Sci. Total Environ. [JOURNAL]
Sun
200 papers
Shade-driven morpho-physiological plasticity buffers light-water co-limitation in Euterpe precatoria under rainfed field conditions.
de Araújo CS, Lunz AMP, Andrade Neto RC … +3 more , Dos Santos VB, Carneiro JC, Cavalcante JPDSS
Sci Total Environ · 2026 Jun · PMID 42263626 · Publisher ↗
In seasonally humid Amazonia, rainfed plant establishment can be co-limited by high irradiance and seasonal water deficits. We tested whether temporary shade reduces leaf evaporative demand without reducing photosyntheti...
In seasonally humid Amazonia, rainfed plant establishment can be co-limited by high irradiance and seasonal water deficits. We tested whether temporary shade reduces leaf evaporative demand without reducing photosynthetic carbon assimilation during early field establishment of the Amazon palm Euterpe precatoria Mart. Seedlings were planted in Rio Branco, Acre, Brazil, and grown under five shade levels: 0, 18, 35, 50, and 65%. From 2 to 12 months after planting (MAP), we measured plant height, stem diameter, canopy diameter, and leaf number. At 6 MAP, we measured leaf gas exchange, chlorophyll index, and leaf microclimate. Antecedent climate was represented by 60-day mean air temperature and cumulative rainfall before each survey. Intermediate shade, especially near 50%, maximized net photosynthetic rate, chlorophyll index, and water-use efficiency, while reducing transpiration and leaf vapor pressure deficit. Growth increments were consistently associated with wetter 60-day windows, whereas temperature effects were not consistent after accounting for developmental stage and previous plant size. ANCOVA indicated that shade-related increases in photosynthesis were not fully explained by stomatal conductance alone, although this inference remains indirect because mesophyll conductance and A-Ci or A-PPFD curves were not measured. Integrated field indicators identified a Pareto-efficient shade window near 45-55%, combining rapid canopy development with lower thermal and evaporative risk. Moderate shade therefore provides a practical management window to improve rainfed plant establishment of E. precatoria in cultivation, agroforestry, and restoration systems.
Mercury binding to selenoproteins and age-related depletion of bioavailable selenium in long-finned pilot whales.
Laitip N, Del Castillo Busto ME, Gajdosechova Z … +5 more , Goenaga-Infante H, Raab A, Ward-Deitrich C, Brownlow A, Feldmann J
Sci Total Environ · 2026 Jun · PMID 42263625 · Publisher ↗
Selenoproteins-particularly glutathione peroxidase (GPx) and selenoprotein P (SelP)-are essential for redox regulation and selenium (Se) homeostasis. However, the influence of aging and mercury (Hg) exposure on these pro...
Selenoproteins-particularly glutathione peroxidase (GPx) and selenoprotein P (SelP)-are essential for redox regulation and selenium (Se) homeostasis. However, the influence of aging and mercury (Hg) exposure on these proteins in marine mammals remains inadequately characterized. In this study, we examined liver, brain, kidney, and muscle tissues from 21 stranded long-finned pilot whales (Globicephala melas), aged 1 to 36 years, using double-affinity high-performance liquid chromatography (AF-HPLC) coupled with inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) to fractionate Se and Hg within selenoprotein-containing fractions. Age-dependent increases in total Hg and Se were observed across all tissues, alongside significant reductions (p < 0.05) in bioavailable selenoproteins-specifically those fractions associated with GPx and SelP-in brain and muscle. Although direct Hg-protein binding was not assessed, co-elution of Hg with Se-containing fractions suggests Hg association with selenoproteins, potentially compromising enzymatic function. These findings indicate a reduction in antioxidant capacity, with SelP potentially facilitating Hg transport into neural tissues, thereby increasing neurotoxic risk in older individuals. Our results underscore the age-related vulnerability of marine mammals to oxidative stress and highlight the need for mechanistic studies to clarify the biochemical basis of HgSe interactions.
Daily water level forecasting with limited data using cluster- and season-based transfer learning.
Huang Z, Sun W, Xie Y … +3 more , Chen X, Rong Z, Hong Y
Sci Total Environ · 2026 Jun · PMID 42263624 · Publisher ↗
The coupling of clustering or classification with deep learning presents an appealing knowledge-guided approach for hydrological prediction. However, applying such an approach to short-term water level forecasting with l...
