Environmental Monitoring and Assessment最新文献

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.

Fecal contamination poses a persistent global threat to drinking water safety, especially in low- and middle-income countries with limited sanitation and treatment infrastructure. Fecal matter transmits a wide array of enteric pathogens like bacteria (Escherichia coli, Vibrio cholerae, Salmonella, Shigella), viruses (norovirus, rotavirus, adenoviruses), protozoa (Giardia, Cryptosporidium), and occasionally fungi and helminths—driving outbreaks of diarrheal diseases, cholera, and other waterborne illnesses that cause substantial morbidity and childhood mortality. This review synthesizes current knowledge on the sources, environmental survival, and transmission behavior of fecal-associated microbes in drinking water systems, including persistence in biofilms, seasonal fluctuations, and distribution network dynamics. It critically compares detection methods, from conventional culture-based techniques to advanced molecular tools that provide greater speed, specificity, and field applicability. The paper also evaluates remediation approaches, contrasting established physical-chemical treatments (coagulation–sedimentation, filtration, UV disinfection, chlorination) with sustainable, eco-friendly alternatives such as bioremediation, phytoremediation, bacteriophage therapy, predatory bacteria, and biosurfactant use, which offer low-cost, chemical-free pathogen control. Effective integration of sensitive detection technologies with targeted, sustainable remediation strategies supported by quantitative microbial risk assessment is essential to reduce fecal pollution risks and protect public health. The review calls for adaptive, multi-barrier water management frameworks to achieve safer drinking water globally.

Graphical Abstract

Remote sensing techniques for total ozone (O₃) quantification have not been extensively explored in Colombia; most investigations rely on in situ measurements from ground-based networks or satellite observations. In topographically constrained urban areas, complex atmospheric dynamics present considerable challenges to conducting remote sensing measurements from ground-based systems. Nevertheless, in this study, the Dobson methodology was employed to estimate the total column O₃ from ground-based ultraviolet solar spectral radiometric measurements in the urban area of a polluted valley in the Colombian Andes. Two measurement campaigns, undertaken in 2020 and 2022 under slightly different atmospheric conditions, were conducted. The findings indicated that even minor variations in local atmospheric pollution levels, associated with air quality and climatological variability, directly influence total ozone estimation. The results were compared with total column O₃ retrieved from the Ozone Monitoring Instrument (OMI) aboard the Aura satellite of the National Aeronautics and Space Administration (NASA). Under partly clear-sky conditions, approximately 50% ((r = 0.55)) of the ozone retrieved from ground-based measurements exhibited a linear relationship with ozone values from OMI. Under partly cloudy-sky and heavily polluted conditions, a lack of correlation was observed.

In Latin America, efforts to accelerate economic growth through agriculture and mining often generate strong tensions with environmental sustainability. We examine this dynamic in Sonsón (Antioquia, Colombia), a municipality at the epicenter of simultaneous agricultural intensification and mineral extraction. To characterize the forces reshaping this landscape from 2000 to 2020, we combined wall-to-wall satellite analysis with evidence from local stakeholders. Annual land-cover trajectories were mapped with the Continuous Change Detection and Classification (CCDC) algorithm using the Landsat archive. In parallel, we surveyed 55 respondents from agricultural, mining, commercial, environmental authority, and other sectors to elicit perceptions of drivers, impacts, and governance. Although most of the territory (≈90.8%) remained stable, this apparent stability masks two distinct waves of transformation: a first wave (2000–2005) concentrated in the Magdalena Medio lowlands with grassland and mining expansion, and a second wave (2015–2020) in the high-altitude “Zona Fria” with the intensification of export-oriented crops and grasslands. Environmental authorities and commerce consistently identified mining as a key driver, a role that industry representatives tended to downplay, while farmers emphasized the shift toward export crops. Taken together, these trends provide an anticipatory warning: despite limited widespread deforestation to date, growing pressures from agriculture and mining threaten forest ecosystems, water quality, and social stability. Strengthened spatial planning, proactive conservation, and cross-sector collaboration are urgently needed to align economic ambitions with ecological integrity. This integrated approach demonstrates how combining long-term Earth observation with social data can strengthen environmental monitoring and land-use assessment in heterogeneous mountain regions.

