Environmental Monitoring and Assessment最新文献

This study concentrated on the Qiantang River in Zhejiang Province, undertaking a comprehensive year-long investigation across 40 sites to quantify conventional pollutants, heavy metals, poly- and perfluoroalkyl substances (PFASs), neonicotinoid insecticides (NNIs), and organophosphate esters (OPEs), while also evaluating plankton diversity. A thorough water safety assessment was subsequently developed. The findings revealed significant pollution levels, with total nitrogen identified as the predominant conventional pollutant, alongside elevated concentrations of Fe and Mn in certain areas. The concentrations of ∑PFASs ranged from 31.2 to 1390 ng/L, ∑NNIs from 10.6 to 115 ng/L, and ∑OPEs from 1.15 to 35.9 ng/L. The ecological risk assessment highlighted moderate to high risks associated with perfluorooctanoic acid, perfluorooctanesulfonic acid, and clothianidin, whereas other emerging pollutants were deemed to pose low risks. Zooplankton analysis identified 109 genera/species, exhibiting notable spatial variations, while the phytoplankton community demonstrated stability. The water safety assessment of the Qiantang River indicated a moderately healthy state with regional disparities: the upstream areas were classified as moderately healthy, the middle reaches as stable, and the downstream regions were under pressure from industrialization and urbanization. Biodiversity and water quality enhance water safety, but emerging pollutants have significant negative impacts and need better control.

Dust fall enriched with heavy metals poses critical risks to both environmental and human health, particularly in arid and semi-arid regions of the Middle East. This study investigates the distribution and source apportionment of 11 heavy metals (Fe, Mg, Mn, Cu, Zn, Ni, Pb, Cr, As, Cd, Ag) in dust fall samples collected over 1 year from three cities in western Iran: Sanandaj, Khorramabad, and Andimeshk. Using receptor modeling techniques-positive matrix factorization (PMF), UNMIX, and principal component analysis (PCA)-major contributing sources were identified. Furthermore, backward trajectory modeling using the HYSPLIT model was applied to assess transboundary dust transport patterns. The results showed that both crustal sources (characterized by Fe, Mg, and Mn) and anthropogenic sources (represented by As, Ag, Pb, Cd, Zn, Cr, Cu, and Ni) were dominant contributors in the study area. PMF identified five key emission sources, while UNMIX and PCA yielded four principal factors. The HYSPLIT analysis confirmed that a significant portion of dust events originated from southeastern Iraq. The integrated modeling approach demonstrated strong agreement among methods and allowed for refined source attribution. Given the elevated levels of toxic metals such as Pb, Cd, and Cr, this study emphasizes the importance of including health risk assessment in future research. The findings offer valuable insights for local air pollution control and underscore the need for regional collaboration in mitigating transboundary air pollution impacts.

The temporal variation of air pollutants and their dispersion patterns in Dattawadi, Nagpur, India, based on twice a week air quality monitoring with 104 measurements over the study period, with HYSPLIT, a trajectories and backward dispersion modeling. sulfur dioxide (SO₂) and nitrogen dioxide (NO₂) were consistently at lower levels compared to their national standards, with median values being 7 µg/m³ and 23 µg/m³, respectively. Particulate matter (PM₁₀, PM_2.5, and TSPM), on the contrary, reached critical levels and consistently exceeded the maximum acceptable limits. The average PM₁₀ was 90 µg/m³ and often surpassed the 100 µg/m³, with 46 occurrences exceeding that level and reaching a daily maximum of 180 µg/m³. PM_2.5 averaged at 62 µg/m³, which is above its 60 µg/m³ standard, with 45 days having daily average excesses and high values up to 127 µg/m³. The TSPM levels also regularly exceeded 159 µg/m3 and even climbed to 250 µg/m³. The seasonal study indicated that meteorological conditions varied in their effect on pollutant dispersion, long-distance transportation of pollutants originating in the North-Northeast, which resulted in significant accumulation of pollutants and high deposition possibilities (up to 5.4 × 10^-10 mg/m² in post-monsoon). Summer displayed stronger winds and contributed to increased vertical mixing, whereas the monsoon season was more favorable due to wet deposition by the southwesterly and westerly winds, leading to cleaner air. These results highlight the need for a season and problem focused approach to air quality management to minimize particulate pollution and protect the health of the population.