The coupling of clustering or classification with deep learning presents an appealing knowledge-guided approach for hydrological prediction. However, applying such an approach to short-term water level forecasting with limited data may result in inadequately populated samples in each cluster for model development. In this study, we developed a framework by combining transfer learning and either seasonal classification or fuzzy C-means (FCM) clustering to tackle this problem. We applied this framework to daily water level prediction at the Lechang Gorge in China. Initially, we explored various combinations of missing data treatment, independent variables, internal parameters, and hyperparameters to pre-train the base Long Short-Term Memory (LSTM) model. The optimally pre-trained model exhibited no significant systematic prediction error (bias = -0.003 m) and achieved an R-value of 0.943, an NSE of 0.457, and an RMSE of 0.889 m for validation. Subsequently, we fine-tuned the pre-trained models using data from four seasons (post-dry, early-flood, post-flood, and early-dry) and two seasons (flood and dry), as well as using cluster data through FCM clustering with either precipitation or water level data. Compared to pre-trained models, the optimally fine-tuned models based on seasonal classification tended to outperform in dry-related periods but underperformed in flood-related seasons. The optimally fine-tuned model based on FCM clustering using precipitation outperformed the baseline in almost all clusters, achieving an RMSE of 0.412 m (a 6.2% improvement) during validation. Overall, transfer learning helps to overcome the limitations of small data by enabling the model to learn from a broader dataset and then adapt to specific conditions within each cluster. This method also enhances the interpretation and helps to identify directions for further improvement of river forecasting models.
Contrasting mechanisms of biochar-nitrogen interactions under inorganic and organic fertilizers: Integrated evidence from incubation, volatilization, leaching, and apparent nitrogen partitioning.
Kohira Y, Fentie D, Lewoyehu M … +5 more , Wutisirirattanachai T, Gezahegn A, Addisu S, Kurosawa N, Sato S
Sci Total Environ · 2026 Jun · PMID 42259230 · Publisher ↗
Biochar is widely promoted as a strategy to mitigate nitrogen (N) losses from soils, yet its effectiveness varies strongly with N source (inorganic or organic) and biochar properties. To clarify these contrasting respons...
Biochar is widely promoted as a strategy to mitigate nitrogen (N) losses from soils, yet its effectiveness varies strongly with N source (inorganic or organic) and biochar properties. To clarify these contrasting responses, we conducted an integrated assessment combining a 98-day soil incubation experiment, a 10-day ammonia (NH) volatilization experiment, a 60-day column leaching experiment, and a scenario-based apparent soil N partitioning framework. When co-applied with urea, biochar suppressed nitrification and reduced soil nitrate-N (NO-N) accumulation consistent with enhanced microbial immobilization and/or ammonium retention. However, relative to the urea control, NH volatilization increased by 32-50% with biochar, and leaching losses also increased: ammonium-N (NH-N) leaching increased by 43-58% at high biochar application rates (20 t ha) while NO-N leaching increased 15-39%. In contrast, biochar combined with anaerobic digestion effluent (ADE) significantly enhanced nitrification and mitigated N losses. Compared with the ADE control, biochar reduced NH volatilization by 15-33% across all biochar treatments and NO-N leaching decreased by 8-10% at the high application rates. However, NH-N leaching remained low across all treatments (0.11-0.60 mg N column). These reductions in N loss were associated with patterns consistent with interactions between ADE-derived organic components and biochar surfaces that may have contributed to reduced NO-N mobility, together with a more balanced microbial community structure. Overall, our results demonstrated that biochar was not a universal mitigation tool. While it exacerbated gaseous and leaching losses when combined with fast-release urea, it substantially enhanced N stabilization when paired with ADE. Fertilizer-specific biochar management, using low application rates with urea and high-temperature biochar at higher rates with ADE, provided a practical pathway to improve N use efficiency while mitigating environmental N losses.
Effects of prescribed burning on fire regime dynamics in Amazonian savanna ecosystems.
Alves DB, Cambraia BC, Berlinck CN
Sci Total Environ · 2026 Jun · PMID 42259229 · Publisher ↗
Prescribed burning has been adopted as a strategic tool within Integrated Fire Management (IFM) of pyrophytic ecosystems, and there is a growing need to provide evidence supporting its effectiveness in reducing wildfire...
Prescribed burning has been adopted as a strategic tool within Integrated Fire Management (IFM) of pyrophytic ecosystems, and there is a growing need to provide evidence supporting its effectiveness in reducing wildfire occurrence. As the first study to systematically quantify the contributions of prescribed burning in Amazonian savanna ecosystems, it evaluates its effects on fire dynamics in the largest savanna enclave in the southern Brazilian Amazon, with emphasis on fire seasonality, fire size, and the extent of fire-affected sensitive vegetation. A fire-scar database was compiled using semi-automated classification procedures applied to Landsat imagery, and records were classified by fire type, seasonality, and size classes. The analysis was segmented into three periods that coincide with abrupt changes in the territorial fire management of the area: T1 (1998-2006), before establishment of Campos Amazônicos National Park; T2 (2007-2015), fire-exclusion policy; T3 (2016-2024), IFM with prescribed burns. Analysis of variance was used to determine whether statistically significant differences occurred among dependent variables derived from fire dynamics patterns across the study periods. Over the past 27 years, a total of 16,589 km of burned area was detected, distributed across 2962 fires. The average percentage of sensitive vegetation burned in wildfires was 3.3 times higher than in prescribed burns (5.2 ± 13.4% for wildfires, compared with 1.6 ± 2.9% for prescribed burns). The use of prescribed burning in T3 was associated with a 31.4% reduction in the percentage prevalence of burned areas in the late dry season compared to T2 (F = 7.297; p = 0.00335), along with a 29.2% reduction in the annual proportion of fire-affected sensitive vegetation. The findings underscore the effectiveness of prescribed burning as a tool for reducing late dry season burned-area prevalence and protecting sensitive vegetation. Prescribed burning should be incorporated into IFM strategies in Amazonian open ecosystems to manage accumulated and continuous fuel in savanna and grassland environments and enhance ecosystem resilience under current and future climate conditions.
