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This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The networks architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a mean squared error (MSE) of 0.02 and a mean absolute error (MAE) of 0.01, indicating high precision in forecasting PM2.5 levels. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

Open-pit coal mining offers high resource recovery, excellent safety conditions, and large-scale production. However, the process generates significant dust, leading to occupational diseases such as pneumoconiosis among miners and adversely affecting nearby vegetation through dust deposition, which hinders photosynthesis and causes ecological damage. This limits the transition of open-pit mining to a green, low-carbon model. Among these processes, blasting generates the most dust and has the widest impact range, but the specific amount of dust generated has not yet been thoroughly studied. This study integrates indoor experiments, theoretical analyses, and field tests, employing the Split Hopkinson Pressure Bar (SHPB) system to conduct impact loading tests on coal–rock samples under pressures ranging from 0.13 MPa to 2.0 MPa. The results indicate that as the impact load increases, the proportion of large-sized blocks decreases while smaller fragments and powdered samples increase, signifying intensified sample fragmentation. Using stress wave attenuation theory, this study translates indoor impact loadings to field blast shock waves, revealing the relationship between blasting dust mass fraction and impact pressure. Field tests at the Haerwusu open-pit coal mine validated the formula. Using image recognition technology to analyze post-blast muck-pile fragmentation, the estimated dust production closely matched the calculated values, with an error margin of less than 10%. This formula provides valuable insights for estimating dust production and improving dust control measures during open-pit mine blasting operations. [ABSTRACT FROM AUTHOR]

In the present study with a novel two-chamber setup (TCS) for dustiness investigations, the relationship between pressure differences as well as air velocities and the resulting dust emissions is investigated. The dust emissions of six particle size fractions of acetaminophen at pressure differences between 0 and 12 Pa are examined. The results show that both simulated and measured air velocities increase with increasing pressure difference. Dust emissions decrease significantly with increasing pressure difference and air velocity. Fine particles cause higher dust emissions than coarse particles. A high goodness of fit is obtained with exponential and quadratic functions to describe the relationship between pressure difference and dust emission, indicating that even moderate increases in pressure may lead to a reduction in the emission. Average air velocities within the TCS simulated with Computational Fluid Dynamics are between 0.09 and 0.37 m/s, whereas those measured experimentally are between 0.09 and 0.41 m/s, both ranges corresponding to the recommended values for effective particle separation in containment systems. These results underline the ability of the novel TCS to control pressure and airflow, which is essential for reliable dust emission measurements and thus provide support for further scientific and industrial applications. [ABSTRACT FROM AUTHOR]

Land cover is a key factor affecting dust emissions. Substantial changes in land cover have occurred due to human activities and climate change in northern China. However, the extent to which these changes influence dust emissions is still controversial. Here, we explored the specific impact of land use type transformation on dust emissions between 2000 and 2020 by using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) from the perspective of land cover changes. Two scenarios were set up and compared to quantify this impact. One scenario was using the land cover data in 2000, and the other scenario was to use the land cover data in 2020. Both scenarios were driven by the initial meteorological conditions in 2020. Results indicated that the land cover changes between 2000 and 2020 reduced dust emissions in northern China, but the weakened intensity showed obvious spatial differences. Different land use conversion types had significant differences in their impact on dust emissions. Transforming bare areas to water bodies can reduce dust emissions by up to -3.62 g m-2 year-1. This is followed by the change from bare areas to sparse vegetation areas, which can decrease dust emissions by -2.9 g m-2 year-1, and then by the conversion from bare areas to cropland, with a reduction of -2.57 g m-2 year-1. These findings can offer methodologies and data support for the quantitative evaluation of the effects of land cover changes on dust emissions. They can also serve as a reference for environmental management when formulating land use policies. [ABSTRACT FROM AUTHOR]