Industrial and domestic waste from the Cikakembang River heavily contributed to the pollution of Citarum River, yet pollution control is hindered by scarce monitoring data. Time-consuming trial-and-error methods often relied to estimate effluent from numerous outfalls. This study applied Bayesian reverse modeling to estimate wastewater discharges from 16 outfalls points under data-scarce conditions. The present study compared the model performance when constrained to hydraulic data versus water quality data. The hydraulic-based approach integrated DREAM with HEC-RAS unsteady flow simulation constrained to the synthetic water stages downstream, while the water quality approach combined DREAM with advection-dispersion equations model within MATLAB constrained to the observed Dissolved Oxygen (DO). Both approaches showed good performance, but differed in efficiency and uncertainty. Several multimodal posteriors were found in both approaches, representing the uncertainty rising from the scarce observation data. Computation wise, the hydraulic-constrained approach required tenfold greater computational time due to heavy load of HEC-RAS simulation, highlighting a trade-off between data availability of obtaining hydraulic data compared to water quality measurements. The findings provide policymakers with a practical tool for efficient resource allocation in data scarce river systems.

The comprehension of soil fertility in mango-supporting ecosystems and factors affecting it is very important, as it significantly influences the production of mango. In this study, soil characteristics were examined across ninety samples collected from eighteen orchards located at the Mango Research Sub-Centre in Rameshwar, Maharashtra. Spatial patterns and nutrient variability were analysed using GPS-based sampling, geostatistical tools, and principal component analysis (PCA). The data indicated that the silt loam soils (20.43-31.38% clay) have extremely acid (pH 4.36) to moderately acid (pH 5.99). In general, the soil samples had very high organic carbon (12.90-33.50 g kg^-1), available nitrogen (191.30-435.90 kg ha^-1), and potassium (145.49-766.53 kg ha^-1), which generally fell within low to medium categories, while phosphorus (21.80-80.79 kg ha^-1) tended to be moderate to high. Exchangeable magnesium (0.50-5.60 cmol⁺ kg^-1) and calcium (1.20-11.10 cmol⁺ kg^-1) were low owing to severe leaching. Among the micronutrient cations, iron, manganese, and copper were above their respective critical limits, whereas zinc (0.21-5.77 mg kg^-1) varied widely (deficient to adequate) across different locations. Variation among soil samples was noticeable for potassium and, to a lesser degree, nitrogen. PCA identified five major components that together accounted for nearly three-quarters of the total variability, with the first two components explaining over 74.9% of total variation. Overall, the non-saline soils of this region had sufficient quantities of available nitrogen, phosphorus, potassium, iron, copper, and zinc, barring small patches for nitrogen and zinc. These results emphasize the heterogeneous distribution of soil nutrients within mango orchards and warrant site-specific nutrient management approaches, which will undoubtedly assist in tailored nutrient regulation for higher productivity of mango in this eco-region.

Microplastic (MPs) pollution in soil has been increasingly reported worldwide; however, data from Thailand remain very scarce, and the issue is largely unexplored. This study addresses that critical knowledge gap by investigating the abundance and characteristics of soil MPs across diverse land use types in Thailand. Topsoil samples were collected from 31 sites representing seven land-use categories: paddy fields, roadside areas, urban parks, forest, university area, sugarcane fields, and cassava fields. MPs concentrations ranged from 83 to 12,100 items/kg of soil, with an average of 3,303 ± 3,749 items/kg. Land use type significantly influenced MPs abundance, with roadside soils showing the highest levels, averaging 7,467 ± 4,020 items/kg, as confirmed by FTIR analysis, and 187.68 mg/g, as determined by pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS). Conversely, cassava fields exhibited the lowest MPs abundance, with 100 ± 45 items/kg and 9.88 mg/kg. Spatial variability in MPs characteristics, including polymer type, shape, size, and color, also closely followed land use patterns. Particles smaller than 0.5 mm were the most dominant size class, while blue and transparent MPs were the most frequently observed colors. Among all soil samples, polyethylene (PE) was the most prevalent polymer (35%), followed by polystyrene (PS) at 32%, as identified by FTIR. Our findings reveal the widespread presence of soil MPs across natural and artificial ecosystems, from urban centers to rural landscapes in Thailand, underscoring the urgent need for improved plastic waste management and greater attention to this emerging environmental threat.

