Environmental Monitoring and Assessment最新文献

Agricultural soils near major transportation corridors increasingly act as repositories for anthropogenic debris, yet the dynamics of this contamination in tropical paddy fields remain under-researched. This study investigates the abundance, morpho-chemical characteristics, and calculated ecological risks of microplastics in paddy field soils along National Highway 66 in Goa, India. Using Raman spectroscopy, scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), and standardized hazard indices, microplastic contamination was found to be ubiquitous, with concentrations ranging from 250 to 423 MP/kg (mean: 336.7 ± 55.47 MP/kg). Population density and proximity to urban centers were key drivers of accumulation, with low-lying paddy regions functioning as depositional sinks for pollutants transported via runoff. Morphological analysis revealed a predominance of fibers (63%) and fine-sized particles (0.1-0.063 mm), indicating high potential for soil mobility. Polypropylene (51.85%) and polycarbonate (17.59%) were the dominant polymers identified. Notably, while the study area is traffic-influenced, tire-wear particles were not detected within the analytical range of the Raman technique employed, with the profile instead reflecting agricultural and consumer-related inputs. SEM analysis highlighted extensive surface weathering, while elemental profiling confirmed the adsorption of heavy metals (Pb, Cu, Fe), establishing these particles as active vectors for contaminants. Ecological risk assessments using the Polymer Hazard Index (PHI), Pollution Load Index (PLI), and Potential Ecological Risk Index (PERI) demonstrated a disconnect between abundance and hazard. These findings suggest that mitigation must prioritize hazard-weighted assessment over simple abundance monitoring to protect agricultural soil health.

Carbon dynamics in alpine inland basins are jointly regulated by hydrothermal conditions, yet the elevational threshold at which hydrothermal drivers shift remains unquantified in the Qinghai Lake Basin. Using multi-source remote sensing data (2003-2023) with piecewise linear regression and structural equation modeling, we examined spatiotemporal patterns and drivers of gross primary productivity (GPP), net primary productivity (NPP), and carbon use efficiency (CUE) during the growing season. The results are as follows: (1) GPP and NPP were low in the northwest and high in the southeast, with multi-year means of 307.9 and 260.23 g C m⁻², respectively, both increasing significantly; (2) a clear ecological threshold at ~3526 m was detected (GPP, 3524.80 ± 7.11 m; NPP, 3527.38 ± 7.21 m), suggesting a shift in the relative importance of hydrothermal drivers across the identified elevation threshold, with productivity transitioning from primarily moisture-constrained to temperature-sensitive; (3) CUE regulation was decoupled from carbon fixation: normalized difference vegetation index (NDVI) had a negative effect (β = -0.28) above 3526 m, suggesting increased vegetation may reduce CUE via enhanced respiration; (4) zoning based on this threshold showed that high and medium carbon sequestration potential areas were almost entirely (> 99%) above the threshold, whereas 99.6% of low-potential areas occurred below it, supporting differentiated basin management. This study quantifies a pivotal elevational threshold, reveals the decoupling between carbon fixation and utilization processes in response to shifts in hydrothermal drivers, and provides a practical framework for carbon cycle prediction and management in alpine inland basins.

Large-scale soil sampling efforts have been undertaken in India since 2015 under the Soil Health Card (SHC) scheme. This study integrates 39 million+ soil measurements from SHC into a geospatial framework to study two important soil properties - Soil Organic Carbon (SOC) content and pH. The study provides maps of mean and uncertainty at village level for SOC content and pH in surface (0-15 cm) agriculture soils in India, and further analyzes the varying relationship between them across major Agro-Ecological Regions (AERs) in the country. The resultant spatial SOC layer also gave an opportunity to assess two global SOC maps - 1. SoilGrids (250 m) and 2. Global Soil Data for Earth System Modelling (GSDE-30 arcsec). Mean SOC content in different AERs varied from 0.39% to 1.06% while mean pH varied from 5.4 to 8.0. An AER-wise analysis indicated a spatially varying relationship between SOC and pH with 11 AERs showing negative correlation and 4 showing positive and no correlation each. The mean SOC contents from GSDE were around half that of SHC for most AERs, while those estimated by SoilGrids were more than twice that of SHC in 16 of the 19 AERs. The implications of these results for Indian SOC stock estimates and climate change mitigation potential are discussed in this paper. Overall, SHC data can complement and augment large scale soil datasets. It can find applications in a diverse set of fields like soil monitoring, carbon budgeting, soil zonation studies, as well as in crop and carbon cycle modelling studies.

