Evaluation of an Aerosol Model: Responses to Meteorology and Emission Scenarios

COVID-19 has claimed over 200 000 lives in the USA and put healthcare workers at risk. Healthcare workers have an increased exposure risk from aerosol-generating procedures such as endotracheal intubation. New barrier designs such as the acrylic box and horizontal plastic drape have emerged to reduce exposure to airborne particles. Particle generating models are needed to test aerosol generating procedure (AGP) barrier designs. To achieve this, an aerosol model that generates a visible and measurable increase in particles which SARS-CoV-2 could travel on and that can also be intubated was created. The model was created using a Laerdal Airway Management Trainer (Laerdal Medical, Stavanger, Norway) combined with a nebuliser and Ambu bag-valve resuscitator (Ambu, Columbia, Maryland, USA). Nebulised Glo Germ (Glo Germ, Moab, Utah, USA) dissolved in saline solution was moved through the tubing and out of the mannequin’s mouth with compression of the Ambu bag. This nebulisation was visualised under ultraviolet light and the quantity of particles between 0.3 and 10.0 μm was measured with a particle counter. Nebulisation was visible exiting the mouth of the mannequin. Nebulised Glo Germ was visualised under ultraviolet light moving in the ambient air. Particles in the size range of 0.3–0.5 µm increased by 20-fold and 1–10 µm increased by 10 252%. SARS-CoV-2 can travel on aerosol and droplet particles and particle generating models are needed to visualise and measure exposure areas and the path particles take during AGPs. We used existing medical and simulation supplies to create a particle simulator.

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The Potential of GRASP/GARRLiC Retrievals for Dust Aerosol Model Evaluation: Case Study during the PreTECT Campaign

Remote Sensing ◽

10.3390/rs13050873 ◽

2021 ◽

Vol 13 (5) ◽

pp. 873

Author(s):

Dimitra Konsta ◽

Alexandra Tsekeri ◽

Stavros Solomos ◽

Nikolaos Siomos ◽

Anna Gialitaki ◽

...

Keyword(s):

Model Evaluation ◽

Dust Concentration ◽

Saharan Dust ◽

North Coast ◽

Concentration Profiles ◽

Future Studies ◽

Aerosol Model ◽

The North ◽

Sun Photometer

We use the Generalized Retrieval of Aerosol Surface Properties algorithm (GRASP) to compare with dust concentration profiles derived from the NMME-DREAM model for a specific dust episode. The GRASP algorithm provides the possibility of deriving columnar and vertically-resolved aerosol properties from a combination of lidar and sun-photometer observations. Herein, we apply GRASP for analysis of a Saharan dust outburst observed during the “PREparatory: does dust TriboElectrification affect our ClimaTe” campaign (PreTECT) that took place at the North coast of Crete, at the Finokalia ACTRIS station. GRASP provides column-averaged and vertically resolved microphysical and optical properties of the particles. The retrieved dust concentration profiles are compared with modeled concentration profiles derived from the NMME-DREAM dust model. To strengthen the results, we use dust concentration profiles from the POlarization-LIdar PHOtometer Networking method (POLIPHON). A strong underestimation of the maximum dust concentration is observed from the NMME-DREAM model. The reported differences between the retrievals and the model indicate a high potential of the GRASP algorithm for future studies of dust model evaluation.

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Optimization of Aerosol Model Selection for TROPOMI/S5P

Remote Sensing ◽

10.3390/rs13132489 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2489

Author(s):

Lanlan Rao ◽

Jian Xu ◽

Dmitry S. Efremenko ◽

Diego G. Loyola ◽

Adrian Doicu

Keyword(s):

Model Selection ◽

Insufficient Information ◽

Satellite Measurements ◽

Model Uncertainties ◽

Bayesian Algorithm ◽

Layer Height ◽

Aerosol Model ◽

Selection For ◽

Temporal Inhomogeneity ◽

Aerosol Properties

To retrieve aerosol properties from satellite measurements, micro-physical aerosol models have to be assumed. Due to the spatial and temporal inhomogeneity of aerosols, choosing an appropriate aerosol model is an important task. In this paper, we use a Bayesian algorithm that takes into account model uncertainties to retrieve the aerosol optical depth and layer height from synthetic and real TROPOMI O2A band measurements. The results show that in case of insufficient information for an appropriate micro-physical model selection, the Bayesian algorithm improves the accuracy of the solution.

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Upper limits on the extent of seafloor anoxia during the PETM from uranium isotopes

Nature Communications ◽

10.1038/s41467-020-20486-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Matthew O. Clarkson ◽

Timothy M. Lenton ◽

Morten B. Andersen ◽

Marie-Laure Bagard ◽

Alexander J. Dickson ◽

...

