Environmental Monitoring and Assessment最新文献

Freshwater ecosystems play a crucial role in maintaining ecological balance and supporting biodiversity. Assessing water quality and identifying pollution factors affected by seasonal changes is of utmost importance. The traditional approach for identifying pollution sources and seasonal water quality assessment is often labor-intensive, time-consuming, and complex. In response, our study introduces a hybrid receptor, multivariate statistical and machine learning (ML) based framework as an alternative. Positive matrix factorization (PMF) and receptor model were used to identify key pollutant sources, while Principal Component Analysis (PCA) was used to corroborate pollutant sources. Additionally, various supervised ML classification algorithms are used to predict and identify pollution sources on full physicochemical datasets and reduced in situ parameter scenarios. ML model stability and generalization capacity were evaluated using learning curves, cross-validation method. The findings indicate significant seasonal variability in water quality index, ranging from 38.5 (poor) to 78.6 (good). PMF and PCA revealed three dominant pollution factors, such as biogeochemical processes, sediment resuspension/nutrient influx, and anthropogenic pollution, affecting the seasonal water quality by 15–30%, 25–45%, and 40–70%, respectively. Model performance indicators using confusion matrices, ROC-AUC, and other evaluation metrics, for full dataset identified XGBoost as the best model, achieving the highest accuracy of 96.3%, followed by CatBoosting (93.6%), Random Forest (93.6%), Support Vector Classifier (91.8%), K-Nearest Neighbors (87.2%) and Decision Tree (84.3%). However, with the in situ data the ML models show stable and consistent classification accuracy in the range of approximately 70–81%. Overall, the modelling framework provides an effective approach for capturing seasonal variability and pollution source attribution, contributing to improved ecological understanding and predictive management of freshwater system.

This study assesses ecological change in two 100 resilient cities (100RC) (Pune, Surat) and two non-100RC cities (Chattogram, Nagpur) in South Asia using an improved remote sensing ecological index (RSEI). The enhanced model incorporates six indicators composite vegetation index (CVI), wetness, normalized difference build-up and bare soil index (NDBSI), impervious surface index (ISI), urban index (UI), and land surface temperature (LST)—with entropy-based weighting and a moving window approach to overcome traditional PCA limitations. The results reveal apparent differences between 100RC and non-100RC cities. Non-100RC cities exhibited high volatility, characterized by sharp ecological declines, partial recoveries, and subsequent collapses. In contrast, 100RC cities followed steadier but persistent downward trajectories: Surat exhibited a monotonic decline, while Pune improved until 2022 before declining in 2024. Hotspot analysis revealed that cold spots expanded dynamically in non-100RC cities, whereas they spread gradually in 100RC cities. Moran’s I confirmed strong spatial clustering (0.89–0.97) across all cities. A two-way ANOVA revealed significant group, year, and interaction effects (p < 0.001). These findings suggest that resilience frameworks reduce ecological instability but are insufficient to halt structural degradation. Strengthening urban resilience requires more rigorous policy enforcement, continuous monitoring, and ecological restoration to achieve sustainable ecological outcomes.

Organotin (OT) compounds, historically dominant in antifouling paints, are persistent organic pollutants known for their severe endocrine-disrupting toxicity. While the global ban on OT shifted to the usage of alternative booster biocides, the environmental behavior, tissue-specific partitioning, and persistence of the replacement compounds remain understudied, particularly in tropical estuarine ecosystems. This study investigates the concentrations and accumulation patterns of OT species of butyltin, known as monobutyltin (MBT), dibutyltin (DBT), tributyltin (TBT), phenyltins of monophenyltin (MPT), diphenyltin (DPT), triphenyltin (TPT), and alternative booster biocides of diuron, dichlofluanid, chlorothalonil, Irgarol 1051, M1, and Sea-Nine 211 across a multitrophic system including seagrass (Enhalus acoroides), bivalves (Pinna bicolor), and two fish species (Thryssa dussumieri and Otolithus ruber) in Pulai River Estuary, Malaysia. Results indicated a shift in pollutant dominance, in which TPT showed higher accumulation between 18.62 and 652.48 µg/kg compared to TBT, with values from 4.05 to 403.47 µg/kg. This suggests that phenyltins currently exhibit higher bioaccumulation potential or persistence in local biota compared to butyltins. Degradation products were also ubiquitously detected, with MBT and DBT ranging from 1.12 to 290.22 µg/kg, and MPT and DPT ranging from 5.13 to 7494.74 µg/kg. Among alternative booster biocides, high concentrations of dichlofluanid, M1, and Sea-Nine 211 were detected in fish cardiac tissues with respective values of 6422.01 µg/kg, 3970.97 µg/kg, and 1495.82 µg/kg. Tissue analysis revealed that Irgarol 1051 and its degradation product M1 were enriched in fish intestines, implicating the ingestion and intestinal absorption of contaminated prey or sediment particles as the primary pathway rather than gill respiration, while detection in seagrass roots indicated sediment-associated exposure. Phenyltins were the dominant OTs, with concentrations exceeding ecological and human health guideline values based on the Oslo and Paris Commission (OSPAR) framework, highlighting trophic transfer and potential consumer risks. A comprehensive monitoring of OTs and booster biocides is strongly recommended to mitigate ecological and human health risks.

