Environmental Monitoring and Assessment最新文献

Fungal disease is on the rise, coupled with fungal pathogens increasing in geographic range. Studies have shown that viable fungal pathogens may be present in beach sand and water, and consequently, efforts are ongoing in Europe to develop guidelines for fungi levels at beaches. In the USA, fungal diseases are a growing concern, and yet, they are not subject to public health reporting, and beach environments are currently not routinely monitored for fungal pathogens. This study measured fungal and enterococci levels at two beaches within a subtropical environment in Miami, FL. Samples were analyzed by culture-based methods, with fungi species confirmation by targeted PCR and sequencing. A unique aspect of this study is the analysis with higher incubation temperatures (37 °C and 40 °C) to isolate mesophilic fungi that can cause invasive infections. Results show that levels of fungi were strongly influenced by sample matrix (sand or water). The most common fungal species observed belonged to the Aspergillus and Candida genera, with the isolation of 25 taxa with reports of causing infections, of which 20 were previously reported to exhibit resistance to some or all classes of antifungals available. Results emphasize the need for fungal specific analyses to better understand beach-related disease risks. Given the current increase in cases of fungal diseases and the presence of viable fungi in the environment, integrating fungal measurements in routine microbiological monitoring programs is critical for assessing the beach transmission of pathogenic fungal disease and the control of emerging fungal pathogens.

In modern agricultural practices, soils are increasingly exposed to multiple anthropogenic and natural pollutants, with heavy metals playing a significant role. Despite their critical environment and health impact, substantial gaps remain in understanding the levels of these contaminants and their outcomes on soil and water systems. To address the lack of pertinent data, this study assessed the speciation and contamination levels of nickel (Ni) and zinc (Zn) in soil samples collected from Varanasi (n = 9) and Mirzapur (n = 6) districts of eastern Uttar Pradesh, India. The modified Tessier method was used for the sequential extraction procedure to understand heavy metals mobility and availability. Among the geochemical fractions (water soluble, exchangeable, organic matter bound, carbonate bound/specifically sorbed, bound to Fe-oxides (Fe-MnOB)), the residual fraction was predominant. For Ni, principal component analysis revealed the highest positive loading factor for soil pH (0.941), and the residual fraction also demonstrated a significant positive loading value (0.779), indicating their stabilizing influence. In the case of Zn, the highest positive loading factor was observed for diethylenetriaminepentaacetic acid-Cu (0.956), while the highest negative loading factor was associated with soil pH (−0.935). For Zn, negative loadings of pH and CEC contrasted with positive associations of DTPA-Cu and Ni, suggesting differential mobility and source behavior. For Zn speciation, the residual fraction showed the highest positive loading (0.944). The study concluded that the majority of contamination indices for Ni and Zn fell within the slightly to moderately contaminated zone, with geogenic sources playing a dominant role compared to anthropogenic inputs. These results offer a valuable reference point for supervising heavy metal contamination and understanding the potential migration of pollutants within soil, water, and human systems. This study may be the first detailed speciation and index-based assessment of Ni and Zn in eastern UP agricultural soils. Periodic assessment and management of these contaminants are recommended to mitigate their environmental and health impacts.

High-resolution real-time monitoring gains increasing importance in environmental monitoring. We employed and evaluated three real-time monitoring techniques to monitor anions at the river Rhine in Koblenz, Germany. These are a sensor for nitrate in situ and ex situ, a colorimetric device for nitrate and nitrite, and an online ion chromatography (IC) for fluoride, bromide, chloride, nitrate, nitrite, phosphate, and sulfate. The optical sensor performed well ex situ while in situ, a higher maintenance effort was needed due to fouling processes. Colorimetry failed to analyze and notify when solutions fouled and required a very high amount of maintenance to reliably analyze nitrate and nitrite. The online–IC was feasible for analyzing seven anions with a low amount of maintenance once the required infrastructure for the instrument was provided. The online high-resolution IC data was compared with conventional monitoring data with, e.g., a routine interval of two weeks, which was shown to fail to capture peak concentrations during high discharge events. Additionally, to monitor short-term events, like the shortest registered peaks of 5 h, monitoring intervals of at least 2 h at the river Rhine are needed. By extending the IC time series to 2.5 years supported by principal component analysis, a dendrogram clustering, and event analysis by QC-diagrams, two major groups of anions were visible. The first group includes discharge-driven anions such as chloride, bromide, sulfate, and fluoride, with fluoride being additionally influenced by the equilibrium precipitation of fluorite. The second group consists of seasonal-driven anions like nitrate, nitrite, and phosphate, with phosphate being also affected by short-term events and hence, remains to be an important indicator in online monitoring setups. To get a complete picture, anion analyses can be combined with, e.g., element or non-target analyses.

