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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.

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 paper presents aspects of monitoring material dust emissions from stationary emission sources (monthly dust measurements performed on cement mill stacks-mill outlet and separator outlet). Additionally, the Portland cement mill technological process (its component parts), as well as the solutions regarding the reduction of the air emissions level, following the emission limit values (VLE), established in the integrated environmental authorization (AIM) from a cement factory in Romania, were analyzed. The paper focused on analyzing the data obtained in three different years for PM10 and dust concentrations (2018-2020). For each year, the measurements have been done in 3 months, each in a different season. The average values for each year for working conditions were: 30.22 mg/m 3 (2018), 27.38 mg/m 3 (2019), and 27.51 mg/m 3 (2020) for working conditions and for normal conditions: 34.22 mg/m 3 (2018), 30.49 mg/m 3 (2019), and 30.16 mg/m 3 (2020). For all 3 years, the values measured in spring were higher than the other two, both for work and normal conditions.

This study presents a statistical comparison between the in situ measurements of the elemental composition of street dust and a forest health status classification derived from aerial hyperspectral image data (HyMap). Combining these two methods allowed us to indirectly pinpoint at a high spatial resolution the atmospheric dust emissions and its effects in a study area around the open-pit lignite mine in Sokolov, Czech Republic. The results reveal a statistically significant relationship between increased Al, Na, Li and Sr levels in street dust and decreased forest health status, and the highest number of statistically significant correlations within a 100 m distance from the street-dust sampling points. Differences in lithological composition were unable to sufficiently explain these changes, therefore anthropogenic factors like dust emissions from coal mining and coal combustion, as well as urbanisation and other industries might be the reason for this link. Such studies are a crucial step in developing new high spatial resolution methods for determining atmospheric dust deposition and their effects.

Tropical cyclones (TCs) can have devastating socioeconomic impacts. Understanding the nature and causes of their variability is of paramount importance for society. However, historical records of TCs are too short to fully characterize such changes and paleo-sediment archives of Holocene TC activity are temporally and geographically sparse. Thus, it is of interest to apply physical modeling to understanding TC variability under different climate conditions. Here we investigate global TC activity during a warm climate state (mid-Holocene, 6,000 yBP) characterized by increased boreal summer insolation, a vegetated Sahara, and reduced dust emissions. We analyze a set of sensitivity experiments in which not only solar insolation changes are varied but also vegetation and dust concentrations. Our results show that the greening of the Sahara and reduced dust loadings lead to more favorable conditions for tropical cyclone development compared with the orbital forcing alone. In particular, the strengthening of the West African Monsoon induced by the Sahara greening triggers a change in atmospheric circulation that affects the entire tropics. Furthermore, whereas previous studies suggest lower TC activity despite stronger summer insolation and warmer sea surface temperature in the Northern Hemisphere, accounting for the Sahara greening and reduced dust concentrations leads instead to an increase of TC activity in both hemispheres, particularly over the Caribbean basin and East Coast of North America. Our study highlights the importance of regional changes in land cover and dust concentrations in affecting the potential intensity and genesis of past TCs and suggests that both factors may have appreciable influence on TC activity in a future warmer climate., Competing Interests: The authors declare no conflict of interest.