Environmental Monitoring and Assessment最新文献

This study developed an integrated pollutant-meteorology-polycyclic aromatic hydrocarbons (PAHs) statistical framework using association rule mining and machine learning to characterize the hourly variability of PAHs bound to fine particulate matter (PM_2.5) in Nagoya, Japan. Association rules identified recurring pollutant-meteorology co-occurrence patterns linked to PAH accumulation, while machine learning models, evaluated via time-blocked cross-validation to avoid leakage from hourly autocorrelation, enabled short-term prediction. Ambient pollutants showed pronounced diurnal patterns, with black carbon and nitrogen oxides peaking during traffic rush hours and photochemical oxidants peaking in the afternoon. Reduced wind speed was the most consistent statistical condition associated with elevated PAH levels, whereas pressure, humidity, and temperature were associated with atmospheric conditions favorable to pollutant accumulation, such as reduced dispersion. Rule structures further revealed shared meteorological patterns and compound-specific association patterns across PAH species. Among the predictive models, eXtreme Gradient Boosting and Random Forest achieved the best performance for high-molecular-weight PAHs (R² = 0.81-0.93, RMSE = 0.02-0.11, MAE = 0.01-0.06), whereas Support Vector Regression and Multilayer Perceptron performed poorly. Pressure, temperature, and humidity dominated feature importance, consistent with the association rule findings. The health-risk assessment showed that benzo[a]pyrene (BaP) dominated carcinogenic toxicity, while estimated incremental lifetime cancer risk values for all population subgroups remained below the U.S. Environmental Protection Agency negligible-risk threshold. Because the identified associations reflect statistical covariation within a limited 10-day observation period, they should be interpreted as site-specific descriptors for local forecasting rather than as evidence of universal atmospheric mechanisms.

Water quality plays a crucial role in human life and ecological environment maintenance. Existing studies have confirmed that land use and landscape patterns exert significant impacts on water quality, with their interactive effects varying markedly across different buffer zones and slope grades. Based on water sampling at 284 sites in the Tumen River basin, the study aimed to identify the optimal buffer zones for water quality under different slopes. The effects of land use and landscape patterns on individual water quality indicators across five slope grades and riparian buffer zones were then revealed via multiple linear regression and random forest-recursive feature elimination models. In the Tumen River basin, the optimal buffer zone is relatively small (50 m) in gentle slope areas, where water quality is mainly affected by landscape patterns. By contrast, steep slope areas tend to require a relatively larger optimal buffer zone (1500 m), in which land use is the primary factor influencing water quality. Dissolved oxygen responds weakly to slope variation and varies significantly within small buffer zones, affected by landscape patterns. Electrical conductivity (EC), total nitrogen (TN), and permanganate index (COD_Mn) are more sensitive to slope changes. EC is primarily controlled by land use, whereas COD_Mn and TN are controlled by landscape patterns. The findings of this study provide a theoretical basis and practical support for the sustainable use of land resources and the improvement of water quality in the Tumen River basin.

Large-scale disturbances can profoundly alter aquatic ecosystems by modifying water chemistry, sediment dynamics, and biodiversity across connected freshwater and marine environments. The 2015 Fundão Dam collapse in Brazil provides an unprecedented context to evaluate how multiple aquatic communities respond to intense and prolonged contamination, revealing key mechanisms of community reorganization under chronic anthropogenic impacts. By a multi-taxa monitoring dataset collected from 62 freshwater and marine sites, we analyzed how different communities, comprising phytoplankton, zooplankton, macroinvertebrates, and fish, responded along a gradient of mining tailing exposure. Applying threshold indicator taxa analysis (TITAN), we identified critical environmental variables, capturing tipping points for positive and negative biotic responses to tailing-related environmental gradients. Our results showed that mining-related contaminants, combined with hydrogeochemical alterations triggered by the dam collapse, have driven long-term changes in biodiversity, reshaping ecosystem functioning. The same stressors that caused widespread negative responses in community structure also promoted localized increases in opportunistic, mainly planktonic taxa, indicating a multidirectional community reorganization. In general, small increases in metals and hydrogeochemical alterations (such as turbidity up to 17 NTU in riverine systems and 2 NTU in the marine sites), resulting from the dam collapse have driven persistent changes in biodiversity, ultimately reshaping ecosystem functioning. Integrating community-level thresholds should represent a critical approach to improve the detection of regime shifts and to support the development of habitat-specific recovery strategies for riverine and marine ecosystems affected by mining disturbances.

