Environmental Monitoring and Assessment最新文献

The Persian leopard (Panthera pardus saxicolor) is a keystone and endangered species in Iran, facing significant threats due to climate change, habitat degradation, and declining prey availability. This study aims to identify suitable habitats for the Persian leopard in Fars Province, located in southern Iran, and to assess the potential impacts of climate change on its future distribution. Habitat suitability modeling was conducted using MaxEnt software, incorporating a range of environmental variables, including topographic, climatic, land use/land cover, and anthropogenic factors. Additionally, to enhance model accuracy, the current and projected distributions of key prey species, such as wild goats and sheep, were incorporated. According to the results, approximately 12.53% of the total area of Fars Province (equivalent to 15,381.86 km²) is currently classified as suitable habitat for the Persian leopard. To predict the effects of climate change by the year 2070, two general circulation models (MRI-ESM2-0 and BCC-CSM2-MR) were applied under the SSP245 and SSP585 climate scenarios. The results indicate that climate change is likely to cause considerable shifts in habitat suitability, with an estimated loss of 23.46 to 39.81% of suitable habitats in Fars Province by 2070. These findings highlight the urgent need to revise current conservation and management strategies, emphasizing the identification and protection of critical habitats in the face of anticipated climate impacts.

Pangolins are the most trafficked mammals in the world. Sunda pangolins (Manis javanica), in particular, are critically endangered due to their proximity to consumption hotspots and the scale of the globalized illegal trade network. Data on their ecological drivers can inform targeted strategies to cauterize supply lines. We used data from 1455 camera-stations deployed between 2008 and 2024 across a heterogeneous mix of landscapes in Sabah, northern Borneo, to model the geomorphological and anthropogenic drivers of Sunda pangolin distribution. Our most parsimonious logistic regression model included six variables: accessibility to human population (β = 0.597, p = 0.004), soil cation exchange capacity (β = −0.665, p = 0.003), soil clay content (β = −0.311, p = 0.051), soil nitrogen concentration (β = 0.9862, p = 0.0001), soil bulk density (β = 0.43, p = 0.143), and topographic position index (β = −0.61, p = 0.005). The model performed well as evaluated using an out-of-sample test dataset (sensitivity = 0.89, specificity = 0.57, and AUC = 0.73). A high proportion (~ 43%) of rural, human-dominated areas were identified as highly suitable pangolin habitat, but only ~ 15% of these areas are protected. We further confirmed the overlap in highly suitable pangolin habitat and human-occupied land using an independent citizen science dataset of pangolin detections collected between 2019 and 2024 (Boyce index = 0.75). Our results illustrate that Sunda pangolins often live in high-risk areas but also suggest an opportunity to develop community-centered conservation strategies to curb poaching and cauterize supply lines fueling the trade of Sunda pangolins in Southeast Asia.

The accurate land use and land cover (LULC) classification in the data-scarce urbanized region of Peshawar remains challenging due to computational limitations, accuracy assessment, and traditional techniques. This study, for the first time, addresses this research gap by introducing different robust machine learning (ML) algorithms in Google Earth Engine (GEE). The crux of this study is to analyze the comparative performances of four classifiers, namely, classification and regression tree (CART), minimum distance (MiD), random forest (RF), and support vector machine (SVM) within GEE using Sentinel data for reliable LULC classification from 2020 to 2024. The performance of each classifier was evaluated by validation and accuracy assessment. The composed points of each class were run in a scripted code and assigned 70% data for training and 30% for testing. The overall accuracy of RF and CART classifiers was 95% followed by the same values of Kappa coefficients. In contrast, MiD shows the weakest performance. The CART and RF classifiers maintain high producer accuracy (PA, > 90) and user accuracy (UA, > 90) for each class. The classification consistency was confirmed with mean Mathew correlation coefficient (MCC) values of 0.98 (for CART) and 0.99 (for RF), with an average F1 score of over 95%. The McNemar test showed no significant difference between CART, RF, and SVM classifiers; however, the confidence interval (CI (=) 95%) confirmed the superior performance of CART and RF. This study confirms that the selected classifiers are transferable for a complex urban environment.

