Environmental Monitoring and Assessment最新文献

As urbanization accelerates, the land use/land cover (LULC) changes significantly impact land surface temperature (LST) and urban heat island effect (UHI). Urban green spaces play a crucial role in regulating the urban thermal environment, typically characterized by the normalized difference vegetation index (NDVI). This study utilizes remote sensing data from Shanghai spanning 2000–2024, combined with the CatBoost model and SHAP method to characterize the nonlinear marginal effects of NDVI on LST and its spatial heterogeneity across different LULC types. The research findings reveal that the regulatory effect of NDVI on LST exhibits significant variations across different land types, demonstrating nonlinear and threshold characteristics. Built-up land types show the strongest cooling sensitivity within the NDVI range of 0.15–0.35, while vegetation land types exhibit saturated regulatory effects with diminishing marginal returns. Water bodies maintain stable negative regulation characteristics, showing insensitivity to NDVI changes. Other land types demonstrate higher uncertainty. Additionally, this study simulates two scenarios to predict LST changes under different LULC–NDVI combinations. The simulation results further validate the significant benefits of enhancing urban green space in built-up areas for mitigating the urban heat island effect, emphasizing that future green infrastructure planning should focus on areas with low green coverage while optimizing the spatial structure of high-vegetation areas. This study provides quantitative evidence to support the achievement of SDG 13 climate action goals and offers guidance for urban green planning and climate adaptation policies.

The integration of metro systems into urban transport networks, alongside persistent road traffic, has amplified concerns about cumulative noise pollution in metropolitan environments. This study presents a novel statistical analysis of environmental noise at metro stations (MSs) and traffic curb areas (TCAs), aiming to decode distinct acoustic signatures. Through 1/3-octave band analysis, metro operations were found to elevate ambient noise levels by 1.5–4.4 dB(A), with a dominant spectral peak consistently emerging at 800 Hz a unique identifier of metro-induced noise. Road traffic exhibited L_eq values between 75.4 and 82.0 dB(A) and background levels (L₉₀) of 70.5–78.0 dB(A), with honking peaks concentrated between 400 and 3150 Hz and idling engine noise spanning 400–1600 Hz. A predictive noise model was developed using multivariate regression, incorporating key acoustic contributors such as honking frequency, heavy-vehicle percentage, and road typology. The model demonstrated robust performance (adjusted R² = 0.81), offering a reliable tool for forecasting urban noise dynamics. This integrative approach, combining real-time spectral profiling with predictive analytics, enables precise identification of high-risk acoustic zones and informs strategic mitigation planning. The study underscores the critical need for frequency-resolved noise monitoring in rapidly urbanizing corridors and advocates for evidence-based acoustic interventions. By decoding the spectral footprints of metro and traffic noise, this research advances urban noise management frameworks toward smarter, more sustainable city soundscapes.

Air pollution has long posed a significant environmental challenge in the North American region, including both the USA and Mexico. Among its impacts, acid rain, which is characterized by a pH lower than 5.6, negatively affects ecosystems and biodiversity. This study evaluates and compares the physicochemical characteristics of precipitation in the urban areas of Denver, New York City, Los Angeles, and the Mexico City Metropolitan Area (MCMA) during the period 2003–2019. Long-term analysis revealed a substantial decline in sulfate concentrations in New York City after 2009, accompanied by a gradual rise in precipitation pH. In contrast, sulfate levels in the MCMA remained elevated and variable, while Los Angeles exhibited persistently acidic precipitation despite relatively low sulfate concentrations. Nitrate trends were weak or irregular across cities, and ammonium increased only in New York City. Results indicated the presence of acid rain in New York City, Los Angeles, and the MCMA, but not in Denver, where precipitation in 2017 did not exhibit acidic pH values. The highest concentrations of SO₄²⁻ and NO₃⁻ in precipitation were observed in the MCMA, likely due to SO₂ emissions from the Tula–Vito–Apasco industrial corridor and high NO_x emissions from mobile sources. The SO₄²⁻/NO₃⁻ ratio was used to determine the predominant ion influencing precipitation acidity. SO₄²⁻ was dominant in New York City and the MCMA, whereas NO₃⁻ was more prevalent in Los Angeles and Denver. Strategies implemented in the USA, such as the transition to cleaner fuels, policies for monitoring emission sources, and the expansion of atmospheric deposition networks, could inform efforts to reduce emissions of acid rain precursors in the MCMA.

