This review examines the transport, fate, modelling, and mitigation of Microplastics (MPs) in urban stormwater infrastructure, with emphasis on pavements, runoff pathways, micro-drainage, and macro-drainage systems. Following a systematic review approach, more than 1000 records were screened and approximately 50 core studies were retained when they addressed urban stormwater or drainage-related MP transport with adequate methodological reporting; marine-only studies and biological-effect studies without direct relevance to transport processes were excluded. The evidence shows that stormwater systems function not merely as passive conduits but as dynamic reactive transport systems with temporary storage, where particle mobilisation, sedimentation, resuspension, and temporary retention regulate MP export. Road surfaces, especially high-traffic areas, are major reservoirs of tyre wear, road-marking, atmospheric, and litter-derived particles that are rapidly mobilised during rainfall. Conventional grab sampling may underestimate MP loads, which in some cases exceed treated wastewater effluent loads by up to six-fold. Drainage structures such as manholes can immobilise up to 17.3
Manzala Lagoon, the largest coastal lagoon in the Nile Delta, has undergone extensive restoration and dredging in recent years (2017–2022), creating an urgent need for accurate and cost-effective bathymetric mapping. Conventional echo-sounder surveys remain reliable but are difficult to implement over such a large, shallow, and vegetated water body. This study developed an interpretable machine-learning framework for satellite-derived bathymetry using Landsat 8 multispectral imagery calibrated with extensive echo-sounder measurements. Seven machine-learning algorithms, comprising tree-based ensembles and boosting-based regressors, were evaluated using regression metrics, complementary depth-class performance metrics, uncertainty estimation, and SHAP-based model interpretation. Tree-based ensembles consistently outperformed boosting-based regressors, with Decision Tree and Extra Trees achieving the highest test-set accuracy under the adopted validation design (r = 0.996, R2 = 0.992, RMSE = 0.073 m). Although both models produced similar accuracy, Extra Trees showed substantially lower prediction uncertainty (≈0.09 m), making it the most reliable model for operational mapping. SHAP analysis identified the near-infrared band as the most influential predictor, likely reflecting surface and turbidity-related effects rather than direct depth sensing, while logarithmic band ratios improved model robustness by capturing nonlinear spectral relationships. The resulting bathymetric maps resolved depth gradients from ≈ −0.6 to −4.0 m and provide an efficient alternative to extensive field surveys. The proposed framework offers a transparent, scalable approach for bathymetric mapping that supports hydrodynamic modeling, sediment transport studies, and sustainable management of shallow coastal lagoons.
Accra, Ghana, faces deteriorating ambient air quality due to rapid urbanization and heavy vehicular traffic. However, site-specific influence of meteorological drivers on PM2.5 at pollution hotspots remains limited. PM2.5 levels and their association with meteorological parameters at nine traffic hotspots were evaluated. Ambient PM2.5 concentrations were monitored to capture both harmattan and non-harmattan seasons, using an Aeroqual Ranger portable dust monitor. Daily meteorological data, including temperature, relative humidity, wind speed, and rainfall, were obtained from automatic weather stations operated by the Ghana Meteorological Agency. Pearson correlation analysis was used to evaluate the relationship between PM2.5 and meteorological parameters. Median PM2.5 concentrations of 54.90 μg/m3 at high-density, 34.26 μg/m3 at commercial/business, and 16.75 μg/m3 at low-density traffic areas were recorded. These values substantially exceed the WHO 24-h guideline of 15 μg/m3. Ashaiman (median = 54.90 μg/m3) is the most polluted traffic site, due to severe congestion, aged vehicles, and extensive roadside commercial activities. Correlation analysis revealed that temperature exhibited a significant positive association with PM2.5 (r = 0.353, p < 0.01). Conversely, rainfall, wind speed, and relative humidity during the non-Harmattan season showed negative correlations, consistent with wet deposition and pollutant dispersion. During the Harmattan season, wind speed showed a weak positive association with PM2.5 (r = 0.079, p = 0.450), attributed to localized dust resuspension and dry atmospheric transport. The findings demonstrate that while meteorological parameters influence PM2.5, the dominant factor remains localized vehicular emissions at traffic hotspots. This underscores an urgent need for emission-focused policies and targeted interventions to protect the health of exposed urban populations in Accra.