Cadmium (Cd) is a toxic heavy metal that is important to consider because of its health risks to humans. Prolonged exposure to Cd in humans presents significant health hazards, including renal, hepatic, and neurological damage, and may also result in carcinogenic effects. This study aims to assess the health risks associated with Cd exposure via multiple media and pathways among preschoolers in a mining city in northwest China. This objective was accomplished by collecting and analyzing samples of commercial food, soil, air PM₁₀, and tap water to determine Cd concentrations. The study also assessed the non-carcinogenic and carcinogenic risks resulting from Cd exposure via multiple media and pathways, as quantified by hazard quotients (HQ) and carcinogenic risk (CR), respectively. The uncertainty and sensitivity of the relevant parameters were quantified using Monte Carlo simulation. The findings indicated that the concentrations of Cd in mung beans, shiitake mushrooms, carp, and air PM₁₀ exceeded national safety standards. The HQ_∑ for boys and girls was 6.64 and 6.39, respectively, while the CR_∑ for both sexes was 2.29 × 10^–4. Inhalation (> 89%) and ingestion (> 95%) were the primary pathways for non-carcinogenic and carcinogenic risks, respectively. These results suggest that preschoolers in the study area may face potential health risks from Cd exposure via multiple media and pathways. This study recommends enhancing the monitoring of Cd in commercial food and improving air quality to mitigate potential health risks from Cd exposure among local preschoolers.

In groundwater contamination source inversion, concentration data from monitoring wells serve as the most crucial known information, directly affecting the inversion accuracy of unknown contamination source parameters. However, existing studies often treat all monitoring data with equal importance, neglecting the varying contributions of individual wells to the inversion results. To address this issue, this study integrates the scaled dot-product attention (SDPA) mechanism into the scalar long short-term memory (sLSTM) model, forming a novel Attention-sLSTM inversion model. In this model, the attention module assigns different weights to monitoring wells, generating a weighted representation of the monitoring data. This weighted representation is then fed into the sLSTM model to learn the non-linear mapping between monitoring data and unknown source parameters. By highlighting critical monitoring data, the attention module improves the inversion accuracy of the sLSTM model. To further improve the attention module, two learnable parameters inspired by quantum theory are introduced into SDPA, yielding a quantum-inspired attention (QIA) mechanism. Compared to SDPA, QIA enables a more comprehensive representation of the complex relationships between monitoring data and contamination source parameters, leading to further gains in inversion accuracy. The proposed QIA-sLSTM model is evaluated through two synthetic groundwater contamination source inversion case studies. Results show that the QIA module consistently enhances inversion accuracy, while also showing the potential to improve model stability and reduce computational costs. Notably, the QIA-sLSTM model significantly outperforms the classical LSTM model, demonstrating superior and stable inversion performance across diverse groundwater contamination scenarios.

This study presents a comprehensive spatiotemporal drought assessment for Çanakkale province, Türkiye, utilizing multi-index remote sensing approaches over a 20-year period (2005–2024) coupled with predictive risk modeling for 2025–2027. Four key environmental parameters were derived through the Google Earth Engine platform: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Standardized Precipitation Index (SPI), and Soil Moisture Condition Index (SMCI). Multiple satellite data sources were integrated, including Landsat 7 ETM + , MODIS/MOD11A1, CHIRPS precipitation dataset, and TerraClimate hydrological data. The retrospective analysis revealed significant climatic variability characterized by inter-annual LST fluctuations, progressive NDVI enhancement toward 2024, and pronounced negative trends in both SPI and SMCI indices during recent years. Particularly, SMCI reached − 1.14 in 2023, indicating severe soil moisture deficit. Spatial heterogeneity was evident across the province, with differential vegetation dynamics and precipitation patterns between coastal and interior regions. A Principal Component Analysis-based integrated drought index was developed, explaining 68.7% of total variance, providing comprehensive drought characterization beyond univariate approaches. A hybrid trend-based forecasting framework incorporating seasonal decomposition, climatological constraints, and stochastic variability was implemented. Model validation demonstrated robust performance for LST (R² = 0.85) and NDVI (R² = 0.88), while SPI and SMCI exhibited challenges inherent to normalized indices with small-magnitude variations. Prospective projections indicate systematic elevation in composite drought risk from 2.58 (2025) to 2.67 (2026–2027), representing a 3.5% increase and persistent moderate-to-high drought vulnerability. These findings provide critical insights for regional water resource management, agricultural planning, and climate adaptation strategies in Mediterranean ecosystems facing intensifying drought pressures.

Remediation of black and odorous water in urban rivers is a key priority for urban water governance, but identifying pollution sources and optimizing strategies remain challenging. This study targeted the 3.2-km Qianjin Canal (Tianjin), integrating the QUAL2Kw model with a self-developed pollution source contribution module (two-step method) to simulate pollutant migration and assess the effectiveness of multiengineering remediation measures. Results showed that the integrated model successfully quantified the contribution of major pollution sources, including point sources (domestic sewage), nonpoint sources (farmland runoff), and source water (rainfall runoff combined with reservoir discharge). Under the combined measures including aeration (20% oxygenation efficiency), water supplementation (0.1 m³/s), and source control, the removal rates of COD, NH₃-N, TN, and TP were 44.0%, 47.5%, 54.1%, and 70.0%, respectively; after treatment, most water quality indicators met GB 3838-2002 Category V. The integrated model effectively supports pollution source identification and remediation strategy optimization for urban black and odorous rivers similar to the Qianjin Canal, providing a targeted scientific framework.

