Dust Levels Research Articles

Development of Livestock-Associated Methicillin-Resistant Staphylococcus aureus (LA-MRSA) Loads in Pigs and Pig Stables During the Fattening Period

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) is widespread in European pig production and poses an occupational hazard to farm workers and their household members. Farm workers are exposed to LA-MRSA through direct contact with pigs and airborne transmission, enabling bacteria to be carried home in the nose or on the skin. Consequently, it is important to consider LA-MRSA in a one-health context, studying human exposure by examining LA-MRSA levels in pigs, air, and dust in the farm environment. In this investigation, LA-MRSA levels were quantified in nasal swab samples from the pigs, air samples, and dust at three different time points in a farm rearing pigs from approx. 30 kg until slaughter. Sampling was repeated across seven batches of pigs, resulting in the analysis of 504 nasal swab samples, alongside air and dust samples. LA-MRSA was cultured and quantified on MRSA2 agar plates. Findings revealed significant batch-to-batch variation and a significant 94.1% decrease in LA-MRSA levels during the rearing period. Despite this decline, all nasal swab samples tested positive, with the highest level reaching 353,000 cfu in a sample. Among the 42 air samples, LA-MRSA levels were low to moderate, with a maximum of 568 and an average of 63 cfu/m3. In the 28 dust samples collected during the second and third sampling periods, LA-MRSA counts were high, reaching up to 37,272 cfu/g, with an average of 17,185 cfu/g. The results suggest that while LA-MRSA levels in pigs decrease with age, reaching low levels before slaughter, the bacterium remains highly abundant in dust, posing an occupational hazard to farm workers.

Unveiling soil coherence patterns along Etihad Rail using Sentinel-1 and Sentinel-2 data and machine learning in arid region

This research applies machine learning to predict soil coherence for Etihad Rail, marking the first comprehensive study in the United Arab Emirates (UAE)'s arid regions. By integrating Sentinel-1 SAR and Sentinel-2 data with MODIS Aerosol Optical Depth (AOD) observations, the study develops detailed models that depict complex soil coherence patterns crucial for urban planning and risk assessment. Findings show variations in soil coherence between operational and under-construction phases, influenced by seasonal changes in aerosol dynamics and sand dust levels. Higher soil coherence is linked with lower annual sand dust deposition and AOD measurements, emphasizing the importance of this data for informed decision-making. The study employs a unique combination of data sources and machine learning algorithms to predict soil coherence, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBOOST), Gaussian Process Regression (GPR), Random Forest (RF), and 1D Convolutional Neural Network (CNN), with the Random Forest model achieving the lowest root mean squared error (RMSE) of 0.0826. These contributions enhance our understanding and provide a valuable framework for infrastructure development in similar environments.

Evaluation of The Initial Growth of Umbuzeiro Rootstocks

Objective: The objective of this study is to investigate the growth of umbuzeiro rootstocks after the application of rock dust and biofertilizer. Theoretical Framewor: In this topic, the main concepts that underpin the research are presented. Techniques to improve the productivity of species native to the Brazilian Semiarid region stand out, providing a solid basis for understanding the context of the investigation. Method: Spondias tuberosa Arruda fruit seeds were germinated in a seedbed and 30 days after emergence, they were transplanted into polyethylene pots. The experiment was carried out in differentiated randomized blocks, with four levels of rock dust and the presence and absence of biofertilizer, distributed in 3 blocks, evaluating the variables of plant height (AP) and stem diameter (DC). Results and Discussion: The results revealed that for AP, treatments were significant for 30 and 60 days, while for DC they were significant for 30, 120 and 150 days. In the discussion section, these results are contextualized in light of the theoretical framework. Research Implications: Practical and theoretical implications are discussed, providing insights into how the results can be applied or practical influences in the field of plant physiology. These implications could encompass the use of rock dust and biofertilizer in agriculture. Originality/Value: This study contributes to the literature on a native fruit species with economic and environmental potential, which still has gaps in knowledge about its nutritional requirements. The relevance of this research is evidenced by the increase in productivity and local social and economic development.

