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A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications.
Environ Monit Assess
November 2024
LETSMP, Department of Physics, Faculty of Science, Ibn Zohr University, Agadir, Morocco.
Article Synopsis
- Advancements in computing power and storage have led to improved data analysis in air quality, enhancing public health protection, although accurate pollutant predictions are still needed.
- This study proposes a new hybrid deep learning model, EFS-GA-LSTM, for predicting multistep hourly PM concentrations using historical data and optimized through modified genetic algorithms and feature selection techniques.
- The EFS-GA-LSTM model outperforms other optimization methods in forecasting tasks, showing better accuracy metrics like root mean square error and correlation coefficient for 3-hour predictions.
Source variations of atmospheric particles in response to control measures in northern China during 2022 winter olympics and paralympics: Evidence from microscopic analysis.
Environ Int
December 2024
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China. Electronic address:
Article Synopsis
- The physicochemical characteristics of atmospheric particles are linked to their sources and formation, but past studies were limited in scope.
- This research used computer-controlled scanning electron microscopy (CCSEM) to efficiently analyze air quality across 15 cities in northern China before, during, and after the 2022 Winter Olympic and Paralympic Games.
- Results indicated improved air quality during the Olympics, shown by reduced particle numbers and types, while conditions worsened afterward, highlighting the effectiveness of control measures in different cities.
Quantification of Airborne Particulate Matter and Trace Element Deposition on and Leaves.
Plants (Basel)
September 2024
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Agencia de Investigación Cientifica, Santa Rosa PC6300, Argentina.
In both developed and developing countries, atmospheric pollution with particulate matter (PM) remains an important issue. Despite the health effects of poor air quality, studies on air pollution are often limited by the high costs of continuous monitoring and the need for extensive sampling. Furthermore, these particles are often enriched with potentially toxic trace elements and organic pollutants.
View Article and Find Full Text PDFA new attention-based CNN_GRU model for spatial-temporal PM prediction.
Environ Sci Pollut Res Int
August 2024
Department of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran.
Accurately predicting the spatial-temporal distribution of PM is challenging due to missing data and selecting an appropriate modeling method. Effective imputation of missing data must consider the relationships between variables while preserving their inherent variability and uncertainty. In this study, we employed machine learning techniques to impute missing data by analyzing the relationships between meteorological variables and other pollutants.
View Article and Find Full Text PDFExploring emission spatiotemporal pattern and potential reduction capacity in China's aviation sector: Flight trajectory optimization perspective.
Sci Total Environ
November 2024
Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA; Institute for a Sustainable Environment, Clarkson University, Potsdam, NY 13699, USA.
China's rapid expansion of civil aviation has led to an increase in pollution-related issues, causing adverse health effects on populations near airports and downwind. Accurately quantifying aviation emissions is essential for effective emission management. Here, we developed a high-resolution aviation emissions inventory for China by employing a bottom-up approach that relied on daily flight schedules.
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