Biodiversity conservation requires rigorous wildlife assessments, and uncrewed aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras offer a promising tool for surveying large mammals. Despite growing use, formal comparisons of UAV survey methodologies remain limited. We evaluated manual versus programmed flight methods and conducted an orthomosaic trial in Chitwan National Park, Nepal, in May 2022, performing six flights per method across four Terai grassland sites. We compared wildlife counts, survey effort (flight length, duration, number of images, and post-processing requirements), and issues regarding image overlap. Manual flights required 35 min on average, covered 6370 m, and generated 86 images per flight, whereas programmed flights averaged 66 min, 9360 m, and 205 images, representing increases of 57% in flight time, 47% in distance, and 138% in image volume for programmed surveys. There was no significant difference in total mammal counts (P = 0.781) or for specific groups such as deer (P = 0.181) and rhinos (P = 0.515) between the manual and programmed flights. However, manual flights yielded imagery that was better suited for species identification. Both approaches were influenced by observer bias, either in real-time species identification during manual flights or post-processing for programmed flights. Our results highlight that for our study area and species of interest, manual UAV flights were able to reduce survey effort while maintaining comparable detection rates and improving species identification. Orthomosaic processing, using both direct georeferencing and Structure-from-Motion, proved largely ineffective for thermal imagery of mobile mammals, as moving animals were often excluded due to image overlap requirements. The study also offers guidance for designing UAV-based wildlife monitoring programs, highlighting the potential of AI, video, and advanced sensors, as well as important limitations to consider before conducting surveys.

Understanding long-term changes in the spatial distribution and habitat suitability of wildlife is critical for effective conservation planning. This study assessed the spatio-temporal distribution of the southern giraffe (Giraffa giraffa) in Hwange National Park (HNP) and examined how environmental variability has influenced habitat suitability over the past two decades. Giraffe occurrence data were obtained from road-count surveys conducted in 2002, 2012, and 2022 and analyzed alongside key environmental variables, including temperature, Normalized Difference Vegetation Index, rainfall, elevation, terrain ruggedness, and distance to water. To ensure model stability, collinearity among variables was tested using the variance inflation factor. Habitat suitability was modelled using an ensemble approach combining support vector machine (SVM), random forest (Sutton et al.), and maximum entropy (MaxEnt) algorithms, implemented in the flexSDM package in R. Results revealed marked spatial and temporal variations in giraffe habitat suitability, with the highest concentrations consistently recorded in the Main Camp management area. Alarmingly, suitable habitat within HNP declined by approximately 60% over the study period, a trend likely driven by both environmental changes and anthropogenic pressures. Habitat preference analyses further indicated that southern giraffes consistently selected mixed woodland–bushland mosaics, which likely provide access to diverse forage resources, predator avoidance opportunities, and thermoregulatory benefits. These findings highlight the vulnerability of giraffe populations to habitat loss and underscore the importance of integrating long-term environmental dynamics into conservation planning. The study provides essential insights to guide targeted conservation interventions for giraffes in HNP, particularly in light of escalating climate variability and human disturbances across the landscape.

A comprehensive assessment of the anthropogenic transformation of soils in the Rostov agglomeration was carried out using principal component analysis. Based on data from 45 chemical and physical parameters, natural (Ah, A, B), buried (Ab, Bb), and anthropogenic (UR) soil horizons were analyzed. The first four principal components were found to explain 78.3% of the total data variance. Two dominant factors of transformation were identified, which are primarily reflected in the first two principal components: PC1 (36.94% of variance), reflecting processes of physical degradation due to technogenic sand input and associated carbonate pollution (high loadings for sand, inorganic carbon (IC), and Ca content), and PC2 (21.41% of variance), associated with toxic pollution by heavy metals and phosphorus (high negative loadings for Pb, As, Zn, Sr, and P). Analysis of the contribution of individual parameters to the total variance revealed the most significant indicators: Mg, Pb, As, Si, Ca, P, Sr, and TOC. A smaller but statistically significant contribution was made by PC3 (14.25%, carbon and alkaline element balance—Ca and Mg) and PC4 (5.67%, which probably reflects the processes of soil acidification and deterioration of its structure). Clustering in the principal component analysis space confirmed a clear separation of horizons by type and degree of anthropogenic impact, mainly for the first two principal components. The results demonstrate that urbanization leads to a complex transformation of the soil cover, expressed in three main processes: physical degradation (technogenic sand input), chemical pollution (heavy metals), and disruption of the carbon balance (decrease in organic and increase in inorganic carbon). The obtained data allow for the ranking of risk factors and form the basis for developing priority measures for monitoring and remediation of soils in large agglomerations of the European South of Russia.