12 citations as recorded by crossref.

1. Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors E. Villanueva et al. 10.3390/s23073776
2. Empowering communities: Advancements in air quality monitoring and citizen engagement H. Relvas et al. 10.1016/j.uclim.2025.102344
3. A negative emission internal combustion engine vehicle? F. Leach 10.1016/j.atmosenv.2022.119488
4. Low-cost electrochemical gas sensing of vertical differences in wintertime air composition (CO, NO, NO2, O3) in Fairbanks, Alaska T. Roberts et al. 10.1039/D4FD00177J
5. Enhancing accuracy of air quality sensors with machine learning to augment large-scale monitoring networks K. Ravindra et al. 10.1038/s41612-024-00833-9
6. A comprehensive review on advancements in sensors for air pollution applications T. Seesaard et al. 10.1016/j.scitotenv.2024.175696
7. Air Pollution Monitoring Using Cost-Effective Devices Enhanced by Machine Learning Y. Colléaux et al. 10.3390/s25051423
8. The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK T. Bush et al. 10.1016/j.buildenv.2023.110330
9. Low-Cost Air Quality Sensors: Biases, Corrections and Challenges in Their Comparability I. Hayward et al. 10.3390/atmos15121523
10. Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods H. Tang et al. 10.3390/s24113448
11. Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations M. Casari et al. 10.1016/j.ecoinf.2024.102781
12. What can we learn from nested IoT low‐cost sensor networks for air quality? A case study of PM2.5 in Birmingham, UK N. Cowell et al. 10.1002/met.2220