Molecular characterization of dissolved organic matter from two eucalypt forests in southwest Australia - legacies of wildfire severity.
Bravo-Escobar A, O'Donnell AJ, Nealon GL … +1 more , Grierson PF
Sci Total Environ · 2026 Jun · PMID 42259228 · Publisher ↗
Wildfires are increasingly affecting forest ecosystems worldwide, with potentially long-lasting impacts on soil organic matter and carbon fluxes. Transformations of dissolved organic matter (DOM) by fire are particularly...
Wildfires are increasingly affecting forest ecosystems worldwide, with potentially long-lasting impacts on soil organic matter and carbon fluxes. Transformations of dissolved organic matter (DOM) by fire are particularly important, as they regulate many biogeochemical and physical processes in soils. We examined changes in DOM from two eucalypt forest ecosystems in southwest Australia that were variably impacted by a large wildfire five years earlier. DOM was extracted from loamy soils under karri forest (Eucalyptus diversicolor F. Muell) and from sandy soils under jarrah forest (Eucalyptus marginata Donn ex. Smith). Soils were collected from low and high severity burned areas and adjacent, unburned sites. To assess molecular characteristics, we combined HNMR with excitation-emission matrices (EEMs). Our results show that fire legacy was more evident in DOM from loamy soils, where the concentration of dissolved organic carbon (DOC) and the molecular weight of DOM was lower in sites burnt at high severity than unburnt sites. The H NMR spectra of unburnt sites in the loamy soils were characterised by oxygenated structures, whereas sites exposed to high severity fires exhibited a dominance of aliphatic structures. By contrast, sandy soils showed little change in H NMR spectra or fluorescence signatures across burned and unburned areas. Importantly, there was no evidence of aromatic structures in burned soils from either forest type, as indicated by the absence of increased aromaticity (SUVA) or aromatic H NMR signals. Our findings highlight that wildfire can impart long-term changes to DOM, particularly in loamy soils, with implications for carbon cycling and ecosystem recovery.
Oil spills in inland freshwaters: A review of recent incidents, mitigation practices, and ecological impacts in the United States.
Olowoyo JO, Zou N, Cooper D … +6 more , Hansen K, Palace V, Lee K, Xin Q, Bassi A, Zheng Y
Sci Total Environ · 2026 Jun · PMID 42259227 · Publisher ↗
Oil spills in inland freshwater environments remain a major but under-examined environmental challenge compared to marine disasters. This study provides the first systematic, decadal review of oil spill incidents across...
Oil spills in inland freshwater environments remain a major but under-examined environmental challenge compared to marine disasters. This study provides the first systematic, decadal review of oil spill incidents across the United States (U.S.) from 2014 to 2024, integrating records from the federal databases such as Environmental Protection Agency, U.S. Coast Guard, and the Office of Pipeline Safety. The analysis identifies national trends in spill frequency, causes, oil types, environmental settings, and response effectiveness. More than 90% of released oil volume was in rivers, while diesel represented the most frequent spill type and crude oil contributed to the greatest cumulative volume. Transportation accidents, including vessel groundings, collisions, and equipment failures, were the leading causes, often linked to aging infrastructure and high-traffic corridors. Ecological recovery was slowest in wetlands and creeks, where hydrocarbons persist in sediments and re-mobilize during flood events. Conventional mechanical containment measures such as booms and skimmers were widely used but often limited by flow conditions and site inaccessibility. In-situ burning, recently trialed in marshes and bayous, achieved up to 90% oil removal but generated combustion residues and smoke that remain poorly characterized. The findings underscore the need for freshwater-specific response frameworks, predictive spill-risk models, and the development of environmentally safe smoke-suppressant technologies. By linking hydrological, chemical, and ecological processes, this review advances a multi-sphere understanding of oil spill dynamics in inland waters and provides a foundation for improved mitigation strategies in freshwater ecosystems worldwide.
A probabilistic analysis of cost-benefit among nitrogen fertilizer application, corn production and drinking water nitrate treatment.
Dunn PJ, Yuan Y
Sci Total Environ · 2026 Jun · PMID 42259226 · Publisher ↗
Nitrate is a drinking water contaminant regulated by the U.S. Environmental Protection Agency, with a maximum contaminant level of 10 mg L as nitrogen. In the U.S. Midwest, nitrate concentration in many drinking water so...
