134 citations as recorded by crossref.

1. Investigation of indoor air quality in university residences using low-cost sensors R. Afroz et al. https://doi.org/10.1039/D2EA00149G
2. AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin M. Casari et al. https://doi.org/10.3390/s23239446
3. Calibration method of ultra-low emission particulate concentration measurement by light scattering method L. Hui https://doi.org/10.1515/teme-2021-0114
4. Effects of aerosol type and simulated aging on performance of low-cost PM sensors J. Tryner et al. https://doi.org/10.1016/j.jaerosci.2020.105654
5. Temporal variability and regional influences of PM2.5 in the West African cities of Abidjan (Côte d'Ivoire) and Accra (Ghana) J. Bahino et al. https://doi.org/10.1039/D4EA00012A
6. Performance evaluation of portable dual-spot micro-aethalometers for source identification of black carbon aerosols: application to wildfire smoke and traffic emissions in the Pacific Northwest M. Chakraborty et al. https://doi.org/10.5194/amt-16-2333-2023
7. Air Quality Monitoring with Low-Cost Sensors: A Record of the Increase of PM2.5 during Christmas and New Year’s Eve Celebrations in the City of Queretaro, Mexico A. Rodríguez-Trejo et al. https://doi.org/10.3390/atmos15080879
8. What can we learn from nested IoT low‐cost sensor networks for air quality? A case study of PM2.5 in Birmingham, UK N. Cowell et al. https://doi.org/10.1002/met.2220
9. Application of PM 2.5 low-cost sensors for indoor air quality compliance monitoring L. Morawska et al. https://doi.org/10.1080/02786826.2025.2457326
10. The inhaled dose of pollution while bike commuting: Interactions between route pollutant concentrations and exertional intensity A. Bonardi et al. https://doi.org/10.1016/j.tbs.2025.101162
11. Insights into low-cost pm sensors using size-resolved scattering intensity of cooking aerosols in a test house A. Thakur et al. https://doi.org/10.1080/02786826.2024.2342722
12. Shadow and Micrometeorological Conditions That Influence the Air Quality in Houses near High Rise Buildings—Field Results R. Vidal-Rojas et al. https://doi.org/10.3390/atmos17050474
13. Assessment of PM2.5 Exposure during Cycle Trips in The Netherlands Using Low-Cost Sensors J. Wesseling et al. https://doi.org/10.3390/ijerph18116007
14. Calibration Method for Particulate Matter Low-Cost Sensors Used in Ambient Air Quality Monitoring and Research J. Venkatraman Jagatha et al. https://doi.org/10.3390/s21123960
15. Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. https://doi.org/10.1039/D4EA00086B
16. Field and laboratory evaluation of PurpleAir low-cost aerosol sensors in monitoring indoor airborne particles S. Park et al. https://doi.org/10.1016/j.buildenv.2023.110127
17. Low-Cost Formaldehyde Sensor Evaluation and Calibration in a Controlled Environment A. Chattopadhyay et al. https://doi.org/10.1109/JSEN.2022.3172864
18. SensEURCity: A multi-city air quality dataset collected for 2020/2021 using open low-cost sensor systems M. Van Poppel et al. https://doi.org/10.1038/s41597-023-02135-w
19. Assessment of Low-Cost Particulate Matter Sensor Systems against Optical and Gravimetric Methods in a Field Co-Location in Norway M. Vogt et al. https://doi.org/10.3390/atmos12080961
20. Challenges in Detecting Clouds in Polar Regions Using a Drone with Onboard Low-Cost Particle Counter J. Inoue & K. Sato https://doi.org/10.1016/j.atmosenv.2023.120085
21. 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
22. First in-Lab Testing of a Cost-Effective Prototype for PM2.5 Monitoring: The P.ALP Assessment G. Fanti et al. https://doi.org/10.3390/s24185915
23. Real-Time Air Quality Monitoring: A Smart IoT System Using Low-Cost Sensors and 3-D Printing A. Osa-Sanchez & B. Garcia-Zapirain https://doi.org/10.1109/JRFID.2025.3541816
24. Development and Testing of a Rocket-Based Sensor for Atmospheric Sensing Using an Unmanned Aerial System R. Thalman https://doi.org/10.3390/s24061768
25. Intelligent and Scalable Air Quality Monitoring With 5G Edge X. Su et al. https://doi.org/10.1109/MIC.2021.3059189
26. Real-time air quality monitoring based on locally developed unmanned aerial vehicle and low-cost smart electronic device J. Taamté et al. https://doi.org/10.1088/1748-0221/19/05/P05036
