Environmental Monitoring and Assessment最新文献

Tropical coastal lagoons often show pronounced seasonal forcing that modulates nutrient supply, light climate, and grazer pressure. We surveyed hypereutrophic Jansen Lagoon (São Luís Island, Brazil) in four campaigns during 2017 (rainy: March and April; dry: September and November) at nine near-surface stations sampled on ebb tide. Phytoplankton and zooplankton were collected and analyzed together with chlorophyll-a and nutrients to assess short-term community responses. Clustering and nMDS revealed clear rainy-dry segregation of communities, and dbRDA linked dry-season samples to higher salinity, turbidity, TP, and silicate, whereas rainy-season samples were associated with higher dissolved oxygen, Secchi depth, ammonium, and DIN. Generalized linear models explained 65% of phytoplankton variance: density increased with DIP and decreased with dissolved oxygen and with the rotifer Filinia longiseta, indicating concurrent bottom-up (nutrients, light/renewal) and top-down (grazing) controls. Microcystis wesenbergii and M. aeruginosa exhibited frequent peaks, underscoring eutrophic risk, though values remained below the bloom threshold applied here. Overall, bottom-up control predominated in the rainy season, whereas grazer pressure intensified in the dry season. Management should couple nutrient-load reductions with measures that shorten residence time, reduce resuspension, and restore macrophytes, with priority to urban margins and semi-enclosed embayments; routine tracking of DO percent saturation, DIN/DIP, Secchi depth, and chlorophyll-a is recommended for long-term assessment.

Dense population, rapid urbanization, and industrialization make India a highly vulnerable country to the consequences of global warming. This study examines spatiotemporal trends of diurnal land surface temperature (LST) over the past 25 years (2000-2024) and analyzes the surface urban heat island (SUHI) intensities across the country and for 50 major cities, respectively, including the influence of zonal biogeography. The significance of the LST trends is statistically confirmed by using the Mann-Kendall test and zonal heterogeneity is analyzed by using ANOVA test. The study covers total span of 25 years (2000-2024) which is classified in two periods, pre-COVID-19 years (2000-2019) and including the post-COVID-19 years (2000-2024). In the period from 2000 to 2019, the mean LST variability range (minimum to maximum) has substantially widened by 7.8 °C and 2.3 °C for daytime and nighttime, respectively. The LST change during the COVID-19 period was significantly hindered; the change in daytime and nighttime LST for May month was 0.18 °C and 0.04 °C, respectively, whereas during 2020-2024, it has become -1.24 °C and -0.2 °C, respectively. In general, the zones follow the country-level LST trends for 2000-2019 as well as for 2020-2024 periods, with variable LST change rates. The highest annual daytime LST growth (+ 0.15 year^-1) is observed for the Desert (DES) zone, whereas the highest nighttime LST rise (+ 0.07 year^-1) is observed for the Western Ghats (WG). Notably, the Himalaya and Trans-Himalaya (HTH) zones exhibit negative LST growth rate (-0.08 and -0.09 for daytime and nighttime, respectively). Further, SUHI analysis indicates that the cities within theIndo-Gangetic Plain (IGP), Semi-Arid Region (SAR), Deccan Plateau (DP), and Western Coastal Region (WCR) zones are found to be largely impacted by SUHI intensification, ranging between 1 and 5 °C for daytime as well as nighttime. Interestingly, even trivial SUHI values of DES cities (1-3 °C for daytime) could be consequential, as the zonal LST is extremely high. The study points out the requirement of urgent policy intervention and mitigation measures.

Soil organic carbon (SOC) is essential for evaluating soil health and ecosystem functionality. Despite the increasing application of machine learning (ML) algorithms to predict the SOC, their accuracy in high-resolution mapping remains underexplored. Accurate spatial SOC prediction is crucial for advising soil/land management and carbon sequestration strategies. The present study was carried out to assess the prediction performance of different ML algorithms for SOC prediction in surface soil (0-30 cm) of South Gujarat region of India at a regional scale. A total of 507 soil samples and 58 environmental covariates (Landsat and Sentinel bands, spectral indices, terrain and bioclimatic variables) selected by Boruta feature selection technique were used to map the SOC in the region. For prediction of SOC, Random Forest (RF), Extreme Gradient Boosting (XGBoost), Cubist and Support Vector Machine (SVM) ML algorithms were employed. The SOC content varied from 0.13 to 3.20% with a mean of 0.95%. The prediction accuracy was obtained superior in the RF model in both calibration and validation datasets (R² = 0.923, RMSE = 0.162% and R² = 0.474 and RMSE = 0.339%, respectively) followed by the XGBoost model (R² = 0.814, RMSE = 0.216% and R² = 0.418 and RMSE = 0.355%, respectively). The lowest performance was shown by the SVM and Cubist models, capturing approximately 34.4% and 30.5% of the variance in the calibration dataset, respectively, and 28.4% and 23.7% of the variance in test sets, respectively. Mean Temperature of Wettest Quarter (BIO8) was a key predictor in the RF, XGBoost and SVM models for SOC prediction. The predicted SOC values varied from 0.32 to 2.25%, 0.02 to 2.67%, 0.31 to 2.11% and 1.64 to 7.79% in RF, XGBoost, Cubist and SVM models, respectively. The RF model demonstrated the highest accuracy and reliability for SOC prediction in the study area with a PICP value of 92.2%. These high-resolution SOC maps help in monitoring soil health and provide significant assistance for sustainable land-use management and planning.