The untreated industrial waste and sewage have been severely polluting the Buriganga River. The primary sources of these heavy metals are industries including metallurgy, tanneries, electroplating and battery recycling plants. Thus, the aquatic ecosystem and health of the people who depend on this river are under threat due to this pollution. Despite some mitigation efforts, the Buriganga River remains severely contaminated. As a result, continuous monitoring, identification of sources and the implementation of mitigation strategies are required. In this study, surface sediment was collected from 12 sampling sites from both banks of the Buriganga River. The concentrations of six metals including Cr, Cd, Pb, Cu, Co and Ni were determined using an atomic absorption spectrophotometer to evaluate the level of contamination and potential ecological risks in the riverine system. To evaluate the pollution status, several contamination indices, such as contamination factor (C_f), pollution load index (PLI), and ecological risk index (R_I), were utilized. Results indicated severe contamination by Cd, evidenced by its high C_f value (3.916), while Pb, Cu, Co and Ni exhibited moderate contamination levels. Cr exhibited minimal contamination due to the relocation of tanning units from Hazaribagh to Savar. The presence of battery, metal-plating and metallurgy industries at site 6A (Babu Bazar) is responsible for the high PLI value. Elevated PLI values at the sampling sites indicate widespread pollution. A R_I value of 151.888 indicates a moderate ecological risk, suggesting potential adverse effects on riverine ecosystems and public health. The findings underscore the need for regulatory authorities to take necessary actions to prevent direct discharge of industrial wastes and untreated sewage. Continuous monitoring of heavy metal contamination, investigation of its bioaccumulation in aquatic organisms and the development of innovative remediation methods to restore the ecological health of the riverine system should be the primary topics of future research.

This study examines the diversity and characteristics of microplastic (MP) contamination in bivalve mollusks collected from the Tam Giang Lagoon, Vietnam—one of Southeast Asia’s largest lagoon systems. A total of 52 specimens representing three species—mussels (Sinanodonta woodiana), green mussels (Perna viridis), and clams (Corbicula subsulcata)—were analyzed, yielding 457 MP particles. Clams contributed the highest proportion (50.55%), followed by green mussels (33.48%) and mussels (15.97%). The average MP density was 3.17 MP particles/g in green mussels, 3.04 MP particles/g in clams, and 0.42 MP particles/g in mussels. Conversely, when normalized per individual, mussels exhibited the highest accumulation (12.17 MP particles/individual), followed by green mussels (10.20 MP particles/individual) and clams (7.45 MP particles/individual), showing a positive correlation between MP density and organism size. Fibers dominated all samples (64.07–84.31%), while fragments were less frequent and pellets were absent. Most MPs were smaller than 0.5 mm, and black was the predominant color in all species. These findings highlight distinct species-specific accumulation patterns likely driven by habitat characteristics, filtration capacity, and feeding behavior. Overall, this research provides essential baseline data on microplastic pollution in the Tam Giang Lagoon and underscores the ecological significance of bivalves as bioindicators of sediment-associated MP contamination in coastal ecosystems.

This study delineates the variability in phytoplankton and microzooplankton community composition and abundance during and after the occurrence of harmful Prorocentrum rhathymum (dinoflagellate) bloom encountered in the coastal waters of Alappuzha, southwest coast of India. During bloom phase (August), prolific growth of P. rhathymum was observed in the nearshore stations, especially in S2 (3200 × 10³ cells/L) with high Chl a (79.43 mg m⁻³), which subsequently declined during non-bloom phase (September). The study area exhibited marked variations in phytoplankton and microzooplankton community composition between the bloom and non-bloom phases, characterized by the transition from phytoflagellates to diatoms and from loricates to aloricates, particularly at the nearshore stations. Our findings suggest that the nutrient enrichment, specifically from enhanced levels of ammonium and phosphate due to coastal upwelling, favoured the bloom. Furthermore, the subsequent decline of the bloom was influenced by the top-down control exerted by microzooplankton, especially loricates, in conjunction with pronounced changes in environmental variables.

Explosive contamination in soil from mining, demolition, and defense activities poses serious threats to both environmental and human health. While remediation methods have been evaluated at the laboratory scale, their performance in the field remains a significant concern. This review consolidates field-tested remediation studies and highlights knowledge gaps, emphasizing the need for regulatory alignment and research focused on scalable, low-impact solutions. Statistical analysis via the Kruskal‒Wallis test (p > 0.05) revealed no significant differences in efficiency, cost, or treatment duration across physical, chemical, and biological technologies, indicating that method selection should be site specific. The Analytical Hierarchy Process (AHP) identified remediation efficiency (45% weight) and cost (12% weight) as the most influential decision factors. Among the biological methods, biopiles achieved the highest efficiency (99.7%) with moderate environmental risk, whereas phytoremediation emerged as the most cost-effective option ($50/tonne) despite its lower efficiency (70%). Zero-valent iron (ZVI) performed best among chemical options, achieving over 96% removal at a reasonable cost. Chemical oxidation, while effective (95%), has raised concerns due to excessive cost and additional chemical requirements. In terms of physical methods, soil washing has moderate efficiency and cost, whereas incineration has the lowest efficiency due to excessive cost and environmental burden. Correlation analysis revealed a strong negative correlation between cost and treatment duration and a positive correlation between cost and efficiency. Integrated approaches that combine biostimulation and composting provide both effectiveness and sustainability. Factors such as contaminant type, soil properties, bioavailability, and climatic conditions significantly influence field-scale performance. Nature-based methods have shown strong potential for long-term ecological benefits.

