Air pollution has created a lot of problems in the developed and developing countries. To avoid or reduce these problems, constant monitoring of the air should be ensured. The conventional techniques is costly because it requires a lot of money and time consuming. Biomonitoring has been the alternative method. Moss, lichens and plants are biomonitors available to entrap air pollutants. The aim of this paper is to discuss one of the ways of monitoring air pollution – Moss bag technique. To do this, types, choice, preparation, handling of bags after preparation of moss were discussed. From the literatures consulted, it was discovered that there were differences in the techniques used by the researchers. In all, the use of mosses with emphasis on the employment of moss bag have proved to be a powerful tool in airborne particulate and toxic elements. To conclude, developing countries should focus more on this technique because it will reduce cost of air monitoring.

Inhalation is the main route of exposure to airborne pollutants. To evaluate the safety and assess the risks of occupational hazards different testing approaches are used. 3D airway epithelial tissues allow to mimic exposure conditions in vitro, generates human-relevant toxicology data, allows to elucidate mode of action of pollutants. Gilian 3500 pumps equipped with Standard Midget Impingers were used to collect the airborne particulate from woodworking and metalworking environments. EpiAirway™ tissues were used to model half working day (4 h), full working day (8 h), and 3 working day exposures to occupational pollutants. Tissue viability was assessed using MTT assay. RT-qPCR analyses performed to analyze the expression of gelsolin, caspase-3, and IL-6. Tissue morphology was assessed by hematoxylin/eosin staining. Acute exposure to workspace pollutants slightly affected tissue viability and did not change the morphology. Both types of particles suppressed expression of gelsolin, with metalworking samples showing the most pronounced effect. A slight reduction in caspase-3 expression was observed. Particles from metalworking suppressed IL-6 expression. 3D Epithelial tissues can be used to model exposures to airborne pollutants. Exposure to particles from woodworking and metalworking had a minor effect on tissue viability but affected the expression of inflammation and apoptosis-related genes.

Both, in developed and developing countries, atmospheric pollution with particulate matter (PM) remains an important issue. Despite the health effects of poor air quality, studies on air pollution are often limited by the high costs of continuous monitoring and the need of extensive sampling. Furthermore, these particles are often enriched in potentially toxic trace elements and organics pollutants. This study evaluates both, the composition of atmospheric dust accumulated during a certain time-span on leaves of Hedera helix and Senecio cineraria, as well as the potential to use them as bio-monitors. The test plants were positioned nearby automatic air quality monitoring stations at four different sites with respectively high, moderate and low traffic intensity. Gravimetric deposition of PM10 and PM2.5 on leaves was consistent with data recorded by the monitoring stations and related to the weather conditions reported by Argentina´s National Meteorological service. To determine the presence of trace elements enriching the PM deposited on leaves, two analytical techniques were applied, XRF (not destructive) and ICP (destructive). The results indicated that only in the unpaved street location (site 2), PM10 and PM2.5 concentrations in the air exceeded WHO guidelines. However, several trace elements were found enriching PM deposited on leaves from all sites. Predominantly, increased concentrations of Cd, Cu, Ti, Mn, Zn and Fe were found, which were associated to construction, traffic and unpaved street sources. Furthermore, based on its high capability to sequester PM and trace elements Senecio cineraria can be take into consideration to be adopted as a bio-monitor or even for PM mitigation.

This study evaluated the use of filtration and UV-A photocatalysis for the reduction of particulate matter (PM) and airborne bacterial pathogens in swine barns. Two MERV filters (8 and 15) were used to mitigate PM concentrations measured at the PM 1, PM 2.5, respirable PM, and PM 10 ranges. Filtration was also used to generate different levels of airborne pathogens to be treated by UV-A. Results show that MERV 8 and 15 filters effectively reduced PM concentrations (96-98%) in air exhausted from a swine barn (p ranged from < 0.01 to 0.04). UV-A photocatalysis did not mitigate PM concentrations. UV-A photocatalysis treatment reduced measured colony-forming units (CFUs) by 15-95%. The CFU percent reduction was higher when airborne PM concentration was low. The numeric results suggested a real mitigation effect despite p-values that did not meet the usual statistical cut-off of <0.05 for significance due to the large variability of the CFU control samples. Normalization of measured airborne pathogen concentrations by smaller PM size range concentrations led to emerging significant treatment differences for CFUs. A significant decrease (~60% reduction; p < 0.03) in the concentration of viable airborne bacteria was shown for all PM below the 10-micron range.

Saussurea neoserrata Nakai offers a reliable and efficient source of antioxidants that can help alleviate adverse skin reactions triggered by air pollutants. Air pollutants, such as particulate matter (PM), have the ability to infiltrate the skin and contribute to the higher occurrence of cardiovascular, cerebrovascular, and respiratory ailments. Individuals with compromised skin barriers are particularly susceptible to the impact of PM since it can be absorbed more readily through the skin. This study investigated the impact of protocatechuic acid and syringin, obtained from the n-BuOH extract of S. neoserrata Nakai, on the release of PGE₂ and PGD₂ induced by PM₁₀. Additionally, it examined the gene expression of enzymes involved in the synthesis of PGE₂ and PGD₂ in human keratinocytes. The findings of this research highlight the potential of utilizing safe and efficient plant-derived antioxidants in dermatological and cosmetic applications to mitigate the negative skin reactions caused by exposure to air pollution.

Particulate matter (PM) contains heavy metals that affect various cellular functions and gene expression associated with an array of acute and chronic diseases, in humans. However, their specific effects on the stem cells remain unclear. Here, we report the effects of PM collected from Jeddah city on bone marrow mesenchymal stem cells (BM-MSCs) on proliferation, cell death, related gene expression and systems biological analysis aiming to understand the underlying mechanisms. Two different sizes (PM_2.5-₁₀) were tested in vitro at various concentrations (15 to 300 µg/ml) and durations (24 to 72 h). PMs induced cellular stress including membrane damage, shrinkage and death. Lower concentrations of PM_2.5 increased BM-MSCs proliferation, while higher concentrations decreased it. PM₁₀ decreased BM-MSCs proliferation in a concentration-dependant manner. The X-Ray Fluorescence spectrometric analysis showed that PM contains high levels of heavy metals. Ingenuity Pathway Analysis (IPA) and hierarchical clustering analyses showed that heavy metals were associated with signalling pathways involving cell stress/death, cancer and chronic diseases. qRT-PCR results showed differential regulation of the apoptosis genes (BCL2, BAX); upregulation of inflammation associated genes (TNF-a and IL-6) and downregulation of cell cycle regulation gene (P53). We conclude that PM could affect different cellular functions and predispose to debilitating diseases.

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data was then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM2.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional data sets can effectively model PM2.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM2.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM2.5 analyzes. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM2.5 to 9 μg/m3, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM2.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM2.5 levels that exceeded 9 μg/m3 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μg/m3 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM2.5 concentrations below 9 μg/m3.

In this work, we introduce the first pipeline that combines a refraction-aware Structure-from-Motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the SfM optimization problem. This way, no refraction correction as post-processing is required. Experiments with simulated data that approach the real-world capturing conditions demonstrate that SfM with refraction correction is extremely accurate, with submillimeter errors. We integrate our refraction-aware SfM within a deep learning framework that also takes into account radiometrical information, developing a combined spectral and geometry-based approach, with further improvements on accuracy and robustness to different seafloor types, both textured and textureless. We conducted experiments with real-world data at 2 locations in the southern Mediterranean Sea, with varying seafloor types which demonstrate the benefits of refraction correction for the deep learning framework. We open-source our refraction-aware SfM, providing researchers in airborne bathymetry with a practical tool to apply SfM in shallow water areas.

Particulate matter (PM) is one of the problems faced in environmental science. It has health effects on man and animals in both developed and developing countries. Research and efforts have been on it several years back. Policy statements and efforts have been published. This review paper is an added information on air pollution. In it, efforts were made in discussing these: classification, effects, methodology, case studies and source apportionment. It is hoped that this paper would contribute to existing knowledge on PM.

The study aims to identify the fine particulate matter (PM2.5) hazard area, mitigation method, and possible sustainable development in a changing global climate. The critical environmental hazards are artificial light at night (ALAN) and air pollution with ambient PM2.5. People use nighttime outdoor environments for their needs, and the nocturnally migrating birds are attracted to urban ALAN during seasonal migration, which could increase the birds' exposure to PM2.5. A comparative study examines PM2.5 concentrations and the spatial correlation between ALAN and PM2.5 within urban versus rural areas. The author used the nighttime data of the artificial light on the Earth's surface and the PM2.5 level of concentration to estimate the extent of air pollution associated with PM2.5 in the ground-level atmosphere. The results can assist in determining the required PM2.5 control areas and designing and executing environmental conservation planning. Furthermore, the results of this study are not only beneficial to understanding accurately the regional differences of spatiotemporal PM2.5 emission dynamics and helpful for proposing alleviation policies in air pollution control and providing scientific support for regional sustainable development in changing climate. The integrated hazards of ALAN and air pollution are most significant and likely to increase within the urban and decrease within rural areas. This study was undertaken by the first author and built upon the context of the academic, scientific, and technological challenges to identify the PM2.5 concentration in urban and rural areas and the expected outcomes.

Recent advances in differential topology single out four-dimensions as being special, allowing for vast varieties of exotic smoothness (differential) structures, distinguished by their handlebody decompositions, even as the coarser algebraic topology is fixed. Should the spacetime we reside in takes up one of the more exotic choices, and there is no obvious reason why it shouldn't, apparent pathologies would inevitably plague calculus-based physical theories assuming the standard vanilla structure, due to the non-existence of a diffeomorphism and the consequent lack of a suitable portal through which to transfer the complete information regarding the exotic physical dynamics into the vanilla theories. An obvious plausible consequence of this deficiency would be the uncertainty permeating our attempted description of the microscopic world. We tentatively argue here, that a re-inspection of the key ingredients of the phenomenological particle models, from the perspective of exotica, could possibly yield interesting insights. Our short and rudimentary discussion is qualitative and speculative, because the necessary mathematical tools have only just began to be developed.

The control of the fine particle emission from the coal-fired power plants has been a big challenge in china. Either the conventional electrostatic precipitator or the fabric filter has some shortages. The electrostatic precipitator is hard to keep high efficiency for the submicron particles. The fabric filter could keep high efficiency but with very high pressure drop. One solution is to combine them together. Several types of hybrid particle collectors (HPC) combining the electrostatic precipitator and the fabric filter together have been developed and applied in a number of projects in China. In recent years, numerical simulation technology has been widely used in the design of the collectors. In this paper, an unsteady numerical model is used to investigate the particle deposition characteristics in a scale hybrid collector. The fluid field is modeled by using incompressible Navier-Stokes equations with the RNG turbulence equations. The corona discharge is solved by using a finite volume method. The particle charging is modeled by using a filed-diffusing combined model. And an unsteady cake formation model is used to simulate the filtration process.

The main objective of this work is to investigate the temporal variation of PM10 concentrations within the urban area of Athens during the years 2001-2015. For this purpose, the time series of the particulate matter with aerodynamic diameter less than 10μm (PM10) is recorded for a 15-year period (2001-2015) in two different monitoring stations located in the urban area of Athens. The results show a totally different behavior of PM10 concentrations between the Athens city center and the suburban areas. It seems that in the city center the main sources of PM10 are traffic and heating systems especially during the cold period of the year. Furthermore, in the city center a significant seasonal variation was found with high concentrations during the cold period of the year and lower concentrations during the warm period of the year. Moreover, it was found that during the weekends, there is a decrease in PM10 concentrations probably due to the fact that majority of people do not use their vehicles. Finally, for both locations a significant temporal decreasing trend of the mean annual PM10 concentrations was found which indicates that during the last years, there have been improvements towards a better air quality.

Asthma is a heterogeneous and very complex group of diseases, which includes different clinical phenotypes depending on symptoms, progression, exacerbation pattern or response to treatment among other characteristics. The allergic phenotype is the most frequent, especially in pediatric asthma. It is characterized by sensitization (production of specific IgE) to allergens, and frequent comorbidity with rhinitis and atopic dermatitis. An allergen is a substance that triggers the production of specific IgE antibodies in susceptible individuals, who present symptoms that will depend on the type and intensity of the allergenic load and the tissue where the interaction occurs. Airborne allergens cause their effects in the respiratory tract and eyes, and can be indoor or outdoor, perennial or seasonal. Although allergens such as mites, pollens or animal dander are generally considered as a single particle, it is important to note that they contain different molecules which could trigger distinct specific IgE molecules on different patients.The key points that a physician who treats asthma should be aware of are:

Particulate organic carbon (POC) is a pivotal component within the marine carbon cycle, actively involved in diverse biogeochemical processes. This study focuses on Mediterranean as the research region, selecting the most influential factors from 47 potential ones influencing surface POC. Using geographic detector for factor identification, ten primary influencers were identified. Preliminarily optimized random forest (BRF), backpropagation neural network, adaptive boosting, and extreme gradient boosting were utilized to construct POC assessment models. Notably BRF exhibited superior performance in estimating the sea surface POC. Further optimization of RF using the tuneRanger R package resulted in R² of 0.868, mean squared error of 1.119 (mg/m³) ², and mean absolute error of 1.041 (mg/m³). Surface POC concentrations in the Mediterranean for May and June 2017 were estimated. Spatial distribution analysis unveiled higher concentrations in the west, lower in the east, higher in the north, lower in the south, with higher levels near the coast and lower far from the coast. Additionally, the study deliberated on the impact of human activities on surface POC in the Mediterranean. This research contributes a high-precision method for satellite retrieval of surface POC concentrations in the Mediterranean, thereby enriching the understanding of POC dynamics in the area.