Plant disease represents one of the most severe threats to agricultural production. Deep learning has emerged as a promising solution for automating the recognition of these diseases, leading to a richness of disease recognition applications based on deep learning. However, most existing applications do not address the challenge of simultaneous multi-disease detection from the same leaf. In this study, we introduce a deep learning-based model designed to detect and recognize multiple diseases from the same leaf simultaneously. Our method enables the recognition of each disease's symptoms separately from small leaf regions, independent of other diseases or specific crop types, through an isolation method. This approach also allows the model to generalize disease detection to new crops not encountered during training. Additionally, our method calculates the prevalence rate of each disease on the leaf and determines the overall extent of all diseases present. To evaluate the effectiveness of our approach, we applied it to the widely recognized PlantVillage dataset, creating a new version for training and testing with three CNN models: Small Inception, MiniVGGNet, and LeNet5. The results demonstrate that the Small Inception architecture outperformed the other two CNNs in terms of classification performance. Despite some class imbalances in the new dataset, which were addressed through the use of class weights, this approach significantly enhanced the model's performance. Furthermore, while the proposed method demonstrates high performance in controlled environments, though its consistency under real field conditions still warrants deeper investigation. Overall, the findings underscore the effectiveness of our method and highlight its potential as an efficient solution applicable across diverse agricultural contexts.

Exploring the intensity of cyclic changes of ecosystems in regional space helps to analyze their variability patterns, which is of great significance for ecological factor monitoring and ecological management. This study proposes a spatial trend analysis method based on spatial spectral data. By extracting pixel gradient and spatial trend in the time domain from multi-temporal spectral data, it reduces feature redundancy while preserving pixel spatial continuity. Using the Yellow River Basin as a case study, monthly vegetation cover data from 2022 were analyzed to examine the spatial distribution characteristics of vegetation cover intensity changes. The results of the study are as follows: (1) Compared to the original gradient distribution, the SIDM method increases Moran's I by 0.106, decreases Geary's C by 0.097, and enhances spatial aggregation. This facilitates clearer depiction of the spatial continuity structure of vegetation changes, providing spatial support for identifying key change areas and implementing zoned management in ecological monitoring. (2) The third quarter of 2022 is the most luxuriant period of vegetation in the watershed, with the highest vegetation cover of 0.772 in August, and the overall increase of the year is 0.033. (3) The gradient of vegetation cover shows an overall northwestern to southeastern upward trend, with a maximum increase of 4.6% and a minimum decrease of -2.4%. The positive change area dominated by Sichuan, Henan, Shanxi and Shandong is 553,750 km², and the negative change area dominated by Ningxia and Inner Mongolia is 171,250 km².

South Africa faces environmental and public health risks due to pollution of various environmental systems by pesticide and pharmaceutical residues resulting from anthropogenic activities. However, regulatory and monitoring mechanisms remain inadequate. This review aimed to assess occurrences, sources, regulatory frameworks, and policy responses related to pesticide and pharmaceutical pollution in South Africa. The review of published (peer-reviewed) articles and government and policy documents found that pharmaceuticals such as acetaminophen and diclofenac, and pesticides such as atrazine, endosulfan, and chlorpyrifos, are commonly reported micropollutants, even though they are banned. The spatial distribution of the reviews shows that Western Cape, Gauteng, and KwaZulu-Natal appear to have more research conducted on these pollutants. Related laws and policies managed by the Department of Agriculture, Land Reform and Rural Development (DALRRD) and the South African Health Products Regulatory Authority (SAHPRA) are insufficient and lack thorough environmental risk assessments, regular monitoring, and strict enforcement. Comparison with the EU, USA, Switzerland, Australia, Japan, and South Korea reveals that these countries have stronger regulatory systems, including obligatory risk assessments, national take-back schemes, and integrated monitoring, which are mostly absent in South Africa. The informal sale of pesticides, misuse, improper disposal of pharmaceutical waste, and the slow implementation of the Integrated Pest Management (IPM) approach further exacerbate the problem. To prevent future risks to ecosystems and public health, the review recommends regulatory adjustments, improved interagency coordination, and enhanced environmental monitoring systems to align South Africa's regulatory framework with world best practices.

