34 citations as recorded by crossref.

1. Detecting and quantifying PM2.5 and NO2 contributions from train and road traffic in the vicinity of a major railway terminal in Dublin, Ireland S. Priyan et al. 10.1016/j.envpol.2024.124903
2. Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors A. Cavaliere et al. 10.5194/amt-16-4723-2023
3. Bayesian Sensor Calibration M. Berger et al. 10.1109/JSEN.2022.3199485
4. Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter M. Aix et al. 10.1016/j.scitotenv.2023.164063
5. Low-Cost Formaldehyde Sensor Evaluation and Calibration in a Controlled Environment A. Chattopadhyay et al. 10.1109/JSEN.2022.3172864
6. Performance and applicability of low-cost PM sensors to assess global pollution variability through machine learning techniques R. Sharma et al. 10.1016/j.aeaoa.2025.100331
7. A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019 X. Weng et al. 10.5194/acp-22-8385-2022
8. Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights D. Topalović et al. 10.1007/s10661-024-13069-0
9. Applying machine learning for large scale field calibration of low‐cost PM2.5and PM10air pollution sensors P. Adong et al. 10.1002/ail2.76
10. A machine learning methodology for the generation of a parameterization of the hydroxyl radical D. Anderson et al. 10.5194/gmd-15-6341-2022
11. 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
12. 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
13. Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors A. Abu-Hani et al. 10.5194/amt-17-3917-2024
14. Low-Cost Sensors for Atmospheric NO2 Measurement: A review C. Ayvaz et al. 10.1016/j.envpol.2025.126418
15. Bayesian Sensor Calibration of a CMOS-Integrated Hall Sensor Against Thermomechanical Cross-Sensitivities M. Berger et al. 10.1109/JSEN.2023.3243783
16. Response of stratospheric water vapour to warming constrained by satellite observations P. Nowack et al. 10.1038/s41561-023-01183-6
17. A Scalable Calibration Method for Enhanced Accuracy in Dense Air Quality Monitoring Networks A. Winter et al. 10.1021/acs.est.4c08855
18. Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO2 and CH4 in air R. Biagi et al. 10.1016/j.heliyon.2024.e29772
19. 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
20. Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana C. McFarlane et al. 10.1021/acsearthspacechem.1c00217
21. 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
22. Minimized Training of Machine Learning-Based Calibration Methods for Low-Cost O3 Sensors S. Tondini et al. 10.1109/JSEN.2023.3339202
23. Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective P. Nowack & D. Watson-Parris 10.5194/acp-25-2365-2025
24. AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin M. Casari et al. 10.3390/s23239446
25. Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors P. Ferrer-Cid et al. 10.3390/s22103964
26. 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
27. Nitrogen Dioxide Monitoring by Means of a Low-Cost Autonomous Platform and Sensor Calibration via Machine Learning with Global Data Correlation Enhancement S. Koziel et al. 10.3390/s25082352
28. A systematic evaluation of high-cloud controlling factors S. Wilson Kemsley et al. 10.5194/acp-24-8295-2024
29. 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
30. Calibration method of particulate matter sensor based on density peaks clustering combined with stacking algorithm J. Lu et al. 10.1016/j.atmosenv.2024.120460
31. Temporal Pattern-Based Denoising and Calibration for Low-Cost Sensors in IoT Monitoring Platforms X. Allka et al. 10.1109/TIM.2023.3239626
32. Enhancing the Reliability of NO2 Monitoring Using Low-Cost Sensors by Compensating for Temperature and Humidity Effects D. Alejo Sánchez et al. 10.3390/atmos15111365
33. Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana G. Raheja et al. 10.1021/acs.est.2c09264
34. A Network of Field-Calibrated Low-Cost Sensor Measurements of PM2.5 in Lomé, Togo, Over One to Two Years G. Raheja et al. 10.1021/acsearthspacechem.1c00391