The main objective of this work is the assessment of the annual number of hospital admissions for respiratory disease (HARD) due to the exposure to in-healable particulate matter (PM10), within the greater Athens area (GAA), Greece. Towards this aim, the time series of the particulate matter with aerodynamic diameter less than 10μm (PM10) recorded in six monitoring stations located in the GAA, for a 13-year period 2001-2013, is used. Initially, a descriptive statistical treatment of PM10 concentrations took place. Furthermore, the AirQ2.2.3 software developed by the WHO was used to evaluate adverse health effects by PM10 in the GAA during the examined period. The results show that, during the examined period PM10 concentrations present a significant decreasing trend. Also, the mean annual HARD cases per 100,000 inhabitants ranged between 20 (suburban location) and 40 (city centre location). Approximately 70% of the annual HARD cases are due to city centre residents. In all examined locations, a declining trend in the annual number of HARD cases is appeared. Moreover, a strong relation between the annual number of HARD cases and the annual number of days exceeding the European Union daily PM10 threshold value was found.

Fungal spores are universal atmospheric components associated to allergic reactions. Alternaria (Ascomycota) is considered the most allergenic spore taxa. Alt a 1 is the major allergen of Alternaria and is present also in other Pleosporales. In this study, standard Hirst-based sampling and analyzing methods for measuring spore daily concentrations of Alternaria, Curvularia, Drechslera-Helminthosporium, Epicoccum, Leptosphaeria, Pithomyces, Pleospora and Stemphyllium (all included in the taxon Pleosporales) have been used besides two high-volume samplers, Burkard Cyclone (2017) and MCV CAV-A/mb (2019-2020), and ELISA Kits for measuring the allergen. The detection and quantification of Alt a 1 was only possible in the samples from the MCV sampler. Although Alt a 1 was better correlated with Alternaria spores than with Pleosporales spores, the three of them showed high correlations. It is shown, for the first time, a high and significant correlation of Alt a 1 with temperature, a negative one with relative humidity and no correlation with precipitation. The aerobiological monitoring of these three elements ensures the best information for understanding the affectation to allergy sufferers but, if not possible, as a minimum public health service aiming at the detection, treatment and prevention of allergy, the study of the airborne Alternaria spores should be ensured.

Based on the Flashing Ratchet Potential (FRP) and Electro-Spectroscopy detection techniques, a new detection method was experimentally examined. This method allows the detection of sub-micro particles in the human exhaled air such as viral particles and other biological agents. The detection signal created by this method is carried out by electron current buildup forming an Electric Current-Spectrum (ECS) distinguishing the contents of the exhaled air samples. The detection method was developed based on FRP and a beam of free charges passing through the electrodes of the FRP. The ECS is characterized by low-energy free charges interacting with the medium through which they pass, where numerous free charges are scattered (delayed) as a result of these interactions. The resulting ECS is characterized by FRP's capacity to drift back the delayed free charges to their original starting positions. By comparing the ECS of two exhale air samples, this new technology can be utilized to identify the presence of viral and microorganisms’ particles. The time-escalating ECS difference quickly indicates the presence of such particles. Moreover, the FRP's optimal operating settings was experimentally defined, in addition to the experimental evaluation of its functionality to confirm the FRP's promising sensing capabilities, which were statistically examined several times to ensure the reproducibility of the results.

A well known technique to enhance the signal to noise ratio (SNR) of repetitive signals is to average them. The coherent parts of the signal add up constructively while the incoherent parts are averaged out. The prerequisite is that the signals are acquired under conditions of high repeatability, i.e.\ the signals must be sufficiently similar. In the present technical note we describe an efficient method for maintaining signal similarity by ensuring spatial and temporal proximity of laser waveform signals obtained by a sensor operated from an airborne platform. The method makes use of a few auxiliary parameters such as laser pulse repetition rate, mirror rotation rate, platform altitude, and flight speed. The method can be extended to be operated in real time.

Airborne Frequency Diversity Array (FDA) radar operating at a high pulse repetition frequency encounters severe range-ambiguous clutter. The slight frequency increments introduced by the FDA result in angle and range coupling. Under these conditions, conventional space-time adaptive processing (STAP) often exhibits diminished performance or fails, complicating target detection. This paper proposes a method combining elevation oblique subspace projection with azimuth-Doppler STAP to suppress range-ambiguous clutter. By analyzing the relationship between elevation frequency and range, the proposed method compensates for quadratic range dependence. Through the construction of an elevation oblique subspace projection technique, an elevation adaptive filter is used to separate clutter from ambiguous regions. Residual clutter suppression is achieved through azimuth-Doppler STAP, enhancing target detection performance. Simulation results demonstrate that the proposed method effectively addresses range dependence and ambiguity issues, improving target detection performance in complex airborne FDA radar environments.

Particulate Organic Carbon (POC) plays a crucial role in the marine carbon cycle, influencing carbon flux and many important ocean biogeochemical processes in the Bay of Bengal. The purpose of this study was to examine the seasonal and annual variability of POC in the Bay of Bengal (BoB) region and its correlation with Sea Surface Temperature (SST) and Chlorophyll-a (Chlor_a). This study uses level-3 POC, Chlor_a, and SST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite. The surface POC concentration in the Bay of Bengal declined at a rate of -1.30 mgm^-3y^-1 over a ten-year period (2003-12) and ranged from 86.48 mgm^-3 to 110.93 mgm^-3 with an average of (93.74±7.50 mgm^-3). Seasonally, the mean POC was highest (97.40 mgm^-3) during the post-monsoon (October-November) and lowest (79.36 mgm^-3) in the pre-monsoon (March-May). Seasons were observed to have statistically significant differences in POC concentration in the study area (F_(3,2155) = 6.298; p <0.05). A strong positive correlation between POC and Chlorophyll was observed whereas a week positive correlation between POC and SST was observed. POC was observed to be very prominent in post-monsoon (97.40 mgm^-3) and in northeast-monsoon (97.14 mgm^-3) and moderate to low in southwest (81.12 mgm^-3) and pre-monsoon (79.36 mgm^-3). The Bay of Bengal’s annual mean POC:Chlor_a ratio ranged from 97.56 mgm^-3 to 332.89 mgm^-3, indicating a significant impact of biological processes on POC composition. To see how POC fluctuation affects biogeochemical processes in the Bay of Bengal, deeper investigation is required.

As climate changes, our daily life has been much influenced by abnormal meteorological phenomena such as heavy rainfall, heat wave, heavy snowfall, and fine dust. Atmospheric air quality is worsening day by day and indoor air quality is also affected by interconnected daily activities throughout the inside and outside of buildings and houses. Nowadays, pollutants from various sources are emitted, transformed by sunlight, vapor, and ozone and transported into the city from country to country. Due to these reasons, there have been high demands to monitor the transportation of particulate matters and improve air quality. Monitoring of pollutants and identification of type and its concentration enables us to track and control its generation and consequently find out the solution. However, monitoring of pollutants, especially, particulate matter generation and its transportation is still not fully operated in atmospheric air due to its open nature and meteorological factors. Even though indoor air is relatively easy to monitor and control than outdoor in the aspect of specific volume and contaminant source, but it still needs to consider the meteorological parameters because indoor air is not fully separated from the outdoor air flow and contaminants transportation. In this study, optical approach using spectral sensor was attempted to reveal the feasibility of wavelength and chromaticity values of reflected light from specific particles. From the analysis of reflected light of various particulate matters according to different liquid additives, parameter studies were performed to investigate which experimental conditions can contribute to the enhanced selective sensing of particulate matters. Five different particulate matters such as household dust, soil, talc powder, gypsum powder and yellow pine tree pollen were utilized and observed to elucidate the relationship between property of particulate matter and detected light spectrum. Applicable approaches to assist current particle matter sensors and improve the selective sensing were suggested.

(1)Background：To date, no studies have epidemiologically examined whether the concentration of particulate matter (PM) 2.5 and its ionic components is related to the prevalence of skin symptoms. Therefore, this study aimed to clarify this association in patients with allergic diseases. (2)Methods: From February 1 to May 31, 2020, we evaluated the daily prevalence of skin symptoms in outpatients with allergic diseases being treated at Fukuoka Hospital, Fukuoka, Japan, and measured the concentration of PM2.5 and its ionic components. （3）Results: Univariate analysis showed a statistically significant association between skin symptoms and the concentration of PM2.5 and the ionic components SO42-, NH4+, K+, and Mg2+, and multivariate analysis showed a statistically significant association between the daily prevalence of skin symptoms and the concentration of the ionic components SO42- or Mg2+. (4)Conclusions: Our findings indicate that the concentration of some PM2.5 ionic components may affect skin symptoms in patients with allergic diseases.

A microstructure-based model is developed to study the effective anisotropic properties (magnetic, dielectric or thermal) of two phase particle-filled composites. The Green’s function technique and the effective field method are used to derive theoretically the homogenized (averaged) properties for a representative volume element containing isolated inclusion and an infinite, chain-structured particles. Those results are compared with the FE approximations conducted for the assumed representative volume element. In addition, as a special case, the Maxwell–Garnett model is retrieved when particle interactions are not taken into consideration. We shall also give some information on the optimal design of the effective anisotropic properties taking into account the shape of magnetic particles.

Since there is not a clear consensus about the possibility for COVID-19 to be an airborne disease, exists a controversy regarding the need to use surgical masks to prevent its spread. Here, using the Kepler conjecture for ideal packaging, the number of virions of different sizes that can be accommodated inside droplets was calculated and are proportional to the 3^rd potency of the droplet/virion diameter. The differences between particles of 5 um and 100 μm are around four orders of magnitude, explaining why the airborne spread is much more difficult but still possible. There is no solid evidence yet that the airborne coronaviruses may reach enough concentration to infect, but this may be the case under certain circumstances. The WHO partially recognizes now this fact in a warning to health workers (from my point of view too late, as it was the declaration of a pandemic). Another issue is whether the virus stays infective in aerosols generated from patients. This has not been directly proved yet except with artificial aerosols, but there are no reasons why the virus cannot remain in the air and be infective if the viral charge and time of exposure are enough. We must also consider whether the virus can infect the intestine; there are some signs in this sense. Finally, and most importantly, we need to reduce interactions by using surgical masks to flatten the curve, leave the quarantine and avoid a rebound. For cultural reasons, a social distance of 2 meters (2M) is extremely hard to manage. Surgical masks do reduce the interactions in conditions of proximity and, therefore, help to “flatten the curve”. The WHO and CDC “laissez-faire” on this matter do not help and we are running out of time. Anticipated actions, such as the use of surgical masks for the general population, are critical.

Because of the near-term risk of extreme weather events and other adverse consequences from climate change, and, at least in the longer term, global fossil fuel depletion, there is world-wide interest in shifting to noncarbon energy sources, especially renewable energy (RE). Because of possible limitations on conventional renewable energy sources, researchers have looked for ways of overcoming these shortcomings by introducing radically new energy technologies. The largest RE source today is bioenergy, while solar energy and wind energy are regarded as having the largest technical potential. This paper reviews the literature on proposed new technologies for each of these three RE sources: microalgae for bioenergy, photolysis and airborne wind turbines. The main finding is that their proponents have underestimated the difficulties facing their introduction on a very large scale.

The study aimed to estimate the prevalence of respiratory symptoms and to assess respiratory health risks associated with Particulate Matter (PM) exposure among the residents of Windhoek, Namibia. Objectives: To measure particulate pollution concentration in Windhoek through monitoring of particulate matter concentration and to identify any associations between particulate pollution, individual location and respiratory health among the Windhoek resident’s. Methods: an adapted standardized self-administered questionnaire was used to collect respiratory health related data as well as previous exposure, while PM monitoring was done using ASTM D1739 reference method. Results: A high prevalence was observed for cough (43%), breathlessness (25%), and Asthma (11.2%). PM was found to be a significant risk factor for episode of cough and phlegm, while high PM exposure category had increased odds ratio for episode of phlegm and cough (OR: 2.5, 95% CI: 0.8-8.0). No association was observed between location and respiratory health outcomes. Conclusion: The study found high levels of PM concentration across all Windhoek suburbs which were above the German, American and EPA. Enactment of legislation relating to the control and monitoring of PM related emissions at point of generation is required at country and city level.

