Search Results (54)

The presence of heavy metals plays a significant role in indoor air quality, which poses a serious public health problem since most of the population spends over 90% of their time in indoor environments. This work investigates heavy metals in indoor dust across different occupational settings in Macao. Field sampling was conducted in five representative locations, which included restaurants, student dormitories, auto repair shops, offices, and parking security rooms, with a total of 11 samples collected in this study. Dust in the form of particulate matter was collected from air conditioning filters to quantify 14 heavy metal contents. The PMF model was applied for source apportionments of the heavy metals, while a health exposure model was used to assess health risks and evaluate the non-carcinogenic and carcinogenic risks in the five representative workplaces. The PMF model identified six major pollution sources: traffic emissions (23.800%), building materials (21.600%), cooking activities (18.500%), chemicals (15.200%), electronic devices (12.300%), and outdoor seaport activities (8.600%). The health risk assessment showed that the overall non-carcinogenic risk (HI = 6.160 × 10⁻⁶ for inhalation, 1.720 × 10⁻³ for oral ingestion, and 2.270 × 10⁻⁵ for dermal contact) and total HI (1.749 × 10⁻³) and carcinogenic risk (6.570 × 10⁻⁹) were below the safety threshold, showing minimal health risk problems. Nevertheless, nickel and chromium were identified as the main contributors to potential long-term risks. Full article

We present an example of how a doctoral network can bring together multidisciplinary expertise and novel scientific advances in atmospheric dust. This network (Dust-DN) has started operations and is a strategic alliance of high-profile partners, able to leverage unique facilities for atmospheric research and innovative space missions. The network aims to improve our understandings of dust processes and microphysics, identify the signature of source regions, address the socio-economic impacts of dust transport and improve the quantification of the role of dust in the climate system. The first results have already been achieved and are shown here, and many more are expected to follow. Full article

Aerosols from natural and anthropogenic sources exert significant yet highly variable influences on the Earth’s radiative balance characterized by pronounced spatial and temporal heterogeneity. Accurate quantification of these effects is crucial for enhancing climate projections and informing effective mitigation strategies. In this study, we evaluated the performance of three widely used aerosol optical depth (AOD) datasets—MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2), MODIS Aqua, and MODIS Terra—by comparing them against ground-based AERONET observations from ten stations located within the dust belt region. Statistical assessments included coefficient of determination (R²), correlation coefficient (R), Index of Agreement (IOA), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Relative Mean Bias (RMB), and standard deviation (SD). The results indicate that MERRA-2 showed the highest agreement (R = 0.76), followed by MODIS Aqua (R = 0.75) and MODIS Terra (R = 0.73). Seasonal and annual AOD climatology maps revealed comparable spatial patterns across datasets, although MODIS Terra consistently reported slightly higher AOD values. These findings provide a robust assessment and reanalysis of satellite AOD products over arid regions, offering critical guidance for aerosol modeling, data assimilation, and climate impact studies. Full article

Accurate identification of bare soil exposure and quantification of associated dust emissions are essential for understanding land degradation and air quality risks in intensively farmed regions. This study develops a monthly monitoring and modeling framework to quantify bare soil dynamics and wind erosion-induced particulate matter (PM) emissions across Shandong Province from 2017 to 2024. By integrating Sentinel-1/2 imagery, climate reanalysis, terrain and soil data, and employing a stacking ensemble classification model, we mapped bare soil areas at 10 m resolution with an overall accuracy of 93.1%. The results show distinct seasonal variation, with bare soil area peaking in winter and early spring, exceeding 25,000 km² or 15% of the total area, which is far above the 6.4% estimated by land cover products. Simulations using the CLM5.0 dust module indicate that annual PM₁₀ emissions from bare soil averaged (2.72 ± 1.09) × 10⁵ tons across 2017–2024. Emissions were highest in March and lowest in summer months, with over 80% of the total emitted during winter and spring. A notable increase in emissions was observed after 2022, likely due to more frequent extreme wind events. Spatially, emissions were concentrated in coastal lowlands such as the Yellow River Delta and surrounding saline–alkali lands. Our approach explicitly advances traditional methods by generating monthly 10 m bare soil maps and linking satellite-derived dynamics with process-based dust emission modeling, providing a robust basis for targeted dust control and land management strategies. Full article

