84 citations as recorded by crossref.

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4. Development of Air Quality Boxes Based on Low-Cost Sensor Technology for Ambient Air Quality Monitoring P. Gäbel et al. 10.3390/s22103830
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10. Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References G. Tancev & F. Toro 10.1109/ACCESS.2022.3145945
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14. Learning Calibration Functions on the Fly: Hybrid Batch Online Stacking Ensembles for the Calibration of Low-Cost Air Quality Sensor Networks in the Presence of Concept Drift E. Bagkis et al. 10.3390/atmos13030416
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18. Assessment of the Performance of a Low-Cost Air Quality Monitor in an Indoor Environment through Different Calibration Models D. Suriano & M. Penza 10.3390/atmos13040567
19. A Low-Cost Calibration Method for Temperature, Relative Humidity, and Carbon Dioxide Sensors Used in Air Quality Monitoring Systems R. González Rivero et al. 10.3390/atmos14020191
20. Calibrating low-cost sensors to measure vertical and horizontal gradients of NO2 and O3 pollution in three street canyons in Berlin S. Schmitz et al. 10.1016/j.atmosenv.2023.119830
21. Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal M. Hasan et al. 10.1021/acsomega.2c07734
22. A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data C. Heffernan et al. 10.1214/23-AOAS1751
23. Evaluation of Low-Cost CO2 Sensors Using Reference Instruments and Standard Gases for Indoor Use Q. Cai et al. 10.3390/s24092680
24. Advantages and limitations of the analytical methods currently employed for the assessment of inorganic pollutants in indoor and outdoor air M. Ródenas et al. 10.1016/j.trac.2024.118034
25. 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
26. The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias G. Tancev & C. Pascale 10.3390/s20216198
27. Efficient and Automated Generation of Orthogonal Atmospheres for the Characterization of Low-Cost Gas Sensor Systems in Air Quality Monitoring G. Tancev et al. 10.1109/TIM.2022.3198747
28. Evaluation and calibration of low‐cost off‐the‐shelf particulate matter sensors using machine learning techniques M. Ghamari et al. 10.1049/wss2.12043
29. Temporal Pattern-Based Denoising and Calibration for Low-Cost Sensors in IoT Monitoring Platforms X. Allka et al. 10.1109/TIM.2023.3239626
30. Sens-BERT: A BERT-Based Approach for Enabling Transferability and Re-Calibration of Calibration Models for Low-Cost Sensors Under Reference Measurements Scarcity M. Narayana et al. 10.1109/JSEN.2024.3362962
31. Low-cost system application for policy assessment: a case study from Berlin A. Caseiro et al. 10.1088/2752-5309/ad56bb
32. Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning I. Apostolopoulos et al. 10.3390/s24134110
33. 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
34. In search of an optimal in-field calibration method of low-cost gas sensors for ambient air pollutants: Comparison of linear, multilinear and artificial neural network approaches D. Topalović et al. 10.1016/j.atmosenv.2019.06.028
35. Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants A. Baruah et al. 10.1039/D2EA00165A
36. Design and Development of an Electronic Board for Supporting the Operation of Electrochemical Gas Sensors D. Suriano 10.3390/hardware2020009
37. First-Principles Algorithm for Air Quality Electrochemical Gas Sensors B. Ouyang 10.1021/acssensors.0c01129
38. A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2019.2929594
39. Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration C. Buehler et al. 10.5194/amt-14-995-2021
40. Machine learning techniques to improve the field performance of low-cost air quality sensors T. Bush et al. 10.5194/amt-15-3261-2022
41. In situ drift correction for a low-cost NO2 sensor network J. Miech et al. 10.1039/D2EA00145D
42. 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
43. Development and evaluation of a robust temperature sensitive algorithm for long term NO2 gas sensor network data correction P. Wei et al. 10.1016/j.atmosenv.2020.117509
44. Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2 P. Han et al. 10.3390/s21010256
45. Mapping the spatial distribution of NO<sub>2</sub> with in situ and remote sensing instruments during the Munich NO<sub>2</sub> imaging campaign G. Kuhlmann et al. 10.5194/amt-15-1609-2022
46. Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network M. Levy Zamora et al. 10.1021/acsestengg.1c00367
47. Review of low-cost sensors for indoor air quality: Features and applications M. Ródenas García et al. 10.1080/05704928.2022.2085734
