A comprehensive risk assessment of microplastics in soil, water, and atmosphere: Implications for human health and environmental safety.

Microplastics (MPs) are pervasive across ecosystems, likely posing significant environmental and health risks based on more and more evidence. In this study, we searched through the Web of Science Core Collection and obtained 1039 papers for visualization and analysis. In order to discuss the chemical composition, migration, transformation and potential risk of MPs, 135 sets of relevant data in soil, water, and atmosphere were collected in China as a typical region, which is a hotspot region for investigation of MPs. The results showed that the primary polymer categories of MPs in the environment to be polypropylene, polyethylene, and polystyrene. The soil contains a significant quantity of MPs, averaging at 12,107.42 items·kgdw-1, while water contains averaging at 97,271.18 items m-3. The total pollution load indexes for all three environments are at risk level I. Based on current risk assessment methods, the potential ecological risk of MPs is low. However, based on the polymer components, migration and transformation patterns, and especially the complexes with other pollutants, it indicates an increasing indirect risk. Interactions with some other pollutants are likely amplify the ecological and health risks associated with MPs. Aggregative results showed that the present risk assessment models could not assess the risks of MPs well. Thus, we suggested develop a risk assessment methodology for MPs based on relevant research progress. Some factors such as the size and form of MPs, sources and distribution, bioaccumulation, social acceptance and economic costs could be considered adding in the present risk assessment models. Finally, promotion of development and application of green chemically synthesized bioplastics such as using synthetic biology to help degrade plastics would be an alternative and sustainable option to relieve the adverse environmental and health concerns of MPs.

Realization of Joints of Aluminosilicate Glass and 6061 Aluminum Alloy via Picosecond Laser Welding without Optical Contact.

To achieve laser direct welding of glass and metal without optical contact is hard, owing to the large difference in thermal expansion and thermal conductivity between glass and metal and an insignificant melting area. In this study, the high-power picosecond pulsed laser was selected to successfully weld the aluminosilicate glass/6061 aluminum alloy with a gap of 35 ± 5 µm between glass and metal. The results show that the molten glass and metal diffuse and mix at the interface. No defects such as microcracks or holes are observed in the diffusion mixing zone. Due to the relatively large gap, the glass collapsed after melting and caulking, resulting in an approximately arc-shaped microcrack between modified glass and unmodified glass or weakly modified glass. The shape of the glass modification zone and thermal accumulation are influenced by the single-pulse energy and linear energy density of the picosecond laser during welding, resulting in variations in the number and size of defects and the shape of the glass modification zone. By reasonably tuning the two factors, the shear strength of the joint reaches 15.98 MPa. The diffusion and mixing at the interface and the mechanical interlocking effect of the glass modification zone are the main reasons for achieving a high shear strength of the joint. This study will provide reference and new ideas for the laser transmission welding of glass and metal in the non-optical contact conditions.

Identifying the habitat suitability of Pteris vittata in China and associated key drivers using machine learning models.

Pteris vittata (P. vittata) possesses significant potential in remediating arsenic (As) soil pollution. Understanding the habitat suitability of P. vittata in China and pinpointing the key drivers that influence its distribution can facilitate the identification of optimal areas for using P. vittata as a remediation tool for As-polluted soils. In this study, a comparative analysis was conducted using ten machine learning models to assess the habitat suitability of P. vittata based on 744 specimen records and 20 environmental factors. The key drivers affecting the distribution of P. vittata were also investigated based on the optimal model. The results indicate that the XGBOOST model was the most reliable and stable, achieving a coefficient of determination of 0.95. Approximately 24.47 % of China's land area was identified as suitable for P. vittata. Particularly, it was predominantly found in Hainan (45.9 %), Guangxi (92.96 %), Guangdong (91.68 %), Hunan (91.26 %), Guizhou (90.83 %), Chongqing (88.17 %), Fujian (85.70 %), Yunnan (77.44 %), Jiangxi (73.99 %) and Zhejiang (57.05 %). Furthermore, this study pinpointed the lowest temperature, annual temperature range, and mining density as key drivers, contributing 45.9 %, 31.9 %, and 7.2 %, respectively. Spatial correlation analysis revealed a significant correlation between mining density and the habitat distribution of P. vittata (Moran' I = 0.519). This study confirmed that both natural factors and anthropogenic activities affect the distribution of P. vittata and provided valuable insights for optimizing the application of P. vittata in soil phytoremediation and reclamation.

Neglected pathways of heavy metal input into agricultural soil: Water-land migration of heavy metals due to flooding events.

