Environmental Monitoring and Assessment最新文献

The growing frequency and extent of wildfires constitute a significant environmental challenge, posing serious threats to ecosystems, biodiversity, and human livelihoods. This study presents a comprehensive wildfire susceptibility assessment for El Tarf Province, one of the most fire-prone yet understudied regions in Algeria. Long-term Landsat imagery (1995–2024) combined with four machine learning algorithms was used to produce high-resolution susceptibility maps and identify the key environmental and bioclimatic drivers of wildfire occurrence. Ten conditioning factors representing topographic, vegetative, edaphic, and climatic conditions were integrated, with elevation, Enhanced Vegetation Index (EVI), wind speed, and precipitation emerging as dominant predictors. Among the tested models, Random Forest achieved the highest predictive performance (ROC–AUC = 0.897), closely followed by XGBoost (0.896), while LightGBM provided an optimal balance between accuracy (0.875) and computational efficiency. Logistic Regression, though simpler, performed reasonably well (0.794). The Landsat-derived wildfire inventory comprised approximately 622,221 burned pixels and was subsequently split into a pre-2017 training set (72.8%) and a post-2017 testing set (27.2%) to evaluate model generalization over time. Spatial block cross-validation was applied to reduce spatial autocorrelation and enhance model generalization. This methodological framework, combining spatial and temporal validation, temporal hold-out, and spatial blocking, strengthens the robustness and reliability of wildfire susceptibility modeling. Interpretability analyses based on SHAP values, Gini importance, and permutation importance identified the contributions of underexplored variables, including vegetation type, soil type, and soil organic carbon (SOC). The resulting susceptibility maps provide valuable insights for spatial planning and ecosystem management, supporting evidence-based strategies to enhance environmental resilience and biodiversity conservation in Mediterranean landscapes.

Regulatory gaps in restart and cold/hot start emissions overlooked by current periodic technical inspection (PTI), and driving behaviors significantly impact plug-in hybrid electric vehicle (PHEV) particle number (PN) emissions under real driving conditions. Using portable emissions measurement systems (PEMS), this study builds cumulative PN emissions by key segments (cold-start, restart) and instantaneous high-emission events across four distinct behaviors. Key findings reveal that calm and normal driving elevate cold-start PN (up to 6.2 ×10¹¹ #/km) due to prolonged engine-off intervals and slow warm-up. Aggressive driving’s frequent restarts yield lower per-event emissions owing to thermal advantages. Adaptive cruise control (ACC) minimizes total PN by combining thermally efficient engine operation with extended zero-emission phases (16–17% duration). Crucially, instantaneous high-emission analysis shows > 80% of PN concentrates in < 20% of driving duration, with emission thresholds varying dramatically (82–1366%) across behaviors—primarily due to divergent dominant modes favored by each behavior. To quantify these behavior-specific modes and their parametric signatures, k-means clustering was applied, and found distinct behavioral associations: aggressive driving predominantly linked to high-load/high-rpm operation (> 2800 rpm or > 80% load), while calm/normal driving elevates cold-start and restart contributions. Consequently, real-world emission monitoring necessitates behavior-adaptive dynamic scenarios, tailoring test focus and parametric design informed by clustered thresholds.

This study assessed leachate and soil contamination at the Igbatoro dumpsite, Akure, Nigeria. Leachate samples were analyzed for heavy metals, BOD, and other physicochemical parameters. Soil samples were collected at depths of 0.5, 1.0, and 1.5 m to evaluate twenty-five (25) parameters, including pH, TDS, DO, COD, and heavy metals. Statistical analyses, including two-way ANOVA and correlation analysis, were performed. Leachate Pollution Index (LPI), revised LPI (r-LPI), Geo-accumulation Index (Igeo), Metal Pollution Index (MPI), Pollution Load Index (PLI), Degree of Contamination (Cdeg), and Potential Ecological Risk Factor (RI) were used to evaluate contamination levels of Igbatoro dumpsite. Results showed that Leachate pH ranged from 5.44 to 6.45 (mean 6.01), and metals like As and Cu exceeded the Federal Environmental Protection Agency (FEPA) limits. Strong negative correlations were observed between pH and most parameters, while temperature showed positive correlations with nitrate and Ni. An LPI of 13.65 and r-LPI of 40.39 exceeded pollution thresholds, indicating significant contamination. Soil analysis revealed elevated metal concentrations compared to the control, with Cd showing the highest Igeo value (–0.60). Location 6 recorded the highest MPI values for Cu and Cr, while most heavy metals fell within pollution classes, reflecting severe contamination. The potential ecological risk factor (RI) value of 174.70 indicates moderate ecological risk, with Cd posing a particularly high risk (Eir = 98.34). Overall, the study highlights considerable environmental and public health risks, underscoring the urgent need for remediation and adoption of sustainable waste management practices.

