Environmental Management最新文献

The accumulation of heavy metals (i.e., As, Cu, Ni, Pb, and Zn) in soils and native plant species near copper, nickel, and pyrite mines in Vietnam was assessed. The highest soil As, Cu, Ni, Pb, and Zn concentrations recorded in mine soils were 42.3, 1570, 9870, 128, and 462 mg/kg, and those in agricultural soils were 11.4, 453, 94.9, 34.4, and 147 mg/kg, respectively. Pollution index (PI) values indicated heavy pollution (PI = 3.99-13.0) for mine soils, and unpolluted to severely polluted (PI = 0.65-2.84) for agricultural soils. Soil enrichment factors had a wide range, from minimal to extreme enrichment of heavy metals (EF = 0.03-91.4). Arsenic minerals may be the main source of high As concentrations in sulfide mines. The As, Cu, Ni, Pb, and Zn concentrations of 20 native plant species near three mines were in the ranges of 0.05-1150, 3.17-123, 0.47-291, 0.08-6.34, and 6.87-168 mg/kg (dry weight, DW), respectively. Based on the recorded hyperaccumulation levels (1150 mg/kg, DW), bioaccumulation factors (BAF = 2.4-90.0), biomass, and rapid growth, Pteris vittata L. is considered a promising plant for phytoextraction of As in soils. Bidens pilosa L. has potential for phytostabilization of sulfide-bearing soils, given its low concentrations of heavy metals in plant shoots, BAF values of <1, high biomass, and wide distribution. Integrated phytoremediation and phytomanagement are applicable to metal-contaminated soils. Phytomining, energy crops, and vegetation cover should be investigated for the phytomanagement of metal-contaminated soils in mining areas.

Browning of surface waters due to increased terrestrial loading of dissolved organic matter (DOM) is observed across the Northern Hemisphere. The effects influence several ecosystem services from freshwater productivity to water purification. Brownification is often explained by changes in large-scale anthropogenic pressures and ecosystem functioning (acidification, climate change, and land cover changes). This study examined the effect of forest use changes on water browning in Finland, considering the effects of global pressures. Our goal was to find the ecosystems and geographic areas that are most sensitive to environmental pressures that increase the loading of DOM. We were also looking for land use strategies that decrease browning. We combined mathematical watershed modelling to scenarios of climate change, atmospheric deposition, and forest use change. Changes included scenarios of forest harvest and protection on forest, that were derived from European Union's regulation. The study area covered 20 watersheds from south to north of Finland. In northern Finland brownification continue. In southern Finland global influence (atmospheric deposition, climate change) seem to weaken, giving more space for local forest use change having an influence on brownification. Forest use change was more influential in river basins dominated by organic soils than in mineral soils. Extending forest protection decreased brownification especially in areas where the influence of atmospheric pressure is decreasing. When forest protection is planned to provide a carbon storage and sequestration potential and to favor biodiversity, it has favorable effect on surface water quality as well.

Rice is an important staple food around the world, the cultivation as sustainable agriculture and food supply are key to achieving the Sustainable Development Goals (SDGs) of 2030. In order to analyze the sustainability of the rice paddy ecosystem, a comparative study was carried out during the rice growing season between paddies with conventional agriculture (CA) and ecological agriculture (EA), integrating analysis of physico-chemical characteristics of soil and soil pore water, pesticide residues, acute toxic effects and potential ecological risk, as well as aquatic invertebrate community structure dynamics. Our study found that total carbon and nitrogen present in soil were significantly higher in CA than in EA, while opposite results were found in soil pore water, implying the improvement on soil properties in EA. Neonicotinoid pesticides (thiamethoxam and thiacloprid) were still detected in EA, although no pesticides were applied after conversing CA to EA. Additionally, toxic effects to zebrafish embryos with a peak toxicity in summer (July, LC₅₀ = 55.26 mg soil equivalent/L) were also found in EA, which was lower than in CA. The dynamics of the aquatic invertebrate community structure were correlated with the toxicity results, with higher diversity recorded in EA. Therefore, for the purpose of ecosystem sustainability, the long-term implementation of EA is highly recommended.

