Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11356-023-30859-0.pdf
Reference87 articles.
1. Al-Ghussain L (2019) Global warming: review on driving forces and mitigation. Environ Prog Sustainable Energy 38(1):13–21. https://doi.org/10.1002/ep.13041
2. Alimissis A, Philippopoulos K, Tzanis CG, Deligiorgi D (2018) Spatial estimation of urban air pollution with the use of artificial neural network models. Atmos Environ 191:205–213. https://doi.org/10.1016/j.atmosenv.2018.07.058
3. Almetwally AA, Bin-Jumah M, Allam AA (2020) Ambient air pollution and its influence on human health and welfare: an overview. Environ Sci Pollut Res 27(20):24815–24830. https://doi.org/10.1007/s11356-020-09042-2
4. Awan FM, Minerva R, Crespi N (2020) Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks. Sensors 20(13):3749. https://doi.org/10.3390/s20133749
5. Baldasano JM (2020) COVID-19 lockdown effects on air quality by NO2 in the cities of Barcelona and Madrid (Spain). Sci Total Environ 741:140353. https://doi.org/10.1016/j.scitotenv.2020.140353
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