75 citations as recorded by crossref.

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16. Vertical variation of atmospheric particulate matter under different pollution levels in the suburbs of Tianjin based on unmanned aerial vehicle S. Li et al. 10.1080/10962247.2022.2134231
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20. Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests S. Mills et al. 10.1016/j.scitotenv.2023.161969
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22. Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India M. Campmier et al. 10.5194/amt-16-4357-2023
23. Improving Aerosol Characterization Using an Optical Particle Counter Coupled with a Quartz Crystal Microbalance with an Integrated Microheater E. Zampetti et al. 10.3390/s24082500
24. 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. 10.1016/j.jaerosci.2021.105833
25. Light painting photography makes particulate matter air pollution visible F. Pope et al. 10.1038/s43247-024-01409-4
26. Schools’ air quality monitoring for health and education: Methods and protocols of the SAMHE initiative and project L. Chatzidiakou et al. 10.1016/j.dibe.2023.100266
27. Calibration Method for Particulate Matter Low-Cost Sensors Used in Ambient Air Quality Monitoring and Research J. Venkatraman Jagatha et al. 10.3390/s21123960
28. Mass concentration measurements of autumn bioaerosol using low-cost sensors in a mature temperate woodland free-air carbon dioxide enrichment (FACE) experiment: investigating the role of meteorology and carbon dioxide levels A. Baird et al. 10.5194/bg-19-2653-2022
29. Long-Term Evaluation of Mid-Cost Optical Particle Counters for PM2.5 Monitoring in an Underground Subway Station: Insights from a 15-Month Study T. Dinh et al. 10.3390/chemosensors13010025
30. Impact of nylon and teflon filter media on the sampling of inorganic aerosols over a high altitude site L. Yang et al. 10.1016/j.envadv.2023.100373
31. Task specific assessment of particle exposure and low-cost sensor performance in indoor construction environments A. Brostrøm et al. 10.1016/j.aeaoa.2025.100336
32. 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
33. PM2.5 exposure differences between children and adults L. Harr et al. 10.1016/j.uclim.2022.101198
34. Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter M. Aix et al. 10.1016/j.scitotenv.2023.164063
35. MitH: A framework for Mitigating Hygroscopicity in low-cost PM sensors M. Casari & L. Po 10.1016/j.envsoft.2024.105955
36. Simultaneous measurement of spatially resolved particle emissions in a pilot plant scale baghouse filter applying distributed low-cost particulate matter sensors P. Bächler et al. 10.1016/j.jaerosci.2020.105644
37. A case study evaluating the performance of a cost-effective optical particle counter coupled with a humidity compensation approach for ambient air monitoring of particulate matter T. Dinh et al. 10.1007/s44273-023-00017-6
38. Improving PM10 sensor accuracy in urban areas through calibration in Timișoara R. Blaga & S. Gautam 10.1038/s41612-024-00812-0
39. Machine learning methods for low-cost pollen monitoring – Model optimisation and interpretability S. Mills et al. 10.1016/j.scitotenv.2023.165853
40. Using low-cost sensors to assess common air pollution sources across multiple residences C. Rathbone et al. 10.1038/s41598-025-85985-1
41. EEATC: A Novel Calibration Approach for Low-Cost Sensors M. Narayana et al. 10.1109/JSEN.2023.3304366
42. Local spatiotemporal dynamics of particulate matter and oak pollen measured by machine learning aided optical particle counters S. Mills et al. 10.1016/j.scitotenv.2024.173450
43. 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. 10.1039/D2EA00124A
44. Air Quality Sensor Networks for Evidence-Based Policy Making: Best Practices for Actionable Insights J. Hofman et al. 10.3390/atmos13060944
45. The Aerosol Research Observation Station (AEROS) K. Ardon-Dryer et al. 10.5194/amt-15-2345-2022
