Sampling Points Research Articles

Unmanned Aerial Vehicle-Neural Radiance Field (UAV-NeRF): Learning Multiview Drone Three-Dimensional Reconstruction with Neural Radiance Field

In traditional 3D reconstruction using UAV images, only radiance information, which is treated as a geometric constraint, is used in feature matching, allowing for the restoration of the scene’s structure. After introducing radiance supervision, NeRF can adjust the geometry in the fixed-ray direction, resulting in a smaller search space and higher robustness. Considering the lack of NeRF construction methods for aerial scenarios, we propose a new NeRF point sampling method, which is generated using a UAV imaging model, compatible with a global geographic coordinate system, and suitable for a UAV view. We found that NeRF is optimized entirely based on the radiance while ignoring the direct geometry constraint. Therefore, we designed a radiance correction strategy that considers the incidence angle. Our method can complete point sampling in a UAV imaging scene, as well as simultaneously perform digital surface model construction and ground radiance information recovery. When tested on self-acquired datasets, the NeRF variant proposed in this paper achieved better reconstruction accuracy than the original NeRF-based methods. It also reached a level of precision comparable to that of traditional photogrammetry methods, and it is capable of outputting a surface albedo that includes shadow information.

Signal Processing for Novel Noise Radar Based on de-chirp and Delay Matching

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal’s complexity and anti-jamming capability. Furthermore, a method for optimizing the processing of echo signals based on de-chirp and delay matching is proposed. The locally generated LFM signal is used to de-chirp the received echoes, resulting in a narrowband difference frequency noise signal. Subsequently, delay matching is performed in the fast time domain using the locally generated NFM signal according to the number of sampling points in the traversal processing period, allowing for the acquisition of target delay information. While reducing the analog-to-digital (A/D) sampling rate, the detection performance for wideband echo signals under high sampling rates is still maintained, with sidelobe levels and range resolution preserved. Accumulating this information in the slow time domain enables accurate target detection. The effectiveness of the proposed method is validated through simulation experiments.

Validation of sampling points for airborne radioactivity in particulate-generating operations

This study introduces an approach to validate sampling points for air monitoring in environments where operations generate significant airborne particulate radioactivity. A case study of repair works in a nuclear spent fuel reprocessing facility is used to synthesis and demonstrate the methodology. We use the probability distribution of air activity measurements and the correlation between two sampling points near high particulate generating operations and the ventilation ducts. The methodology developed in this paper can be applied in task-related air monitoring scenarios for validation of sampling points. This will augment internal exposure control measures during works involving physical entries in areas with high potential of escalation of air activity (e.g., during major repairs, during decommissioning of nuclear facilities etc.) and to evaluate the sufficiency of respiratory protection.

Simulation of spatially varying non-stationary seismic waves using random frequencies and non-uniform fast fourier transform

The simulation of spatially varying non-stationary seismic waves is a crucial consideration for the seismic analysis of engineering structures. The traditional approach often incurs substantial computational expenses and suffers from low efficiency due to the extensive implementation of large-scale matrix factorizations at numerous sampling points in both time and frequency domains. In this study, an enhanced approach is developed for the efficient simulation of non-stationary seismic waves. The core of this approach lies in the utilization of random frequency sampling coupled with time-domain interpolation to reduce matrix factorizations, and non-uniform fast Fourier transform for expediting the summation of trigonometric functions. A parametric analysis is conducted to investigate the effect of vital parameters, including the decoupling strategy of time-frequency spectrum, the number of random frequencies, and the number of interpolation knots over time, on both the efficiency and accuracy of the enhanced approach. Consequently, the preferred values for these parameters are determined. By considering the simulation of seismic waves acting on a group of bridges, the enhanced approach is validated in the view of correlation functions. The results demonstrate a high level of fidelity of the simulated seismic waves and proves the effectiveness of the enhanced approach in the efficient simulation of spatially varying non-stationary earthquake ground motions. The developed approach can be used for the simulation of non-stationary seismic waves for a wide range of engineering structures.

Real-time non-line-of-sight computational imaging using spectrum filtering and motion compensation.

Non-line-of-sight (NLOS) imaging aims at recovering the shape and albedo of hidden objects. Despite recent advances, real-time video of complex and dynamic scenes remains a major challenge owing to the weak signal of multiply scattered light. Here we propose and demonstrate a framework of spectrum filtering and motion compensation to realize high-quality NLOS video for room-sized scenes. Spectrum filtering leverages a wave-based model for denoising and deblurring in the frequency domain, enabling computational image reconstruction with a small number of sampling points. Motion compensation tailored with an interleaved scanning scheme can compute high-resolution live video during the acquisition of low-quality image sequences. Together, we demonstrate live NLOS videos at 4 fps for a variety of dynamic real-life scenes. The results mark a substantial stride toward real-time, large-scale and low-power NLOS imaging and sensing applications.