Keyword(s):

Carbon Cycle ◽

Global Scale ◽

Uranium Isotopes ◽

Biogeochemical Model ◽

Emission Scenarios ◽

Isotope Data ◽

Upper Limit ◽

Thermal Maximum ◽

Upper Limits ◽

Anoxic Events

AbstractThe Paleocene Eocene Thermal Maximum (PETM) represents a major carbon cycle and climate perturbation that was associated with ocean de-oxygenation, in a qualitatively similar manner to the more extensive Mesozoic Oceanic Anoxic Events. Although indicators of ocean de-oxygenation are common for the PETM, and linked to biotic turnover, the global extent and temporal progression of de-oxygenation is poorly constrained. Here we present carbonate associated uranium isotope data for the PETM. A lack of resolvable perturbation to the U-cycle during the event suggests a limited expansion of seafloor anoxia on a global scale. We use this result, in conjunction with a biogeochemical model, to set an upper limit on the extent of global seafloor de-oxygenation. The model suggests that the new U isotope data, whilst also being consistent with plausible carbon emission scenarios and observations of carbon cycle recovery, permit a maximum ~10-fold expansion of anoxia, covering <2% of seafloor area.

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Analysis and prediction of LUCC change in Huang-Huai-Hai river basin

Open Geosciences ◽

10.1515/geo-2020-0112 ◽

2020 ◽

Vol 12 (1) ◽

pp. 1406-1420

Author(s):

Jianwei Wang ◽

Kun Wang ◽

Tianling Qin ◽

Hanjiang Nie ◽

Zhenyu Lv ◽

...

Keyword(s):

Land Use ◽

River Basin ◽

Land Use Planning ◽

Climate Model ◽

Dynamic Change ◽

Cultivated Land ◽

Emission Scenarios ◽

Future Change ◽

Land Use Types ◽

Downstream Area

AbstractLand use/cover change plays an important role in human development and environmental health and stability. Markov chain and a future land use simulation model were used to predict future change and simulate the spatial distribution of land use in the Huang-Huai-Hai river basin. The results show that cultivated land and grassland are the main land-use types in the basin, accounting for about 40% and 30%, respectively. The area of cultivated land decreased and artificial surfaces increased from 1980 to 2010. The degree of dynamic change of land use after the 1990s was greater than that before the 1990s. There is a high probability of exchange among cultivate land, forest and grassland. The area of forest decreased before 2000 and increased after 2000. Under the three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) of IPSL-CM5A-LR climate model, the area of cultivated land will decrease and that of grassland will increase in the upstream area while it will decrease in the downstream area. The above methods and rules will be of great help to future land use planning.

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Improved tropospheric and stratospheric sulfur cycle in the aerosol–chemistry–climate model SOCOL-AERv2

Geoscientific Model Development ◽

10.5194/gmd-12-3863-2019 ◽

2019 ◽

Vol 12 (9) ◽

pp. 3863-3887 ◽

Cited By ~ 2

Author(s):

Aryeh Feinberg ◽

Timofei Sukhodolov ◽

Bei-Ping Luo ◽

Eugene Rozanov ◽

Lenny H. E. Winkel ◽

...

Keyword(s):

Climate Model ◽

Model Performance ◽

Stratospheric Aerosol ◽

Sulfur Cycle ◽

Sulfate Aerosol ◽

Tropospheric Chemistry ◽

Sulfur Deposition ◽

Aerosol Chemistry ◽

Deposition Fluxes ◽

Aerosol Model

Abstract. SOCOL-AERv1 was developed as an aerosol–chemistry–climate model to study the stratospheric sulfur cycle and its influence on climate and the ozone layer. It includes a sectional aerosol model that tracks the sulfate particle size distribution in 40 size bins, between 0.39 nm and 3.2 µm. Sheng et al. (2015) showed that SOCOL-AERv1 successfully matched observable quantities related to stratospheric aerosol. In the meantime, SOCOL-AER has undergone significant improvements and more observational datasets have become available. In producing SOCOL-AERv2 we have implemented several updates to the model: adding interactive deposition schemes, improving the sulfate mass and particle number conservation, and expanding the tropospheric chemistry scheme. We compare the two versions of the model with background stratospheric sulfate aerosol observations, stratospheric aerosol evolution after Pinatubo, and ground-based sulfur deposition networks. SOCOL-AERv2 shows similar levels of agreement as SOCOL-AERv1 with satellite-measured extinctions and in situ optical particle counter (OPC) balloon flights. The volcanically quiescent total stratospheric aerosol burden simulated in SOCOL-AERv2 has increased from 109 Gg of sulfur (S) to 160 Gg S, matching the newly available satellite estimate of 165 Gg S. However, SOCOL-AERv2 simulates too high cross-tropopause transport of tropospheric SO2 and/or sulfate aerosol, leading to an overestimation of lower stratospheric aerosol. Due to the current lack of upper tropospheric SO2 measurements and the neglect of organic aerosol in the model, the lower stratospheric bias of SOCOL-AERv2 was not further improved. Model performance under volcanically perturbed conditions has also undergone some changes, resulting in a slightly shorter volcanic aerosol lifetime after the Pinatubo eruption. With the improved deposition schemes of SOCOL-AERv2, simulated sulfur wet deposition fluxes are within a factor of 2 of measured deposition fluxes at 78 % of the measurement stations globally, an agreement which is on par with previous model intercomparison studies. Because of these improvements, SOCOL-AERv2 will be better suited to studying changes in atmospheric sulfur deposition due to variations in climate and emissions.

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