Investigation of the Minimum Ignition Energy Required for Combustion of Coal Dust Blended with Fugitive Methane

Ventilation Air Methane (VAM) significantly contributes to global warming. Capturing and mitigating these emissions can help combat climate change. One effective method is the thermal decomposition of methane, but it requires careful control to prevent explosions from the high temperatures involved. This research investigates the influence of methane concentration and coal dust particle properties on the minimum ignition energy (MIE) required for fugitive methane thermal decomposition and flame propagation properties. This knowledge is crucial for the mining industry to effectively prevent and mitigate accidental fires and explosions in VAM abatement plants. Coal dust samples from three different sources were selected for this study. Experiments were conducted using a modified Hartmann glass tube and a Thermal Gravimetric Analyser (TGA). The chemical properties of coal dust were determined through ultimate and proximate analysis. The particle size distribution was determined using a Mastersizer 3000 apparatus (manufactured by Malvern Panalytical, Malvern, UK). The results showed that the MIE is significantly affected by coal dust particle size, with smaller particles (<74 µm) requiring less energy to ignite compared to coarser particles. Additionally, blending methane with coal dust further reduces the MIE. Introducing methane concentrations of 1% and 2.5% into the combustion space reduced the MIE by 25% and 74%, respectively, for the <74 µm coal dust size fraction. It was observed that coal dust concentration can either raise or lower the MIE. Larger coal dust concentrations, acting as a heat sink, reduce the likelihood of ignition and increase the MIE. This effect was noted at a methane concentration of 2.5% and coal dust levels above 3000 g/m3. In contrast, small amounts of coal dust had little impact on MIE variation. Moreover, the presence of methane during combustion increased the upward flame travel distance and propagation velocity. The flame’s vertical travel distance increased from 124 mm to 300 mm for a coal dust concentration of 300 g·m−3 blended with 1% and 2.5% methane, respectively.

Measurements of dust and respirable crystalline silica during indoor demolition and renovation.

Increased focus on renovating and maintaining the existing building stock is an integral part of the circular economy, however this might pose challenges to workers health. The aim of this study was to assess the renovation workers' exposure to inhalable dust, thoracic dust, respirable dust, and respirable crystalline silica (RCS). Personal aerosol samples were collected as full shift samples from 92 workers to a total of 407 samples. Fourteen locations around Oslo, Norway was visited for multiple days with repeated measurements of the same individual. Particulate matter from 3 aerosol fractions, respirable, thoracic, and inhalable, were analyzed gravimetrically, and the respirable fraction was analyzed for RCS by NIOSH 7500 method for X-ray diffraction (XRD)with low temperature plasma ashing sample preparation. The total measured concentrations of respirable dust (n = 192) had a geometric mean (GM) of 0.88mg/m3, RCS concentrations (n = 182) had a GM of 0.040mg/m3, thoracic dust (n = 131) had GM 2.4mg/m3, and inhalable dust (n = 84) had a GM of 8.5mg/m3. The maximum measured concentrations were 29mg/m3, 3.2mg/m3, 65mg/m3, and 163mg/m3, respectively. Workdays involving tasks such as mechanical demolition and clearing out demolished materials led to the highest exposure levels of both dust and RCS. However, other workers at the renovation sites were indirectly exposed to a considerable amount of RCS. This study revealed substantial exposure to both RCS and dust during renovation, and protective measures are warranted to reduce exposure levels in the industry.

Verification of On-Site Applicability of Rainwater Road Surface Spraying for Promoting Rainwater Utilization and Analyzing the Fine Dust Reduction Effect

This study conducted a pilot test and field demonstration to analyze the feasibility of using rainwater for road surface sprinkling and its effectiveness in reducing fine dust to create sustainable cities. The pilot test results verified the effectiveness of rainwater sprinkling on road surfaces in reducing fine dust. Subsequently, to ensure field applicability, a test bed with different pavement sections was established along Ojeong-ro in Bucheon, Gyeonggi-do, and a circulatory system for reusing the runoff rainwater was installed and tested for its acceptable dust reduction effectiveness. The results showed that the section with porous pavement exhibited a significant reduction in acceptable dust levels, and a trend of decreasing pollutant levels due to the initial washing effect of rainwater was observed. However, the water quality and quantity analysis revealed that additional purification processes are necessary to reuse the water for road surface sprinkling. Based on these findings, this study suggests that expanding porous pavement and introducing purification systems are essential for installing rainwater utilization facilities to reduce fine dust in urban areas. This research provides critical foundational data that can contribute to urban environmental improvement and establish a sustainable water circulation system.