Optimized design of energy absorption of aluminum foam filled CFRP thin-walled square tube based on agent model

PurposeAluminum foam-filled thin-walled unit structures have received much attention for their excellent energy absorption properties. To improve the energy absorption effect of car energy absorption box under axial compression, this paper optimizes the fiber lay-up sequence, fiber angle and aluminum foam density of aluminum foam filled carbon fiber reinforced plastic (CFRP) thin-walled square tubes.Design/methodology/approachDesign of sample points required to construct the proxy model using design of experiments (DOE) method, and the data sample points of different models are obtained through Abaqus simulation and test. A double high-precision proxy model with the maximum specific energy absorption (SEA) and the minimum initial peak crash force (PCF) as the evaluation index is constructed based on the response surface function method. The NSGA-II multi-objective genetic algorithm was used to optimize the design parameters and obtain the optimal solution for the Pareto front, and the results were verified by using the multi-objective optimization toolbox in design-expert.FindingsThe results show that the optimal solution to the multi-objective optimization problem with the inclusion of the lay-up sequence is ρ = 0.5g/cm3 for a fiber lay-up angle varying in the range ±15–90° and an aluminum foam density varying in the range 0.2g/cm3-0.5g/cm3, with a lay-up method of [±87°/±16°/±15°/±89°]. The two optimization methods correspond to SEA and PCF errors of 2.109% and 4.1828%, respectively. The optimized SEA value is 18.2 J/g and the PCF value is 18,230 N. The optimized design reduces the peak impact force and increases the specific energy absorption, which improves the energy absorption effect of thin-walled energy-absorbing boxes for automobiles.Originality/valueIn this paper, the impact resistance of CFRP thin-walled square tubes filled with aluminum foam is optimized. Based on numerical simulations and experiments to obtain the sample point data for constructing the dual-agent model, we investigate the effect of filling with different densities of aluminum foam under the simultaneous change of fiber lay-up angle and order on its mechanical properties in this process, and carry out the multi-objective optimization design with NSGA-II algorithm.

River suspended-sand flux computation with uncertainty estimation using water samples and high-resolution ADCP measurements

Abstract. Measuring suspended-sand fluxes in rivers remains a scientific challenge due to their high spatial and temporal variability. To capture the vertical and lateral gradients of concentration in the cross-section, measurements with point samples are performed. However, the uncertainty related to these measurements is rarely evaluated, as few studies of the major sources of error exist. Therefore, the aim of this study is to develop a method to determine the cross-sectional sand flux and estimate its uncertainty. This SDC (for sand discharge computing) method combines suspended-sand concentrations from point samples with ADCP (acoustic Doppler current profiler) high-resolution depth and velocity measurements. The MAP (for multitransect averaged profile) method allows obtaining an average of several ADCP transects on a regular grid, including the unmeasured areas. The suspended-sand concentrations are integrated vertically by fitting a theoretical exponential suspended-sand profile to the data using Bayesian modeling. The lateral integration is based on the water depth as a proxy for the local bed shear stress to evaluate the bed concentration and sediment diffusion along the river cross-section. The estimation of uncertainty combines ISO standards and semi-empirical methods with a Bayesian approach to estimate the uncertainty due to the vertical integration. The new method is applied to data collected in four rivers under various hydro-sedimentary conditions: the Colorado, Rhône, Isère, and Amazon rivers, with computed flux uncertainties ranging between 18 % and 32 %. The relative difference between the suspended-sand flux in 21 cases calculated with the proposed SDC method compared to the International Organization for Standardization (ISO) 4363 standard method ranges between −40 % and +23 %. This method that comes with a flexible, open-source code is the first to propose an applicable uncertainty estimation that could be adapted to other flux computation methods.

