97 citations as recorded by crossref.

1. Evaluation of optical particulate matter sensors under realistic conditions of strong and mild urban pollution A. Masic et al. https://doi.org/10.5194/amt-13-6427-2020
2. Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors P. Ferrer-Cid et al. https://doi.org/10.3390/s22103964
3. A hybrid machine learning and Extended Kalman filtering framework for sensor fusion of low-cost and high-precision dissolved oxygen sensors under environmental variability . Ambuj et al. https://doi.org/10.1016/j.measurement.2026.121033
4. Real-time air quality prediction using traffic videos and machine learning L. Deveer & L. Minet https://doi.org/10.1016/j.trd.2025.104688
5. Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network M. Levy Zamora et al. https://doi.org/10.1021/acsestengg.1c00367
6. Calibrating low-cost sensors using MERRA-2 reconstructed PM2.5 mass concentration as a proxy V. Malyan et al. https://doi.org/10.1016/j.apr.2023.102027
7. Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: Aerosol composition dependencies and sensitivity analyses A. May & H. Li https://doi.org/10.1080/02786826.2022.2114312
8. In chamber calibration and performance evaluation of air quality low-cost sensors D. Agrawal et al. https://doi.org/10.1016/j.apr.2024.102299
9. Toward Personalized Short-Term PM2.5 Forecasting Integrating a Low-Cost Wearable Device and an Attention-Based LSTM C. Mountzouris et al. https://doi.org/10.3390/air3040029
10. Using low-cost sensors for source attribution and health assessment: An air quality study in Brownsville, Texas S. Pinakana et al. https://doi.org/10.1016/j.aeaoa.2025.100405
11. Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor K. Barkjohn et al. https://doi.org/10.5194/amt-14-4617-2021
12. A Smoke Chamber Study on Some Low-Cost Sensors for Monitoring Size-Segregated Aerosol and Microclimatic Parameters L. Bencs & A. Nagy https://doi.org/10.3390/atmos15030304
13. Low-Cost Air Quality Sensors: Biases, Corrections and Challenges in Their Comparability I. Hayward et al. https://doi.org/10.3390/atmos15121523
14. Calibration strategies and measurement frameworks for low-cost particulate matter sensors: a comprehensive review Z. Feng et al. https://doi.org/10.1088/1361-6501/ae6298
15. Spatial Distribution and AirQ + -Based Health Risk Analysis of Particulate Matter in Bangladesh’s Industrial Hubs M. Hossain et al. https://doi.org/10.1007/s11270-026-09294-7
16. Calibration of Low-Cost LoRaWAN-Based IoT Air Quality Monitors Using the Super Learner Ensemble: A Case Study for Accurate Particulate Matter Measurement G. Balagopal et al. https://doi.org/10.3390/s25051614
17. A machine learning field calibration method for improving the performance of low-cost particle sensors S. Patra et al. https://doi.org/10.1016/j.buildenv.2020.107457
18. A quality control method based on physical constraints and data-driven collaborative artificial intelligence for wind observations along high-speed railway lines X. Xiong et al. https://doi.org/10.5194/amt-18-737-2025
19. A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways T. Akinosho et al. https://doi.org/10.1016/j.ecoinf.2022.101609
20. Evaluation of Low-cost Air Quality Sensor Calibration Models K. Aula et al. https://doi.org/10.1145/3512889
21. Evaluation and calibration of low‐cost off‐the‐shelf particulate matter sensors using machine learning techniques M. Ghamari et al. https://doi.org/10.1049/wss2.12043
22. Intelligent PM 2.5 mass concentration analyzer using deep learning algorithm and improved density measurement chip for high-accuracy airborne particle sensor network S. Lee et al. https://doi.org/10.1016/j.jaerosci.2022.106097
23. Assessing the spatial transferability of calibration models across a low-cost sensors network V. Malyan et al. https://doi.org/10.1016/j.jaerosci.2024.106437
24. Investigating IoT-based low-cost sensor network for real-time hyper-local air quality monitoring and exposure assessment M. Zaid et al. https://doi.org/10.1016/j.apr.2025.102749
