16 citations as recorded by crossref.

1. Spatial and temporal dynamics of major air pollutants and the driving mechanisms in Tianshui City, China J. Xing et al.
2. Random forest models for PM2.5 speciation concentrations using MISR fractional AODs G. Geng et al.
3. Satellite mapping of PM2.5 episodes in the wintertime San Joaquin Valley: a “static” model using column water vapor R. Chatfield et al.
4. Machine Learning-Based Estimation of Surface NO2 Concentrations over China: A Comparative Analysis of Geostationary (GEMS) and Polar-Orbiting (TROPOMI) Satellite Data Y. Ma et al.
5. Ambient traffic related air pollution in relation to ovarian reserve and oocyte quality in young, healthy oocyte donors S. LaPointe et al.
6. A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation N. Murray et al.
7. On the added value of satellite AOD for the investigation of ground-level PM2.5 variability J. Handschuh et al.
8. A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation T. Li et al.
9. Combining low-cost, surface-based aerosol monitors with size-resolved satellite data for air quality applications P. deSouza et al.
10. Evolving trends in application of low-cost air quality sensor networks: challenges and future directions E. Bagkis et al.
11. Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes M. Sorek-Hamer et al.
12. Impact of atmospheric condition on aerosol optical properties in urban and rural environment S. Kłapiński et al.
13. TROPOMI-Based PM2.5 Estimates and Their Evaluation During a High-Pollution Event in Germany J. Handschuh et al.
14. MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm J. Limbacher et al.
15. Air pollution in Germany: Spatio-temporal variations and their driving factors based on continuous data from 2008 to 2018 X. Liu et al.
16. Unequal spatio-temporal distribution of population-weighted pollution extremes through deep learning K. Rautela et al.