Open-pit coal mining offers high resource recovery, excellent safety conditions, and large-scale production. However, the process generates significant dust, leading to occupational diseases such as pneumoconiosis among miners and adversely affecting nearby vegetation through dust deposition, which hinders photosynthesis and causes ecological damage. This limits the transition of open-pit mining to a green, low-carbon model. Among these processes, blasting generates the most dust and has the widest impact range, but the specific amount of dust generated has not yet been thoroughly studied. This study integrates indoor experiments, theoretical analyses, and field tests, employing the Split Hopkinson Pressure Bar (SHPB) system to conduct impact loading tests on coal–rock samples under pressures ranging from 0.13 MPa to 2.0 MPa. The results indicate that as the impact load increases, the proportion of large-sized blocks decreases while smaller fragments and powdered samples increase, signifying intensified sample fragmentation. Using stress wave attenuation theory, this study translates indoor impact loadings to field blast shock waves, revealing the relationship between blasting dust mass fraction and impact pressure. Field tests at the Haerwusu open-pit coal mine validated the formula. Using image recognition technology to analyze post-blast muck-pile fragmentation, the estimated dust production closely matched the calculated values, with an error margin of less than 10%. This formula provides valuable insights for estimating dust production and improving dust control measures during open-pit mine blasting operations.

The identification of driving factors that contribute to dust emissions holds great significance on studying global climate change. In the present study, we constructed a new index, Dust Sensitivity Index, which allowed us to identify the sensitivity of dust emissions responding to variations in driving factors over 2003–2017 in Earth's main drylands. We found that dust emissions were sensitive to driving factor variability in eastern Brazil, the southern drylands of the Sahel, eastern Africa, eastern Australia and parts of northern Eurasia, where the aridity index (AI) is relatively high. The main factor affecting the DSI varies geographically over the Earth's main dryland regions. In total, wind speed made the largest relative contribution to DOD sensitivity (11.3%), followed by soil moisture (10.8%) and vegetation (10.4%). In addition, wind, vegetation, and soil moisture interact to impose complex and varying limitations on dust activity. 39.3% of the Earth's main drylands were limited by vegetation, 31.2% by wind and 29.5% by soil moisture. Our study also demonstrates that the residual effects of previous dust-driving factors have impacts on contemporary conditions. We found that regions characterized by lower DSI values displayed the most prominent residual effects in general.

Background. Effective management of dust emissions from industrial enterprises should be based on reliable data on emission sources and emitted components. Correct estimation of geoecological impacts exerted by dust emissions is a complicated process due to chemical diversity of the soil component in them. Aim. The aim was to test a practical approach to identifying actual ambient air pollution with solid particles in a zone influenced by an economic entity for correct estimation of impacts and monitoring of ambient air quality. Materials and methods. Samples of industrial dust emissions were analyzed with a scanning electronic high-resolution microscope (magnification was from x5 to x300,000; acceleration voltage varied between 0.3 to 30 kW) with S3400N HITACHI x-ray fluorescent device (the limit of detection was approximately 5–10 mass % and the minimum field of view was 100 µm). X-ray photographs were processed by using XRD 6000/7000 software, Version 5.21. The results obtained by calculating dispersion of the dust component were mapped with ArcView 3.2 and ArcGIS 9.3.1., GIS-platforms created by ESRI. Results. Complex investigations of dust emissions were performed at a selected enterprise and this made it possible to identify a profile of the dust component in emissions from major sources. Actual composition of dusts emitted by the analyzed enterprise was established to differ fundamentally from the data declared in the inventory lists of emissions issued by the enterprise. The results of dispersion calculations gave grounds for spotting out substances that were priority (marker) ones for the analyzed production and we also estimated actual effects produced by dust emissions on ambient air. Conclusions. The study results allow concluding that a dust emission profile is a reliable instrument for estimating actual impacts of an industrial enterprise on ambient air. It can be applied to solve tasks within setting standards, monitoring and estimating actual impacts exerted by anthropogenic dust emissions when environmental measures are being planned and developed.

In the present study with a novel two-chamber setup (TCS) for dustiness investigations, the relationship between pressure differences as well as air velocities and the resulting dust emissions is investigated. The dust emissions of six particle size fractions of acetaminophen at pressure differences between 0 and 12 Pa are examined. The results show that both simulated and measured air velocities increase with increasing pressure difference. Dust emissions decrease significantly with increasing pressure difference and air velocity. Fine particles cause higher dust emissions than coarse particles. A high goodness of fit is obtained with exponential and quadratic functions to describe the relationship between pressure difference and dust emission, indicating that even moderate increases in pressure may lead to a reduction in the emission. Average air velocities within the TCS simulated with Computational Fluid Dynamics are between 0.09 and 0.37 m/s, whereas those measured experimentally are between 0.09 and 0.41 m/s, both ranges corresponding to the recommended values for effective particle separation in containment systems. These results underline the ability of the novel TCS to control pressure and airflow, which is essential for reliable dust emission measurements and thus provide support for further scientific and industrial applications.