Airborne virus, such as COVID-19, caused pandemics all over the world. Virus-containing particles produced by infected individuals are suspended in the air for extended periods of time, actually results in viral aerosols and the spread of infectious diseases. Aerosol collection and detection devices are essential for limiting the spread of airborne virus diseases. This review provides an overview of the primary mechanisms and enhancement techniques for collecting and detecting airborne viruses. Indoor virus detection strategies for scenarios with varying ventilations are also summarized based on the excellent performance of existing advanced comprehensive devices. This review provides guidance for the development of future aerosol detection devices and aids in the control of airborne transmission diseases, such as COVID-19, monkeypox, and other airborne transmission viruses.

This study investigates the application of frequency augmentation techniques to improve condition-based maintenance (CBM+) systems for airborne weapon systems, focusing on the predictive accuracy of velocity and acceleration parameters. Utilizing MIL-STD-1553B MUX data from 138 sorties of fixed-wing aircraft, we explored six augmentation methods, including traditional interpolations (linear, quadratic, cubic spline) and advanced machine learning models (K-Nearest Neighbor, LSTM, and Bi-LSTM). Our findings indicate that while traditional methods like linear interpolation are more effective for velocity parameters, advanced ML techniques, particularly Bi-LSTM, provide better results for acceleration parameters, which exhibit more complex and rapid variability. The study underscores the necessity of tailoring augmentation approaches to specific parameter characteristics and highlights the potential of ML models in capturing intricate patterns within time-series data. These insights are critical for advancing CBM+ systems in military avionics, enhancing reliability and operational efficiency through improved data processing strategies. Future work should explore more performant augmentation techniques, such as by integrating multi-dimensional datasets, and explore ways to develop automated tools for a more powerful predictive maintenance framework.

The analysis of particulate matter (PM) in dilute solutions is an important target for environmental, geochemical and biochemical researches. Here we show how the microdrop technology may allow to control, through the evaporation of small droplets, the deposition of insoluble materials dispersed in a solution on a well-defined area with a specific spatial pattern. Using this technology the superficial density of the deposited solute can be accurately controlled. In particular, it becomes possible to deposit an extremely reduced amount of insoluble material -in the order of few μg- on a confined area, thus allowing a relatively high superficial density to be reached within a limited time. In this work we quantitatively compare the microdrop technique for the preparation of particulate matter samples with the classical filtering technique. After having been optimized, the microdrop technique allows to obtain a more homogeneous deposition and limit sample consumption of a factor ~25. This method is potentially suitable for many novel applications in different scientific fields.

Particulate matter (PM) represents an air quality management challenge for confined swine production systems. Because of the limited space and ventilation rate, PM can reach relatively high concentrations in swine barns. PM in swine barns possesses different physical, chemical, and biological characteristics than that in the atmosphere and other indoor environments. As a result, it exerts different environmental and health effects and creates some unique challenges regarding PM measurement and mitigation. Numerous research efforts have been made, generating massive data and information. However, relevant review reports are sporadic. This study aims to provide an updated comprehensive review of swine barn PM, focusing on publications since 1990. It covers various topics, including PM characteristics, sources, measurement methods, and in-barn mitigation technologies. Since PM in swine barns is of primarily biological origins, bioaerosols are reviewed in great detail. Relevant topics include bacterial/fungal counts, viruses, microbial community composition, antibiotic-resistant bacteria, antibiotic resistance genes, endotoxins, and (1→3)-β-D-glucans. For each topic, existing knowledge is summarized and discussed and knowledge gaps are identified. Overall, PM in swine barns is complicated in chemical and biological composition and highly variable in mass concentrations, size, and microbial abundance. Feed, feces, and skins constitute the major PM sources. Regarding in-barn PM mitigation, four technologies (oil/water sprinkling, ionization, alternation of feed and feeders, and recirculating air filtration) are dominant. However, none of them have been widely used in commercial barns. A collective discussion of major knowledge gaps and future research needs is offered at the end of the report.

The NP method was used to elucidate the contributions of vehicles and coal combustion to seasonal and long-term atmospheric PM-bound Pyr and 1-NP concentrations in Kanazawa, Kitakyushu, Shenyang, and Shanghai in the Western Pacific region from 1997 to 2021. Among the four cities, Kanazawa demonstrated the lowest Pyr concentration. The contribution of vehicles to Pyr before and after 2010 were 35% and 5%, respectively. The 1-NP concentration reduced by a factor of more than 1/10. These changes can be attributed to the emission control from vehicles. Kitakyushu revealed the second lowest Pyr and the lowest 1-NP concentrations. Coal combustion was found to be the main contributor to Pyr, while its contribution to 1-NP increased from 9% to 19%. The large contribution of coal combustion is attributed to iron manufactures. Shenyang demonstrated the highest atmospheric Pyr concentration with its largest seasonal change. Vehicles are the largest contributors to 1-NP. However, coal combustion, including winter coal heating, contributed 97% or more to Pyr and more than 14% to 1-NP. Shanghai revealed the second highest Pyr and 1-NP concentrations, but the former was substantially lower than in Shenyang. Coal combustion was the major contributor, but the contribution of vehicles to Pyr was larger before 2010 similar to Kanazawa.

The rapid development of LiDAR technology has promoted great changes in forest resource surveys. The airborne LiDAR point cloud can provide precise tree height and detailed vertical structure of the tree stands. Coordinating some representative ground sample plots, LiDAR can be used to estimate key forest resource indicators such as forest stock volume, diameter at breast height, and forest biomass at a large scale. By establishing relationship models between the forest parameters of sample plots and the calculated parameters of LiDAR, these developments may eventually expand the models to large-scale forest resource surveys of entire areas. In this study, eight sample plots in northeast China are used to verify and update the information using point cloud obtained by the LiDAR scanner riegl-vq-1560i. Firstly, the tree crowns are segmented using the profile-rotating algorithm, and dominant trees height are used to check and rectify the tree locations. Secondly, considering the correlation between forestry parameters and tree species, we establish models to distinguish between species using geometric characteristics of tree crowns. Thirdly, when the tree species is known, parameters such as height, crown width, diameter at breast height, biomass and stock volume can be extracted from trees. The prediction models of forestry parameters can also be verified, which can be extended to accurate large-scale forestry surveys based on LiDAR data. Finally, experiment results demonstrate that the F-score of the eight plots in the tree segmentation exceed 0.95, the accuracy of tree species correction exceeds 90%, and the R2 of tree height, east-west canopy width, north-south canopy width, diameter at breast height, above-ground biomass and stock volume are 0.893, 0.757, 0.694, 0.840, 0.896 and 0.891, respectively. The above results indicate that the LiDAR-based estimation of forestry parameters is practical and that these forestry parameter prediction models can be widely applied in forest resource monitoring.

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km² agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-structures were equally present, the airborne gamma-ray data permits us shedding light on finer features. Map areas with higher clay content were coincident with paleo-channels crossing the Mezzano Lowland in Etruscan and Roman periods, confirmed by the hydrographic setting of historical maps and by the geo-morphological features of the study area.

Covid-19 is an airborne disease, with the vast majority of infections occurring indoors. By comparison, little transmission occurs outdoors. Here, we investigate the airborne transmission pathways that differentiate the indoors from outdoors, and conclude that profound differences exist, which help to explain why SARS-CoV-2 transmission is much more prevalent indoors. Near and far-field transmission pathways are discussed along with factors that affect infection risk, with aerosol concentration, air entrainment, thermal plumes, and occupancy duration all identified as being influential. In particular, we present the fundamental equations that underpin the Wells-Riley model, and show the mathematical relationship between inhaled virus particles and quanta of infection. A simple model is also presented for assessing infection risk in spaces with incomplete air mixing. Transmission risk is assessed in terms of aerosol concentration using simple 1D equations, followed by a description of thermal plume-ceiling interactions. With respect to this, we present new experimental results using Schlieren-visualisation and Computational Fluid Dynamics (CFD) based on the Eulerian-Lagrangian approach. Pathways of airborne infection are discussed, with the key differences identified between indoors and outdoors. In particular, the contribution of thermal and exhalation plumes is evaluated, and the presence of near-field/far-field feedback loop postulated, which is absent outdoors.

Invasive species are significant threats to global agriculture and food security being the major causes of crop loss. An operative biosecurity policy requires full automation of detection and habitat identification of the potential pests and pathogens. Unmanned Aerial Vehicles (UAVs) mounted thermal imaging cameras can observe and detect pest animals and their habitats, and estimate their population size around the clock. However, their effectiveness becomes limited due to manual detection of cryptic species in hours of captured flight videos, failure in habitat disclosure and the requirement of expensive high-resolution cameras. Therefore, the cost and efficiency trade-off often restricts the use of these systems. In this paper, we present an invasive animal species detection system that uses cost-effectiveness of consumer-level cameras while harnessing the power of transfer learning and an optimised small object detection algorithm. Our proposed optimised object detection algorithm named Optimised YOLO (OYOLO) enhances YOLO (You Only Look Once) by improving its training and structure for remote detection of elusive targets. Our system, trained on the massive data collected from New South Wales and Western Australia, can detect invasive species (rabbits, Kangaroos and pigs) in real-time with a higher probability of detection (85–100 %), compared to the manual detection. This work will enhance the visual analysis of pest species while performing well on low, medium and high-resolution thermal imagery, and equally accessible to all stakeholders and end-users in Australia via a public cloud.

The mass concentration of particulate matter (PM) has been systematically used in epidemiological studies as exposure indicator, to relate airborne concentrations with a wide variety of human health effects, which can be hardly explained by using this single parameter. In fact, PM is a “particle cocktail” that includes a complex mixture of compounds with a wide range of sizes, chemical compositions and emission sources. Current research hypothesizes that many of the adverse health effects are derived from oxidative stress in biological systems caused by the deposition of PM into the lungs. This emerging hypothesis is called the oxidative stress paradigm. In this commentary article we analize how this new paradigm could help to answer the as-of-yet unanswered questions related to the mechanism of action of PM pollution on human health. Acellular oxidative potential (OP) assays have been emerged as a promising approach to quantify the PM potential to induce oxidative stress and to relate it with the chemical composition and size distribution of PM. Recent researches have shown that the OP is related to the presence of metals, organic carbon, polyaromatic hydrocarbons and quinones. However, the association between PM and particle-induced toxicity is still largely unknown. Therefore, additional research is needed to identify the specific PM characteristic(s), such as its specific size, emission source or chemical content, which contribute the most to its redox activity. Thus, the OP measurements provide information that allows us to evaluate and integrate the toxic potential of PM in a unique parameter, whose relationships with emission sources, size distribution and/or chemical composition should be faced in the near future.

As semi-airborne mineral exploration has limited budgets, it is critical to design experimental procedures that generate data maximizing the desired information We investigate the effects of transmitter-receiver geometries for a variety of anomalies and semi-airborne layouts. Our simulations indicate that 200 m flight line spacing and 100 m point distance are the optimal trade-off between coverage and survey progress for various targets. Based on the target size and distance between the transmitter and the target, the transmitter length should be at least equal to the length of the target and for more than 1 km, at least two or three times the target size. Likewise important are the location and direction of the transmitter cables, which can have great impact on the result of inversion and should be parallel to the target strike. By using more than one transmitter, better results are obtained. If the strike of the target is known, transmitters should be parallel to each other and if not, it is better to use perpendicular transmitters. Results showed that the optimal distance between transmitters is 3 km. Our simulations show that it is even possible to recover targets just below the transmitter in corresponding areas of masked data.

The Compact Midwave Imaging System (CMIS) is a wide field of view, multi-angle, multi-spectral pushframe imager that relies on the forward motion of the satellite to create a two-dimensional (2D) image swath. An airborne demonstration of CMIS was successfully completed in January-February 2021 on the NASA Langley Research Center Gulfstream III. The primary objective of the four-flight campaign was to demonstrate the capability of this unique instrument to perform stereo observations of clouds and other particulates (e.g. smoke) in the atmosphere. It is shown that the midwave infrared (MWIR) spectral bands of CMIS provide a unique 24/7 capability with high resolution for accurate stereo sensing. The instrument relies on new focal plane array (FPA) technology, which provides excellent sensitivity at much warmer detector temperatures than traditional technologies. This capability enabled a compact, low-cost design that can provide atmospheric motion vectors and cloud heights to support requirements for atmospheric winds in the 2017-2027 Earth Science Decadal Survey. Applications include day/night observations of the planetary boundary layer, severe weather, and wildfires. A comparison with current space-based earth science instruments demonstrates that the SWIR/MWIR multi-spectral capability of CMIS is competitive with larger, more expensive instrumentation. Imagery obtained over a controlled burn and operating nuclear power plant demonstrated the sensitivity of the instrument to temperature variations. The system relies on a mature stereoscopic imaging technique applied to the same scene from two independent platforms to unambiguously retrieve atmospheric motion vectors (AMVs) with accurate height assignment. This capability has been successfully applied to geostationary and low-earth orbit satellites to achieve excellent accuracy. When applied to a ground-point validation case, the accuracy for the CMIS aircraft observations was 20 m and 0.3 m/s for cloud heights and motion vectors, respectively. This result was confirmed by a detailed error analysis with analytical and covariance models. The results for CMIS cases with underflights of Aeolus, CALIPSO and Aqua provided a good validation of expected accuracies. The paper also showed the feasibility of accommodating CMIS on CubeSats to enable multiple instruments to be flown in a leader-follower mode.

