean fine-mode fraction was 0.94, 0.20 and 0.16, respectively, clearly indicating the dominance of fine-mode anthropogenic aerosol during the first type of events, whereas coarse-mode dust aerosol dominated during the other two types of events. This was corroborated by the relationships among various aerosol parameters (AOD vs. AE, and EAE vs. AAE, SSA and RRI). The mean aerosol radiative forcing (ARF) at the top of the atmosphere (ARFTOA), the bottom of the atmosphere (ARFBOA), and in the atmosphere (ARFATM) were −35 ± 7, −73 ± 16, and 38 ± 17 Wm−2 during strong anthropogenic events, −48 ± 12, −85 ± 24, and 37 ± 15 Wm−2 during strong dust event, and −68 ± 19, −117 ± 38, and 49 ± 21 Wm−2 during extreme dust events. Increase in aerosol loading enhanced the aerosol-induced atmospheric heating rate to 0.5–1.6 K day−1 (strong anthropogenic events), 0.4–1.9 K day−1 (strong dust events) and 0.8–2.7 K day−1 (extreme dust events). The source regions of air masses to Dushanbe during the onset of such events are also identified. Our study contributes to the understanding of dust and anthropogenic aerosols, in particular the extreme events and their disproportionally high radiative impacts over Central Asia.
- ItemOn the feasibility of using open source solvers for the simulation of a turbulent air flow in a dairy barn(Amsterdam [u.a.] : Elsevier, 2020) Janke, David; Caiazzo, Alfonso; Ahmed, Naveed; Alia, Najib; Knoth, Oswald; Moreau, Baptiste; Wilbrandt, Ulrich; Willink, Dilya; Amon, Thomas; John, VolkerTwo transient open source solvers, OpenFOAM and ParMooN, and the commercial solver Ansys Fluent are assessed with respect to the simulation of the turbulent air flow inside and around a dairy barn. For this purpose, data were obtained in an experimental campaign at a 1:100 scaled wind tunnel model. All solvers used different meshes, discretization schemes, and turbulence models. The experimental data and numerical results agree well for time-averaged stream-wise and vertical-wise velocities. In particular, the air exchange was predicted with high accuracy by both open source solvers with relative differences less than 4% and by the commercial solver with a relative difference of 9% compared to the experimental results. With respect to the turbulent quantities, good agreements at the second (downwind) half of the barn inside and especially outside the barn could be achieved, where all codes accurately predicted the flow separation and, in many cases, the root-mean-square velocities. Deviations between simulations and experimental results regarding turbulent quantities could be observed in the first part of the barn. These deviations can be attributed to the utilization of roughness elements between inlet and barn in the experiment that were not modeled in the numerical simulations. Both open source solvers proved to be promising tools for the accurate prediction of time-dependent phenomena in an agricultural context, e.g., like the transport of particulate matter or pathogen-laden aerosols in and around agricultural buildings. © 2020 The Authors