This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The network’s architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a mean squared error (MSE) of 0.02 and a mean absolute error (MAE) of 0.01, indicating high precision in forecasting PM2.5 levels. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

A measurement campaign was undertaken April–October 2021 using PM10 filter samplers to collect 24 h samples downwind of the Oceano Dunes State Vehicular Recreation Area (ODSVRA), an area that allows off-highway driving on its coastal dunes. The PM10 samples were analyzed and these data were used to identify the sources that contributed to the PM10 under varying meteorological conditions. Exposed filters were weighed to calculate mass concentration and analyzed using X-ray fluorescence to quantify elemental composition, ion chromatography to quantify water-soluble ions, and thermal/optical reflectance to quantify organic carbon and elemental carbon in the particulate matter. These speciated data were used to attribute the sources of PM10 for eight days that exceeded the California state 24 h mean PM10 standard and 39 days that were below the standard. The mean attribution of sources for the eight identified exceedance days was mineral dust (43.1%), followed by sea salt (25.0%) and the unidentified category (20.4%). The simultaneous increase in the mineral dust and unidentified categories with increasing levels of PM10 arriving from the direction of the ODSVRA suggests that the unidentified components were unmeasured oxides of minerals and carbonate. This increases the attribution of mineral dust for a mean exceedance day to 63.5%. The source of the mineral dust component of the PM10 is attributable to wind-driven saltation and dust emission processes within the ODSVRA. [ABSTRACT FROM AUTHOR]

As a natural aerosol with the largest emissions on land, dust has important impacts on atmospheric environment and climate systems. Both the emissions and transport of dust aerosols are tightly connected to meteorological conditions and as a result are confronted with strong modulations by the changing climate. Here, we project the changes of global dust emissions and loading by the end of the 21st century using an ensemble of model outputs from the Coupled Model Intercomparison Project version 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs). Based on the validations against site-level observations, we select 5 out of 10 models and estimate an ensemble global dust emission of 3311 Tg a−1 (1 Tg = 1012 g) at present day, in which 75 % is dry deposited and 25 % is wet deposited. Compared to 2005–2014, global dust emissions show varied responses with a reduction of 15.8 Tg a−1 under the SSP3-7.0 scenario but increased emissions up to 53.4 Tg a−1 under the SSP5-8.5 scenario at 2090–2099. For all scenarios, the most significant increase of dust emissions appears in North Africa (0.4 %–4.7 %) due to the combined effects of reduced relative humidity and precipitation but strengthened surface wind. In contrast, all scenarios show decreased emissions in central Asia (−0.6 % to −20 %) and Middle East (0 to −2.8 %) because of the increased precipitation but decreased wind speed regionally. The dust loading shows uniform increases over North Africa (1 %−12.5 %) and the downwind Atlantic following the increased emissions, but decreases over East Asia (−3.4 % to −15.2 %) and the downwind Pacific due to enhanced local precipitation that promotes wet deposition. As a result, global dust loading will increase by 2.1 %–9.3 % at the end of the 21st century under different climate scenarios, suggesting a likely strengthened radiative and climatic perturbations by dust aerosols in a warmer climate. [ABSTRACT FROM AUTHOR]