25. Smart sensors, smart calibration: Applications in machine learning for coal dust monitoring N. Amoah et al. https://doi.org/10.1016/j.gsme.2025.09.010
26. Research progress on calibration of bridge structural health monitoring sensing system Y. Yang et al. https://doi.org/10.1186/s43251-024-00143-3
27. Evaluation and calibration of low-cost particulate matter sensors for respirable coal mine dust monitoring Z. Feng et al. https://doi.org/10.1080/02786826.2023.2288609
28. Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations M. Casari et al. https://doi.org/10.1016/j.ecoinf.2024.102781
29. Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance F. Bulot et al. https://doi.org/10.1016/j.heliyon.2023.e15943
30. Analytical performance and calibration strategies of low-cost particulate matter sensors for indoor and workplace monitoring—a review Z. Feng et al. https://doi.org/10.1039/D5AY01554E
31. Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors M. El Adoui et al. https://doi.org/10.1145/3590956
32. City Wide Participatory Sensing of Air Quality A. Rebeiro-Hargrave et al. https://doi.org/10.3389/fenvs.2021.773778
33. EEATC: A Novel Calibration Approach for Low-Cost Sensors M. Narayana et al. https://doi.org/10.1109/JSEN.2023.3304366
34. Embedded System Application for Establishing Variability and the Relationship Between Meteorological Parameters and Particulate Matter Pollution in a Lagos Site O. Odubanjo et al. https://doi.org/10.62292/njtep.v3i4.2025.101
35. Performance and applicability of low-cost PM sensors to assess global pollution variability through machine learning techniques R. Sharma et al. https://doi.org/10.1016/j.aeaoa.2025.100331
36. Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece I. Stavroulas et al. https://doi.org/10.3390/atmos11090926
37. A self-calibration algorithm for soil moisture sensors using deep learning D. Aranda Britez et al. https://doi.org/10.1007/s10489-024-05921-0
38. Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art M. Narayana et al. https://doi.org/10.3390/s22010394
39. A novel approach combining indoor mobile measurements and interpretable machine learning to unveil highly-resolved indoor air pollution Z. Tang et al. https://doi.org/10.1016/j.buildenv.2025.112552
40. Spatiotemporal distribution prediction for PM2.5 based on STXGBoost model and high-density monitoring sensors in Zhengzhou High Tech Zone, China S. Zhao et al. https://doi.org/10.1016/j.jenvman.2024.123682
41. Evaluation of nine machine learning regression algorithms for calibration of low-cost PM2.5 sensor V. Kumar & M. Sahu https://doi.org/10.1016/j.jaerosci.2021.105809
42. Optimizing Air Quality Monitoring: Comparative Analysis of Linear Regression and Machine Learning in Low-Cost Sensor Calibration R. Fang et al. https://doi.org/10.1007/s44408-025-00009-x
43. Evaluation of a low-cost dryer for a low-cost optical particle counter M. Chacón-Mateos et al. https://doi.org/10.5194/amt-15-7395-2022
44. Visibility-Based Calibration of Low-Cost Particulate Matter Sensors: Laboratory Evaluation and Theoretical Analysis A. Ronen https://doi.org/10.3390/s25226995
45. A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives B. Alfano et al. https://doi.org/10.3390/s20236819
46. Long-term Evaluation of Machine Learning Based Methods for Air Emission Monitoring M. Si et al. https://doi.org/10.1007/s00267-024-02057-2
47. Low-Cost Particulate Matter Mass Sensors: Review of the Status, Challenges, and Opportunities for Single-Instrument and Network Calibration J. Zhang et al. https://doi.org/10.1021/acssensors.4c03293
48. Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device E. Bagkis et al. https://doi.org/10.3390/atmos12020251
49. XGBoost-based calibration of the portable PAQMON 1.0 PM monitor in outdoor conditions D. Topalović et al. https://doi.org/10.5937/bakar2501055T
