{"type": "FeatureCollection", "features": [{"id": "10.1016/j.scitotenv.2024.170593", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:17:30Z", "type": "Journal Article", "created": "2024-02-01", "title": "Science of the Total Environment", "description": "Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1\u00b0 resolution daily AOD data set over Europe over the period 2003-2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23-92\u00a0%, 11-13\u00a0% and 115-132\u00a0% higher than the corresponding satellite or reanalysis products, respectively. Over 91.6\u00a0%, 81.6\u00a0%, and 88.9\u00a0% of QML AOD, fAOD and cAOD predictions fall within \u00b120\u00a0% Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R\u00a0=\u00a00.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment.", "keywords": ["cAOD", "Satellite", "13. Climate action", "Simulaci\u00f3 per ordinador", "11. Sustainability", "fAOD", "Aerosol Optical Depth", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", "14. Life underwater", "Atmospheric aerosols", "Particulate matter", "Aerosol", "3. Good health"]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2024.170593"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2024.170593", "name": "item", "description": "10.1016/j.scitotenv.2024.170593", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2024.170593"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-01T00:00:00Z"}}, {"id": "10.1029/2023jd040657", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:18:17Z", "type": "Journal Article", "created": "2024-06-11", "title": "Impact of Dust Source Patchiness on the Existence of a Constant Dust Flux Layer During Aeolian Erosion Events", "description": "Abstract

Dust emission fluxes during wind soil erosion are usually estimated using a dust concentration vertical gradient, by assuming a constant dust flux layer between the surface and the dust measurement levels. Here, we investigate the existence of this layer during erosion events recorded in Iceland and Jordan. Size\uffe2\uff80\uff90resolved dust fluxes were estimated at three levels between 2 and 4\uffc2\uffa0m using the eddy\uffe2\uff80\uff90covariance method. Dust fluxes were found mainly constant only between the two upper levels in Iceland, the lower dust flux being often stronger and richer in coarse particles, while dust fluxes in Jordan were nearly constant across all levels. The wind dynamics could not explain the absence of a constant dust flux layer in Iceland. We show that the presence of stationary dust source patches in Iceland, related to surface humidity, created a non\uffe2\uff80\uff90uniform dust layer near the surface, named dust roughness sublayer (DRSL), where individual plumes behind each patch interact but do not fully mix. The lowest dust measurement level was probably located within this sublayer while the upper ones were located above, such that there the emitted dust became spatially well\uffe2\uff80\uff90mixed. This explains near the surface in Iceland, the more intermittent dust concentration, its low correlation with the dust concentrations above, and the richer dust flux in coarse particles due to their lower deposition contribution. Our findings highlight the importance of estimating dust fluxes above a dust blending height whose characteristics depend on the dust source patchiness caused by surface humidity or the presence of sparse non\uffe2\uff80\uff90erosive elements.Mineral dust produced by wind erosion of arid and semiarid surfaces is a major component of atmospheric aerosol that affects climate, weather, ecosystems, and socioeconomic sectors such as human health, transportation, solar energy, and air quality. Understanding these effects and ultimately improving the resilience of affected countries requires a reliable, dense, and diverse set of dust observations, fundamental for the development and the provision of skillful dust-forecast-tailored products. The last decade has seen a notable improvement of dust observational capabilities in terms of considered parameters, geographical coverage, and delivery times, as well as of tailored products of interest to both the scientific community and the various end-users. Given this progress, here we review the current state of observational capabilities, including in situ, ground-based, and satellite remote sensing observations in northern Africa, the Middle East, and Europe for the provision of dust information considering the needs of various users. We also critically discuss observational gaps and related unresolved questions while providing suggestions for overcoming the current limitations. Our review aims to be a milestone for discussing dust observational gaps at a global level to address the needs of users, from research communities to nonscientific stakeholders.