Fine particulate matter (PM₂.₅) pollution poses a serious environmental and public health threat in rapidly urbanizing regions in Africa, yet high-quality, long-term measurements remain rare across sub-Saharan Africa. This two-year study (2021–2022) delivers one of the few spatiotemporally resolved PM₂.₅ datasets in Ghana, generated using USEPA Federal Reference Method (FRM) gravimetric samplers. Over 300 samples were collected at three contrasting sites in Accra, Ghana: Adabraka (AD), adjacent to the Agbogbloshie e-waste burning area; Dansoman (DA), a mixed residential–commercial neighborhood; and Legon (UG), a suburban reference site. Mean 24-h concentrations exceeded WHO guidelines by a factor of about 3–4 at all sites, with AD recording the highest levels (60.76 µg/m³ in 2021; 60.54 µg/m³ in 2022) due to persistent, localized emissions. While interannual variability was minimal, spatial contrasts were pronounced, reflecting stable and dominant anthropogenic sources. Principal component analysis identified temperature (positive correlation) and relative humidity (negative correlation) as key meteorological drivers of PM₂.₅ variability. Health risk assessment indicated hazard quotients (HQ) above 1 for both adults and children, with markedly higher risks in children due to physiological and behavioral factors. Beyond its local implications, this dataset fills a critical regional gap, providing a benchmark for validating satellite-based estimates and regional chemical transport models, and informing targeted interventions in resource-limited regions.

Human–wildlife conflict (HWC) is a critical issue in the agricultural and horticulture fields of the study area. Livestock depredation by wildlife is common in the Baspa Valley of Kinnaur district. The study has focused on the nature of human–wildlife conflicts in the study area and the animals involved in HWC. Uncia uncia and Ursus thibetanus were found to be major carnivores linked to livestock depredation around human settlements in the area’s pasturelands. Macaca mulatta and Semnopithecus ajax are the major animals involved in HWC in the study area and are associated with crop raiding. Pseudois nayaur causes damage to fields mainly during the harvest season. The study has highlighted Ochotona roylei as a pest of the apple tree, marking the first record of O. roylei as an apple pest in India and the mitigation measures adopted by the locals to alleviate the human–wildlife conflict.

The Air Quality Index (AQI) is a widely used indicator for assessing ambient pollution levels and associated health risks. However, AQI classification using data-driven approaches is often challenged by severe class imbalance, where extreme pollution categories such as Poor and Very Poor are sparsely represented. In this study, machine learning models were developed for multi-class AQI classification using a dataset comprising major air pollutants (PM₂.₅, PM₁₀, NO, NO₂, NOx, SO₂, CO, O₃, and volatile organic compounds) along with key meteorological variables, including relative humidity, wind speed, wind direction, solar radiation, and vertical wind speed. To improve model robustness, data preprocessing techniques such as handling missing values, distribution normalisation, and cyclical encoding of wind direction were applied. To address the pronounced class imbalance, a cluster-aware synthetic oversampling (CASO) framework was implemented by integrating random oversampling, Edited Nearest Neighbours (ENN), KMeans-SMOTE, and final class equalisation. Five machine learning models—Logistic Regression, Decision Tree, Random Forest, XGBoost, and LightGBM—were evaluated using accuracy, balanced accuracy, and macro-averaged F1-score. The experimental results demonstrate that ensemble gradient-boosting models, particularly LightGBM and XGBoost, consistently outperform other classifiers, achieving the highest test balanced accuracy values (≥0.96) after resampling. The findings confirm that combining cluster-aware synthetic resampling with ensemble learning significantly enhances AQI classification performance under severe data imbalance, offering a reliable and interpretable framework for air quality assessment.

Forest extent, assemblage, and regeneration pattern influence the biodiversity and ecosystem functions, which are often sensitive to climatic and anthropogenic correlates, especially in tropical forest systems. We quantified the diversity, regeneration potential and mapped the forest types in Panna Tiger Reserve, Central India, using Sentinel-2A multi-temporal data with a Random Forest classifier. A total of 153 stratified random plots were sampled with a focus on trees, saplings, and seedlings. Of the six forest types in the region, five forest types, except Boswellia forest, could be mapped with an overall accuracy of 88.9% and a Kappa coefficient of 0.81. Area-wise, Northern dry mixed deciduous forest (NDDF) was the most widespread forest type (36.88%), followed by Dry deciduous scrub (DDS) (16.7%), Dry teak forest (DTF) (7.64%), Dry bamboo brakes (DBB) (3.78%), and Anogeissus pendula forest (APF) (0.62%), while non-forest and water consist of 33.26% of the reserve. Overall, 64 species from 48 genera and 25 families were identified. Trees (35 species), saplings and seedlings (39 species each) had the highest species richness across all three life stages in NDDF. APF had the highest tree density of 625 individuals ha⁻¹ and sapling density (634 ind. ha⁻¹), while NDDF had the highest seedling density (1621 ind. ha⁻¹). DDS had the highest regeneration potential (75%), followed by NDDF (74.5%) and BF (46%). Our results highlight that mapping forest types, together with assessing structural attributes, diversity, and regeneration potential, can contribute to better conservation planning and management actions.

