210 citations as recorded by crossref.

1. Highly resolved spatiotemporal variability of fine particle number concentrations in an urban neighborhood Y. Etzion & D. Broday https://doi.org/10.1016/j.jaerosci.2018.01.004
2. Air Quality Impacts at an E‐Waste Site in Ghana Using Flexible, Moderate‐Cost and Quality‐Assured Measurements L. Kwarteng et al. https://doi.org/10.1029/2020GH000247
3. Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing H. Mei et al. https://doi.org/10.3390/s20164381
4. Spatial calibration and PM2.5 mapping of low-cost air quality sensors H. Chu et al. https://doi.org/10.1038/s41598-020-79064-w
5. Sustaining low-cost PM2.5 monitoring networks in South-Asian Countries: technical challenges and solutions T. Saeed et al. https://doi.org/10.1016/j.envint.2026.110275
6. Laboratory assessment of low-cost PM monitors A. Manikonda et al. https://doi.org/10.1016/j.jaerosci.2016.08.010
7. Evaluation methods for low-cost particulate matter sensors J. Bean https://doi.org/10.5194/amt-14-7369-2021
8. Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors T. Sayahi et al. https://doi.org/10.1016/j.envpol.2019.113131
9. Effects of spatial sensitivity on mass sensing with bulk acoustic mode resonators A. Zielinski et al. https://doi.org/10.1016/j.sna.2015.11.003
10. Effects of Road Traffic on the Accuracy and Bias of Low-Cost Particulate Matter Sensor Measurements in Houston, Texas T. Oluwadairo et al. https://doi.org/10.3390/ijerph19031086
11. Observation of PM2.5 using a combination of satellite remote sensing and low-cost sensor network in Siberian urban areas with limited reference monitoring C. Lin et al. https://doi.org/10.1016/j.atmosenv.2020.117410
12. Development and testing a low-cost device for airborne PM monitoring in highly anthropized areas: The international maritime hub of Civitavecchia (Rome, Italy) F. Lucci et al. https://doi.org/10.1016/j.geogeo.2022.100120
13. 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
14. 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
15. Evaluation and environmental correction of ambient CO2 measurements from a low-cost NDIR sensor C. Martin et al. https://doi.org/10.5194/amt-10-2383-2017
16. Comparison of Low-Cost Particulate Matter Sensors for Indoor Air Monitoring during COVID-19 Lockdown M. Kaliszewski et al. https://doi.org/10.3390/s20247290
17. Assessment and Improvement of Two Low-Cost Particulate Matter Sensor Systems by Using Spatial Interpolation Data from Air Quality Monitoring Stations C. Liang & P. Yu https://doi.org/10.3390/atmos12030300
18. Indoor Air Pollution and Health: Bridging Perspectives from Developing and Developed Countries A. Pillarisetti et al. https://doi.org/10.1146/annurev-environ-012220-010602
19. A Survey on Real-Time Communications in Wireless Sensor Networks B. Kim et al. https://doi.org/10.1155/2017/1864847
20. Long-term field evaluation of the Plantower PMS low-cost particulate matter sensors T. Sayahi et al. https://doi.org/10.1016/j.envpol.2018.11.065
21. Field Assessment of the Village Green Project: An Autonomous Community Air Quality Monitoring System W. Jiao et al. https://doi.org/10.1021/acs.est.5b01245
22. What does the Shinyei PPD42NS Low-Cost Dust Sensor Really Measure? M. Canu et al. https://doi.org/10.18178/ijesd.2021.12.1.1310
23. Review of low-cost sensors for indoor air quality: Features and applications M. Ródenas García et al. https://doi.org/10.1080/05704928.2022.2085734
24. How Sensors Might Help Define the External Exposome M. Loh et al. https://doi.org/10.3390/ijerph14040434
25. 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
26. The Aerosol Research Observation Station (AEROS) K. Ardon-Dryer et al. https://doi.org/10.5194/amt-15-2345-2022
27. A tunable high-pass filter for simple and inexpensive size-segregation of sub-10-nm nanoparticles N. Surawski et al. https://doi.org/10.1038/srep45678
28. Evaluation of the Performance of Low-Cost Air Quality Sensors at a High Mountain Station with Complex Meteorological Conditions H. Li et al. https://doi.org/10.3390/atmos11020212