Although machine learning (ML) has been increasingly used to predict pesticide sorption, most existing models rely primarily on molecular and soil descriptors and rarely account for the influence of solution chemistry. This limitation reduces their relevance for environmental monitoring, particularly under variable water quality conditions, such as salinity. In this study, an interpretable ensemble ML framework was developed to predict the soil–water sorption coefficient (K) by integrating pesticide molecular properties, soil characteristics, and electrolyte descriptors. A dataset of 975 laboratory-derived batch sorption observations, covering 65 pesticides, 23 soils, and multiple electrolyte types and concentrations, was compiled from peer-reviewed studies. Six tree-based ensemble models were trained and evaluated, with XGB achieving the best predictive performance (R² = 0.938; RMSE = 0.248) on an independent test set. Model interpretation using SHAP revealed that electrolyte parameters were the dominant predictors, with electrolyte molecular mass (MME, 28%) and electrolyte concentration (CE, 25%) contributing more than traditional descriptors such as octanol water coefficient (KOW, 18%) and soil organic carbon (OC, 12%). Nonlinear response patterns were consistent with ionic strength effects and cation-bridging mechanisms, supporting the learned relationships’ mechanistic plausibility. External validation under unseen conditions confirmed robust generalization (R² = 0.950). This cost-effective, interpretable framework enables regulators and agronomists to simulate pesticide mobility across varying water quality conditions, thereby supporting sustainable pesticide management and targeted risk assessment.

Microplastics are emerging pollutants of interest in agricultural soils, as they are persistent and may have significant ecological effects. The paper examined the occurrence, properties, and distribution of microplastics in peri-urban agricultural soils in Ranchi, Jharkhand. Soil samples (n = 165; 11 sites × 3 depths × 5 replicates) were collected from ten agricultural fields and one control area at three depths (0–15 cm, 15–30 cm, and 30–45 cm). Microplastics were isolated using density separation and oxidation methods, and then morphologically categorized. FTIR spectroscopy was employed to analyze the polymer composition. Quantification of associated metals was performed using atomic absorption spectrophotometry (AAS) after acid digestion, while physicochemical parameters of the soils were measured following the protocols of APHA to establish their relationships with the presence of microplastic. The findings showed that fibers had the highest prevalence at all depths (80 ± 8 to 3320 ± 36 particles kg⁻¹ dry soil) with the highest concentration in the surface layers. Fragments, films, and microbeads were found to decrease significantly with depth, suggesting a lack of vertical migration, except in finer fibers. Polymers were mainly polypropylene (34.3%), polyethylene (16.7%), polypropylene diene rubber (14.7%), LDPE and polyethylene-based foaming material (11.8% each), and polypropylene copolymer (10.8%). The Microplastic Pollution Load Index (MPPLI) was used to identify pollution hotspots associated with the intensive use of plastics. The interrelations between microplastic abundance and soil organic matter, as well as electrical conductivity, suggest the potential impact of microplastic on soil quality and properties (Supplementary Figs. 1, 2, and 3). Vertical distribution of microplastics shows that shallow-rooted crops face surface microplastics, while deep-rooted crops interact with finer particles in soil highlighting the need to enhance the management of plastic waste to protect soil health and sustainability in agriculture.

Microplastics (MPs) accumulation can potentially influence soil fertility and plant productivity. MP pollution and spatial distribution in terrestrial environments, particularly in agricultural soils surrounding the municipal solid waste (MSW) dumping site in southwest Bangladesh, is still not investigated. The objective of this study was to quantify, characterize, and assess the ecological risks of MPs in agricultural soils surrounding the MSW dumpsite of Khulna City Corporation (KCC) in southwest Bangladesh. Ten (10) composite agricultural soil samples surrounding the MSW dumping site were collected and processed for MPs and trace metals analysis. We investigated the abundance, spatial distribution, characteristics, polymer types, and ecological risk of observed MPs. The results exhibited that the mean abundance of MPs was 5567 items kg⁻¹ (range from 1667 to 11,000 items kg⁻¹), and the dominant shape, size, and color were found in fibers (~ 43%), < 1 mm, and blue, respectively. Polyvinyl chloride (PVC) was the dominant polymer followed by HDPE and nylon among the 14 identified polymer types across the study site’s soils. Spatial analysis showed that the old dumpsite, along with agricultural soils in the vicinity of the active dumpsite, was polluted with the MPs and heavy metals. The investigated sites were classified as risk category I due to MP pollution, and the findings indicate a decrease in MP abundance as the distance from the dumping site increases. Principal component and correlation analysis indicate different anthropogenic sources of MPs and heavy metals pollution, namely the solid waste dumpsite and improper agricultural activities (p < 0.05). The findings highlight the need for integrated management strategies to prevent microplastic pollution in agricultural soils to ensure soil health and crop productivity.