Airborne wind energy (AWE) systems use tethered flying devices to harvest higher-altitude winds to produce electricity. For a successful deployment of these systems, it is crucial to understand how the public perceives them. If public concerns about the technology are not taken seriously, implementation could be delayed or, in some cases, prevented, resulting in increased costs for project developers and a lower contribution of the sector to renewable energy targets. This literature review assessed the current state of knowledge on public responses to AWE. An exhaustive literature search led to the identification of 40 relevant publications that were reviewed. The literature assumed that the safety, visibility, acoustic emissions, ecological impacts, and the siting of AWE systems shape public responses to the technology. The reviewed literature views people’s responses to AWE very optimistically but lacks scientific evidence to back up its claims. It seems to overlook that the influence of AWE’s characteristics (e.g., visibility) on public responses will also depend on a range of situational and psychological factors (e.g., people’s general attitude towards AWE, the public’s trust in project developers). Therefore, empirical social scientific research is needed to increase the field’s understanding of public responses to AWE and thereby facilitate deployment.

Influenza A viruses (IAV) are widespread viruses affecting avian and mammalian species worldwide. Outbreaks of IAV in poultry are usually associated with substantial morbidity and mortality, significantly affecting the poultry industry and food security. IAVs from avian species can be transmitted to mammals including humans and, thus, they are of inherent pandemic concern. Most of the efforts to understand the pathogenicity and transmission of avian origin IAVs have been focused on H5 and H7 subtypes due to their highly pathogenic phenotype in poultry. However, IAV of the H9 subtype that circulate endemically in poultry flocks in some regions of the world have also been associated with cases of zoonotic infections. As a result, the World Health Organization includes avian origin H9N2 IAV among the top in the list of IAVs of pandemic concern. In this review, we discuss the interspecies transmission of H9N2 between avian and mammalian species and the molecular factors that are thought relevant for this spillover. Additionally, we discuss factors that have been associated with the ability of these viruses to transmit through the respiratory route in mammalian species.

This paper presents a comparison between a kite model with a constant-length tether and a model based on a system identification algorithm. The concept of system identification is applied to predict the uncertainties related to the variation of the wind speed and the shape deformation of the tethered membrane wing during flight. A pole-placement controller is used to ensure that the kite follows the planned flight path. Thus, we can determine the required locations of the closed loop poles, and then enforce them by changing the controller's gains in real-time. The capability of the system identification algorithm to recognize sudden changes in the dynamic model, and the ability of the controller to stabilize the system in the presence of such changes are confirmed. Furthermore, the system identification algorithm is applied to determine the parameters of a kite with variable-length tether used in a flight test of the 20 kW kite power system of TU Delft. Experimental data of this test were compared with the system identification results in real-time and significant changes were observed in the parameters of the dynamic model which heavily affect the resulting response.

Microplastics (of size < 5mm) pollution in our environment is of current concern by researchers, public media and non-governmental organizations. Implications by their presence in aquatic and soil ecosystems have been well studied and documented, but less attention has been paid on airborne microplastics (MPs). Studies concerning airborne microplastics started from 2016 and only a few (n=7) have been published till date. Although, studies may increase in the following years, since air is very important for human survival. Microplastics have been observed in atmospheric fallouts in indoor and outdoor environments using a sampling or vacuum pump, rain sampler and/or particulate fallout collector. Identification and quantification have been carried out by visual, spectroscopic and spectrometric techniques. Factors such as meterological, climatic and anthropogenic influence the distribution and movement of airborne MP. Human exposure may be through inhalation or dermal route with their potential biopersistence and translocation. Ingestion may cause localized inflammation and cancer due to responses by the immune cells, especially in individuals with compromised metabolism and poor clearance mechanisms. Ecological risks involve possible contamination of the ecosystem through a dynamic relationship of MPs in soil, water and air forming a MP contamination cycle. The present review aimed at providing a comprehensive overview of current knowledge or information regarding microplastics in air, identifies gap in knowledge and give suggestions for future research.

Recent omics technologies have revolutionized the study of molecular plant responses to air pollution, overcoming previous limitations. This review synthesizes the latest advancements in molecular plant responses to major air pollutants, emphasizing ozone (O3), nitrogen oxides (NOX) and particulate matter (PM) research. These pollutants induce stress responses common to other abiotic and biotic stresses, including the activation of reactive oxygen species (ROS)-scavenging enzymes and hormone signaling pathways. New evidence showed the central role of antioxidant phenolic compound biosynthesis via phenylpropanoids pathway in air pollution stress responses. Transcription factors like WRKY, AP2/ERF, and MYB, which connect hormones signaling to antioxidant biosynthesis, were also affected. To date, research has predominantly focused on laboratory studies analyzing individual pollutants. This review highlights the need for comprehensive field studies and the identification of molecular tolerance traits, which are crucial for the identification of tolerant plant species aimed to the development of sustainable Nature Based Solutions (NBS) to mitigate urban air pollution.

Recognizing the critical impact of environmental factors on individuals, this study explores the relationship between environmental indoor pollutants (PM1, PM2.5, PM10, and CO₂) and students' basic emotional responses based on Ekman’s categorization. Conducted in a secondary school in Spain, it integrates IoT and image processing technologies to monitor air quality and emotions in real-time. Participants included 76 secondary school students and three teachers, with data collected over two months. The results showed significant correlations between PM levels and negative emotions such as anger and disgust, while CO₂ levels were associated with both negative and positive emotions, including happiness. Regression models demonstrated that environmental variables significantly predict basic emotions, highlighting the influence of indoor air quality. These findings emphasize the need for proactive air quality management in educational settings to enhance learning environments and support students' emotional well-being.

A probe of a patient, seeking help in an emergency ward of a French hospital in late December 2019 because of Influenza like symptoms, was retrospectively tested positive to COVID-19. Despite the early appearance of the virus in Europe, the prevalence and virulence appeared to be low for several weeks, before the spread and severity of symptoms increased exponentially, yet with marked spatial and temporal differences. Here, we compare the possible linkages between peaks of fine particulate matter (PM2.5) and the sudden, explosive increase of hospitalizations and mortality rates in the Swiss Canton of Ticino, and the Greater Paris and London regions. We also discuss the influence of Saharan dust intrusions on the COVID-19 outbreak observed in early 2020 on the Canary Islands. We find that high PM2.5 concentrations – possibly favored by air temperature inversions or Saharan dust intrusions – are not only modulating but even more so boosting severe outbreaks of COVID-19. We conclude that the overburdening of the health services and hospitals as well as the high over-mortality observed in various regions of Europe in spring 2020 can be linked to peaks of PM2.5 and likely particular weather situations that have favored the spread and enhanced the virulence of the virus. In the future, we recommend to monitor not only the prevalence of the virus, but also to consider the occurrence of weather situations that can lead to sudden, very explosive COVID-19 outbreaks.

Coal-fired thermal plants (CTPP) are known to pollute the atmosphere with emission of many greenhouse gases and particulate matter. The power generation from these thermal plants cannot be stopped completely because it forms the backbone of the grid power supply. It is necessary to study the dispersion patterns of pollutants that affect the health of the people. The dispersion patterns are location specific since they depend on local meteorological conditions. In this study, the dispersion of particulate matter (PM) and sulphur dioxide (SO₂) from a CTPP with 275 m high stack are studied under different atmospheric boundary layer (ABL) of neutral, stable and unstable conditions up to a distance of 30 km from the stack. The plume of the PM spreads under all conditions. During some parts of the day, PM settles around the stack while at other times PM keeps suspending in the air for the full distance under study. Sulphur dioxide dilutes to concentrations below the detection limits in about 12-13 km from the stack for neutral and unstable ABL whereas for the stable ABL, the dispersion is up to 30 km. The 24 h weighted average concentration of sulphur dioxide, at 10 m height from the ground, is 14.2 mg/m³ at a distance of 25 kms from the CTPP, which is comparable with the value of 9.2 mg/m³ measured at the Air Quality Stations located around the same distance. Based on the results policy changes that need to be implemented are suggested.

A study is reported on the electrochemical alloying-dealloying properties of Mg₂Sn intermetallic compounds. ¹¹⁹Sn Mössbauer spectra of β-Sn powder, thermally alloyed cubic-Mg₂Sn and an intermediate MgSn nominal composition are used as references. The discharge of a Mg/micro-Sn half-cell led to significant changes in spectra line shape that are explained by a multiphase mechanism involving the coexistence of c-Mg₂Sn, distorted Mg_2-δSn and Mg-doped β-Sn. Capacities and capacity retention were improved by using nanoparticulate tin electrodes. This material reduces significantly the diffusion lengths for magnesium and contains surface SnO and SnO₂, which are partially electroactive. The half-cell potentials were suitable to be combined versus MgMn₂O₄ cathodes. Energy density and cycling properties of the resulting full Mg-ion cells are also scrutinized.

In this paper we present AWEbox, a Python toolbox for modeling and optimal control of multi-aircraft systems for airborne wind energy (AWE). AWEbox provides an implementation of optimization-friendly multi-aircraft AWE dynamics for a wide range of system architectures and modeling options. It automatically formulates typical AWE optimal control problems based on these models, and finds a numerical solution in a reliable and efficient fashion. To obtain a high level of reliability and efficiency, the toolbox implements different homotopy methods for initial guess refinement. The first type of methods produces a feasible initial guess from an analytic initial guess based on user-provided parameters. The second type implements a warmstart procedure for parametric sweeps. We investigate the software performance in two different case studies. In the first case study we solve a single-aircraft reference problem for a large number of different initial guesses. The homotopy methods reduce the expected computation time by a factor of 1.7 and and the peak computation time by a factor of 8, compared to when no homotopy is applied. Overall, the CPU timings are competitive to timings reported in the literature. When the user initialization draws on expert a priori knowledge, homotopies do not increase expected performance, but the peak CPU time is still reduced by a factor of 5.5. In the second case study, a power curve for a dual-aircraft lift-mode AWE system is computed using the two different homotopy types for initial guess refinement. On average, the second homotopy type, which is tailored for parametric sweeps, outperforms the first type in terms of CPU time by a factor of 3. In conclusion, AWEbox provides an open-source implementation of efficient and reliable optimal control methods that both control experts and non-expert AWE developers can benefit from.

The objective of this research is to analyze metal elements like Na (sodium), Mg (magnesium), Al (aluminum), Si (silicon), Pb (lead), K (potassium), Ca (calcium), and Fe (iron) found in dust particles within two distinct areas from which the sampling has been done. First sampling was taken from the road verge of a highly trafficked road section, while the second sampling was done in a residential garden area 90 m away from the road. Several metal elements have been detected, with a high difference for Silicon Si, which present higher concentrations in the dust sampled from the road verge area. Pb has only been detected in the samples taken from the road verge, which could be explained by residual remnants from old lead gasoline, and wheel weights. Additionally, airborne particulate matter (PM) concentrations have been measured, during the same episode, in comparison between the road verge and the garden area, presenting a substantial difference in the concentration levels, deducing that dense vegetation is protecting and blocking a majority of airborne PM. A literature highlight of health effects of different metal elements and PM concentrations is presented.

Seasonal variations in atmospheric ultrafine particulate matter (PM0.1) were monitored in Bangkok, Thailand, from 2016 to 2017. PM0.1 bound organic carbon (OC), and elemental carbon (EC) were collected by a cascade air sampler that can collect PM0.1 and estimated by a thermal-optical carbon analyzer following the IMPROVE-TOR protocol. The annual average PM0.1 in Bangkok was 14.5 ± 4.7 µg/m3, higher than in large Asian cities like Shanghai and Hanoi. Biomass burning from neighboring areas was shown to increase the particle concentration. Apparent risings in carbon species as OC, EC, and OC/EC ratios in the wet and dry seasons were observed, and Char-EC/Soot-EC displayed that the PM0.1 in the Bangkok atmosphere was influenced mainly by the vehicle exhaust, even though the influence of biomass burning was more sensitive during the dry season. The effective carbon ratio (ECR) displays that carbonaceous aerosol is light-absorbing; the dry season increase contributed to climate and air quality. The finding of this work is of great importance to air pollutants control policies in urban areas.