Atmospheric aerosols impact environmental quality and health, requiring accurate quantification. This study employed the PMeye scanning LiDAR, a UV system operating at 355 nm by Raymetrics S.A. for continuous, high-resolution monitoring in two campaigns: May 2024 (Vasilikos Power Station, Cyprus) and June 2024 (Port of Piraeus, Greece). Measurement days with dust presence were selected via AERONET-based aerosol classification and validated using a SKIRON model. A novel horizontal scanning method at 355 nm distinguished dust from anthropogenic emissions. Results showed higher pollution in Cyprus (~500 μg/m³) due to dust and chimney emissions, versus ~150 μg/m³ in Piraeus from dust and ship exhausts. Full article

Amino acids (AAs), a type of nitrogen-based organic compounds in the atmosphere, are directly and indirectly related to climate change, and as their link to allergic diseases becomes more known, the need for quantitative analysis of ultrafine dust (PM_2.5) will become increasingly necessary. When sensing water-soluble AAs using a gas chromatograph combined with a tandem mass spectrometer (GC-MS/MS), derivatization should be considered to increase the volatility and sensitivity of target analytes. In this study, two methods were used to compare and evaluate 13 AA derivatives in PM_2.5 samples: N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide with 1% tert-butyldimethylchlorosilane (MTBSTFA w/1% t-BDMCS), which is preferred for silylation, and ethyl chloroformate (ECF) with methanol (MeOH) for chloroformate derivatization. The most appropriate reaction conditions for these two derivative methods, such as temperature and time, and the analytical conditions of GC-MS/MS for the qualitative and quantitative analysis of AAs were optimized. Furthermore, the calibration curve, detection limit, and recovery of both methods for validating the quantification were determined. The two derivative methods were applied to 23 actual PM_2.5 samples to detect and quantify target AAs. The statistical significances between pairwise measurements of individual AAs detected by both methods were evaluated. This study will help in selecting and utilizing appropriate derivative methods for the quantification of individual AAs in PM_2.5 samples. Full article

This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). Despite ICESat-2’s design primarily as an altimetry mission with a single-wavelength, low-power, high-repetition-rate laser, ICESat-2 effectively captures global aerosol distribution patterns and can provide valuable insights to bridge the observational gap between the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) missions to support future spaceborne lidar mission design. The machine learning approach outperforms traditional thresholding methods, particularly in complex conditions of cloud embedded in aerosol, owing to a finer spatiotemporal resolution. Our results show that annually, between 60°S and 60°N, 78.4%, 17.0%, and 4.5% of aerosols are located within the 0–2 km, 2–4 km, and 4–6 km altitude ranges, respectively. Regional analyses cover the Arabian Sea (ARS), Arabian Peninsula (ARP), South Asia (SAS), East Asia (EAS), Southeast Asia (SEA), the Americas, and tropical oceans. Vertical aerosol structures reveal strong trans-Atlantic dust transport from the Sahara in summer and biomass burning smoke transport from the Savanna during dry seasons. Marine aerosol belts are most prominent in the tropics, contrasting with earlier reports of the Southern Ocean maxima. This work highlights the importance of vertical aerosol distributions needed for more accurate quantification of the aerosol–cloud interaction influence on radiative forcing for improving global climate models. Full article

Occupational health monitoring in demolition environments requires precise detection of blast-dust-induced pulmonary pathologies. However, it is often hindered by challenges such as contaminated imaging biomarkers, limited access to medical resources in mining areas, and opaque AI-based diagnostic models. This study presents a novel computational framework that combines industrial-grade robustness with clinical interpretability for the diagnosis of pulmonary nodules. We propose a hybrid framework that integrates morphological purification techniques (multi-step filling and convex hull operations) with multi-dimensional features fusion (radiomics + lightweight deep features). To enhance computational efficiency and interpretability, we design a soft voting ensemble classifier, eliminating the need for complex deep learning architectures. On the LIDC-IDRI dataset, our model achieved an AUC of 0.99 and an accuracy of 0.97 using standard clinical-grade hardware, outperforming state-of-the-art (SOTA) methods while requiring fewer computational resources. Ablation studies, feature weight maps, and normalized mutual information heatmaps confirm the robustness and interpretability of the model, while uncertainty quantification metrics such as the Brier score and Expected Calibration Error (ECE) better validate the model’s clinical applicability and prediction stability. This approach effectively achieves resource-accuracy co-optimization, maintaining low computational costs, and is highly suitable for resource-constrained clinical environments. The modular design of our framework also facilitates extensions to other medical imaging domains without the need for high-end infrastructure. Full article

