Environmental Monitoring and Assessment最新文献

Plastic pollution has become a widespread global threat, putting significant pressure on aquatic ecosystems and other ecological areas. Chironomid larvae are effective bioindicators of microplastic pollution in freshwater environments because of their sediment-dwelling behavior and environmental sensitivity. This study investigated microplastic contamination in the Ergene River, an ecosystem significantly affected by intensive industrial activity, using chironomid larvae. In May 2025, larvae were collected from five stations along the Ergene River and its tributaries, especially in areas with high industrial activity. The results showed an average microplastic concentration of 63.28 ± 33.59 particles per gram of wet weight in the larvae. Fibers were the most common shape, accounting for 62% of particles. Color analysis indicated that most particles were black (34%), transparent (32%), and blue (22%). Raman spectroscopy revealed that the main polymers were polyacrylamide (PAA, 36%), polyamide (PA, 28%), and polyethylene terephthalate (PET, 15%). Based on the Polymer Hazard Index (PHI), PAA (PHI = 8280) and PA (PHI = 1400) were classified as “high hazard” (level IV), while PET (PHI = 60) and polystyrene (PS, PHI = 90) were categorized as “low hazard” (level II). The study suggests that the primary sources of microplastics in the region are discharges from organized industrial zones and that chironomid larvae may serve as potential bioindicators of this type of pollution.

The pollution in Caohai Lake, a small section in the north of Dianchi Lake, Kunming, Yunnan Province, China, has increased in severity in recent years. We extracted sediment cores from four sampling points in Caohai Lake. We then determined the total phosphorus (TP), total nitrogen (TN), total carbon (TC), chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), cadmium (Cd), and lead (Pb) concentrations; the organic matter content (OM), and the carbon/nitrogen (C/N) ratio at different depths in the sediment cores. We evaluated the ecological risk from the heavy metals in the sediments with the geoaccumulation index and the potential ecological risk index. Overall, the study area was slightly polluted by heavy metals. The potential ecological risk index values indicated slight ecological hazards from all six heavy metals, with the highest risks from Cu and Pb. The combined potential ecological hazard index for the four sites was low. Correlation analysis showed that some of the heavy metals were strongly correlated, which indicated similar sources. The average total phosphorus (TP) concentration and the average mass ratio of OM in the sediments of Caohai Lake were 714.67 mg/kg and 28.92%, respectively. The TN and TC levels in Caohai Lake were high and varied considerably with depth, but were stable at a depth of about 1.3 m. We compared the data for different depths in the sediment cores and found that the TN/TC mass ratio was much higher in the surface sediments than in the deeper layers. The C/N ratio averaged 14, which indicates that the OM in Caohai Lake was from a mixture of sources.

Heavy metal pollution in agro-mining zones threatens ecosystems and human health through bioaccumulation and food-chain transfer. This study assessed heavy metal pollution and associated health risks in the Likuyu River catchment by measuring concentrations of zinc (Zn), cadmium (Cd), nickel (Ni), copper (Cu), and lead (Pb). During the rainy season, samples of water, sediment, soil, and locally cultivated vegetables were systematically collected along a 22.7 km stretch of the river. Heavy metal concentrations were analyzed by flame atomic absorption spectroscopy (FAAS), providing a multi-media view of contamination and exposure pathways. Although water showed low ionic concentrations, Cd exceeded the WHO limit (0.01 mg/L). Sediments revealed elevated Zn at Site A (108.0 mg/kg) and Ni at Site E (35.97 mg/kg), with Ni surpassing the threshold effect concentration (TEC) of 22.7 mg/kg. However, overall sediment pollution was low: Zn enrichment was slight (CF = 1.14), and a negative Igeo with PLI < 1 indicated unpolluted conditions. Soil from Site D had Ni at 107.2 mg/kg, exceeding the Tanzania Bureau of Standards (TBS) limits, with CF = 1.58 and Igeo =  + 0.07, indicating moderate pollution. Other sites showed CF < 1, negative Igeo, and low PLI, reflecting minimal contamination. Cowpea (Vigna unguiculata (L.) Walp.) and Napa cabbage (Brassica rapa subsp. pekinensis (Lour.) Hanelt) accumulated high levels of Ni and Pb, with Pb exceeding the Codex Alimentarius limits and Ni surpassing the European Food Safety Authority (EFSA) limits. Bioaccumulation was evident (BAF > 1), and health indices indicated noncarcinogenic (HI > 1) and carcinogenic risks (CR > 10⁻⁴), especially in cowpea (CR = 0.347). Elevated Ni and Pb in soils and vegetables indicated localized exposure risks, highlighting the need for targeted monitoring, informed vegetable selection, and coordinated mitigation in agro-mining areas.