Deep Learning-Based Dust Detection on Solar Panels: A Low-Cost Sustainable Solution for Increased Solar Power Generation

The world is shifting towards renewable energy sources due to the harmful effects of fossils fuel-based power generation in the form of global warming and climate change. When it comes to renewable energy sources, solar-based power generation remains on top of the list as a clean and carbon cutting alternative to the fossil fuels. Naturally, the sites chosen for installing solar parks to generate electricity are the ones that get maximum solar radiance throughout the year. Consequently, such sites offer challenges for the solar panels such as increased temperature, humidity and high dust levels that negatively affect their power generation capability. In this work, we are more concerned with the detection of dust from the images of the solar panels so that the cleaning process can be done in time to avoid power loses due to dust accumulation on the surface of solar panels. To this end, we utilize state-of-art deep learning-based image classification models and evaluate them on a publicly available dataset to identify the one that gives maximum classification accuracy for dusty solar panel detection. We utilize pre-trained models of 20 deep learning models to encode the images that are then used to train and validate four variants of a support vector machine. Among the 20 models, we get the maximum classification of 86.79% when the images are encoded with the pre-trained model of DenseNet169 and then use these encodings with a linear SVM for image classification.

‘New mining scheme, same old problems?’; risk factors and health challenges in underground community mining in Ghana

Abstract Ghana’s small-scale mining sector has long been a cornerstone of its economy and a vital source of livelihood for many communities. However, the sector is plagued by significant challenges, including environmental degradation, health and safety risks, and illegal practices. In response, the Ghanaian government introduced the Community Mining Scheme (CMS), a new mining model designed to promote local community participation in artisanal small-scale gold mining (ASGM). This study examines the workplace risk factors and associated health challenges faced by ASGM miners under the CMS, drawing on interviews with fifty underground CMS miners in Ghana. Our findings reveal that miners continue to face numerous health and safety challenges, including inadequate lighting, poor ventilation, exposure to noise and odors, unsupported or poorly supported loose materials, non-use of personal protective equipment, high dust levels, and unsafe blasting practices. Additionally, health concerns such as skin rashes, musculoskeletal injuries, and the risk of mercury poisoning persist. These challenges mirror those reported in the literature on unregulated and illegal ASGM in Ghana, indicating that existing issues remain unresolved under the CMS. To prevent the undermining of this initiative, it is crucial to adopt progressive practices, such as localized coping strategies and the recruitment of trained professionals to lead ASGM operations within the CMS. Enhancing miners’ risk perception through targeted outreach and awareness campaigns is also essential. Further recommendations for mitigating safety risks and health challenges are discussed in the article.

Spatiotemporal spreading characteristics of dust aerosols by coal mining machine cutting and "partition/multi-level composite field" atomization dust purification technology

To locally atomize the spatial confinement of primary dust generated by the shearer cut-off, the CFD-DPM mathematical model was used to simulate and analyze the wind-borne dust transport law, which resulted in the heavily polluted area of primary dust. A multi-level and multi-dimensional coal machine spray system was developed for the polluted area, its atomization effect was verified by using the TAB breakup model. The results show that the heavily polluted area of primary dust is mainly concentrated in the position of fully mechanized mining face Z=24–39 m area of shearer, and the average concentration is 738.2 mg/m³. The average concentration of droplets generated by the multi-level and multi-dimensional shearer spray system at the shearer drum is 0.66 kg/m³, with a percentage of droplets with particle size between 40 and 160 μm exceeding 85 %. The coverage rate of the shearer drum is 95 %. The relationship between droplet concentration and time is consistent with the Boltzmann function fitting model. Following the implementation of the multi-level and multi-dimensional coal machine spray system in the field, it was observed that the average reduction in dust levels was 90.9 % for total dust and 85.7 % for exhaled dust in the synthesized mining face.