Abstract. Quantifying the dust optical depth (DOD) and its uncertainty across spatiotemporal scales is key to understanding and constraining the dust cycle and its interactions with the Earth System. This study quantifies the DOD along with its monthly and year-to-year variability between 2003 and 2017 at global and regional levels based on the MIDAS (ModIs Dust AeroSol) dataset, which combines Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua retrievals and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis products. We also describe the annual and seasonal geographical distributions of DOD across the main dust source regions and transport pathways. MIDAS provides columnar mid-visible (550\u2009nm) DOD at fine spatial resolution (0.1\u2218\u00d70.1\u2218), expanding the current observational capabilities for monitoring the highly variable spatiotemporal features of the dust burden. We obtain a global DOD of 0.032\u00b10.003 \u2013 approximately a quarter (23.4\u2009%\u00b12.4\u2009%) of the global aerosol optical depth (AOD) \u2013 with about 1\u00a0order of magnitude more DOD in the Northern Hemisphere (0.056\u00b10.004; 31.8\u2009%\u00b12.7\u2009%) than in the Southern Hemisphere (0.008\u00b10.001; 8.2\u2009%\u00b11.1\u2009%) and about 3.5 times more DOD over land (0.070\u00b10.005) than over ocean (0.019\u00b10.002). The Northern Hemisphere monthly DOD is highly correlated with the corresponding monthly AOD (R2=0.94) and contributes 20\u2009% to 48\u2009% of it, both indicating a dominant dust contribution. In contrast, the contribution of dust to the monthly AOD does not exceed 17\u2009% in the Southern Hemisphere, although the uncertainty in this region is larger. Among the major dust sources of the planet, the maximum DODs (\u223c1.2) are recorded in the Bod\u00e9l\u00e9 Depression of the northern Lake Chad Basin, whereas moderate-to-high intensities are encountered in the Western Sahara (boreal summer), along the eastern parts of the Middle East (boreal summer) and in the Taklamakan Desert (spring). Over oceans, major long-range dust transport is observed primarily along the tropical Atlantic (intensified during boreal summer) and secondarily in the North Pacific (intensified during boreal spring). Our calculated global and regional averages and associated uncertainties are consistent with some but not all recent observation-based studies. Our work provides a simple yet flexible method to estimate consistent uncertainties across spatiotemporal scales, which will enhance the use of the MIDAS dataset in a variety of future studies.

", "keywords": ["Mineral dusts", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia [\u00c0rees tem\u00e0tiques de la UPC]", "Physics", "QC1-999", "MIDAS global dataset", "16. Peace & justice", "01 natural sciences", "Atmospheric Sciences", "Climate Action", "Chemistry", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia", "13. Climate action", "Mineral dust particles", "Simulaci\u00f3 per ordinador", "Pols", "Meteorology & Atmospheric Sciences", "Datasets", "Dust optical depth (DOD)", "Earth System", "QD1-999", "Astronomical and Space Sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://acp.copernicus.org/articles/22/3553/2022/acp-22-3553-2022.pdf"}, {"href": "https://escholarship.org/content/qt9v38c6qs/qt9v38c6qs.pdf"}, {"href": "https://doi.org/10.5194/acp-22-3553-2022"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/acp-22-3553-2022", "name": "item", "description": "10.5194/acp-22-3553-2022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/acp-22-3553-2022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-19T00:00:00Z"}}, {"id": "10.5194/acp-2021-4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:22:17Z", "type": "Journal Article", "created": "2021-01-18", "title": "Contribution of the world's main dust source regions to the global cycle of desert dust", "description": "

Abstract. Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, the relative contributions of the world\u2019s major dust source regions to the global dust cycle remain poorly constrained. This problem hinders accounting for the potentially large impact of regional differences in dust properties on clouds, the Earth's energy balance, and terrestrial and marine biogeochemical cycles. Here, we constrain the contribution of each of the world\u2019s main dust source regions to the global dust cycle. We use an analytical framework that integrates an ensemble of global model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We obtain a data set that constrains the relative contribution of each of nine major source regions to size-resolved dust emission, atmospheric loading, optical depth, concentration, and deposition flux. We find that the 22\u201329\u2009Tg (one standard error range) global loading of dust with geometric diameter up to 20\u2009\u03bcm is partitioned as follows: North African source regions contribute ~50\u2009% (11\u201315\u2009Tg), Asian source regions contribute ~40\u2009% (8\u201313\u2009Tg), and North American and Southern Hemisphere regions contribute ~10\u2009% (1.8\u20133.2\u2009Tg). Current models might on average be overestimating the contribution of North African sources to atmospheric dust loading at ~65\u2009%, while underestimating the contribution of Asian dust at ~30\u2009%. However, both our results and current models could be affected by unquantified biases, such as due to errors in separating dust aerosol optical depth from that produced by other aerosol species in remote sensing retrievals in poorly observed desert regions. Our results further show that each source region's dust loading peaks in local spring and summer, which is partially driven by increased dust lifetime in those seasons. We also quantify the dust deposition flux to the Amazon rainforest to be ~10\u2009Tg/year, which is a factor of 2\u20133 less than inferred from satellite data by previous work that likely overestimated dust deposition by underestimating the dust mass extinction efficiency. The data obtained in this paper can be used to obtain improved constraints on dust impacts on clouds, climate, biogeochemical cycles, and other parts of the Earth system.