Global Forest Change (GFC) provides a widely accessible tool for monitoring tree cover loss and is frequently used by both professionals and the public in discussions of forest dynamics. However, its reliability in specific regions has not been sufficiently evaluated. We assessed its accuracy in part of Slovakia through visual interpretation of Sentinel 2 imagery conducted independently by three operators. The GFC dataset achieved 95% producer’s accuracy and 78% user’s accuracy in detecting tree cover loss. Therefore, it could slightly overestimate loss in the study area, especially in small patches and edge pixels, as the marginality of pixels explained a large part of the disagreement (AUC = 0.76). However, we could also underestimate the loss during its visual identification, as it proved more challenging than anticipated (average agreement between operators was 85%). To complement this analysis, we compared GFC tree cover loss with official forestry logging records across Slovakia. Both datasets reported similar total areas of loss and largely consistent spatial patterns, with a median difference of 6% in their ratio within forestry mapping units. Nonetheless, we identified regions where GFC reported substantially more loss, potentially reflecting errors in forestry records, as well as regions where forestry records indicated more loss, possibly due to temporal misclassification. Overall, the GFC dataset represents an appropriate source for monitoring tree cover loss in Slovakia, provided that users are aware of its limitations.

Land-use and land-cover change (LULCC) is one of the key drivers altering terrestrial ecosystem carbon storage. Accurate simulation of LULCC is crucial for assessing ecosystem sustainability and formulating global climate change mitigation strategies. Within this context, this study proposes a novel deep learning model integrating U-Net architecture, an attention mechanism, and ConvLSTM—termed U-Net-Attention-ConvLSTM (UNA-CL), to enhance the accuracy of LULCC simulation. The model’s effectiveness was validated using land use and land cover (LULC) data from Kunming (2000–2020) and compared with a widely applied convolutional neural network (CNN) model (CNNA-CL) and Random Forest-Cellular Automata (RF-CA) model. Furthermore, this study coupled the UNA-CL model with the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which is designed for ecosystem service assessment, to jointly reveal the spatiotemporal evolution characteristics of future LULC patterns and carbon storage. The results indicate that (1) the UNA-CL model outperformed the comparative models in classification accuracy, achieving an overall accuracy (OA) of 94.36%, which is 5.72% and 0.5% higher than the CNNA-CL and RF-CA models, respectively; (2) in terms of spatial allocation accuracy, the UNA-CL model not only accurately simulated land cover categories with complex distribution patterns but also mitigated the simulation bias caused by spatial heterogeneity in the RF-CA model; (3) from 2000 to 2030, the net increase in carbon storage was 3.74 Mega tons (Mt), exhibiting a trend of increase followed by decrease. Specifically, the conversion of grassland and cultivated land to forest land led to an accumulation of 3.81 Mt during 2000–2020. However, from 2020 to 2030, a combination of forest land loss and continued construction land expansion resulted in a net decrease of 0.07 Mt.

Ponds, ubiquitous in subtropical regions, play a pivotal role in regulating the regional hydrological cycle, fostering biodiversity, and providing livelihood opportunities. This study assesses the water quality and ecological health of ten rural and urban ponds in Gaya district, Bihar, analysing PO₄³⁻, SO₄²⁻, NO₃⁻, Chl-a, DO, BOD, COD, calcium, magnesium, chloride, and bicarbonate, during February–March 2021. Anthropogenic activities, including grey water, domestic waste, and agricultural runoff, have led to elevated nutrient and organic loads, with biochemical oxygen demand (BOD) ranging from 35.0 to 60.0 mg/L and chemical oxygen demand (COD) ranging from 130.0 to 290.0 mg/L, exceeding limits prescribed by the Central Pollution Control Board (CPCB). All ponds were classified as hypertrophic, based on Carlson’s Composite Trophic Status Index (CTSI > 90), indicating excessive nutrient enrichment. High concentrations of total nitrogen (TN-66–236 mg/L) and total phosphorus (TN-0.45–12.2 mg/L) indicate strong phosphorus and nitrogen-driven trophic pressure throughout the system. Persistently higher TSI(TN) than TSI(TP) across rural and urban sites indicates nitrogen loading as the primary regulator of trophic state. Furthermore, the Water Quality Index (WQI, 207.1–350.9) indicated unsuitability for domestic use. Globally, small ponds are being lost due to intensive agriculture, encroachment and urban sprawl, even though they are increasingly recognised as critical regulators of nutrient cycling, refuges for freshwater biodiversity, buffers against droughts and floods, and serve as potential sources of irrigation and drinking water. Therefore, assessing their ecological health for human use is vital for advancing sustainable development and guiding rejuvenation strategies and policy frameworks to strengthen conservation practices.