The frequent outbreak of dust storms every spring is one of the extreme weathers in East Asia. Geostationary meteorological satellite thermal infrared (TIR) imagery provides observations for high‐frequency, all‐day, large‐scale monitoring of dust sources and transport. This study proposes an integrated method to detect dust over East Asia by using the multispectral and multi‐temporal Himawari‐8/Advanced Himawari Imager (AHI) TIR observations, based on a Feedforward Neural Network (FNN) model and the Robust Principal Component Analysis based Anomalous Dust Detection (ADD) method. The FNN model is trained to map the multispectral Brightness Temperatures (BTs) to the three category probabilities (i.e., cloud, dust, and clear sky), and the ADD method calculates the dust score by normalizing multi‐temporal BT difference anomalous variation. The Integrated Dust Index (IDI) reflects the quantified dust confidence of the AHI pixel, which is defined as the sum of the dust probability and the dust score. The IDI was evaluated using 2 months of the Sentinel‐5P TROPOMI Ultraviolet Aerosol Index and achieved a total dust detection accuracy above 90%. A comparison with Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation nighttime observations shows that the IDI can also detect dust at night. The times series IDI of the massive 14–16 March 2021 dust storm show its capabilities to trace dust sources and monitor dust transport. Key Points: The dust has distinct thermal infrared spectral features and can cause significant anomalous variations in satellite observationsThe feedforward neural network model and the robust principal component analysis are combined to calculate the Integrated Dust Index (IDI)Multi‐source validation data have proved the IDI to be an effective metric for dust detection and transmission monitoring [ABSTRACT FROM AUTHOR]

The Temblor Mountain Special Recreation Area (SRMA) on the western flank of the San Joaquin Valley, CA, is located in the endemic area of Coccidioides, a fungal pathogen responsible for the increasing incidence of coccidioidomycosis (Valley fever). Recreationists in the SRMA, such as off-highway vehicle (OHV) drivers and mountain bikers who disturb the soils, are at risk of being exposed to airborne arthroconidia (asexual spores) of the pathogen. To reduce the risk of pathogen exposure for visitors, the Bureau of Land Management (BLM) plans to limit recreational activities to areas with a reduced pathogen presence. They envision an official OHV park in the future, by also restricting access to areas with ongoing restoration efforts and by limiting soil erosion in sensitive areas. To investigate which soils in the Temblor SRMA are most likely to support the growth of Coccidioides spp., soil samples were collected over a 3-year period from dominant soil types in a northern and a southern sampling area and analyzed for the pathogen using a culture-independent PCR-based method. In addition, soil pH and electrical conductivity were determined. The results of this study revealed slight genetic variance in the Coccidioides sequences obtained from the soils of the Temblor SRMA. An analysis of variance (ANOVA) could not confirm differences in soil pH and electrical conductivity (EC) between the different soil types investigated and between sites where the pathogen was detected compared to sites where it could not be found. However, the year of sampling appeared to have an influence on observed soil pH and EC, and the presence of the pathogen. Of all dominant soil types investigated, those belonging to the Littlesignal–Cochora association were the least likely to contain the pathogen, whereas soils of the Beam–Panoza–Hillbrick association appeared more supportive. In addition to pointing out OHV areas with lower pathogen exposure risk in the Temblor SRMA, recommendations were made to educate visitors and BLM workers about the risk of contracting Valley fever. [ABSTRACT FROM AUTHOR]

The climate effect and environmental pollution caused by dust discharged into the atmosphere have attracted much attention. However, the driving factors of dust emissions have not been studied thoroughly. Here, spatiotemporal variations in dust emissions and the relationship between dust emissions and large-scale atmospheric circulation in East Asia from 2000 to 2021 were investigated using Modern-Era Retrospective Analysis for Research and Applications version 2, Cloud-Aerosol Lidar Pathfinder Satellite Observations, ERA5 reanalysis data, and climate indices. Results showed that the Taklimakan Desert in the Tarim Basin, the Gurbantonggut Desert in the Junggar Basin, the Turpan Basin, and the Gobi Desert in western Inner Mongolia and southern Mongolia are the main sources of dust emissions in East Asia. The period of strong dust emissions is from March to May, and emissions to the atmosphere were mainly distributed at 0–4 km in the troposphere. In the eastern and southwestern Tarim Basin, northern Junggar Basin, and parts of the Gobi Desert in southern Mongolia, dust emissions have significantly increased over the past 22 years, whereas in the southwestern Tibetan Plateau, southwestern Inner Mongolia, and a small part of the northern Mongolian Gobi Desert there was a significant decreasing trend. The winter North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were significantly negatively correlated with East Asian dust emissions the following spring. The various phases of the AO/NAO coupling have clear different effects on East Asian dust emissions in the spring. When the AO/NAO coupling was negative (positive), the East Asian trough and Siberian High were strengthened (weakened), the frequency of cold air activity increased (weakened), 800 hPa wind speed strengthened (weakened), and East Asian emissions increased (decreased). In AO−/NAO+ years, the Asian polar vortex was stronger to the south and the East Asian trough was stronger to the west. The Lake Baikal trough was in the deepening phase, which caused more polar cold air to move into East Asia, aggravating the intensity of dust activity. In the AO+/NAO− years, the Siberian High and East Asian trough weakened, which was unfavorable to the southward movement of cold air from Siberia. Therefore, the frequency of windy weather in East Asia decreased, partly weakening dust emissions. However, a positive geopotential anomaly in northeast China and a negative geopotential anomaly in South Asia triggered an anomalous enhancement in easterly wind in the tropospheric area over northwest China. Strengthening of the Balkhash trough provides favorable conditions for gale weather in northwest China. The frequency of gale weather increased, and dust emissions were enhanced in northwest China. [ABSTRACT FROM AUTHOR]