From Glory to Decline: Uncovering Causes of Oak Decline in Iran

ABSTRACTOak decline is a major concern in Iran. A literature search with the terms ‘Iran’ AND ‘oak’ OR ‘Quercus’ from 1963 through 2023 resulted in 635 articles with over 200 dealing specifically with oak decline. In Iran, oak decline is most severe in the Zagros forests, followed by the Arasbaran and Hyrcanian forests. Of Iran's 11 native oak species, Quercus brantii is the most affected. Several factors have been identified as contributing to oak decline in Iran. Some economic and social factors include high rural unemployment, livestock overgrazing and uncontrolled cutting of live tree branches with the wood used for firewood and foliage used as food for farm animals. These pruning wounds can serve as entry points for pathogens and attract insects. Important abiotic factors included extended drought, increasing air temperature and wind speed, and increased levels of airborne dust and heavy metals. Important pathogens identified in the early 2000s included Armillaria mellea and charcoal rot fungus (Biscogniauxia mediterranea). More recently, many other fungal pathogens (e.g., Discula quercina, Neoscytalidium dimidiatum, Obolarina persica, Paecilomyces formosus and Phaeoacremonium tuscanicum) and bacterial pathogens (e.g., Bacillus pumilus, Brenneria goodwinii, Gibbsiella quercinecans, Rahnella victoriana and Stenotrophomonas maltophilia) were found in association with declining oaks. Among the insects, severe oak defoliation has resulted from various Lepidoptera in the families Erebidae, Geometridae and Tortricidae. Similarly, several bark‐ and wood‐boring beetles in the families Buprestidae and Cerambycidae were reported from declining oaks, with the buprestid Agrilus hastulifer being one of the most common. It is hoped that this review will improve our understanding of the many factors involved in oak decline in Iran and promote the development of management strategies.

Residential proximity to agricultural herbicide and fungicide applications and dust levels in homes of California children

BackgroundFew studies of the relationship between residential proximity to agricultural pesticide applications and pesticide levels in the home have incorporated crop location or wind direction. We evaluated the relationship between agricultural pesticide applications using the California Pesticide Use Reporting (CPUR) database and pesticide concentrations in carpet dust accounting for land use and wind direction. MethodsWe measured concentrations (ng/g) of seven herbicides and two fungicides in carpet dust samples from 578 California homes (2001–2007). We created three metrics by computing the density (kg/km2) of use of each pesticide reported in CPUR within 0.5-, 1-, 2-, and 4-km buffers around homes 180- and 365-days before sampling (CPUR metric). We apportioned applications to the crop area within the buffers (CROP-A metric) and weighted CPUR applications by the proportion of days that the home was within ±45° of the downwind direction (W-CPUR metric). We modeled natural-log concentrations (Tobit regression) and dust detections (logistic regression) adjusting for season/year, occupation, and home/garden use. ResultsDetections were >90 % for glyphosate, 2,4-D, and simazine. Detection rates and dust concentrations increased with increasing CPUR densities for all herbicides and one fungicide. Compared to homes without applications within 4 km, the highest tertile of 365-day glyphosate use was associated with ∼100 % higher concentrations (CPURT3>9.2kg/km2 %change = 110, 95 %CI = 55, 183; CROP-AT3>13.4kg/km2 %change = 144, 95 %CI = 81, 229; and W-CPURT3>2.1kg/km2 %change = 102, 95 %CI = 50, 171). The highest density tertiles of 2,4-D, simazine, and trifluralin were associated with 2- to 6-times higher concentrations, respectively; that was similar across metrics. Across all metrics, agricultural use of dacthal, dicamba, and iprodione were associated with 5- to 10-times higher odds of dust detections. Associations were unclear for 2-methyl-4-chlorophenoxyacetic acid and null for chlorothalonil. ConclusionsAgricultural herbicide and fungicide use was an important determinant of indoor contamination within 4 km of homes. Accounting for crops and wind direction did not substantially change these relationships.