Expression profiles of circulating miRNAs in an endangered Piedmontese sheep breed during the estrus cycle

IntroductionThe preservation of locally endangered breeds is essential for maintaining ecosystem services that benefit both society and the environment. Reproductive fitness becomes a crucial consideration in this context. MicroRNAs (miRNAs) are small non-coding RNA molecules that play a key role in post-transcriptional regulation. Typically, they function within the tissues where they are produced. However, when they are released into extracellular fluid, they are referred to as circulating miRNAs (c-miRNAs). C-miRNAs may serve as potential biomarkers, whose profile changes under different physiological states. The purpose of this study is to establish a connection between distinctive variations in the expression of c-miRNAs and specific estrus cycle phases in Frabosana-Roaschina sheep, an endangered Piedmontese breed.MethodsTwo trials, each involving 20 ewes with different reproductive efficiencies (nulliparous in the first trial and pluriparous in the second trial), were sampled on alternate days after synchronization for blood, saliva, and feces. Ultrasound scans were performed during the induced estrus cycle. The animals’ behaviors were assessed through video recordings.ResultsIn the first trial, play behaviors were detected without sexual behaviors, whereas in the second trial, sexual behaviors were observed without play behaviors. Based on plasma trends of 17β-estradiol and progesterone and ultrasound images, two moments were identified for miRNAs analyses: the beginning of the follicular phase (day 2) and the beginning of the luteal phase (day 11). C-miRNAs of six representative animals from the second trial were sequenced. Analyses of the sequencing data have identified 12 c-miRNAs that were differentially expressed (DE) when comparing day 11 with day 2: five miRNAs were found to be upregulated, whereas seven miRNAs were downregulated. An enrichment analysis, based on predicted targets, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases was performed. Many of these genes regulate reproductive pathways with the possible involvement of miRNAs. Finally, qRT-PCR was conducted to validate the DE miRNAs in all ewes. Differences in gene expression between the two sampling points and the two trials were observed, in line with existing literature.DiscussionInvestigating the role of these miRNAs in regulating estrus could improve the reproductive performance and welfare of Frabosana-Roaschina ewes.

Acoustic detection rate can outperform traditional survey approaches in estimating relative densities of breeding waders

Passive acoustic devices are increasingly being used to monitor biodiversity. However, few studies have compared the accuracy of acoustic surveys and traditional surveys against ground‐truthed data. Here, we assess whether acoustic recorders used in conjunction with an artificial intelligence (AI) classifier can predict the relative breeding density of four wader species better than traditional fieldworker transect surveys. In a 27‐km2 upland study site, acoustic data were collected at 83 sampling points and analysed using the BirdNet bird‐sound classifier to estimate vocal detection rate at each location; we also carried out concurrent transect bird surveys. To ground‐truth these approaches, intensive field surveys were undertaken to identify each breeding territory of our focal species. With both the acoustic dataset and the transect dataset, we used similar analytical approaches (random forest regression trees) to predict relative territory density across the study site, and then compared these predictions with the territory density obtained from the intensive field surveys. The classifier performed well at identifying the presence of target species' vocalizations within 3‐s periods for Lapwing (accuracy = 0.911), Curlew (0.826) and Oystercatcher (0.841), but less well for Golden Plover (0.699). For Curlew and Oystercatcher, the predictions obtained from the acoustic approach were a better fit to actual territory density than the transect approach. In contrast, for Lapwing and Golden Plover, the transect predictions outperformed the acoustic predictions, with the acoustic model particularly poor for Golden Plover. We attributed these differences to the performance of the classifier, species' ecology and vocal behaviour. Data gathering for the acoustic approach was more time‐efficient than the transect surveys, requiring less than a quarter of the fieldworker days. We conclude that there is high potential for acoustic approaches to augment traditional methods, although species' ecological characteristics should be considered: species that vocalize more frequently, at higher amplitudes and hold larger territories will be better‐suited to sampling‐based acoustic methods.

Multimodel-based quantitative source apportionment and risk assessment of soil heavy metals: A reliable method to achieve regional pollution traceability and management

To strengthen the control of pollution sources and promote soil pollution management of agricultural land, this study constructed a comprehensive source apportionment framework, which significantly improved the reliability of potential source analysis compared with the traditional single model. The spatial distribution pattern of agricultural soil heavy metals (SHMs) content in Lintong, a typical river valley city in China, was determined and the degree of contamination was evaluated. A scientific source apportionment methodological framework was constructed through correlation analysis methods together with multiple source apportionment receptor models. Finally, the Monte Carlo simulation method was used to derive the results of the human health risk assessment (HHRA). The results revealed the following: (1) Agricultural soils were moderately and mildly polluted, accounting for 28.8 % and 71.2 % of the total number of sampling points, respectively. (2) The overall correlation of heavy metals (HMs) was strong according to the coupling analysis of the SHMs, in which a strong correlation (0.8–1) was reached among Cu, Ni, Pb, Cr and Zn, indicating that these HMs were most likely homologous or composite. (3) Multimodel analysis of the SHMs sources revealed that the first and second principal components were agricultural (41.36 %) and industrial (19.69 %) sources, respectively, and the remaining principal components were road traffic, natural factors, and atmospheric deposition or surface runoff, respectively. (4) The average comprehensive noncarcinogenic health risk indices for adults and children were 4.2259E-02 and 1.4194E-01, respectively, which were within the slight risk range, indicating that the risk caused by SHMs to the human body can be almost negligible. This study adopted a mixed method to reveal the risk of SHMs pollution and its sources, which provides some reference and technical support for traceability analysis, zoning control, and health risk studies of regional pollutants and is helpful for formulating scientific management measures and targeting control policies.

