11 citations as recorded by crossref.

1. A new aggregation and riming discrimination algorithm based on polarimetric weather radars A. Blanke et al. https://doi.org/10.5194/acp-25-4167-2025
2. Molecular characterization of atmospheric organic aerosols: Contemporary applications of high-resolution mass spectrometry Q. Xie & A. Laskin https://doi.org/10.1016/j.trac.2024.117986
3. Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning Y. Xu et al. https://doi.org/10.1016/j.scitotenv.2024.172822
4. Health impact assessment of occupational exposure to PM2.5 during welding operations S. Liu et al. https://doi.org/10.1016/j.jhazmat.2025.140987
5. Exploring AI potential in optical analysis of organic and elemental carbons: A review of emerging applications in airborne particulate characterization A. Agibayeva et al. https://doi.org/10.1016/j.apr.2025.102839
6. Selective capture of PM2.5 by urban trees: The role of leaf wax composition and physiological traits in air quality enhancement D. Chen et al. https://doi.org/10.1016/j.jhazmat.2024.135428
7. Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry J. Perez Chavez et al. https://doi.org/10.5194/amt-19-2575-2026
8. Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS C. Cuss et al. https://doi.org/10.1039/D5JA00179J
9. Deep learning based aerosol particle classification for the detection of ship emissions G. Wang et al. https://doi.org/10.1016/j.scitotenv.2025.180041
10. Advanced Characterization of Industrial Smoke: Particle Composition and Size Analysis with Single Particle Aerosol Mass Spectrometry and Optimized Machine Learning Y. Ye et al. https://doi.org/10.1021/acs.analchem.4c05988
11. Real-Time Detection of Urban Atmospheric Micro–Nanoplastics and Their Chemical Mixing State Using Bioaerosol Single-Particle Mass Spectrometry C. Zhang et al. https://doi.org/10.1021/acs.est.5c06513