Nitrate is a drinking water contaminant regulated by the U.S. Environmental Protection Agency, with a maximum contaminant level of 10 mg L as nitrogen. In the U.S. Midwest, nitrate concentration in many drinking water sources exceeds that threshold due to non-point source pollution of nitrogen fertilizer loss from cropland, which is compounded by extensively installed tile drainage (subsurface drainage). To maintain compliance with the EPA drinking water standard, community water systems (CWSs) must utilize costly treatment methods to reduce nitrate concentrations in their source water. On the other hand, many studies show that reducing fertilizer use could be an effective way for water protection from nitrogen pollution. However, reducing fertilizer use could reduce crop yield and jeopardize farmers' benefit. Therefore, the overall objective of this study was to analyze tradeoffs between potential loss to farmer revenue and savings to CWSs for different fertilization scenarios. A Monte Carlo analysis was utilized to estimate changes to farmer revenue and nitrate loads associated with various nitrogen fertilizer rate scenarios. Two CWSs in the Midwest were chosen to estimate how changes to nitrogen fertilizer application rates would affect nitrate drinking water treatment costs. Farmer net revenue was not significantly increased by utilizing nitrogen fertilizer application rates above 150 kg of nitrogen per hectare. In contrast, drainage nitrate loads increased with increased nitrogen application rates, which led to disproportionately large impacts on the frequency of nitrate treatment at the CWSs. According to the results, applying between 100 and 150 kg of nitrogen per hectare for corn farming could significantly reduce nitrate-related water treatment expenses without noticeably impacting farmers' income in this region.
Exposure to second-generation anticoagulant rodenticides is widespread in the non-target British wild mammals Erinaceus europaeus and Vulpes vulpes.
Seilern-Macpherson K, Lawson B, Charman S … +6 more , Jones S, Rainford J, Macarthur R, Kent AJ, Cunningham AA, Barnett L
Sci Total Environ · 2026 Jun · PMID 42247865 · Publisher ↗
Anticoagulant rodenticides (ARs), molluscicides, and neonicotinoids are widely used biocides for plant protection and pest control and are considered a potential health risk to non-target wildlife. Here, we investigate t...
Anticoagulant rodenticides (ARs), molluscicides, and neonicotinoids are widely used biocides for plant protection and pest control and are considered a potential health risk to non-target wildlife. Here, we investigate these compounds in convenience samples derived from 100 European hedgehogs (Erinaceus europaeus) and 181 red foxes (Vulpes vulpes) from England (2013 to 2020), screening liver tissues using liquid chromatography-tandem mass spectrometry. Whilst no molluscicides or neonicotinoids were detected, except for low level clothianidin in a single fox, ARs were found in 62% of hedgehog and 90% of fox samples at variable concentrations (0.05-3430 ng/g). In 83% of animals with detectable ARs, more than one AR compound was found, consistent with multiple, cumulative exposures. Additionally, 25% of hedgehog and 64% of fox samples contained summed AR concentrations above the commonly used reference value of 100 ng/g for potential poisoning or regulatory concern. There was no significant association with geographical region or sex (for fox or hedgehog), or with age, location, habitat type, or the assigned category of cause of death (for hedgehog). Despite the high AR levels detected, there was no evidence of acute haemorrhage indicative of AR toxicosis in hedgehogs. However, since hedgehogs are of conservation concern in Great Britain, further investigation of potential adverse health impacts of ARs and other pesticides is warranted. Our study findings indicate widespread AR exposure among the sampled animals and support further structured monitoring of these omnivorous non-target mammals. Within our convenience sample, we found no evidence of reduced AR exposure following the introduction of the UK rodenticide stewardship scheme in 2016.
Rural Airborne PM-bound Microplastics and Heavy Metals: GIS-AI Risk Assessment Review.
Ashlin A, Gautam S, Kumar RP … +1 more , Ho CH
Sci Total Environ · 2026 Aug · PMID 42241934 · Publisher ↗
Fine particulate matter (PM) is a critical air pollutant and carrier of toxic co-contaminants, including microplastics (MPs) and heavy metals (HMs), posing severe human health risks. Rural exposure studies lag behind urb...