Graphical Abstract

As a crucial freshwater resource, groundwater plays an indispensable role in arid and semi-arid regions characterized by low annual precipitation and frequent droughts. Developing computational frameworks for groundwater level prediction is essential to advance sustainable water resource management. This study proposes a hybrid deep learning model (CNN-GRU-Attention) for groundwater depth forecasting, integrating convolutional neural networks (CNN), gated recurrent units (GRU), and attention mechanisms. The methodological framework commenced with a spatiotemporal analysis of groundwater depth dynamics in Zhengzhou, China. Subsequently, multiple machine learning and deep learning algorithms were systematically evaluated to predict groundwater depth using four input variables: monthly evaporation, precipitation, average temperature, and groundwater extraction. These variables were rigorously selected through the Shannon entropy method. Model performance was quantified using three statistical metrics: MAE, RMSE, and R². Results indicate that the CNN-GRU-Attention model demonstrates superior performance in groundwater depth forecasting, achieving MAE values of 0.4–0.5, RMSE values of 0.5–0.6, and R² values of 0.8–0.9. To fully evaluate the performance of the model, we designed two hypothetical scenarios. First, we analyzed changes in the model’s predictive performance under conditions of reduced data, when the data volume is reduced by 10–25%, the CNN-GRU-Attention model still outperforms other models in predictive performance. Second, to maintain stable groundwater depth under drought-induced rainfall reduction conditions, controlled extraction measures should be implemented to balance recharge and withdrawal. Under this special rainfall scenario, a reduction in extraction volume of 42 million m³ is more conducive to maintaining groundwater stability. This model provides an effective predictive framework and offers valuable insights for sustainable groundwater management in arid regions.

This study presents an airborne radiometric assessment of natural gamma radiation across selected areas in Ondo and Osun States, Nigeria, utilising GIS gridded airborne geological map data from Sheets 263 and 264 obtained from the Nigerian Geological Survey Agency. The data, organised in a gridded format, were analysed using Oasis Montaj software to quantify the concentrations of naturally occurring radioelements, ²³⁸U, ²³²Th, and ⁴⁰K, and to estimate the corresponding terrestrial gamma dose rates. Results indicate that, in Sheet 264, concentrations of ²³⁸U, ²³²Th, and ⁴⁰K range from 4.7 to Bq/kg, 20.6 to 172.7 Bq/kg, and 96.6 to 1560.50 Bq/kg, with mean values of 48.2, 81.3, and 609.0 (Bq/kg) respectively. For Sheet 263, ²³⁸U concentrations span 0.1–94.0 Bq/kg, ²³²Th from 5.90 to 162.70 Bq/kg, and ⁴⁰K from 59.50 to 943.00 Bq/kg with the average values of 37.2, 56.9, and 331.70 (Bq/kg) accordingly. The calculated absorbed dose rates in both sheets spanned between 37.8 and 191.6 and 14.3 and 161.8 for Sheet 264 and Sheet 263 with mean values of 100.5 nGy/h and 67.8 nGy/h. The absorbed dose rate for most of the locations in the study area exceeds the global average of 59 nGy/h, with generally higher values observed in Sheet 264. The high values of standard deviation and coefficient of variation, particularly for ⁴⁰K, indicate strong spatial heterogeneity in radioelement distribution, suggesting varying lithological and mineralogical compositions within the study area. The elevated levels of primordial radioelements and associated radiation doses in several locations suggest a potential public health concern due to chronic exposure to ionising radiation. Given the geological context and ongoing anthropogenic activities in the region, routine environmental monitoring is recommended to mitigate potential radiological risks to local populations.

Plastic waste is a major source of microplastic (MP) pollution, posing adverse environmental and public health risks. This study assessed MP abundance in 24 urban ponds and lakes within the Kolkata Municipal Corporation, India, during the post- and pre-monsoon seasons of 2022–2023. Results showed that MP concentrations were significantly higher in the post-monsoon season (20 ± 3.46 items L⁻¹; ANOVA, p < 0.05), with red, white, and black particles being the most prevalent. MPs prevalently ranged between 500 and 1000 µm with fibers constituting 58–59% of the total MPs. Fourier-transform infrared (FTIR) analysis identified polyethylene (PE) as the dominant polymer. MP abundance showed negative correlations with dissolved oxygen and turbidity, and positive associations with pH, TDS, BOD₅, and total coliform counts. A Support Vector Machine (SVM) model was developed for morphology-based MP classification, achieving an accuracy of 89%. Additionally, remote sensing and GIS techniques were used to develop index-based models for water-body identification and MP quantification (items L⁻¹) using spectral data from Sentinel-2 imagery. These models demonstrated high validation accuracy of 98.01% and 92.60%, respectively. Metal analysis of sediment and MPs of water detected chromium, suggesting possible MP–metal interactions within sediments. Although the Pollution Load Index (PLI) indicated relatively low contamination levels, the Polymer Hazard Index (PHI) exceeded 1000, indicating substantial ecological risk. Future studies should focus on long-term monitoring, socio-economic and health impact assessments and development of effective mitigation strategies particularly addressing plastic-waste derived-MPS.

Graphical Abstract