Environmental DNA from bulk materials can be analyzed to gain an understanding of the bacterial, fungal, plant, and/or arthropod communities present. DNA metabarcoding is widely used to characterize these biological communities, by amplifying “barcode” regions and sequencing these amplicons via next-generation sequencing. The Earth Microbiome Project (EMP) adopted the use of indexed primers, PCR primers containing Illumina^® adapter sequences and a unique 12-nucleotide Golay barcode to simplify the identification of bacterial taxa via the 16S barcode. We sought to develop a wet laboratory workflow utilizing indexed primers that could cost-effectively reduce bench time while simultaneously targeting multiple DNA barcode regions to characterize bacterial (16S), fungal (ITS1), plant (ITS2, trnL p6 loop), and arthropod (COI) communities. The EMP primer constructs for 16S were modified to accommodate our DNA barcode regions of interest while also permitting successful demultiplexing following sequencing. A single indexed primer pair was designed for ITS1 and trnL p6 loop, and two primer pairs were developed for ITS2 and COI. To test the workflow, a total of 648 soil and 336 dust samples were processed, with key steps including DNA isolation, total DNA quantification, amplification with indexed primers, library purification and quantification, and Illumina MiSeq sequencing. Based on raw read counts and analysis of positive controls, the trnL p6 loop and ITS2 a primer pairs performed comparably to the originally designed 16S primers. Both COI primers pairs, ITS1 and ITS2 b primers, had lower raw reads compared to the other three primer pairs. The combination of the three plant targets successfully recovered all plant taxa in the positive controls except for Nephrolepis exaltata [Nephrolepidaceae] and the COI primers recovered all arthropod taxa except for the beetle. Notably, none of the taxa in the fungal positive control were recovered using ITS1. For environmental samples, sequencing was successful for all primers except COI c, and primer biases were observed for all three plant primers, in which a small number of families were uniquely amplified for each primer pair. This workflow can be applied to many disciplines that utilize DNA metabarcoding given its customizability and flexibility with Illumina sequencing chemistry. Full article

High-intensity coal mining significantly impacts the surrounding soil moisture (SM) through water seepage, artificial watering for dust suppression, and geomorphological changes, which will lead to ecological degradation. This study explores the impact of open-pit mines on surface SM in an arid–semiarid open-pit mine area of China over the period from 2000 to 2021. Using the temperature vegetation dryness index (TVDI), derived from the Land Surface Temperature–Normalized Difference Vegetation Index (LST-NDVI) feature space, this paper proposes a method—the TVDI of climate factor separation (TVDI-CFS)—to disentangle the influence of climate factors. The approach employs the Geographically and Temporally Weighted Regression (GTWR) model to isolate the influence of temperature and precipitation, allowing for a precise quantification of mining-induced disturbances. Additional techniques, such as buffer analysis and the Dynamic Time Warping (DTW) algorithm, are used to examine spatiotemporal variations and identify disturbance years. The results indicate that mining impacts on surface SM vary spatially, with disturbance distances of 420–660 m and strong distance decay patterns. Mining expansion has increased disturbance ranges and intensified cumulative effects. Inter-annual TVDI trends from 2015 to 2021 reveal clustered disturbances in alignment with mining directions, with the largest affected area in 2016. These findings provide a systematic valuable insights for ecological restoration and sustainable environmental management in mining-affected areas. Full article

Aligned with the EU Sustainable Development Goals 2030 (EU SDG2030), extensive research is dedicated to enhancing the sustainable use of biomass waste for the extraction of pharmaceutical and nutritional compounds, such as (poly-)phenolic compounds (PC). This study proposes an innovative one-step hydrothermal extraction (HTE) at a high temperature (120 °C), utilizing environmentally friendly acidic natural deep eutectic solvents (NADESs) to replace conventional harmful pre-treatment chemicals and organic solvents. Brewer’s spent grain (BSG) and novel malt dust (MD) biomass sources, both obtained from beer production, were characterized and studied for their potential as PC sources. HTE, paired with mild acidic malic acid/choline chloride (MA) NADES, was compared against conventional (heated and stirred maceration) and modern (microwave-assisted extraction; MAE) state-of-the-art extraction methods. The quantification of key PC in BSG and MD using liquid chromatography (HPLC) indicated that the combination of elevated temperatures and acidic NADES could provide significant improvements in PC extraction yields ranging from 251% (MD-MAC-MA: 29.3 µg/g; MD-HTE-MA: 103 µg/g) to 381% (BSG-MAC-MA: 78 µg/g; BSG-HTE-MA: 375 µg/g). The superior extraction capacity of MA NADES over non-acidic NADES (glycerol/choline chloride) and a traditional organic solvent mixture (acetone/H₂O) could be attributed to in situ acid-catalysed pre-treatment facilitating the release of bound PC from lignin–hemicellulose structures. Qualitative ¹³C-NMR and pyro-GC-MS analysis was used to verify lignin–hemicellulose breakdown during extraction and the impact of high-temperature MA NADES extraction on the lignin–hemicellulose structure. This in situ acid NADES-catalysed high-temperature pre-treatment during PC extraction offers a potential green pre-treatment for use in cascade valorisation strategies (e.g., lignin valorisation), enabling more intensive usage of available biomass waste stream resources. Full article

