Environmental Monitoring and Assessment最新文献

Accurate gap-filling of daily soil CO₂ flux is essential for understanding forest carbon cycling and supporting the achievement of carbon neutrality goals. Using observations from the Ailaoshan forest site (2010–2013), we evaluated five machine-learning models with inputs including environmental variables, eddy covariance products such as ecosystem respiration and gross primary productivity, and a seasonal phase indicator. Seasonal patterns remained stable during the 4-year period, and no long-term trend was detected after quality control. After preprocessing, multiple machine learning models were compared and evaluated through rolling-window cross-validation. Model robustness and interpretability were further examined using complexity curves, SHAP analysis, and ablation experiments. The empirical Q10 × SWC model served as a baseline, and independent data from the Xishuangbanna site (2003–2008) were used for external validation. CatBoost achieved the best balance between accuracy and complexity and remained stable across rolling windows. Incorporating GPP, RE, and cos_doy improved test performance, raising R² from ~ 0.85 to ~ 0.90 and reducing RMSE from ~ 0.66 to ~ 0.57. Compared with Q10 × SWC, CatBoost achieved an average Skill score of 0.18, corresponding to a 3–32% reduction in RMSE. External validation at Xishuangbanna further confirmed model robustness (Skill ≈ 0.81). The proposed framework provides an effective tool for generating continuous and reliable daily soil CO₂ flux datasets, offering methodological support for forest carbon cycle research and the realization of “dual carbon” goals.

The increasing encroachment of micro-nano-plastics (MNPs) in various ecosystems raised considerable concerns about their impact on living beings, environmental systems, biological waste treatment schemes—particularly anaerobic digestion (AD) of organic waste. MNPs can originate from the disintegration of conventional plastics or through leachate released from consumer goods, eventually merging with waste streams treated in anaerobic systems. Moreover, bioplastics (BPs) being promoted as bio-based or biodegradable also end up in biological waste treatment units including AD. This literature review examines fate and impact of MNPs and BPs over AD systems, with comprehensive focus on mechanistic insights, promotion or inhibition aspects in MNPs and BPs containing AD systems and impact on microbial communities. The review emphasizes that MNPs impact microbial diversity and their structural orientation, enzymatic action, and syntrophic interactions, which are vital for biomethane production from AD system. The MNPs may either absorb into microbial cell walls—disrupting cell integrity or may even introduce toxic additives thereby inhibiting hydrolysis and methanogenesis. Nevertheless, at lower concentrations, MNPs could also instigate microbial activity by functioning as biofilm and promoting direct interspecies electron transfer for enhanced degradation. In addition, most of the BPs were reported as interrupting material for adequate biomethane production, nevertheless few even contributing to methane yield and degrading over the course of AD. Despite such findings, long-term impact and degradation pattern of such polymers in AD systems are still underexplored. The review further recommends methodological standardization to assess MNPs/BPs impact over AD systems.

The Ashebeka River supplies drinking water to Assela City and its surrounding areas, yet rising pollutant loads threaten water quality, supply reliability, and treatment efficiency. This study, conducted in 2023, applied a sediment fingerprinting approach to quantify suspended sediment sources and inform the Water Safety Plan (WSP) implementation. Composite soil samples (n = 57) were collected across four sub-catchments: upper (UC), middle (MC), middle left (MLC), and lower (LC). In addition, 45 composite water samples were taken at the catchment outlet during the rainy season (June–August). Thirteen environmentally relevant geochemical tracers (Fe, Mn, Ni, Co, Cu, Zn, Cd, Hg, Pb, As, B, Se, Cr) were analyzed and screened for redundancy via variance and correlation analysis. Source apportionment employed discriminant function analysis (DFA) with non-parametric Kruskal-Wallis tests to accommodate non-normal data. Analytical recovery (82–105%) validated methodological robustness. Results revealed sediment contributions from the UC, MC, MLC, and LC sub-catchments were 22%, 45%, 19%, and 14%, respectively. The dominance of the MC reflects intensive cultivation on erosion prone slopes, marking it as a priority intervention area. Targeted soil conservation and agroforestry in the MC and LC are recommended to curb sediment influx and reduce downstream treatment costs. These findings equip government agencies, Assela City municipality, and stakeholders with quantitative evidence to prioritize hotspots and execute efficient catchment-scale water safety measures.

Graphical Abstract

Operationalizing the planetary boundaries (PB) framework at a regional scale is a critical sustainability challenge. This study addresses this gap within the Yangtze River Economic Belt (YREB), a globally significant nexus of urban, agricultural, and ecological systems. By integrating 30 m land use data with socioeconomic statistics, we developed an improved ecosystem service value (({V}_{E})) calculation method and coupled ecological carrying capacity (EC) with ecological footprint (EF) to establish an ecological boundary (EB) model. Our findings reveal three key insights: (1) Total ESV increased by 92.4%, with forests and water bodies contributing 83.6%, forming a spatial differentiation pattern centered on the Yangtze mainstream; (2) Additionally, significant differences in ecological boundary values were observed, with eastern regions exhibiting values 2.3–4.8 times higher than those in the west; (3) Geodetector analysis identified per capita GDP, R&D intensity, and urbanization rate as key drivers of ecological boundary transgressions. This research validates the application of downscaled planetary boundaries for robust ecological governance and introduces a holistic framework for “boundary delineation–mechanism deconstruction–policy coordination” tailored to regional ecological conditions.. This research provides two primary contributions. Scientifically, it pioneers a robust methodology for regionalizing the PB framework, offering a new paradigm for assessing socio-ecological systems. Practically, it provides an evidence-based blueprint for a differentiated, interregional governance strategy, advocating for technological compensation from the east to support targeted ecological conservation in the west. This offers a scalable model for reconciling development with resilience in critical river basins globally.