Revolutionizing urban emission tracking: Enhanced vehicle ratios via remote sensing techniques

This study presents research that examines the use of several remote sensing methods to quantify automobile emissions in China. The research provides a unified data processing technique, using methodologies such as plume chasing (PCH) and point sampling (P-sampling) to measure vehicle emissions accurately. This method accurately calculates the ratios of gaseous combustion, namely NOx/CO2, in different driving scenarios and verifies the technique using controlled vehicle emissions measurements. The results demonstrate the method’s ability to identify variations in emission levels linked to engine operation and alterations in after-treatment systems. This provides significant information for monitoring emissions in metropolitan areas such as Wuhan, China. The suggested method has substantial promise for improving air quality initiatives in China.

Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events.

Characteristics of PM2.5-bound water-soluble inorganic ions generated during cooking

Commercial cooking is an important source of PM2.5, which adversely affects air quality, posing threats to human health. However, few studies have examined the effects of commercial cooking on outdoor air quality. We aimed to analyze characteristics of outdoor PM2.5 and ions from commercial cooking during cooking and non-cooking times in a popular restaurant area at Nankai University. The acidity and alkalinity of atmospheric PM2.5 based on the equivalent values of anions and cations and the formed compounds based on the ion concentrations and correlations between them were determined. The results showed that the mean PM2.5 concentration at the snack street during cooking times was 120.39 ± 58.52 μg/m3, approximately 1.4 times higher than at the comparison point. The total concentration of water-soluble inorganic ions accounted for approximately half of the PM2.5 emissions at both sampling points. High concentrations of Na+, NO3−, and SO42− were found at both sampling points. We found that NH4NO3, CaSO4, and CaCl2 formed at the snack street, whereas Ca(NO3)2, NH4NO3, and (NH4)2SO4 formed at the comparison point. Based on our analysis of the acid-base balance slopes, the atmospheric PM2.5 was alkaline. These results elucidate the characteristics of PM2.5-bound water-soluble inorganic ions formed during cooking.

Investigating Illicit Drug Hotspots and Daily Variations Using Sewer-Network Wastewater Analysis

Previous wastewater-based epidemiology (WBE) research on illicit drug use has predominantly focused on wastewater treatment plant (WWTP) influents, but information on sewer-network wastewater is very limited. This study represents a pioneering small-scale WBE investigation based on the analysis of sewer-network wastewater samples from different sewer manholes in suburban (Tamsui region) and urban areas (Zhongshan and Wanhua regions) and a comparison of the results with those obtained from corresponding WWTP influents. Among sixteen illicit drugs, methamphetamine exhibited the highest concentration in sewer-network wastewater across both areas. Suburban–urban variations were observed, with more types of illicit drugs detected in the suburban area. Back-calculation indicated that methamphetamine and ketamine were the most-consumed illicit drugs in both sewer-network wastewaters and WWTP influents. Similar types of illicit drugs were detected in the sewer-network wastewaters and WWTP influents, indicating the representativeness of WWTP influents in assessing regional illicit drug abuse. Nevertheless, the sewer-network wastewater results offered additional information making it possible to pinpoint potential hotspots of illicit drug and identify peak usage periods throughout the day, in contrast to the WWTP influent results. In the non-suspected suburban area of Tamsui, high potential hotspots of methamphetamine (sampling points 3 and 6) and ketamine (sampling points 1 and 8) were identified. Although the Zhongshan and Wanhua regions were chosen as suspected hotspots of illicit drug abuse, more severe illicit drug use was observed in Wanhua. Moreover, a trend toward higher illicit drug use from early morning to morning was observed. Despite sampling challenges and higher costs, small-scale WBE via sewer-network wastewater analysis provides superior identification of drug abuse hotspots and peak usage periods. Therefore, this study provides valuable insights for law enforcement and can help prevent and combat illicit drug abuse by targeting potential hotspots and understanding daily illicit drug use dynamics.

