A systematic literature review of deep learning neural network for time series air quality forecasting
Rapid progress of industrial development, urbanization and traffic has caused air quality
reduction that negatively affects human health and environmental sustainability, especially …
reduction that negatively affects human health and environmental sustainability, especially …
[HTML][HTML] Towards federated learning and multi-access edge computing for air quality monitoring: literature review and assessment
Systems for monitoring air quality are essential for reducing the negative consequences of
air pollution, but creating real-time systems encounters several challenges. The accuracy …
air pollution, but creating real-time systems encounters several challenges. The accuracy …
[HTML][HTML] Short-term prediction of particulate matter (PM10 and PM2. 5) in Seoul, South Korea using tree-based machine learning algorithms
BY Kim, YK Lim, JW Cha - Atmospheric Pollution Research, 2022 - Elsevier
In this study, highly accurate particulate matter (PM 10 and PM 2.5) predictions were
obtained using meteorological prediction data from the local data assimilation and …
obtained using meteorological prediction data from the local data assimilation and …
Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter
B Sadeghi, M Ghahremanloo, S Mousavinezhad… - Environmental …, 2022 - Elsevier
From hourly ozone observations obtained from three regions⸻ Houston, Dallas, and West
Texas⸻ we investigated the contributions of meteorology to changes in surface daily …
Texas⸻ we investigated the contributions of meteorology to changes in surface daily …
Improving the accuracy of O3 prediction from a chemical transport model with a random forest model in the Yangtze River Delta region, China
K Xiong, X Xie, J Mao, K Wang, L Huang, J Li… - Environmental Pollution, 2023 - Elsevier
Due to inherent errors in the chemical transport models, inaccuracies in the input data, and
simplified chemical mechanisms, ozone (O 3) predictions are often biased from …
simplified chemical mechanisms, ozone (O 3) predictions are often biased from …
[HTML][HTML] Spatiotemporal air pollution forecasting in houston-TX: a case study for ozone using deep graph neural networks
The presence of pollutants in our atmosphere has become one of humanity's greatest
challenges. These pollutants, produced primarily by burning fossil fuels, are detrimental to …
challenges. These pollutants, produced primarily by burning fossil fuels, are detrimental to …
Deep learning based emulator for simulating CMAQ surface NO2 levels over the CONUS
This study details the development and evaluation of an emulator model of the Community
Multiscale Air Quality (CMAQ) model, utilizing a U-Net deep learning architecture to …
Multiscale Air Quality (CMAQ) model, utilizing a U-Net deep learning architecture to …
Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast
Forecasting ambient PM2. 5 concentrations with spatiotemporal coverage is key to alerting
decision makers of pollution episodes and preventing detrimental public exposure …
decision makers of pollution episodes and preventing detrimental public exposure …
Spatiotemporal heterogeneity of the relationships between PM2. 5 concentrations and their drivers in China's coastal ports
Y Zhang, Y Yang, J Chen, M Shi - Journal of Environmental Management, 2023 - Elsevier
PM 2.5 is one of the primary air pollutants that affect air quality and threat human health in
the port areas. To prevent and control air pollution, it is essential to understand the …
the port areas. To prevent and control air pollution, it is essential to understand the …
[HTML][HTML] Ozone response modeling to NOx and VOC emissions: Examining machine learning models
Current machine learning (ML) applications in atmospheric science focus on forecasting and
bias correction for numerical modeling estimations, but few studies examined the nonlinear …
bias correction for numerical modeling estimations, but few studies examined the nonlinear …