29. Comparison of methods for converting Dylos particle number concentrations to PM2.5 mass concentrations R. Franken et al. https://doi.org/10.1111/ina.12546
30. 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
31. Calibration and validation-based assessment of low-cost air quality sensors J. Dong et al. https://doi.org/10.1016/j.scitotenv.2025.179364
32. Hygroscopic properties of particulate matter and effects of their interactions with weather on visibility W. Won et al. https://doi.org/10.1038/s41598-021-95834-6
33. Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection J. Bi et al. https://doi.org/10.1016/j.envint.2021.106897
34. Evaluation of a Low-Cost Aerosol Sensor to Assess Dust Concentrations in a Swine Building S. Jones et al. https://doi.org/10.1093/annhyg/mew009
35. Re-calibrating DIY: Testing digital participation across dust sensors, fry pans and environmental pollution H. Pritchard et al. https://doi.org/10.1177/1461444818777473
36. Estimating Hourly Concentrations of PM2.5 across a Metropolitan Area Using Low-Cost Particle Monitors N. Zikova et al. https://doi.org/10.3390/s17081922
37. Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments L. Chatzidiakou et al. https://doi.org/10.5194/amt-12-4643-2019
38. Building and optimising an automatic monitoring system network for outdoor PM2.5: a case study of Ho Chi Minh City L. Bui et al. https://doi.org/10.1007/s10661-023-11319-1
39. Observation and modeling of vertical carbon dioxide distribution in a heavily polluted suburban environment Z. BAO et al. https://doi.org/10.1080/16742834.2020.1746627
40. Evaluating the Performance of Low-Cost Air Quality Monitors in Dallas, Texas H. Khreis et al. https://doi.org/10.3390/ijerph19031647
41. The use of low-cost measuring devices for testing air quality in hard-to-reach locations M. Rogulski et al. https://doi.org/10.1051/e3sconf/20184400151
42. Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors J. Kuula et al. https://doi.org/10.5194/amt-13-2413-2020
43. Response Characterization of an Inexpensive Aerosol Sensor J. Kuula et al. https://doi.org/10.3390/s17122915
44. Relationship Between the Concentrations Of PM2.5 Indoors Obtained by Using the Optical and Gravimetric Methods: Preliminary Analysis J. PASTUSZKA https://doi.org/10.21307/acee-2020-035
45. An Integrated Sensor Data Logging, Survey, and Analytics Platform for Field Research and Its Application in HAPIN, a Multi-Center Household Energy Intervention Trial D. Wilson et al. https://doi.org/10.3390/su12051805
46. Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles Y. Guan et al. https://doi.org/10.1080/01621459.2019.1665526
47. Performance of low-cost indoor air quality monitors for PM2.5 and PM10 from residential sources Z. Wang et al. https://doi.org/10.1016/j.buildenv.2020.106654
48. Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network S. Ali et al. https://doi.org/10.3390/s23020854
49. Visualization of Dust Generation in Outdoor Workplaces Using A Wearable Particle Monitor and Global Navigation Satellite System S. SEKOGUCHI et al. https://doi.org/10.7888/juoeh.44.1
50. Experimental and modeling assessment of a novel automotive cabin PM2.5 removal system J. Vande Hey et al. https://doi.org/10.1080/02786826.2018.1490694
51. Field Testing of Low-Cost PM Sensors in Animal Production Facilities S. Uğuz et al. https://doi.org/10.33462/jotaf.1555650
52. Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors S. Jha et al. https://doi.org/10.1109/JSEN.2021.3118454
53. 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
54. 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
55. Usability of a Personal Air Pollution Monitor: Design-Feedback Iterative Cycle Study G. Duncan et al. https://doi.org/10.2196/12023
56. 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
57. Validation of a light-scattering PM2.5 sensor monitor based on the long-term gravimetric measurements in field tests J. Shi et al. https://doi.org/10.1371/journal.pone.0185700
58. Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring L. Crilley et al. https://doi.org/10.5194/amt-11-709-2018