This study experimentally investigates the near-wake dispersion of gaseous pollutants emitted from a light-duty truck operating at two representative speeds (42 and 55 km/h) with diesel and a B50 biodiesel-diesel blend. A 1:20-scale truck model was tested in a controlled wind tunnel to examine the effects of vehicle speed and fuel type on the dispersion of CO, CO₂, NOx, and NO₂. Results show that increasing speed increases vehicle-induced turbulence and accelerates pollutant diffusion, resulting in lower near-field concentrations. At 55 km/h, the diesel-biodiesel blend produces a higher exhaust jet velocity and greater longitudinal dispersion than neat diesel along the vehicle axis, especially in the near-tailpipe region. Utilizing biodiesel reduces CO₂ but increases CO, NOx, and NO₂ emissions due to its higher oxygen content and combustion temperature. These findings demonstrate that fuel composition and vehicle speed jointly determine the vertical and longitudinal dispersion characteristics of exhaust plumes. Beyond the experimental observations, the results provide practical insights for urban air quality monitoring and emission modeling. Specifically, understanding how the biodiesel content of the fuel mixture and vehicle speed alter plume geometry can support optimized placement of roadside sensors and improve the interpretation of remote-sensing data in traffic corridors with mixed biodiesel usage. The presented dataset and analysis thus offer a foundation for refining pollutant dispersion models and developing evidence-based emission management strategies.

Polyhydroxybutyrate (PHB), a biodegradable polyester, exhibits unique advantages in biosynthesis, degradability, and plastic-like physical properties, making its research and application of great significance. In this study, artificially prepared simulated wastewater was used as influent, with inoculated sludge acclimated for salt tolerance from a municipal wastewater treatment plant. Three parallel sequencing batch reactors (SBRs) were operated at carbon-to-nitrogen ratios (C/N) of 1, 3, and 5 to investigate pollutant removal efficiency and its impact on PHB synthesis. Key findings are as follows: (1) The effect of C/N on pollutant removal varied by indicator: a high C/N (5) promoted ammonium nitrogen and total phosphorus (TP) removal but inhibited total organic carbon (TOC) removal due to deteriorated sludge settleability; a moderate C/N (3) showed optimal comprehensive performance in pollutant removal and system stability. (2) Microbial genera acclimated under low C/N conditions exhibited stronger growth capacity, while those under high C/N conditions showed superior PHB synthesis capability. (3) PHB synthesis increased significantly by 64.7% when C/N increased from 1 to 3, but the growth rate slowed to 9.0% when C/N further increased to 5, indicating a threshold effect.

Tropical dry forests (TDFs) provide essential ecosystem services yet are notoriously difficult to map using remote sensing data due to their spectral similarity to open fields, variability in forest regrowth, and factors such as seasonal leaf phenology and landscape fragmentation that hinder their discrimination. These limitations are critical in data-scarce regions where traditional classification methods struggle. This study introduces a novel semi-supervised deep learning (DL) framework for land use and land cover (LULC) change detection, combining synthetic aperture radar (SAR) and optical satellite imagery for TDF change detection. The proposed framework combines unsupervised pseudo‑labeling and a custom Y‑Net architecture to fuse optical and radar imagery, enabling accurate change detection with limited labeled data. The framework achieves state-of-the-art results, with a mean overall accuracy of 95.3% and a mean Intersection over Union (mIoU) of 88.1%, outperforming established models like standard U-Net and PSPNet. Even in scenarios where only 60% of the dataset is labeled, the semi-supervised method maintains accuracy above 90%, demonstrating its robustness in limited-data conditions. The proposed semi-supervised framework is applied to reveal TDF changes in the Cauca River Valley in Antioquia (Colombia) using satellite images between 2017 and 2021. These findings provide a valuable foundation for advancing remote sensing applications in environmental monitoring, conservation planning, and sustainable resource management.

As rapid urbanization poses profound threats to environmental sustainability, the primary objective of this study was to assess the ecological risk by examining the intricate interactions among land-use and structural change indicators, ecological stressors, and anthropogenic pressures within the Kamrup Metropolitan District. A multi-method framework was applied: fuzzy decision-making trial and evaluation laboratory (DEMATEL) to identify key determinants and generate the ecological risk map; total interpretive structural modeling (TISM) to reveal hierarchical and causal pathways among influencing factors; and a hybrid cellular Automata-Markov chain (CA-Markov) model to simulate land use and land cover dynamics and predict future ecological risk under different scenarios. The results showed that fuzzy DEMATEL identified population density, land-use change rate, built-up footprint, and fractional vegetation cover as the dominant drivers of ecological stress, and the TISM hierarchy demonstrated how these drivers trigger a cascading sequence leading to vegetation loss, fragmentation, elevated land surface temperatures, and declining air, water, and plant diversity. Supported by an AUC value of 0.779, the model also pinpointed the clustering of high-risk zones within the urban core, where intense anthropogenic pressure coincides with ecological sensitivity. Scenario projections further showed that ecological vulnerability escalates under an over-expansion pathway, whereas conservation-oriented land management substantially limits future risk. By providing location-specific management recommendations, this research emphasizes the necessity of proactive planning to mitigate ecological deterioration and bolster resilience amid rapid urban growth.