Environmental Monitoring and Assessment最新文献

This study presents a Geospatial Artificial Intelligence (GeoAI) framework for high-resolution Zoonotic Cutaneous Leishmaniasis (ZCL) risk mapping, correlation analysis, and scenario-based projection, integrating geographic information systems (GIS), remote sensing, and neural network architecture. Historical disease maps and multi-temporal satellite-derived environmental layers were jointly modeled using a multilayer perceptron (MLP), two-dimensional convolutional neural networks (2D-CNNs), and three-dimensional CNNs (3D-CNNs). The principal methodological contribution is the implementation of a 3D-CNN, which enables explicit learning of spatiotemporal transmission dynamics. Environmental-disease relationship analyses, based on Pearson coefficients and regression models, identified temperature as the dominant positive environmental driver of ZCL risk. Model performance assessment using root mean square error (RMSE), mean absolute error (MAE), and the area under the receiver operating characteristic curve (AUC) indicates that the 3D-CNN consistently outperforms alternative architectures in capturing complex spatial and temporal patterns. Elevated risk was concentrated in warmer western and southern regions, whereas cooler northern and eastern mountainous areas exhibited lower susceptibility. By 2030, ZCL risk is projected to undergo a spatial shift, with risk decreasing in western regions and intensifying in southern areas, which has direct implications for targeted surveillance and intervention efforts.

Landscape spatial pattern analysis is a viable approach for examining the arrangement of landscape features and their relationships to ecological processes, especially when large datasets are unavailable. This study quantified landscape metrics and explored their potential relationships with water quality parameters at river monitoring stations in Laguna Lake, Philippines, from 2017 to 2023 using correlational analysis. Using FRAGSTATS 4.2 and the Environmental Systems Research Institute (ESRI) Sentinel-2 annual land cover, landscape metrics were derived within a 500 m buffer around the river monitoring stations and correlated with selected water quality parameters. Significant positive correlations were observed between specific built-up landscape metrics (percentage of landscape, most extensive patch index, and effective mesh size) and pollutants (fecal coliforms, ammonia, and phosphates), suggesting that larger, more connected built-up areas are associated with higher pollutant concentrations. This pattern may reflect the presence of extensive impervious surfaces, which are commonly linked to greater runoff and wastewater discharges. In contrast, the same metrics applied to cropland areas showed significant negative correlations with fecal coliform, ammonia, and phosphate, and a significant positive correlation with nitrate. Hence, this indicates that better-connected cropland is associated with lower pollutant levels, possibly reflecting vegetation that intercepts or filters runoff before it reaches rivers. The study demonstrates the use of landscape spatial pattern analysis as a potential indicator of water-quality degradation in rivers and lakes. It identifies innovations to improve the health of the riverine ecosystem.

Bacillus thuringiensis (Bt) protein released from transgenic crops and their subsequent complexation with heavy metals pose potential ecological risks that remain inadequately assessed. Understanding the interfacial behavior of these complexes is essential for predicting their mobility and bioavailability in soils. Thus, this study investigated the co-adsorption of Bt protein (Cry1Ac) with Zn²⁺ and Cd²⁺ onto soil minerals (SiO₂ and Al₂O₃) using dissipative quartz crystal microbalance (QCM-D). Results showed that the adsorption capacity reached a maximum at pH 6. The increased ionic strength suppressed adsorption on the negatively charged SiO₂ surface, but enhanced adsorption on the positively charged Al₂O₃ surface. Furthermore, the maximum equilibrium sorption capacity, determined from isotherm analysis, was significantly higher for the Cry1Ac-Zn²⁺ (2.957 × 10^-3 mg·cm⁻²) than for the Cry1Ac-Cd²⁺ complex (7.250 × 10^-4 mg·cm⁻²) the Al₂O₃ surface. However, the opposite trend was observed on the SiO₂ surface. The analysis of the adsorption mechanism revealed that the primary driving force was electrostatic interaction between mineral surfaces and Cry1Ac-Cd²⁺/Zn²⁺. Furthermore, the formation of complexes between the metal ions and the protein, potentially leading to metal ion bridging and subsequent bilayer adsorption, constituted a significant secondary mechanism contributing to the overall adsorption capacity and layer structure. These findings highlight the critical role of mineral surfaces in modulating the transport and potential bioavailability of heavy metals in the presence of Bt proteins. The study provides key parameters for improving risk assessment of heavy metal mobility in areas cultivated with transgenic Bt crops, supporting more accurate evaluation and mitigation of associated ecological impacts.