Industrial dumpsites are potential hotspots of environmental radiation exposure, yet predictive assessments of dose distribution remain limited in Nigeria contexts. This study evaluates the use of supervised machine learning techniques to predict absorbed gamma dose rate at the NAK Steel dumpsite in Katsina State, Nigeria, using a dataset of 36 soil sampling locations (n = 36). The predicted absorbed dose rates were subsequently used to estimate annual gonadal dose (AGD) as an organ-specific dose indicator and excess lifetime cancer risk (ELCR) as a radiological risk metric. Four learning algorithms: support vector machine (SVM), linear regression (LR), random forest (RF), and decision tree (DT), were trained and tested under identical preprocessing and validation conditions. Across all performance metrics, RF consistently outperformed the other techniques. It achieved the lowest prediction errors (RMSE = 0.0131; MAE = 0.0101), the highest coefficient of determination (R2 = 0.902), and the strongest hotspot classification performance (accuracy = 0.89; kappa = 0.77; AUC = 0.93). In contrast, LR and SVM tended to smooth spatial variability and underestimate localized anomalies, while DT produced abrupt spatial patterns suggestive of overfitting. Predicted AGD values ranged from 0.03 to 0.27 mSv y⁻1, while the corresponding ELCR estimates reflected the spatial variability in annual effective dose with the highest values occurring near the principal hotspot (S11). These findings demonstrate that random forest provides a reliable framework for predicting absorbed gamma dose rate in heterogeneous industrial environments and for supporting subsequent radiological dose and risk assessment using internationally accepted conversion models. Although the relatively small dataset (n = 36) limits model generalization, the proposed framework provides a practical tool for environmental radiation monitoring and decision-making in data-sparse regions.
Arsenic (As) contamination in rice is a major dietary exposure risk in South and Southeast Asia, yet As migration during traditional rice processing remains poorly resolved. This study presents the first continuous, integrated assessment of As migration from raw paddy (RP) to cooked rice through parboiling (steaming, soaking, and boiling) and cooking. Ten independently collected raw paddy (RP) samples representing eight rice cultivars (range: from 144 to 387 µg/kg As) and seven groundwater sources spanning safe to extreme contamination (range: < 3–450 µg/L As) were examined. Total As concentrations were quantified in intermediate paddy fractions including steamed paddy (STP), soaked paddy (SOP), and boiled paddy (BP); final edible fractions including parboiled rice grain (PRG) and cooked rice grain (CRG); and discarded processing waters including steamed water (STW), soaking water (SOW), boiling water (BW), and total discarded water (TDW). Parboiling substantially amplified As retention, with boiling identified as the dominant accumulation stage, increasing BP As from 193 to 759 µg/kg across the water contamination gradient. Although discarded waters removed considerable As (STW 20–315 µg/L, SOW 16–341 µg/L, and BW 28–345 µg/L), removal efficiency declined sharply with increasing water As. Cooked rice grain exhibited a very strong positive linear relationship with raw water As (Pearson’s r = 0.9722, p = 0.0002 increasing from 152 µg/kg under As-safe water (< 3 µg/L) to 813 µg/kg at 450 µg/L. Strong predictive relationships were observed for PRG (R2 = 0.9730), CRG (R2 = 0.9452), and TDW (R2 = 0.9327). Integrated intrachain analysis revealed that when contaminated groundwater was used throughout the traditional processing sequence, the overall processing sequence was associated with higher grain As concentrations despite the concurrent removal of As in discarded processing waters. In contrast, consistent use of groundwater containing < 3 µg/L As (referred to as As-safe water in this study) achieved up to a 46
Vegetated riparian buffers play a critical role in maintaining ecological health and water quality, yet efficient characterization and large-scale monitoring remain challenging due to the resource demands of traditional field campaigns. This study, conducted in an agricultural setting, introduces a straightforward, image-based methodology for riparian buffer characterization, exploiting advancements in deep convolutional neural networks (DCNN) and very high spatial resolution satellite imagery. Leveraging a large Riparian Strip Quality Index (RSQI) field dataset, the proposed approach adapts a Multi-View DCNN (MVDCNN) architecture, originally developed for 3D object recognition, to correlate satellite images of riparian strips with RSQI metrics. Of the seven spectral band combinations, multiple input views, and two training modes evaluated, the configuration using four views with RGB bands from a pretrained network achieved the best results. However, the alternative spectral band combinations produced similar levels of performance, suggesting that texture and shape information are key factors in the model’s effectiveness. Comparisons with a conventional workflow involving object-based land cover classification followed by RSQI calculation indicate that the trained MVDCNN achieves stronger correlations between imagery and RSQI scores (average RMSE = 7.35, R2 = 0.93 using RGB bands) compared to the object-based method (RMSE = 11.25, R2 = 0.87). To our knowledge, this is the first direct application of DCNNs to riparian buffer quality assessment. Requiring minimal preprocessing and no photo-interpretation expertise, the proposed approach leverages existing field data to facilitate more accessible, scalable, and adaptable riparian buffer monitoring, with potential for application in diverse environmental contexts.