Associations between bioaerosols, lung function work-shift changes and inflammatory markers: A study of recycling workers.

We investigated associations between bioaerosol exposures and work-shift changes in lung function and inflammatory markers among recycling workers. Inhalable dust was measured with personal samplers and analyzed for endotoxin, bacteria, and fungi (incubated at 25 °C and 37 °C) levels. Lung function (FEV1, FVC) was measured before and after work-shifts and serum concentrations of inflammatory markers (CRP, SAA, CC16, IL1B, IL2, IL4, IL5, IL6, IL8, IL10, IL13, and TNF) after the shift. Associations were explored by linear mixed-effects models. We included 170 measurements from 88 production workers exposed to inhalable dust, endotoxin, bacteria, and fungi (25 °C and 37 °C) at geometric mean levels of 0.6 mg/m3, 10.7 EU/m3, 1.6×104 CFU/m3, 4.4×104 CFU/m3, and 103 CFU/m3, respectively, and 14 administrative workers exposed at 7-fold lower levels. No associations were observed between bioaerosol exposures and work-shift change in lung function. IL2, IL6, IL10, and TNF concentrations were positively associated with inhalable dust levels, SAA and IL6 with bacteria, CRP, SAA, IL8, and TNF with fungi (25 °C or 37 °C), with the latter being the only statistically significant finding (exp(β) 1.40, 95% confidence interval 1.01-1.96). This study of recycling workers exposed to bioaerosol levels generally below those of farmers and compost workers and above background levels did not indicate any acute effect on lung function. Several inflammatory markers tended to increase with exposure, suggesting a systemic effect. Future research should combine data from bioaerosol-exposed workers to uncover health risks that may form the basis for health-based occupational exposure limits.

The level, source, and health outcome of PM2.5 exposure in Southwest Iran

IntroductionDusty storms considerably increase airborne particles in dry and semi-dry locations, such as deserts with no plants and strong winds. Therefore, the environment and people are affected severely. Ahvaz, an important metropolis, is often polluted by neighboring nations. The present research studies the concentration, source, and calculation of these particles' effects. Material and methodFor health consequences evaluation, the WHO suggests the Air Quality Health Impact Evaluation Programmed (Air Q 2.2.3). Khuzestan Meteorology Office recorded particulate matter measurements on both hazy and clear days. The data was gathered voluminously in 2023. ResultAccording to data collected from Khuzestan province's meteorology documents, 49 days in 2023 had very unsafe air quality. The most polluted months in terms of the number of dust days are as follows: January (14 days) > December (12 days) > November (11 days) > August (5 days) > May (3 days) > September (2 days) > March, February, June, July, October (1 day) > April (0 days). HYSPLIT maps indicate that Iran causes dust in March, the Great Arabian Desert in December and August, Iraq in April, September, and October, Kuwait in January, Turkey in February and July, Egypt in June and May, and Oman in November. ConclusionThe meteorology database reveals that Ahvaz is highly polluted and that 49 days had unacceptable dust levels. Based on assessments obtained employing the Air Q+ programs, the people of Ahvaz encountered heart disease, respiratory disease, and stroke caused by their exposure to PM2.5 particulates.

Analyzing Trends in Saharan Dust Concentration and Its Relation to Sargassum Blooms in the Eastern Caribbean