Understanding vegetation dynamics in terrestrial ecosystems is critical under the pressures of global climate change and anthropogenic activities. This study analyzes vegetation dynamics from 2001 to 2024 in Türkiye's 13 biodiverse terrestrial ecoregions using a multi-scale approach, focusing not only on the main drivers, but also on the interactive and context-specific role of these factors. Using the Google Earth Engine (GEE) platform and MODIS Normalized Difference Vegetation Index (NDVI) data, integrated through a structured multi-scale framework, we applied statistical methods including the Mann-Kendall test, Kruskal-Wallis test, and LOESS curves optimized with leave-one-out cross-validation (LOOCV) to objectively model non-linear dynamics. The results revealed statistically significant positive NDVI change tendencies in all 13 terrestrial ecoregions (Mann-Kendall test, p < 0.05). Forest-dominated ecoregions exhibited the strongest greening, with Sen's slope values reaching +0.0032 NDVI year⁻¹ in the Euxine-Colchic Broadleaf Forests, whereas steppe ecosystems showed more moderate increases (e.g., Central Anatolian Steppe: + 0.0019 NDVI year⁻¹). Across elevation gradients, the most consistent and strongest greening occurred at mid-elevations (500-1500 m; τ ≈ 0.40-0.65), while change tendencies weakened and became more heterogeneous at higher elevations. Land-cover-based analysis further showed that natural vegetation types exhibited the strongest nationwide tendencies ("Trees": τ = 0.71; "Shrubland": τ = 0.66), whereas anthropogenically influenced classes displayed weaker or regionally variable responses. Beyond documenting general greening patterns, the original contribution of this study lies in introducing a structured multi-scale framework that integrates long-term MODIS NDVI time series (2001-2024) with ecoregion, elevation, and land cover contexts. This approach enables the objective identification of ecosystem-specific and scale-dependent vegetation dynamics that cannot be captured by conventional single-scale or linear trend-based analyses commonly applied in previous studies in Türkiye.

Aquatic and coastal ecosystems are increasingly altered by human activities, particularly through the removal of riparian vegetation, pollution, urbanization, and biological invasions. This study aimed to assess environmental conditions and identify a gradient of human disturbance in the Guaraguaçu River, located on the coastal plain of southern Brazil and subjected to multiple stressors. To achieve this, we evaluated land use, water and sediment quality, and the dominance of invasive macrophytes, and applied a rapid physical habitat assessment protocol quarterly throughout 2024 at 22 sites along the main channel of the Guaraguaçu River and its two tributaries, the Pery and Pombas Rivers. Data were summarized using principal coordinate analysis (PCoA), and the axis scores were used to define the environmental gradient. The results revealed a clear gradient in environmental conditions, likely driven by anthropogenic pressures across the watershed. Among the various stressors analyzed, water pollution emerged as the primary driver of environmental degradation in the Guaraguaçu River Basin. Conductivity, salinity, ammonia, and orthophosphate contributed most significantly to the observed gradient. The highest values for these parameters were recorded in the Pery River and downstream from its confluence with the Guaraguaçu River, indicating a decline in environmental quality in this tributary and, consequently, in the lower basin sector. The results showed that environmental conditions varied along a gradient significantly influenced by both seasonality and river zone, suggesting that seasonal dynamics are a key driver of this pattern. This influence of seasonality was also supported by the PCoA results, which reflected clear temporal patterns in environmental variability. Furthermore, human-induced changes to the Pery River, particularly water pollution and hydrological modifications, have impaired ecological conditions and intensified environmental degradation in downstream areas of the Guaraguaçu River basin. These findings highlight the need for targeted management strategies, including improved wastewater treatment, to mitigate cumulative stressors and enhance the resilience of this coastal river ecosystem.