Flooding, carrying sediments, inundates farmlands across the world due to extreme adverse weather conditions. The casualties and property damage associated with flooding are important direct impacts. However, there is currently insufficient understanding of the remobilization and distribution of heavy metals (HMs) caused by flooding. Few studies have specifically considered flooding as a pathway for HMs contamination of soil. Herein, a novel methodological framework for revealing the input pathways of HMs in agricultural soils in mining-intensive areas is proposed and applied. Flooding is considered one of the pathways for HMs inputs during source apportionment. The results demonstrated a high degree of overlap between the distribution characteristics of major HMs in agricultural soils and sediments. The degree of soil Cd pollution was significantly positively correlated with the inundation depth in the flooded area. It took 8.4-11.5 times of flood inundation or 98.5-119.9 years of accumulation of atmospheric deposition to reach HMs contamination levels in the soil of the study area. Flooding brought in most of the soil Cd, while atmospheric deposition was the primary input pathway for soil Pb and Zn. Our results identified the role of flood inundation on the input of HMs in mining-intensive areas. These results demonstrated the value of our framework for studying the impact of flooding on HMs in agricultural soils from the perspective of input pathways, providing new insights not only into identifying the sources of soil HMs but also into enhancing understanding of the impact of flooding on soil environments. With the potential increase in the frequency and intensity of flooding inundating farmlands in the future, it is essential to consider flooding as a pathway for HMs inputs in order to comprehensively assess their environmental impact.

Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in mining and smelting area in southwestern China.

Accurate prediction of spatial distribution of potentially toxic elements (PTEs) is crucial for soil pollution prevention and risk control. Achieving accurate prediction of spatial distribution of soil PTEs at a large scale using conventional methods presents significant challenges. In this study, machine learning (ML) models, specially artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB), were used to predict spatial distribution of soil PTEs and identify associated key factors in mining and smelting area located in Yunnan Province, China, under the three scenarios: (1) natural + socioeconomic + spatial datasets (NS), (2) NS + irrigation pollution index (IPI) datasets, (3) NS + IPI + deposition (DEPO) datasets. The results highlighted the combination of NS+IPI+DEPO yielded the highest predictive accuracy across ML models. Particularly, XGB exhibited the highest performance for As (R2 =0.7939), Cd (R2 =0.6679), Cu (R2 =0.8519), Pb (R2 =0.8317), and Zn (R2 =0.7669), whereas RF performed the best for Ni (R2 =0.7146). The feature importance and Shapley additive explanation (SHAP) analysis revealed that DEPO and IPI were the pivotal factors influencing the distribution of soil PTEs. Our findings highlighted the important role of DEPO in spatial distribution prediction of soil PTEs, which has often been ignored in previous studies.

Integrating high-throughput phenotyping and genome-wide association studies for enhanced drought resistance and yield prediction in wheat.

Drought, especially terminal drought, severely limits wheat growth and yield. Understanding the complex mechanisms behind the drought response in wheat is essential for developing drought-resistant varieties. This study aimed to dissect the genetic architecture and high-yielding wheat ideotypes under terminal drought. An automated high-throughput phenotyping platform was used to examine 28 392 image-based digital traits (i-traits) under different drought conditions during the flowering stage of a natural wheat population. Of the i-traits examined, 17 073 were identified as drought-related. A genome-wide association study (GWAS) identified 5320 drought-related significant single-nucleotide polymorphisms (SNPs) and 27 SNP clusters. A notable hotspot region controlling wheat drought tolerance was discovered, in which TaPP2C6 was shown to be an important negative regulator of the drought response. The tapp2c6 knockout lines exhibited enhanced drought resistance without a yield penalty. A haplotype analysis revealed a favored allele of TaPP2C6 that was significantly correlated with drought resistance, affirming its potential value in wheat breeding programs. We developed an advanced prediction model for wheat yield and drought resistance using 24 i-traits analyzed by machine learning. In summary, this study provides comprehensive insights into the high-yielding ideotype and an approach for the rapid breeding of drought-resistant wheat.

Facet-dependent synthesis of H2O2 from H2 and O2 over single Pt atom-modified Pd nanocrystal catalysts.