", "keywords": ["Atmospheric sciences", "550", "QC1-999", "Global dust cycle", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida", "01 natural sciences", "Atmospheric Sciences", "Atmospheric models", "Earth's atmosphere", "Simulaci\u00f3 per ordinador", "Meteorology & Atmospheric Sciences", "Dust; Aerosols; Climate Models; Earth System Models;", "14. Life underwater", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", " environment", "Life Below Water", "QD1-999", "0105 earth and related environmental sciences", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", " Atmosphere", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "info:eu-repo/classification/ddc/550", "Atmosphere", "Climate change science", "ddc:550", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Physics", "Aerosol model simulations", "15. Life on land", "Atmosfera -- Aspectes ambientals", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "Climate Action", "Earth sciences", "Chemistry", "13. Climate action", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida [\u00c0rees tem\u00e0tiques de la UPC]", "Air quality", "Earth Sciences", "Aerosols--Measurement", "Desert dust", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Astronomical and Space Sciences"]}, "links": [{"href": "https://boa.unimib.it/bitstream/10281/321610/1/Kok_2021_ACP_Dust-global.pdf"}, {"href": "https://acp.copernicus.org/articles/21/8169/2021/acp-21-8169-2021.pdf"}, {"href": "https://acp.copernicus.org/articles/21/8169/2021/acp-21-8169-2021-supplement.pdf"}, {"href": "https://escholarship.org/content/qt31s4c3tr/qt31s4c3tr.pdf"}, {"href": "https://escholarship.org/content/qt4f95b02f/qt4f95b02f.pdf"}, {"href": "https://doi.org/10.5194/acp-2021-4"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Goldschmidt2021%20abstracts", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/acp-2021-4", "name": "item", "description": "10.5194/acp-2021-4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/acp-2021-4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5194/acp-21-8127-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:22:18Z", "type": "Journal Article", "created": "2021-05-27", "title": "Improved representation of the global dust cycle using observational constraints on dust properties and abundance", "description": "

Abstract. Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, atmospheric models struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused by fundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of 2\u00a0relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with a geometric diameter up to 20\u2009\u00b5m (PM20) is approximately 5000\u2009Tg\u2009yr\u22121, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded datasets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this dataset is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

", "keywords": ["Atmospheric sciences", "550", "QC1-999", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida", "Dust emissions", "01 natural sciences", "Earth system -- environmental sciences", "Atmospheric Sciences", "Dust; Aerosol; Climate Models; Earth System Models;", "Atmospheric models", "Simulaci\u00f3 per ordinador", "Meteorology & Atmospheric Sciences", "Atmospheric model simulations", "QD1-999", "Earth system", "0105 earth and related environmental sciences", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "info:eu-repo/classification/ddc/550", "Atmosphere", "Climate change science", "ddc:550", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Physics", "Dust", "Computer simulation", "15. Life on land", "Atmosfera -- Aspectes ambientals", "520", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "Climate Action", "Earth sciences", "Chemistry", "Model simulation", "13. Climate action", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida [\u00c0rees tem\u00e0tiques de la UPC]", "Earth Sciences", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", "Aerosols--Measurement", "Desert dust", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Astronomical and Space Sciences", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]"]}, "links": [{"href": "https://boa.unimib.it/bitstream/10281/321612/2/10281-321612_VoR.pdf"}, {"href": "https://acp.copernicus.org/articles/21/8127/2021/acp-21-8127-2021.pdf"}, {"href": "https://acp.copernicus.org/articles/21/8127/2021/acp-21-8127-2021-supplement.pdf"}, {"href": "https://escholarship.org/content/qt5g7457v8/qt5g7457v8.pdf"}, {"href": "https://doi.org/10.5194/acp-21-8127-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/acp-21-8127-2021", "name": "item", "description": "10.5194/acp-21-8127-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/acp-21-8127-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-23T00:00:00Z"}}, {"id": "10.5194/essd-2022-31", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:22:29Z", "type": "Journal Article", "created": "2022-01-25", "title": "European primary emissions of criteria pollutants and greenhouse gases in 2020 modulated by the COVID-19 pandemic disruptions", "description": "

