Coarse Samples Research Articles

Scale and suction effects on compressibility and time-dependent deformation of mine waste rock material

Designing high mine waste rock piles for long-term behavior requires material mechanical characterization over a large range of stresses and variable environmental conditions. However, representative coarse samples cannot be handled by standard testing devices and the common approach is to test small-scaled samples at the laboratory, which might be affected by particle size effects when compared to the field material. Several reported results indicate that coarser samples present higher amount of particle crushing than small-scaled samples, thus lower dilatancy and higher compressibility. However, specific studies of size effects on time-dependent deformation are lacking. The aim of this paper is to identify the effects of particle size and suction on stress-deformation mechanism of partially saturated mine waste rock. Oedometric compression tests on two parallel graded samples are presented: the gravelly fraction (dmax=50 mm) and the sandy fraction (dmax=2.36 mm). Each stress increment triggers « instantaneous » and delayed strains. The results reveal the combined effects of particle size and humidity on the mechanical behavior. Coarser samples exhibit higher total compressibility and creep deformation, which also increases with the material humidity. The results give empirical support for the development of scaling laws and suggest that total deformation can be decoupled considering a suction dependent index for creep deformation.

Pasta Drying Defects as a Novel Ingredient for Hard Dough Biscuits: Effect of Drying Temperature and Granulation on Its Functionality.

Various drying temperatures impact the texture of pasta and cause different drying defects. These by-products could reflect techno-functional characteristics which are suitable for cereal products. This research addresses the influence of low (LT) and high (HT) drying pasta defects with two granulations on the theoretical and functional characteristics of hard dough biscuits. By shifting from a LT to HT drying temperature, a higher onset and peak temperature was found due to the higher mobility of starch molecules with increasing crystalline stability. The lowest transition enthalpy of biscuit formulation was also observed for higher incorporation of fine HT pasta regrinds. The algebraic model of dough with consistography determined the poor-extensible gluten and a high resistance with a greater value of P/L and P indices for LT regrinds. Scanning electron microscopy revealed a heavy and dense texture with immersed starch granules for additional fine regrinds while coarse samples caused swell granules with greater diameter. Moreover, fine HT regrinds reflected the lowest L* value for biscuit due to heat gradient tension with the hard milling process which leads to protein denaturation with decreasing nitrogenous.

DiffBEV: Conditional Diffusion Model for Bird’s Eye View Perception

BEV perception is of great importance in the field of autonomous driving, serving as the cornerstone of planning, controlling, and motion prediction. The quality of the BEV feature highly affects the performance of BEV perception. However, taking the noises in camera parameters and LiDAR scans into consideration, we usually obtain BEV representation with harmful noises. Diffusion models naturally have the ability to denoise noisy samples to the ideal data, which motivates us to utilize the diffusion model to get a better BEV representation. In this work, we propose an end-to-end framework, named DiffBEV, to exploit the potential of diffusion model to generate a more comprehensive BEV representation. To the best of our knowledge, we are the first to apply diffusion model to BEV perception. In practice, we design three types of conditions to guide the training of the diffusion model which denoises the coarse samples and refines the semantic feature in a progressive way. What's more, a cross-attention module is leveraged to fuse the context of BEV feature and the semantic content of conditional diffusion model. DiffBEV achieves a 25.9% mIoU on the nuScenes dataset, which is 6.2% higher than the best-performing existing approach. Quantitative and qualitative results on multiple benchmarks demonstrate the effectiveness of DiffBEV in BEV semantic segmentation and 3D object detection tasks.

