Background Area Research Articles

Estimation of Carbon and Nitrogen Contents in Forest Ecosystems in the Background Areas of the Russian Arctic (Murmansk Region)

In this study, carbon and nitrogen contents in the undisturbed terrestrial ecosystems in the northern taiga zone of Russia’s Murmansk region were estimated. The goal of this study was to examine the carbon and nitrogen dynamics in atmospheric precipitation, assimilating organs of coniferous trees (Picea obovata and Pinus sylvestris), needle litter, soils, and soil water. The objects of our research were the most common dwarf shrub-green moss spruce forests and lichen-dwarf shrub pine forests of the boreal zone. The study was carried out on permanent plots between 1999 and 2020. The long-term dynamics of carbon concentrations in snow demonstrated a trend towards increasing carbon concentrations in forested and treeless areas of the Murmansk region. It was shown that in representative spruce and pine forests, the concentrations and atmospheric precipitation of carbon compounds and carbon leaching with soil water were higher below the tree crowns, compared to between the crowns. In soil water, a decrease was found in carbon concentration with the soil profile depth. For soils, the highest carbon concentrations were found in the organic and illuvial soil horizons. The main soil sinks of carbon and nitrogen in northern taiga forests were found to be located in the organic soil horizon below the crowns. In northern taiga forests, the carbon content of living Picea obovata and Pinus sylvestris needles and Pinus sylvestris needle litter had minor variability; no significant interbiogeocoenotic and age differences were found. We found that the nitrogen content in brown needles and needle litter was significantly lower compared to photosynthetically active needles, probably due to retranslocation processes (withdrawal before needle abscission), corroborating the literature in the results session. The largest stocks of carbon and nitrogen in northern taiga forests are concentrated in the soil organic horizon, and the removal of these elements with soil water is insignificant. Carbon and nitrogen stocks in living and fallen needles are lower than in soil. The least amount of carbon and nitrogen is contained in atmospheric precipitation.

Approaches to the Development of an Assessment of the Quality and Authenticity of Natural Drinking Water Supplied from Lake Baikal

The article discusses approaches to the organization of assessment of the quality and authenticity of natural drinking water supplied from Lake Baikal. These developments are based on molecular biology methods to determine the quality and authenticity of natural drinking water supplied from Lake Baikal. A unique property of bacteria to form restriction enzymes (ER) as a reaction to the ingress of extraneous microflora into the habitat has been revealed. This method of searching and determining ER enzymes is widely used in scientific research. Quality control and authenticity of Baikal drinking water when it is bottled for sale is not carried out. In this work, it was found that in strains of bacteria isolated from areas of anthropogenic influence, where allochthonous (extraneous) microflora falls, strains of bacteria with ER enzymes were detected. In water samples taken in clean background areas (pelagic lakes, deep-sea sediments), bacteria with enzymes (ER) are not detected. The fact is substantiated that the detection of ER enzymes in bacterial strains is evidence that the water is taken either in areas where extraneous microflora enters, or water is not from a natural source – Lake Baikal. This property of bacteria is the basis for assessing the quality and authenticity of natural drinking water supplied from Lake Baikal. This approach makes it possible to establish the authenticity of Baikal drinking water and the exclusion of fakes.

