49 citations as recorded by crossref.

1. Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device E. Bagkis et al. 10.3390/atmos12020251
2. Evaluation of optical particulate matter sensors under realistic conditions of strong and mild urban pollution A. Masic et al. 10.5194/amt-13-6427-2020
3. Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors P. Ferrer-Cid et al. 10.3390/s22103964
4. 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
5. Calibrating low-cost sensors using MERRA-2 reconstructed PM2.5 mass concentration as a proxy V. Malyan et al. 10.1016/j.apr.2023.102027
6. 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. 10.1029/2020JD032712
7. Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain G. Kosmopoulos et al. 10.3390/s23146541
8. 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 10.1080/02786826.2022.2114312
9. 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
10. Low-Cost Formaldehyde Sensor Evaluation and Calibration in a Controlled Environment A. Chattopadhyay et al. 10.1109/JSEN.2022.3172864
11. Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing H. Mei et al. 10.3390/s20164381
12. Development and application of a United States-wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor K. Barkjohn et al. 10.5194/amt-14-4617-2021
13. A Smoke Chamber Study on Some Low-Cost Sensors for Monitoring Size-Segregated Aerosol and Microclimatic Parameters L. Bencs & A. Nagy 10.3390/atmos15030304
14. A machine learning field calibration method for improving the performance of low-cost particle sensors S. Patra et al. 10.1016/j.buildenv.2020.107457
15. A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways T. Akinosho et al. 10.1016/j.ecoinf.2022.101609
16. Inter- versus Intracity Variations in the Performance and Calibration of Low-Cost PM2.5 Sensors: A Multicity Assessment in India S. V et al. 10.1021/acsearthspacechem.2c00257
17. Evaluation of Low-cost Air Quality Sensor Calibration Models K. Aula et al. 10.1145/3512889
18. Evaluation and calibration of low‐cost off‐the‐shelf particulate matter sensors using machine learning techniques M. Ghamari et al. 10.1049/wss2.12043
19. Significance of sources and size distribution on calibration of low-cost particle sensors: Evidence from a field sampling campaign V. Malyan et al. 10.1016/j.jaerosci.2022.106114
20. 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. 10.1016/j.jaerosci.2022.106097
21. 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
22. Apis-Prime: A deep learning model to optimize beehive monitoring system for the task of daily weight estimation O. Anwar et al. 10.1016/j.asoc.2023.110546
23. Development of a physics-based method for calibration of low-cost particulate matter sensors and comparison with machine learning models B. Prajapati et al. 10.1016/j.jaerosci.2023.106284
24. Development and evaluation of correction models for a low-cost fine particulate matter monitor B. Nilson et al. 10.5194/amt-15-3315-2022
25. Voice Calibration Using Ambient Sensors J. Chen et al. 10.1142/S0218126623500433
26. Evaluation and calibration of low-cost particulate matter sensors for respirable coal mine dust monitoring Z. Feng et al. 10.1080/02786826.2023.2288609
27. Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance F. Bulot et al. 10.1016/j.heliyon.2023.e15943
28. 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
29. Utilization of scattering and absorption-based particulate matter sensors in the environment impacted by residential wood combustion J. Kuula et al. 10.1016/j.jaerosci.2020.105671
30. Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors M. Adoui et al. 10.1145/3590956
31. City Wide Participatory Sensing of Air Quality A. Rebeiro-Hargrave et al. 10.3389/fenvs.2021.773778
32. EEATC: A Novel Calibration Approach for Low-Cost Sensors M. Narayana et al. 10.1109/JSEN.2023.3304366
33. Missing Data Imputation on IoT Sensor Networks: Implications for on-Site Sensor Calibration N. Okafor & D. Delaney 10.1109/JSEN.2021.3105442
34. Evaluating the performance and influencing factors of three portable black carbon monitors for field measurement L. Wu et al. 10.1016/j.jes.2023.05.044
35. 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. 10.3390/atmos12101306
36. 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
37. Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece I. Stavroulas et al. 10.3390/atmos11090926
38. Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes K. Kelly et al. 10.1021/acs.est.0c02341
39. Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art M. Narayana et al. 10.3390/s22010394
40. Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network E. Considine et al. 10.1016/j.envpol.2020.115833
41. 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
42. Exploration of intra-city and inter-city PM2.5 regional calibration models to improve low-cost sensor performance S. Jain & N. Zimmerman 10.1016/j.jaerosci.2024.106335
43. Evaluation of nine machine learning regression algorithms for calibration of low-cost PM2.5 sensor V. Kumar & M. Sahu 10.1016/j.jaerosci.2021.105809
44. Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring R. Kureshi et al. 10.3390/s22031093
45. Evaluation of a low-cost dryer for a low-cost optical particle counter M. Chacón-Mateos et al. 10.5194/amt-15-7395-2022
46. 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. 10.1016/j.buildenv.2023.110953
47. A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives B. Alfano et al. 10.3390/s20236819
48. Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams A. Akram Abdulrazzaq et al. 10.1155/2022/2682287
49. Quantitative Analysis for Application Specific Calibration Approaches for Low-Cost Sensors for Air Quality Monitoring M. Narayana et al. 10.1541/ieejeiss.142.1166