Kinetics of Estonian Phosphate Rock Dissolution in Hydrochloric Acid

The kinetics of the dissolution of Estonian phosphate rock and the governing reaction mechanisms in hydrochloric acid in technological processes were investigated. The influences of particle size and acid concentrations of 0.5–1.5 M on the reaction rate and the pH variation during the process were studied at a dosage of 2.1 moles of HCl per mole of calcium for 60 min. The results indicated that the solubility of phosphorus reached 94%–100% for the fine samples and 82%–99% for the coarse samples. The time required to achieve an apparent steady-state pH reduced with the increasing acid concentrations and decreasing particle sizes. It was determined that the CaF2 precipitation in solutions starting at 1 M was faster at higher concentrations. The SEM surface analysis of the insoluble particles proved the existence of etch pit formation. The XPS and EDX analyses affirmed that the dissolution was incongruent. The surface composition of the unreacted particles gave a stoichiometry of CaF1.8, showing the formation of CaF2 on the surface. The dissolution kinetics were analyzed using the shrinking core model and showed a combination of chemical reaction, diffusion or interfacial transfer, and diffusion, sequentially for coarse particles or simultaneously for fine fractions.

Stability of calcined clays during storage

AbstractUsage of calcined clay as a cement main constituent is spotlighted as an important part of the decarbonisation of the cement industry. In particular, little knowledge is available on the storage stability of calcined clays. Therefore, the influences of fineness, temperature and humidity for different timescales of storing were determined. Two samples of a calcined kaolinitic‐illitic clay of different fineness (crushed and ground) were stored sealed in a desiccator, as well as at 20 °C and 60 % rel. humidity, and at 40 °C and 100 % rel. humidity. The clay's mineralogical composition, moisture and chemically bound water content were analysed at 7, 28, and 91 days of storage respectively. Additionally, their reactivity was determined acc. ASTM C 1897‐20 and acc. EN197‐1.The calcined clays which were stored in a desiccator showed only slight alteration over the period of 91 days. Samples stored at 20 °C and 60 % rel. humidity, contained small amounts of moisture and chemically bound water, regardless of their fineness. Storage at 40 °C and 100 % rel. humidity led to a huge increase in moisture and chemically bound water, especially at high fineness. The reactive silicon of the fine samples was diminished independent of storage conditions over time, whereas the coarse samples showed no changes. Samples stored at 40/100 showed a strong decrease in their heat of hydration over 91 days.This study implicates, that the analysed calcined clay could be stored as coarsegrained fraction at normal conditions.

Microstructure evolution of porous calcium hexaluminate compacts during first heating

Porous calcium hexaluminate (CaAl12O19 or CA6) structures show long-term stability at high temperatures, high refractoriness, low thermal conductivity, and excellent chemical resistance, thus being Taylor-made for thermal insulation. Previous works attained porous structures from in situ formation of CA6 combining CaO and Al2O3 sources in solid-state reactive sintering. Although the approach is time-and-energy-saving regarding the production of large parts of complex shapes, it faces considerable difficulties related to the expansive formation of calcium aluminates. To overcome such drawbacks, pre-formed porous CA6 aggregates improve the system's dimensional stability before sintering. Despite the straightforward processing, few studies have investigated such materials systemically. This article addresses the combination of pre-formed porous CA6 aggregates and organic and inorganic binders for the production of porous structures by two shaping processes, namely, uniaxial pressing and direct casting of aqueous suspensions. After drying, the samples' microstructure and physical properties evolution were investigated up to sintering (1100-1500 °C) through total porosity, Young's modulus, compression strength, pore diameter, and thermal conductivity measurements, dilatometric analyses, and scanning electron microscopy. Reference samples of coarse calcined alumina were tested under the same conditions to highlight the impacts of CA6 particles' morphology. Compared to them, the CA6-containing samples showed almost no variation in total porosity and average pore size levels during thermal treatments, although their strength and rigidity increased significantly after sintering. Their microstructure remained practically unchanged after drying, showing clusters of large asymmetrical CA6 crystals bonded to each other by their edges, and surrounding a large fraction of 1.8–2.2 μm pores. According to the results, although water content, processing method, and compacting levels are important parameters, particles' microstructure, ratio of intra-particle pores, and asymmetrical shape are key variables for the development of physical properties. Such characteristics strongly contributed to their densification resistance and lower thermal conductivity after exposure to high temperatures.

