13 citations as recorded by crossref.

1. A deep learning model incorporating frequency domain information for ultra multi-step air pollutant forecasting: A case study of Shanghai H. Huang et al. 10.1016/j.apr.2024.102247
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4. Advances in amelioration of air pollution using plants and associated microbes: An outlook on phytoremediation and other plant-based technologies A. James et al. 10.1016/j.chemosphere.2024.142182
5. Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al. 10.3390/atmos15050594
6. Assessing the Impact of a Low-Emission Zone on Air Quality Using Machine Learning Algorithms in a Business-As-Usual Scenario M. Doval-Miñarro et al. 10.3390/su17083582
7. A neural operator for forecasting carbon monoxide evolution in cities S. Bedi et al. 10.1038/s44407-024-00002-5
8. Investigating the causes and reduction approaches of nocturnal ozone increase events over Tai'an in the North China Plain J. Li et al. 10.1016/j.atmosres.2024.107499
9. Advancing Spatiotemporal Pollutant Dispersion Forecasting with an Integrated Deep Learning Framework for Crucial Information Capture Y. Wang et al. 10.3390/su16114531
10. Forecasting particulate matter concentration in Shanghai using a small-scale long-term dataset A. Salcedo-Bosch et al. 10.1186/s12302-025-01068-y
11. The Possibilities of Using Big Data Technologies in Solving Problems of Processing Data on Atmospheric Air Pollution D. Bogomolov & S. Plotnikov 10.33693/2313-223X-2024-11-1-162-170
12. Water Quality Classification and Machine Learning Model for Predicting Water Quality Status—A Study on Loa River Located in an Extremely Arid Environment: Atacama Desert V. Flores et al. 10.3390/w15162868
13. A Hybrid Autoformer Network for Air Pollution Forecasting Based on External Factor Optimization K. Pan et al. 10.3390/atmos14050869