Hydrogen peroxide (H2O2) is one of the most valuable clean energy sources with a rapidly growing requirement in industry and daily life. The direct synthesis of H2O2 from hydrogen and oxygen is considered to be an economical and environmentally friendly manufacturing route to replace the traditional anthraquinone method, although it remains a formidable challenge owing to low H2O2 selectivity and production. Here, we report a catalyst consisting of Pd(111) nanocrystals on TiO2 modified with single Pt atoms (Pt1Pd(111)/TiO2), which displays outstanding reactivity, producing 1921.3 µmol of H2O2, a H2 conversion of 62.2% and H2O2 selectivity of 80.3% over 30 min. Kinetic and isotope experiments confirm that the extraordinary catalytic properties are due to stronger H2 activation (the rate-determining step). DFT calculations confirm that Pt1Pd(111) exhibits lower energy barriers for H2 dissociation and two-step O2 hydrogenation, but higher energy barriers for side reactions than Pt1Pd(100), demonstrating clear facet dependence and resulting in greater selectivity and amount of H2O2 produced.

dbEBV: A database of Epstein-Barr virus variants and their correlations with human health.

Since Epstein-Barr virus (EBV) was discovered in 1964, it has been reported to be associated with various malignancies as well as benign diseases, and the pathogenicity of EBV has been widely studied. Several databases have been established to provide comprehensive information on the virus and its relation to diseases and introduce convenient analysis tools. Although they have greatly facilitated the analysis of EBV at the genome, gene, protein, or epitope level, they did not provide enough insight into the genomic variants of EBV, which have been suggested as relevant to diseases by multiple studies. Here, we introduce dbEBV, a comprehensive database of EBV genomic variation landscape, which contains 942 EBV genomes with 109,893 variants from different tissues or cell lines in 24 countries. The database enables the visualization of information with varying global frequencies and their relationship with the human health of each variant. It also supports phylogenetic analysis at the genome or gene level in subgroups of different characteristics. Information of interest can easily be reached with functions such as searching, browsing, and filtering. In conclusion, dbEBV is a convenient resource for exploring EBV genomic variants, freely available at http://dbebv.omicsbio.info.

Spatial and temporal variations in environmental impacts of heavy metal emissions from China's non-ferrous industry: An enterprise-specific assessment.

In China, the non-ferrous metal industry is the sector with the highest emissions of arsenic, cadmium, mercury and lead, causing serious impacts on human health and the ecosystem. However, current heavy metal emission inventories are inadequate for figuring out their exposures and associated environmental impacts due to the lack of detailed data. Here, we constructed a high-resolution, enterprise-specific, and long-term dataset detailing heavy metal emissions from the non-ferrous industry in China from 1981 to 2020, using comprehensive enterprise information. Furthermore, an environmental impact assessment was performed using the characterization factors of the IMPACT World + model. Results show that: (1) from 1981 to 2020, the total heavy metal emissions of China's non-ferrous industry reached 144,697 tons (t), with atmospheric emissions (104,524 t) exceeding aquatic ones (40,173 t). (2) The industry's emissions showed a rising and then declining trend, with significant spatial heterogeneity, where heavy metal emissions concentrated in the central and western parts of Yunnan, the southern part of Hunan, the northern part of Guangxi, Henan along the Yellow River, the intersection of Gansu and Shaanxi, the central and eastern parts of Liaoning, and the eastern part of Inner Mongolia. (3) The environmental impact on human health was 1.19 × 107 DALY, and the value of ecosystem quality was 7.26 × 109 species·yr. The top 10 % of enterprises with the largest environmental impacts contributed over 60 % of human health risks and 62 % of ecosystem quality impacts. Improving the removal efficiency of heavy metals by 10 % within the four major industry classes could lead to a 9.92 % reduction in human health impacts and a 9.77 % reduction in ecosystem quality impacts within the non-ferrous metals industry. The findings of this study can provide insights for pollution control, environmental risk reduction, and sustainable development in the non-ferrous metals industry.

Diagnostic features emerging in near-infrared reflectance spectroscopy for low petroleum hydrocarbon pollution after spectral subtraction.

Diagnostic features in near-infrared reflectance spectroscopy (NIRS) are the foundation of knowledge-based approach of petroleum hydrocarbon determination. However, a significant challenge arises when analyzing samples with low levels of petroleum hydrocarbon pollution, as they often lack distinctive diagnostic features in their sample NIRS spectra, limiting the effectiveness of this approach. To address this issue, we have developed a technical workflow for diagnostic spectrum construction and parameterization based on spectral subtraction. This method was applied on a set of NIRS spectra from soil samples that were contaminated with petroleum hydrocarbons (ranged between 178 and 1716 mg/kg of total petroleum hydrocarbon). Then two diagnostic features for low-level petroleum hydrocarbon pollution were found: (1) An overall downward concave emerged on diagnostic spectrum within both 2290-2370 nm and 1700-1780 nm for all low pollution levels even below 200 mg/kg; (2) An indicative pattern of asymmetric "W-shaped" double absorption valley occurred for those exceeding 1000 mg/kg, and its valleys located near 2310 nm, 2348 nm or 1727 nm, 1762 nm stably. These two features on diagnostic spectrum could be parameterized to detect, and the detection limit was at least about 10-50 times lower than that based on sample spectrum. These findings update our understanding on the detectability of spectral response from low petroleum hydrocarbon pollution, and widely extend the application of knowledge-based NIRS approach in either field detection or remote sensing identification for environmental management.