Under the dual pressures of accelerating global warming and intensifying human activities, arid and semi-arid ecosystems are confronting unprecedented ecological challenges. Fractional Vegetation Cover (FVC), as a critical indicator of vegetation dynamics and ecosystem health, has attracted increasing research attention; however, the spatiotemporal evolution patterns of FVC and its underlying response mechanisms to drought stress remain insufficiently understood. Using a dimidiate pixel model, FVC was derived from Landsat imagery during 1990–2022 for the arid northwestern China. Its spatiotemporal dynamics and drought responses were analyzed by integrating trend analysis, spatial statistics, and multi-timescale Standardized Precipitation Evapotranspiration Index (SPEI) data. The results indicate that: (1) Over the past 33-year, FVC exhibited an overall increasing trend at a rate of 0.98×10^-4yr^-1 , albeit with considerable interannual variability. The sharp decline in FVC in 2001 was closely associated with an extreme drought event, while the pronounced increase after 2010 coincided temporally with the implementation of regional ecological restoration programs. (2) Spatially, FVC displayed a heterogeneous pattern, with relative stability in the west and dynamic changes in the east. Low-high clusters dominated the local spatial autocorrelation structure, reflecting a fragmented vegetation distribution under drought stress, while high-high clusters showed progressive spatial expansion in oasis areas and ecological restoration zones. (3) The response of FVC to drought was scale-dependent: at the growing season scale (SPEI-4), FVC exhibited a positive correlation with drought indices, whereas at the interannual scale (SPEI-12), this relationship shifted to a negative correlation in certain sub-basins. Vegetation persistence was predominantly associated with no-to-mild drought conditions, while severe and extreme drought events significantly suppressed FVC. These findings results important theoretical and practical implications for ecosystem restoration efforts globally.
Nanoplastics are increasingly recognised as relevant contaminants in environmental biota, yet their integration into biomonitoring remains limited by analytical and interpretative uncertainties. This review critically examines nanoplastic detection in biological matrices, evidence of uptake and tissue distribution, and biomarkers of early biological effects. Although experimental studies suggest that model nanoplastics may reach internal tissues, quantitative confirmation of intact-particle translocation remains limited, and fluorescence-based findings may be affected by dye leaching, tissue autofluorescence, external particle association, and insufficient chemical confirmation. Oxidative stress, immune and inflammatory responses, genotoxicity, and metabolic disturbances are frequently reported across taxa, but their low specificity limits their use as stand-alone indicators. We therefore outline a tiered conceptual approach for future biomonitoring, integrating the following: (i) polymer-specific particle confirmation, (ii) assessment of internal burden using complementary mass-, number-, and size-based metrics, and (iii) interpretation through mechanistically linked multi-biomarker panels. This approach distinguishes environmental occurrence from internal exposure and biological effect and highlights the need for contamination control, complementary analytical methods, environmentally relevant exposure conditions, and transparent uncertainty reporting. Nanoplastics are not yet ready for routine biomonitoring, but coordinated analytical validation and mechanism-based interpretation may support their future inclusion in environmental monitoring and risk assessment.
