Environmental Monitoring and Assessment最新文献

Satellite hyperspectral remote sensing has proven to be reliable and cost-efficient for estimating the concentration levels and spatial distribution of potentially toxic elements (PTEs) in soil, thereby supporting contaminated soil management and environmental sustainability. However, satellite hyperspectral estimation of soil PTEs remains challenging because it is difficult to obtain an effective set of spectral features for model development, as PTEs occur at trace levels and their spectral signals are often mixed with those of major soil elements and environmental factors under field conditions. To address this problem, we conducted controlled spectral experiments using artificially prepared Pb-contaminated soil samples, in which interfering factors affecting the spectral features of soil Pb were removed. The spectral features of soil Pb were effectively identified using the SPA-SHAP algorithm, and a new spectral index for Pb-contaminated soil (Pb-SI) was developed. Based on these results, a novel spectral feature set (NSFS) for soil Pb was established. Subsequently, the NSFS was extrapolated to satellite hyperspectral datasets via the direct standardization algorithm. Building upon this, a satellite-based estimation model for soil PTEs was established by integrating the NSFS with the random forest (RF) algorithm. Results shown that (1) the NSFS consisted of eight spectral parameters: R₄₇₉, R₇₁₈, R₈₄₃, R₁₇₂₇, R₂₀₁₅, R₂₁₉₀, R₂₃₅₃ and Pb-SI; (2) the satellite-based soil Pb estimation model, constructed via the integration of NSFS and RF algorithm, attained R² = 0.76 and RPD = 2.07, showing a 25.45% improvement in RPD compared with models that use traditional spectral feature sets; and (3) high Pb concentrations are mainly observed in the northern part of the study area, which are closely related to mining sites and transportation routes. This study provides an effective satellite hyperspectral framework leveraging the synergy between the NSFS and RF model for the estimation and spatial characterization soil PTEs and is useful for soil pollution control and the protection of human health.

Temporary ponds represent highly dynamic freshwater habitats that support unique biological communities and are particularly sensitive to environmental disturbance. Despite their ecological importance, little is known about how protection regimes and human pressures influence the ecological condition of these systems. In this study, diatom-based indices were used to assess water quality in 24 temporary ponds located in two contrasting environments in Central Italy: 12 ponds in Castelporziano (CP, representative of the Mediterranean site), characterized by restricted public access, and 12 ponds on the Campo Imperatore plateau (GS, representative of the Alpine site), which are frequently exposed to tourism and livestock activity. Thirteen diatom indices were calculated to compare ecological conditions between the two systems. Results showed generally higher Specific Pollution Sensitivity Index (IPS) and Eutrophication Pollution Index (EPID) values (better water quality) in CP, while GS ponds displayed markedly lower Trophic Diatom Index (TDI) values, indicating lower trophic conditions but greater ecological variability. Alpine ponds also showed higher SPEAR herbicides index (SPEAR) values, suggesting assemblages dominated by sensitive taxa. These findings highlight how differences in disturbance regimes and protection levels can influence temporary pond ecosystems and water quality, emphasizing the need for targeted management and conservation strategies.

Global climatic variations and intensified human activities have altered land use structures and disrupted landscape patterns, increasing landscape ecological risk. Assessing this risk contributes to the formulation of ecological conservation and sustainable development strategies. Research on landscape ecological risk assessment and its drivers in Africa, particularly in rapidly developing East Africa, remains insufficient, which hinders regional ecological risk management and sustainable economic development. We investigated landscape patterns and spatiotemporal variations in regional ecological risk from 1992 to 2022 in East Africa using Geodetector and Random Forest models, measured the explanatory power and interaction effects of natural and human drivers, and quantified their contributions. We found that landscape patterns changed drastically in East Africa. Woodland and sparse vegetation increased by 12.30 × 10⁴ km² and 2.89 × 10⁴ km², respectively; shrubland and bare land decreased by 11.10 × 10⁴ km² and 5.91 × 10⁴ km², respectively. Ecological risk showed a north-high, south-low spatial pattern with positive autocorrelation (P < 0.001). High, sub-high, and medium risk areas decreased, suggesting a downward trend. The distribution of landscape ecological risk in East Africa was mainly influenced by vegetation conditions and annual precipitation; vegetation change dominated the landscape ecological risk pattern, and natural and human factors influenced landscape resilience. Overall, our findings may inform ecological conservation planning in East Africa. Ecological management should give greater attention to vegetation protection and restoration, while landscape planning should consider regional precipitation conditions to help reduce ecological risk and promote sustainable development.