Influence of biochar on the arsenic phytoextraction potential of Pteris vittata in soils from an abandoned arsenic mining site.

Biochar (BC) has a strong potential for activating arsenic (As) in soil; thus, the phytoremediation efficiency of As-polluted soils is enhanced with Pteris vittata L. A pot experiment was conducted to investigate the potential of BC to assist in phytoremediation with P. vittata. The effects of BC on physicochemical properties, available As, enzyme activities, and the bacterial community in the rhizosphere soil were investigated, and the biomass, physiology, and As uptake of P. vittata were analyzed. The results indicated that applying BC facilitated available As in the P. vittata rhizosphere soil, and the phytoremediation efficiency percentage increased in the As-polluted soils, such as 3.80% and 8.01% under the 2% and 5% BC treatments compared to the control, respectively. Phytoremediation with P. vittata and BC significantly improved soil organic matter content, available N, P, and K, enzyme activities, and the bacterial community. BC promoted Streptomyces (26.6-54.2%) and Sphingomonas (12.3-30.8%) abundance which regulated the growth and As uptake by P. vittata. Moreover, applying BC increased the biomass, and As uptake by P. vittata. Overall, BC strengthened the phytoremediation of As-polluted soils by improving soil pH, nutrient concentrations, enzyme activities, bacterial community structure, and soil arsenic activation, growth, and absorption by P. vittata.

Acute toxicity of cerium to neonatal Daphnia magna: Responses of antioxidant systems, influence of environmental factors and development of a biotic ligand model.

The bioavailability of cerium (Ce) and its toxic effects on aquatic organisms are still unclear, which limits the toxicity prediction and pollution control for this element. Here, the acute toxicity of Ce to Daphnia magna neonates and the responses of the antioxidant system were investigated, and the quantitative relationships between the toxicity of Ce and environmental factors were determined. The 24 and 48 h EC50Ce-D values based on the dissolved concentration of Ce in Daphnia magna were 60.6 and 10.9 µM, respectively, and the EC50Ce3+ values were 23.4 and 3.73 µM, respectively. After Ce exposure at environmentally relevant concentrations (0.5-3.5 µM), significant increases in superoxide dismutase activity and malondialdehyde content were observed in Daphnia magna, while significant decreases in catalase activity and H2O2 content occurred. Low levels of Ce cause oxidative damage to Daphnia magna and adverse impacts on the antioxidant system; however, further molecular-based studies are needed. The addition of Ca2+ or Na+ reduced the acute toxicity of Ce to Daphnia magna. In contrast, Mg2+ (MgSO4) promoted Ce toxicity, which is a new finding related to the interaction effects between cations and rare earth elements on biological ligands; however, the effects of SO42+ could not be distinguished. Complexation with organic ligands could significantly reduce the toxicity of Ce to Daphnia magna; however, complexes of Ce with citric acid and malic acid might be bioavailable to Daphnia magna. In the absence of organic ligands and competing metals, the binding constant of Ce3+ to Daphnia magna at toxic concentrations was 5.83. The log K values for the competitive effects of Ca2+ and Na+ were 3.73 and 2.59, respectively, while the log K value for the protective effect of fulvic acid was 3.76. These results contribute to understanding the toxicity of Ce and will help predict the toxicity of Ce in freshwater.

Parameters estimation-based calibration method for rotational axis of line-structured light 3D reconstruction.

Rotation is a critical component in 3D reconstruction systems, where accurate calibration of rotation axis parameters is essential for 3D stitching. In this study, what we believe to be a novel parameters estimation-based method for calibrating rotation axis parameters using 2D planar targets is proposed. Compared to traditional circle fitting methods, this method takes both orientation and position information into account, resulting in better precision performance. By leveraging the transmission of spatial pose relationships, the parameters estimation-based calibration method also effectively mitigates the impact of noise for more accurate calibration of rotation axis parameters. Error validation and 3D reconstruction experiments proved the superior performance of the proposed method. The experiment results demonstrate the effectiveness and applicability of the approach in enhancing the calibration of rotation axis parameters for 3D reconstruction systems.