Fine particulate matter (PM) is a critical air pollutant and carrier of toxic co-contaminants, including microplastics (MPs) and heavy metals (HMs), posing severe human health risks. Rural exposure studies lag behind urban ones. This review systematically evaluates the sources, spatial and temporal patterns, analytical methods, and health risks of PM-bound MP and HM across 110 articles (2008-2025) retrieved from the Web of Science database. The bibliometric analysis performed in the VoS viewer revealed sharp growth in publications post-2018, peaking in 2024. Major contributors are China (38.12%), the United States (15.45%), and India (10%), with the Chinese Academy of Sciences being the most productive institution. The keyword network identified HM, PM, and MP as the dominant research themes. Rural concentrations of these PM-bound contaminants vary widely due to seasonal factors, biomass burning, agriculture, and long-range transport. Although urban levels are often higher, rural risks may be equal to or exceed health risks observed in urban areas due to prolonged exposure and occupational hazards. Inhalation is identified as the primary exposure pathway, followed by ingestion and dermal contact. Health risks are commonly assessed using United States Environmental Protection Agency (USEPA) based cancer and non-cancer risk metrics, while Fourier Transform Infrared (FTIR), Raman spectroscopy, Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) remain the most widely applied analytical techniques. Existing studies exhibit considerable methodological inconsistency, limited integration of toxicology, and minimal use of Geographic Information Systems (GIS) and artificial intelligence (AI). The integration of GIS and AI approaches offers significant potential to improve source identification, spatial visualization and quantitative mapping, hotspot analysis, and predictive modeling, particularly in under-studied rural regions. These findings support the World Health Organization air quality guidelines and Sustainable Development Goals 3, 7, and 11.
AI and computer vision for wildlife identification in camera trap images: Fine-tuning SpeciesNet outperforms local models for species classification.
Rajmohan PP, Sharma R, Amir Z … +4 more , Bruce T, Brook BW, Morris D, Luskin MS
Sci Total Environ · 2026 Aug · PMID 42241933 · Publisher ↗
Wildlife camera traps generate millions of images that exceed the capacity of manual processing. Computer vision (CV), a branch of artificial intelligence (AI) and machine learning (ML), helps ecologists process images e...
Wildlife camera traps generate millions of images that exceed the capacity of manual processing. Computer vision (CV), a branch of artificial intelligence (AI) and machine learning (ML), helps ecologists process images efficiently. The CV workflow generally starts with animal detection (e.g., with MegaDetector) and then, for those images with animals, the cropped image containing the animal (i.e., snip) is passed to a classifier to identify species. SpeciesNet is an open-source AI/ML classifier that recognises 2498 classes (mostly species-level) globally, and is therefore a 'global model'. However, SpeciesNet has substantial geographic and taxonomic gaps. Ecologists working in areas or with species beyond its scope may therefore build local classifiers for their particular sites. We hypothesised that a blended approach, fine-tuning SpeciesNet, could harness global feature representations and local taxonomic specialisation (i.e., classes limited to the study region). Within this context, we address three questions: (i) How do global, local, and fine-tuned classifiers compare? (ii) How many training images are required? (iii) How does performance vary between random distribution and out-of-distribution testing? We used the Wildlife Observatory of Australia's tagged image repository for the 'Wet Tropics' rainforests (n = 454 camera deployments, 2,184,664 images, 121 species), and refined this to a balanced dataset of the 15 most common species for CV modelling. We found that (i) fine-tuning SpeciesNet delivered the highest performance, often exceeding 95% F1-score, (ii) performance plateaued after 250-500 local training images per class (species) for all three approaches, and (iii) these advantages were pronounced in out-of-distribution testing (i.e., for new cameras withheld from any model training). We conclude that fine-tuning SpeciesNet reconciles the longstanding tension between broad applicability and site-specific precision, accelerating image-to-inference workflows to achieve results within management-relevant timelines. Such advances move cameras further towards being an automated, easy, affordable, and efficient solution for wildlife monitoring, research, and conservation.
Corrigendum to "Pristine and iron-engineered animal- and plant-derived biochars enhanced bacterial abundance and immobilized arsenic and lead in a contaminated soil" [Sci. Total Environ. (763) 2021, 144218].
Pan H, Yang X, Chen H … +8 more , Sarkar B, Bolan N, Shaheen SM, Wu F, Che L, Ma Y, Rinklebe J, Wang H
Sci Total Environ · 2026 Jul · PMID 42236418 · Publisher ↗
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Radiological characterisation and inter-laboratory comparison of naturally occurring radioactive materials (NORM) in building materials used in Ireland.
Djabou RE, Dodek M, Sainz C … +5 more , Fuente I, Vintró LL, Hanley O, Goggins J, Foley M
Sci Total Environ · 2026 Aug · PMID 42229306 · Publisher ↗
Naturally occurring radioactive materials (NORM), primarily derived from the uranium-238 and thorium-232 decay series and potassium-40, are present in raw and processed construction materials and can contribute to indoor...