In this study, a facile, rapid, and cost-effective method was developed for the determination of lead ions using copper-metal organic framework (Cu-MOF) nanoparticles based dispersive solid phase extraction (DSPE) in slotted quartz tube-flame atomic absorption spectrometry (SQT-FAAS) system. Cu-MOF nanoparticles were used as sorbents, and SQT was used to increase the residence time of lead atoms in the light path. The limit of detection (LOD) and limit of quantification (LOQ) of the Cu-MOF-DSPE-SQT-FAAS system were calculated as 7.1 µg L^-1 and 23.5 µg L^-1 under optimum experimental conditions, respectively. The regression coefficient (R²) was found to be 0.9967, and the linear operating range was determined between 15 and 300 µg L^-1. Thanks to the developed method, a 103.7-fold improvement was achieved for the sensitivity of the traditional FAAS system by comparing the slopes of the linear calibration plot equations. The feasibility of the proposed method was investigated by spiking experiments with utilizing the stream water samples. The good recovery results obtained in the range of 90.8% to 127.1% demonstrated the applicability of the developed method to river water samples with high accuracy and precision. Cu-MOF structures have been employed for the first time for the preconcentration of Pb ions, and their prominent surface properties suggest that they may also be applicable for other analytical processes for different analytes.

Marine litter pollution poses significant ecological and socio-economic challenges, particularly in estuarine environments where waste accumulation is exacerbated by anthropogenic activities. This study assesses the spatial distribution and composition of macro litter (> 5 mm) in the Escravos Estuary, Southern Nigeria, an ecologically and economically vital region with limited empirical data on marine debris. Using a systematic spatial sampling approach, macro litter was collected biweekly from June 2024 to August 2024 at six designated sites across Okerenkoko and Kurutie communities. Litter categorisation followed the OSPAR and COLLECT frameworks, and statistical analyses, including t-tests, ANOVA, and chi-square tests, were conducted to examine spatial patterns. Results indicate a total of 346 macro litter items, with Okerenkoko exhibiting significantly higher accumulation (201 items) compared to Kurutie (145 items). Single-use plastics, particularly beverage bottles (132 items) and food containers (37 items), dominated the litter composition, collectively accounting for 48.8% of total waste. Despite an overall declining trend in litter abundance from Week 1 (79 items) to Week 6 (35 items), no significant temporal variations were detected (p = 0.757), suggesting consistent litter deposition. A significant association (p = 0.0009) was found between litter types and locations, highlighting localised waste sources. These findings establish baseline data crucial for evidence-based policymaking and intervention strategies. Addressing macro litter pollution in the Escravos Estuary requires integrated waste management, policy enforcement, and community engagement to mitigate long-term ecological and economic impacts. This study provides the first quantitative assessment of macro litter in the region, contributing novel insights into estuarine litter dynamics in developing coastal nations.