Abstract. We present a European dataset of daily-, sector-, pollutant- and country-dependent emission adjustment factors associated to the COVID-19 mobility restrictions for the year 2020. The resulting dataset covers a total of nine emission sectors, including road transport, energy industry, manufacturing industry, residential and commercial combustion, aviation, shipping, off-road transport, use of solvents, and fugitive emissions from transportation and distribution of fossil fuels. The dataset was produced to be combined with the Copernicus CAMS-REG_v5.1 2020 business-as-usual (BAU) inventory, which provides high resolution (0.1 \u00d7 0.05 deg.) emission estimates for 2020 omitting the impact of the COVID-19 restrictions. The combination of both datasets allows quantifying spatially- and temporally-resolved reductions in primary emissions from both criteria pollutants (NOx, SO2, NMVOC, NH3, CO, PM10 and PM2.5) and greenhouse gases (CO2 fossil fuel, CO2 biofuel and CH4), as well as assessing the contribution of each emission sector and European country to the overall emission changes. Estimated overall emission changes in 2020 relative to BAU emissions were as follows: \u221210.5 % for NOx (\u2212602 kt), \u22127.8 % (\u2212260.2 Mt) for CO2 from fossil fuels, \u22124.7 % (\u2212808.5 kt) for CO, \u22124.6 % (\u221280 kt) for SO2, \u22123.3 % (\u221219.1 Mt) for CO2 from biofuels, \u22123.0 % (\u221256.3 kt) for PM10, \u22122.5 % (\u2212173.3 kt) for NMVOC, \u22122.1 % (\u221224.3 kt) for PM2.5, \u22120.9 % (\u2212156.1 kt) for CH4 and \u22120.2 % (\u22128.6 kt) for NH3. The most pronounced drop in emissions occurred in April (up to \u221232.8 % on average for NOx) when mobility restrictions were at their maxima. The emission reductions during the second epidemic wave between October and December, were three to four times lower than those occurred during the Spring lockdown, as mobility restrictions were generally softer (e.g., curfews, limited social gatherings). Italy, France, Spain, the United Kingdom and Germany were, together, the largest contributors to the total EU27 + UK absolute emission decreases. At the sectoral level, the largest emission declines were found for aviation (\u221251 to \u221256 %), followed by road transport (\u221215.5 % to \u221218.8 %), the latter being the main driver of the estimated reductions for the majority of pollutants. The collection of COVID-19 emission adjustment factors (https://doi.org/10.24380/k966-3957, Guevara et al., 2022) and the CAMS-REG_v5.1 2020 BAU gridded inventory (https://doi.org/10.24380/eptm-kn40, Kuenen et al., 2022) have been produced in support of air quality modelling studies.

", "keywords": ["QE1-996.5", "330", "Mobility restrictions COVID-19", "Geology", "COVID-19 (Malaltia)", "01 natural sciences", "7. Clean energy", "3. Good health", "Environmental sciences", "COVID-19 (Disease)", "Greenhouse gases", "13. Climate action", "Simulaci\u00f3 per ordinador", "Air quality", "11. Sustainability", "GE1-350", "Air--Pollution", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", "Confinament", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://essd.copernicus.org/articles/14/2521/2022/essd-14-2521-2022.pdf"}, {"href": "https://doi.org/10.5194/essd-2022-31"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-2022-31", "name": "item", "description": "10.5194/essd-2022-31", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-2022-31"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-25T00:00:00Z"}}, {"id": "2117/405673", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:26:33Z", "type": "Journal Article", "created": "2024-02-01", "title": "Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1\u00b0 resolution to facilitate air quality assessments", "description": "Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1\u00b0 resolution daily AOD data set over Europe over the period 2003-2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23-92\u00a0%, 11-13\u00a0% and 115-132\u00a0% higher than the corresponding satellite or reanalysis products, respectively. Over 91.6\u00a0%, 81.6\u00a0%, and 88.9\u00a0% of QML AOD, fAOD and cAOD predictions fall within \u00b120\u00a0% Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R\u00a0=\u00a00.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment.", "keywords": ["cAOD", "Satellite", "13. Climate action", "Simulaci\u00f3 per ordinador", "11. Sustainability", "fAOD", "Aerosol Optical Depth", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", "14. Life underwater", "Atmospheric aerosols", "Particulate matter", "Aerosol", "3. Good health"]}, "links": [{"href": "https://doi.org/2117/405673"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/405673", "name": "item", "description": "2117/405673", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/405673"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-01T00:00:00Z"}}, {"id": "2117/371172", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:26:33Z", "type": "Journal Article", "created": "2022-01-25", "title": "European primary emissions of criteria pollutants and greenhouse gases in 2020 modulated by the COVID-19 pandemic disruptions", "description": "