Large-scale biomass-mix burning of Holika Dahan (HD) is celebrated before the Indian traditional festival Holi, which has adverse effects on air quality and health. This study aimed to assess chemical characteristics of airborne PM_2.5, source contributions, emission dispersions, and related health risks during pre-HD, HD, and post-HD sequential nights for a residential area and a commercial area in Lucknow. Compared with pre-HD night, concentrations of ambient PM_2.5 were higher during HD night by factors of 2.0 and 1.4 in the respective residential and commercial areas. Tracer signatures of PM_2.5 emissions from HD biomass-mix combustion ranged from 1.2 to 2.7 times higher during HD night than during pre-HD night. Further, the peaks in the spectrum of ATR-FTIR functional group markers of PM_2.5 samples indicate the impact of HD biomass-mix combustion on local air quality. In addition to peaks in marker species of HD emission, PCA and HCA statistics further confirmed that HD biomass-burning is the predominant source of PM_2.5 at both study sites. The HYSPLIT simulated the HD emission transport paths towards primary eastward and south-eastward. The AERMOD dispersion hotspot isopleths indicated the extent of localised propagation of HD emissions up to 1.2 km and 1.5 km for residential and commercial areas, respectively. The hazard index (HI) values were found below the USEPA permissible limit, indicating negligible non-carcinogenic health risk despite the record of high HIs on HD night. Whereas the cancer risk (CR) exceeded USEPA safe limits due to HD impact at both the sites, however, peak CR was observed during HD night. The findings of the study highlighted short-term air quality degradation and potential long-term health concerns associated particularly with Holi festival-related HD combustion.

Accurate estimation of above-ground biomass (AGB) is essential for sustainable forest resource management and carbon stock assessment. This study focuses on the Pembi Forest, Telangana, India, where synthetic aperture radar (SAR) data from ALOS-2 PALSAR-2 L-band and multispectral data from Sentinel-2 were integrated with biophysical information to develop AGB prediction models. Predictor variables included SAR backscatter and polarization-derived features, Sentinel-2 spectral indices and band combinations, principal components, leaf area index (LAI), and global canopy height. Light Gradient Boosting Machine (LGBM), Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), Artificial Neural Network (ANN), and a weighted voting ensemble were evaluated. The ensemble model achieved the highest predictive performance, with R² = 0.67, RMSE = 24.35 t ha⁻¹ and MAE = 14.97 t ha⁻¹. Spatial AGB distribution maps showed prominent variation in the study area, providing valuable information for resource management and conservation planning. The findings highlight the potential of combining SAR and multispectral remote sensing data with machine learning techniques to improve accuracy of AGB estimation, surpassing the performance of conventional single-model methods and advancing the understanding of forest biomass dynamics.

The pathways of nontarget exposure to anticoagulant rodenticides (ARs) are poorly understood and have yet to be examined in Ontario, Canada. We sought to determine if second-generation anticoagulant rodenticide (SGAR) baiting practices used by pest management professionals targeting commensal rodents may be causing rodenticide spillover into nontarget rodents in urban wildland areas, which could act as a vector of ARs to predators. We used snap traps to capture white-footed mice (Peromyscus leucopus) in urban wildland areas of Toronto and Vaughan, Ontario, near ongoing rodenticide baiting programs utilizing bromadiolone and difethialone products. We detected bromadiolone in only nine individuals and difethialone in two individuals trapped out of a total of 111. These exposed white-footed mice were caught at only five out of seven study sites. AR concentrations were between 0.008 and 0.03 µg/g of liver tissue for bromadiolone and 0.008 and 0.023 µg/g for difethialone, which may be sublethal for raptors. SGAR residues were detected in individuals caught at 0 m, 5 m, 20 m, 40 m, 70 m, and 100 m from active rodenticide stations. SGAR-positive mice did not differ significantly from SGAR-negative mice in terms of sex, age, body condition, distance to the AR source, capture date, or capture site. Four exposed individuals were in “poor” body condition. This indicates that the pest management industry’s use of rodenticides in these urban and suburban settings is likely causing some degree of nontarget spillover in Toronto and the Greater Toronto Area. Additionally, nearby residential SGAR usage may have accounted for some of these exposures.

Agricultural application of treated sewage sludge supports nutrient and organic matter recovery but can also transfer emerging contaminants retained in or associated with sludge during wastewater treatment to soil. This review evaluates microplastics (MPs), per- and polyfluoroalkyl substances (PFAS), and pharmaceutical residues across their sources, sludge association, treatment response, agricultural loading, environmental fate, exposure pathways, potential effects, and regulation. Evidence identified through targeted searches of the Web of Science Core Collection and Scopus was integrated using a structured narrative review and comparative qualitative synthesis. These groups share a sludge-to-soil pathway but exhibit fundamentally different environmental profiles. MPs are retained mainly through physical association with solids and remain environmentally relevant through persistence, fragmentation, and size-dependent redistribution. PFAS behavior reflects compound-specific partitioning, the high persistence of many perfluoroalkyl acids (PFAAs), precursor transformation, and the greater mobility of many shorter-chain PFAAs under some soil conditions. Pharmaceutical residues show compound-specific variation in sorption, degradation, transformation, mobility, biological activity, and plant uptake. Conventional sludge treatments often redistribute contaminants or modify measured profiles without establishing destructive removal. High-temperature thermal processes may achieve greater destruction of some contaminants under suitable operating conditions, but transformation-aware whole-process mass balances remain necessary. Existing European Union and US sludge frameworks remain centered largely on conventional pollutants, although PFAS-related assessment and management are developing. The comparison demonstrates that occurrence alone does not establish risk and that a single uniform monitoring or management framework is inadequate. Class-specific monitoring, treatment evaluation, and long-term field evidence are needed to support environmentally defensible land application.