Intensified agricultural activities increase wastewater discharge into coastal lagoon systems, with high levels of organic matter, nutrients, and pesticides. This study monitored and assessed environmental conditions at a key agricultural drainage site to assess ecological risk to a Ramsar lagoon system. The Water Pollution Index (WPI) and Risk Quotient (RQ) were applied jointly to identify priority pollutants and their potential impact on the lagoon system of international ecological importance. Sampling was conducted over 4 years in an agricultural drain in northwestern Mexico, where farming, livestock, and poultry effluents converge. Physicochemical parameters, nutrients, and pesticides were determined using standardized methods and gas chromatography coupled with mass spectrometry. The overall WPI value was 1.07, ranking the water as highly polluted. Chemical oxygen demand (1051 mg/L) and total suspended solids (155 mg/L) contributed most to the index, reflecting a high load of recalcitrant organic matter. Eighteen pesticides from different chemical groups were identified, including thiamethoxam, acetamiprid, chlorpyrifos, and diazinon, which had the highest RQ values (≥ 0.8), indicating a significant ecological risk to aquatic organisms. The coexistence of organic pollutants and nutrients suggests cumulative and synergistic effects that compromise the stability of the lagoon system. There is technical evidence of the need to implement strategies for the treatment and sustainable management of agricultural wastewater, promoting its reuse in line with the principles of the circular economy.

Although mechanical harvesting has substantially increased the efficiency of agricultural production, the manner in which it influences the abundance and distribution pattern of microplastics (MPs) in the soil remains unknown. Specially, the relationships between MPs and soil physicochemical properties are unclear. Herein, soil samples before and after mechanical harvesting in Anhui Province are collected and segmented into three layers: top (0–10 cm), middle (10–20 cm), and bottom (20–30 cm). Besides analyzing MPs, standardized tests are conducted on the soil porosity, organic carbon, total nitrogen, and moisture content. Results demonstrate that the mechanical harvesting decreases the abundance of soil MPs in the top and middle layers but increases that in bottom layer. However, a considerable positive correlation between soil MP levels before and after harvesting is identified; their distribution characteristics were compared as well. We suppose that mechanical harvesting introduces a small amount of additional MPs while facilitating the penetration capacity of existing MPs. Although the soil organic carbon, total nitrogen, and moisture content are observed to positively correlate with each other, they are not directly related to MPs. Furthermore, we assume that mechanical harvesting physically drives the connection of soil porosity with other parameters, as their positive correlations become evident after harvesting. This study provides a valuable finding: MP penetration can be facilitated by mechanical harvesting, which should be considered for the prevention of potential MP-induced ecological risks on deep soil and groundwater.

Water cleanliness and safety are fundamental to sustaining human activities and maintaining ecological stability. In this study, a self-powered water-quality sensing system is developed based on contact electrification and the distinct charge-transfer behaviors of different pollutants at the liquid–solid interface. When water samples containing heavy metal ions, microplastics, or rust flow through a conductive sponge, contact friction between the pollutants and the flexible porous structure generates differentiated triboelectric signals, which are continuously collected using an electrometer and a data acquisition card. By further integrating a Light Gradient Boosting Machine (Light GBM) model, a mapping relationship between signal features and pollutant types and concentrations is established for water-quality prediction. Experimental results demonstrate that the system can effectively identify heavy metal ions (Zn²⁺, Ba²⁺, and Al³⁺), polypropylene (PP) microplastics, and rust (Fe₂O₃), achieving an average classification accuracy of 86.67%. Validation experiments using municipal water samples from Kunming supplemented with quantified rust further confirm the reliability of the system. Under varying temperature (4.36–42.75 °C), pH (3–11), and turbidity conditions, the system maintains stable and accurate pollutant recognition, with detection accuracy reaching up to 100%. This study integrates liquid–solid triboelectric sensing with machine learning, providing a promising strategy for intelligent water-quality monitoring.

Manzala Lagoon, the largest coastal wetland of Egypt, lies within the Nile Delta and serves as an essential sanctuary for both resident and migratory birds. Despite its importance for regional biodiversity, the ecosystem faces significant anthropogenic pressures, with recent dredging activities constituting a major disturbance. This study aimed to evaluate dredging impacts on bird diversity and environmental health in the Ashtoum El-Gamil Protected Area. Seasonal monitoring in 2024, combining camera-based morphological identification with molecular barcoding of feathers (n = 13; cytochrome oxidase 1 gene), documented 123 species across 11 orders and 23 families, with 51 species consistently observed year-round. Health assessments in the endemic Coturnix coturnix (common quail) were conducted by measuring genotoxic damage, DNA repair capacity (via poly(ADP)-ribosylation), and total antioxidant capacity (TAC) in the gut, liver, and gonads. Results revealed reduced DNA recovery, elevated antioxidant capacity, and a prominent 40 kDa PARP immunoreactive band, particularly in gut and gonads. These oxidative stress indicators were independent of low heavy metal loads, implicating factors like rising temperatures may be the primary drivers. These findings highlight dredging’s limited immediate effects on species diversity but underscore subtle health risks, advocating sustained, long-term monitoring and targeted management to safeguard wetland biodiversity.