The classification of a hydropower scheme as run-of-the-river (or run-of-river; ROR) evokes an image of a low-impact installation; however, examination of eight case studies worldwide shows that substantial negative societal and ecological impacts are tied to them, albeit in somewhat different ways. We conclude that ROR dams not only potentially displace communities, disrupt livelihoods, and degrade environments in surrounding areas, but they also divert water from areas of need, impact aquatic ecology through habitat destruction and disruption of fish migrations, emit non-trivial amounts of greenhouse gases over the lifespan of the project, and disrupt streamflow in downstream river sections. While these negative impacts vary on a case-by-case basis, medium and large ROR dams consistently have multiple and cumulative impacts, even when not having appreciable reservoirs. We contend that many impactful dams do not qualify as low-impact ROR projects, despite being defined as such. Such mislabeling is facilitated in part by the ambiguous definition of the term, which risks the ROR concept being used by proponents of impactful structures to downplay their negative effects and thus mislead the public or gain status, including within the Clean Development Mechanism in relation to mitigating climate change.

Permanent and temporary ponds are considered peculiar ecosystems which provide important ecosystem functions, services, supporting biodiversity on small and large scales. Pond's conservation status is globally critical. Moreover, their ecological functioning and conservation status is frequently overlooked, because of the habitat small size, their seasonal occurrence and their unique appearance. While a certain attention is given to Mediterranean Temporary Ponds and, in general, to low altitude ponds, the ecological importance of high-altitude ponds is critically unrecognized, especially in the Italian peninsula. The main aim of this research is to create the first georeferenced checklist of Italian high-altitude ponds. In order to achieve this goal, we integrated spectral, spatial characteristics, and morphological operations based on Sentinel-1 and Sentinel-2 image data using the Google Earth Engine (GEE). Overall, 2156 ponds were identified: 62% (n = 1343) in the Alps and 38% (n = 813) in the Apennines. The highest number of ponds was detected in Central Alps (n = 642), followed by Western Alps (n = 479), Central Apennines (n = 412), Eastern Alps (n = 222), Southern Apennines (n = 216) and Northern Apennines (n = 185). For what concerns the Alps, the average altitude was estimated in 2428 m a.s.l., while in the Apennines the average altitude was estimated in 784 m a.s.l. The total area covered from ponds has been estimated in 4.258.640 m², with a mean of 1716 m². Ponds were described as 20% temporary (n = 445) and 80% permanent (n = 1711). Considering the land use, 83% (n = 1797) of ponds were described as "natural" and 17% (n = 359) as "anthropized". Identification and georeferentiation of high-altitude ponds are primary actions to the application of management plans and this research could be considered the first step towards the safeguard of these threatened ecosystems.

Machine learning (ML) techniques have been researched and used in various environmental monitoring applications. Few studies have reported the long-term evaluation of such applications. Discussions regarding the risks and regulatory frameworks of ML applications in environmental monitoring have been rare. We monitored the performance of six predictive models developed using ML and statistical methods for 28 months. The six models used to predict NO_x emissions were developed using six different algorithms. The model developed with a moderate complexity algorithm, adaptive boosting, had the best performance in long-term monitoring, with a root mean square error (RMSE) of 0.48 kg/hr in the 28-month monitoring period, and passed two of the three relative accuracy test audits. High complexity models based on gradient boosting and neural network algorithms had the best training performance, with a minimum RMSE of 0.23 kg/hr and 0.26 kg/hr, but also had the worst RMSE scores, of 0.51 kg/hr and 0.57 kg/hr, during the monitoring period. In addition, all six models failed all three relative accuracy test audits. The following problems were observed: (1) Complex ML models tended to have overfitting problems, thus indicating the importance of the trade-off between model accuracy and complexity. (2) Model input sensor drift or out of high-frequency ranges from the training data resulted in inaccurate predictions or an accuracy lower than the minimum allowed by regulators. (3) Existing regulatory frameworks must be modernized to keep pace with current machine learning practices. Some statistical tests are unsuitable for applications developed by using ML methods.