Background: Exposure in utero to particulate matter (PM2.5 and PM10) is associated with maladaptive health outcomes. Although exposure to prenatal PM2.5 and PM10 have cord blood DNA methylation signatures at birth, signature persistence into childhood and saliva cross-tissue applicability has not been tested. Methods: In the Fragile Families and Child Wellbeing Study, a United States 20-city birth cohort, average residential PM2.5 and PM10 during pregnancy was estimated using air quality monitors with inverse distance weighting. Saliva DNA methylation at ages 9 (n=749) and 15 (n=793) was measured using the Illumina HumanMethylation 450k BeadArray. Cumulative DNA methylation scores for particulate matter were estimated by weighting participant DNA methylation at each site by meta-analysis effect estimates from Gruzieva et al. 2019 and standardizing the sums. Using mixed effects regression analysis, we tested the associations between cumulative DNA methylation scores at ages 9 and 15 and PM exposure during pregnancy, adjusted for child sex, age, race/ethnicity, maternal income to needs ratio, nonmartial birth status, and saliva cell type proportions. Results: Our study sample was 50.5% male, 56.3% non-Hispanic Black, and 19.8% Hispanic, with median income to needs ratio of 1.4. In the third trimester, mean PM2.5 exposure levels were 27.9 ug/m3/day (standard deviation: 7.0, 23.7% of observations exceeded safety standards) and PM10 were 15.0 ug/m3/day (standard deviation: 3.1). An interquartile range increase in PM2.5 exposure (10.73 g/m3/day) was associated with -0.0287 standard deviation lower cumulative DNA methylation score for PM2.5 (95% CI: -0.0732, 0.0158, p=0.20) across all participants. An interquartile range increase in PM10 exposure (3.20 g/m3/day) was associated with -0.1472 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: -0.3038, 0.0095, p=0.06) across all participants. The PM10 findings were driven by the age 15 subset where an interquartile range increase in PM10 exposure was associated with -0.024 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: -0.043, -0.005, p=0.012). Findings were robust to adjustment for PM exposure at ages 1 and 3. Conclusion: In utero PM10 associated DNA methylation differences persist until age 15 and can be detected in saliva. Benchmarking the persistence and cell type generalizability is critical for epigenetic exposure biomarker assessment.

Evidence for the potential for airborne transmission of SARS-CoV-19 continues to accumulate, with important implications for healthcare workers, as well as the general public. Three lines of evidence support this conclusion.

Emergence of zoonotic-human pathogens is proven to be a lethal threat to public health, and RNA virus including influenza viruses, severe acute respiratory syndrome coronavirus, middle east respiratory syndrome coronavirus, Wuhan coronavirus (COVID-19), plays a pivotal role. As those viruses as airborne microorganisms spread mainly by tiny airborne particles, it is important to de-active those airborne particles before their entry into human bodies. In this study, we investigated the effect of far infrared (FIR) radiation on inhibition of airborne microorganisms. The result confirmed that double stand DNA from airborne microorganisms were stable under mild FIR radiation. However, single strand RNA from them was found to be sensitive to FIR radiation, indicating that RNA virus in airborne particles is instable under FIR radiation. Based on this observation, two models on usage of FIR radiation to prevent RNA virus transmission and cure RNA virus infection were proposed, implying that FIR radiation might be a cheap, convenient, and efficient method in clinic to treat RNA virus.

In this paper, an offshore airborne wind energy (AWE) farm consisting of three non-reversing pumping mode AWE systems is modelled and simulated. The AWE systems employ permanent magnet synchronous generators (PMSG). A direct interconnection technique is developed and implemented for AWE systems. This method is a new approach invented for interconnecting offshore wind turbines with the least number of required offshore-based power electronic converters. The direct interconnection technique can be beneficial in improving the economy and reliability of marine airborne wind energy systems. The performance and interactions of the directly interconnected generators inside the energy farm internal power grid are investigated. The results of the study conducted in this paper, show the directly interconnected AWE systems can exhibit a poor load balance and significant reactive power exchange which must be addressed. Power control strategies for controlling the active and reactive power of the AWE farm are designed, implemented, and promising results are discussed in this paper.

Multi-object tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, size, appearance and motion of the moving objects as well as occlusions due to the interaction between the moving objects and with other static objects in the scene.To deal with these problems, this work proposes a four-stage Hierarchical Association framework for multiple object Tracking in Airborne video (HATA). The proposed framework combines data association-based tracking (DAT) methods and target tracking using a Compressive Tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne video datasets show significant tracking improvement compared to existing state-of-art methods.

In airborne sensor networks (ASNs), the media access control (MAC) protocol is facing with serious unfairness problem due to the traditional protection mechanism of air-to-air communications among aircrafts. Actually by using the binary exponential back-off algorithm at high traffic loads to minimize collisions among users, the latest successful node can always benefit from this kind of MAC to obtain channel resources. Moreover, when taking the existence of the hidden nodes in ASNs into account, the inaccurate traffic load information will further aggravate the system’s unfairness. In this paper, a neighbor-channel-aware (NCA) protocol is proposed to improve the fairness of MAC protocol in ASNs. In the proposal, the NCA frame is firstly added and exchanged between neighbor nodes periodically, which helps to resolve the inaccurate traffic load information, so as to avoid reducing the probability of successful message transmission. Then a traffic-loading based back-off algorithm is involved to make the neighbor nodes cooperatively adjust the inter-frame space (IFS) interval to further reduce the unfairness. The simulation results show that, the proposed MAC protocol can guarantee the satisfied fairness, simultaneously avoiding heavy network overloads to protect key messages’ successful transmissions in ASNs.

: This study was aimed at assessing adaptation and bonding of discontinuous (short) glass fiber-reinforced composite to intraradicular dentin EverX Flow (GC Corporation, Tokyo, Japan), when used as intracanal composite filling and anchorage instead of traditional fiber posts. (2) Methods: Seventy intact extracted human teeth were endodontically treated and randomly divided into 6 groups (n=10), depending on the materials used in the post space. In Group 1, a 2-bottle universal adhesive G2 Bond Universal + EverX Flow were tested. In group 2, a single-component universal adhesive G-Premio Bond + EverX Flow were used. In groups 3 and 4 the same materials are tested, but after cleaning of the canal walls with 17% EDTA and final irrigation with 5.25% NaOCl Ultrasound Activated. In the last three Groups (5-7) traditional prefabricated GC Fiber Posts 1.6 mm silanized with G-Multi Primer for 1 minute are cemented with a dual-cured composite resin cement (GradiaCore), after ultrasonic irrigation in the groups 6 and 7. In each group, 1 mm-thick slices from each sample (n=10) were cut for light microscope and SEM inspection for study materials adaption to the dentin and for measuring push-out strength of post / cemement material to the dentin / prefabricated post. These results were statistically analyzed: as the data distribution was not normal, the Kruskal-Wallis Analysis of Variance by Ranks had to be applied. The level of significance was set at p<0.05. Results: Push-out forces varied between 6.66-8.37 MPa. No statistically significant differences were recorded among the groups. Microscopic examination showed that ultrasonic irri-gation increased adaptation of the materials to the dentin surface. There was a trend of higher bond strength among the tested groups when EverX Flow was used. Also, the type of failure was more often cohesive when ultrasonic irrigation and two-step adhesive system were used. Conclusions: Within the limitations of this in vitro study, it may be concluded that when EverX Flow was used for intracanal anchorage in the post-endodontic recon-struction, similar push-out retentive forces and strength to those of traditional fiber posts cemented with particulate filler resin composite cements were achieved. Although further studies are necessary, EverX Flow represents an effective alternative to traditional fiber post adhesion in particular when used in combination with the two-step adhesive system and ultrasonic activation.

In northern Thailand, the problem of small particulate matter happens every year, with the pri-mary source being agricultural weed burning and wildfire. The tourism industry is strongly impacted and has been the spotlight for the past few years. Thus, the study aims to investigate the effect of small particulate matter on tourism and related SMEs in Chiang Mai, Thailand. The data was collected from 286 entrepreneurs in the tourism and related SMEs sectors. The data was analyzed using data mining and association rule techniques. The study revealed that small particulate matter has a considerable impact on customer factors, especially the number of customers has decreased. Operational factors and prod-uct/service factors are also affected by the dust in the form of adjustments to keep the business running and the protection of the health of employees and customers. Certainly, financial factors are affected by the small particulate matter situation, both lower revenues and higher costs.

Mapping and prediction of inundated areas is increasingly important for climate change adaptation and emergency preparedness. Flood forecasting tools and flood risk models have to be compared to observed flooding patterns for training, calibration, validation and benchmarking. At regional to continental scale, satellite earth observation is the established method for surface water extent (SWE) mapping and several operational global-scale data products are available. However, the spatial resolution of satellite-derived SWE maps remains a limiting factor, especially in low-lying areas with complex hydrography, such as Denmark. We collected thermal imagery using an unmanned airborne system (UAS) for three areas in Denmark shortly after major flooding events. We combined the thermal imagery with an airborne lidar-derived high-resolution digital surface model of the country to retrieve high-resolution (40 cm) SWE maps. The resulting SWE maps were compared to low-resolution SWE maps derived from satellite earth observation (EO). We conclude that UAS have significant potential for SWE mapping at intermediate scales, can bridge the scale gap between ground observations and satellite EO and can be used to benchmark and validate SWE mapping products derived from satellite EO as well as models predicting inundation.

Airborne transmission of seasonal and pandemic influenza viruses is responsible for their epidemiological success and public health burden in humans. Efficient airborne transmission of H1N1 influenza virus relies on receptor specificity and pH of fusion of the surface glycoprotein hemagglutinin (HA). In this study, we examine the role of HA pH of fusion on transmissibility of a cell culture-adapted H3N2 virus. Mutations in the HA head at positions 78 and 212 of A/Perth/16/2009 (H3N2), which were selected after cell culture adaptation, decrease the acid stability of the virus from a pH of 5.5 (WT) to 5.8 (mutant). In addition, we observed that this mutant H3N2 virus replicated to higher titers in cell culture but had reduced airborne transmission in the ferret model. These data demonstrate that, like H1N1 HA, the pH of fusion for H3N2 HA is a determinant of efficient airborne transmission. Surprisingly, we demonstrate that the NA segment noncoding regions can impact the pH of fusion of reassortant viruses. Taken together, our data confirm that HA acid stability is an important characteristic of epidemiologically successful human influenza viruses and is influenced by HA/NA balance.

The science on the anthropogenic airborne aerosols impacting upon the World Heritage marble monument, the Taj Mahal, at Agra has been studied in the light of modern physico-chemical approaches. The study is an effort to understand yet unrecognized airborne species which were found on the surface of the Taj Mahal monument. These species have been analyzed in the light of current analytical methods to impart characterization features and their possible impacts on the surface of the marble. Chemical constituents of these substrates which were incorporated over the top surface of the monument have been identified. Interestingly, the carbon particulates which were thought in the micro level, popularly called “particulate matters” has now been identified even in the nano domain entity, which are chemically more reactive, have been found on the surface of the monument. Because of their high chemical activity these nano carbons do play newer chemistry in the presence of air and sunlight generating several reactive oxygen species (ROS).These ROS are capable to respond to complicated chemical reactions on the surface of the marble in association with deposited cyanophyceae and other deposits of plant origin causing rapid degradation. This study provides the nature of onslaught borne out by such monument exposed under the prevalent smoggy environmental scenario.

This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMS) under high humidity conditions. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data was analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMS can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data.

Inhaled particulate matter (PM) is a key player in the millions of yearly deaths worldwide related to air pollution. The oxidative potential of PM provides an indication of its ability to promote an oxidative environment. When the human lungs are exposed to PM, the reactive oxygen species (ROS) generated in such oxidative environment, if excessive, may lead to cell damage via oxidative stress. Inflammation, endoplasmic reticulum stress, airway remodeling, and many different cell death modes (apoptosis, ferroptosis, pyroptosis, among others) can be triggered by oxidative stress via complex molecular pathways, which also involve the adaptive cellular response. But ROS can also directly interact with macromolecules such as proteins and lipids, as well as nucleic acids, having been reported to induce DNA damage and epigenetic modifications, which jeopardizes homeostasis. These facts have been extensively studied in in vitro models and confirmed in in vivo models.In this review, we delve beyond conventional assessments of airborne particles, discussing their oxidative potential and the PM-induced ROS-mediated cellular damage observed in in vitro models. The close link between oxidative stress and inflammation, and the manifestation of different cell death modes is highlighted by reviewing the latest literature. The effects of ROS on the balance of DNA damage and repair, carcinogenicity and epigenetics interplay are then analyzed. Finally, we expand on the latest reports on the antioxidants’ potential to counteract the deleterious effects of ROS, and disclose future perspectives for the topic.