A platform for whole-genome speed introgression from Aegilops tauschii to wheat for breeding future crops.

Breeding new and sustainable crop cultivars of high yields and desirable traits has been a major challenge for ensuring food security for the growing global human population. For polyploid crops such as wheat, introducing genetic variation from wild relatives of its subgenomes is a key strategy to improve the quality of their breeding pools. Over the past decades, considerable progress has been made in speed breeding, genome sequencing, high-throughput phenotyping and genomics-assisted breeding, which now allows us to realize whole-genome introgression from wild relatives to modern crops. Here, we present a standardized protocol to rapidly introgress the entire genome of Aegilops tauschii, the progenitor of the D subgenome of bread wheat, into elite wheat backgrounds. This protocol integrates multiple modern high-throughput technologies and includes three major phases: development of synthetic octaploid wheat, generation of hexaploid A. tauschii-wheat introgression lines (A-WIs) and homozygosis of the generated A-WIs. Our approach readily generates stable introgression lines in 2 y, thus greatly accelerating the generation of A-WIs and the introduction of desirable genes from A. tauschii to wheat cultivars. These A-WIs are valuable for wheat-breeding programs and functional gene discovery. The current protocol can be easily modified and used for introgressing the genomes of wild relatives to other polyploid crops.

Correlation analysis between Raman spectral signature and transcriptomic features of carbapenem-resistant Klebsiella pneumoniae.

The Raman microspectroscopy technology has been successfully applied to evaluate the molecular composition of living cells for identifying cell types and states, but the rationale behind it was not well investigated. In this study, we acquired single-cell Raman spectra (SCRS) of three Klebsiella pneumoniae (K. pneumoniae) strains with different Carbapenem resistant mechanisms and analyzed them with machine learning algorithm. Two carbapenem resistant Klebsiella pneumoniae (CRKP) strains can be successfully distinguished from susceptible strain and CRKP with KPC or IMP carbapenemases can be classified with an overall accuracy achieving 100 %. Furthermore, we performed a correlation analysis between transcriptome and Raman spectra, and found that Raman shifts such as 752 and 1039 cm-1 highly correlated with drug resistance genes expression and could be regarded as Raman biomarkers for CRKP with different mechanisms. The findings of the study provide a theoretical basis for identifying the relationship between Raman spectra and transcriptome of bacteria, as well as a novel method for rapid identification of CRKP and their carbapenemases types.

[Accumulation Characteristics, Sources, and Health Risks of Soil Lead of Urban Parks in Beijing].

Urban parks have multiple functions such as social culture, economy, and environmental services during urban development. The rapid development of cities and economy may lead to the accumulation of heavy metals in the soil of urban parks, which may threaten human health. A total of 140 soil samples were collected in 32 typical parks in Beijing. The accumulation characteristics of Pb in the soil of urban parks were analyzed using the single-factor pollution and geo-accumulation indices. The sources of Pb pollution in soils were quantitatively analyzed using the stable isotope of Pb, and the health risk was assessed using the probabilistic risk assessment method based on Monte Carlo simulation. The results showed that the geometric mean of Pb in soils of urban parks in Beijing was 38.63 mg·kg-1, which was 1.48 times the background value. However, it did not exceed the risk screening value(GB 36600-2018). The accumulation of soil Pb in urban parks increased with the increase in the proximity between the park and the central urban area and the increase in the establishment time. The soil Pb pollution index of 2 ring, 2-4 ring, and 4-6 ring parks were 0.16, 0.10, and 0.09, which did not reach the pollution level, and the geo-accumulation indices were 0.80, 0.07, and -0.31, respectively. Except for the no-moderate pollution level in ring 2 and ring 2 to ring 4, the other rings did not reach the pollution level. The sources of Pb pollution in urban parks were coal combustion, road dust, and paint, with the contributions of 45.4%, 19.6%, and 13.9%, respectively. The 95% quantiles of hazard index(HI) of soil Pb in the park for different age groups were 1.11E-01, 8.57E-02, 6.39E-02, 1.64E-02, 1.36E-02, 1.26E-02, 1.64E-02, and 1.78E-02, respectively, which indicated that there was no potential non-carcinogenic risk(HI<1). Exposure duration was the most sensitive to non-carcinogenic risks in people aged 0-18 years, and soil Pb concentration was the most sensitive to non-carcinogenic risks in people aged 18-80 years. The increase in body weight often reduced the non-carcinogenic risks. These results can provide theoretical basis for soil environmental risk control in urban parks.