Wetlands are among the most essential and diverse ecosystems, providing a wide range of intrinsic ecological functions that sustain biodiversity and contribute to the well-being of surrounding communities. However, they are increasingly threatened by water quality degradation resulting from both natural processes and anthropogenic pressures. This study presents an assessment of water quality in two Ramsar-designated wetlands in northern Mexico: the Cañón de Fernández State Park and the Laguna de Santiaguillo. In situ measurements and laboratory analyses were conducted to evaluate physicochemical, microbiological, and emerging contaminants, with a focus on pharmaceuticals. Multivariate statistical methods, specifically cluster and correlation analyses, were applied to identify spatial patterns, contaminant sources, and potential environmental stressors. Additionally, a Weighted Arithmetic Water Quality Index (WAWQI) was calculated to provide an integrated evaluation of water quality. Results revealed distinct contaminant profiles across sites, reflecting both geogenic inputs (e.g., Al, As, Fe) and anthropogenic influences, including wastewater discharge, agricultural runoff, and seasonal tourism. The presence of pharmaceuticals, such as diclofenac, along with high coliform counts and chemical oxygen demand, indicates the occurrence of emerging pollutants in protected areas. The WQI classified water quality as "poor" in Cañón de Fernández and "unsuitable" in Laguna de Santiaguillo, mainly associated with elevated arsenic levels and low dissolved oxygen. These findings highlight the need for integrated watershed management, improved wastewater infrastructure, and monitoring strategies that include emerging contaminants, while emphasizing the value of wetlands and the critical need for conservation efforts that acknowledge both their ecological functions and the benefits they provide to humanity.

Reliable assessment of effluent chemical oxygen demand (COD) and total nitrogen (TN) is essential for regulatory compliance and operational optimization in wastewater treatment plants (WWTPs). However, conventional laboratory-based assays are costly, labor-intensive, and often delayed, limiting their utility for real-time management. To overcome these economic and temporal constraints, this study proposes a cost-effective assessment strategy that leverages routine online monitoring data-specifically flow rate, ammonia nitrogen, total phosphorus, and pH-as accessible surrogates to estimate complex water quality parameters. A novel hybrid deep learning framework, integrating Long Short-Term Memory (LSTM) networks with Extreme Gradient Boosting (XGBoost), was developed using long-term time-series data from a full-scale WWTP. Data quality was ensured through wavelet denoising and outlier treatment. The proposed strategy demonstrated superior performance on the test set, achieving coefficients of determination (R²) of 0.913 for COD and 0.923 for TN, significantly outperforming standalone Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) baselines. These results validate that the hybrid strategy effectively captures the nonlinear dynamics of the treatment process using only standard online sensors. Consequently, this approach serves as a reliable soft-sensing tool, reducing reliance on frequent laboratory testing and enabling high-frequency, cost-efficient effluent quality assessment.

The presence of antiviral drugs in aquaculture systems has received increasing attention due to their potential environmental implications. In this study, a sensitive and reliable liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was established and validated for the simultaneous determination of amantadine (AMT) and rimantadine (RMT) in aquaculture pond sediments. The method showed excellent linearity (R² > 0.9990) over the tested concentration range, with limits of detection and quantification of 0.5 μg/kg and 1.0 μg/kg, respectively. Satisfactory recovery (91.2~104.8%) and good precision were obtained at different spiking levels, confirming the suitability of the method for complex sediment matrices. Sediment samples were collected from 10 aquaculture ponds, including 5 intensive and 5 semi-intensive systems. AMT and RMT were detected in 80% and 60% of the samples, respectively. Concentrations of AMT ranged from 1.2 to 6.5 μg/kg, while RMT concentrations ranged from 0.9 to 4.2 μg/kg. Notably, residue content in sediments from intensive systems was consistently higher than those from semi-intensive systems, suggesting that management practices and drug input intensity strongly influence the accumulation of antiviral drug residues in sediments. These findings indicate that aquaculture sediments act as a significant environmental sink for veterinary pharmaceuticals, with potential ecological and food safety implications. Residues of AMT and RMT in sediments may be re-mobilized into the water column, bioaccumulate in aquatic organisms, and ultimately drive the development of drug resistance. The validated method provides a robust tool for routine environmental monitoring and contributes baseline data on the occurrence of AMT and RMT in aquaculture systems. Overall, this research underscores the need for stricter drug management policies, improved aquaculture practices, and further studies on the long-term environmental fate and risks of antiviral residues in aquatic ecosystems.