Two methods for thermal conduction analysis of bi-porous sintered structures

Loop heat pipes (LHPs) have consistently garnered attention for effectively managing thermal conditions in critical electronic components of spacecraft and satellites. Particularly intriguing are LHPs equipped with bi-porous capillary wicks, demonstrating commendable flow and thermal dissipation capabilities. Despite its significance for evaluating the phase-transition heat transfer in porous structures, the effective thermal conductivity (ETC) has not been understood mechanistically. This study aims to establish an efficient procedure for predicting ETC in bi-porous wicks through a comparative analysis of analytical and numerical methods. The analytical ETC expression was formulated using thermal resistance network and neck formation theory. Meanwhile, the numerical model utilized the Finite volume method (FVM) combined with a 3D porous structure created by Diffusion-Limited Aggregation (DLA) algorithm. The investigation revealed a congruence between two methods and the measured ETCs of interstitial-pore samples containing fine nickel powders. However, undesirable results were shown for coarse samples due to the irregular particle shapes. In samples with formation pores, the numerical model excels in characterizing the spatial distribution and scale of pores, albeit at the expense of time consumption and model complexity compared to the analytical one. Both methods prove valuable for ETC modeling of sintered bi-porous structures according to desired applications.

Filtration of fine mineral particles assisted by nanobubbles

This study investigates the effect of nanobubbles (NBs) on the filtration rate of quartz and iron ore tailings (fine and coarse samples) using a bench-scale press filter operating at 8 bar. The NBs dispersion was generated through hydrodynamic cavitation in a needle valve after 25 min of air saturation in water at constant pressure (Psat = 2.5 bar). To assess the influence of particle size and hydrophobicity (amine conditioning) on the filtration rate, quartz samples with varying particle size distributions were employed. Subsequently, fine (Dm = 7 µm) and coarse (Dm = 90 µm) tailings were examined as a case study. These tailings were collected from the desliming hydrocyclone (prior to the flotation stage) and the floated product (silica and silicate particles) from the reverse cationic flotation of a Brazilian iron ore mill. Filtration tests were conducted with the quartz samples after treatment with 180 g t−1 of dodecyl-ether-amine at pH 7.5. The results revealed that NBs significantly improved the filtration rate (approximately 20%) for fine (Dm = 7 µm) and medium-sized quartz samples (Dm = 129 µm). Conversely, no substantial effect was observed with the coarse material (Dm = 307 µm), due to its higher porosity. The enhanced filtration rate was validated with fine iron tailings, where NB conditioning reduced the filtration time by about 30%. Similar to the results with quartz, this positive effect diminished when fines were mixed with coarse tailings because of the enhanced porosity of the resulting cake. The underlying operating mechanism seems to be the enhancement of mineral particle hydrophobicity induced by the NBs. In addition, theses bubbles reduce the capillary forces responsible for retaining liquid within the cake's porous section, allowing a more rapid flow of the filtrate. This technique appears to hold substantial potential for tailings management in the iron mineral industry, and extended trials at a pilot scale will be scheduled.

Space–Time Sampling for Network Observability

Designing sparse sampling strategies are crucial in having resilient estimation and control in networked systems as they make sampling less expensive and networks more resilient w.r.t where and when samples are collected. It is shown under what conditions taking coarse samples from a network will contain the same amount of information as a finer set of samples. Our goal is to estimate the initial condition of linear time-invariant networks using a set of noisy measurements. The observability condition is reformulated as the frame condition, where one can easily trace location and time stamps of each sample. We compare estimation quality of various sampling strategies using estimation measures, which depend on spectrum of the corresponding frame operators. Using properties of the minimal polynomial of the state matrix, deterministic and randomized methods are suggested to construct observability frames. Intrinsic tradeoffs assert that collecting samples from fewer subsystems dictates taking more samples (in average) per subsystem. Three scalable algorithms are developed to generate sparse space-time sampling strategies with explicit error bounds.