The biodiversity crisis in freshwater habitats is intensifying due to human-induced changes, leading to significant biodiversity loss. Freshwater fish, in particular, are experiencing alarming population declines. Effective monitoring by direct capture is critical to track and mitigate these losses, but small water bodies often present challenges for active monitoring methods. In such cases, passive sampling techniques, like traps, offer a practical alternative for conservation agencies, though their effectiveness in monitoring fish biodiversity for conservation purposes is not yet fully understood. This study evaluates the effectiveness of two passive sampling techniques fyke nets and umbrella-shaped traps (umbrella traps) for capturing fish diversity in small freshwater habitats. Sampling was conducted overnight in 39 water bodies, and fish density was quantified using catch per unit effort (CPUE). A beta regression model was applied to analyze species capture rate and the influence of habitat parameters on catch success. The CPUE revealed a 6.82-fold higher mean individual capture rate and a 2.05-fold higher species richness per deployment for umbrella traps compared to fyke nets indicating significantly higher sampling efficiency under comparable conditions. Rarefaction curves and non-metric multidimensional scaling analyses demonstrated that umbrella traps more effectively characterized littoral fish diversity and captured higher species richness than fyke nets in small freshwater bodies. The study underscores the utility of umbrella traps as a reliable tool for assessing fish diversity through direct capture, particularly for monitoring elusive or invasive non-native species. These findings aim to enhance conservation oriented species capture and inform conservation planning in often-overlooked small freshwater ecosystems, which play a crucial role in the long-term conservation of fish species.

This study investigated the accumulation levels, ecological and health risks, and the impact of socio-economic development on potentially toxic elements (PTEs) present in road dust collected from the major roads of low latitude industrialized City, Dongguan, China. The concentrations of PTEs decreased in the order of zinc (Zn) > lead (Pb) > chromium (Cr) > arsenic (As) > cadmium (Cd) > mercury (Hg). Similarly high levels of Cd and Zn accumulation were observed, and other PTEs were generally moderately accumulated or enriched. The ecological risk was relatively high for Cd and Hg and low for other PTEs. Most of sampling sites were moderately to heavily polluted, and the ecological risk was generally moderate to very high. The non-carcinogenic risks to both adults and children were safe at internationally accepted levels. However, higher levels of carcinogenic risk were observed in males and females, mainly due to the contribution of Cr and As. Spatial distribution patterns revealed higher accumulation level, ecological and health risks in districts of Binhai, Central City with higher GDP. In the present study, a positive relationship was observed between PTEs concentrations and GDP of Dongguan's six districts (R = 0.66, p = 0.15) and a similar significant pattern was revealed for the first time at the global level (R = 0.66, p = 0.0021). It is evident that socio-economic development had a significant impact on PTEs contamination and the resulting ecological and health risks. Therefore, industrial cities such as Dongguan need to adopt broader strategies that decouple the relationship between socio-economic development and the emission of PTEs concentration (e.g. Cd, Cr and As), to mitigate this emission during economic growth and transition towards a more sustainable development model.

Freshwaters are among the most vulnerable ecosystems, yet the scarcity of biodiversity assessments prevents the detection of changes incurred by neobiota. Minnows of the genus Phoxinus were long thought to be represented by a single species in Eurasia, the common minnow P. phoxinus, but the genus now includes more than 25 valid species. However, their distributions do not follow drainage boundaries, there are known cases of human translocations, and morphological species assignation is difficult due to intra- and interpopulation phenotypic diversity. Hence, the species were delimited and are now determined mostly using molecular methods. In Austria, recent studies have identified at least four different species of Phoxinus, three of which are considered native and one introduced. However, more data were needed; thus, extensive collecting and DNA barcoding of minnow populations was undertaken with the help of recreational fishers, school pupils, and field biologists. DNA barcodes of museum specimens and environmental DNA collected from water samples were also included. Altogether, the genetic lineage of 258 new Phoxinus specimens was determined. The results confirmed the distribution of P. marsilii in eastern Austria, P. lumaireul in southern Austria and P. csikii in central and western Austria. Additional populations of the introduced P. phoxinus were identified. Most importantly, a new species record for Austria, P. cf. morella, was discovered, yet it is unclear whether its distribution in Austria is natural. This study also confirmed the potential of citizen science for biodiversity monitoring, with the number of specimens analyzed increasing fourfold in just two years.

We used relationships between diatom species composition and phosphorus concentration in periphyton (mat P) to develop a monitoring and assessment program for wetlands in the Big Cypress National Preserve (BCNP) in Florida, USA. Cluster analysis and regression showed limitation of taxonomic composition of assemblages to a few low phosphorus (P) taxa in low mat P conditions, with additional species being able to colonize habitats having higher mat P concentrations. TITAN (Threshold Indicator Taxa Analysis) and regression respectively identified 11 and 8 low P taxa and both methods identified 47 high P taxa. Congruence of low and high P taxa traits determined in our study and results of prior phosphorus experiments confirmed P caused taxon responses. We varied taxa traits (low and high P taxa), method for determining traits, and trait calculation method to evaluate metric performance. Metrics of biological condition, whether using the low or high P taxa traits, were most highly related to mat P when using TITAN derived taxa traits and a novel relative abundance calculation using log-transformed relative abundances. Benchmarks for management targets were determined for minimally disturbed conditions and for greater than usual changes in assemblages along the P gradient. Diatom metrics were more highly correlated with distance from P sources than mat P, indicating species-based metrics have high value for monitoring and assessment. Our results can be used for a monitoring and assessment program with effects-based management targets in BCNP, and our methods provide examples for applications in other ecological settings.