IMAGE FEATURE EXTRACTION METHODS FOR STRUCTURE DETECTION FROM UNDERWATER IMAGERY

Abstract. The use of autonomous underwater vehicles (AUVs) for surveying underwater infrastructure presents a potential cost saving in comparison to remotely operated vehicles (ROVs). One of the challenges when processing images of underwater structures captured by an AUV, is that vast number of images captured during the mission usually do not show the structure. For instance, images captured during the dive to the structure or of the sea floor, or of the deep sea facing away from the structure. Too many images captured, without relevant information for a 3D reconstruction of the structure, leads to increased processing time and issues during the reconstruction process. There are two solutions to reduce the images to only images showing the structure. Firstly, only images of the structure are captured in the first place or remove images that are not useful after the capture and before further processing. This study developed and evaluated techniques that would enable the first strategy to be applied in an AUV. To apply this strategy in an AUV, would require an on-board structure detection system to ensure that they are correctly orientated for capturing useful footage during a survey mission. However, the marine environment poses several challenges to image-based object detection. Furthermore, small AUVs have limited power and computational resources available while deployed on a mission. To investigate the suitability of creating a lightweight structure detection model for the purpose of image evaluation, three computationally efficient image feature extraction methods (colour moments, local binary patterns (LBP), and Haar wavelet decomposition) were evaluated for their ability to distinguish underwater structures from background areas using unsupervised k-means models. LBP was found to be an effective method for identifying underwater structures in open water conditions. For identifying a structure against the seabed, colour moments were identified as the most effective method.

Road dust exposure and human corneal damage in a plateau high geological background provincial capital city: Spatial distribution, sources, bioaccessibility, and cytotoxicity of dust heavy metals

Ocular surface diseases are common in the plateau city, Kunming China, the continued daily exposure to heavy metals in dust may be an important inducement. In this study, the 150 road dust samples from five functional areas in Kunming were collected. The concentrations, distribution, possible sources, and bioaccessibility of heavy metals were analyzed. The adverse effects of dust extracts on human corneal epithelial cells and the underlying mechanisms were also assessed. The concentrations (mg·kg−1) of As (19.1), Cd (2.67), Cr (90.5), Cu (123), Pb (78.4), and Zn (389) in road dust were higher than the soil background, with commercial and residential areas showing the highest pollution. Their bioaccessibility in artificial tears was As (6.59 %) > Cu (5.11 %) > Ni (1.47 %) > Cr (1.17 %) > Mn (0.84 %) > Cd (0.76 %) > Zn (0.50 %) > Pb (0.31 %). The two main sources of heavy metals included tire and mechanical abrasion (24.5 %) and traffic exhaust (21.6 %). All dust extracts induced cytotoxicity, evidenced by stronger inhibition of cell viability, higher production of ROS, and altered mRNA expression of antioxidant enzymes and cell cycle-related genes, with commercial- areas-2 (CA2)-dust extract showing the greatest oxidative damage and cell cycle arrest. Our data may provide new evidence that dust exposure in high geological background cities could trigger human cornea damage.

Multi-scale feature decoupling and similarity distillation for class-incremental defect detection of photovoltaic cells

Existing vision-based photovoltaic cell defect detection methods usually update models with all defect data of both old and new categories to adapt to new classes emerging in the dynamic data stream from realistic production lines. This model updating strategy wastes resources and sometimes is infeasible due to the confidentiality of historical data. In this paper, we propose a novel distillation-based model updating method, i.e., multi-scale Feature Decoupling and Similarity Distillation (mFDSD), which updates the model with only new data from the dynamic data stream but can effectively identify both old and new categories. In mFDSD, we design a mask-based feature decoupling distillation module and a similarity-regulated feature distillation module to adaptively regulate distillation losses assigned to important defective areas, less-important background areas, and discarded areas of feature maps. Experimental results demonstrate that our mFDSD outperforms the current state-of-the-art distillation-based model updating methods for the class-incremental defect detection of photovoltaic cells.