This research investigated the effects of seasonal variation and geographical distribution on 1,4-dioxane concentrations in four lakes and a wastewater treatment plant (WWTP) in Florida, USA. The results showed significant seasonal variation, with the highest 1,4-dioxane concentrations detected during spring and the lowest during summer in both surface water and wastewater. Regression analyses revealed that 1,4-dioxane concentrations were negatively correlated with both water temperature and precipitation, suggesting that hot and wet conditions favored relatively lower dioxane concentrations because of dilution. Spatially, the lake with the highest concentration (Lake Talquin) was associated with a large mixed-use watershed and long residence time, whereas the lake with the lowest concentration had limited connectivity and fewer potential contamination sources. Hurricanes had immediate impacts on the 1,4-dioxane concentrations in lakes. However, the impacts varied from lake to lake, depending on the balance between increased 1,4-dioxane input and runoff dilution. The highest wastewater concentration was measured in the tertiary effluent, which may be attributed to in-situ formation through oxidation of 1,4-dioxane precursors during chlorination. Statistical analyses indicated significant seasonal, spatial, and treatment-stage effects (p < 0.05).
This study aimed to estimate the excess lifetime cancer risk (ELCR) from internal and external exposures to naturally occurring radionuclides of 226Ra, 232Th, and 40K in soil from three artisanal mining and resident agricultural regions in Niger State, North Central Nigeria. A NaI(Tl) detector was used to determine NORM concentrations in the soil samples, and the ELCR for artisanal miners and resident farmers was simulated using the RESRAD (residual radioactivity) code. The average activity concentrations of NORMs (226Ra, 232Th, and 40K) were estimated to be 18.79 ± 0.91, 7.87 ± 1.23, and 346.39 ± 10.35 Bq/kg, respectively. The excess cancer risks from the exposure to these radionuclides were estimated by the application of the RESRAD code. The simulation results indicate that the ELCR ranged from 2.33 × 10⁻⁶ to 5.40 × 10⁻⁶ within the evaluated time period (1–100 years), with the minimum value associated with 232Th and the maximum value associated with 22⁶Ra. These results show that 226Ra has the highest ELCR over 100 years, followed by 232Th in the soil from the artisanal mining areas. The results show that the dominant exposure pathways were through direct radiation from soil (direct and airborne) and inhalation. The findings of this study could help determine the risk of environmental natural radioactivity as well as in making decisions about radiation protection.
Emerging pollutants pose an escalating threat to the stability of marine ecosystem, underscoring the urgent need for comprehensive understanding and evidence-based interventions. This paper examines several classes of emerging pollutants, taking four typical types as representatives: endocrine-disrupting compounds (EDCs), per-and polyfluoroalkyl substances (PFAS), microplastics and antibiotics, providing a detailed account of their sources in the marine environment, transmission pathways, and impacts on marine organisms and ecosystems. Mitigation strategies currently available for each pollutant class are also critically evaluated. This review systematically analyzes more than 100 studies published from 1993 to 2025, with the majority concentrated from 2010 to 2025, from a global perspective. Through a synthesis and critical analysis of relevant literature and research findings, this study aims to provide robust scientific evidence and evidence-based recommendations for marine environmental management and policy for marine environmental protection and the advancement of sustainable marine development.
Heavy metal (Hg, As, and Cr) concentrations were measured in the muscle tissue of four wild sparid species (Sparus aurata, Pagrus pagrus, Pagellus erythrinus, and Pagellus acarne) from the central coast of Algeria. The relationships between muscle metal content and fish size (fork length and total weight) were also examined. Accumulation patterns based on mean metal concentrations in sampled fish muscle tissues showed the same descending order in all species: As > Hg > Cr. Concentration ranges of the analyzed metals (mg kg–1 wet weight) were as follows: As (1.323–3.396), Hg (0.102–2.004), and Cr (0.232–0.915). The highest mean Cr concentration (0.65 ± 0.02 mg kg–1) was recorded in the muscle of Sparus aurata, while the highest levels of Hg (0.98 ± 0.10 mg kg–1) and As (2.70 ± 0.07 mg kg–1) were found in Pagrus pagrus. Strong positive linear correlations (r = 0.92–0.99, p < 0.01) were observed between fish size and muscle metal concentrations in all cases. The mean metal concentrations in the selected sparid species were within the safe limits established by international organizations for human consumption.