An analysis of the methods used in Bulgaria for estimating CO2, SO2 and dust emissions has been conducted. The first methodology, which is officially used by all energy auditors at the Agency for Sustainable Energy Development targets the energy efficiency of combustion devices installed mainly at industrial enterprises. The second methodology, used by the Ministry of Environment and Water, is more comprehensive and can be applied to thermal power plants, small combustion plants as well as industrial systems. In recent years, many projects related to energy efficiency and renewable energy projects, including hydrogen technologies, which require an assessment of reduced greenhouse gas emissions, have been implemented as a priority. The use of reliable and accurate methods is essential in the assessment of greenhouse emissions. A novel methodology, based on stoichiometric equations of the combustion process for solid, liquid and gaseous fuels has been proposed and comprised. This novel methodology is characterized by higher precision compared to the methods currently in place and this is achieved through calculating emissions from the combustion of energy fuels accounting for the full elemental composition of the fuel and its heating value, whereas the current commonly applied methods use only the fuel type and the carbon content. A further benefit of the proposed methodology is the ability to estimate emissions of fuels for which there is no alternative method for calculating CO2, SO2 and dust. Results of emission calculations according to the analysed methods are presented. Finally, a comparative analysis between the presented methodologies including an assessment of their accuracy and universal applicability has been made. [ABSTRACT FROM AUTHOR]

In order to comply with the provisions of the normative acts in the field of air quality (STAS 12574/1987 - maximum allowed concentrations of sedimentable powders [1]), it was proposed to build a water mist dedusting facility within the coal deposit to stop the dust emissions at the source. The installation must ensure long-term operation in harsh conditions, with resistance to wear and minimal maintenance: operating time 12-14 hours/day, 365 days/year. The dedusting plant is composed of the water house, the distribution network, the fog production plants and the electricity supply plant. The process used to create the mist is based on the use of high pressure pumps. The equipment consists of fog guns equipped with high-pressure water spray nozzles and dispersion fans that are purchased fully equipped including a remote control installation; 6 spray guns were provided. [ABSTRACT FROM AUTHOR]

A measurement campaign was undertaken April–October 2021 using PM10 filter samplers to collect 24 h samples downwind of the Oceano Dunes State Vehicular Recreation Area (ODSVRA), an area that allows off-highway driving on its coastal dunes. The PM10 samples were analyzed and these data were used to identify the sources that contributed to the PM10 under varying meteorological conditions. Exposed filters were weighed to calculate mass concentration and analyzed using X-ray fluorescence to quantify elemental composition, ion chromatography to quantify water-soluble ions, and thermal/optical reflectance to quantify organic carbon and elemental carbon in the particulate matter. These speciated data were used to attribute the sources of PM10 for eight days that exceeded the California state 24 h mean PM10 standard and 39 days that were below the standard. The mean attribution of sources for the eight identified exceedance days was mineral dust (43.1%), followed by sea salt (25.0%) and the unidentified category (20.4%). The simultaneous increase in the mineral dust and unidentified categories with increasing levels of PM10 arriving from the direction of the ODSVRA suggests that the unidentified components were unmeasured oxides of minerals and carbonate. This increases the attribution of mineral dust for a mean exceedance day to 63.5%. The source of the mineral dust component of the PM10 is attributable to wind-driven saltation and dust emission processes within the ODSVRA.