Abstract. We present a European dataset of daily-, sector-, pollutant- and country-dependent emission adjustment factors associated to the COVID-19 mobility restrictions for the year 2020. The resulting dataset covers a total of nine emission sectors, including road transport, energy industry, manufacturing industry, residential and commercial combustion, aviation, shipping, off-road transport, use of solvents, and fugitive emissions from transportation and distribution of fossil fuels. The dataset was produced to be combined with the Copernicus CAMS-REG_v5.1 2020 business-as-usual (BAU) inventory, which provides high resolution (0.1 \u00d7 0.05 deg.) emission estimates for 2020 omitting the impact of the COVID-19 restrictions. The combination of both datasets allows quantifying spatially- and temporally-resolved reductions in primary emissions from both criteria pollutants (NOx, SO2, NMVOC, NH3, CO, PM10 and PM2.5) and greenhouse gases (CO2 fossil fuel, CO2 biofuel and CH4), as well as assessing the contribution of each emission sector and European country to the overall emission changes. Estimated overall emission changes in 2020 relative to BAU emissions were as follows: \u221210.5 % for NOx (\u2212602 kt), \u22127.8 % (\u2212260.2 Mt) for CO2 from fossil fuels, \u22124.7 % (\u2212808.5 kt) for CO, \u22124.6 % (\u221280 kt) for SO2, \u22123.3 % (\u221219.1 Mt) for CO2 from biofuels, \u22123.0 % (\u221256.3 kt) for PM10, \u22122.5 % (\u2212173.3 kt) for NMVOC, \u22122.1 % (\u221224.3 kt) for PM2.5, \u22120.9 % (\u2212156.1 kt) for CH4 and \u22120.2 % (\u22128.6 kt) for NH3. The most pronounced drop in emissions occurred in April (up to \u221232.8 % on average for NOx) when mobility restrictions were at their maxima. The emission reductions during the second epidemic wave between October and December, were three to four times lower than those occurred during the Spring lockdown, as mobility restrictions were generally softer (e.g., curfews, limited social gatherings). Italy, France, Spain, the United Kingdom and Germany were, together, the largest contributors to the total EU27 + UK absolute emission decreases. At the sectoral level, the largest emission declines were found for aviation (\u221251 to \u221256 %), followed by road transport (\u221215.5 % to \u221218.8 %), the latter being the main driver of the estimated reductions for the majority of pollutants. The collection of COVID-19 emission adjustment factors (https://doi.org/10.24380/k966-3957, Guevara et al., 2022) and the CAMS-REG_v5.1 2020 BAU gridded inventory (https://doi.org/10.24380/eptm-kn40, Kuenen et al., 2022) have been produced in support of air quality modelling studies.

", "keywords": ["QE1-996.5", "330", "Mobility restrictions COVID-19", "Geology", "COVID-19 (Malaltia)", "01 natural sciences", "7. Clean energy", "3. Good health", "Environmental sciences", "COVID-19 (Disease)", "Greenhouse gases", "13. Climate action", "Simulaci\u00f3 per ordinador", "Air quality", "11. Sustainability", "GE1-350", "Air--Pollution", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", "Confinament", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://essd.copernicus.org/articles/14/2521/2022/essd-14-2521-2022.pdf"}, {"href": "https://doi.org/2117/371172"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/371172", "name": "item", "description": "2117/371172", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/371172"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-25T00:00:00Z"}}, {"id": "10138/579229", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:25:27Z", "type": "Journal Article", "created": "2024-06-11", "title": "Impact of Dust Source Patchiness on the Existence of a Constant Dust Flux Layer During Aeolian Erosion Events", "description": "Abstract

Dust emission fluxes during wind soil erosion are usually estimated using a dust concentration vertical gradient, by assuming a constant dust flux layer between the surface and the dust measurement levels. Here, we investigate the existence of this layer during erosion events recorded in Iceland and Jordan. Size\uffe2\uff80\uff90resolved dust fluxes were estimated at three levels between 2 and 4\uffc2\uffa0m using the eddy\uffe2\uff80\uff90covariance method. Dust fluxes were found mainly constant only between the two upper levels in Iceland, the lower dust flux being often stronger and richer in coarse particles, while dust fluxes in Jordan were nearly constant across all levels. The wind dynamics could not explain the absence of a constant dust flux layer in Iceland. We show that the presence of stationary dust source patches in Iceland, related to surface humidity, created a non\uffe2\uff80\uff90uniform dust layer near the surface, named dust roughness sublayer (DRSL), where individual plumes behind each patch interact but do not fully mix. The lowest dust measurement level was probably located within this sublayer while the upper ones were located above, such that there the emitted dust became spatially well\uffe2\uff80\uff90mixed. This explains near the surface in Iceland, the more intermittent dust concentration, its low correlation with the dust concentrations above, and the richer dust flux in coarse particles due to their lower deposition contribution. Our findings highlight the importance of estimating dust fluxes above a dust blending height whose characteristics depend on the dust source patchiness caused by surface humidity or the presence of sparse non\uffe2\uff80\uff90erosive elements.