Wildfires are a major disturbance in Mediterranean forests, yet long-term burned-area accounting remains uncertain in fragmented landscapes where coarse-resolution global products may smooth burn perimeters and miss small scars. This study presents a forest-constrained regional reconstruction of annual forest burned area at 30 m resolution for northern Morocco over 1984–2025, excluding 2012, using the Landsat archive, spectral change metrics, and supervised machine learning. Annual mapping was constrained by Landsat-derived forest-domain masks anchored to an official 2018 forest inventory. Burned/unburned discrimination used paired pre- and post-fire composites and three predictors: ΔNBR, ΔMIRBI, and ΔBAI. Five classifiers were evaluated under a strict chronological design, with 2001–2018 for training, 2019–2021 for validation, and 2022–2024 for independent testing. A calibrated radial-basis support vector machine performed best on the independent sample-based test period, with PR-AUC = 0.988, ROC-AUC = 0.989, F1 = 0.948, Brier score = 0.035, and a fixed operating threshold of τ = 0.50. The reconstructed record showed strong interannual variability, with a median of 609 ha and a range of 135–19,451 ha, and closely matched official annual burned-area statistics (r = 0.998, NRMSE = 16.97
Sandy beach ecosystems are increasingly affected by climatic variability and human pressures. The ghost crab Ocypode quadrata is widely used as a bioindicator of environmental conditions on sandy beaches. However, important gaps remain in understanding how natural sources of variability, such as seasonality and beach morphodynamics, influence its population dynamics. We evaluated seasonal and spatial variation in burrow density, biomass, carapace dimensions, and body condition, used here as morphological and population descriptors of O. quadrata, along morphodynamic gradient of sandy beaches in southern Brazil. Biomass and carapace dimensions, obtained through direct census, varied significantly among beaches and seasons, with higher values generally recorded on the reflective beach during summer, whereas intermediate and dissipative beaches showed higher values during winter. In contrast, body condition consistently peaked in autumn across all beaches. Burrow density, estimated through indirect census, did not vary significantly across seasons or beaches, but was positively associated with vegetation area, as were the other descriptors examined. These findings suggest that seasonal dynamics and habitat characteristics, including vegetation area, may be associated with variation in O. quadrata population descriptors across beaches differing in morphodynamic condition. Accounting for these natural sources of variability may support more reliable use of O. quadrata as a bioindicator in sandy beach ecosystems.
The distribution of entomopathogenic nematodes (EPNs) in agricultural soils is influenced by complex interactions between edaphic factors and crop management practices, yet these relationships remain poorly understood under tropical conditions. This study evaluated the spatial distribution of EPNs and their association with soil chemical attributes in five crop systems in southeastern Brazil. A total of 82 soil samples were collected and analyzed using insect-baiting techniques and geospatial tools, including kernel density and interpolation methods. EPN occurrence varied across crops and was positively associated with higher concentrations of potassium and iron, while an inverse pattern was observed for copper. These results suggest that micronutrients may indirectly influence EPN distribution by modulating soil microbial communities and plant–soil interactions. Although causal relationships cannot be definitively established, the spatial patterns identified highlight the importance of soil chemical composition in structuring beneficial soil fauna. The integration of geospatial analysis provides a valuable framework for optimizing the application of biological control agents in Integrated Pest Management (IPM) programs. These findings contribute to a better understanding of soil ecological dynamics and support the development of more sustainable agricultural practices.