Graphical Abstract

Seagrass meadows are increasingly recognized for their role in mitigating climate change through blue carbon sequestration and their influence on local carbonate chemistry. This study investigates the spatial variability of aragonite saturation state (Ω_arag) and assesses the blue carbon storage potential of seagrass meadows along the Palk Bay, Southeast Coast of India. Subsurface water samples were collected across multiple seagrass-dominated stations between May and June 2024. Key seawater carbonate system parameters, including pH, temperature, total alkalinity (TA), and salinity, were measured to calculate Ω_arag using CO2SYS software. Sediment cores were analyzed for organic carbon content and bulk density to estimate carbon stock. Results revealed significant spatial variation in Ω_arag, influenced by seagrass density, species composition (Cymodocea serrulata and C. rotundata), and hydrodynamic conditions. Stations with dense C. serrulata beds showed elevated Ω_arag values, suggesting local amelioration of acidification stress. The mean carbon stock was estimated at 1.97 Mg C/ha⁻¹, with higher values in more mature (> 60% cover) and dense seagrass patches. These findings highlight the dual ecological function of seagrass meadows in enhancing local carbonate saturation and functioning as effective carbon storage systems, underlining their significance in coastal ecosystem-based climate mitigation strategies.

New models quantifying tree volume, biomass, and carbon have recently been implemented across the USA, termed the National-Scale Volume and Biomass (NSVB) framework. Little research has been done to quantify the implications of these new models on the standing dead tree (SDT) carbon pool, which occupies a substantial portion of the nation’s total forest ecosystem carbon stocks. This project compared predictions of carbon stocks in SDTs using the previous estimation approach (the Component Ratio Method) and NSVB using the most recent FIA data collected across the conterminous USA. Equivalence tests were conducted to compare predictions using the two modeling systems. Results show similar findings to what has been recently observed for live trees, namely that NSVB produces predictions of carbon in SDTs larger than those produced from CRM. However, predictions produced from NSVB and CRM for SDTs in advanced stages of decay (i.e., decay classes 4 and 5) are similar, likely due to the lack of tops and limbs in these SDTs, which has been suggested as a reason for the increased carbon stock predictions produced in live trees using the NSVB framework. Individual SDT predictions have important implications when scaled to the population (e.g., condition and state levels), where we similarly observed NSVB predictions to be larger compared to CRM predictions. These results highlight the role that biometrical methods play in determining carbon in SDTs, such as decay class and structural loss reduction factors and species-specific carbon fractions that are implemented in the NSVB framework.

Most population-aggregated cities in developing countries are facing severe air pollution due to high concentrations of PM_2.5 and O₃, which could be associated with the impacts of local mesoscale circulations. The river land breeze (RLB) and mountain valley breeze (MVB) circulations made PM_2.5 and O₃ pollution causes more complicated in inland regions. By combining monitoring and simulating data from the inland region, this study analyzed the influence of local mesoscale circulations on pollutant concentrations and their underlying mechanisms. The daily average and peak concentrations of pollutants in RLB days and MVB days were different from those in non-RLB days and non-MVB days, indicating that local mesoscale circulations exerted distinct influences on regional pollutant concentrations. Moreover, hourly PM_2.5 concentrations had a notable reduction during winter in RLB days when compared to non-RLB days (1.6–18.7 μg/m³), meanwhile, they increased the most during spring (1.3–7.0 μg/m³) and decreased the most during winter (2.5–14.3 μg/m³) in MVB days when compared to non-MVB days. Hourly O₃ concentrations increased the least during winter in RLB days compared to non-RLB days (6.6–22.4 μg/m³), meanwhile, they showed the largest increase during spring (32.3 μg/m³) and the smallest increase during winter in MVB days compared to non-MVB days (13.2 μg/m³). Both the RLB and MVB could disperse particulate horizontally through the lower branches, with the extent of dispersion varying according to wind speeds in different seasons. The recirculation dominated by stronger branches (river/valley breezes) could enhance the formation of both O₃ and secondary PM_2.5 by prolonging the residence time within the region and promoting the pollutant mixing. Based on those diverse contributions, targeted management strategies will be highly desirable in regions over riverine and mountainous terrains in the inland region in Yichang, China, and similar cities around the world.

