Environmental Monitoring and Assessment最新文献

It was presented a reliable statistical assessment of regional background element concentrations with a geochemical framework using the concentrations of Cr, Fe, Ni, Cu, Zn, As, Mo, Pb, and Hg in soils and Hg in air (atmospheric and soil) on the example of the area with ore mineralization and superimposed historical anthropogenic mining activity (Sarala gold-ore group; the Republic of Khakassia, Russia). The most reproducible results were obtained by the 2sd (mean ± 2σ) and 2MAD (median ± 2MAD) statistical methods. The TIF (Tukey inner fence) gave the widest background range. The 2sd method can be used as well as 2MAD, but with careful statistical pre-processing of data. It was found that the upper threshold values of background concentrations of Cr, Fe, Ni, Cu, Zn, As, Mo, Pb, and Hg in soils of the Sarala gold-ore area exceeded the concentrations in the upper continental crust, soils of 4 neighboring objects of the Siberian region, and maximum permissible concentrations. Hg concentrations in air (atmospheric and soil) were higher than in the Northern and Southern Hemispheres and did not exceed the maximum permissible concentration for residential areas. The patterns are typical for areas where ore mineralization zones are located. Based on the comparison of geological and geochemical data with the results of ANOVA (one-way analysis of variance) tests and correlation analysis, the sources of elements were recognized in the study area (geogenic, technogenic, and mixed). The investigation with this kind of statistical approach was realized for the first time in Russia for such a natural-technogenic object, providing research novelty. The results provide reference data for comparing background concentrations in soils in similar mining areas worldwide and can help with making decisions about environmental strategies for specialized measures to ensure public safety.

This study provides a comprehensive long-term assessment of air quality in Balıkesir, northwestern Türkiye, focusing on the spatiotemporal variability of PM₁₀ and SO₂ during 2013–2023. The analysis integrates pollutant observations with ERA5 reanalysis meteorological data to examine seasonal and diurnal variations, long-term trends, and the role of temperature inversions and atmospheric circulation in shaping pollution episodes. Results indicate that both pollutants reach their highest concentrations in winter due to intensified domestic heating and stagnant meteorological conditions, with temperature inversions acting as a key driver of near-surface accumulation. The Theil–Sen trend analysis revealed an insignificant long-term change in PM₁₀ but a statistically significant upward trend in SO₂ (+ 0.5 µg m⁻³ year⁻¹), suggesting persistent reliance on sulfur-rich fuels during the cold season. However, a short-term decline after 2020 reflected the combined effects of cleaner fuels and pandemic-related emission reductions. Correlation analysis demonstrated that PM₁₀ and SO₂ are moderately interrelated (r = 0.49) and primarily controlled by high-pressure, low-wind conditions. HYSPLIT trajectory analyses confirmed that severe winter episodes result from the combined influence of local stagnation and regional air mass transport. These findings highlight the dual importance of emission control and meteorological monitoring in managing winter pollution. Policy recommendations include stricter regulation of heating fuels, inversion-based early-warning systems, and enhanced regional cooperation to mitigate transported particulate matter.

Constructed wetlands (CWs) operate based on natural processes to remove pollutants. Despite public health risks, the discharge of untreated septic sludge into environmental media continues in many developing countries. This study evaluates the quality of effluents from CWs fed with screened substrate during the rainy and dry seasons. It employed an analytical design on purposively selected CWs. The CWs had, in sequence, coarse (8-16 mm) and fine (4-8 mm) granite, coarse (2-4 mm) and fine (0.15-2 mm) sand. The experimental CWs included Acroceras zizanioides dandy, while the control was unplanted. The flow rate through the subsurface filter was 0.01 m³/hour, operated twice weekly, with a hydraulic loading rate of 2-20 cm/day. Samples were collected and analysed using standard methods for pathogen reduction, suspended solids(SS), nitrate(NO₃), phosphate(PO₄), biochemical oxygen demand(BOD₅), lead(Pb), and zinc(Zn). Results were presented using descriptive and t-test statistics (P < 0.05), and mean residual concentrations were compared to national discharge guidelines. In planted CWs, phosphate (5.7 ± 0.7 mg/L), BOD₅ (53.1 ± 14.2 mg/L), and zinc(1.2 ± 0.4 mg/L) exceeded regulatory thresholds, while SS(27.9 ± 6.2 mg/L), nitrate (5.4 ± 0.8 mg/L), and lead(0.0 ± 0.0 mg/L) were permissible and significantly reduced. The coliforms log-reduction value was 0.79. The experimental CWs showed promise but should be arranged in series to enhance effluent quality to meet national regulatory standards before environmental discharge.

