12 citations as recorded by crossref.

1. Neighborhood-scale air quality, public health, and equity implications of multi-modal vehicle electrification M. Visa et al. https://doi.org/10.1088/2634-4505/acf60d
2. CFD- and BPNN- based investigation and prediction of air pollutant dispersion in urban environment X. Lin et al. https://doi.org/10.1016/j.scs.2023.105029
3. Neighborhood-Scale O3, NO2, and PM2.5 in Washington, D.C. from a 1 × 1 km2 WRF–SMOKE–CMAQ Framework H. Hallaji et al. https://doi.org/10.1021/acsestair.6c00155
4. A two-way coupled regional urban–street network air quality model system for Beijing, China T. Wang et al. https://doi.org/10.5194/gmd-16-5585-2023
5. Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing Y. Wang et al. https://doi.org/10.1038/s43247-023-01119-3
6. Machine learning and causal inference for disentangling air pollution reduction during the Asian Games in megacity Hangzhou F. Zhang et al. https://doi.org/10.1016/j.envpol.2025.126775
7. An explainable artificial intelligence based assessment of differential impacts of road hierarchy on urban emissions in Riyadh City J. Mallick & S. Alqadhi https://doi.org/10.1007/s00477-025-03147-1
8. Bridging data-driven models and computational fluid dynamics for enhanced urban air pollution and analysis: A review A. Imami et al. https://doi.org/10.1016/j.uclim.2026.103125
9. Artificial neural network modeling for predicting PM10, PM2.5, NOX, and SO2 in coal mining areas A. Mishra et al. https://doi.org/10.1007/s12145-025-01922-w
10. Key factors in epidemiological exposure and insights for environmental management: Evidence from meta-analysis Y. Wang et al. https://doi.org/10.1016/j.envpol.2024.124991
11. Atmospheric aging effects of carbonaceous aerosols in a near-roadside environment: Insights from chemical composition and optical properties X. Xu et al. https://doi.org/10.1016/j.envpol.2026.128201
12. Recent progress, bottlenecks, and outlook of multiscale air quality modelling: a review Y. Dai et al. https://doi.org/10.1016/j.aeaoa.2026.100435