Pretreatment of Bituminous Coal By-Products for the Hydrometallurgical Extraction of Rare Earth Elements

Low-temperature plasma (LTP) oxidation has been widely used to study the mineralogy of the mineral matter existing in coal sources. The current study investigated the potential of LTP oxidation as a pre-treatment method to improve rare earth element (REE) leachability from coal and its by-products. Representative density-fractionated samples of Baker and Fire Clay coarse refuse seam materials were ground to a top size of 180 µm and subjected to low-temperature plasma oxidation. Subsequently, the treated samples were leached at 1% w/v solids concentration and 75 °C for 5 h using (i) de-ionized (DI) water, (ii) 0.1 mol/L of ammonium sulfate, and (iii) 1.2 mol/L of sulfuric acid. It was determined that LTP treatment improved REE leaching characteristics, especially the leaching of heavy REEs (HREE), existing in the lighter density fractions of the Baker seam coarse refuse material. For instance, the HREE recovery for the 1.6 specific gravity (SG) float fraction increased from 8% to 33% using 0.1 mol/L of ammonium sulfate solution after 32 h of LTP treatment. This finding indicated that HREEs associated with the organic matter were released by the LTP treatment and adsorbed onto the surfaces of highly negative charged mineral matter and was exchanged with ammonium to allow their recovery. Similarly, when using 1.2 mol/L of sulfuric acid, the HREE recovery increased from 23% to 53% for the 1.6 SG float fraction. Interestingly, LTP oxidation did not provide significant improvement in REE recovery from the 2.2 sink density fractions, which was likely due to its lower organic content. No significant benefits were observed when treating the Fire Clay coarse refuse material, which was likely due to the lack of organic affinity and the difficult-to-leach REE minerals associated with the coal source such as monazite, xenotime, and zircon. Conversely, high-temperature oxidation within a temperature range of 600–750 °C significantly improved REE leaching characteristics for both coal sources. Improvement in REE recovery was due to decarbonization of the material, clay dehydroxylation and subsequent conversion of liberated REE-bearing minerals into a more leachable form. However, increasing the temperature above 800 °C decreased REE recovery due to the conversion of meta-kaolinite into mullite, which is chemically stable.

Experimental study on the high-speed frictional behavior between the tire and asphalt pavement

Driving speed, pavement type, surface characteristics, intermediate media, and tire properties are the key factors influencing the frictional behavior between tire and pavement. Although there are many kinds of equipment for friction coefficient tests based on the rubber-pavement contact mechanism, there is still a lack of equipment that can simulate the skid resistance behavior of high-speed vehicles based on real tire in the laboratory. Therefore, High-speed Friction Testing (HSFT) equipment was developed based on real tire loading, which well solves the deficiency of the current test methods. Based on this test platform, multi-factor coupling skid resistance tests were carried out on the asphalt concrete (AC-10/13/16) and the Open-Graded Friction Coarse (OGFC-10/13/16) samples. Statistical comparison and nonlinear fitting analysis were conducted on the results under various loading conditions The results show that driving speed, water film thickness, and tire load are all negatively correlated with skid resistance, and their influence extent can be ranked as water film thickness > tire load > driving speed. The established μHSFT skid resistance model based on multi-factor coupling shows high reliability, which can be used as the basis for pavement construction and is an important reference for future unmanned driving technology.

Simultaneous Effects of Normal Stress and Sand Grain Size on Fluid Storativity, Transmissibility, and Structural Strength of Artificial Sandstones

Investigation of granular materials’ geomechanical and geological characteristics could provide invaluable information to researchers working in petroleum and civil engineering, geoscience, and petrophysics. In this study, the effects of normal stress and sand grain size on the petrophysical and geomechanical characteristics of artificial samples (made of a wide range of sand grains and cement) were investigated, while the chemical composition and mineralogy of all the samples were kept identical. The results showed that the rock porosity and permeability, the main criteria for evaluating rock storativity and transmissibility, respectively, were adversely affected by applying normal stress and the increase of grain size. Based on the results, there is a critical range of grain size (i.e., 0.1–0.2 mm) above which rock permeability decreases sharply with an increase in normal stress. The geomechanical outcomes revealed that improving the cementation process by applying compaction force, and thus, increasing the degree of hydration has a greater effect on ameliorating rock strength than reducing rock porosity. Besides, the uniaxial compressive strength (UCS) and Brazilian test results indicated that fine-grained samples fail at lower normal stress (i.e., at 38% and 66% of the coarse samples, respectively) compared with coarse-grained samples because of the large number of grain-to-grain boundaries. The fracture patterns showed a straight line for very fine and fine-grained samples, while an irregular shape appeared for medium and coarse-grained samples. In the end, a comparison of distinct petrophysical and geomechanical rock properties, such as X-ray fluorescence analysis, UCS, porosity, and permeability, between artificial and natural samples has been made to analyze the difference between them.