Sediment production is often intensified in peri-urban areas, due to the transition from predominantly rural to urbanized landscapes. Although sediment dynamics in urbanizing areas remain complex and poorly understood, urban development can generate significant fine sediment, emphasizing the importance of monitoring the impacts of each stage of urban development. We monitored suspended solids concentration and loads in six stormwater drainage systems with small, street-scale catchments for 6 months using low-cost automatic monitoring stations in southeast Australia. The study was conducted in a peri-urban area undergoing urbanization, where different stages of urban development were observed, ranging from bulk earthworks and road construction to fully urbanized sites. The results showed that during urbanization, event mean suspended solids concentrations commonly exceed 5 g/L (~100× more than fully urbanized sites). Suspended solids yields (SSY) in early-stage urbanization areas can be up to 30 times higher than in fully developed areas. The median particle size of sediments in early development stages was up to six times finer than those in the later development stages. The results highlight that early urbanization stages contribute significantly to fine sediment production, presenting a high risk to sensitive water bodies. The findings highlight the value of combining innovative IoT (Internet of Things) monitoring technologies, with geospatial and time series analysis to better understand sediment dynamics in a complex and rapidly urbanizing landscape. Additionally, the findings underscore that erosion and sediment control measures are vital, particularly during the early stages of urbanization, requiring proactive management throughout this process to mitigate fine sediment impacts and protect downstream waterbodies.

Traditional assessments of ecological sensitivity rely on static variables, failing to capture the pronounced seasonal dynamics of temperate mountain ecosystems, which leads to a mismatch between management and actual risks during critical seasons. Taking Mount Tai, China, as a case study, we established a dynamic evaluation framework. We processed Sentinel-2 imagery (2018–2024) on the Google Earth Engine (GEE) to derive seasonal NDVI and NDWI as key biophysical proxies. These were combined with static factors (topography, land use) and integrated using an AHP model validated by Random Forest (RF). The results reveal a significant “summer–winter dual-core driving” mechanism. In summer, patterns are vegetation-dominated (NDVI > 0.6), with high-sensitivity areas (HSA) accounting for 17.79% and clustered in the core forest zone. The winter pattern shifts to be controlled by hydrological-cryospheric factors, where HSA within the 0–50 m water buffer reaches 37.56%. RF analysis confirmed the water factor’s dominance in the cold season. Furthermore, phenological analysis showed that the Start of Season (SOS) exhibits substantially higher interannual variability (σ = 17.3 days) than the End of Season, describing spring as a transitional period with higher interannual variability in early-season vegetation growth. Compared to static assessments, the dynamic framework proved necessary by revealing a severe underestimation of risks around water bodies in winter. This study advances ecological sensitivity assessment from static pattern description to spatiotemporal process simulation. The proposed framework is highly repeatable and transferable, providing spatial decision support for seasonal adaptive management in temperate mountain scenic areas.

This study investigates the seasonal dynamics of phytoplankton and zooplankton diversity in the Batapady mangrove estuary, southwest India, from October 2023 to June 2024. Monthly sampling across five stations captured variations in environmental parameters (temperature, salinity, pH, dissolved oxygen, biological oxygen demand, and nutrients) and plankton community metrics. A total of 21 phytoplankton and 11 zooplankton genera were identified, with Bacillariophyceae and Copepoda as dominant groups, respectively. Diversity indices Shannon–Wiener (H′), Margalef’s richness (d), and Pielou’s evenness (J′) peaked in March, corresponding to stable hydrographic conditions and nutrient availability. Redundancy Analysis (RDA) revealed that plankton communities respond linearly to environmental forcing, with Water Temperature and Salinity identified as the primary active drivers of succession. While phytoplankton dynamics were strongly coupled with hydrography (Adj. R² = 0.147), zooplankton exhibited weaker abiotic associations (Adj. R² = -0.022), suggesting the influence of unmeasured biotic interactions. The findings suggest seasonal hydrography, particularly salinity and nutrient dynamics appear to influence plankton succession patterns in this estuarine ecosystem. This work highlights the ecological significance of mangrove-estuarine interfaces and advocates plankton-based monitoring for establishing ecological baselines in tropical coastal waters.

Red mud, a hazardous byproduct generated from the Bayer process in aluminium production, poses environmental risks due to its alkalinity and toxic elements content. This study provides a comprehensive physico-chemical characterization of red mud from an industrial landfill near Mostar, Bosnia and Herzegovina, to assess environmental impact and valorisation potential. Basic water parameters (pH, temperature, and electrical conductivity) of the flooded landfill section were measured on-site. Red mud samples showed pH values between 8.04–9.16 and a point of zero charge (pHpzc) of 8.7. Moisture content ranged from 20.08% to 37.21%, with the majority of particles < 0.25 mm. Mineral content varied between 12.45% and 17.77%, while bulk and true densities were 1.01–1.58 g/mL and 2.74–3.31 g/mL, respectively. Gamma spectrometry revealed radionuclide activity concentrations in the order ²³²Th > ²²⁶Ra > ²³⁸U. FAAS analysis showed pseudo-total heavy metal contents in the sequence Fe > Mn > Cr > Ni > Pb > Zn > Co > Cu > Cd. EDX analysis confirmed Fe₂O₃, CaO, and Al₂O₃ as dominant components (85.62%–92.35%), corroborated by FTIR spectroscopy. Heavy metal total content in plant material followed the trend Fe > Mn > Zn > Cu < Cr, Ni > Cd, with cobalt below detection limits. The results obtained in this study could provide guidelines for using red mud as a secondary raw material. Furthermore, current research highlights the importance of recycling and valorising valuable red mud components to secure future resources and reduce the irrational exploitation of conventional mineral sources.