To assess the soil quality in the Yushu area, eastern Qinghai-Tibet Plateau, we collected and analyzed 1706 topsoil samples and conducted an evaluation using the soil quality index (SQI) method. This is the first comprehensive soil quality assessment ever carried out in Yushu. Results revealed a generally high SQI in the study area (ranging from 0.4 to 0.7). Research indicates that geological background is the dominant factor shaping the soil elemental composition and quality in this area, while the impact of human activities is minimal. The core objectives of this study are to reveal the impacts of factors (e.g., geological background) on SQI and identify the key driving factors, as well as to clarify the formation mechanism of alpine soil quality. The established soil quality assessment data baseline can supplement the Qinghai-Tibet Plateau soil health database and provide regional references for soil ecological protection and agricultural optimization at the plateau scale.

The Sukinda mining complex, one of the largest chromite deposits globally, is severely affected by heavy metal (HM) contamination, posing acute ecological risks. This study integrates environmental pollution assessment with multivariate analysis (MVA) to identify native metallophytes for eco-restoration of mining-impacted landscapes. From 2023 to 2024, soil and associated vegetation were sampled from contaminated sites, documenting 38 native plant species (36 genera and 21 families). 10 HMs (Cd, Cr, Mg, Pb, Ni, Co, Cu, Fe, Zn and Mn) were quantified by atomic absorption spectrophotometry (AAS). Pollution indices: geoaccumulation index (I_geo), contamination factor (CF), enrichment factor (EF), pollution load index (PLI), and potential ecological risk index (PERI)- indicated Cd, Cr, Pb, Ni, and Cu as priority pollutants. MVA and Hierarchical cluster analysis (HCA) revealed patterns of metal accumulation, inter-metal relationships, and species clustering. Bioaccumulation factor (BAF) for 23 priority taxa (Cr: 9, Pb: 14, Cu: 16, Cd: 1 species) with BAF > 0.5 (accumulators) and > 1 (hyperaccumulators), demonstrating strong adaptability to metal-enriched substrates, and suitability for phytoremediation. The study provides the first documented HM profiles for C. flexuousus, C. martinii, C. zizanioides, C. purpureus, D. montana, S. siamea, J. gossypiifolia, S. glauca, C. umbellata, Anisomeles indica, and V. denticulata, expanding the phytoremediation knowledge base. The integrated framework establishes a pathway from contamination diagnosis to species prioritisation, supporting large-scale eco-restoration. Leveraging native flora offers a cost-effective, sustainable approach to rehabilitate HM-polluted environments, enhance soil health, conserve biodiversity, and inform pilot-scale remediation strategies, including intercropping with priority species for long-term ecosystem conservation.

