Artificial neural network an innovative approach in air pollutant prediction for environmental applications: A review

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102305 - 102305

Published: May 22, 2024

Air pollution in the environment is growing daily as a result of urbanization and population growth, which causes numerous health issues. Information about air quality environmental risks provided by pollutant data crucial for management. The use artificial neural network (ANN) approaches predicting pollutants reviewed this research. These methods are based on several forecast intervals, including hourly, daily, monthly ones. This study shows that ANN techniques contaminants more precisely than traditional methods. It has been discovered input parameters architecture-type algorithms used affect accuracy prediction models. therefore accurate reliable other empirical models because they can handle wide range meteorological parameters. Finally, research gap networks identified. review may inspire researchers to certain extent promote development intelligence prediction.

Language: Английский

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High-Resolution Estimation of Daily PM2.5 Levels in the Contiguous US Using Bi-LSTM with Attention

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 126 - 126

Published: Jan. 2, 2025

Estimating surface-level PM2.5 concentrations at any given location is crucial for public health monitoring and cohort studies. Existing models datasets this purpose have limited precision, especially on high-concentration days. Additionally, due to the lack of open-source code, generating estimates other areas time periods remains cumbersome. We developed a novel deep learning-based model that improves concentration by capitalizing temporal dynamics air quality. Specifically, we improve estimation precision developing Long Short-Term Memory (LSTM) network with Attention integrating multiple data sources, including in situ measurements, remotely sensed data, wildfire smoke density observations, which model’s ability capture events. rigorously evaluate against existing products, demonstrating 2.2% improvement overall RMSE, 9.8% reduction RMSE days, highlighting superior performance our approach, particularly Using model, produced comprehensive dataset from 2005 2021 contiguous United States are releasing an framework ensure reproducibility facilitate further adaptation quality

Language: Английский

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Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China

Atmosphere, Journal Year: 2024, Volume and Issue: 15(12), P. 1432 - 1432

Published: Nov. 28, 2024

Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, data in Dezhou City China are collected from January 2014 to December 2023, multiple deep learning models used forecast PM2.5 concentrations. The ability of the is evaluated compared with observed using various statistical parameters. Although all eight can accomplish forecasting assignments, precision accuracy CNN-GRU-LSTM method 34.28% higher than that ANN method. result shows has best performance other seven models, achieving an R (correlation coefficient) 0.9686 RMSE (root mean square error) 4.6491 μg/m3. values CNN, GRU LSTM 57.00%, 35.98% 32.78% method, respectively. results reveal predictor remarkably improves performances benchmark overall forecasting. This research provides a new perspective for predictive ambient model provide scientific basis prevention control.

Language: Английский

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Response of Microbial Communities to Antiviral Drug Stress in Surface Water in Beijing

Water, Journal Year: 2025, Volume and Issue: 17(1), P. 118 - 118

Published: Jan. 4, 2025

The environmental persistence of antiviral drugs poses serious safety hazards to aquatic ecosystems through their selective pressure on microorganisms, yet the understanding drugs’ impact microbial community structures remains limited. In this study, surface water samples from Beijing were analyzed for drug concentrations using UPLC-MS/MS, and abundance was assessed via 16S amplicon sequencing. Employing these methods, we investigated mechanisms which may exert ecological risks communities. Our findings reveal that significantly increase Enhydrobacter Nitrospira microbiota. concentration DNA polymerase inhibitor is positively correlated with Peredibacter, Enterococcus, Aeromonas, Aquabacterium, Alloprevotella, Ruminococcus. Antiviral also found reduce digestive system-related functions in organismal systems, while promoting processes associated carbohydrate metabolism influencing metabolic activity bacterial Co-occurrence network analysis showed disrupt original key communities, Bdellovibrio Candidatus omnitrophus emerging as new indicating rare communities can play an important role maintaining system stability. Total phosphorus (TP) dissolved oxygen (DO) identified factors shifts. underscore potential contribution wide-scale usage bacteria, yielding novel perspectives sustainable management urban riverine environments.

Language: Английский

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Short-Sequence Machine Learning Framework for Predicting Constitutive Relationships of Sand

Geotechnical and Geological Engineering, Journal Year: 2025, Volume and Issue: 43(2)

Published: Jan. 11, 2025

Language: Английский

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Synergistic reduction of air pollutants and carbon emissions in Chengdu-Chongqing urban agglomeration, China: Spatial-temporal characteristics, regional differences, and dynamic evolution

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144929 - 144929

Published: Feb. 1, 2025

Language: Английский

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

Atmosphere, Journal Year: 2025, Volume and Issue: 16(3), P. 292 - 292

Published: Feb. 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.

Language: Английский

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Decoding PM2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM

Algorithms, Journal Year: 2025, Volume and Issue: 18(3), P. 167 - 167

Published: March 14, 2025

With the rapid development of industrialization and urbanization, air pollution is becoming increasingly serious. Accurate prediction PM2.5 concentration great significance to environmental protection public health. Our study takes Nanning urban area, which has unique geographical, climatic source characteristics, as object. Based on dual-time resolution raster data China High-resolution High-quality Dataset (CHAP) from 2012 2023, carried out using SARIMA, Prophet LightGBM models. The systematically compares performance each model spatial temporal dimensions indicators such mean square error (MSE), absolute (MAE) coefficient determination (R2). results show that a strong ability mine complex nonlinear relationships, but its stability poor. obvious advantages in dealing with seasonality trend time series, it lacks adaptability changes. SARIMA based series theory performs well some scenarios, limitations non-stationary heterogeneity. research provides multi-dimensional reference for subsequent predictions, helps researchers select models reasonably according different scenarios needs, new ideas analyzing change patterns, promotes related field science.

Language: Английский

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Dynamic prediction of PM2.5 concertation in China using experience replay with multi-period memory buffers

Atmospheric Research, Journal Year: 2025, Volume and Issue: unknown, P. 108063 - 108063

Published: March 1, 2025

Language: Английский

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Toward causal artificial intelligence approach for PM2.5 interpretation: A discovery of structural causal models

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103115 - 103115

Published: March 1, 2025

Language: Английский

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