Dynamic thermal simulation of a tree-shaped district heating network based on discrete event simulation

The computational complexity involved in modelling district heating (DH) networks impedes the integration of network operations into comprehensive DH system studies. We developed a flexible, accurate, and fast dynamic thermal simulation model utilising discrete event simulation (DES). This model is versatile, suitable for any tree-shaped DH network with a central heating plant and can estimate node temperatures and calculate pipe heat losses. The speed of the model is improved via using variable time steps and by incorporating two advanced techniques: lazy evaluation and a customised priority queue. To further improve the computational speed, we developed a technique to eliminate redundant sampling points. This model was tested and demonstrated excellent consistency with actual measurements. Remarkably, reducing sampling points can speed up the simulation by a factor of three without compromising the temperature accuracy. A 72-day simulation of a network with 102 pipes was completed within 0.219 s. Our findings highlight the significant potential of the DES model for large-scale dynamic network simulations and offer a promising solution for DH network simulations and system optimisation.

Variations in the level of available phosphorus with changes in the status of water-soluble organic matter derived from different organic materials in soil

The combined application of organic material and phosphorus fertilizer is an effective method to enhance phosphorus use efficiency for plant growth. This is partly because the presence of water-soluble organic matter (WSOM) derived from different organic materials can enhance the level of available phosphorus in the soil; however, it is poorly understood how this level varies with changes in the WSOM status (i.e., decomposed, dissolved, and retained) in the soil depending on WSOM types. This study aimed to (i) understand how changes in the WSOM status enhances the available phosphorus level in the soil, and (ii) determine the WSOM type that contributes to such enhancement. The incubation test showed that fractions of 73%–92% and 8%–27% of WSOM-derived organic carbon were retained and dissolved, respectively, at the beginning of incubation, while 31%–45% was decomposed during the incubation period. The WSOM derived from cattle manure compost (CM) and sewage sludge compost (SSC) that was initially retained was maintained until the late stage of the incubation test, whereas that derived from hydrothermal decomposed liquid fertilizer (HDLF) was rapidly desorbed during the first 14 days of the incubation period. The available phosphorus level was higher under the combined application of CM- and SSC-derived WSOM than under the single phosphorus application throughout the incubation period, while it was high only during the first 3 days of incubation under the application of HDLF-derived WSOM. The amounts of retained organic carbon at each sampling point during the incubation period compared to those at the beginning were positively and linearly correlated to the available phosphorus levels that were enhanced by the WSOM present in the soil. This study for the first time provides quantitative experimental evidence that 1) the longer the WSOM continues to be retained, the higher the amount of available phosphorus remaining in the soil, and 2) the available phosphorus level decreases with WSOM sorption or decomposition. Furthermore, it was shown that highly humified WSOM has a great potential for the maintenance of higher available phosphorus levels. This study provides the insight that a combined application of highly humified organic materials with a chemical fertilizer is necessary for not only cost effective but also sustainable fertilization design.

Assessment of the water quality pollution index and ecological risk of microplastic pollution along the Tambakoso River in Surabaya, Indonesia

Increasing human activities and improper waste disposal will cause microplastic pollution in surface water. This study analyzed the abundance and characteristics of microplastics, pollution index based on water quality and its relationship with microplastic pollution, and the potential ecological risk of microplastics along the Tambakoso River which is influenced by various land uses of housing, industry, agriculture, and ponds from 16 sampling points. The average abundance of microplastics in the river was 91.80 particles/L. The Kruskal Wallis test showed that there were significant differences between microplastic pollution at each sampling location (Pvalue <0.05). In general, microplastics were mostly found in the form of fragments (48.36 %), transparent color (73.81 %), SMP size (<1 mm) (81.6 %), and dominated by PVC and nylon polymers. However, the characteristics of microplastics at each sampling location varied. The water quality pollution index value showed a slightly polluted category at most points. Redundancy analysis (RDA) showed that the characteristics of the shape and color of microplastics correlated with water quality parameters. The potential ecological risk based on microplastic pollution showed minor, moderate, and high categories at points with industrial land use. This indicates that the distribution of microplastics is closely related to human activities in the area. The level of ecological risk from microplastics depends on the percentage of each plastic polymer, along with its abundance in the environment. This study offers an important basis for designing efficient countermeasures to reduce microplastic pollution and improve water quality in surface waters.