In this study, the potential and limitations of iNaturalist for monitoring mosses in Poland (2019-2025) were assessed using 17,451 verified "Research Grade" records. Rarefaction enabled diversity pattern analysis while accounting for sampling effort differences. Observational activity peaked in early-spring (March) and autumn (October). Analysis revealed extreme asymmetry in research effort (G_users = 0.89): the 12 most active users contributed 65.5% of national records, creating a "super-user effect." Rarefaction corrected this bias, identifying the Pomeranian Voivodeship as having the highest standardized diversity (S_rar = 80.9). Citizen science effectively supports specific moss monitoring aspects. Observers documented 151 unique records of the alien moss Campylopus introflexus, representing over 60% of sites recorded during 30 years of traditional floristic research. Despite the "microscope barrier" restricting the database largely to visually identifiable bryophytes (e.g., reducing nearly the entire genus Hypnum to H. cupressiforme; G_taxa = 0.76), iNaturalist serves as a crucial early-warning system. Ultimately, large-scale citizen scientists datasets provide valuable guidance for identifying priority areas for future bryological research.

Commercially important fish from the Sur coastal fishery, Sultanate of Oman, have not previously been subject to comprehensive characterisation for potentially toxic elements (PTEs). The present study quantifies ten PTEs across multiple tissues in five species - pharaoh cuttlefish, Indian mackerel, narrow-barred kingfish, Indian oil sardine and longtail tuna - by inductively coupled plasma spectrometry, across three independent laboratories, one ISO/IEC 17025:2017-accredited (n = 535 determinations). Mercury was below detection in 87.9% of determinations. All detected values in the five focal species complied with EU 2023/915 maximum levels. Cadmium showed a tissue accumulation gradient (liver ≈ kidney >> gill ≈ muscle, liver n = 3; Kruskal-Wallis H = 25.15, p = 1.44 × 10^-5), consistent with metallothionein-mediated sequestration in an upwelling-influenced system. Lead was undetected in commercial muscle, but two goatfish composites exceeded the EU lead maximum level (0.327 against 0.300 mg/kg), attributed to sediment exposure in benthic feeders. Applying the EPA IRIS 2025 oral reference dose for inorganic arsenic (6 × 10^-5 mg/kg-day) and a precautionary 10% inorganic fraction, Hazard Index values ranged from 0.96 (mackerel) to 7.28 (cuttlefish), exceeding 1.0 in four of five species; three still exceeded 1.0 at the 4% fraction recommended by US EPA guidance. All five exceeded the carcinogenic risk threshold (1 × 10^-4) under the revised cancer slope factor (32 per mg/kg-day), robust across the published range of inorganic fractions. These results constitute the first quantitative baseline for the Ash Sharqiyah coast, and designate arsenic speciation as the primary research priority.

This study provides the first comprehensive hydrochemical and health risk assessment of groundwater in Dalbandin, which is an arid, mineral-rich area of Balochistan, Pakistan. Fifty groundwater samples (T1-T50) were collected from inhabited and agricultural zones and analyzed for physicochemical parameters (pH, EC, TDS) and heavy metals (Mn, Cd, Cr, Cu, Pb, Na, Ca, Mg, HCO₃, Cl, SO₄) using atomic absorption spectroscopy and portable meters. Positive Matrix Factorization (PMF) resolved four partially resolved statistical sources: (1) an Mn-rich pattern, (2) a Pb-Cd-Cu-rich pattern, (3) a mixed sulfate-major-ion pattern, and (4) a Na-Cl-Ca-rich salinity pattern. PCA and APCS-MLR gave some support but were not as successful at separating sources as PMF. All heavy metals exceeded WHO drinking water guidelines, with Pb (mean: 0.16 mg/L, 16 × limit), Cd (mean: 0.03 mg/L, 10 × limit), Cu (mean: 4.39 mg/L, 2 × limit), Mn (mean: 0.29 mg/L, 3 × limit), and Cr (mean: 0.16 mg/L, 3 × limit) being of particular concern. All the samples were found to be "Unsafe" (mean 1,115.7) using the Drinking Water Quality Index (DWQI), and 74% of samples were found to be in moderate to high restriction zone using the Irrigation Water Quality Index (IWQI). Modeled risk assessment (total Cr conservatively assumed to be Cr(VI)) showed HI > 1 and TCR > 10⁻⁴ in all samples. The 3.5-fold child/adult difference applied to HI, not TCR. Monte Carlo simulation, 100% of simulated values exceeded the screening thresholds under the assumed inputs; this is a model result and not a probability of disease, with mean values of HI_Adult = 9.74, HI_Child = 34.08, TCR_Adult = 1.12 × 10⁻³, and TCR_Child = 7.86 × 10⁻⁴. These results indicate potentially significant public health risks that warrant confirmatory monitoring and risk-management attention in this geogenically contaminated area. The integrated approach illustrates the benefits of integrating a receptor model with a probabilistic health risk assessment in guiding groundwater management in areas with mineral resources in arid areas.