Transcriptome and histological analyses on the uterus of freckle egg laying hens

BackgroundIn this study, we explored the characteristics and causes of freckle formation. We collected 15 normal and freckled eggs each for eggshell index testing and hypothesized that the structure and function of the uterus would have a direct effect on freckled egg production given that eggshells are formed in the uterus. To test this hypothesis, we collected uterine tissue from laying hens (418 days of age) that laid normal (Group C, n = 13) and freckled (Group T, n = 16) eggs for 7 consecutive days.ResultsWhen we examined the eggshell quality, we found that the L value was significantly lower (P < 0.05) in the freckled site group of freckled eggs compared to the normal egg group during the detection of blunt pole, equator, and sharp pole of the eggshell color. The a-values of the three positions were significantly higher (P < 0.05) in the freckled site group of freckled eggs, and the a-values of the blunt pole were significantly lower (P < 0.05) in the background site group of freckled eggs, compared to the normal egg group. The b-values were significantly higher (P < 0.05) at three locations in the freckled site group of freckled eggs compared to the normal egg group. During the detection of eggshell thickness, the blunt pole was significantly higher (P < 0.05) in the freckled egg site group of freckled eggs compared to the normal egg group, and there was no significant difference between the other groups (P > 0.05). There was no significant difference (P > 0.05) between the transverse and longitudinal diameters of the eggs in each group.We then performed histopathology and transcriptome analyses on the collected tissue. When compared with group C, uterine junctional epithelial cells in group T showed significant defects and cilia loss, and epithelial tissue was poorly intact. From transcriptomics, genes that met (|log2FC|) ≥ 1 and P < 0.05 criteria were screened as differentially expressed genes (DEGs). We identified a total of 136 DEGs, with 101 up- and 35 down-regulated genes from our RNA-seq data. DEGs identified by enrichment analyses, which were potentially associated with freckled egg production were: IFI6, CCL19, AvBD10, AvBD11, S100A12, POMC, and UCN3. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that pathways were associated with immunoreaction and stress stimulation, e.g., complement activation, interleukin-1 cell reactions, viral responses, cell reactions stimulated by corticotropin releasing hormone, steroid hormone mediated signaling pathways, staphylococcal infections, B cell receptor signaling pathways, and natural killer cell mediated cytotoxicity.ConclusionsFrom these data, freckled areas deepen freckled eggshell color, but background areas are not affected. At the same time,we reasoned that freckle eggs may result from abnormal immune responses and impaired uterine functions induced by stress. Therefore, the uterus of laying hens in a state of stress and abnormal immune function can cause the appearance of freckled eggs.

To the study of the features of the heavy metals deposition in the mosses of the Samara region

The features of the deposition of heavy metals by mosses from the atmosphere in the identified background area (Krasnosamarsky forest) for the Samara region were studied. Pleurozium schreberi showed the highest metal accumulating capacity, Brachythecium salebrosum - average, Dicranum polysetum - the lowest. Climacium dendroides showed high species specificity in the accumulation of heavy metals among the studied species, which may be related to its water regime. A comparative analysis of the chemical “fingerprint” of the average content of heavy metals for the mosses of the Krasnosamarsky forest area according to to Markert revealed a negative deviation from the average content of heavy metals in the bryophytes of the Samara region, which meant that there were no atmospheric sources of heavy metal pollution in the study area. Mosses growing in the Krasnosamarksky forest can be used as reference species for the region. The revealed increased level of accumulation of a number of elements (Cr, V, Co, Ni, As, Fe, Zn) demonstrated the biogeochemical features of the Samara region in comparison with the reference moss species Pleurozium schreberi in Western Europe. This also indicates the need to use indicators of species growing in the region as reference values.

An image registration-based self-supervised Su-Net for carotid plaque ultrasound image segmentation

Background and ObjectivesTotal Plaque Area (TPA) measurement is critical for early diagnosis and intervention of carotid atherosclerosis in individuals with high risk for stroke. The delineation of the carotid plaques is necessary for TPA measurement, and deep learning methods can automatically segment the plaque and measure TPA from carotid ultrasound images. A large number of labeled images is essential for training a good deep learning model, but it is very difficult to collect such large labeled datasets for carotid image segmentation in clinical practice. Self-supervised learning can provide a possible solution to improve the deep-learning models on small labeled training datasets by designing a pretext task to pre-train the models without using the segmentation masks. However, the existing self-supervised learning methods do not consider the feature presentations of object contours. MethodsIn this paper, we propose an image registration-based self-supervised learning method and a stacked U-Net (SSL-SU-Net) for carotid plaque ultrasound image segmentation, which can better exploit the semantic features of carotid plaque contours in self-supervised task training. ResultsOur network was trained on different numbers of labeled images (n = 10, 33, 50 and 100 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The network trained on the entire SPARC dataset was then directly applied to an independent dataset collected in Zhongnan hospital (n = 497, Wuhan, China). For the 44 subjects tested on the SPARC dataset, our method yielded a DSC of 80.25-89.18% and the produced TPA measurements, which were strongly correlated with manual segmentation (r = 0.965-0.995, ρ< 0.0001). For the Zhongnan dataset, the DSC was 90.3% and algorithm TPAs were strongly correlated with manual TPAs (r = 0.985, ρ< 0.0001). ConclusionsThe results demonstrate that our proposed method yielded excellent performance and good generalization ability when trained on a small labeled dataset, facilitating the use of deep learning in carotid ultrasound image analysis and clinical practice. The code of our algorithm is available https://github.com/a610lab/Registration-SSL.