27. Long‐Term Characterization of Indoor Air Quality at a Research Area Building: Comparing Reference Instruments and Low‐Cost Sensors M. Calvello et al. https://doi.org/10.1155/2024/8799498
28. Comparison of calibration models for low-cost PM2.5 sensors in high-concentration occupational environments R. Fang et al. https://doi.org/10.1093/annweh/wxag021
29. Added Value of Vaisala AQT530 Sensors as a Part of a Sensor Network for Comprehensive Air Quality Monitoring T. Petäjä et al. https://doi.org/10.3389/fenvs.2021.719567
30. Response of low-cost sensors to high PM 2.5 concentrations during bushfire and haze events X. Liu et al. https://doi.org/10.1080/02786826.2024.2368733
31. Performance evaluation of the Alphasense OPC-N3 and Plantower PMS5003 sensor in measuring dust events in the Salt Lake Valley, Utah K. Kaur & K. Kelly https://doi.org/10.5194/amt-16-2455-2023
32. Laboratory evaluation of the effects of particle size and composition on the performance of integrated devices containing Plantower particle sensors Y. Zou et al. https://doi.org/10.1080/02786826.2021.1905148
33. Performance Evaluation and Calibration of Low-Cost PurpleAir PM2.5 Sensors in South Asian Conditions: Dhaka, Bangladesh C. Hossain et al. https://doi.org/10.1021/acsestair.5c00105
34. 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. https://doi.org/10.3390/atmos13030416
35. EEATC: A Novel Calibration Approach for Low-Cost Sensors M. Narayana et al. https://doi.org/10.1109/JSEN.2023.3304366
36. Design and Evaluation of a Calibration Chamber for Low-Cost PM Sensors–Part 2: Mass Concentration-Based Assessment V. Malyan et al. https://doi.org/10.1021/acsestair.6c00161
37. City-Scale Air Quality Network of Low-Cost Sensors A. Masic https://doi.org/10.3390/atmos15070798
38. Quantitative validation in indoor dispersion modeling: Comparing large-eddy simulation results with experimental measurements M. Auvinen et al. https://doi.org/10.1063/5.0276393
39. Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors N. Nalakurthi et al. https://doi.org/10.3390/s24113650
40. Improving PM10 sensor accuracy in urban areas through calibration in Timișoara R. Blaga & S. Gautam https://doi.org/10.1038/s41612-024-00812-0
41. Sizing Accuracy of Low-Cost Optical Particle Sensors Under Controlled Laboratory Conditions P. Gautam et al. https://doi.org/10.3390/atmos16050502
42. High-resolution large-eddy simulation of indoor turbulence and its effect on airborne transmission of respiratory pathogens—Model validation and infection probability analysis M. Auvinen et al. https://doi.org/10.1063/5.0076495
43. Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes K. Kelly et al. https://doi.org/10.1021/acs.est.0c02341
44. SENSORES DE MATERIAL PARTICULADO EN SUSPENSIÓN DE BAJO COSTO: INTEGRACIÓN AL MONITOREO DE LA CALIDAD DEL AIRE D. Gomez & J. Vassallo https://doi.org/10.22201/iingen.0718378xe.2023.16.3.86568
45. Silicon Nitride Photonic Particle Detector—Experiments and Model Assessment A. Buchberger et al. https://doi.org/10.1109/JSEN.2021.3087633
46. Site-Specific Calibration of Low-Cost Particulate Matter (PM) 2.5 Monitors in the United States: A Comparison of Industrial and Non-Industrial Communities J. Lee et al. https://doi.org/10.1007/s44408-026-00112-7
47. 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
48. Selective Detection of Functionalized Carbon Particles based on Polymer Semiconducting and Conducting Devices as Potential Particulate Matter Sensors Y. Song et al. https://doi.org/10.1002/smll.202310527
49. 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
50. Particulate matter in a lockdown home: evaluation, calibration, results and health risk from an IoT enabled low-cost sensor network for residential air quality monitoring N. Cowell et al. https://doi.org/10.1039/D2EA00124A
51. Statistics of a Sharp GP2Y Low-Cost Aerosol PM Sensor Output Signals K. Bučar et al. https://doi.org/10.3390/s20236707
52. Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution—Part B—Particle Number Concentrations F. Bulot et al. https://doi.org/10.3390/s23177657