Arbitrary-Oriented Ellipse Detector for Ship Detection in Remote Sensing Images

At present, in arbitrary-oriented object detection, the angular periodicity problem of rotated bounding box described by angle causes an object to have different numerical representations, which leads to uncertainty of rotated bounding box regression. To eliminate the angular periodicity problem, in this paper, we propose a novel and simple ellipse parameters representation method for arbitrary-oriented object, which hides the angle of object in the focal vector of ellipse to avoid direct angle prediction. Moreover, the proposed representation method can enable the arbitrary-oriented object to have only one numerical representation, which is beneficial to alleviate the uncertainty of bounding box regression. In order to adapt the proposed ellipse parameters representation method, we adopt 2D Gaussian distribution label assign for coarse samples selection, then the KLD loss and SimOTA are used to refine the coarse samples to obtain the best positive samples. We extend the YOLOX with medium parameters as an oriented ship detector according to the proposed ellipse parameters representation method, and conduct the experiments on HRSC2016, RSDD-SAR and RHRSID to demonstrate the effectiveness of the proposed method. The experimental results show that the proposed representation method achieves impressive results compared with state-of-the-art arbitrary-oriented object detection methods.

CoF-Net: A Progressive Coarse-to-Fine Framework for Object Detection in Remote-Sensing Imagery

Object detection in remote-sensing images is a crucial task in the fields of Earth observation and computer vision. Despite impressive progress in modern remote-sensing object detectors, there are still three challenges to overcome: 1) complex background interference; 2) dense and cluttered arrangement of instances; and 3) large-scale variations. These challenges lead to two key deficiencies, namely, coarse features and coarse samples, which limit the performance of existing object detectors. To address these issues, in this article, a novel coarse-to-fine framework (CoF-Net) is proposed for object detection in remote-sensing imagery. CoF-Net mainly consists of two parallel branches, namely, coarse-to-fine feature adaptation (CoF-FA) and coarse-to-fine sample assignment (CoF-SA), which aim to progressively enhance feature representation and select stronger training samples, respectively. Specifically, CoF-FA smoothly refines the original coarse features into multispectral nonlocal fine features with discriminative spatial–spectral details and semantic relations. Meanwhile, CoF-SA dynamically considers samples from coarse to fine by progressively introducing geometric and classification constraints for sample assignment during training. Comprehensive experiments on three public datasets demonstrate the effectiveness and superiority of the proposed method.

Effect of planting density on root biomass and distribution, and soil organic carbon stock of Acacia decurrens stands in Northwestern Ethiopia

Background:The expandingAcacia decurrenswoodlots in Northwestern Ethiopia is recognized for carbon storageviaroot biomass and soil organic carbon (SOC) enhancement but its planting densities have varied considerably. This study evaluated the effect of planting density on the root biomass, SOC stock, and vertical distributions in the stands.Methods:Five planting densities (0.5 m x 0.5 m, 0.75 m x 0.75 m, 1 m x 1 m, 1.25 m x 1.25 m, and 1.5 m x 1.5 m) were replicated four times with randomized complete block design. Soil core (6.67 cm diameter) and pit (900 cm2area) methods were used to collect fine and coarse root samples within 0–50 cm soil depth (having five soil layers in 10 cm intervals), respectively. Fine root biomass samples were classified as live and dead (necromass) and further as tree and herbaceous root. All root biomass samples were washed, oven-dried, weighed, and standardized into gram per meter square (g m-2) for root biomass comparisons for each planting density and soil depth, then summed up for 0–50 cm depth as a total root biomass. The vertical distributions of fine and coarse root biomass at each depth were expressed as a percentage (in decimal) of the total root biomass (0–50 cm).Conclusions:Planting density had significant effects on root biomass, SOC stock, and root distributions (P < 0.05) but inconsistent for the percentage of SOC stock at all soil layers except at 40-50 cm. PlantingA. decurrenswith high density is recommended to increase root biomass, SOC, and percentage of roots in deep soil layers. Further study is suggested for the effects of stand age on root biomass dynamics and SOC stock with large scale.