Continuous assessment of ecosystem health is fundamental to wildlife conservation. Heavy metal pollution poses persistent risks to ecosystems and wildlife, yet contamination may be difficult to detect in relatively intact landscapes with limited anthropogenic disturbance. We used wildlife feces as a non-invasive biomonitoring matrix and quantified Cd, Pb, Cr, Hg, and As in 50 fecal and 52 environmental samples collected from the human–tiger coexistence landscape of the Tumen River Basin during 2018–2021. At least one target element was detected in 90 of the 102 fecal and environmental samples (88.2
Microplastic (MP) pollution is an emerging concern along tropical seaweed-producing coasts, where aquaculture occurs within broader coastal systems influenced by settlements, fisheries, rivers, and shoreline use. This study assessed the occurrence, spatial patterns, short-term variability, and particle characteristics of MP pollution in surface seawater and surface sediment along the Takalar coast, South Sulawesi, Indonesia. Water and sediment samples were collected from 12 stations, comprising nine seaweed-farming sites and three non-farming sites, during two sampling repetitions in January and March 2022. After laboratory extraction, MPs were visually identified and characterized by shape, color, and size, and the polymer type of a subset of representative particles was determined using Fourier Transform Infrared (FTIR) spectroscopy. MP abundance ranged from 300 to 1600 particles/m3 in surface seawater from seaweed-farming sites and from 500 to 800 particles/m3 at non-farming sites. MP abundance in surface sediment ranged from 10 to 170 particles/kg dry weight (DW) at seaweed-farming sites and from 30 to 110 particles/kg DW at non-farming sites. Mixed-effects analysis showed no significant effects of site category, sampling campaign, or their interaction on MP abundance in either matrix. Fibers and fragments dominated the particles, accounting for more than 60
Riverbed mining exerts significant pressure on Himalayan river systems, yet its influence on trace metal dynamics in sediments remains poorly understood across elevation gradients. This study quantifies the concentration and ecological risk of six trace metals Cr, Ni, Co, Cu, Zn, and Pb in surface sediments from the Chandra River (Upper Himalayas), Beas River (Middle Himalayas), and Chakki Khad (Lower Himalayas), Himachal Pradesh, India. Ten surface sediment samples were collected from each river and evaluated using pollution indices (Igeo, PINemerow, PLI, PERI, MERMQ, and MEC), sediment quality guidelines (SQGs), Pearson’s correlation, and hierarchical cluster analysis. Mean metal concentrations in Chandra and Beas Rivers followed Cr > Zn > Ni > Pb > Cu > Co, while Chakki Khad showed Cr > Ni > Zn > Pb > Cu > Co. Chromium exceeded probable effect concentration (PEC) values in 40
The relevance of this study stems from the need to assess the impact of mining activities on aquatic environments. This work aimed to identify patterns of component migration and accumulation in waters associated with abandoned tin-tungsten mines and to evaluate the environmental status of these waters. Water samples were collected from mine workings and waste rock dumps at the Dedovogorsk tungsten and Angatuy tin deposits, located in Eastern Transbaikalia, Russia. The waters are predominantly sodium-calcium bicarbonate and sulfate-bicarbonate types, with total dissolved solids (TDS) less than 200 mg/L and a neutral pH. Trace element concentrations generally range from single-digit to hundreds of micrograms per liter. Two distinct types of chemical element associations were identified: a technogenic association, reflecting the influence of mining activities, and a regional association, developing under natural conditions. The technogenic association is characterized by elevated concentrations of Co, Cu, Zn, and Cd, while the regional association exhibits accumulation of rare earth elements (REEs) and reduced concentrations of heavy metals. The environmental assessment revealed significant pollution in waters from adit drainage, the open-pit, and tailings storage facilities. Based on component concentrations, these waters were classified as areas of ecological emergency and disaster.
To explore the pollution status of heavy metals in surface dust and soil at bus stops in Weiyang District, Xi'an, 52 surface dust and soil samples were collected. Pollution assessment methods such as the Index of Geo-accumulation (Igeo) and the Nemerow Comprehensive Pollution Index were used to assess the pollution status of six heavy metals, Cr, Mn, Cu, Zn, As, and Pb, in 104 samples. To our knowledge, for the first time, the coupling coordination of heavy metal concentrations between dust and soil was studied. The average concentrations of heavy metals in dust and soil, except for As and Mn, were higher than the background values of soil in Shaanxi Province. Spatially, the concentrations of heavy metals in dust were generally higher in the east and lower in the west, while in the soil, they were higher in the south and lower in the north. The main sources were traffic, natural, and industrial sources. Hand-to-mouth ingestion was the main exposure route for both adults and children. The non-carcinogenic risk for children was higher than that for adults. The coupling coordination degree of heavy metal concentrations between surface dust and soil was at a moderate level, which means that there is a certain interactive migration between dust and soil heavy metals, and the pollution sources could be similar. Overall, our study provides everyone with references for single-medium and multi-medium heavy metal migration models.