Urban rivers are increasingly facing contamination and degradation due to unregulated human activities and are becoming significant reservoirs for pollutants, including toxic heavy metals (HMs) and antibiotics. This co-occurrence of antibiotics and HMs in water bodies may pose greater health and environmental concerns as HMs can co-select for antibiotic-resistant bacteria, leading to a higher prevalence of antibiotic resistance (ABR) genes/bacteria. This study investigates the co-selection of HMs and ABR in Enterobacteriaceae strains isolated from the polluted urban stretch of the Mula-Mutha River flowing through Pune Metropolitan (India). Water samples were collected from five different sites of the river, and physicochemical parameters and metal concentrations were assessed, along with microbiological profiling. Elevated levels of toxic HMs, including arsenic (As, 1010 µg/L), lead (Pb, 90 µg/L), cadmium (Cd, 42 µg/L), and chromium (Cr, 163 µg/L) were recorded at one of the urban sites (Mhatre Bridge), surpassing the World Health Organization (WHO) thresholds, marking it as a contamination hotspot. Eighty-eight Enterobacteriaceae isolates were obtained, of which 41 (46.5%) were multidrug-resistant (MDR), while one was extensively drug-resistant (XDR). Out of these, 33 isolates were used for further investigation. ABR levels were highest against cephalosporins (93.9%) and β-lactam/β-lactamase inhibitor combinations (67.7%). Thirteen isolates (39.3%) exhibited multiple antibiotic resistance (MAR) indices ≥ 0.2, suggesting exposure to high-risk environments. Heavy metal resistance (HMR) profiling revealed minimum inhibitory concentrations (MICs) exceeding 1500 mg/L for Mn, Cr, As, and Pb in multiple isolates, while the multiple HM resistance (MHMR) index reached a value of up to 1.0 at two urban sampling sites. Pearson correlation analysis revealed strong associations between HMR and ABR, including arsenic-lomefloxacin (r = 0.43), copper-cefotaxime (r = 0.46), and lead-piperacillin tazobactam (r = 0.365), suggesting co-selection via shared resistance mechanisms. These findings hold significance and highlight the convergence of chemical and biological pollution in shaping resistant microbial communities and emphasize the urgent need for integrated river basin monitoring and pollution control strategies.

Urban soil pollution is inherently complex, and integrating environmental magnetism with geochemistry provides an effective strategy for precise source apportionment and targeted remediation. This study systematically characterized the magnetic and geochemical pollution of surface soils in Hangzhou, employing correlation analysis, principal component analysis (PCA), and the absolute principal component scores-multiple linear regression (APCS-MLR) model to trace and quantify the sources of magnetic particles and heavy metal contaminants. Results indicate that topsoil magnetism is dominated by low-coercivity pseudo-single-domain (PSD) and multi-domain (MD) ferromagnetic minerals, with pronounced enrichment in industrial zones and traffic-congested areas. Spatial distribution patterns of geochemical elements exhibit a clear anthropogenic-natural composite signature, with elevated concentrations of Ca, Cu, Pb, and Zn primarily in Gongshu, Xihu, and Shangcheng districts, while Al, Fe, Ti, Ce, and V are largely controlled by natural backgrounds. Significant correlations between χ_lf, χ_ARM, SIRM, SOFT and Ca, Zn, Cr confirm the utility of magnetic parameters as proxies for heavy metal monitoring. APCS-MLR analysis identified five major soil pollution sources: industrial emissions (28.29%), natural background (25.29%), traffic emissions (18.03%), construction dust (17.48%), and agricultural inputs (10.91%). Magnetic particles predominantly originate from industrial activities, whereas heavy metals result from multiple combined sources. Notably, heavy metal pollution in Hangzhou's topsoil has transitioned from a conventional "industrial + traffic" pattern to a more complex "industrial + traffic + construction dust" regime. Collectively, these findings provide a scientific basis for precision soil management in Hangzhou and introduce an innovative magnetic-geochemical coupled framework for urban soil source apportionment, offering methodological novelty and technical guidance for pollution control in complex urban environments.