Naturally occurring radioactive materials (NORM), primarily derived from the uranium-238 and thorium-232 decay series and potassium-40, are present in raw and processed construction materials and can contribute to indoor gamma radiation exposure. This study evaluates the activity concentrations of Ra-226, Th-232 and K-40 in building materials used in Ireland and assesses their radiological suitability for construction applications. Twenty representative building materials were analysed in two phases. The first phase included aggregates, construction sands, plaster and concretes from pre-1964 residential demolition sites. The second phase focused on twelve materials, including particle-size fractions of concrete and aggregates, and aggregates from low- and high-radioactivity regions, cementitious additives (ground granulated blast-furnace slag (GGBS) and two fly ash samples), and imported ceramic tiles. High-resolution gamma spectrometry measurements were conducted in up to three laboratories (Environmental Protection Agency, EPA; University College Dublin, UCD; and University of Cantabria, UC) for inter-laboratory comparison. The Gamma Index (Iγ) was calculated in accordance with European Commission guidelines. Ra-226 activity concentrations ranged from 5.5 to 169 Bq·kg, Th-232 from below detection limits to 206 Bq·kg, and K-40 from 5.4 to 1155 Bq·kg. Concrete, plaster, sands and low-activity aggregates exhibited low radionuclide levels, with Iγ values ≤ 0.4, well below the screening limit for unrestricted use. Particle-size fractionation had little effect on radionuclide concentrations in concrete, while greater variation was observed in Ra-226 in aggregate fractions. One aggregate source showed elevated activity (Iγ ≈ 2.0 ± 0.03), highlighting the importance of geological screening. Cementitious additives and ceramic tiles showed higher activity levels than concrete but remained below regulatory limits. This study supports more sustainable construction practices by providing data for the safe reuse and recycling of building materials. The findings expand the available NORM dataset for Irish construction materials and support their safe use in the built environment.
Substrate-specific bacterial and fungal communities in the Jukskei River plastisphere revealed by full-length amplicon sequencing.
Chigwada AD, Kalu CM, Tekere M
Sci Total Environ · 2026 Aug · PMID 42229305 · Publisher ↗
The escalating crisis of plastic pollution in aquatic ecosystems has created a novel ecological niche known as the plastisphere, where microbial communities colonize plastic surfaces, influencing biogeochemical cycles, p...
The escalating crisis of plastic pollution in aquatic ecosystems has created a novel ecological niche known as the plastisphere, where microbial communities colonize plastic surfaces, influencing biogeochemical cycles, pollutant degradation, and ecosystem health. Despite global plastisphere research, studies in subtropical, eutrophic African urban rivers remain scarce, limiting insights into substrate-specific microbial assembly and bioremediation potential in polluted freshwater systems. Plastic debris in the Jukskei River, an urban waterway in Johannesburg, South Africa, hosts distinct bacterial and fungal communities on polyethylene (PE) and polystyrene (PS) surfaces. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy revealed oxidative weathering (carbonyl indices 0.08-0.28) consistent with environmental aging. Targeted amplicon sequencing (full-length 16S rRNA and ITS regions via PacBio HiFi) demonstrated substrate-specific community composition (PERMANOVA, pseudo-F{1,8} = 3.45, R = 0.40, p < 0.01), with PE supporting higher genus-level evenness and taxa such as Romboutsia, Cutibacterium, and Sphingomonas, while PS was characterized by greater representation of Lactococcus, Clostridium sensu stricto, and lactic acid bacteria. Low-abundance potential pathogens at the genus level (Escherichia-Shigella, Streptococcus) showed statistically significant correlations with eutrophication indicators (BOD₅, nitrate) and cadmium, although no causal mechanisms were established. Additionally, the amplicon-based approach used was unable to confirm both species-level resolution and virulence potential. PICRUSt2 and FUNGuild predictions indicated the presence of broadly distributed metabolic pathways and guilds, but these inferences do not constitute evidence of active plastic biodegradation or specialized ecological roles. This study provides the first amplicon-based characterization of the plastisphere in a polluted African urban river, revealing substrate-driven bacterial assembly patterns and highlighting the need for future functional validation to assess bioremediation potential and public health risks.
Corrigendum to "Effects of polypropylene micro(nano)plastics on soil bacterial and fungal community assembly in saline-alkaline wetlands" [Sci. Total Environ. 945 (2024) 173890].
Zhang L, Zhang G, Shi Z … +3 more , He M, Ma D, Liu J
Sci Total Environ · 2026 Jun · PMID 42225500 · Publisher ↗
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GeoAI for polar vegetation mapping and hydrological interactions: A systematic review.
Amarasingam N, Vomero M, Platel A … +3 more , Newman C, Robinson SA, Bollard B
Sci Total Environ · 2026 Aug · PMID 42224875 · Publisher ↗
Remote sensing (RS) and Artificial intelligence (AI) are increasingly applied to monitor vegetation and hydrology in the Arctic and Antarctic, where logistical and environmental constraints make fieldwork difficult. Thes...