53. Performance Evaluation of Five Different Low-Cost Particulate Matter Sensors for Monodisperse Test Aerosols M. Nothhelfer et al. https://doi.org/10.1007/s44408-025-00019-9
54. Evaluation and Application of a Novel Low-Cost Wearable Sensing Device in Assessing Real-Time PM2.5 Exposure in Major Asian Transportation Modes W. Wang et al. https://doi.org/10.3390/atmos12020270
55. 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
56. Assessment of aerosol persistence in ICUs via low-cost sensor network and zonal models K. Glenn et al. https://doi.org/10.1038/s41598-023-30778-7
57. 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
58. Citizen-operated mobile low-cost sensors for urban PM2.5 monitoring: field calibration, uncertainty estimation, and application A. Hassani et al. https://doi.org/10.1016/j.scs.2023.104607
59. Spatio-temporal analysis of bicyclists’ PM2.5 exposure levels in a medium sized urban agglomeration M. Tames et al. https://doi.org/10.1007/s10661-024-13356-w
60. Field evaluation of community condensation particle counters, optical particle counters, and a low-cost particle sensor X. Wang et al. https://doi.org/10.1080/02786826.2025.2590103
61. Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning A. Hassani et al. https://doi.org/10.1021/acs.est.3c03661
62. Real-time in situ monitoring of dust particle growth in a low-pressure nanodusty plasma based on laser-induced photodetachment T. Donders & J. Beckers https://doi.org/10.1063/5.0188876
63. 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
64. Assessment of IoT low-cost sensor networks for long-term outdoor and indoor air quality monitoring: a case study in Dublin J. Chen et al. https://doi.org/10.1016/j.apr.2025.102651
65. Seasonal effects in the application of the MOment MAtching (MOMA) remote calibration tool to outdoor PM2.5 air sensors L. Weissert et al. https://doi.org/10.5194/amt-18-3635-2025
66. Size-Resolved Field Performance of Low-Cost Sensors for Particulate Matter Air Pollution E. Molina Rueda et al. https://doi.org/10.1021/acs.estlett.3c00030
67. Application and validation of a wearable monitor for assessing time- and location-resolved exposures to particulate matter in California’s Central Valley X. Li et al. https://doi.org/10.1080/02786826.2024.2415481
68. Monitoring Air Pollution in Wartime Kyiv (Ukraine): PM2.5 Spikes During Russian Missile and Drone Attacks K. Bondar et al. https://doi.org/10.3390/urbansci9110477
69. 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
70. Design and testing of a low-cost sensor and sampling platform for indoor air quality J. Tryner et al. https://doi.org/10.1016/j.buildenv.2021.108398
71. Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative F. Reisen et al. https://doi.org/10.3390/s21217206
72. From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors M. Giordano et al. https://doi.org/10.1016/j.jaerosci.2021.105833
73. The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias G. Tancev & C. Pascale https://doi.org/10.3390/s20216198
74. 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
75. Ambient characterisation of PurpleAir particulate matter monitors for measurements to be considered as indicative A. Caseiro et al. https://doi.org/10.1039/D2EA00085G
76. Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India M. Campmier et al. https://doi.org/10.5194/amt-16-4357-2023
77. Exploring particle concentrations and inside-to-outside ratios in vehicles: A real-time road test study D. Wang et al. https://doi.org/10.1016/j.scitotenv.2024.170783
78. Ten questions concerning low-cost indoor air quality sensors: Perspectives from research and practice T. Parkinson et al. https://doi.org/10.1016/j.buildenv.2026.114737
79. Open-source Internet of Things (IoT)-based air pollution monitoring system with protective case for tropical environments E. Collado et al. https://doi.org/10.1016/j.ohx.2024.e00560
80. Varying Performance of Low-Cost Sensors During Seasonal Smog Events in Moravian-Silesian Region V. Nevrlý et al. https://doi.org/10.3390/atmos15111326
81. 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