The rapid increase of urban contaminated sites along China's urbanization during the last 30 years.

Contaminated sites pose serious threats to the soil environment and human health. However, the location and temporal changes of urban contaminated sites across China remain unknown due to data scarcity. Here, we developed a machine-learning model to identify the contaminated sites using public data. Results show that the trained model with 2,005 surveyed site samples and six variables can achieve a model performance evaluation value of 0.86. 43,676 contaminated sites were identified from 83,498 polluting enterprise plots in China. However, these contaminated sites have significant spatiotemporal heterogeneity, mainly located in economically developed provinces, urban agglomerations, and core urban areas. Moreover, the contaminated sites increased by 325% along with urban expansion from 1990 to 2018. The abandoned contaminated sites increased rapidly, but the contaminated sites in production decreased continuously. This methodological framework and our findings contribute to the precise management of contaminated sites and provide insights into urban sustainable development.

[Soil Pollution Prediction from Enterprise for Coking Industry in China Based on Machine Learning and Scenario Analysis].

Owing to the lack of sequential monitoring data of soil pollutants in coking industry enterprises, it is hard to accurately predict their soil pollution. To predict the trend of soil pollution of coking industry enterprises in the future, a prediction model should be developed using machine learning based on the influencing factors. A total of 13 potential factors were selected from the enterprise characteristics, enterprise management level, pollutant characteristics, and natural factors, and the main controlling factors were identified. On this basis, the prediction models were developed using a support vector machine, BP neural network model, decision tree model, and logistic model, and then the pollution situation of enterprises in the coking industry in 2025 and 2030 was predicted under different scenarios. The results indicated that time of service for the enterprise, time of establishment for the enterprise, the environmental illegal record, soil clay, and annual wind speed were the major controlling factors of soil pollution of enterprises in the coking industry. Compared with the support vector machine, BP neural network model, and decision tree model, the logistic model had a robust performance index, with an area under the receiver operating characteristic curve of 0.91. The accurate rate and recall rate were 84% and 88%, respectively. Under the optimistic scenario, there will be 1599 and 1695 plots with a high probability of pollution in the coking industry in 2025 and 2030, respectively; under the pessimistic scenario, there will be 1671 and 1715 plots with a high probability of pollution in the coking industry in 2025 and 2030, respectively. The results of this study provided a scientific basis for soil environmental remediation and eco-environmental strategy development for the coking industry.

High-speed nonlinear frequency sweeping signal distance extraction algorithm based on the table lookup method.

In the domain of frequency sweeping interferometry, the accurate extraction of distance information from nonlinear frequency scanning signals holds paramount significance in ensuring meticulous measurements of high precision. This paper presents a novel, to the best of our knowledge, high-speed distance extraction algorithm based on the table lookup method and validates its feasibility through theoretical models, simulations, and practical experiments. The proposed algorithm achieves comparable accuracy to traditional methods involving resampling and Hilbert transform. However, it outperforms them in robustness against noise and variations in sampling points. This method can accurately process signals sampled even below the Nyquist sampling rate. The simplicity and computational efficiency of the proposed approach make it suitable for various nonlinear sampling applications, promising broad applicability in scientific and engineering contexts.

Hair-biomonitoring assessment of rare-earth-element exposure in residents of the largest rare-earth mining and smelting area of China.

The long-term and large-scale mining of rare earth minerals may lead to an accumulation of rare earth elements (REEs) in the environment, posing potential health risks to residents. We collected scalp hair (n = 254) from residents of a smelting area, a mining area, and a reference area to clarify human exposure to REEs. The contents of 15 REEs investigated in human hair samples were notably higher in the mining and smelting areas than in the reference area. Significant differences between some REEs were observed between the mining and smelting areas, for instance, cerium (Ce), dysprosium, and praseodymium. In the study areas, exposure to different sources of REEs may be one of the factors that contributed to the variations of REE correlations and clusters in human hair. Furthermore, in the smelting area, Ce contents in hair decreased with increasing age of children. However, Ce contents in the hair of adults increased with age. In contrast, Ce accumulation continuously increased in the reference area residents' hair with age. Regression results indicated that age and location were more important than sex when considering the influence on REE accumulation in residents' hair. The results of this study may help policymakers to implement guidelines to alleviate residents' exposure to REE in mining and smelting areas.