Effects of pristine and photoaged tire wear particles and their leachable additives on key nitrogen removal processes and nitrous oxide accumulation in estuarine sediments

Journal of Hazardous Materials, Год журнала: 2025, Номер 487, С. 137136 - 137136

Опубликована: Янв. 6, 2025

Язык: Английский

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Phytomonitoring of air pollution around brick kilns in urban area: Exploring the potential of plants for the remediation of pollutants

Chemosphere, Год журнала: 2024, Номер 368, С. 143721 - 143721

Опубликована: Ноя. 1, 2024

Язык: Английский

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Distribution, removal and potential factors affecting antibiotics occurrence in leachate from municipal solid waste incineration plants in China

Water Research, Год журнала: 2025, Номер 275, С. 123187 - 123187

Опубликована: Янв. 23, 2025

Язык: Английский

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Spatial-Temporal Variations of Inorganic Contaminants and Associated Risks for Sediment of Felent Stream Basin Flowing Along with Silver Mines in the Midwestern Türkiye

Soil and Sediment Contamination An International Journal, Год журнала: 2025, Номер unknown, С. 1 - 18

Опубликована: Фев. 11, 2025

Язык: Английский

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Exploring the synergistic effect of NaOH/NaClO absorbent in a novel wet FGD scrubber to control SOx/NOx emissions

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(2)

Опубликована: Янв. 14, 2025

Язык: Английский

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The influence of the ultrasonic field on the efficiency of leachate treatment in the SBR reactor

Desalination and Water Treatment, Год журнала: 2025, Номер unknown, С. 101025 - 101025

Опубликована: Янв. 1, 2025

Язык: Английский

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A hybrid deep learning air pollution prediction approach based on neighborhood selection and spatio-temporal attention

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 29, 2025

Air pollution is a critical global environmental issue, further exacerbated by rapid industrialization and urbanization. Accurate prediction of air pollutant concentrations essential for effective prevention control measures. The complex nature data influenced fluctuating meteorological conditions, diverse sources, propagation processes, underscores the crucial importance spatial temporal feature extraction accurately predicting concentrations. To address challenges redundancy diminished long-term accuracy observed in previous studies, this paper presents an innovative approach to predict leveraging advanced analysis deep learning methods. proposed approach, termed KSC-ConvLSTM, integrates k-nearest neighbors (KNN) algorithm, spatio-temporal attention (STA) mechanism, residual block, convolutional long short-term memory (ConvLSTM) neural network. KNN algorithm adaptively selects highly correlated neighboring domains, while enhanced with STA extracts features from input data. ConvLSTM processes output STA-ConvNet capture high-dimensional features. effectiveness KSC-ConvLSTM was validated through predictions PM2.5 Beijing its surrounding urban agglomeration. experimental results indicate that outperforms benchmark approaches single-step, multi-step, trend prediction. It demonstrates superior fitting predictive performance. Quantitatively, reduces root mean square error (RMSE) 4.216–8.458 averages 1–12 h Beijing, compared approach. findings show shows considerable potential predicting, preventing, controlling pollution.

Язык: Английский

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Advancements in Hybrid and Combined Biological Technologies for Treating Polluted Gases: A Comprehensive Review

Water Air & Soil Pollution, Год журнала: 2025, Номер 236(3)

Опубликована: Янв. 30, 2025

Язык: Английский

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A systematic review of potentially toxic elements (PTEs) in river sediments from China: evaluation of associated non-dietary health risks

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(3)

Опубликована: Фев. 11, 2025

Язык: Английский

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Which Pollutants Should Be Prioritized for Control in Multipollutant Complex Contaminated Groundwater of Chemical Industrial Parks?

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

Increasing chemical pollutants in groundwater within industrial parks pose a critical environmental challenge, necessitating innovative strategies to address contaminants with the highest risks health and ensure sustainable management. Herein, we investigated 277 from 367 sampling points across 10 rounds, totaling 1,016,590 measured data points. An prioritization index (EHPI) was proposed applied integrate multiple criteria: occurrence, migration, persistence, bioaccumulation, acute chronic toxicity, effects rank target for priority control. Thirty were classified as top-priority group 81 high-priority, metals, polycyclic aromatic hydrocarbons, haloalkanes ranking highest, while emerging of concern ranked lower. The top 6 beryllium, benzo(g,h,i)perylene, nickel, benzo(a)pyrene, chrysene, arsenic. EHPI method compared against five other weighting schemes, including AHP (analytic hierarchy process), entropy, AHP-entropy, AHP-TOPSIS (technique order preference by similarity ideal solution), entropy-TOPSIS. effectively captured integrated results more simplistic schemes. Overall, 38 are recommended inclusion control list, focusing on high detection exceedance categories. This framework provides guidance focused monitoring, assessment, highest-risk pollutants, supporting effective human protection.

Язык: Английский

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