Unveiling air pollution patterns in Yemen: a spatial–temporal functional data analysis

Hael, Mohanned Abduljabbar

doi:10.1007/s11356-023-25790-3

Cited by 7 publications

(2 citation statements)

References 79 publications

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“…In both the fall and summer samples, there is a positive link connecting temperature and PM2.5 concentrations, according to statistical analysis. Nevertheless, the investigation has also revealed a negative correlation in summer data and a positive correlation in fall samples between relative humidity and PM2.5 concentration (Hael, 2023). In addition, several other variables, including geography, temperature, and precipitation, may have a big impact on PM2.5 concentrations.…”

Section: Correlation Evaluation Between Pm25 Particles and Other Factorsmentioning

confidence: 87%

Deep Learning Techniques for Air Quality Prediction: A Focus on PM2.5 and Periodicity

Shankar,

Arasu

2023

Migrat. Lett.

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The rapid increase in traffic, urbanization, and industrial expansion has all contributed to a decrease in air quality, which has a vital impact on both the long-term feasibility of the environment and the health of humans, particularly in industrialized nations. Numerous studies have explored using machine learning for air quality forecasting to reduce pollution. While shallow machine learning architectures offer less accurate forecasts, deep learning, a recent advancement in computational intelligence, has immense potential in predicting air quality. Deep learning frameworks can identify intricate correlations and patterns in data on air quality, resulting in more accurate and dependable predictions. Several aspects, including climatic conditions, emission sources, and geographical characteristics, may be considered by these models, which can help one better understand and anticipate air pollution levels. This research investigates deep learning applications' periodic changes in air quality. Hybrid deep learning methods utilize optimization, data decomposition, and correlation evaluation between PM2.5 particles and other factors to overcome limitations. This study contrasts various deep learning algorithms for forecasts of air quality and demonstrates that hybrid deep learning is more accurate compared to each model alone at predicting future periods of air quality. It proposes future research directions for the future generation of models. The literature summary provides valuable insights for academics seeking future studies in this field.

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Section: Correlation Evaluation Between Pm25 Particles and Other Factorsmentioning

confidence: 87%

Deep Learning Techniques for Air Quality Prediction: A Focus on PM2.5 and Periodicity

Shankar,

Arasu

2023

Migrat. Lett.

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“…These correlation coefficients provide insights into the varying strengths of the relationships among the variables under study. The heatmap derived from the datasets (Hael, 2023) of a real thermal power plant examined in this article reveals that the relationship between O 2 and nitrogen oxides is stronger than that of O 2 and other gases. This means that excess air is supplied to meet the O 2 requirement for the combustion process from the air, and the excess air supplied includes nitrogen along with oxygen.…”

Section: Interrelationships Among Emission Gases and Other Variablesmentioning

confidence: 90%

Data-Driven Modeling for the Prediction of Stack Gas Concentration in a Coal-Fired Power Plant in Türkiye

Mohammadi,

Saloglu,

Dertli

et al. 2024

Water Air Soil Pollut

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In this research, deep learning and machine learning methods were employed to forecast the levels of stack gas concentrations in a coal-fired power plant situated in Türkiye. Real-time data collected from continuous emission monitoring systems (CEMS) serves as the basis for the predictions. The dataset includes measurements of carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx), oxygen (O2), and dust levels, along with temperatures recorded. For this analysis, deep learning methods such as multi-layer perceptron network (MLP) and long short-term memory (LSTM) models were used, while machine learning techniques included light gradient boosted machine (LightGBM) and stochastic gradient descent (SGD) models were applied. The accuracy of the models was determined by analysing their performance using mean absolute error (MAE), root means square error (RMSE), and R-squared values. Based on the results, LightGBM achieved the highest R-squared (0.85) for O2 predictions, highlighting its variance-capturing ability. LSTM excelled in NOx (R-squared 0.87) and SO2 (R-squared 0.85) prediction, while showing the top R-squared (0.67) for CO. Both LSTM and LGBM achieved R-squared values of 0.78 for dust levels, indicating strong variance explanation. Conclusively, our findings highlight LSTM as the most effective approach for stack gas concentration forecasting, closely followed by the good performance of LightGBM. The importance of these results lies in their potential to effectively manage emissions in coal-fired power plants, thereby improving both environmental and operational aspects. Graphical Abstract

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Dynamic clustering of spatial–temporal rainfall and temperature data over multi-sites in Yemen using multivariate functional approach

Hael,

Ma,

Al-Sakkaf

et al. 2024

Stoch Environ Res Risk Assess

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Unveiling air pollution patterns in Yemen: a spatial–temporal functional data analysis

Cited by 7 publications

References 79 publications

Deep Learning Techniques for Air Quality Prediction: A Focus on PM2.5 and Periodicity

Deep Learning Techniques for Air Quality Prediction: A Focus on PM2.5 and Periodicity

Data-Driven Modeling for the Prediction of Stack Gas Concentration in a Coal-Fired Power Plant in Türkiye

Dynamic clustering of spatial–temporal rainfall and temperature data over multi-sites in Yemen using multivariate functional approach

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