Systematic literature review of healthcare services for the elderly: trends, challenges, and application scenarios

In recent years, healthcare for the elderly has received attention from researchers worldwide in the context of global aging. This paper provides a comprehensive overview of healthcare services for older people from 2019 to 2023, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to screen published papers in the Web of Science database, and identifies three different application scenarios for healthcare services for older people after analyzing the research objectives of the selected papers. This study examined the common health problems and needs of older adults, and the key parameters used to assess healthcare services for this population. This review also explores how healthcare providers can promote healthy aging and prevent age-related diseases in older patients. This study further evaluates the background and application areas of the papers and explains the methodology of the main ideas proposed in the studies. The review concludes with an analysis of the main trends and challenges that are likely to emerge in healthcare services for older people.

High-performance deep transfer learning model with batch normalization based on multiscale feature fusion for tomato plant disease identification and categorization

Deep learning and machine learning are cutting-edge methods for analysing images that have considerable potential. Artificial Neural Networks (A-NNs), one of the most well-known methods of computer intelligence, are now used in machine learning (ML) and deep transfer learning (DL) to raise plant production and quality. Identification and primary prevention of plant diseases at the appropriate time are essential for boosting productivity. Due to the phenomenon of minimally intense data in the background and foreground areas of the image, the extensive colour similarity between regions of unhealthy and normal leaves, the presence of noise in the sampling data, and changes in the location, size, and shape of plant leaf, it is difficult to correctly identify and classify plant diseases. In an effort to address these issues, a reliable technique for classifying plant diseases was developed by using a deep AlexNet CNN architecture as the main network with batch normalisation. In the three-step process, the first annotation is made to obtain the RoI (region of interest). The AlexNet CNN is therefore suggested for deep primary feature extraction in a constructed efficient network. The research demonstrates that the existing strategy is superior to more recent ones in terms of accuracy and dependability in recognising diseases in plants. Based on a deep transfer AlexNet CNN model, this research work developed a model for diseases identification and classification in plant leaves. It is trained using additional datasets that include a variety of plant leaf classifications and background images. From Plant Village and Kaggle, we gathered data on healthy and diseased tomato plant leaves. We are obtaining a near-balanced dataset containing ten different leaf disease kinds, such as bacterial, fungal, viral, and nutrient insufficiency. Ten classes have been considered for this research by gathering a dataset with associated images of the typical and abnormal tomato plant leaves. Considered in this work were the various labels for healthy and diseased tomato leaves, such as early blight, Bacterial spot, late bright mold, healthy, etc. Since deep CNN models have shown notable machine vision results, they are used in this case to diagnose and categorise plant illnesses from their leaves. As a result, the proposed CNN models can thus now be evaluated from confusion matrix using data analysis criteria, primarily focusing on metrics for evaluation like training and validation accuracy, loss, Recall, Precision, F1 score, processing speed, and performance.

Co-exposure of heavy metals in rice and corn reveals a probabilistic health risk in Guizhou Province, China

The adverse effects of heavy metals have arousing concern in the high geological background area, especially in southwestern Guizhou, China. However, the pollution status of heavy metals are still unclear when exposed to rice and corn in Guizhou province. Therefore, the concentration, pollution level, spatial distribution, and probabilistic health risks of Ni, Cr, Pb, Cu, and Zn are estimated in rice and corn. A total of 241 samples (117 for rice and 124 for corn) were collected from Guizhou province and measured by a method of inductively coupled plasma-mass spectrometry (ICP-MS). The results showed that rice and corn were contaminated with Ni and Cr. High concentrations of Ni were presented in the southeast of rice. It indicated that 22.0 % of rice samples were contaminated with Ni. HI values for children and adults exceeded 1.0 in rice and corn, suggesting that humans might be subject to probabilistic non-carcinogenic risks. FTCR demonstrated that rice and corn might cause probabilistic carcinogenic risks to children and adults, which were both greatly higher than 1.0 × 10−4. Moreover, the contributions of Ni to the HI and FTCR were the highest for adults and children. Therefore, more attention should be paid to the exposure of heavy metals in rice and corn, especially in Ni. The results would provide a novel prospective for pollution control and be helpful for environmental regulation.

Spatiotemporal Variability of Urban Air Pollution in Bucharest City

Urban air pollution is one of the major challenges that cities around the world face. Particulate matter (PM), nitrogen dioxide (NO2), volatile organic compounds (VOCs), and other pollutants are many times over the recommended airborne exposure, generating a strong impact on human health and city well-being. Considering Bucharest as a case study, this study aimed to investigate the patterns of particulate matter and nitrogen dioxide concentrations. Multiyear data from the Romanian National Air Quality Monitoring Network were used to investigate spatial and temporal variability. All air pollutants presented a typical bimodal trend during the day, with specific double peaks corresponding to the morning rush hours and nighttime. Spatial variability in NO2 concentrations was observed, with almost double the concentration values in the city center during midday compared with those for the background and industrial areas. A weekly pattern of PM was noticed, with lower concentrations during the weekends in comparison with those during weekdays, more pronounced in the case of PM10 compared with the case of PM2.5. The fine particle fraction presented monthly and seasonal variability, with higher levels during the cold months compared with the warm months, mainly corresponding to the increased household heating. The estimated proportion of mortality attributable to annual exposure to an air PM2.5 above 5 μg/m3 in Bucharest ranged between 7.55% and 8.26%, with the maximum from 2021. By contrast, the estimated proportion of mortality attributable to PM10 and NO2 above 10 μg/m3 was significantly lower, with values around 4%. The results are useful in supporting environmental planning measures to decrease urban air pollution.

Background concentrations and spatial distribution of PFAS in surface waters and sediments of the greater Melbourne area, Australia

A survey of per- and polyfluoroalkyl substances (PFAS) was conducted in Melbourne, Australia to determine background concentrations in residential, industrial, municipal wastewater treatment plants, and rural land uses. Surface water and sediment samples collected from 65 sites with different catchment land uses were analysed for thirty-three PFAS. Twenty-two out of thirty-three targeted PFAS were detected, with at least one PFAS species was detected in 98% water samples and 8% sediment samples. One site was determined to have point-source pollution from an airport (surface water Σ33PFAS = 4261 ng/L) and was excluded from statistical analyses. The median Σ33PFAS concentration in surface water was 63.5 ng/L and the average was 78.6 ng/L (range < DL-526 ng/L). PFAS species with the highest median concentrations were PFBA (11.3 ng/L), PFHxA (9.2 ng/L), PFOA (8.3 ng/L), PFOS (8.0 ng/L), PFPeA (7.5 ng/L), PFHpA (3.2 ng/L), and PFHxS (2.9 ng/L).The average Σ4PFAS in sediments was 0.35 ng/g d.w. (range = <DL-6.7 ng/g) and the congeners detected were PFOS, N-MeFOSAA, N-EtFOSAA, and N-EtFOSE. Significantly higher concentrations of all PFAS were found in urban areas than rural, except for PFBA which was evenly dispersed throughout all areas. Short-chain perfluoroalkyl carboxylic acids (PFCAs), PFBA, PFPeA, PFHxA, PFHpA, and the PFSA, PFBS were evenly dispersed throughout urban and industrial areas. PFOA, PFNA, and PFDA were higher, but not significantly, in residential catchments than industrial catchments. Industrial areas had significantly higher concentrations of perfluoroalkyl sulfonic acids (PFSAs), PFPeS, PFHxS, and PFOS, and 6:2 FTS than other urbanised areas. The distribution of PFAS throughout catchment types in this study displayed similar trends to other studies including findings of PFBA in background areas. Ongoing monitoring of defined catchments is recommended to improve understanding of PFAS sources and changes in environmental profiles, and to assess the implications of its occurrence throughout the environment.

Enrichment Characteristics and Correlation Analysis of Se and Heavy Metals in Soil-Blueberry Fruit Systems in the Different Geological Background Areas of Guizhou Province

Enrichment Characteristics and Correlation Analysis of Se and Heavy Metals in Soil-Blueberry Fruit Systems in the Different Geological Background Areas of Guizhou Province

Determining the number and size of background samples derived from an area adjacent to the target sample that provide the greatest support for a POI in a target sample

When sampling an item or surface for DNA originating from an action of interest, one is likely to collect DNA unrelated to the action of interest (background DNA). While adding to the complexity of a generated DNA profile, background DNA has been shown to aid in resolving the genotypes of contributors in a targeted sample, and where references of donors to the background DNA are not available, strengthen the LR supporting a person of interest contributing to the targeted sample. This is possible thanks to advances in probabilistic genotyping, where forensic labs are able to deconvolute complex DNA profiles to obtain lists of genotypes and their associated weights. Coupled with DBLR™, one can then compare multiple evidentiary profiles to each other to determine the contribution of common, but unknown, contributors. Here, we consider factors associated with taking background samples and whether one should collect multiple background samples that all relate to a single target sample, or if one should collect larger background samples rather than smaller samples. Background samples consisted of DNA accumulated on the items primarily by one or both occupants of a single household, while targeted samples were generated from touch deposits, or saliva deposits that had been left to air dry. Samples were collected from areas of various sizes, consisting of only the background, the target and the background directly beneath it, and the target and additional surrounding background. A broad range of DNA quantities were recovered, with larger background samples (400 cm2) yielding significantly more DNA than smaller background samples (30 cm2). Significant differences in DNA quantities between target samples were not observed. Generated DNA profiles were interpreted using STRmix™ and DBLR™, and where there was support for a common donor between the background and target sample, pairwise comparisons were performed to observe the effect on the LR supporting the target DNA donor contributing to the targeted sample when conditioning on one (or two) common donor between the targeted sample and 1–8 background samples. Multiple background samples gave significantly higher LRs compared to a single background sample, the larger sampled background area resulted in larger LR gains than the smaller areas, and four or more background samples reduced LR variability considerably. Here we provide recommendations for the minimum and ideal number of additional background samples that should be collected, and that several smaller samples may be more beneficial than a single larger sample.

Spatial-temporal variation, source apportionment and risk assessment of lead in surface river sediments over ∼20 years of rapid industrialisation in the Pearl River Basin, China

Lead (Pb) is a highly toxic element and is not essential to the human body. Lead pollution caused by human activities and a high geological background is considered a global environmental issue. According to the China Geochemical Baseline (CGB) project, the Pearl River Basin had the highest Pb content in alluvial sediments of 30 first-level basins in China. For this reason, it is of great significance to determine the temporal and spatial variations in Pb and their influencing factors in the Pearl River Basin. In this study, 956 stream sediment samples collected in the 1980 s (early stage) and 129 river sediment samples collected from 2008 to 2010 (late stage) were used to study the background value and spatial-temporal variation characteristics of Pb in river sediments in the Pearl River Basin. The Pb source apportionment and an ecological risk assessment were also carried out. The background value of Pb (36.2 mg·kg–1) in the river sediments of the Pearl River Basin was significantly higher than that in China (22.1 mg·kg–1). The parent rocks determine the Pb background in sediments and the high Pb background areas mainly comprised carbonate rocks and acid volcanic rocks. Over 20 years of rapid industrialisation, the average Pb increased from 43.3 to 68.3 mg·kg–1 in the Pearl River Basin. The BCR analysis revealed that Pb mainly existed in the reducible phase (48 % on average) and residue phase (42 % on average). The enrichment factor and geo-accumulation index indicated that the late-stage sediments experienced more Pb pollution than the early-stage sediments. However, the risk assessment code (RAC) showed that there was a low ecological risk of Pb in the late-stage sediments. The factor analysis results for the two rounds of data were significantly different. The Pb content in early-stage sediments was closely related to Al2O3 and Zr, while Pb in the late-stage sediments was mainly related to Zn, As, Sb, Au and Hg, indicating that the increase in Pb in the later samples was mainly influenced by human activities. The Pb isotope composition of the late-stage sediments confirmed that low Pb content was mainly controlled by natural sources, while high Pb content was significantly affected by anthropogenic sources. Combining the results of spatial-temporal variation, chemical speciation and source apportionment indicated that the rapid rise of Pb in late-stage sediments in certain areas could be attributed to mining and smelting activities during the process of industrialisation over 20 years. The anthropogenic exogenous Pb could be immobilised by Fe-Mn (hydro)oxides when it entered the soil, so although there was a high background the ecological risk of Pb in river sediments was low. In the future, Pb pollution control and remediation needs to be strengthened in the Pearl River Basin to avoid the outbreak of potential ecological risks linked to Pb.

Machine learning-based prediction of cadmium bioaccumulation capacity and associated analysis of driving factors in tobacco grown in Zunyi City, China

Tobacco grown in areas with high-geochemical backgrounds exhibits considerably different cadmium (Cd) bioaccumulation abilities due to regional disparities and environmental changes. However, the impact of key factors on the Cd bioaccumulation ability of tobacco grown in the karst regions with high selenium (Se) geochemical backgrounds is unclear. Herein, 365 paired rhizospheric soil-grown tobacco samples and 321 topsoil samples were collected from typical karst tobacco-growing soil in southwestern China and analyzed for Cd and Se. XGBoost was used to predict and evaluate the Cd bioaccumulation ability of tobacco and potential influencing factors. Results showed that regional geochemical characteristics, such as soil Cd and Se contents, soil type, and lithology, have the highest influence on the Cd bioaccumulation ability of tobacco, accounting for 46.5% of the overall variation. Moreover, soil Se contents in high-geochemical background areas considerably affect Cd bioaccumulation in tobacco, with a threshold for the mutual suppression effects of Cd and Se at a soil Se content of 0.8 mg/kg. According to the results of bivariate local indicators of spatial association analysis, tobacco cultivated in the central, northeast, and southeast regions of Zunyi City carries a lower risk of soil Cd contamination. This study provides new insights for managing tobacco cultivation in karst regions.

STOCKS OF POTENTIALLY TOXIC ELEMENTS IN THE GROUND COVER OF NORTHERN TAIGA PINE FORESTS UNDER AEROTECHNOGENIC POLLUTION

The paper presents the results of studying the distribution of stocks of potentially toxic elements (Ni, Cu) in subcrown spaces and “windows” of stands in medium-aged pine forests in the background area of the Kola Peninsula and that under environmental pollution by atmospheric emissions of the Severonickel Combine (Monchegorsk, Murmansk Region). On the basis of previously obtained data on the stocks of aboveground biomass of the components of the ground cover, mortmass of plant debris and forest litter and data of chemical analysis by means of atomic absorption spectrometry of leaves of dominant shrub species, aboveground parts of the moss Pleurozium schreberi (Brid.) Mitt, and lichen Cladonia stellaris (Opiz) Pouzar et Vězda, as well as plant waste and organogenic horizon (forest litter) of Al-Fe-humus podzols, Ni and Cu stocks in different components of forest ecosystems were calculated. It was found that the sharp decrease in atmospheric emissions by the Severonickel Combine over the last 20 years has not affected the level of habitat contamination, and the stock of heavy metals in the upper organogenic horizon of Al-Fe-humus podzols remains very high, especially in the impact zone, which does not even allow to start the process of restoration of the ground cover in this area. The stock of heavy metals in the plant waste consistently increases with the increase in the level of forest litter contamination when approaching the source of pollution, but the scale of this increase is significantly smaller in the mortmass compared to the litter. The pattern of distribution of heavy metal stocks in components of ground cover, plant waste and forest litter over the phytocenosis area is fundamentally different in background conditions and under aerotechnogenic pollution, which is due to differences in the accumulation of organic matter in different types of microsites. The ratio of Ni : Cu concentrations and stocks also differs in the soil-vegetation cover in pine forests of the background area and under the influence of aerotechnogenic pollution.

Measurement of transfer factors from soil-to-plant/food crop of Naturally Occurring Radionuclide Materials (NORMs) in Nigeria: a review

One of the most crucial factors in the environmental safety assessment for the local and global communities, situated either in normal background areas or around nuclear facilities, is the uptake of radionuclides from soil by plants, which is typically stated as soil-to-plant Transfer Factor (TF) or Transfer Coefficient (TC). Such a metric, which can be defined as the ratio of radioactivity unit per dry crop mass to that of unit per dry soil mass, is necessary for environmental transfer models, which help to estimate the radionuclide concentrations in agricultural crops and calculate the dose exposure to people. In this article, a number of research studies conducted by scholars in Nigeria, published in both national and international journals, were compiled and reviewed for the purpose of providing an integrated and synthesized overview of the current state of knowledge on NORMs’ transfer factors in Nigeria. The activity concentrations of 40K, 226Ra, 228Ra, 232Th and 238U were measured by different research groups using gamma ray spectrometry coupled with either NaI(Tl) or HPGe detector. The activity concentrations of natural radioactivity in soils samples were generally higher than the values recorded in plant and food crop samples in almost all the research studies considered in the review. About 79% of the values of soil-to-plant transfer factor of 40K in all the studies under review were greater than the recommended value, while the values of transfer factors reported for 232Th and 238U were all greater than the recommended values.

Transfer of heavy metals from soil to tea and the potential human health risk in a regional high geochemical background area in southwest China

Tea is one of the most consumed nonalcoholic beverages. The collaborative analysis of heavy metals soil-to-tea transfer and the associated potential risk to human health is important. This study features a survey of As, Cd, Pb and Cr in 144 paired soils and tea leaves from six main tea-growing regions (Fengqing, Linxiang, Yongde, Mangshi, Longling, and Yunlong) in Yunnan, China. The data showed soil acidification (pH = 4.77–5.17) in tea plantations, affecting heavy metals bioavailability thereby the transfer to tea leaves. Soil total and bioavailable As, Cd, Pb and Cr concentrations were 1.45–117, 0.025–0.67, 15.2–153, 3.8–409 mg kg−1 and 0.03–0.22, 0.011–0.38, 0.59–17, 0.013–0.47 mg kg−1, respectively. Specifically, As concentration in 20.8 % of the soil samples exceeded the standard value at 40 mg kg−1, while the standard-exceeding ratio of Cr was low at 9 %. Besides, Cd showed high bioavailability at 44–56.1 %, while Cr was low at 0.12–0.34 %. Arsenic, Cd, and Pb in tea leaves were within the standard values at 2, 1 and 5 mg kg−1. However, though soil Cr was low in standard-exceeding ratio and bioavailability, Cr accumulation in tea showed high standard-exceeding ratio (72.2 %). This indicated that soil heavy metals concentration and bioavailability are not necessarily to predict the pollution risk in tea leaves. Besides, tea favors to accumulate Cd, with 16 % showing BAF > 1. Though Cr in tea leaves was highly standard-exceeded and Cd was uptake-preferred, the target hazard quotients (THQ; <1) and aggregate risk hazard indexes (HI; 0.046) suggested that there was no potential risks to human health. This indicated that high pollution risk in tea leaves is not necessarily to induce risk to human health. The information helps to better understand the efficiency and influencing factors for heavy metals soil-to-tea leaves transfer and strategize how to more accurate evaluate the risks in soil pollution, food safety and human health.