10 citations as recorded by crossref.

1. Features of extreme $$\textrm{PM}_{\mathrm {2.5}}$$ pollution and its influencing factors: evidence from China L. Deng & X. Liu 10.1007/s10661-024-12990-8
2. Enhancing real-time PM2.5 forecasts: A hybrid approach of WRF-CMAQ model and CNN algorithm Y. Lee et al. 10.1016/j.atmosenv.2024.120835
3. Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring B. Chauhan et al. 10.3390/atmos16040359
4. Development and interpretation of PM2.5 estimation model for the Seoul Metropolitan Area using machine learning and explainable AI M. Kim et al. 10.1016/j.apr.2025.102672
5. An Air Pollutants Prediction Method Integrating Numerical Models and Artificial Intelligence Models Targeting the Area around Busan Port in Korea H. Hong et al. 10.3390/atmos13091462
6. An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters C. Jin et al. 10.5194/gmd-16-4137-2023
7. A Development of PM2.5 Forecasting System in South Korea Using Chemical Transport Modeling and Machine Learning Y. Koo et al. 10.1007/s13143-023-00314-8
8. Forecast of Fine Particles in Chengdu under Autumn–Winter Synoptic Conditions J. Yang et al. 10.3390/toxics11090777
9. Development of PM2.5 Forecast Model Combining ConvLSTM and DNN in Seoul J. Koo et al. 10.3390/atmos15111276
10. Development of a deep neural network for predicting 6 h average PM2.5 concentrations up to 2 subsequent days using various training data J. Lee et al. 10.5194/gmd-15-3797-2022