Graphical Abstract

Natural habitats with transitional buffer zones between humans and wildlife pose the most vulnerable landscape for human-tiger interaction. The Jim Corbett National Park (JCNP) is one such tiger habitat in the foothills of the Himalayas in India. There are several segments of transitional buffer zones along the periphery of JCNP, but the eastern part poses the most vulnerable segment since witnesses’ maximum annual events of human-tiger conflicts cause injuries and loss of human life and livestock; therefore, it was selected as a study area for case illustration using geospatial technology. The key objective of the study was to appraise the adverse ecological changes in the transitional buffer zone and their impacts on tourism dynamics and spatiotemporal trends of human-tiger conflicts. Result suggests that over the last three decades (1991–2025), the study area has witnessed multiple adverse ecological changes. Subsequently, the conflicts between humans and tigers have been increasing at 3% annually. The area under the very high risk zone of conflicts has been increasing at the highest rate, standing at 0.88% annually, followed by high (0.48%) and moderate risk zones (0.08%), respectively. Due to that, the area of the low-risk zone has been decreasing at a 0.12% annual rate. Extending risk zones and increasing conflict events are causing an increase in the conflict hotspots, annual rates of human injuries, human loss, livestock injuries, and livestock loss with consistently increasing rates. Therefore, as a need of the hour, this study suggests a holistic mitigation approach, keeping in consideration the human-tiger conflict risk zones, hotspots, and ecotourism-based measures. It is strongly believed that the proposed study will be very useful for planners and administrators of JCNP to implement sustainable development planning in the study area as well as other similar buffer zones across the park, for the scientific fraternity to enhance their research work in the field of wildlife ecology and habitat management, and for individuals for their safety in terms of life and injuries due to tiger attacks. Further, the study advocates that the situation will get worse if necessary mitigation measures for human-tiger conflicts are not taken in a timely manner.

Artificial neural networks (ANNs) are widely applied in air quality modelling because they can capture nonlinear interactions among pollutants and support reliable air pollutant index (API) forecasting. This study aims to identify the pollutants that most strongly influence API variability and to evaluate the performance of two merged hybrid ANN models for forecasting API in the northern region of Peninsular Malaysia. Two hybrid frameworks were developed: an ANN integrated with sensitivity analysis (ANN-SAM) to identify influential pollutants and an ANN combined with principal component analysis (ANN-PCA) to reduce dimensionality while retaining key information. Sensitivity analysis identified O₃, SO₂, PM₁₀, and PM_2.5 as the most influential pollutants, whereas PCA retained all variables except SO₂. These selected inputs were used to develop the MLP-FF-ANN-SAM and MLP-FF-ANN-PCA models. Both models achieved strong predictive performance, with R2 values ranging from 0.821 to 0.826 and RMSE values between 5.922 and 5.982. The slight improvement after removing NO₂ indicates that it contributes limited independent predictive value due to its collinearity with particulate matter. Seasonal increases in PM₁₀ and PM_2.5 during haze periods further highlight the influence of regional transboundary pollution. Using 5 years of multi-station data, this study demonstrates that merged ANN-SAM and ANN-PCA frameworks can provide accurate, efficient, and interpretable API forecasts. These findings support the development of simplified and computationally efficient tools for operational air quality assessment and early-warning applications in Malaysia.

The widespread use of pesticides has significantly contributed to managing pest populations in both agricultural and forest ecosystems, yet concerns about their unintended impacts on non-target habitats continue to grow. Tebufenozide, a pesticide commonly used to control forest defoliator pests, including spongy moth (Lymantria dispar dispar), is known for its selective action on Lepidopteran larvae. Despite its targeted mode of action, the potential transport and fate of tebufenozide into sensitive forested aquatic habitats, such as vernal ponds, is not well understood. This study examines the spatial distribution of tebufenozide in 41 vernal ponds located within three state forests in central Pennsylvania (Bald Eagle, Rothrock, and Tuscarora) by analyzing sediment and water samples collected within and outside designated spray blocks. Tebufenozide was detected in 39 water samples and 40 sediment samples, including 27 unsprayed water and sediment samples, indicating possible pesticide drift or runoff into non-target areas. We used a Mann-Whitney U test to reveal significantly higher concentrations of tebufenozide in ponds within spray blocks for both sediment (W = 241.5, p = 0.0161) and water (W = 316.5, p = 2.769e-06). Tebufenozide concentrations were higher in ponds closer to spray zones, suggesting proximity influences pesticide levels, though no clear directional dispersal patterns emerged. These findings underscore the vulnerability of vernal ponds, essential breeding habitats for amphibians and other organisms, to pesticide contamination. Enhanced management strategies, such as wider buffer zones and alternative pest control measures, may be necessary to safeguard these critical ecosystems.