Abstract. Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, the relative contributions of the world\u2019s major dust source regions to the global dust cycle remain poorly constrained. This problem hinders accounting for the potentially large impact of regional differences in dust properties on clouds, the Earth's energy balance, and terrestrial and marine biogeochemical cycles. Here, we constrain the contribution of each of the world\u2019s main dust source regions to the global dust cycle. We use an analytical framework that integrates an ensemble of global model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We obtain a data set that constrains the relative contribution of each of nine major source regions to size-resolved dust emission, atmospheric loading, optical depth, concentration, and deposition flux. We find that the 22\u201329\u2009Tg (one standard error range) global loading of dust with geometric diameter up to 20\u2009\u03bcm is partitioned as follows: North African source regions contribute ~50\u2009% (11\u201315\u2009Tg), Asian source regions contribute ~40\u2009% (8\u201313\u2009Tg), and North American and Southern Hemisphere regions contribute ~10\u2009% (1.8\u20133.2\u2009Tg). Current models might on average be overestimating the contribution of North African sources to atmospheric dust loading at ~65\u2009%, while underestimating the contribution of Asian dust at ~30\u2009%. However, both our results and current models could be affected by unquantified biases, such as due to errors in separating dust aerosol optical depth from that produced by other aerosol species in remote sensing retrievals in poorly observed desert regions. Our results further show that each source region's dust loading peaks in local spring and summer, which is partially driven by increased dust lifetime in those seasons. We also quantify the dust deposition flux to the Amazon rainforest to be ~10\u2009Tg/year, which is a factor of 2\u20133 less than inferred from satellite data by previous work that likely overestimated dust deposition by underestimating the dust mass extinction efficiency. The data obtained in this paper can be used to obtain improved constraints on dust impacts on clouds, climate, biogeochemical cycles, and other parts of the Earth system.

", "keywords": ["550", "3702 Climate change science (for-2020)", "QC1-999", "Global dust cycle", "0201 Astronomical and Space Sciences (for)", "0401 Atmospheric Sciences (for)", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida", "3701 Atmospheric Sciences (for-2020)", "01 natural sciences", "Meteorology & Atmospheric Sciences (science-metrix)", "Atmospheric Sciences", "Atmospheric models", "Earth's atmosphere", "Simulaci\u00f3 per ordinador", "14 Life Below Water (sdg)", "Meteorology & Atmospheric Sciences", "Dust; Aerosols; Climate Models; Earth System Models;", "14. Life underwater", "Life Below Water", "QD1-999", "0105 earth and related environmental sciences", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "info:eu-repo/classification/ddc/550", "3701 Atmospheric sciences (for-2020)", "Climate change science", "Atmosphere", "ddc:550", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Physics", "Aerosol model simulations", "15. Life on land", "Atmosfera -- Aspectes ambientals", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "Climate Action", "Earth sciences", "Chemistry", "37 Earth Sciences (for-2020)", "13. Climate action", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida [\u00c0rees tem\u00e0tiques de la UPC]", "Air quality", "Earth Sciences", "13 Climate Action (sdg)", "Aerosols--Measurement", "Desert dust", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Astronomical and Space Sciences"]}, "links": [{"href": "https://boa.unimib.it/bitstream/10281/321610/1/Kok_2021_ACP_Dust-global.pdf"}, {"href": "https://acp.copernicus.org/articles/21/8169/2021/acp-21-8169-2021.pdf"}, {"href": "https://acp.copernicus.org/articles/21/8169/2021/acp-21-8169-2021-supplement.pdf"}, {"href": "https://escholarship.org/content/qt31s4c3tr/qt31s4c3tr.pdf"}, {"href": "https://escholarship.org/content/qt4f95b02f/qt4f95b02f.pdf"}, {"href": "https://doi.org/10281/321610"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Goldschmidt2021%20abstracts", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10281/321610", "name": "item", "description": "10281/321610", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10281/321610"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10281/321612", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:25:37Z", "type": "Journal Article", "created": "2021-05-27", "title": "Improved representation of the global dust cycle using observational constraints on dust properties and abundance", "description": "

Abstract. Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, atmospheric models struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused by fundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of 2\u00a0relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with a geometric diameter up to 20\u2009\u00b5m (PM20) is approximately 5000\u2009Tg\u2009yr\u22121, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded datasets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this dataset is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