Coastal marine ecosystems are key components of biodiversity and ecosystem functioning but have been increasingly degraded by human activities. One of the most severe environmental disasters in Brazil occurred in November 2015, when the Fundão tailings dam collapsed in Mariana (Minas Gerais), releasing approximately 40 million m³ of iron ore waste into the Rio Doce basin and adjacent coastal environments. To evaluate the long-term biological consequences of this event, we analyzed the taxonomic composition and diversity of marine communities using environmental DNA (eDNA) metabarcoding from sediment cores collected in 2018 across three coastal sectors—Front (mouth of the Doce River), North, and South. A total of 761,517 reads generated 11,061 unique amplicon sequence variants (ASVs) assigned to 148 taxa revealing significant spatial variation in taxonomic (species-level) composition and diversity indices (PERMANOVA, pseudo-F = 16.55; p = 0.047). The South region exhibited the highest species richness (q₀ = 103 taxa), followed by the North (97) and Front (70). Cluster and SIMPER analyses indicated two distinct biological assemblages: (1) the Front region, dominated by diatoms (Mediophyceae, Bacillariophyceae) and protists tolerant to metal enrichment, and (2) the North–South regions, characterized by higher evenness and presence of benthic invertebrates such as Holothuroidea and nematodes (Desmodorida). Species abundance distribution (SAD) models differed among areas, reflecting ecological gradients associated with the dispersal and chronic accumulation of mining residues. These results demonstrate a persistent imbalance in marine communities near the Doce River mouth, suggesting that the legacy of historical contamination and the Fundão dam failure continues to shape benthic biodiversity patterns more than three years after the disaster.

The Gansu-Qinghai contiguous region of the upper Yellow River occupies a strategic position in China’s ecological security framework. However, comprehensive long-term assessment of ecological quality changes and their mechanisms in this ecologically fragile zone remains limited. Systematically evaluating ecological quality dynamics is necessary for supporting high-quality development strategies in the Yellow River Basin. This study utilizes MODIS series remote sensing imagery from 2000 to 2022, accessed through the Google Earth Engine platform. The Remote Sensing Ecological Index (RSEI) was constructed via principal component analysis (PCA). Theil-Sen median trend analysis, Mann-Kendall tests, and coefficient of variation methods were applied to examine spatiotemporal patterns and stability. Pearson correlation analysis and random forest modeling were employed to quantify the contributions of ten driving factors. Results indicate that the ecological quality of the study area showed an overall improving trend with local fluctuations from 2000 to 2022. Spatially, it exhibited a west-high and east-low pattern, with “good” and “excellent” areas continuously expanding. About 43% of the region experienced ecological improvement, 20% showed degradation, and over 86% remained highly stable. Vegetation greenness was the dominant positive driver, while land surface temperature and dryness index had significant negative impacts. Precipitation and humidity displayed threshold responses, and socioeconomic factors such as GDP and population density mainly influenced local ecology through land-use intensity. Overall, ecological quality was jointly regulated by vegetation dynamics, hydrothermal conditions, and human activities. This study establishes baseline data for systematic ecological monitoring in high-altitude ecologically sensitive regions. The findings demonstrate that targeted ecological restoration projects have achieved measurable effectiveness, while emphasizing the necessity of integrating climate change considerations into future conservation management strategies for the upper Yellow River.

Most of the waterbodies are getting polluted due to wastewater discharges from the adjoining drains or channels. The present study focuses on flow dynamics and the physical separation of suspended solids in flowing wastewater drains. Two experimental setups were employed: a multi-tray sedimentation model without baffles and a modified model with vertical flow baffles. Laboratory experiments were conducted at flow rates of 60–240 mL min⁻¹, established through Froude-based dynamic similarity to represent typical urban drain hydraulics. Results demonstrated that the baffled model significantly outperformed the plain sedimentation model, achieving removal rates of total suspended solids (TSS) up to 88% and 70%, respectively. The staged baffle reduced turbulence, created quiescent zones, and facilitated the progressive settling of both larger and finer particles. Particle size analysis (1–2400 µm) further confirmed the system’s efficiency, with median particle size (D₅₀) reduced from 1727 µm at the inlet to 1.35 µm at the outlet in the baffled system. The findings validate the hypothesis that engineered in situ sedimentation models can significantly improve TSS and organic removal in urban wastewater drains, offering a scalable solution for liquid waste management.