Remote sensing (RS) and Artificial intelligence (AI) are increasingly applied to monitor vegetation and hydrology in the Arctic and Antarctic, where logistical and environmental constraints make fieldwork difficult. These technologies offer new opportunities to track ecological change, but the extent, consistency, and methodological quality of current applications have not been systematically reviewed. This study presents the first PRISMA 2020 based systematic synthesis of AI enhanced RS, collectively termed GeoAI, applied to Arctic and Antarctic environments (2005-2025; 116 studies). Publication activity has expanded significantly since 2018, driven by the convergence of uncrewed aerial vehicle (UAV), multispectral imaging, satellite archives, and deep learning (DL). Bibliometric and conceptual-network analyses reveal a rapid shift from isolated ecological monitoring toward integrated, data-fusion frameworks linking vegetation, hydrology, and climate processes. Classical machine learning approaches remain foundational, while DL-based convolutional neural-network architectures are emerging as powerful tools for fine-scale segmentation and prediction. Most studies still operate at the landscape scale, with few achieving full UAV-to-satellite integration, exposing persistent spatial-resolution and validation gaps. Vegetation hydrology coupling is reported in most cases, though subsurface and process-based monitoring remain limited. Spectral-index analysis reveals a persistent reliance on greenness metrics, yet there is a growing shift toward pigment, moisture, and cryptogam-sensitive indices that more accurately capture plant physiological function and microclimatic interactions. This review establishes the empirical foundation for next-generation polar monitoring, emphasising hierarchical UAV-to-satellite fusion, open benchmark datasets, and explainable, ecologically grounded AI as essential pathways for scalable, climate-adaptive conservation of Earth's fastest-changing regions.
Machine learning-based prediction of antibiotic resistance gene distribution in agricultural soils under different climate change scenarios.
Kenzi M, Benbernou M, Khelifa H … +1 more , Tbahriti HF
Sci Total Environ · 2026 Aug · PMID 42224874 · Publisher ↗
Antibiotic resistance genes (ARGs) in agricultural soils represent a major public health concern, as climate change is believed to augment their dissemination and abundance. Understanding the impact of future climate cha...
Antibiotic resistance genes (ARGs) in agricultural soils represent a major public health concern, as climate change is believed to augment their dissemination and abundance. Understanding the impact of future climate change scenarios on ARG abundance is essential to implement predictive and proactive One Health strategies. In this study, a total of 2301 soil samples from 67 countries across six continents were compiled from three global metagenome databases, namely NCBI SRA, MG-RAST, and JGI IMG/M. Six machine learning models, namely LightGBM, XGBoost, Random Forest, Support Vector Machines, Deep Neural Networks, and Logistic Regression, were used to predict ARG distribution patterns in agricultural soils, and their performance was evaluated using stratified 10-fold cross-validation with metrics such as AUC-ROC, precision, recall, F1 score, and Matthews Correlation Coefficient. WorldClim 2.1 and CMIP6 models were used to project ARG distribution under three Representative Concentration Pathway scenarios, namely RCP 2.6, RCP 4.5, and RCP 8.5, for the years 2050 and 2070. The LightGBM model achieved the best predictive performance, with an AUC-ROC of 0.957 (95% CI: 0.951-0.963), substantially higher than that of the other models, while the Deep Neural Networks model achieved an AUC-ROC of 0.891. The LightGBM model demonstrated high stability across cross-validation folds, with minimal fold-to-fold variance, defined as the standard deviation of AUC-ROC scores across the 10 folds (SD = 0.008). SHAP feature importance analysis identified soil temperature, pH, and organic carbon content as the top three factors influencing ARG relative abundance, with SHAP values of 0.342, 0.287, and 0.251, respectively. Annual precipitation and soil moisture level were also identified as significant contributors to ARG distribution. SHAP dependency plots revealed critical thresholds for ARG relative abundance, with a sharp increase observed independently when soil temperature exceeds 18 °C and when soil pH drops below 6.5. Furthermore, a non-linear accelerating increase in ARG abundance risk was observed as climate change intensity worsened across scenarios. Projections for future climate change scenarios indicate a potential 34.7% increase in high-risk ARG zones by the year 2070, with the largest changes expected in South Asia, Sub-Saharan Africa, and Mediterranean regions. Paired t-tests revealed significant differences in performance among all models (p < 0.001). These findings demonstrate that gradient-boosting methods such as LightGBM outperform deep learning approaches for ARG prediction from soil microbiome data, offering higher accuracy and interpretability. As climate change is projected to increase ARG risks in a non-linear manner, the development of climate-adaptive agricultural practices and global surveillance systems is urgent. This framework provides actionable risk-mapping tools to support precision farming and region-specific policy interventions within the One Health approach.
Silent Spill - maritime microplastics from vessel coatings.
Maes T, Lusher AL, Pazdro K … +5 more , Pouch A, Mazurkiewicz M, Hjelset S, Folbert M, Lagiewka K
Sci Total Environ · 2026 Aug · PMID 42224873 · Publisher ↗
Paint-derived particles are increasingly recognised as a major contributor to marine microplastic pollution, yet emissions from active vessels under routine operational conditions remain poorly characterised. We tested t...