Weakly Supervised Discriminative Learning With Spectral Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection.

Anomaly detection (AD) using hyperspectral images (HSIs) is of great interest for deep space exploration and Earth observations. This article proposes a weakly supervised discriminative learning with a spectral constrained generative adversarial network (GAN) for hyperspectral anomaly detection (HAD), called weaklyAD. It can enhance the discrimination between anomaly and background with background homogenization and anomaly saliency in cases where anomalous samples are limited and sensitive to the background. A novel probability-based category thresholding is first proposed to label coarse samples in preparation for weakly supervised learning. Subsequently, a discriminative reconstruction model is learned by the proposed network in a weakly supervised fashion. The proposed network has an end-to-end architecture, which not only includes an encoder, a decoder, a latent layer discriminator, and a spectral discriminator competitively but also contains a novel Kullback-Leibler (KL) divergence-based orthogonal projection divergence (OPD) spectral constraint. Finally, the well-learned network is used to reconstruct HSIs captured by the same sensor. Our work paves a new weakly supervised way for HAD, which intends to match the performance of supervised methods without the prerequisite of manually labeled data. Assessments and generalization experiments over real HSIs demonstrate the unique promise of such a proposed approach.

Chemical Composition and Toxicity of PM10 and PM0.1 Samples near Open-Pit Mines and Coal Power Stations.

Particulate matter (PM) <10 μm in size represents an extremely heterogeneous and variable group of objects that can penetrate the human respiratory tract. The present study aimed to isolate samples of coarse and ultrafine PM at some distance from polluting industries (1–1.5 km from the border of open-cast mines). PM was collected from snow samples which allowed the accumulation of a relatively large amount of ultrafine particles (UFPs) (50–60 mg) from five objects: three open-cast mines, coal power plants, and control territories. The chemical composition of PM was examined using absorption spectroscopy, luminescence spectroscopy, high-performance liquid chromatography, X-ray diffraction (XRD), and X-ray fluorescence (XRF) analyses of solid particle material samples. Toxicity was assessed in human MRC-5 lung fibroblasts after 6 h of in vitro exposure to PM samples. The absorption spectra of all the samples contained a wide non-elementary absorption band with a maximum of 270 nm. This band is usually associated with the absorption of dissolved organic matter (DOM). The X-ray fluorescence spectra of all the studied samples showed intense lines of calcium and potassium and less intense lines of silicon, sulfur, chlorine, and titanium. The proliferation of MRC-5 cells that were exposed to PM0.1 samples was significantly (p < 0.01) lower than that of MRC-5 cells exposed to PM10 at the same concentration, except for PM samples obtained from the control point. PM0.1 samples—even those that were collected from control territories—showed increased genotoxicity (micronucleus, ‰) compared to PM10. The study findings suggest that UFPs deserve special attention as a biological agent, distinct from larger PMs.

Vibration and tribological properties of epoxy-granite composites used as novel foundations for machine elements

Abstract Composites have become attractive to many industries today and are a good alternative to many traditional materials. Epoxy granite (EG) is used as a competitive alternative to ground ceramics, especially as a foundation for machine tools. This is because of their high damping ratio compared to other traditional materials such as cast iron. In the present study, a lightweight and cost-effective EG composite material was developed as a new foundation for machine elements. The composite material EG was prepared by mixing epoxy resin (12 wt %) and granite particles by casting method. The crushed granite particles were sieved and separated into coarse particles ≤2.36 ≥ 1.18 mm, medium particles ≤1.18 ≥ 0.6 and fine particles ≤0.6 mm. Vibration modal analysis is performed using an impact hammer to measure the natural frequencies occurring at each material size and the damping ratios. Microbial and fungal resistance is tested to understand applicability in humid environments, and water absorption and soak resistance were also measured. Scratch tests are performed using a top scratch tester to measure scratch width for fine and coarse samples. The electrostatic discharges generated by friction on the rubber and granite-epoxy composite sole are measured. It was found that as the particle size decreases, the damping ratio increases and the damping capability is improved, both microbial and fungal resistance increases, the scratch width is small for fine particles, while it is difficult to achieve for other granite sizes. Electrostatic discharges show a better feel for the composite material, but it is better with fine particles.