This study investigates the temporal trends and correlations between Saharan dust mass concentration densities (DMCD) and Sargassum concentrations (SCT) in the tropical North Atlantic. Average DMCD data for June, July, and August from 1980 to 2022, alongside SCT data for the same months from 2012 to 2022, were analyzed using Mann–Kendall tests for trends and lagged regression models to assess whether higher Saharan dust levels correlate with Sargassum outbreaks in the region. A comprehensive analysis reveals a significant upward trend in Saharan dust quantities over the study period, with the summer months of June, July, and August exhibiting consistent increases. Notably, 2018 and 2020 recorded the highest mean DMCD levels, with June showing the most significant increasing trend, peaking in 2019. These findings are consistent with previous studies indicating a continuous elevation in Saharan dust concentrations in the tropical atmosphere of the North Atlantic. Simultaneously, Sargassum concentrations also show a notable increasing trend, particularly in 2018, which experienced both peak SCT and elevated DMCD levels. Mann–Kendall tests confirm statistically significant upward trends in both Saharan dust and Sargassum concentrations. Simple linear regression and lagged regression analyses reveal positive correlations between DMCD and SCT, highlighting a temporal component with stronger associations observed in July and the overall June–July–August (JJA) period. These results underscore the potential contribution of elevated Saharan dust concentrations to the recent surge in Sargassum outbreaks in the tropical North Atlantic. Furthermore, the results from forward stepwise regression (FSR) models indicate that DMCD and chlorophyll (CHLO) are the most critical predictors of SCT for the summer months, while sea surface temperature (SST) was not a significant predictor. These findings emphasize the importance of monitoring Saharan dust and chlorophyll trends in the Eastern Caribbean, as both factors are essential for improving Sargassum modeling and prediction in the region. This study provides valuable insights into the climatic factors influencing marine ecosystems and highlights the need for integrated environmental monitoring to manage the impacts on coastal economies.

Occupational Exposure to Silica Dust and Silicosis Risk in Chinese Noncoal Mines: Qualitative and Quantitative Risk Assessment.

Despite increasing awareness, silica dust-induced silicosis still contributes to the huge disease burden in China. Worryingly, recent silica dust exposure levels and silicosis risk in Chinese noncoal mines remain unclear. We aimed to determine recent silica dust exposure levels and assess the risk of silicosis in Chinese noncoal mines. Between May and December 2020, we conducted a retrospective cohort study on 3 noncoal mines and 1 public hospital to establish, using multivariable Cox regression analyses, prediction formulas of the silicosis cumulative hazard ratio (H) and incidence (I) and a cross-sectional study on 155 noncoal mines in 10 Chinese provinces to determine the prevalence of silica dust exposure (PDE), free silica content, and total dust and respirable dust concentrations. The qualitative risk of silicosis was assessed using the International Mining and Metals Commission's risk-rating table and the occupational hazard risk index; the quantitative risk was assessed using prediction formulas. Kaplan-Meier survival analysis revealed significant differences in the silicosis probability between silica dust-exposed male and female miners (log-rank test χ21=7.52, P=.01). A total of 126 noncoal mines, with 29,835 miners and 4623 dust samples, were included; 13,037 (43.7%) miners were exposed to silica dust, of which 12,952 (99.3%) were male. The median PDE, free silica content, total dust concentration, and respirable dust concentration were 61.6%, 27.6%, 1.30 mg/m3, and 0.58 mg/m3, respectively, indicating that miners in nonmetal, nonferrous metal, small, and open-pit mines suffer high-level exposure to silica dust. Comprehensive qualitative risk assessment showed noncoal miners had a medium risk of silicosis, and the risks caused by total silica dust and respirable silica dust exposure were high and medium, respectively. When predicting H and I over the next 10, 20, and 30 years, we assumed that the miner gender was male. Under exposure to current total silica dust concentrations, median I10, I20, and I30 would be 6.8%, 25.1%, and 49.9%, respectively. Under exposure to current respirable silica dust concentrations, median I10, I20, and I30 would be 6.8%, 27.7%, and 57.4%, respectively. These findings showed that miners in nonmetal, nonferrous metal, small, and open-pit mines have a higher I and higher qualitative silicosis risk. Chinese noncoal miners, especially those in nonmetal, nonferrous metal, small, and open-pit mines, still suffer high-level exposure to silica dust and a medium-level risk of silicosis. Data of both total silica dust and respirable silica dust are vital for occupational health risk assessment in order to devise effective control measures to reduce noncoal mine silica dust levels, improve miners' working environment, and reduce the risk of silicosis.