Research on a Multi-source RFI Mitigation Algorithm Using a Reference Antenna Array

We propose a multi-source radio frequency interference (RFI) mitigation method based on a reference antenna array to address the challenge of RFI from multiple directions in radio observation equipment. It introduces a sampling point correction technique using a multi-channel parallel cross-correlation computation method, enhancing the effectiveness of frequency domain adaptive RFI fast mitigation algorithms. The design implements an RFI component detection method based on cross-correlation coefficient thresholds to effectively reduce new interference frequency components introduced by the reference antenna array. Simulated RFI signals and baseband signals of pulsar J0332+5434 observed by the Nanshan 26 m Radio Telescope (NSRT) were used to test the algorithm proposed in this paper. Simulation results demonstrate that the simulated radio telescope signals after RFI mitigation closely match the original pulsar data in profile and phase, confirming the effectiveness of the proposed method.

Native accumulator plants with a differential mercury phytoremediation potential in a region in Southern Amazon.

Mercury (Hg) is a non-essential trace metal, toxic to living beings and complex to quantify and mitigate in the environment. In this study, 25 plant species native to an Amazon-Cerrado transition area were tested for use in Hg remediation. Species identification, Hg quantification in plant biomass and soil at each sampling point, and evaluation of Hg compartmentalization in each plant were carried out. The results were subjected to statistical tests and evaluated using translocation coefficients (FT), bioconcentration (FBC), and bioaccumulation (FB). The results demonstrated that the distribution and accumulation of Hg differed between species and between the parts of the plant evaluated. Soil was the predominant source of Hg in the study area. The study highlighted seven species with Hg phytoremediation potential. Five translocator species were characterized, among these a preferentially bioaccumulating and bioconcentrating species, in addition to a bioconcentrating species and a preferentially bioconcentrating and bioaccumulating species of Hg. Potentially accumulating species stood out, Blechnum serrulatum Rich. (Blechnaceae), Mauritia flexuosa L.f. (Arecaceae), and Montrichardia arborescens (L.) Schott (Araceae), all widely distributed in tropical regions, characterized as rooted, terrestrial, or amphibious and associated with ruderal environments.

Mediating effect of oxidative stress on blood pressure elevation in workers exposed to low concentrations of benzene, toluene, and xylene (BTX).

To investigate the mediating effect of oxidative stress on the relationships between low-concentration benzene, toluene, and xylene (BTX) exposure and blood pressure in workers. A cross-sectional study involving 841 workers from a petroleum refining enterprise in Hainan, China, was conducted. Among the workers, 615 workers were exposed to low-concentration BTX, and 216 workers were in the control group. S-phenylmercapturic acid (S-PMA), hippuric acid (HA), and methyl hippuric acid (MHA, including the three isomers 2-MHA, 3-MHA, and 4-MHA) were measured in the urine of workers via high-performance liquid chromatography‒tandem triple quadrupole mass spectrometry to assess the internal BTX burden. Oxidative stress markers, blood pressure, and their correlations were analysed in both the exposed and control groups of workers. Mediation analysis was used to investigate the potential role of oxidative stress in the relationship between BTX exposure and blood pressure. The concentrations of BTX at the sampling points in the enterprise were all below the limits stipulated in China's national occupational health criteria: occupational exposure limits for hazardous agents. With respect to the internal burden of BTX, the concentrations of the benzene metabolite S-PMA, the toluene metabolite HA, and the xylene metabolites 3-MHA and 4-MHA in the urine samples in the exposure group were greater than those in the control group (P < 0.05). The correlation analysis results revealed that the concentration of the benzene metabolite S-PMA in workers' urine was positively correlated with diastolic blood pressure (DBP) (r = 0.265, P < 0.05). Compared with those in the control group, DBP was greater (β = 1.363, 95% CI 0.088 -2.639), serum superoxide dismutase (SOD) activity was lower (β = - 0.037, 95% CI - 0.060 to - 0.013), and the serum malondialdehyde (MDA) concentration was greater (β = 0.066, 95% CI 0.022-0.110) in the exposure group. Partial correlation analysis revealed a positive correlation between DBP and MDA (rs = 0.115, P < 0.01). The results of the mediation analysis indicated that MDA was a complete mediator between low BTX exposure and DBP (P < 0.05). Occupational exposure to low concentrations of BTX elevates blood pressure and oxidative stress among workers. A positive correlation between DBP and MDA was observed, with MDA acting as a complete mediator between low-concentration BTX exposure and DBP elevation.