82. Field evaluation of low-cost particulate matter (PM) sensor instruments in indoor and outdoor environments: A university lecture hall and urban southeastern United States S. Westgate et al. https://doi.org/10.1080/02786826.2025.2484244
83. Exploring Environmental and Temporal Performances of Machine Learning Models for Calibration of a Low-Cost PM2.5 Sensor R. Wathore et al. https://doi.org/10.1007/s44408-026-00094-6
84. A scalable framework for harmonizing, standardization, and correcting crowd-sourced low-cost sensor PM2.5 data across Europe A. Hassani et al. https://doi.org/10.1016/j.jenvman.2025.125100
85. Development of Drone-Mounted Multiple Sensing System with Advanced Mobility for In Situ Atmospheric Measurement: A Case Study Focusing on PM2.5 Local Distribution H. Madokoro et al. https://doi.org/10.3390/s21144881
86. Traceable PM2.5 and PM10 Calibration of Low-Cost Sensors with Ambient-like Aerosols Generated in the Laboratory S. Horender et al. https://doi.org/10.3390/app11199014
87. Performance evaluation of MOMA (MOment MAtching) – a remote network calibration technique for PM2.5 and PM10 sensors L. Weissert et al. https://doi.org/10.5194/amt-16-4709-2023
88. Recent developments in polysaccharide-based electrospun nanofibers for environmental applications Z. Raza et al. https://doi.org/10.1016/j.carres.2021.108443
89. An evaluation of the U.S. EPA's correction equation for PurpleAir sensor data in smoke, dust, and wintertime urban pollution events D. Jaffe et al. https://doi.org/10.5194/amt-16-1311-2023
90. The value of adding black carbon to community monitoring of particulate matter R. Sugrue et al. https://doi.org/10.1016/j.atmosenv.2024.120434
91. Using low-cost particle sensors in HVAC ducts V. Gentile et al. https://doi.org/10.1080/02786826.2025.2475085
92. 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
93. Low-cost sensors and Machine Learning aid in identifying environmental factors affecting particulate matter emitted by household heating A. Hassani et al. https://doi.org/10.1016/j.atmosenv.2023.120108
94. Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana C. McFarlane et al. https://doi.org/10.1021/acsearthspacechem.1c00217
95. Farmers and Local Residents Collaborate: Application of a Participatory Citizen Science Approach to Characterising Air Quality in a Rural Area in The Netherlands A. Woutersen et al. https://doi.org/10.3390/s22208053
96. A Real-Time Comparison of Four Particulate Matter Size Fractions in the Personal Breathing Zone of Paris Subway Workers: A Six-Week Prospective Study R. Pétremand et al. https://doi.org/10.3390/su14105999
97. Spectral analysis approach for assessing the accuracy of low-cost air quality sensor network data V. Kumar et al. https://doi.org/10.5194/amt-16-5415-2023
98. Low-cost air quality monitoring system design and comparative analysis with a conventional method M. Jacob et al. https://doi.org/10.1007/s40095-021-00415-y
99. Evaluating the PurpleAir monitor as an aerosol light scattering instrument J. Ouimette et al. https://doi.org/10.5194/amt-15-655-2022
100. A Swirling-Flow-Enhanced Triboelectric Nanogenerator for Improved Dilute-Phase Particle Sensing M. Zhang et al. https://doi.org/10.3390/s26082284
101. Laboratory Evaluation of Low-Cost Optical Particle Counters for Environmental and Occupational Exposures S. Sousan et al. https://doi.org/10.3390/s21124146
102. GeoAir—A Novel Portable, GPS-Enabled, Low-Cost Air-Pollution Sensor: Design Strategies to Facilitate Citizen Science Research and Geospatial Assessments of Personal Exposure Y. Park et al. https://doi.org/10.3390/s21113761
103. Integrated evanescent field detector for ultrafine particles—theory and concept A. Buchberger et al. https://doi.org/10.1364/OE.394396
104. Performance assessment of NOVA SDS011 low-cost PM sensor in various microenvironments A. Božilov et al. https://doi.org/10.1007/s10661-022-10290-7
105. DATIV—Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors G. Hasanuzzaman et al. https://doi.org/10.3390/s24134314
106. One-Point Calibration of Low-Cost Sensors for Particulate Air Matter (PM) Concentration Measurement L. Russi et al. https://doi.org/10.3390/s25030692
107. 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