", "keywords": ["Atmospheric sciences", "550", "QC1-999", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida", "Dust emissions", "01 natural sciences", "Earth system -- environmental sciences", "Atmospheric Sciences", "Dust; Aerosol; Climate Models; Earth System Models;", "Atmospheric models", "Simulaci\u00f3 per ordinador", "Meteorology & Atmospheric Sciences", "Atmospheric model simulations", "QD1-999", "Earth system", "0105 earth and related environmental sciences", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "info:eu-repo/classification/ddc/550", "Atmosphere", "Climate change science", "ddc:550", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Physics", "Dust", "Computer simulation", "15. Life on land", "Atmosfera -- Aspectes ambientals", "520", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "Climate Action", "Earth sciences", "Chemistry", "Model simulation", "13. Climate action", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida [\u00c0rees tem\u00e0tiques de la UPC]", "Earth Sciences", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", "Aerosols--Measurement", "Desert dust", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Astronomical and Space Sciences", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]"]}, "links": [{"href": "https://boa.unimib.it/bitstream/10281/321612/2/10281-321612_VoR.pdf"}, {"href": "https://acp.copernicus.org/articles/21/8127/2021/acp-21-8127-2021.pdf"}, {"href": "https://acp.copernicus.org/articles/21/8127/2021/acp-21-8127-2021-supplement.pdf"}, {"href": "https://escholarship.org/content/qt5g7457v8/qt5g7457v8.pdf"}, {"href": "https://doi.org/10281/321612"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10281/321612", "name": "item", "description": "10281/321612", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10281/321612"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-23T00:00:00Z"}}, {"id": "11386/4857971", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:25:52Z", "type": "Journal Article", "created": "2023-08-23", "title": "Observing Mineral Dust in Northern Africa, the Middle East, and Europe: Current Capabilities and Challenges ahead for the Development of Dust Services", "description": "Abstract

Mineral dust produced by wind erosion of arid and semiarid surfaces is a major component of atmospheric aerosol that affects climate, weather, ecosystems, and socioeconomic sectors such as human health, transportation, solar energy, and air quality. Understanding these effects and ultimately improving the resilience of affected countries requires a reliable, dense, and diverse set of dust observations, fundamental for the development and the provision of skillful dust-forecast-tailored products. The last decade has seen a notable improvement of dust observational capabilities in terms of considered parameters, geographical coverage, and delivery times, as well as of tailored products of interest to both the scientific community and the various end-users. Given this progress, here we review the current state of observational capabilities, including in situ, ground-based, and satellite remote sensing observations in northern Africa, the Middle East, and Europe for the provision of dust information considering the needs of various users. We also critically discuss observational gaps and related unresolved questions while providing suggestions for overcoming the current limitations. Our review aims to be a milestone for discussing dust observational gaps at a global level to address the needs of users, from research communities to nonscientific stakeholders.

Abstract. Quantifying the dust optical depth (DOD) and its uncertainty across spatiotemporal scales is key to understanding and constraining the dust cycle and its interactions with the Earth System. This study quantifies the DOD along with its monthly and year-to-year variability between 2003 and 2017 at global and regional levels based on the MIDAS (ModIs Dust AeroSol) dataset, which combines Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua retrievals and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis products. We also describe the annual and seasonal geographical distributions of DOD across the main dust source regions and transport pathways. MIDAS provides columnar mid-visible (550\u2009nm) DOD at fine spatial resolution (0.1\u2218\u00d70.1\u2218), expanding the current observational capabilities for monitoring the highly variable spatiotemporal features of the dust burden. We obtain a global DOD of 0.032\u00b10.003 \u2013 approximately a quarter (23.4\u2009%\u00b12.4\u2009%) of the global aerosol optical depth (AOD) \u2013 with about 1\u00a0order of magnitude more DOD in the Northern Hemisphere (0.056\u00b10.004; 31.8\u2009%\u00b12.7\u2009%) than in the Southern Hemisphere (0.008\u00b10.001; 8.2\u2009%\u00b11.1\u2009%) and about 3.5 times more DOD over land (0.070\u00b10.005) than over ocean (0.019\u00b10.002). The Northern Hemisphere monthly DOD is highly correlated with the corresponding monthly AOD (R2=0.94) and contributes 20\u2009% to 48\u2009% of it, both indicating a dominant dust contribution. In contrast, the contribution of dust to the monthly AOD does not exceed 17\u2009% in the Southern Hemisphere, although the uncertainty in this region is larger. Among the major dust sources of the planet, the maximum DODs (\u223c1.2) are recorded in the Bod\u00e9l\u00e9 Depression of the northern Lake Chad Basin, whereas moderate-to-high intensities are encountered in the Western Sahara (boreal summer), along the eastern parts of the Middle East (boreal summer) and in the Taklamakan Desert (spring). Over oceans, major long-range dust transport is observed primarily along the tropical Atlantic (intensified during boreal summer) and secondarily in the North Pacific (intensified during boreal spring). Our calculated global and regional averages and associated uncertainties are consistent with some but not all recent observation-based studies. Our work provides a simple yet flexible method to estimate consistent uncertainties across spatiotemporal scales, which will enhance the use of the MIDAS dataset in a variety of future studies.