The research is focusing on analyzing the concentrations of particulate matter (PM) next to a highly congested road section, with 39,900 as a maximum number of vehicles per 24 h, in the vicinity of Timisoara, Romania. Concentrations have been measured in multiple episodes using two different measurement methods, gravimetric and dynamic light scattering, showing a disperse range of PM concentrations. Presence of metal particles in the samples have been analyzed using an electron microscope. Additionally, impact on human health is described by evaluating results for inhalable, thoracic, and alveolar sized particles.

World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing PM level, the procedure for measuring PM value is needed firstly. We use Tapered Element Oscillating Microbalance (TEOM)-based PM measuring sensors because it shows higher cost-effectiveness than Beta Attenuation Monitor (BAM)-based sensor. However, TEOM-based sensor has higher probability of malfunctioning than BAM-based sensor. In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that named as Hypothesis Pruning Generative Adversarial Network (HP-GAN). We experimentally compare the several anomaly detection architectures to certify ours performing better.

The digital model of airborne frequency selective surface radomes (AFSSRs) is the basis of design, simulation analysis, manufacturing, and other related research of AFSSRs. This paper proposes a rapid modeling method of AFSSRs based on dynamic customizable primitives. Firstly, a layered digital model construction scheme of AFSSR is presented based on the typical radome wall structure. Then, according to the characteristics of various surface configurations and complex wireframe information of AFSSRs, the dynamic primitives are raised to express the boundary and contour information of all kinds of radomes. Focusing on the undevelopable characteristics of the aero-dynamic shape surface of the AFSSR, the arrangement solution and mapping method for frequency selective elements on undevelopable surfaces are proposed. Furthermore, the imple-mentation logic of this method on the creation of each layer model and the assembly of the whole machine model is introduced. Finally, according to this method, a rapid modeling system (RMS) is established, and the modeling effect and efficiency of the RMS are demonstrated and compared, which verifies the feasibility and effectiveness of this method.

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing, data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai'i island, using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that, while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m x 2 m M. polymorpha presence dataset and a 30 m x 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy.

Proper treatment of infectious air could potentially mitigate the spread of airborne viruses such as porcine reproductive and respiratory syndrome virus (PRRSV). The objective of this research is to test the effectiveness of ultraviolet (UV) in inactivating aerosolized PRRSV, specifically, four UV lamps, UV-A (365 nm, both fluorescent and LED-based), "excimer" UV-C (222 nm), and germicidal UV-C (254 nm), were tested. The two UV-C lamps effectively irradiated fast-moving PRRSV aerosols with short treatment times (<2 s). One-stage and two-stage UV inactivation models estimated the UV doses needed for target percentage (%) reductions on PRRSV titer. UV-C (254 nm) dose needed for 3-log (99.9%) reduction was 0.521 and 0.0943 mJ/cm2, respectively, based on one-stage and two-stage models. An order of magnitude lower UV-C (222 nm) doses were needed for a 3-log reduction, i.e., 0.0882 and 0.048 mJ/cm2, based on one-stage and two-stage models, respectively. However, the cost of 222-nm excimer lamps is still economically prohibitive for scaling-up trials. The UV-A (365 nm) lamps could not reduce PRRSV titers for tested doses up to 4.11 mJ/cm2. Pilot-scale or farm-scale testing of UV-C on PRRSV aerosols simulating barn ventilation rates are recommended based on its effectiveness and reasonable costs comparable to HEPA filtration.

Today, air pollution is the greatest threat to organismal healthspan. The environment of our planet earth, the habitat of over eight billion humans and estimated twenty billion billions other animals, is contaminated with a wide variety of pollutants. Unfortunately, humans, out of billions and billions of living organisms on earth, are solely responsible for polluting the environment through emitting pollutants like particulate matter from industry, fuel engine vehicles, biomass combustion, toxic fumes from blasting, and wildfire. In the modern world, human-caused air pollutants induce massive oxidative stress and inflammation, the major contributors in initiation and progression of many diseases including pulmonary, cardiovascular, renal, hepatic, reproductive, neurological, mental, and accelerated biological aging. The provocative question is the following: how can we solve this human-created problem? As it is not realistic to clean the environment at once from human-caused pollution, initiatives have been undertaken to develop novel therapeutic approaches to control air-pollutant-induced oxidative stress and inflammation to protect humans from pollution-induced devastating diseases. In this article, I discuss the key findings of numerous recent preclinical studies documenting first, the role of air pollutant PM2.5 in augmentation of inflammation, oxidative stress, and associated diseases; and second, the efficacies of different natural and synthetic compounds in amelioration of PM2.5-induced oxidative stress, inflammation, pyroptosis, and associated pathologies.

Riverine particulates dominate the transport of vital nutrients like Si, Fe or P to the ocean mar-gins, where they may increase primary production by acting as slow release fertilizer. Further-more, the supply of particulate surface area to the ocean is considered to be a major control or-ganic carbon burial. Taken together, these observations suggest a close link between the supply of riverine particulate material and the organic carbon cycle. To explore this link, we conducted microcosm experiments to measure the growth of the marine diatom Thalassiosira weissflogii in the presence and absence of different types and concentrations of riverine particulate material. Results demonstrate a strong positive effect of riverine particulate material on diatom growth with increased total diatom concentrations and slowed post-exponential death rates with in-creasing particulate concentration. Moreover, SEM and optical microscope investigations con-firm that riverine particulates facilitates organic carbon burial through their role in the aggrega-tion and sedimentation of phytoplankton. The supply of riverine particulate material has been shown to be markedly climate sensitive with their fluxes increasing dramatically with increas-ing global temperature and runoff. This pronounced climate sensitivity implies that riverine particulates contribute substantially in regulating atmospheric CO2 concentrations through their role in the organic carbon cycle.

Every day around 93% of children under the age of 15 (1.8 billion children) breathe outdoor air that is so polluted it puts their health and development at serious risk. Due to the pandemic, however, ventilation of buildings using outdoor air has become an important safety technique to prevent the spread of COVID-19. With the mounting ev-idence suggesting that air pollution is impactful to human health and educational out-comes, this contradictory guidance may be problematic in schools with higher air pol-lution levels, but keeping kids COVID-19 free and in school to receive their education is now more pressing than ever. To understand if all schools in an urban area are ex-posed to similar outdoor air quality and if school infrastructure protects children equally indoors, we installed research grade sensors to observe PM2.5 concentrations in indoor and outdoor settings to understand how unequal exposure to indoor and out-door air pollution impacts indoor air quality among high- and low-income schools in Salt Lake City, Utah. Based on this approach, we found that during atmospheric inver-sions and dust events, there was a lag ranging between 35 to 73 minutes for the out-door PM2.5 concentrations to follow a similar temporal pattern as the indoor PM2.5. This lag has policy and health implications and may help to explain the rising concerns re-garding reduced educational outcomes related to air pollution in urban areas. These data and resulting analysis show that poor air quality may impact school settings, and the potential implications with respect to environmental inequality.

The quality of photogrammetric-based derived products like orthophotos, digital terrain models (DTMs) and digital line maps as well as the global digital elevation models (DEM) are rigorously dependent on the accuracy of image orientation. This paper evaluates the vertical accuracy of aerial photogrammetric Digital Terrain Model (DTM), Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), and TerraSAR-X's twin satellite of TanDEM-X (TDX) datasets against in-situ orthometric heights computed from ellipsoidal heights and the 2008 Earth Gravitational Model (EGM2008) derived geoid heights in Ethiopia. The quality of the four global digital elevation models was also validated against the aerial photogrammetric DTM measurements. Besides, the accuracies of the photogrammetric DTM and the four DEM products were checked for their compliance to the American Society for Photogrammetry and Remote Sensing (ASPRS) standards as well as the Ethiopian national vertical data evaluation standards. The study showed that the photogrammetric DTM is in a good agreement with the reference orthometric heights compared to SRTM, ASTER and TDX datasets. More precisely, the result has an absolute accuracy of 1.67 m at Linear Error (LE) 95% confidence level, while the absolute accuracy of SRTM3 arc seconds (SRTM3) at LE 90% (11.91 m) is better than its product specification (16 m). The absolute accuracy of SRTM1 arc second (SRTM1) (7.70 m at LE 90%) surpasses that of SRTM3, whereas the absolute accuracy of ASTER DEM is somehow below its product specification. TDX also has the same vertical accuracy (10.29 m at LE 90%) compared to its product specification (10 m). Furthermore, the vertical accuracy of the photogrammetric DTM meets the100 cm vertical accuracy of the 2015 ASPRS standard. However, it does not meet the Ethiopian national vertical data accuracy requirement standard, i.e., RMSEz of ± 0.45 m. In general, the photogrammetric DTM, SRTM1, and TDX have been proven a superior product over the SRTM3 and ASTER DEMs, and better to use these products for high-level precision and accuracy required applications.

The pollution caused by nitrogen dioxide is a major environmental problem in China. This study introduces a new type of atmospheric trace gas remote-sensing instrument, an airborne fiber imaging spectrometer. This spectrometer has a spectral range of 300–410 nm and works in push-broom mode with a 30° field of view on a flight path. Flight experiments were conducted on December 30, 2022 and January 5, 2023, covering heavily polluted areas of east of Beijing and Tangshan. This equipment obtained the density distribution of NO2 over the flight area. The results showed that pollution was mainly concentrated in the Caofeidian area and at the power station in the north, and the main source of pollution was anthropogenic. Satellite and airborne data near the pollution points were compared, and the two datasets showed a positive correlation with a correlation coefficient of 0.78 and 0.7, on the two days, respectively. This study demonstrates the capability of an airborne fiber imaging spectrometer for NO2 regional emission remote sensing and identifying the pollution points.

The dual-station Interferometric Synthetic Aperture Radar (InSAR) system can overcome the physical limitations imposed by the baseline of single-station dual-antenna InSAR. It provides greater flexibility and can enhance elevation measurement accuracy through a well-designed baseline configuration.Unmanned aerial vehicles (UAV) equipped with dual-station InSAR, with relatively low cost and high flexibility, are useful for mapping and land resource exploration. However, due to challenges including spatiotemporal synchronization and motion errors, there are limited reports on UAV-borne dual-station InSAR. This paper proposes a comprehensive method for processing data from small UAV-borne dual-station InSAR by integrating two-way synchronization chain signals. The proposed method includes compensation for time and phase synchronization errors, trajectory refinement with synchronized chain and Position and Orientation System (POS) data, high-precision dual-station InSAR imaging and interferometric processing. Height inversion results based on the proposed method is also provided, demonstrating the effectiveness of the proposed method and improving interferometric accuracy at calibration points from 0.66 meters to 0.42 meters.

This study aimed to investigate the airborne release of N-nitrosodimethylamine (NDMA) as a result of the dry heat cooking of some meats using charcoal grilling and pan broiling methods. Three types of meat, beef sirloin, pork belly, and duck, were chosen and cooked in a temporary building using the above methods. Air samples were collected in Thermosorb-N cartridges, which were qualitatively and quantitatively analyzed for NDMA using ultra-high performance liquid chromatography–mass spectrometry and high-performance liquid chromatography–fluorescence detection, respectively. Overall, the charcoal grilling method showed higher average NDMA concentrations than the pan broiling method for all types of meat. The highest average concentration was observed for charcoal-grilled beef sirloin (410 ng/m3) followed by pork belly, suggesting that meat protein content and cooking duration are important determinants of NDMA formation. Cancer risk assessment showed that the charcoal grilling of such meats can pose an additional cancer risk for restaurant customers.

Air pollution, particularly fine particulate matter (PM2.5), poses a significant threat to public health, especially in rapidly urbanizing areas like Lagos State. Despite the severity of the issue, there is a lack of comprehensive data on PM2.5 levels and the Air Quality Index (AQI) in Lagos. This study leverages data from AirQo, an innovative air quality monitoring network using low-cost sensors, to provide a detailed assessment of PM2.5 concentrations and AQI across eight locations in Lagos State, Nigeria. This approach offers real-time, continuous monitoring, filling a critical data gap in the region's air quality management efforts. This study uses AirQo data to assess the concentration levels and spatial distribution of PM2.5 and AQI across the selected eight locations. Data on PM2.5 concentrations were obtained from AirQo sensors deployed across the eight strategic locations in Lagos. The spatial distribution of PM2.5 levels and AQI were analyzed, and the results were compared with national and international air quality standards. The findings revealed that PM2.5 levels (minimum – 6.28 µg/m3 (Ikeja) and maximum - 204.68 µg/m3 (Banana Island) in many areas of Lagos exceed annual WHO and national air quality guidelines, indicating significant health risks. The spatial analysis identified pollution hotspots, particularly in densely populated and industrial regions. The data suggested vehicular emissions and industrial activities as major contributors to high PM2.5 levels. This study underscores the severe air quality issues in Lagos State, emphasizing the need for immediate and targeted interventions to mitigate PM2.5 pollution. The use of AirQo sensors proved effective in providing accurate and timely data for air quality assessment. It is recommended that Lagos State implements stricter emission controls, promotes cleaner technologies, and enhances public transportation systems to reduce traffic-related pollution. Additionally, expanding the air quality monitoring network and incorporating more environmental data will provide a more comprehensive understanding of air pollution impacts, thereby facilitating better policy and public health decisions.

Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM_2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM_2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM_2.5 concentrations on average (16.60 _± 4.29 µg/m³), while tgbwp hosted the highest BC concentrations (2.31 _± 1.94 µg/m³). Furthermore, STURLA combined with machine learning modeling was able to correlate PM_2.5 (R²= 0.68, RMSE 2.82 µg/m³) and BC (R² = 0.64, RMSE 0.75 µg/m³) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.

The outbreak of COVID-19 has caused a global public health crisis. The spread of SARS-CoV-2 by contact is widely accepted, but the relative importance of aerosol transmission for the spread of COVID-19 is controversial. Here we characterize the distribution of SARA-CoV-2 in 123 aerosol samples, 63 masks, and 30 surface samples collected at various locations in Wuhan, China. The positive percentages of viral RNA included 21% of the aerosol samples from an intensive care unit and 39% of the masks from patients with a range of conditions. A viable virus was isolated from the surgical mask of one critically ill patient while all viral RNA positive aerosol samples were cultured negative. The SARS-CoV-2 detected in masks from patients, ambient air, and respirators from health workers compose a chain of emission, transport, and recipient of the virus. Our results indicate that masks are effective in protecting against the spread of viruses, and it is strongly recommended that people throughout the world wear masks to break the chain of virus transmission and thus protect themselves and others from SARS-CoV-2.

As broiler farming facilities have become larger and more concentrated in response to external environmental changes, there is a possibility of increased concentrations of fine dust and aerosols inside these facilities due to enclosure. In particular, workers are exposed to high concentrations of organic particulate matter and harmful gases while performing their tasks, and as they age, they become more vulnerable to respiratory diseases. It is essential to directly monitor the concen-trations to which workers are exposed, along with the spatial distribution of aerosols inside broiler house. In this study, we analyzed the regional aerosol concentrations using passive samplers in commercial tunnel-ventilated broiler farms. Simultaneously, we employed active samplers at the height of the workers' breathing zones to monitor real-time aerosol concentrations by particle size along their work routes. Spatial aerosol concentrations generally increased from the inlet to the exhaust in the breathing zone. The average aerosol concentrations were TSP -1,042 µg/m³, PM-10 718 µg/m³, and PM-2.5 137 µg/m³. To analyze the workers' exposure environments, we categorized the tasks in the barn into Static work period (SWP) and Moving work period (MWP) based on video analysis. The results showed that during MWP, fine dust concentrations exceeded the standards by up to 214%. Particularly, during MWP, the concentrations were 1.74 times higher for TSP, 1.40 times higher for PM-10, and 1.22 times higher for PM-2.5 compared to SWP. It was observed that during the movement of workers, physical generation of particles around 10 µm, such as feces, feed, and bedding, occurred due to the movement of chicken, which influenced the aerosol concentration.

Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor-intensive and time-consuming. To address this limitation, remote sensing tech-nology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Talladega Na-tional Forest by employing ALS-derived data and aerial photography. The quality of predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species-group models. This study confirms that quality level 2 LiDAR data was sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82) when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species-group predictive models were of higher quality than general predictive models. Lastly, landscape-level maps were created from the predictive models, and these may be helpful to plan-ners, forest managers, and landowners in their management efforts.

Background: SARS-CoV-2 continues to impact human health globally, with airborne transmission being a significant mode for transmission. In addition to tools like vaccination and testing, counter-measures that reduce viral spread in indoor settings are critical. This study aims to assess the efficacy of UV-C light, utilizing the Violett sterilization device, as a countermeasure against airborne transmission of SARS-CoV-2 in the highly susceptible Golden Syrian hamster model. Methods: Two cohorts of naïve hamsters were subjected to airborne transmission from experimentally infected hamsters; one cohort was exposed to air treated with UV-C sterilization, while the other cohort was exposed to un-treated air. Results: Treatment of air with UV-C light prevented the airborne transmission of SARS-CoV-2 from experimentally exposed hamster to naïve hamsters. Notably, this protection was sustained over a muti-day exposure period during peak viral shedding from hamsters. Conclusion: These findings demonstrate the efficacy of the UV-C light to mitigate airborne SARS-CoV-2 transmission. As variants continue to emerge, UV-C light holds promise as a tool to reduce infections in diverse indoor settings, ranging from healthcare facilities to households. This study reinforces the urgency of implementing innovative methods to reduce airborne disease transmission and safeguard public health against emerging biological threats.

This study is describing airborne geophysical survey, carried out in Northeastern Russia (Archangelsk region) in order to aimed at geological mapping of Grib kimberlite pipe, which is known to have strong presence of remanent magnetization. Airborne data were inverted using two techniques, the magnetization vector inversion (MVI) and a QDIK inversion, a proprietary algorithm developed at the Irkutsk State University (ISTU), which allows extracting four parameters from airborne magnetic data (magnetic susceptibility, inclination, declination and Koeniksberger ratio). The inversion results were further analyzed and verified against the known geology of the Grib kimberlite. The study allowed to better map geology of Grib kimberlite using only uninvasive (remote sensing) technology

Topography represented by high resolution digital elevation models are able to inform past and present morphological process on the terrain. High resolution LiDAR data taken by the General Directorate of Map at the surroundings of the Bergama city shows great opportunities to understand the morphological process on alluvial fan on which the city is located and the flood plain of Bakırçay river near the alluvial fan. In this paper the LiDAR data collected in 2015 have been used to create DEM’s to understand the geomorphological evolution of the alluvial fan and the flood plain around it. Since the proximal roots and medial parts of the alluvial fan have been the scene for a long human settlement most topographical traces of the morphological process have been distorted. Nevertheless, the traces of past and present morphological process at the distal fan which consist the contact zone with the flood plain are very clear on the DEM created from LiDAR data. The levees and some old courses of Bergama and Bakırçay rivers have been shown on the maps which are also important to understand the ancient roads which follows these levees.

This study aims to elucidate the influence of meteorological conditions on particle levels in Cergy-Pontoise. It explores the temporal variability of PM10 pollution days by associating them with the vertical temperature profile derived from conventional radiosondes from 2013 to 2022 (regional station). The results indicate that nearly 80% of exceedance days were associated with thermal inversions, primarily observed in winter and typically lasting 1 to 3 days. Analysis of winter thermal inversion characteristics suggests that those linked to pollution primarily occur near the ground, with higher intensity in December (12.1°C) and lower in February (10.3°C). Persistent inversions (extended nocturnal by diurnal inversion) account for 91.4% of the total inversions associated with high concentrations. Captive balloon soundings and temperature measurements at different altitudes were conducted during the winter of 2022/2023 to clarify thermal inversion in the Oise Valley at the center of Cergy-Pontoise. The results highlight three nocturnal wind circulation mechanisms in the valley, including downslope flow, circulation influenced by an urban heat island, and mechanical air evacuation under an inversion layer towards the less steep East side of the valley. Analysis of PM with the temperature gradient in the Oise Valley shows a significant correlation, suggesting an increase in concentrations during locally detected inversions and a decrease during atmospheric disturbance.

Polycyclic aromatic hydrocarbons (PAHs) have been reported to cause oxidative stress in metabolic processes. This study aimed to evaluate the relationship between exposure to PAHs, including benzo[a]pyrene (BaP) and 1-nitropyrene (1-NP), in the atmosphere and oxidative stress levels in the human body. This study included 44 Korean adults who lived in Cheongju, South Korea. Atmospheric BaP and 1-NP concentrations and urinary 6-hydroxy-1-nitropyrene (6-OHNP), N-acetyl-1-aminopyrene (1-NAAP), and 1-hydroxypyrene (1-OHP) concentrations were measured. The oxidative stress level was assessed by measuring urinary thiobarbituric acid reactive substances (TBARS) and 8-hydroxydeoxyguanosine (8-OHdG) concentrations. Urinary TBARS and 6-OHNP concentrations significantly differed between winter and summer. BaP exposure was significantly associated with urinary 8-OHdG concentrations in summer. However, atmospheric 1-NP did not show a significant correlation with oxidative stress marker concentrations. Urinary 1-NAAP concentration was a significant determinant for urinary 8-OHdG concentration in summer. Oxidative stress in the body increases in proportion to inhalation exposure to BaP, and more 8-OHdG is produced in the body as the amount of 1-NP, which is metabolized to 1-AP or 1-NAAP, increases.

Several studies have identified a relationship between air pollution and depression, particularly in relation to fine particulate matter (PM2.5) exposure. However, the strength of this association appears to be moderated by variables such as age, gender, genetic vulnerability, physical activity and climatic conditions, and has not been assessed at a cross-national level to date. The current study examines the association between the prevalence of depression in each country, based on the most recent Global Burden of Disease Study data, and the average national level of PM2.5 based on the World Health Organization’s database. The observed associations were adjusted for age, gender, level of physical activity, income, education, population density, climate, and type of depression. It was observed that there was a modest but significant positive correlation between PM2.5 level and the prevalence of depression even after adjusting for the above confounders. This association was more marked above a certain threshold and applied chiefly to major depressive episodes. These findings are of significant public health importance in terms of preventive strategies aimed at reducing the population-level burden of depression.

The transmission and analysis of data is one of the challenges of the 21st century. In the field of environmental measurement technology, existing broadband and wireless technologies have not been able to transmit data reliably and cost-effectively over long distances and in hard-to-reach places. LoRaWAN, an IoT technology, could be an energy-efficient, cost-effective and secure alternative as a narrowband technology in combination with battery-powered sensors and thus make an important contribution to the intelligent, largely wireless networking of objects, plants and machines (IoT), for example in the municipal sector. In addition to ecological and economic benefits, the quality of life in modern, intelligently networked cities can be enhanced by real time data acquisition. However, the prerequisite is that the quality of the data acquired via this method is sufficiently good. This paper therefore addresses the question of the quality of particulate matter data collected by low-cost sensors. To determine this, an SDS011 particulate matter sensor from Nova Fitness was ported to LoRaWAN. The sensor was installed next to a governmental measurement station. In a test that lasted five weeks, data from the SDS011 sensor were compared with those from the governmental station. Differences were identified and a correction approach was developed and applied. The efficiency of the approach was verified. Based on the results, it can be seen that the use of the low cost sensors has weaknesses. Problems can only be partially reduced. Nevertheless, the use of the low-cost sensors can be helpful for a flexible and cost effective collection of environmental data.

Detecting, tracking, and classifying unmanned aerial vehicles (UAVs) in a swarm presents significant challenges due to their small and diverse radar cross-sections, multiple flight altitudes, velocities, and close trajectories. To overcome these challenges, adjustments of the radar parameters and/or position of the radar (for airborne platforms) are often required during runtime. The runtime adjustments help to overcome the anomalies in the detection, tracking, and classification of UAVs. The runtime adjustments are performed either manually or through fixed algorithms, each of which can have its limitations for complex and dynamic scenarios. In this work, we propose the use of multi-agent reinforcement learning (RL) to carry out the runtime adjustment of the radar parameters and position of the radar platform. The radar used in our work is a multibeam multifunction phased array radar (MMPAR) placed onboard UAVs. The simulations show the cognitive adjustment of the MMPAR parameters and position of the airborne platform using RL helps to overcome anomalies in the detection, tracking, and classification of UAVs in a swarm. A comparison with other artificial intelligence (AI) algorithms shows that RL performs better due to runtime learning of the environment through rewards.

Over the years, researchers have been developing different ventilation strategies as a potential solution for controlling infectious airborne transmissions in healthcare facilities. The effects of ventilation systems on airflow characteristics have been extensively discussed among researchers since SARS-CoV-2 outbreak in 2019. Based on a bibliometric study of 154 publications from 1970 to 2021 retrieved from the Web of Science (WoS) database, this paper examines the research landscape on the effects of ventilation strategies on airflow analysis in healthcare facilities. Results show that China is the most productive country due to its significant contributions to the top prolific authors and funding agencies, which have promoted the research development of the topic. Several research hotspots were identified through keyword co-occurrence analysis. Cluster 1 highlights the ventilation studies involving ventilation layouts and ventilation rates in healthcare facilities. Cluster 2 underlines the computational analysis of ventilation performance for reducing the exposure risk of occupants, which promotes the development of infection control measures. Cluster 3 strengthens the knowledge of the emission and dispersion characteristics of respiratory droplets in healthcare facilities. Overall, this paper highlights the future direction of the current research field other than identifying strategic collaborators and suitable publication outlets for interested researchers.