50. Seasonal multisite low-cost sensor measurements to estimate spatial and temporal variability of particulate matter pollution in Nairobi, Kenya E. Nyaga et al. https://doi.org/10.1016/j.apr.2025.102630
51. A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors S. Smith et al. https://doi.org/10.3390/s25041028
52. Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan R. Huda et al. https://doi.org/10.3390/ijerph21050623
53. Field calibration of fine particulate matter low-cost sensors in a highly industrialized semi-arid conurbation M. Villarreal-Marines et al. https://doi.org/10.1038/s41612-024-00837-5
54. Evaluating Wildfire Smoke Transport Within a Coupled Fire‐Atmosphere Model Using a High‐Density Observation Network for an Episodic Smoke Event Along Utah's Wasatch Front D. Mallia et al. https://doi.org/10.1029/2020JD032712
55. Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain G. Kosmopoulos et al. https://doi.org/10.3390/s23146541
56. 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. https://doi.org/10.1109/JSEN.2024.3362962
57. Low-Cost Formaldehyde Sensor Evaluation and Calibration in a Controlled Environment A. Chattopadhyay et al. https://doi.org/10.1109/JSEN.2022.3172864
58. Low-Cost Sensor Systems and IoT Technologies for Indoor Air Quality Monitoring: Instrumentation, Models, Implementation, and Perspectives for Validation S. Lopes et al. https://doi.org/10.3390/s25247567
59. Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing H. Mei et al. https://doi.org/10.3390/s20164381
60. A chip-based radio frequency sensor for soil moisture measurements: A machine learning and deep learning calibration approach J. Progga et al. https://doi.org/10.1016/j.jafr.2025.102591
61. Comparison of PM2.5 trends and source factors in urban and rural locations in Bangladesh S. Zaman et al. https://doi.org/10.1016/j.apr.2025.102744
62. A Machine Learning-Based Calibration Framework for Low-Cost PM2.5 Sensors Integrating Meteorological Predictors X. Ma et al. https://doi.org/10.3390/chemosensors13120425
63. Inter- versus Intracity Variations in the Performance and Calibration of Low-Cost PM2.5 Sensors: A Multicity Assessment in India S. V et al. https://doi.org/10.1021/acsearthspacechem.2c00257
64. Significance of sources and size distribution on calibration of low-cost particle sensors: Evidence from a field sampling campaign V. Malyan et al. https://doi.org/10.1016/j.jaerosci.2022.106114
65. Performance Assessment of Low- and Medium-Cost PM2.5 Sensors in Real-World Conditions in Central Europe B. Atfeh et al. https://doi.org/10.3390/atmos16070796
66. Explainable AI for rule reduction in fuzzy models for air pollution measurement adjustment P. Kowalski et al. https://doi.org/10.1016/j.envsoft.2025.106734
67. Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana G. Raheja et al. https://doi.org/10.1021/acs.est.2c09264
68. Apis-Prime: A deep learning model to optimize beehive monitoring system for the task of daily weight estimation O. Anwar et al. https://doi.org/10.1016/j.asoc.2023.110546
69. Development of a physics-based method for calibration of low-cost particulate matter sensors and comparison with machine learning models B. Prajapati et al. https://doi.org/10.1016/j.jaerosci.2023.106284
70. Development and evaluation of correction models for a low-cost fine particulate matter monitor B. Nilson et al. https://doi.org/10.5194/amt-15-3315-2022
71. Metrological Validation of Low-Cost DS18B20 Digital Temperature Sensors Using the TH-001 Procedure: Calibration Models, Uncertainty, and Reproducibility J. Rodríguez-Rama et al. https://doi.org/10.3390/metrology6010021
72. Evaluation of calibration performance of a low-cost particulate matter sensor using collocated and distant NO2 K. Ko et al. https://doi.org/10.5194/amt-17-3303-2024
73. Voice Calibration Using Ambient Sensors J. Chen et al. https://doi.org/10.1142/S0218126623500433
74. Building governance infrastructure for a technological and resident network for low-cost neighborhood air quality monitoring K. Stevens et al. https://doi.org/10.1088/1748-9326/ade72a
75. Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data K. Barkjohn et al. https://doi.org/10.1021/acsestair.4c00125
76. Calibration method of particulate matter sensor based on density peaks clustering combined with stacking algorithm J. Lu et al. https://doi.org/10.1016/j.atmosenv.2024.120460
77. Utilization of scattering and absorption-based particulate matter sensors in the environment impacted by residential wood combustion J. Kuula et al. https://doi.org/10.1016/j.jaerosci.2020.105671
78. Missing Data Imputation on IoT Sensor Networks: Implications for on-Site Sensor Calibration N. Okafor & D. Delaney https://doi.org/10.1109/JSEN.2021.3105442
79. Evaluating the performance and influencing factors of three portable black carbon monitors for field measurement L. Wu et al. https://doi.org/10.1016/j.jes.2023.05.044
80. A Bayesian-Optimized Surrogate Model Integrating Deep Learning Algorithms for Correcting PurpleAir Sensor Measurements M. Ahmed et al. https://doi.org/10.3390/atmos15121535
81. Non-Cumulative, Size-Specific Calibration of Low-Cost Particulate Matter Sensors Under Simulated Construction Drilling Events A. Komiljon & J. Choi https://doi.org/10.3390/atmos17060561
82. Assessment and Calibration of a Low-Cost PM2.5 Sensor Using Machine Learning (HybridLSTM Neural Network): Feasibility Study to Build an Air Quality Monitoring System D. Park et al. https://doi.org/10.3390/atmos12101306
83. Assessment of the Performance of a Low-Cost Air Quality Monitor in an Indoor Environment through Different Calibration Models D. Suriano & M. Penza https://doi.org/10.3390/atmos13040567
84. Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes K. Kelly et al. https://doi.org/10.1021/acs.est.0c02341
85. Filling gaps in PM2.5 time series: A broad evaluation from statistical to advanced neural network models R. Safarov et al. https://doi.org/10.1371/journal.pone.0330211
86. Improving Low-Cost Optical PM Sensor Accuracy in Humid Conditions via Aerosol Liquid Water Estimation Using U.S. EPA CSN Data Y. Guo et al. https://doi.org/10.1021/acsestair.5c00225
87. Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network E. Considine et al. https://doi.org/10.1016/j.envpol.2020.115833
88. A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data C. Heffernan et al. https://doi.org/10.1214/23-AOAS1751
89. Exploration of intra-city and inter-city PM2.5 regional calibration models to improve low-cost sensor performance S. Jain & N. Zimmerman https://doi.org/10.1016/j.jaerosci.2024.106335
90. An overview of outdoor low-cost gas-phase air quality sensor deployments: current efforts, trends, and limitations K. Okorn & L. Iraci https://doi.org/10.5194/amt-17-6425-2024
91. Deep learning calibration model for PurpleAir PM2.5 measurements: Comprehensive Investigation of the PurpleAir network M. Ghahremanloo et al. https://doi.org/10.1016/j.atmosenv.2025.121118
92. In-situ validation of embedded physics-based calibration in low-cost particulate matter sensor for urban air quality monitoring Z. Feng et al. https://doi.org/10.1016/j.uclim.2025.102289
93. Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring R. Kureshi et al. https://doi.org/10.3390/s22031093
94. Correction of PM2.5 underestimation in low-cost sensors under elevated dust loading using only sensor measurements K. Kaur et al. https://doi.org/10.5194/amt-19-1077-2026
95. Performance validation and calibration conditions for novel dynamic baseline tracking air sensors in long-term field monitoring H. Mei et al. https://doi.org/10.5194/amt-18-1771-2025
96. A review of machine learning approaches for improving calibration and performance of low-cost air quality sensor networks C. Yan https://doi.org/10.1108/SR-10-2025-0761
97. Real-time indoor sensing of volatile organic compounds during building disinfection events via photoionization detection and proton transfer reaction mass spectrometry X. Ding et al. https://doi.org/10.1016/j.buildenv.2023.110953