108. Fine particulate matter (PM2.5) exposure assessment among active daily commuters to induce behaviour change to reduce air pollution A. Ilenič et al. https://doi.org/10.1016/j.scitotenv.2023.169117
109. SenseBike: A New Low-Cost Mobile-Networked Sensor System for Cyclists to Monitor Air Quality and Automatically Measure Passing Distances in Urban Traffic A. Tenbeitel et al. https://doi.org/10.3390/s25227099
110. Design and Evaluation of a Calibration Chamber for Low-Cost PM Sensors–Part 1: Number Concentration-Based Assessment V. Malyan et al. https://doi.org/10.1021/acsestair.5c00262
111. Factors influencing particle resuspension and dispersion: An experimental study using test-track measurements B. Obeid et al. https://doi.org/10.1016/j.trd.2024.104321
112. Measurement variability in residential PM2.5: an evaluation of a low-cost sensor in the Netherlands J. Holtjer et al. https://doi.org/10.1007/s11869-025-01833-1
113. AIoT-Powered Real-Time Sensing and Calibration of Low-Cost Particulate Matter Sensors Using the TCM Network G. Msigwa et al. https://doi.org/10.1109/JIOT.2025.3631631
114. Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science J. Hofman et al. https://doi.org/10.3390/s24175653
115. Quantification of aerosol dispersal from suspected aerosol-generating procedures R. Strand-Amundsen et al. https://doi.org/10.1183/23120541.00206-2021
116. Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network W. Wang et al. https://doi.org/10.3390/s20175002
117. Can low-cost sensors (LCS) enhance air quality monitoring for personal pollution exposure assessment? A. Ilenič et al. https://doi.org/10.1088/2515-7620/ae02ef
118. Fundamental understanding of low-cost sensor response S. Kookkal & S. Dhaniyala https://doi.org/10.1080/02786826.2026.2649803
119. Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter M. Aix et al. https://doi.org/10.1016/j.scitotenv.2023.164063
120. Evaluation of a new low-cost particle sensor as an internet-of-things device for outdoor air quality monitoring F. Roberts et al. https://doi.org/10.1080/10962247.2022.2093293
121. Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring B. Chauhan et al. https://doi.org/10.3390/atmos16040359
122. Understanding the effect of outdoor pollution episodes and HVAC type on indoor air quality T. Mangin et al. https://doi.org/10.1016/j.buildenv.2025.112978
123. Spatial and temporal variability of surface dust concentration in western Crete (Greece) – Development of a monitoring network S. Bitzan et al. https://doi.org/10.1016/j.aeolia.2025.101005
124. The sphere of exposure: centering user experience in community science air monitoring M. Westbrook et al. https://doi.org/10.3389/fenvs.2024.1433489
125. 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
126. 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
127. Deployment and Evaluation of a Network of Open Low-Cost Air Quality Sensor Systems P. Schneider et al. https://doi.org/10.3390/atmos14030540
128. Wildfire smoke impacts on indoor air quality assessed using crowdsourced data in California Y. Liang et al. https://doi.org/10.1073/pnas.2106478118
129. Laboratory evaluation of the Alphasense OPC-N3, and the Plantower PMS5003 and PMS6003 sensors K. Kaur & K. Kelly https://doi.org/10.1016/j.jaerosci.2023.106181
130. Insights into the utility of small form air quality monitoring in health care environments: lessons learned from the University Hospitals Birmingham NHS Foundation Trust N. Cowell et al. https://doi.org/10.3389/fpubh.2025.1634075
131. A review of extreme air pollution measurement and modeling techniques with applications K. Rautela et al. https://doi.org/10.1016/j.rser.2026.117178
132. Outward and inward protection efficiencies of different mask designs for different respiratory activities X. Koh et al. https://doi.org/10.1016/j.jaerosci.2021.105905
133. Towards a hygroscopic growth calibration for low-cost PM2.5 sensors M. Patel et al. https://doi.org/10.5194/amt-17-1051-2024
134. Reliability Assessment of Low-Cost PM Sensors Under High Humidity and High PM Level Outdoor Conditions G. Kumar et al. https://doi.org/10.1109/JSEN.2025.3592796