", "keywords": ["Mineral dusts", "3702 Climate change science (for-2020)", "QC1-999", "0201 Astronomical and Space Sciences (for)", "0401 Atmospheric Sciences (for)", "3701 Atmospheric Sciences (for-2020)", "01 natural sciences", "Meteorology & Atmospheric Sciences (science-metrix)", "Atmospheric Sciences", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia", "Simulaci\u00f3 per ordinador", "Pols", "Meteorology & Atmospheric Sciences", "Datasets", "Dust optical depth (DOD)", "Earth System", "QD1-999", "0105 earth and related environmental sciences", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia [\u00c0rees tem\u00e0tiques de la UPC]", "3701 Atmospheric sciences (for-2020)", "Physics", "MIDAS global dataset", "16. Peace & justice", "Climate Action", "Chemistry", "37 Earth Sciences (for-2020)", "13. Climate action", "Mineral dust particles", "13 Climate Action (sdg)", "Astronomical and Space Sciences"]}, "links": [{"href": "https://acp.copernicus.org/articles/22/3553/2022/acp-22-3553-2022.pdf"}, {"href": "https://escholarship.org/content/qt9v38c6qs/qt9v38c6qs.pdf"}, {"href": "https://doi.org/2117/364526"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/364526", "name": "item", "description": "2117/364526", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/364526"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-19T00:00:00Z"}}, {"id": "3185943994", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:27:20Z", "type": "Journal Article", "created": "2022-03-17", "title": "Quantification of the dust optical depth across spatiotemporal scales with the MIDAS global dataset (2003\u20132017)", "description": "

Abstract. Quantifying the dust optical depth (DOD) and its uncertainty across spatiotemporal scales is key to understanding and constraining the dust cycle and its interactions with the Earth System. This study quantifies the DOD along with its monthly and year-to-year variability between 2003 and 2017 at global and regional levels based on the MIDAS (ModIs Dust AeroSol) dataset, which combines Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua retrievals and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis products. We also describe the annual and seasonal geographical distributions of DOD across the main dust source regions and transport pathways. MIDAS provides columnar mid-visible (550\u2009nm) DOD at fine spatial resolution (0.1\u2218\u00d70.1\u2218), expanding the current observational capabilities for monitoring the highly variable spatiotemporal features of the dust burden. We obtain a global DOD of 0.032\u00b10.003 \u2013 approximately a quarter (23.4\u2009%\u00b12.4\u2009%) of the global aerosol optical depth (AOD) \u2013 with about 1\u00a0order of magnitude more DOD in the Northern Hemisphere (0.056\u00b10.004; 31.8\u2009%\u00b12.7\u2009%) than in the Southern Hemisphere (0.008\u00b10.001; 8.2\u2009%\u00b11.1\u2009%) and about 3.5 times more DOD over land (0.070\u00b10.005) than over ocean (0.019\u00b10.002). The Northern Hemisphere monthly DOD is highly correlated with the corresponding monthly AOD (R2=0.94) and contributes 20\u2009% to 48\u2009% of it, both indicating a dominant dust contribution. In contrast, the contribution of dust to the monthly AOD does not exceed 17\u2009% in the Southern Hemisphere, although the uncertainty in this region is larger. Among the major dust sources of the planet, the maximum DODs (\u223c1.2) are recorded in the Bod\u00e9l\u00e9 Depression of the northern Lake Chad Basin, whereas moderate-to-high intensities are encountered in the Western Sahara (boreal summer), along the eastern parts of the Middle East (boreal summer) and in the Taklamakan Desert (spring). Over oceans, major long-range dust transport is observed primarily along the tropical Atlantic (intensified during boreal summer) and secondarily in the North Pacific (intensified during boreal spring). Our calculated global and regional averages and associated uncertainties are consistent with some but not all recent observation-based studies. Our work provides a simple yet flexible method to estimate consistent uncertainties across spatiotemporal scales, which will enhance the use of the MIDAS dataset in a variety of future studies.

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