Persistent organic pollutants (POPs) remain a major environmental challenge because of their persistence, long-range transport, bioaccumulation, and resistance to degradation. Conventional monitoring and remediation systems provide reliable analytical performance but are largely fragmented, laboratory dependent, and unable to deliver the continuous, predictive, and adaptive management required for complex, dynamic contamination scenarios. Recent literature reports substantial advances in biosensors, Internet of Things (IoT)-enabled sensing, machine learning (ML), and artificial intelligence (AI) for pollutant detection, forecasting, source identification, and remediation optimization. However, these developments often operate separately, with limited coordination between environmental sensing, data analytics, treatment processes, and decision-making. The literature further highlights persistent barriers to field deployment, including limited and geographically heterogeneous datasets, inconsistent sensor performance and calibration, restricted model transferability, insufficient independent validation, uncertainty in AI predictions, and the lack of mature regulatory frameworks for AI-assisted environmental assessment. From our perspective, the next advance in POP management should move beyond isolated AI applications toward integrated, closed-loop AI–IoT architectures that continuously connect sensing, prediction, remediation, and feedback control. Such systems could transform POP management from reactive assessment to proactive and adaptive environmental governance. Future progress will depend on standardized, Findable, Accessible, Interoperable, and Reusable (FAIR) environmental datasets, explainable and uncertainty-aware AI, robust field validation, and interoperable sensing infrastructures.

Accurate prediction of dissolved oxygen is important for water-quality management, yet monitoring series are usually noisy, nonlinear, and non-stationary, which limits conventional forecasting models. This study proposes a WOA-EEMD-CNN-LSTM framework in which EEMD is used to decompose the original series into intrinsic mode functions and a residual component for noise reduction and multi-scale representation, WOA is used to optimize the key EEMD parameters and improve decomposition quality, and CNN-LSTM is used to capture local features and long-term temporal dependencies for component-wise prediction before final aggregation. The method was evaluated on a 4 h monitoring series from the Xiaojiang estuary and a public Refuge dataset. In the locked seed-123 evaluation, the WOA-EEMD-CNN-LSTM model achieved an RMSE of 0.32867 and an (R^2) of 0.95070 on Xiaojiang, and an RMSE of 0.28874 and an (R^2) of 0.93429 on Refuge. The resulting workflow provides entropy-guided parameter adaptation, causal multi-scale representation, component-wise learning, timestamp-aligned aggregation, and an auditable leakage-controlled evaluation process. These properties make the framework a practical and reproducible option for dissolved-oxygen forecasting in noisy water-quality monitoring series and provide a clear basis for later multi-seed and multi-site validation.

Carbon monoxide (CO) and carbon dioxide (CO₂) are key tracers when evaluating atmospheric pollution and greenhouse gas emissions, with their enhancement ratio (ΔCO:ΔCO₂) serving as a widely used metric for assessing combustion efficiency and estimating emissions across major industrial sectors at different spatial scales. However, most previous studies have been conducted based on near-surface observations (i.e. a few meters above ground), limiting the spatial representativeness of emission sources. In contrast, high-altitude mountain observations can provide a more comprehensive perspective for emission assessment at regional scale. In this study, we conducted year-round atmospheric concentration observations of CO and CO₂ at a mountain top site (119.00°E, 30.03°N, ∼1500 m a.s.l.) in the Yangtze River Delta (YRD) region, China. By integrating these observations with an atmospheric transport model and prior emissions, we examined regional combustion efficiency and used the observed relationship to evaluate wintertime CO and CO₂ emissions for the YRD region. We found that (1) the observed wintertime ΔCO:ΔCO₂ ratio was 13.6 ± 0.1 ppb/ppm, lower than earlier values reported in China but still higher than those in many European regions, indicating improved yet incomplete combustion efficiency in the YRD region. (2) The simulated ΔCO:ΔCO₂ ratio was 16.1 ± 1.2 ppb/ppm, suggesting that the inventory overestimates CO:CO₂ emissions ratio by 18.4(±8.8)%. (3) The prior wintertime CO emissions are underestimated by around 20.0(± 8.0)%, and prior wintertime CO₂ emission are underestimated by a much larger ratio of 36.8(±17.6)%. Furthermore, the sectoral characteristics of ΔCO:ΔCO₂ suggest that future mitigation efforts should prioritize sources with higher ratios, particularly in the residential and industrial sectors.