Multi-source ferrous metallurgical dust and sludge are significant components of iron-containing solid waste in the iron and steel industry. It is crucial for the sustainable operation of steel enterprises to recycle iron from ferrous metallurgical dust and sludge (FMDS) for use in steel smelting. However, besides Fe, FMDS also contains valuable elements such as Zn, Pb, K, and Na, among others. While these valuable elements hold high recovery value, they impede the direct reuse of FMDS by iron and steel enterprises. This paper introduces the compositional characteristics of multi-source ferrous metallurgical dust and sludge, analyzes the main recycling technologies associated with FMDS at the present stage of development, and discusses the characteristics of different technologies. In view of this, a new idea of the “cooperative treatment of multi-source ferrous metallurgical dust and sludge—full quantitative recovery of valuable elements” is put forward. This new idea integrates a variety of treatment processes to directly recycle FMDS within the steel plant, enhancing the adequacy of dust and sludge recovery and reducing the risk of environmental pollution. This paper provides a reference for achieving the full quantification and utilization of high-value-added FMDS in steel plants. Full article

Traditional spectral energy distribution (SED) fitting techniques face uncertainties due to assumptions in star formation histories and dust attenuation curves. We propose an advanced machine learning-based approach that enhances flexibility and uncertainty quantification in SED fitting. Unlike the fixed NGBoost model used in mirkwood, our approach allows for any scikit-learn-compatible model, including deterministic models. We incorporate conformalized quantile regression to convert point predictions into error bars, enhancing interpretability and reliability. Using CatBoost as the base predictor, we compare results with and without conformal prediction, demonstrating improved performance using metrics such as coverage and interval width. Our method offers a more versatile and accurate tool for deriving galaxy physical properties from observational data. Full article

Road dust is a mixture of fine and coarse particles released into the air due to an external force, such as tire–ground friction or wind, which is harmful to human health when inhaled. Continuous dust emission from the road surfaces is detrimental to the road itself and the road users. Due to this, multiple dust monitoring and control techniques are currently adopted in the world. The current dust monitoring methods require expensive equipment and expertise. This study introduces a novel pragmatic and robust approach to quantifying traffic-induced road dust using a deep learning method called semantic segmentation. Based on the authors’ previous works, the best-performing semantic segmentation machine learning models were selected and used to identify dust in an image pixel-wise. The total number of dust pixels was then correlated with real-world dust measurements obtained from a research-grade dust monitor. Our method shows that semantic segmentation can be adopted to quantify traffic-induced dust reasonably. Over 90% of the predictions from both correlations fall in true positive quadrant, indicating that when dust concentrations are below the threshold, the segmentation can accurately predict them. The results were validated and extended for real-time application. Our code implementation is publicly available. Full article

Perfluorooctanoic acid (PFOA) in environmental matrices is increasingly being studied due to its environmental persistence, global occurrence, bioaccumulation, and associated human health risks. Some indoor environments can significantly impact the health of occupants due to pollutants in indoor air and household dust. To investigate the potential exposure of individuals to PFOA in specific confined environments, this study reports an analytical method and results concerning the determination of PFOA in household dust, used as a passive sampler. To the best of our knowledge, this paper represents one of the first studies concerning PFOA concentrations in indoor dust collected in the south of Italy, within the European region. A total of twenty-three dust samples were collected from two different areas of Sicily (Palermo and Milena), extracted, and analyzed by an UHPLC-QTOF-MS/MS system. Finally, PFOA exposure was estimated using a new index (Indoor PFOA Exposure Index, IPEX) that incorporates the PFOA levels in dust, exposure time, and the correlation between the PFOA in dust and blood. It was then compared across four different exposure groups, revealing that PFOA exposure for people working in chemistry laboratories was evaluated to be ten times higher than the exposure for homemakers. Full article