Heavy metal levels of outdoor dust from the Eastern Mediterranean Sea region and assessment of the ecological and health risk.

As a result of some chemical element (heavy metals) pollution of dust, environmental pollution of dust has become an increasing concern, necessitating an assessment of risks to both ecology and human health, particularly in urban areas. Most of these pollutants settle on the outdoors and eventually become part of the outdoor dust. These will have negative long-term repercussions on ecosystems and human health. In this research, energy dispersive X-ray fluorescence (EDXRF) spectrometry analytical method was used to assess the pollution characteristics of the eight heavy metals (HMs): Mn, Cu, As, Hg, Ni, Cr, Zn, and Pb in the East Mediterranean Sea area. The concentration of As, Mn, Cr, Cu, Hg, Ni, Pb, and Zn analyzed in outdoor dust samples varied from 0.94 to 19.52 mg kg-1, 190.08 to 1019.7 mg kg-1, 20.46 to 45.9 mg kg-1, 19.5 to 62.56 mg kg-1, 0.01 to 0.93 mg kg-1, 10.48 to 40.64 mg kg-1, 12. 6 to 36.1 mg kg-1, and 48.96 to 112.41mg kg-1, respectively. HMs have been detected in the outdoor dust samples analyzed in the study, and, as a result, mean concentrations followed the order Mn > Zn > Cu > Cr > Ni > Pb > As > Hg, respectively. The ecological risk was observed at various contamination levels, with As and Hg pollution being the most severe. The highest hazard quotient (HQ) for adults and children was determined as a result of As and Cr, respectively. According to the US-EPA health risk threshold, the cancer risk in the study area is negligible.

Quantifying the Contribution of Multiple Processes to the Dust Decreasing Trend in the Guliya Ice Core Over the Past 50 Years

AbstractDust records extracted from ice cores can facilitate the reconstruction of historical atmospheric dust levels and climate change. However, interpreting dust variations in ice cores is intricate because of the compounded influence of emission, transport, and deposition processes. This study investigated dust records retrieved from the Guliya ice cap drilled in 2015 on the West Tibetan Plateau using a mean trajectory transport and deposition model. Results showed that the Guliya dust concentration has exhibited a declining trend since the 1960s (−751 μg kg−1 yr−1). Applying an attribution approach, we discovered that low dust emission (80.3%) was the main cause of the drop in dust concentration, with changes related to transportation (5.2%) and deposition (14.5%) making only minor contributions. The weakening of surface wind speed in the desert and increasing precipitation in both the desert and glacier were the primary factors driving the decrease in Guliya dust concentration.

Polybrominated diphenyl ethers in paired dust-breast milk samples: Levels, predictors of contamination, and health risk assessment for infants and mothers

An integrated study on the levels of 7 polybrominated diphenyl ethers (PBDEs) in house dust and breast milk samples from women (N = 30) living in these households was conducted. ∑PBDEs ranged from <LOD to 523 ng g−1 dust (median 4.9 ng g−1 dust), and from 0.16 to 6.3 ng g−1 lipid weight (lw) (median 0.49 ng g−1 lw), in house dust and breast milk samples, respectively. Statistically significant correlations were observed between the mass fractions of BDE-153 from breast milk, and BDE-47, BDE-99 and BDE-100 from house dust, congeners that all originate from the “penta” formulation, indicating its dominance as PBDE exposure source in the examined household dusts. The number of electronic and electrical devices, and age of the building were positive, and the number of upholstered furniture and household area were negative predictors of PBDE dust levels. Mothers age was negative predictor of the PBDE levels in breast milk, possibly due to dilution of PBDEs in total body fat accumulated over the years. The results of health risk assessment suggest that detected PBDE levels don’t pose non-carcinogenic risk to the study population.

Combined impact of heat and dust on diabetes hospitalization in Kuwait

IntroductionIn Kuwait, a severe diabetes and obesity epidemic coexists with intense dust storms and harsh summer heat. While, theoretically, this interplay between dust, heat, and diabetes presents a serious public...

Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces

Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces.