Towards an ecosystem capacity to stabilise organic carbon in soils.

Soil organic carbon (SOC) accrual, and particularly the formation of fine fraction carbon (OCfine), has a large potential to act as sink for atmospheric CO2. For reliable estimates of this potential and efficient policy advice, the major limiting factors for OCfine accrual need to be understood. The upper boundary of the correlation between fine mineral particles (silt + clay) and OCfine is widely used to estimate the maximum mineralogical capacity of soils to store OCfine, suggesting that mineral surfaces get C saturated. Using a dataset covering the temperate zone and partly other climates on OCfine contents and a SOC turnover model, we provide two independent lines of evidence, that this empirical upper boundary does not indicate C saturation. Firstly, the C loading of the silt + clay fraction was found to strongly exceed previous saturation estimates in coarse-textured soils, which raises the question of why this is not observed in fine-textured soils. Secondly, a subsequent modelling exercise revealed, that for 74% of all investigated soils, local net primary production (NPP) would not be sufficient to reach a C loading of 80 g C kg-1 silt + clay, which was previously assumed to be a general C saturation point. The proportion of soils with potentially enough NPP to reach that point decreased strongly with increasing silt + clay content. High C loadings can thus hardly be reached in more fine-textured soils, even if all NPP would be available as C input. As a pragmatic approach, we introduced texture-dependent, empirical maximum C loadings of the fine fraction, that decreased from 160 g kg-1 in coarse to 75 g kg-1 in most fine-textured soils. We conclude that OCfine accrual in soils is mainly limited by C inputs and is strongly modulated by texture, mineralogy, climate and other site properties, which could be formulated as an ecosystem capacity to stabilise SOC.

Revealing degradation pathways of soluble and dissolved organic matter in alluvial-lacustrine aquifer systems impacted by high levels of geogenic ammonium.

The excessive presence of geogenic ammonium (NH4+) in groundwater poses a global environmental concern, commonly linked to the degradation of nitrogen-containing dissolved organic matter (DOM). However, there is a gap in systematic studies on the combination of soluble organic matter (SOM) in sediments and DOM in groundwater, with few indoor incubation experiments to validate their degradation pathways. This study utilized ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry to analyze the molecular characteristics of DOM and SOM in aquifer systems affected by geogenic NH4+. Subsequently, indoor incubation experiments spanning up to 140 d were conducted to verify the degradation pathways. The experimental results revealed a two-phase degradation process for both the DOM and SOM. The initial stage was characterized by the degradation of aliphatic compounds (ALC) with the production of polyphenols (PPE) and highly unsaturated compounds (HUC). The second stage was dominated by the degradation of PPE and HUC, accompanied by the re-consumption of some ALC, while more recalcitrant HUC persisted. Notably, the first stage of SOM degradation exceeded that of DOM degradation, indicating that SOM exhibited greater resistance to aging. This phenomenon may be attributed to a wider range of active enzymes in sediments, the rapid replenishment of SOM by organic matter in sediments, or the accelerated degradation of DOM. The experimental results aligned with the molecular characterization of DOM and SOM in actual aquifer systems. It is hypothesized that NH4+ produced through the direct mineralization of SOM may contribute more to the enrichment of NH4+ in groundwater than that produced through the mineralization of DOM. This study is the first to analyze DOM and SOM together in aquifer systems and validate their degradation pathways through incubation experiments, thereby providing novel insights into the enrichment of geogenic NH4+ in groundwater.

Tracing groundwater nitrate sources in an intensive agricultural region integrated of a self-organizing map and end-member mixing model tool.

The traceability of groundwater nitrate pollution is crucial for controlling and managing polluted groundwater. This study integrates hydrochemistry, nitrate isotope (δ15N-NO3- and δ18O-NO3-), and self-organizing map (SOM) and end-member mixing (EMMTE) models to identify the sources and quantify the contributions of nitrate pollution to groundwater in an intensive agricultural region in the Sha River Basin in southwestern Henan Province. The results indicate that the NO3--N concentration in 74% (n = 39) of the groundwater samples exceeded the WHO standard of 10 mg/L. According to the results of EMMTE modeling, soil nitrogen (68.4%) was the main source of nitrate in Cluster-1, followed by manure and sewage (16.5%), chemical fertilizer (11.9%) and atmospheric deposition (3.3%). In Cluster-2, soil nitrogen (60.1%) was the main source of nitrate, with a significant increase in the contribution of manure and sewage (35.5%). The considerable contributions of soil nitrogen may be attributed to the high nitrogen fertilizer usage that accumulated in the soil in this traditional agricultural area. Moreover, it is apparent that most Cluster-2 sampling sites with high contributions of manure and sewage are located around residential land. Therefore, the arbitrary discharge and leaching of domestic sewage may be responsible for these results. Therefore, this study provides useful assistance for the continuous management and pollution control of groundwater in the Sha River Basin.

The heterogeneous distribution of heavy metal(loid)s at a smelting site and its potential implication on groundwater.

The downward migration of soil heavy metal(loid)s (HMs) at smelting sites poses a significant risk to groundwater. Therefore, it is requisite for pollution control to determine the pollution characteristics of soil HMs and their migration risks to groundwater. 198 soil samples collected from a Pb-Zn smelting site were classified into 6 clusters by self-organizing map (SOM) and K-means clustering. Cd, Zn, As, and Pb were identified as the characteristic contaminants of the site. The driving factors for the heterogeneous distribution of HMs have been validated through the implementation of K-means clustering and multiple-hits calculation. Using ultrafiltration extraction and microscopic analysis, the soil colloids were identified as crucial carriers facilitating the migration of HMs. Specifically, the colloidal fractions of Cd, Zn, and As, Pb in deep soil (3-4 m) accounted for 91 %, 78 %, 88 %, and 82 %, respectively, consistently surpassing those found in topsoil (0-0.5 m). It was primarily attributed to the strong affinity of HMs toward soil colloids (franklinite, PbS, and kaolinite) and dissolved organic matter (humic acids and protein). The research findings highlight the potential risk of colloidal HMs to groundwater contamination, providing valuable insights for the development of targeted management and remediation strategies.

The critical role of organic matter for cadmium-lead interactions in soil: Mechanisms and risks.

Understanding the interaction mechanisms between complex heavy metals and soil components is a prerequisite for effectively forecasting the mobility and availability of contaminants in soils. Soil organic matter (SOM), with its diverse functional groups, has long been a focal point of research interest. In this study, four soils with manipulated levels of SOM, cadmium (Cd) and lead (Pb) were subjected to a 90-day incubation experiment. The competitive interactions between Cd and Pb in soils were investigated using Fourier transform infrared spectrometer (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and X-ray adsorption near-edge structure (XANES) analysis. Our results indicate that Pb competed with Cd for adsorption sites on the surface of SOM, particularly on carboxyl and hydroxyl functional groups. Approximately 22.6 % of Cd adsorption sites on humus were occupied by Pb. The use of sequentially extracted exchangeable heavy metals as indicators for environment risk assessments, considering variations in soil physico-chemical properties and synergistic or antagonistic effects between contaminants, provides a better estimation of metal bioavailability and its potential impacts. Integrating comprehensive contamination characterization of heavy metal interactions with the soil organic phase is an important advancement to assess the environmental risks of heavy metal dynamics in soil compared to individual contamination assessments.

Missing genotype imputation in non-model species using self-organizing maps.

Current methodologies of genome-wide single-nucleotide polymorphism (SNP) genotyping produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Genotype imputation is routinely used in well-studied species to buffer the impact in downstream analysis, and several algorithms are available to fill in missing genotypes. The lack of reference haplotype panels precludes the use of these methods in genomic studies on non-model organisms. As an alternative, machine learning algorithms are employed to explore the genotype data and to estimate the missing genotypes. Here, we propose an imputation method based on self-organizing maps (SOM), a widely used neural networks formed by spatially distributed neurons that cluster similar inputs into close neurons. The method explores genotype datasets to select SNP loci to build binary vectors from the genotypes, and initializes and trains neural networks for each query missing SNP genotype. The SOM-derived clustering is then used to impute the best genotype. To automate the imputation process, we have implemented gtImputation, an open-source application programmed in Python3 and with a user-friendly GUI to facilitate the whole process. The method performance was validated by comparing its accuracy, precision and sensitivity on several benchmark genotype datasets with other available imputation algorithms. Our approach produced highly accurate and precise genotype imputations even for SNPs with alleles at low frequency and outperformed other algorithms, especially for datasets from mixed populations with unrelated individuals.

Not all soil carbon is created equal: Labile and stable pools under nitrogen input.

Anthropogenic activities have raised nitrogen (N) input worldwide with profound implications for soil carbon (C) cycling in ecosystems. The specific impacts of N input on soil organic matter (SOM) pools differing in microbial availability remain debatable. For the first time, we used a much-improved approach by effectively combining the 13C natural abundance in SOM with 21 years of C3-C4 vegetation conversion and long-term incubation. This allows to distinguish the impact of N input on SOM pools with various turnover times. We found that N input reduced the mineralization of all SOM pools, with labile pools having greater sensitivity to N than stable ones. The suppression in SOM mineralization was notably higher in the very labile pool (18%-52%) than the labile and stable (11%-47%) and the very stable pool (3%-21%) compared to that in the unfertilized control soil. The very labile C pool made a strong contribution (up to 60%) to total CO2 release and also contributed to 74%-96% of suppressed CO2 with N input. This suppression of SOM mineralization by N was initially attributed to the decreased microbial biomass and soil functions. Over the long-term, the shift in bacterial community toward Proteobacteria and reduction in functional genes for labile C degradation were the primary drivers. In conclusion, the higher the availability of the SOM pools, the stronger the suppression of their mineralization by N input. Labile SOM pools are highly sensitive to N availability and may hold a greater potential for C sequestration under N input at global scale.

The predictive value of hematological inflammatory markers for severe oral mucositis in patients with nasopharyngeal carcinoma during intensity-modulated radiation therapy: A retrospective cohort study.

BACKGROUND: This study aims to investigate the predictive value of the circulating blood cell count, including neutro-philto-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), and thesystemic inflammation index (SII) for the development of severe oral mucositis (SOM) induced by radiation in patients undergoing radiotherapy for nasopharyngeal carcinoma (NPC). METHODS: In this retrospective study, 142 NPC patients were screened, and based on mucositis toxicity grade, they were categorized into two groups: SOM and nonSOM. Peripheral blood cell counts were conducted prior to Intensity-Modulated Radiation Therapy (IMRT). Associations between blood cell count, NLR, PLR, SII, and SOM occurrence were examined. RESULTS: Revealed elevated NLR and SII levels, along with reduced lymphocyte (LYM), eosinophil (EOS), and basophil (BAS) in patients with SOM. LYM, EOS, BAS, NLR, and SII were effective predictors of the severity of radiation-induced oral mucositis (RIOM) in NPC patients. CONCLUSIONS: The occurrence of SOM was strongly linked to the hematological status at the start of Radiation Therapy (RT). Integrating BAS count and NLR into comprehensive risk prediction models could prove valuable for predicting SOM in NPC patients.

Impact of Climate on Soil Organic Matter Composition in Soils of Tropical Volcanic Regions Revealed by EGA-MS and Py-GC/MS.

Soil organic matter (SOM) crucially influences the global carbon cycle, yet its molecular composition and determinants are understudied, especially for tropical volcanic regions. We investigated how SOM compounds change in response to climate, vegetation, soil horizon, and soil properties and the relationship between SOM composition and microbial decomposability in Tanzanian and Indonesian volcanic regions. We collected topsoil (0-15 cm) and subsoil (20-40 cm) horizons (n = 22; pH: 4.6-7.6; SOC: 10-152 g kg-1) with undisturbed vegetation and wide mean annual temperature and moisture ranges (14-26 °C; 800-3300 mm) across four elevational transects (340-2210 m asl.). Evolved gas analysis-mass spectrometry (EGA-MS) documented a simultaneous release of SOM compounds and clay mineral dehydroxylation. Subsequently applying double-shot pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) at 200 and 550 °C, we detailed the molecular composition of topsoil and subsoil SOM. A minor portion (2.7 ± 1.9%) of compounds desorbed at 200 °C, limiting its efficacy for investigating overall SOM characteristics. Pyrolyzed SOM closely aligns with the intermediate decomposable SOM pool, with most pyrolysates (550 °C) originating from this pool. Pyrolysates composition suggests tropical SOM is mainly microbial-derived and subsoil contains more degraded compounds. Higher litter inputs and attenuated SOM decomposition due to cooler temperatures and lower soil pH (<5.5) produce less-degraded SOM at higher elevations. Redundancy analyses revealed the crucial role of active Al/Fe (oxalate-extractable Al/Fe), abundant in low-temperature/high-moisture conditions, in stabilizing these less-degraded components. Our findings provide new insights into SOM molecular composition and its determinants, critical for understanding the carbon cycle in tropical ecosystems.

Relationship between nitrate, heavy metal, and sterols contents in Japanese agricultural soils with risk of groundwater pollution.

In Japanese agricultural lands, nitrate-nitrogen contamination of soil and groundwater often occurs due to the application of livestock excrements and compost. Therefore, rural soils in Japan were sampled and analyzed for nitrate-nitrogen leaching, heavy metal content, and sterols associated with livestock excrement and compost to calculate contamination risk indicators. The results were analyzed using self-organizing maps and cluster analysis. Nitrate-nitrogen content using water extraction was detected in most of the sampled soils. In addition, many samples from areas that were already severely contaminated with nitrate-nitrogen showed particularly high concentrations. Coprostanol, an indicator of fecal contamination, was detected in more than half of the samples. The main source of nitrate-nitrogen contamination in these areas is livestock excrement and compost. Self-organization maps showed that areas with high nitrate-nitrogen contamination also corresponded to areas with high copper and zinc soil contents. The self-organization maps and cluster analysis resulted in five clusters: a nitrate-contaminated group mainly originating from livestock excrement and compost, a heavy metal-contaminated group, a general group, a nitrate-contaminated group mainly originating from chemical fertilizers, and a contaminated group with potentially hazardous substances requiring attention. Authorities and decision-makers can use the results to prioritize areas requiring remediation.

Unveiling dissociation mechanisms and binding patterns in the UHRF1-DPPA3 complex via multi-replica molecular dynamics simulations.

CONTEXT: Ubiquitin-like with PHD and RING finger domain containing protein 1 (UHRF1) is responsible for preserving the stability of genomic methylation through the recruitment of DNA methyltransferase 1 (DNMT1). However, the interaction between Developmental pluripotency associated 3 (DPPA3) and the pre-PHD-PHD (PPHD) domain of UHRF1 hinders the nuclear localization of UHRF1. This disruption has implications for potential cancer treatment strategies. Drugs that mimic the binding pattern between DPPA3 and PPHD could offer a promising approach to cancer treatment. Our study reveals that DPPA3 undergoes dissociation from the C-terminal through three different modes of helix unfolding. Furthermore, we have identified key residue pairs involved in this dissociation process and potential drug-targeting residues. These findings offer valuable insights into the dissociation mechanism of DPPA3 from PPHD and have the potential to inform the design of novel drugs targeting UHRF1 for cancer therapy. METHODS: To comprehend the dissociation process and binding patterns of PPHD-DPPA3, we employed enhanced sampling techniques, including steered molecular dynamics (SMD) and conventional molecular dynamics (cMD). Additionally, we utilized self-organizing maps (SOM) and time-resolved force distribution analysis (TRFDA) methodologies. The Gromacs software was used for performing molecular dynamics simulations, and the AMBER FF14SB force field was applied to the protein.

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections.

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol-Gasoline Blends by Raman Spectroscopy.

Methanol-gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol-gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol-gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)-PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol-gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

Spatial response of water level and quality shows more significant heterogeneity during dry seasons in large river-connected lakes.

The spatial response mechanism of hydrology and water quality of large river-connected lakes is very complicated. In this study, we developed a spatial response analysis method that couples wavelet correlation analysis (WTC) with self-organizing maps (SOM), revealing the spatial response and variation of water level and water quality in Poyang Lake, China's largest river-connected lake, over the past decade. The results show that: (1) there was significant spatial heterogeneity in water level and quality during the dry seasons (2010-2018) compared to other hydrological stages. (2) We identified a more pronounced difference in response of water level and quality between northern and southern parts of Poyang Lake. As the distance increases from the northern lake outlet, the impact of rising water levels on water quality deterioration intensified during the dry seasons. (3) The complex spatial heterogeneity of water level and quality response in the dry seasons is primarily influenced by water level fluctuations from the northern region and the cumulative pollutant entering the lake from the south, which particularly leads to the reversal of the response in the central area of Poyang Lake. The results of this study can contribute to scientific decision-making regarding water environment zoning management in large river-connected lakes amidst complex environment conditions.

Efficiency of clustering methods and self-organizing maps of Adriatic sardines and anchovies regarding organochlorine and fatty acid burden.

The Adriatic Sea plays a crucial role as both a significant fishing ground and a thriving trading market for small pelagic edible fish. Recognized for their nutritional value, these fish are esteemed for their high protein content and abundance of polyunsaturated omega-3 and omega-6 fatty acids, making them a sought-after and healthful food choice. Nevertheless, pelagic species can also serve as a reservoir for lipophilic organochlorine pollutants, posing potential risks to human health. In this study, we compared traditional classification methods traditional principal component analysis (PCA) and Ward's clustering with an advanced self-organizing map (SOM) algorithm in determining distribution patterns of 24 organochlorines and 19 fatty acids in sardine and anchovy samples taken from the eastern Adriatic. The outcomes reveal the strengths and weaknesses of the three approaches (PCA, Ward's clustering, and SOM). However, it is evident that SOM has proven to be the most effective in offering detailed information and data visualization. Although sardines and anchovies exhibit similar distribution patterns for p,p'-DDE, PCB-28, PCB-138, PCB-153, PCB-118, and PCB-170, they differ in the concentrations of fatty acids such as stearic, palmitic, myristic, oleic, docosapentaenoic, and docosahexaenoic acid. Our findings supply valuable insights for environmental authorities and fish consumers concerning the potential risks associated with organochlorines in these two types of fish.

Analysis of heavy metal and polycyclic aromatic hydrocarbon pollution characteristics of a typical metal rolling industrial site based on data mining.

With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.

Identification of hydrogeological and geoelectrical parameters influencing the productivity of boreholes in the commune of Daloa (Central-Western of Côte d'Ivoire).

This study was to determine the hydrogeological and geoelectrical parameters affecting the productivity of water boreholes in the Daloa commune. Using the Schlumberger configuration, we carried out 15 parallel and perpendicular electrical profiling and 40 Vertical Electrical Soundings (VES). We also performed Electrical Resistivity Tomography (ERT) with a pole-dipole configuration at one site. The data processing allowed the identification of the different layers of the subsurface and their geoelectric properties that influence the productivity of the boreholes in the study area. These geoelectric data have shown that the productivity of the boreholes is more related to the conductive anomalies (W and U) than to the vertical electrical sounding. The processing of the hydrogeological data has shown that borehole productivity in the study area using Self-Organising Map (SOM) is a function of the hydraulic conductivity but also the thickness and density of the fracturing. Sixteen new boreholes on the main campus of Jean Lorougnon Guede University, Daloa, validate these results. The yield of these productive boreholes varies between 7.5 and 22 m3/h, with an average of 15.2 m3/h, four times higher than the values usually obtained in the study area. The success rate of these boreholes is about 70 %. This study has shown how effectively integrating hydrogeological and geoelectrical parameters provides information on the lithology and structural conditions in the subsurface necessary for productive drilling.

Exploring the effect of different application rates of biochar on the accumulation of nutrients and growth of flue-cured tobacco (Nicotiana tabacum).

Background: Biochar application has become one of the most potential tools to improve soil fertility and plant growth for sustainable and eco-friendly agriculture. However, both positive and negative effects of biochar application have been recorded on plant growth and soil fertility. Methods: This study investigated the impact of different application rates (0, 600, 900, 1200, and 1800 kg/ha) of biochar on the soil nutrient contents, accumulation of nutrients and dry matter in different plant parts, and growth of flue-cured tobacco plants under field conditions. Results: Results demonstrated that soil organic carbon pool and carbon/nitrogen ratio were increased proportionally with the increasing dosage of biochar, 25.54 g/kg and 14.07 g/kg compared with control 17 g/kg and 10.13 g/kg, respectively. The contents of soil total nitrogen were also significantly increased after biochar application in the middle (1.77 g/kg) and late-growth (1.54 g/kg) stages of flue-cured tobacco than in control (1.60 g/kg and 1.41 g/kg, respectively). The contents of soil nitrate nitrogen were also higher under low (600 and 900 kg/ha) application rates of biochar and reduced when higher (1200 and 1800 kg/ha) dosages of biochar were applied. However, it was observed that varying application rates of biochar had no impact on soil ammonium nitrogen content during the growth period of flue-cured tobacco plants. The nutrient accumulation (N, P, K) in different parts of flue-cured tobacco plants was significantly increased under a low application rate of biochar, which enhanced the soil and plant analyzer development values, effective leaves number, growth, dry matter accumulation, and leaf yield of flue-cured tobacco. In contrast, the high biochar application rate (1200 and 1800 kg/ha) negatively impacted nutrient accumulation and growth of flue-cured tobacco. Conclusion: Conclusively, the optimum application of biochar (600 and 900 kg/ha) is beneficial for plant growth, soil fertility, accumulation of nutrients, and dry matter in different plant parts. However, excessive biochar application (> 900 kg/ha) could inhibit flue-cured tobacco plant growth. This study provides a theoretical foundation for biochar application in tobacco and other crop production to obtain agricultural sustainability and economic stability.

Exploring research on ecotechnology through artificial intelligence and bibliometric maps.

Ecotechnology, quintessential for crafting sustainable socio-environmental strategies, remains tantalizingly uncharted. Our analysis, steered by the nuances of machine learning and augmented by bibliometric insights, delineates the expansive terrain of this domain, elucidates pivotal research themes and conundrums, and discerns the vanguard nations in this field. Furthermore, we deftly connect our discoveries to the United Nations' 2030 Sustainable Development Goals, thereby accentuating the profound societal ramifications of ecotechnology.

Analysis and modelling of profiles to understand fractionation processes for contaminations with polychlorinated biphenyls observed in fish.

Polychlorinated biphenyls (PCB) both continue to spread into the environment and to bioaccumulate from primary urban and industrial sources as well as from secondary sources such as soils and the oceans. Fractions of congeners in PCB mixtures, i.e. PCB profiles, can be used as fingerprints to trace contamination pathways from sources to sinks because PCB mixtures fractionate during transport due to congener specific phase changes and degradation. Using a statistical analysis of a total of 8584 PCB profiles with seven congeners (CB28, CB52, CB101, CB118, CB138, CB153, CB180) for contaminated fish from two international datasets as well as a modelling of profiles, two major fractionation processes related to distinct contamination pathways were identified: (1) A relative enrichment of lighter congeners (CB28, CB52, CB101) in seawater fish due to a predominantly atmospheric transport, whereas freshwater and some coastal fish had higher fractions of heavier congeners (CB138, CB153) because those were mainly contaminated by particle-sorbed PCB from surface runoff. (2) A temperature driven fractionation tended to affect congeners with a medium molecular weight (CB118) as well as the heaviest congeners (CB180), a fractionation process which was conceptually associated with transport of PCB from secondary sources. Specifically, medium chlorinated PCB is sufficiently volatile and persistent for a preferred transport into cooler waters. In warmer climates, only the highest chlorinated congeners are persistent enough to ultimately accumulate in fish. Our analysis and modelling provide a starting point for the development of systems to trace - better than before - sources of PCB contaminations observed in fish.