The creation of effective aspiration systems for use in tipping systems for railroad cars (rotary car dumpers) is considered. An aspiration system with air-jet barriers is introduced. Dust emissions are localized with at least 90% efficiency. In cyclones, the level of processing is 92.4%. [ABSTRACT FROM AUTHOR]

Lead dust emissions can occur when lead powder it is transported via ship-loading facilities. Significant health and environmental hazards of fine lead dusts mean that minimising dust emissions during ship-loading is critical for industry, yet there is limited information on controlling such emissions for lead powder. This study investigated and quantified the impact of moisture on lead dust emissions via both single drop and continuous drop tests in the range 0% to 11% moisture. With increasing moisture, image analysis of single drop tests found increased agglomeration of small plate-like particles into larger spherical clusters. Average particle areas of particles dispersed to the outside of a central core of powder were shown to increase linearly in the range 0% to 11% moisture. Continuous drop tests showed that increasing powder moisture from 0% to 7% could reduce dust emissions by three orders of magnitude, demonstrating the importance and potential benefits of moisture control in ship-loading operations. Modelling of bulk lead powder and lead particle drying were used to identify critical issues and constraints in controlling moisture. Humidity control was identified as a critical control measure during the final stage of ship-loading, where drying rates are fast. [Display omitted] • Single drop and continuous drop tests performed on lead concentrate powder. • Dust investigations between 0% to 11% moisture were undertaken. • Images show agglomeration of smaller particles into larger spheres. • Dust emissions can be substantially reduced using moisture control. • Particle drying must be limited via control of ambient humidity. [ABSTRACT FROM AUTHOR]

The Oceano Dunes State Vehicular Recreation Area (ODSVRA) allows off-highway vehicle (OHV) activity on this coastal dune system. Three sources of data were examined to determine if OHV activity increased wind-blown dust emissions originating from the ODSVRA. Measurements of emissivity (mg m−2 s−1) of particulate matter (PM) from dune sands were made using the PI-SWERL® instrument from 2013 through to 2020 in the area with OHV activity and in areas where OHV access is not permitted. These measurements indicated that the mean emissivity of the riding area was two to three times higher than the mean of the non-riding areas, for wind shear velocity (u∗, m s−1) conditions well-above threshold (u∗ > 0.5 m s−1). Measurements of Wind Power Density (WPD, W m−2) and suspended particulate matter (PM, μg m−3) at monitoring stations in the riding areas and downwind of the riding areas made between May and September 2019 indicate that PM concentrations increased 12% per month for similar WPD conditions. In 2020, OHV activity was prohibited beginning in March due to the SARS-CoV-2 pandemic. Network measurements of PM and WPD, April to August 2020 indicated a 12% decrease in PM concentrations per month for similar WPD conditions, suggesting the cessation of OHV activity resulted in the dunes becoming less emissive through time. Measurements of wind speed and suspended PM at a monitoring station downwind of a dune preserve area (no OHV activity allowed) for 2019 and 2020 indicate that PM and WPD measurements do not follow the same temporal trends for the in-Park and downwind of riding area influenced area stations, further suggesting that OHV activity influences dune emissivity.

The Temblor Mountain Special Recreation Area (SRMA) on the western flank of the San Joaquin Valley, CA, is located in the endemic area of Coccidioides, a fungal pathogen responsible for the increasing incidence of coccidioidomycosis (Valley fever). Recreationists in the SRMA, such as off-highway vehicle (OHV) drivers and mountain bikers who disturb the soils, are at risk of being exposed to airborne arthroconidia (asexual spores) of the pathogen. To reduce the risk of pathogen exposure for visitors, the Bureau of Land Management (BLM) plans to limit recreational activities to areas with a reduced pathogen presence. They envision an official OHV park in the future, by also restricting access to areas with ongoing restoration efforts and by limiting soil erosion in sensitive areas. To investigate which soils in the Temblor SRMA are most likely to support the growth of Coccidioides spp., soil samples were collected over a 3-year period from dominant soil types in a northern and a southern sampling area and analyzed for the pathogen using a culture-independent PCR-based method. In addition, soil pH and electrical conductivity were determined. The results of this study revealed slight genetic variance in the Coccidioides sequences obtained from the soils of the Temblor SRMA. An analysis of variance (ANOVA) could not confirm differences in soil pH and electrical conductivity (EC) between the different soil types investigated and between sites where the pathogen was detected compared to sites where it could not be found. However, the year of sampling appeared to have an influence on observed soil pH and EC, and the presence of the pathogen. Of all dominant soil types investigated, those belonging to the Littlesignal–Cochora association were the least likely to contain the pathogen, whereas soils of the Beam–Panoza–Hillbrick association appeared more supportive. In addition to pointing out OHV areas with lower pathogen exposure risk in the Temblor SRMA, recommendations were made to educate visitors and BLM workers about the risk of contracting Valley fever.

This study explores the characteristics of crucial dust sources and changes in their emissions over the Arabian Peninsula (AP) over the 2000–2022 period using high-resolution dust aerosol optical depth data from the Moderate Resolution Imagining Spectroradiometer (MODIS) aerosol measurements onboard Terra and Aqua platforms. The MODIS dust retrievals successfully unravel the hitherto-unexplored key dust source regions and spatial heterogeneity in dust emissions. Critically, MODIS-defined dust sources display a robust geomorphological signature. In Iraq, the Tigris and Euphrates River basins contain extensive dust sources; the Euphrates dust sources are stronger and more widespread. Localized dust sources are noticed over Syria. In the Kingdom of Saudi Arabia (KSA), the eastern province particularly facilitates extensive dust activity. Oman is the prominent dust source in the southern AP due to the presence of intruding sand dunes. Dust emissions in the Iraq and KSA regions exhibit a significant negative correlation with the Standardized Precipitation-Evapotranspiration Index, a drought index, establishing that the local droughts enhance the dust emissions in these regions. The recent sustained droughts from 2008 to 2013 caused a remarkable escalation in the dust emissions in these regions through the modification of land surface conditions.

The climate effect and environmental pollution caused by dust discharged into the atmosphere have attracted much attention. However, the driving factors of dust emissions have not been studied thoroughly. Here, spatiotemporal variations in dust emissions and the relationship between dust emissions and large-scale atmospheric circulation in East Asia from 2000 to 2021 were investigated using Modern-Era Retrospective Analysis for Research and Applications version 2, Cloud-Aerosol Lidar Pathfinder Satellite Observations, ERA5 reanalysis data, and climate indices. Results showed that the Taklimakan Desert in the Tarim Basin, the Gurbantonggut Desert in the Junggar Basin, the Turpan Basin, and the Gobi Desert in western Inner Mongolia and southern Mongolia are the main sources of dust emissions in East Asia. The period of strong dust emissions is from March to May, and emissions to the atmosphere were mainly distributed at 0–4 km in the troposphere. In the eastern and southwestern Tarim Basin, northern Junggar Basin, and parts of the Gobi Desert in southern Mongolia, dust emissions have significantly increased over the past 22 years, whereas in the southwestern Tibetan Plateau, southwestern Inner Mongolia, and a small part of the northern Mongolian Gobi Desert there was a significant decreasing trend. The winter North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were significantly negatively correlated with East Asian dust emissions the following spring. The various phases of the AO/NAO coupling have clear different effects on East Asian dust emissions in the spring. When the AO/NAO coupling was negative (positive), the East Asian trough and Siberian High were strengthened (weakened), the frequency of cold air activity increased (weakened), 800 hPa wind speed strengthened (weakened), and East Asian emissions increased (decreased). In AO−/NAO+ years, the Asian polar vortex was stronger to the south and the East Asian trough was stronger to the west. The Lake Baikal trough was in the deepening phase, which caused more polar cold air to move into East Asia, aggravating the intensity of dust activity. In the AO+/NAO− years, the Siberian High and East Asian trough weakened, which was unfavorable to the southward movement of cold air from Siberia. Therefore, the frequency of windy weather in East Asia decreased, partly weakening dust emissions. However, a positive geopotential anomaly in northeast China and a negative geopotential anomaly in South Asia triggered an anomalous enhancement in easterly wind in the tropospheric area over northwest China. Strengthening of the Balkhash trough provides favorable conditions for gale weather in northwest China. The frequency of gale weather increased, and dust emissions were enhanced in northwest China.