Coastal wetlands, unique ecosystems situated at the interface of land and coastal waters, are the principal retention pools for environmental microplastics, which can be degraded at higher rates by their anomalous hydrological characteristics. The Ashtamudi coastal wetland, a Ramsar wetland, is increasingly affected by anthropogenic and natural microplastic pollution and nutrient enrichment. To evaluate the present status and spatial heterogeneity in this ecosystem, water quality and microplastic distribution were studied at seven sites. Extensive physicochemical analysis was carried out with key physicochemical parameters such as BOD, potassium, nitrogen, phosphorus, TDS, turbidity, and chloride, and microplastic characterization using FTIR spectroscopy, zeta potential, and UV–visible analysis. Statistical analysis showed significant spatial heterogeneity in water quality and microplastic characteristics. CPCB limit exceedance was found at all the sites for BOD, chloride, and water quality index (WQI), suggesting extreme organic pollution, salinity stress, and overall water quality impairment. High levels of nitrogen (~ 5.5 mg/L) and phosphorus (~ 1.2 mg/L) at all the sites, particularly at Thekkumbhagam, suggest advanced eutrophication, leading to algal blooms, oxygen depletion, and aquatic ecosystem disturbance. Mukkadu and Ashtamudi presented relatively lower values, but all the sites exceeded wetland nutrient thresholds. FTIR detected microplastic polymers like polypropylene (PP), polyethene (PE), polyvinyl chloride (PVC), and polyethene terephthalate (PET), with differences in particle size and distribution. Besides identification, microplastics are carriers of toxic pollutants, alter aquatic food chains, and produce ingestion-related health risks to organisms. The current study points out the interlinked consequences of nutrient enrichment, salinity, ecological concerns and microplastic dispersal, suggesting the ecological vulnerability of the Ashtamudi wetland. The study outlines the future scope and possible solutions for the mitigation of microplastic pollution in wetlands. These implications need immediate pollution control and conservation measures to ensure the long-term environmental sustainability of this critical wetland ecosystem.

Graphical Abstract

Honey bees (Apis mellifera L.) have been established as environmental monitors to assess the aerosol contamination of the environment in the vicinity of beehives. During their wide-ranging foraging trips, these hymenopterans catch particles in flight and while collecting nectar, pollen, or water. This study demonstrates that honeybees can be employed not only to detect major particulate matter emitters, such as mines or industrial plants, but also smaller, localized aerosol sources, such as shooting ranges. During background monitoring of particulate matter pollution, worker bees were collected from hives in rural areas of the canton and the city of Fribourg (Switzerland). The head, wings, and hind legs of the bees were investigated with Scanning Electron Microscopy coupled with X-ray spectroscopy (SEM–EDX). The analyzed particles reflect the vigorous dairy farming activity in the region (home of Gruyère cheese-making), but particles from one beehive were very exotic and typical of gunshot residues. Indeed, a shooting range was within the foraging range of the corresponding beehive. Bees, therefore, could be an ideal tool not only for monitoring significant aerosol sources but also for monitoring small ones.

Accurately classifying forest successional stages remains a major challenge in applied ecology due to the continuum of succession, ecological heterogeneity, and limited interpretability of many machine learning (ML) approaches. Prevailing models typically rely on black-box algorithms that, while accurate, often lack ecological transparency, limiting their practical use in restoration and regulatory contexts. Here, we introduce and evaluate an ecology-informed symbolic machine learning (EISy-ML) framework that integrates symbolic regression with adaptive ecological constraints, specifically monotonic biomass trajectories and structural complexity proxies, derived from allometric functions. Using field data from 467 plots in the Subtropical Atlantic Forest, Brazil, EISy-ML generated interpretable and biologically plausible equations for successional classification. Performance was benchmarked against eight standard ML classifiers using balanced accuracy, macro F1, Cohen’s kappa, and Matthews correlation coefficient. EISy-ML achieved the highest test accuracy (0.899), F1 (0.905), Kappa (0.829), and MCC (0.803), with no statistically significant difference compared to the next best models. The symbolic framework offers substantial improvements in transparency, reproducibility, and ecological coherence over conventional approaches, enabling direct application in restoration monitoring and environmental auditing. These results validate the hypothesis that symbolic ML integrated with ecological constraints produces models that are both robust and operationally interpretable. Future research should extend EISy-ML validation to other biomes, incorporate temporal and functional trait data, and explore uncertainty-aware or fuzzy logic extensions for handling transitional successional states.