13 citations as recorded by crossref.

1. Low‐cost air quality monitoring networks for long‐term field campaigns: A review F. Carotenuto et al. 10.1002/met.2161
2. Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study S. Diez et al. 10.5194/amt-17-3809-2024
3. Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning I. Apostolopoulos et al. 10.3390/s24134110
4. Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants A. Baruah et al. 10.1039/D2EA00165A
5. On Memory-Based Precise Calibration of Cost-Efficient NO2 Sensor Using Artificial Intelligence and Global Response Correction S. Koziel et al. 10.1016/j.knosys.2024.111564
6. A portable nitrogen dioxide instrument using cavity-enhanced absorption spectroscopy S. Bailey et al. 10.5194/amt-17-5903-2024
7. Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing S. Koziel et al. 10.1016/j.jestch.2024.101729
8. Calibration of NO, SO2, and PM using Airify: A low-cost sensor cluster for air quality monitoring M. Ionascu et al. 10.1016/j.atmosenv.2024.120841
9. Review of low-cost sensors for indoor air quality: Features and applications M. Ródenas García et al. 10.1080/05704928.2022.2085734
10. High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling S. Koziel et al. 10.1038/s41598-024-77214-y
11. Cost-Efficient measurement platform and machine-learning-based sensor calibration for precise NO2 pollution monitoring A. Pietrenko-Dabrowska et al. 10.1016/j.measurement.2024.115168
12. Two step calibration method for ozone low-cost sensor: Field experiences with the UrbanSense DCUs J. Sá et al. 10.1016/j.jenvman.2022.116910
13. Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning S. Koziel et al. 10.1038/s41598-024-59993-6