Vegetation phenology needs to be considered in the assessment of agricultural drought severity, especially in drylands such as Botswana. Using Remote Sensing time-series data (2000 – 2020), this study evaluated vegetation productivity using the annual sum of the Enhanced Vegetation Index (EVI_sum) and computed changes as trends (EVIsum_trend) in agricultural lands comprising grasslands and croplands. To assess agricultural drought severity, a weighted linear combination was applied to the EVI-based Vegetation Condition Index (VCI_wlc) and compared to the conventional Standardized Precipitation Index. To detect how agricultural drought has impacted vegetation phenology, we identified dynamics in vegetation greenup, maturity, peak, senescence and dormancy and correlated these to the EVI_sum and VCI_wlc. Differentiating between croplands and grasslands, vegetation productivity in grasslands was consistently lower than in croplands during droughts. The seasonal agriculture-related phenology, such as late vegetation greenup — the start of the season — is correlated with reduced vegetation productivity and severe agricultural droughts as evidenced by lower EVI_sum and VCI_wlc values, respectively. Notable phenology patterns include delayed greenup in grasslands compared to croplands, while the peak period often overlaps between grasslands and croplands. Overall, phenological shifts detected over agricultural lands in Botswana were about 58 days delayed greenup between the earliest and latest detection, whereas for peak, senescence and dormancy, delays were between 60 and 61 days. Findings provide valuable insights into how vegetation responds over time to changing environmental stressors such as drought. Understanding and monitoring how drought impacts agriculture-related phenology in dryland ecosystems is essential to informing national drought policies.

This pilot project utilised a novel boat design to skim and remove algae from river water and evaluated the entrainment of microplastics in surface water algae. The removed algae were estimated to contain a concentration of 55.33 microplastics per kg of algae. The microplastic concentration, normalised to the volume of water passed through the skimmer, was calculated at 1.8 microplastics per cubic metre of water. A sample of river water adjacent to the algae was also analysed as a non-direct comparison to the concentration of microplastics in the algae. Open river water adjacent to algae was calculated to have a microplastic concentration of 0.10 microplastics per cubic metre of water. These preliminary results suggest that by removing river algae, you also remove a significant amount of microplastics from freshwater systems. This novel approach can be seen as an advantage ecologically, as the main objective is to remove potentially harmful algae blooms, where the by-product of this methodology also removes microplastics from the environment. The research represents one of the first quantitative assessments of microplastics entrained in river algae collected by a skimmer. This research could be further validated and incorporated into existing reporting framework and even potentially citizen/community science protocols.

The underwater light environment is important for lake ecosystem function and structure. In the summer of 2023, the Yangtze River basin suffered an extreme drought, and Poyang Lake experienced its lowest water level since 1951. However, there remains a gap in understanding the impact of extreme drought on the underwater light environment. In this study, the underwater light environment in Poyang Lake was quantitatively investigated in late July 2023, and its influencing factors were explored. The results showed that the underwater light environment in Poyang Lake attenuated most drastically within the 0–0.4 m depth range, where the underwater spectrum changes significantly, especially in the visible light range (400–700 nm). With increasing depth, the proportion of red and green light increased, whereas that of blue light decreased rapidly. However, in regions with high Secchi depth (SD), even at 1.2 m depth, blue light still accounted for 2.03–9.15%, forming a distinct underwater light environment. In other regions with low SD and high suspended solids (SS), blue light attenuated severely and red light became dominat. Although the red-to-blue light ratio varied across Poyang Lake, its maximum values were consistently distributed in the west. Attenuation of blue, green, and red light, as well as photosynthetically active radiation, in the four lake regions followed the order: northern > western > southern > eastern, with blue light attenuating the most rapidly. In contrast to previous findings, SD was the key factor driving changes in the underwater light environment of Poyang Lake, followed by SS. Future efforts should prioritize enhanced monitoring of SD and the underwater light environment in Poyang Lake.

Heavy metal contamination poses a serious threat to watershed ecosystem health, and predicting ecological risk from toxicity data is crucial for watershed protection and sustainable development. However, ecotoxicological data are time-consuming and costly to obtain, resulting in limited samples that hinder the application of machine learning models. To address this issue, this study first employed the Synthetic Minority Oversampling Technique for Regression with Gaussian Noise (SMOGN) to generate high-quality synthetic samples and augment the original toxicity dataset. Using the augmented data, we developed and compared three regression models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—and applied Shapley Additive Explanations (SHAP) to interpret the key drivers of predicted toxicity for Cr, Mn, and Cu. Results show that synthetic data substantially improved model performance, notably increasing the RF test R² from 0.696 to 0.977; overall, all models achieved higher accuracy with the augmented dataset than with the original data. Risk quotient (RQ) assessment indicated maximum RQ values of 3.290 for Cr and 2.132 for Cu in the Chaohu Lake Basin, signifying elevated ecological risk. The study demonstrates the feasibility and effectiveness of SMOGN under data-scarce conditions in ecotoxicology and provides robust data support and scientific evidence for monitoring, assessment, and pollution-control decision-making for heavy metal contamination in the Chaohu Lake Basin.