Quantifying regional background pollution, driven by long-range transport and diffuse emissions from surrounding regions, is crucial for isolating the impact of local sources on air quality. The existing methods to estimate background such as statistical clustering and spectral filtering have the limitation to not account for the spatial resolution that is essential to define background pollution. In this study, we adopted a signal processing method to obtain a spatiotemporal distribution of PM_2.5 background concentrations over India using MERRA2 reanalysis dataset. This method utilizes wavelet transforms to capture the coherence of low-frequency components in time series from neighborhood regions. The derived PM_2.5 background map captures the major seasonal and regional pollution regimes across India, including winter buildup over the Indo-Gangetic Plain (IGP), pre-monsoon dust-driven enhancements in the northwest. The method reproduces expected transitions between polluted and clean periods, yielding a consistent background pattern. A comparison of derived PM_2.5 background with those obtained from the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) at four major Indian cities also showed good agreement with MAD ranging from 2.2 (Kolkata) to 10.8 (Delhi) µg/m³ across cities. Analysis of high- and low-emission grids showed that the method correctly yields higher background contributions at low-emission grids, reflecting dominant regional transport and higher increments at high-emission grids linked to local sources. Finally, evaluation against 5th percentile PCHIP-derived background concentrations shows moderate agreement across the Delhi monitoring network (MAE = 21.95 µg/m³, RMSE = 29.45 µg/m³), improving substantially at the background site in Delhi (MAE = 14.03 µg/m³, RMSE = 17.65 µg/m³), consistent with reduced local-source influence at this location and supporting the reliability of the proposed method.

Coastal mangrove forests can influence the retention and redistribution of sediments and associated contaminants in coastal environments. The primary objective of this work was to examine the distribution and environmental implications of heavy metal contaminants (lead (Pb), copper (Cu), and nickel (Ni)) in mangrove sediments through a concentration analysis, geochemical pollution indices, and elemental profiling through scanning electron microscopy-energy-dispersive spectroscopy (SEM-EDS). Sediment materials were obtained from three locations within a coastal mangrove region. At each station, surface sediments were obtained to represent site-specific conditions. The geoaccumulation index (I_geo), contamination factor (Cf), and pollution load index (PLI) were applied to assess pollution levels. The results indicated that Pb, Cu, and Ni concentrations at all locations were below the sediment quality guideline (SQG) thresholds based on the TEL/PEL and ERL/ERM criteria, indicating a low probability of adverse biological effects according to these selected SQGs, within the scope of the metals analyzed and the sampling period. All I_geo values were negative and classified as class 0 (unpolluted), while Cf values indicated low contamination for Cu and Ni and moderate contamination for Pb at one station. PLI values at all stations were below one, confirming that the sediments were generally unpolluted within the sampled stations and sampling period. SEM-EDS analysis revealed porous and aggregated sediment morphologies with relatively uniform distributions of Pb and Cu. Elemental spectra were dominated by silicate and aluminosilicate minerals, with Pb and Cu occurring only as trace elements. These findings indicate that the sediments currently exhibit low levels of heavy metal contamination and suggesting that the sediment matrix may contribute to the retention of Pb, Cu, and Ni under the conditions investigated. However, this interpretation is limited to the measured total metal concentrations, pollution indices, and SEM-EDS observations from one station and does not directly demonstrate long-term accumulation or an ecosystem-scale sink function.

River systems in uranium-rich areas are key pathways for human exposure to natural radionuclides. However, radiological assessments remain limited in sub-Saharan Africa. This study evaluates ²²⁶Ra, ²³²Th, and ⁴⁰K in the Likuyu River, southern Tanzania, approximately 51.5 km downstream of the Mkuju River Uranium Project. Triplicate water and sediment samples were collected from five sites along 22.7 km during the rainy season and analysed by high-purity germanium gamma spectrometry. Established models were used to compute radiological indices, including annual effective dose (AED), radium equivalent activity (Ra_eq), external and internal hazard indices (Hₑₓ, Hᵢₙ), annual ingestion dose (D_ing), and excess lifetime cancer risk (ELCR). Water showed spatial variation in radionuclide concentrations. Site A had the highest ²²⁶Ra activity concentration (2.71 ± 0.33 Bq L⁻¹). Site C exhibited an elevated ⁴⁰K activity concentration (120.06 ± 1.62 Bq L⁻¹), and the total annual ingestion dose from the measured radionuclides at this site exceeded the World Health Organisation (WHO) total indicative dose criterion of 0.1 mSv year⁻¹. Annual ingestion doses at Sites A (0.743 ± 0.109 mSv year⁻¹) and C (0.623 ± 0.007 mSv year⁻¹) exceeded WHO guidelines. The corresponding ELCRs (28.6 × 10^-4 and 23.97 × 10^-4) also exceeded the ICRP tolerable risk of 1 × 10^-4. Sediments showed localised enrichment at Sites B and D, but radiological indices remained below safety limits (Ra_eq, 150.8-247.4 Bq kg⁻¹; AED, 0.090-0.139 mSv year⁻¹; Hₑₓ and Hᵢₙ < 1). Principal Component Analysis revealed uranium-thorium co-enrichment and potassium-driven inputs, suggesting lithogenic and mineralogical influences. Untreated river water at Sites A and C may pose a significant radiological health risk. In contrast, sediment exposure remains within regulatory thresholds. These findings provide baseline data for ongoing monitoring and support water safety initiatives in uranium-affected agro-mining regions of East Africa.