TEMDI: A Temporal Enhanced Multisource Data Integration model for accurate PM2.5 concentration forecasting

Atmospheric Pollution Research, Год журнала: 2024, Номер 15(11), С. 102269 - 102269

Опубликована: Июль 30, 2024

Язык: Английский

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Air pollutant prediction based on a attention mechanism model of the Yangtze River Delta region in frequent heatwaves

Atmospheric Research, Год журнала: 2024, Номер unknown, С. 107701 - 107701

Опубликована: Сен. 1, 2024

Язык: Английский

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An enhanced interval-valued PM2.5 concentration forecasting model with attention-based feature extraction and self-adaptive combination technology

Expert Systems with Applications, Год журнала: 2024, Номер 264, С. 125867 - 125867

Опубликована: Ноя. 29, 2024

Язык: Английский

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Using a Citizen-installed Network of PM2.5 Sensors to Predict Hourly PM2.5 Airborne Concentration

Water Air & Soil Pollution, Год журнала: 2025, Номер 236(2)

Опубликована: Янв. 10, 2025

Язык: Английский

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Tracking of Active Sites as Well as the Compositing Effect over a Cu/Ce-Based Catalyst with Superior Catalytic Activity

JACS Au, Год журнала: 2025, Номер 5(2), С. 975 - 989

Опубликована: Янв. 29, 2025

The replacement of a noble metal catalyst by base metals presents great challenge for low-temperature CO and volatile organic compounds oxidation. Cu/Ce-based catalysts are expected to achieve this goal with excellent performance, among which the main active sites still need be further explored. For reason, CuCe were compounded typical elements (cobalt, Co) study compositing effect in-situ enhanced Raman ultralow-temperature DRIFTS technologies. site both CuCoCe was same Cu–OV–Ce at copper–cerium interface, named as asymmetric oxygen vacancy (ASOv). dispersion CuO CeO2 species promoted, formation energy ASOv decreased significantly from 1.502 0.854 eV after addition Co, leads an increase in concentration. A small cobalt added can form more Co2+ species, improving activity stability. Cu1Co0.5Ce3 improved 100% conversion toluene 96 227 °C. Here, studied relative quantification, showing consistency catalytic Meanwhile, dynamic exchange reactions tracked, indicating that redox equilibrium continuously produce new ASOV cause long-term In addition, it is almost difficult CoCe CoCu samples ASOv, interaction between also weaker than catalysts.

Язык: Английский

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Time-Series Data-Driven PM2.5 Forecasting: From Theoretical Framework to Empirical Analysis

Atmosphere, Год журнала: 2025, Номер 16(3), С. 292 - 292

Опубликована: Фев. 28, 2025

PM2.5 in air pollution poses a significant threat to public health and the ecological environment. There is an urgent need develop accurate prediction models support decision-making reduce risks. This review comprehensively explores progress of concentration prediction, covering bibliometric trends, time series data characteristics, deep learning applications, future development directions. article obtained on 2327 journal articles published from 2014 2024 WOS database. Bibliometric analysis shows that research output growing rapidly, with China United States playing leading role, recent increasingly focusing data-driven methods such as learning. Key sources include ground monitoring, meteorological observations, remote sensing, socioeconomic activity data. Deep (including CNN, RNN, LSTM, Transformer) perform well capturing complex temporal dependencies. With its self-attention mechanism parallel processing capabilities, Transformer particularly outstanding addressing challenges long sequence modeling. Despite these advances, integration, model interpretability, computational cost remain. Emerging technologies meta-learning, graph neural networks, multi-scale modeling offer promising solutions while integrating into real-world applications smart city systems can enhance practical impact. provides informative guide for researchers novices, providing understanding cutting-edge methods, systematic paths. It aims promote robust efficient contribute global management protection efforts.

Язык: Английский

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Multi-granularity PM2.5 concentration long sequence prediction model combined with spatial–temporal graph

Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106400 - 106400

Опубликована: Март 1, 2025

Язык: Английский

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PM2.5 probabilistic forecasting system based on graph generative network with graph U-nets architecture

Journal of Central South University, Год журнала: 2025, Номер 32(1), С. 304 - 318

Опубликована: Янв. 1, 2025

Язык: Английский

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A new hybrid deep neural network for multiple sites PM2.5 forecasting

Journal of Cleaner Production, Год журнала: 2024, Номер 473, С. 143542 - 143542

Опубликована: Сен. 1, 2024

Язык: Английский

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Dynamic synchronous graph transformer network for region-level air-quality forecasting

Neurocomputing, Год журнала: 2024, Номер unknown, С. 128924 - 128924

Опубликована: Ноя. 1, 2024

Язык: Английский

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