48. A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN B. Montrucchio et al. 10.1109/TVT.2020.3035554
49. Air quality measurement, prediction and warning using transfer learning based IOT system for ambient assisted living S. Sonawani & K. Patil 10.1108/IJPCC-07-2022-0271
50. Long-term behavior and stability of calibration models for NO and NO<sub>2</sub> low-cost sensors H. Kim et al. 10.5194/amt-15-2979-2022
51. Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors S. Schmitz et al. 10.5194/amt-14-7221-2021
52. 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
53. Distributed Multi-Scale Calibration of Low-Cost Ozone Sensors in Wireless Sensor Networks J. Barcelo-Ordinas et al. 10.3390/s19112503
54. Air pollution prediction system using XRSTH-LSTM algorithm H. Srivastava & S. Kumar Das 10.1007/s11356-023-28393-0
55. STCM: A spatio-temporal calibration model for low-cost air monitoring sensors Y. Zhang et al. 10.1016/j.ins.2023.119307
56. Garbage in, gospel out? – Air quality assessment in the UK planning system A. Mills & S. Peckham 10.1016/j.envsci.2019.06.010
57. 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
58. Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors E. Villanueva et al. 10.3390/s23073776
59. Estimation of Surface-Level NO2 Using Satellite Remote Sensing and Machine Learning: A review M. Siddique et al. 10.1109/MGRS.2024.3398434
60. Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors P. Ferrer-Cid et al. 10.3390/s22103964
61. Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network S. Ali et al. 10.3390/s23020854
62. Low-cost urban carbon monitoring network and implications for china: a comprehensive review H. Jiang et al. 10.1007/s11356-023-29836-4
63. Integration and calibration of non-dispersive infrared (NDIR) CO<sub>2</sub> low-cost sensors and their operation in a sensor network covering Switzerland M. Müller et al. 10.5194/amt-13-3815-2020
64. Minimized Training of Machine Learning-Based Calibration Methods for Low-Cost O3 Sensors S. Tondini et al. 10.1109/JSEN.2023.3339202
65. Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles R. Mawrence et al. 10.3390/s20247332
66. Data reconstruction applications for IoT air pollution sensor networks using graph signal processing P. Ferrer-Cid et al. 10.1016/j.jnca.2022.103434
67. Learning to Identify Malfunctioning Sensors in a Large-Scale Sensor Network T. Lin et al. 10.1109/JSEN.2021.3138250
68. Air pollution measurement errors: is your data fit for purpose? S. Diez et al. 10.5194/amt-15-4091-2022
69. Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources J. Thorson et al. 10.3390/s19173723
70. Reliability of Lower-Cost Sensors in the Analysis of Indoor Air Quality on Board Ships O. Schalm et al. 10.3390/atmos13101579
71. Calibration of SO2 and NO2 Electrochemical Sensors via a Training and Testing Method in an Industrial Coastal Environment S. Ahumada et al. 10.3390/s22197281
72. Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art M. Narayana et al. 10.3390/s22010394
73. Indoor Air Sensing: A Study in Cost, Energy, Reliability and Fidelity in Sensing P. Sharma et al. 10.1007/s11220-023-00412-x
74. EEATC: A Novel Calibration Approach for Low-Cost Sensors M. Narayana et al. 10.1109/JSEN.2023.3304366
75. Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors I. Christakis et al. 10.3390/signals5010004
76. 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
77. A fast calibration algorithm for Non-Dispersive Infrared single channel carbon dioxide sensor based on deep learning K. Mao et al. 10.1016/j.comcom.2021.08.003
78. Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning I. Vajs et al. 10.3390/s21103338
79. Air Pollution Monitoring via Wireless Sensor Networks: The Investigation and Correction of the Aging Behavior of Electrochemical Gaseous Pollutant Sensors I. Christakis et al. 10.3390/electronics12081842
80. Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants T. Leo Hohenberger et al. 10.1016/j.envint.2022.107329
81. Annual NO2 as a Predictor of Hourly NO2 Variability: Do Defra UK’s Heuristics Make Sense? A. Mills & S. Peckham 10.3390/atmos12030385
82. Data Driven Concept for Sensor Data Adaptation of Electrochemical Sensors for Mobile Air Quality Measurements E. Esatbeyoglu et al. 10.1149/1945-7111/ab74bd
83. Effect of Relative Humidity and Air Temperature on the Results Obtained from Low-Cost Gas Sensors for Ambient Air Quality Measurements A. Samad et al. 10.3390/s20185175
84. Evaluation of the Performance of Low-Cost Air Quality Sensors at a High Mountain Station with Complex Meteorological Conditions H. Li et al. 10.3390/atmos11020212