Storm-driven nonpoint-source (NPS) export in steep mountain basins is highly sensitive to runoff peaks, yet nutrient monitoring is typically monthly, limiting event-scale calibration. This study proposes an event-aware SWAT+ calibration framework for the Qingjiang River Basin (China) by coupling Hippopotamus Optimization (HO) with SUFI-2 uncertainty fitting. The objective retains monthly Nash-Sutcliffe efficiency (NSE) for discharge and nutrients and adds an event regularization term computed from daily discharge within objectively delineated runoff events (peak magnitude, time-to-peak, and recession slope). Using 2008-2018 for split-sample calibration/validation and 2020-2024 for an out-of-sample test without parameter retuning, HO-SUFI-2 improves monthly discharge NSE from 0.72 to 0.79 (calibration) and from 0.70 to 0.73 (validation) while tightening uncertainty (P-factor 83%→87%; R-factor 1.21→1.09). Peak constraints reduce median relative peak-magnitude error from 0.104 to 0.069 at Gaobazhou and from 0.114 to 0.069 at Changyang, and tighten peak-timing dispersion (|TPK| IQR 2→1 days at Gaobazhou; median |TPK| 1→0 day at Changyang), without degrading monthly nutrient skill (TP NSE 0.70→0.73). In 2020-2024, monthly discharge NSE remains 0.62-0.67 and TN/TP NSE ≥ 0.42 at both stations; remaining biases may reflect post-2020 management and reservoir operations not explicitly represented. The framework provides a practical way to constrain storm hydrograph dynamics under data limitations and to report uncertainty-aware diagnostics for storm-period risk screening in mountainous basins.

This study assessed arsenic (As) contamination in cow milk across eight locations (A-H) in Sahibganj, Jharkhand, India, where the average As concentration in livestock drinking water was 0.44 mg/L. Milk As levels ranged from BDL (Location H, analyzed as zero) to 0.026 mg/kg (Location A), showing strong correlations with water (r = 0.983, p < 0.05) and fodder (r = 0.970, p < 0.05) concentrations. Principal component analysis (PCA) revealed that the first three principal components (PC) collectively explained 87.53% of total variance, effectively capturing the major trends in the data. PC1, explaining 65.97% of the variance, was primarily loaded with high positive contributions from arsenic in water (0.392), fodder (0.403), and soil (0.410). Deterministic and probabilistic risk assessment, performed using a Monte Carlo simulation, identified significant health threats. Deterministic non-carcinogenic risks (HQ) peaked at Location A for age groups 0-3 and 3-12 years (HQ > 1), while the other locations generally showed lower HQ values across all age groups. The probabilistic 95th percentile HQ also exceeded 1 for age groups 0-3 and 3-12 at Location A and for the 0-3 years age group at Locations B and C. Similarly, the deterministic carcinogenic risk (CR) reached 8.69E-04 for the 0-3 years age group at Location A, exceeding the USEPA limit (E-04-E-06), with the probabilistic 95th percentile at 1.15E-03; the lowest CR was 4.77E-06 at Location E for the 36-60 age group. These findings underscore a critical public health concern, particularly for vulnerable populations, necessitating urgent mitigation strategies to reduce As exposure through milk consumption in the region.

Environmental pollution caused by rapid industrialization is becoming a growing concern and challenge for humans. Environmental pollutants are hazardous and toxic substances that are released into the environment, causing lethal effects on living organisms and ecosystems. Water pollution is the contamination of water bodies by harmful pollutants such as organic and inorganic pollutants, chemicals, excessive nutrients, pathogens, and heavy metals. Heavy metals occur naturally, but large amounts of heavy metals are present in industrial effluents and are highly prevalent. Lead, mercury, cadmium, chromium, and arsenic, found in industrial wastewater, are non-biodegradable and can cause serious health disorders, including nervous disorders, respiratory disorders, and cancer. Conventional methods used for the remediation of heavy metals from industrial effluents include physical, chemical, and biological methods such as ion exchange, chemical precipitation, bioremediation, adsorption, and soil washing. However, these methods are not eco-friendly, produce secondary waste, and require sophisticated machinery and trained professionals. Furthermore, these are expensive, take a longer time for treatment, and require optimal conditions for effective treatment. In contrast, nano-biosystems and synthesized nanomaterials offer a promising and more efficient alternative. According to the latest findings, carbon-based nanomaterials (such as carbon nanotubes and graphene), metal-oxide nanoparticles, magnetic nanocomposites, and bio-supported nanosorbents are examples of nanoadsorbents that exhibit exceptionally high adsorption capacities, selective affinity toward specific heavy metals, and tolerance to stressful environmental conditions, making them highly effective even at trace contamination levels. Strong binding is made possible by their large surface area, flexible surface chemistry, and functionalization with particular ligands by complexation, sorption-reduction, and electrostatic attraction. Moreover, some nanomaterials can be magnetically recovered and reused, thereby improving their sustainability and enabling scale-up and commercial-level applications. The potential of nanoparticles to effectively eliminate various pollutants from industrial effluents makes them a promising choice for future applications. The use of nano-biosystems worldwide can create a cleaner, safer, and healthier environment for future generations.