• Sensitivity of dust emissions to wind's variations is higher than those of vegetation and soil moisture. • The residual effect weakens the sensitivity of dust emissions to driving factors. • Key driving factor of dust emissions is spatially differentiated in Earth's main drylands. The identification of driving factors that contribute to dust emissions holds great significance on studying global climate change. In the present study, we constructed a new index, Dust Sensitivity Index, which allowed us to identify the sensitivity of dust emissions responding to variations in driving factors over 2003–2017 in Earth's main drylands. We found that dust emissions were sensitive to driving factor variability in eastern Brazil, the southern drylands of the Sahel, eastern Africa, eastern Australia and parts of northern Eurasia, where the aridity index (AI) is relatively high. The main factor affecting the DSI varies geographically over the Earth's main dryland regions. In total, wind speed made the largest relative contribution to DOD sensitivity (11.3%), followed by soil moisture (10.8%) and vegetation (10.4%). In addition, wind, vegetation, and soil moisture interact to impose complex and varying limitations on dust activity. 39.3% of the Earth's main drylands were limited by vegetation, 31.2% by wind and 29.5% by soil moisture. Our study also demonstrates that the residual effects of previous dust-driving factors have impacts on contemporary conditions. We found that regions characterized by lower DSI values displayed the most prominent residual effects in general. [ABSTRACT FROM AUTHOR]

• Aeolian horizontal flux and wind measurements were analyzed for a site in Senegal. • Wind erosion and erosivity were compared with sites in Central Sahel (Mali, Niger) • The seasonality of wind erosivity and wind erosion is not homogeneous over the Sahel. • Wind erosivity in Western (Central) Sahel mostly occurs during the dry (rainy) season. • Annual bare-soil aeolian horizontal flux is lower in Western than in Central Sahel. Wind erosion is a major phenomenon in the Sahel, and can affect soil fertility. Studies of Sahelian aeolian erosion or erosivity are scarce and have been mainly focused on the Central Sahel. Since February 2020, the number of saltating particles and the horizontal flux of aeolian sediment were monitored in Bambey (Senegal) in combination with long-term 5-minutes wind measurements (2014–2021). These datasets enabled to assess the consistency of wind erosion and wind erosivity estimates, and thus to further analyze wind erosivity over pluriannual periods, as wind speed time-series are available over longer terms than horizontal aeolian flux. As a result, the seasonality of wind erosivity largely differs between Western and Central Sahel. In Western Sahel, wind erosivity is related to medium wind speeds during the dry season, while in Central Sahel it is mostly due to high wind speeds occurring at the monsoon onset. Additionally, horizontal flux of aeolian sediments during the dry season are of the same order in Senegal as in Western Niger, but lower than in Eastern Niger. Horizontal flux of aeolian sediments during the rainy season are lower in Senegal than in Western Niger and Eastern Niger. Altogether, annual aeolian flux thus appears significantly lower in Western than in Central Sahel, and mostly related to the dry season. [ABSTRACT FROM AUTHOR]

The detachment of fine adhered dust particles during bulk particle contacts is studied with an adhesive Discrete Element Method (DEM) to describe the carrier and the dust particle motion. Further, dust detachment functions are realized in a non-adhesive DEM, in which only the coarse carrier particles are tracked as one composed bulk particle. The implemented detachment functions include three criteria, a normal and a tangential lift-off condition as well as a rotational condition. These functions are benchmarked based on dust-resolved adhesive DEM simulations by varying crucial parameters in the course of particle-particle and particle-wall contacts. In advance, the adhesive force is calibrated using experimental single drop tests. The detachment criteria reflect the physical detachment of individual dust particles well assuming dust particles with a narrow size distribution. The application of the enhanced dust detachment functions therefore allows to predict dust release of individual dust particles with significantly less computing time. [Display omitted] • Prediction of the detachment of fine adhered dust particles with an adhesive DEM. • Realization of dust detachment functions including three detachment criteria. • Benchmark and validation based on vertical and oblique bulk particle impacts. • Main goal: Prediction of detachment without modelling each individual dust particle. [ABSTRACT FROM AUTHOR]