Monitoring crown closure evolution using multi-temporal Light Detection and Ranging (LiDAR) surveys is a method that we expect to be increasingly adopted given the availability of LiDAR sensors and the accumulating survey archives. However, little attention was devoted to comparing crown closure estimates from independent surveys. Although survey parameters cannot be modified after the data collection, we speculate that the error associated to crown closure estimates comparison can be reduced by selecting optimal post-survey parameters. In this study, we compared crown closure estimates of three airborne LiDAR surveys from 2018 (40 pt/m²) used as a reference, and two lower-density surveys from 2016 (4.5 pt/m²) and 2018 (2 pt/m²). We studied the effect of the height threshold used to separate canopy points and the grid resolution, using skewness and variance of lagged difference of crown closure. Crown closure estimates using low height thresholds were more different across surveys, resulting in higher root mean squared error (RMSE), bias and more different variograms. Results show that optimal height threshold was 3 m and grid resolution was 25 m, although there was room for decision (RMSE of 7% and 5%, and bias of 4% and 0% for 2016 and 2018 low-density surveys).

The escalating prevalence of particulate matter (PM) has raised serious concerns regarding its detrimental effects on human health. This study aimed to investigate the potential of fermented blueberry and black rice (FBBR) in mitigating the effects of PM2.5, both in SH-SY5Y cells and mice exposed to PM2.5. Various assays, including MTT, NO, western blot, ELISA, and behavioral studies (MWM and Y-maze) were conducted. Our results demonstrated that PM2.5 induced significant cytotoxicity and elevated nitric oxide (NO) production at a concentration of 100μg/mL of PM2.5 in SH-SY5Y cells. Additionally, administration of FBBR effectively attenuated PM2.5-induced cytotoxicity and suppressed NO production in SH-SY5Y cells. In an intranasal instilled mice model, exposure to 10 mg/kg body weight of PM2.5 resulted in cognitive impairments. However, FBBR treatment ameliorated these impairments in both the Y-maze and MWM tests in PM2.5-exposed mice. Furthermore, FBBR administration increased the expression of brain-derived neurotrophic factor (BDNF) and reduced inflammatory markers in the brains of PM2.5-exposed SH-SY5Y cells. These findings underscore the detrimental effects of PM2.5 on the nervous system and highlight the potential of FBBR as a nutraceutical agent for mitigating these effects. Overall, this study emphasizes the urgency of addressing the harmful impact of PM2.5 on the nervous system and suggests the promising role of FBBR as a protective intervention against the adverse effects associated with PM2.5 exposure.

A study was carried out in a Waste Sorting Plant (WSP) located in France and treating dry recyclable household waste (DRHW) as well as dry recyclable commercial and industrial waste (DRCIW). The aim was to investigate the exposure to bioaerosol and dust. The aim of the study was to assess the extent to which the measurement of concentration, species composition and particle size distribution can contribute to a better assessment of the biological risks associated with exposure. Both stationary and personal bioaerosol and dust samples were collect in the (WSP). The results confirm that waste and waste sorting activities are sources of airborne fungi. Indeed, ambient concentrations ranged from 7.3×103 to 8.5×105 CFU/m3 for culturable moulds and up to up to 4 mg/m3 for dust. Personal exposure to inhalable dust was found up to 3 mg/m3 for dust and ranged from 8.6×103 to 1.5×106 CFU/m3 for fungi. Airborne fungal communities were found to be dominated by the Penicillium genera in both bioaerosols and settled dust samples, followed by the Aspergillus, Cladosporium, Wallemia, Mucor, and Rhizopus genera. Fungi were carried by particles of aerodynamic diameters mainly between around 2.0 and 10.0 µm. The findings dealing with size distribution and biodiversity of bioaerosols suggest that employees are exposed to complex bioaerosols during their work and helps to make a finer diagnosis of the risks involved, which is often difficult in the absence of any OEL for bioaerosols in general.

Urban air pollution is a significant threat to both the environment and public health. Primarily originating from traffic emissions, particulate matter is identified as the most prevalent and toxic pollutant in cities. In recent years, street vegetation has emerged as a nature-based solution to improve urban air quality. However, to achieve this goal, proper placement of vegetation is crucial. This study critically investigates how different street tree placement strategies affect the local air quality, based on a model calibrated using existing air quality measurements from one of the most polluted streets in Glasgow. We then test the local air quality effects of tree orientation, spacing, shape, porosity, size, species, and wind directions. The findings suggest that trees effectively enhance air quality, particularly when implemented on the leeward side of the street. To achieve optimal effectiveness, the spacing of trees require careful alignment with the aspect ratio, as haphazard planting could lead to unfavorable results. The insights on improving air quality in Glasgow's street canyons, complements the objectives of the ‘Glasgow Avenues Programme’ and can be included in its Low Emission Zones to co-benefit the environment and the wellbeing of the local community.

This paper describes the indoor air pollution in urban (Lahore, Faisalabad, Gujranwala, Rawalpindi and Karachi) Pakistani regions while considering the outdoor air and the health and wellbeing of the occupants present indoors. It also aims to assess previous methodological approaches Such as Air Conditioning Systems, used by developed countries to improve air quality to demonstrate the improvements that can be made in air quality in Pakistani urban areas. This research also investigates the causes, concentration and effects of poor air quality in Pakistani urban areas such as particulate matter (PM2.5), carbon oxides (COx), nitrogen oxides (NOx), sulphur oxides (SOx) and volatile organic compounds (VOC’s). The average concentration of these pollutants in Pakistan’s indoor air is, particulate matter (PM): [100 – 250 µg/m3 for PM2.5, 200 – 600 µg/m3 for PM10] 1 – 20 ppm for carbon monoxide (CO), nitrogen oxides (NOx): [50 – 100 µg/m3 for NO2, 20 – 40 µg/m3 for NO], 20 – 50 µg/m3 for sulfur dioxide (SO2), volatile organic compounds (VOCs): 0.1 – 0.5 ppm for formaldehyde, 5 – 10 µg/m3 for benzene and 20 – 30 µg/m3 for toluene. It should be noted that these concentrations can differ depending on the origin of pollution, location, and time of day. The literature review is made up of a combination of descriptive research methods that are used to inform the research background of ambient and indoor air quality, thus the literature review provides a theoretical and methodological background about the air pollution and its improvement in different parts of the world. According to the World Air Quality Index, Pakistan is the third most polluted country in terms of average PM2.5 concentration which is 14.2 times the WHO annual air quality guideline value. As a result, it is suggested that a PM2.5 concentration reduction program be implemented in urban Pakistani regions' indoor air.

Currently available nationwide prediction models for fine particulate matter (PM_2.5) lack prediction confidence intervals and usually do not describe cross validated model performance at different spatiotemporal resolutions and extents. We used 41 different spatiotemporal predictors, including data on land use, meteorology, aerosol optical density, emissions, wildfires, population, traffic, and spatiotemporal indicators to train a machine learning model to predict daily averages of PM_2.5 concentrations at 0.75 sq km resolution across the contiguous United States from 2000 through 2020. We utilized a generalized random forest model that allowed us to generate asymptotically-valid prediction confidence intervals and took advantage of its usefulness as an ensemble learner to quickly and cheaply characterize leave-one-location-out CV model performance for different temporal resolutions and geographic regions. Using a variable importance metric, we selected 8 predictors that were able to accurately predict daily PM_2.5, with an overall leave-one-location-out cross validated median absolute error of 1.20 ug/m³, an R² of 0.84, and confidence interval coverage fraction of 95%. When considering aggregated temporal windows, the model achieved leave-one-location-out cross validated median absolute errors of 0.99, 0.76, 0.63, and 0.60 ug/m³ for weekly, monthly, annual, and all-time exposure assessments, respectively. We further describe the model’s cross validated performance at different geographic regions in the United States, finding that it performs worse in the Western half of the country where there are less monitors. The code and data used to create this model are publicly available and we have developed software packages to be used for exposure assessment. This accurate exposure assessment model will be useful for epidemiologists seeking to study the health effects of PM across the continental United States, while possibly considering exposure estimation accuracy and uncertainty specific to the the spatiotemporal resolution and extent of their study design and population.

Responding to an urgent call for effective and cost-efficient enforcement of emission regulations from ocean-going vessels (OGVs) at sea, the Royal Belgian Institute of Natural Sciences, one of the 17 official partners of the Belgian coastguard structure, launched its airborne sniffer program in 2015. A custom-built sniffer sensor was installed on board of the Belgian coastguard aircraft and has been used for the in-situ measurement of SO2 and NOx in OGV exhausts, providing non-compliance alerts to port inspection authorities in Belgium and other EU member states. To guarantee the consistency in quality and reliability of the airborne non-compliance alerts, the standard operational procedures and measurement methodology have been described and recorded in a sniffer quality management system (SQMS). As part of the SQMS, alerting thresholds for non-compliance with SO2 and NOx standards were defined based on the emission limits and the measurement uncertainty. In addition, compliant measurements for the fuel sulfur content (FSC) were shared through Thetis-EU, which is the sulfur inspection database hosted by the European Maritime Safety Agency (EMSA). By providing both non-compliance alerts and compliant measurements to port inspection authorities, the coastguard aircraft fulfills a first-step in the enforcement chain. This article demonstrates that the reported alerts have not only led to several sanctions, but the analysis also showed that the provision of airborne alerts improved the efficiency of the port inspection authorities drastically, resulting in a cost-efficient improvement of their enforcement strategy. Port inspection authorities were able to follow-up on 46% of the generated FSC alerts. In addition, 43% of the alerted OGVs, which were followed up, were confirmed as non-compliant after a port inspection. Accordingly, conditions were in place to effectively sanction 20% of the non-compliant observations. For NOx alerts, a limited follow-up was done by port inspection authorities and none of the alerts were confirmed by those inspections, which is mainly due to the lack of suitable inspection mechanisms and the absence of additional EU implementation regulations on NOx emission inspections. Aiming at paving the way for a stricter enforcement of airborne non-compliance alerts, a validation analysis was done between the airborne FSC measurements and reference FSC measurements. In addition to onboard measurements from exhaust gas cleaning systems (EGCS), analyzes of fuel samples were also carried out by port inspection authorities in order to acquire reference measurements. The validation analysis presented in this study showed that the empiric deviation of the airborne FSC measurements with the reference measurements was significantly lower than the uncertainty used in the reporting thresholds. It has also been shown that aerial measurements provide evidence-based data that, given an appropriate confidence interval, has the potential to be considered reliable legal evidence.

There is a lot of discussion underway with conflicting opinions examining the airborne nature of the SARS-CoV2 virus. Surprisingly, important phenomena prevalent with respect to aerosols (suspended droplets) have not been considered. In this Technical Note, we propose a methodology for the coupling of aerosol phenomena (such as evaporation, particle transport accounting for drag) to accurately establish the lifetimes of the droplets. A characteristic time analysis illustrates the time scales for evaporation and settling: for example, the characteristic time for evaporation of a 10 µm droplet is 0.036 s at a relative humidity of 25%; compared to a settling time of about 500 s. For any particle smaller than ~ 100 µm, the evaporation of the emitted or exhaled droplet has to be considered. Coupling evaporation of the droplet as it settles, we estimate the horizontal distance traversed. Trajectories of a 10 µm and 100 µm particle emitted with a typical initial velocity of release associated with coughing and sneezing indicates the greater spread in the horizontal direction when evaporation is accounted for. The life time of the 10 µm particle increases from 8.3 min to 12 hours (will be intercepted prior and the actual airborne time will then be shorter); and for a 100 µm particle from 4.9 s to 39.4 s.

This publication aims to disseminate a step-by-step process that walks through the conceptualization and building of a low-cost (~ $150 monitoring device for airborne fine particulate matter (PM2.5), based on miniaturized sensors and components. Details on the implementation of the hardware and software are provided which facilitate the data acquisition, capture and analysis. The central components and their setup discussed in what follows include: the sensor device (called “P.ALP” – Ph.D. Air quality Low-cost Project), Arduino IDE (Integrated Development Environment) and R code (open-access software). A monitoring device for PM2.5, using low-cost sensors and technologies was successfully conceptualized, designed, and implemented. The P.ALP monitoring system was designed and developed to be a basic device, which can be further customized and implemented using the wide range of low-cost sensors available on the market.

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aims of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant Arabis alpina in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs rivalled the accuracy of LiDAR, putting the current paradigm of LiDAR being the more accurate of the two methods into question. We obtained DEMs at spatial resolutions of 6.25cm-8m for PHOTO and 50cm-32m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, we found that SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50cm in such studies.