Paint-derived particles are increasingly recognised as a major contributor to marine microplastic pollution, yet emissions from active vessels under routine operational conditions remain poorly characterised. We tested the hypothesis that exposed deck coatings on operational ships continuously generate and accumulate paint-derived micro- and meso-particles in the absence of maintenance activities. Samples were collected from accumulation zones adjacent to drainage points on three vessel types: an offshore support vessel, a container ship, and a liquefied natural gas carrier. Size-fractionated material was analysed using microscopy, FTIR/NIR spectroscopy, and multispectral imaging. Across vessels, more than 90% of classified particles were paint-derived fragments or iron oxide-rich corrosion products, while a minor fraction consisted of synthetic polymer fibres and fragments. Although the study design does not allow calculation of emission rates per unit area, the consistent presence and dominance of coating-derived debris across vessel types provide field-based evidence that routine weathering of deck coatings represents an ongoing and currently unregulated source of micro-sized particulate pollution. These findings identify exposed ship superstructures as a previously undercharacterised emission pathway and support the inclusion of coating degradation in ship-borne microplastic inventories and maritime sustainability frameworks.
A scalable Earth observation-based machine learning framework for high-resolution mapping and uncertainty assessment of climate-sensitive child health vulnerability in Bangladesh.
Moniruzzaman M
Sci Total Environ · 2026 Aug · PMID 42224872 · Publisher ↗
Bangladesh remains highly vulnerable to climate-induced hazards that disproportionately affect child health, yet spatially explicit assessments of these risks remain limited. This study develops a scalable Earth observat...
Bangladesh remains highly vulnerable to climate-induced hazards that disproportionately affect child health, yet spatially explicit assessments of these risks remain limited. This study develops a scalable Earth observation-driven machine learning framework to map climate-sensitive child health vulnerability across Bangladesh at 1-km spatial resolution. Anthropometric and health indicators from the 2022 Bangladesh Demographic and Health Survey (DHS) were integrated with multi-sensor satellite-derived environmental variables to generate high-resolution vulnerability surfaces and quantify prediction uncertainty. Georeferenced data from 674 DHS clusters (n = 8784 children) were combined with environmental covariates, including Land Surface Temperature (MODIS), precipitation (CHIRPS), vegetation indices (NDVI and EVI), and population density. Three supervised learning algorithms-Random Forest, Gradient Boosting Machine, and XGBoost-were trained using 80% of the dataset, with hyperparameters optimized through 5-fold cross-validation. The ensemble framework achieved the strongest predictive performance (R = 0.683; RMSE = 7.65), outperforming individual models by 3.8%. Thermal stress, rainfall variability, and ecological productivity emerged as the dominant environmental determinants of vulnerability, collectively explaining 62% of model variance. The resulting maps revealed pronounced spatial disparities, with Rangpur exhibiting the highest vulnerability levels (67.3), while comparatively lower scores were observed in parts of Barishal and Sylhet (39.1). Bootstrap-based uncertainty analysis using 1000 iterations produced a mean uncertainty index of 4.8 ± 1.2, with elevated uncertainty concentrated in topographically complex regions. Hotspot analysis identified vulnerable clusters encompassing approximately 6.3 million children under five. The proposed framework provides a transferable approach for precision-oriented climate-health surveillance in data-constrained regions.
Soil microplastics as carriers of organic pollutants: Sorption mechanisms and environmental behavior.
Peng J, Cheng B, Jiang L … +1 more , Du Q
Sci Total Environ · 2026 Aug · PMID 42224871 · Publisher ↗
Soil is a major aggregate of microplastics and organic pollutants (antibiotics, pesticides). The long-term coexistence of microplastics and these organic pollutants in soil leads to inevitable interactions, which in turn...
Soil is a major aggregate of microplastics and organic pollutants (antibiotics, pesticides). The long-term coexistence of microplastics and these organic pollutants in soil leads to inevitable interactions, which in turn affect their environmental behaviors. Based on recent research findings on microplastics in agricultural and urban soils, it is found that agricultural soils receive a large amount of microplastic inputs mainly through plastic mulching films, sewage sludge, compost, and atmospheric deposition, while microplastics in urban soils mainly originate from traffic, landfills, and industrial activities. Besides, the inherent characteristics of microplastics (polymer type, particle size, crystallinity, surface chemical properties), the chemical characteristics of antibiotics and pesticides (ionization, hydrophobicity, functional groups), and key environmental parameters (pH, ionic strength, soil depth, mineral composition, and dissolved organic matter) all affect the adsorption behavior and mechanism of microplastics on organic pollutants. Aging, dissolved organic matter, clay minerals, and biofilms can significantly alter the surface of microplastics, transforming the adsorption mechanism from simple hydrophobic partitioning to a complex interplay involving hydrogen bonds, electrostatic attraction, and π-π interactions. These processes can change the mobility, bioavailability, and persistence of the combined pollutants, thereby having a continuous negative impact on soil organisms, terrestrial food webs, and potential groundwater pollution. Therefore, it is imperative to reduce emissions at the source and develop soil risk management strategies as soon as possible to mitigate the long-term threats these emerging compound pollutants pose to soil ecological functions and human health.
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