The changes triggered by human activities in marine environments entail the enrichment of harmful microorganisms and may contribute to the translocation of pathogens between wildlife and humans, thus increasing potential health risks. Therefore, comprehensive biomonitoring of marine-environmental health is fundamental for mitigating impacts on both marine biodiversity and public health. Here, we review the catalog of bacterial genera potentially related to diseases in marine organisms (BGPRDs) as a tool for effective biomonitoring of marine health. These bacteria serve not only as indicators of environmental imbalance but also as markers of susceptibility to disease across multiple marine phyla. The catalog is composed of bacteria described as etiological agents of diseases in specific or nonspecific hosts for eight phyla of marine organisms (Rhodophyta, Ochrophyta, Heterokontophyta, Cnidaria, Mollusca, Arthropoda, Echinodermata, and Chordata). The abundance and distribution of BGPRDs can indicate (i) disturbances in marine-environmental health, (ii) the probability of interactions between pathogens and their hosts, and (iii) more susceptible marine organisms based on the abundance of their potential pathogens.

Haveli district lies within the outer Himalayan range and supports a temperate climate with cold winters and mild summers. Despite its ecological significance, plant diversity in this region remains poorly documented. This study investigated species composition and diversity across elevational, habitat, and aspect gradients within this Himalayan biodiversity hotspot. Vegetation surveys were conducted along 24 altitudinal transects (1329–1982 m), revealing a pronounced mid-elevation peak in biodiversity (1345–1586 m). A total of 101 vascular plant species from 44 families were recorded, with Asteraceae (14 spp.), Rosaceae (8 spp.), Fabaceae (6 spp.), and Lamiaceae (6 spp.) being the most dominant. Diversity indices varied across sites, with the highest values recorded at Chanjal (1502 m) and the lowest at Hillan (1982 m). Species richness peaked in Kalamula (1.427) and declined to 0.909 at Kalali. Soil properties also varied substantially, with maximum pH (8.54) at Khurshadabad, bulk density (1.18 g.cm³) at Sangal, total nitrogen (0.06%) at Kala Mulla and Budal, and organic matter (3.09%) at Budal. Regression coefficients indicated weak but measurable effects of elevation on diversity, climatic, and physicochemical traits. NDVI analysis revealed healthier and denser vegetation in central and eastern uplands, while peripheral zones exhibited sparser cover linked to human disturbance. Land-use mapping showed forest dominance in central highlands, transitioning to mixed forest and built-up areas at lower elevations. These findings highlight Haveli Kahuta’s biodiversity significance and underscore the need for conservation strategies addressing deforestation, overgrazing, and land-use pressures.

This study investigated the concentrations of six heavy metals—manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), and lead (Pb)—in poultry eggs consumed in Dhaka, Bangladesh, and evaluated the associated human health risks. A total of 84 eggs were collected from seven major city markets, with the white and yolk portions analysed separately using atomic absorption spectroscopy. The results showed that Fe was the most abundant metal in both egg white (mean: 9.34 mg kg⁻¹) and yolk (mean: 69.95 mg kg⁻¹), followed by Zn, Ni, Cu, Mn, and Pb. Concentrations of Ni and Pb in both egg portions exceeded maximum permissible limits in several samples. Although all target hazard quotient (THQ) and hazard index (HI) values were below the United States Environmental Protection Agency (USEPA) safety thresholds, indicating minimal non-carcinogenic risk, the cancer risk (CR) values for Ni exceeded the acceptable benchmark (1 × 10⁻⁴) for both children and adults, suggesting potential long-term health hazards. Principal component analysis (PCA) revealed two dominant sources of contamination: (i) feed-derived (Fe, Zn) and (ii) anthropogenic activities (Ni, Pb) likely related to industrial emissions, electroplating, and smelting. These findings underscore the need for stringent monitoring, effective regulatory enforcement, and targeted interventions to reduce heavy metal contamination in the poultry supply chain.