Road tunnel-derived coarse, fine and ultrafine particulate matter: physical and chemical characterization and pro-inflammatory responses in human bronchial epithelial cells

BackgroundTraffic particulate matter (PM) comprises a mixture of particles from fuel combustion and wear of road pavement, tires and brakes. In countries with low winter temperatures the relative contribution of mineral-rich PM from road abrasion may be especially high due to use of studded tires during winter season. The aim of the present study was to sample and characterize size-fractioned PM from two road tunnels paved with different stone materials in the asphalt, and to compare the pro-inflammatory potential of these fractions in human bronchial epithelial cells (HBEC3-KT) in relation to physicochemical characteristics.MethodsThe road tunnel PM was collected with a vacuum pump and a high-volume cascade impactor sampler. PM was sampled during winter, both during humid and dry road surface conditions, and before and after cleaning the tunnels. Samples were analysed for hydrodynamic size distribution, content of elemental carbon (EC), organic carbon (OC) and endotoxin, and the capacity for acellular generation of reactive oxygen species. Cytotoxicity and pro-inflammatory responses were assessed in HBEC3-KT cells after exposure to coarse (2.5–10 μm), fine (0.18–2.5 μm) and ultrafine PM (≤ 0.18 μm), as well as particles from the respective stone materials used in the pavement.ResultsThe pro-inflammatory potency of the PM samples varied between road tunnels and size fractions, but showed more marked responses than for the stone materials used in asphalt of the respective tunnels. In particular, fine samples showed significant increases as low as 25 µg/mL (2.6 µg/cm2) and were more potent than coarse samples, while ultrafine samples showed more variable responses between tunnels, sampling conditions and endpoints. The most marked responses were observed for fine PM sampled during humid road surface conditions. Linear correlation analysis showed that particle-induced cytokine responses were correlated to OC levels, while no correlations were observed for other PM characteristics.ConclusionsThe pro-inflammatory potential of fine road tunnel PM sampled during winter season was high compared to coarse PM. The differences between the PM-induced cytokine responses were not related to stone materials in the asphalt. However, the ratio of OC to total PM mass was associated with the pro-inflammatory potential.

Assessment of the Effect of Soil Sample Preparation, Water Content and Excitation Time on Proximal X-ray Fluorescence Sensing.

X-ray fluorescence (XRF) spectroscopy offers a fast and efficient method for analysing soil elemental composition, both in the laboratory and the field. However, the technique is sensitive to spectral interference as well as physical and chemical matrix effects, which can reduce the precision of the measurements. We systematically assessed the XRF technique under different sample preparations, water contents, and excitation times. Four different soil samples were used as blocks in a three-way factorial experiment, with three sample preparations (natural aggregates, ground to ≤2 mm and ≤1 mm), three gravimetric water contents (air-dry, 10% and 20%), and three excitation times (15, 30 and 60 s). The XRF spectra were recorded and gave 540 spectra in all. Elemental peaks for Si, K, Ca, Ti, Fe and Cu were identified for analysis. We used analysis of variance (anova) with post hoc tests to identify significant differences between our factors and used the intensity and area of the elemental peaks as the response. Our results indicate that all of these factors significantly affect the XRF spectrum, but longer excitation times appear to be more defined. In most cases, no significant difference was found between air-dry and 10% water content. Moisture has no apparent effect on coarse samples unless ground to 1 mm. We suggested that the XRF measurements that take 60 s from dry samples or only slightly moist ones might be an optimum option under field conditions.