46. Evaluation of calibration performance of a low-cost particulate matter sensor using collocated and distant NO2 K. Ko et al. 10.5194/amt-17-3303-2024
47. Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study D. Bousiotis et al. 10.5194/amt-14-4139-2021
48. Spatial Distribution of PM2.5 Mass and Number Concentrations in Paris (France) from the Pollutrack Network of Mobile Sensors during 2018–2022 J. Renard et al. 10.3390/s23208560
49. A study on the performance of low-cost sensors for source apportionment at an urban background site D. Bousiotis et al. 10.5194/amt-15-4047-2022
50. Seasonal Changes in Urban PM2.5 Hotspots and Sources from Low-Cost Sensors L. Harr et al. 10.3390/atmos13050694
51. Quantification of within-vehicle exposure to NOx and particles: Variation with outside air quality, route choice and ventilation options V. Matthaios et al. 10.1016/j.atmosenv.2020.117810
52. Air quality assessment in three East African cities using calibrated low-cost sensors with a focus on road-based hotspots A. Singh et al. 10.1088/2515-7620/ac0e0a
53. Evaluation of low-cost formaldehyde sensors calibration M. Justo Alonso et al. 10.1016/j.buildenv.2022.109380
54. 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
55. Long-term airborne measurements of pollutants over the United Kingdom to support air quality model development and evaluation A. Mynard et al. 10.5194/amt-16-4229-2023
56. Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science J. Hofman et al. 10.3390/s24175653
57. A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques A. Baruah et al. 10.1038/s41612-024-00859-z
58. Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors D. Bousiotis et al. 10.1016/j.envint.2023.107907
59. Real-Time Source Apportionment of Particulate Matter from Low-Cost Particle Sensors Using Machine Learning V. Kumar et al. 10.1007/s41810-024-00271-3
60. In-situ validation of embedded physics-based calibration in low-cost particulate matter sensor for urban air quality monitoring Z. Feng et al. 10.1016/j.uclim.2025.102289
61. Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges L. Liang 10.1016/j.envres.2021.111163
62. Assessment of the Feasibility of a Future Integrated Larger-Scale Epidemiological Study to Evaluate Health Risks of Air Pollution Episodes in Children S. Nauwelaerts et al. 10.3390/ijerph19148531
63. Testing a New “Decrypted” Algorithm for Plantower Sensors Measuring PM2.5: Comparison with an Alternative Algorithm L. Wallace 10.3390/a16080392
64. Performance evaluation of MOMA (MOment MAtching) – a remote network calibration technique for PM2.5 and PM10 sensors L. Weissert et al. 10.5194/amt-16-4709-2023
65. Low-Cost Particulate Matter Mass Sensors: Review of the Status, Challenges, and Opportunities for Single-Instrument and Network Calibration J. Zhang et al. 10.1021/acssensors.4c03293
66. Distant calibration of low-cost PM and NO2 sensors; evidence from multiple sensor testbeds J. Hofman et al. 10.1016/j.apr.2021.101246
67. Potential of low-cost PM monitoring sensors to fill monitoring gaps in areas of Sub-Saharan Africa G. Gualtieri et al. 10.1016/j.apr.2024.102158
68. Impacts of daily household activities on indoor particulate and NO2 concentrations; a case study from oxford UK A. Singh et al. 10.1016/j.heliyon.2024.e34210
69. Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information E. Considine et al. 10.1021/acs.est.2c06626
70. Towards comprehensive air quality management using low-cost sensors for pollution source apportionment D. Bousiotis et al. 10.1038/s41612-023-00424-0
71. Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany J. Venkatraman Jagatha et al. 10.3390/s24134193
72. SensEURCity: A multi-city air quality dataset collected for 2020/2021 using open low-cost sensor systems M. Van Poppel et al. 10.1038/s41597-023-02135-w
73. Particle generation and dispersion from high-speed dental drilling M. Kumar et al. 10.1007/s00784-023-05163-3
74. Spatial calibration and PM2.5 mapping of low-cost air quality sensors H. Chu et al. 10.1038/s41598-020-79064-w
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