59. Dissecting PM sensor capabilities: A combined experimental and theoretical study on particle sizing and physicochemical properties X. Qin et al. https://doi.org/10.1016/j.envpol.2024.124354
60. Health Benefit Assessment of Running in Urban Areas against the Background of Particulate Matter 2.5 Concentration: The Munich Olympic Park L. Zeindl & J. Koenigstorfer https://doi.org/10.3390/urbansci4040062
61. Spatial and temporal variability of urban cyclists’ exposure to PM2.5 in Medellín, Colombia J. Martínez et al. https://doi.org/10.1016/j.apr.2023.101946
62. 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
63. A comprehensive review on PM, gaseous and biological emissions: monitoring techniques, health impacts and mitigation strategies R. Yasmeen https://doi.org/10.1080/00439339.2025.2563560
64. Real-time sensors for indoor air monitoring and challenges ahead in deploying them to urban buildings P. Kumar et al. https://doi.org/10.1016/j.scitotenv.2016.04.032
65. Potential of a low-cost gas sensor for atmospheric methane monitoring M. van den Bossche et al. https://doi.org/10.1016/j.snb.2016.07.092
66. 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
67. Efficient field correction of low-cost particulate matter sensors using machine learning, mixed multiplicative/additive scaling and extended calibration inputs S. Koziel et al. https://doi.org/10.1038/s41598-025-02069-w
68. Inadequacy of air purifier for indoor air quality improvement in classrooms without external ventilation Y. Choe et al. https://doi.org/10.1016/j.buildenv.2021.108450
69. Solar-powered air quality monitor applied under subtropical conditions in Hong Kong: Performance evaluation and application for pollution source tracking P. Wei et al. https://doi.org/10.1016/j.atmosenv.2019.116825
70. Field and Laboratory Evaluations of the Low-Cost Plantower Particulate Matter Sensor M. Levy Zamora et al. https://doi.org/10.1021/acs.est.8b05174
71. Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile M. Tagle et al. https://doi.org/10.1007/s10661-020-8118-4
72. Small, Smart, Fast, and Cheap: Microchip-Based Sensors to Estimate Air Pollution Exposures in Rural Households A. Pillarisetti et al. https://doi.org/10.3390/s17081879
73. Effects of fireworks on particulate matter concentration in a narrow valley: the case of the Medellín metropolitan area C. Hoyos et al. https://doi.org/10.1007/s10661-019-7838-9
74. Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods M. Si et al. https://doi.org/10.5194/amt-13-1693-2020
75. Development and performance evaluation of a mixed-sensor system for fine particles and road traffic noise C. Wu et al. https://doi.org/10.1016/j.eti.2022.102902
76. Humidity, density, and inlet aspiration efficiency correction improve accuracy of a low-cost sensor during field calibration at a suburban site in the North-Western Indo-Gangetic plain (NW-IGP) H. Pawar & B. Sinha https://doi.org/10.1080/02786826.2020.1719971
77. Using crowd-sourced low-cost sensors in a land use regression of PM2.5 in 6 US cities T. Lu et al. https://doi.org/10.1007/s11869-022-01162-7
78. 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. https://doi.org/10.5194/amt-14-4139-2021
79. Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? N. Castell et al. https://doi.org/10.1016/j.envint.2016.12.007
80. Air Pollution Effects on Mental Health Relationships: Scoping Review on Historically Used Methodologies to Analyze Adult Populations K. Leontjevaite et al. https://doi.org/10.3390/air2030016
81. 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
82. Advancements in Air Quality Monitoring Systems: A Comprehensive Review of Emerging Technologies for Enhancing Environmental Health S. Ahmed et al. https://doi.org/10.1155/ina/3080684
83. Characterization of a commercial lower-cost medium-precision non-dispersive infrared sensor for atmospheric CO2 monitoring in urban areas E. Arzoumanian et al. https://doi.org/10.5194/amt-12-2665-2019
84. The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors J. Kim et al. https://doi.org/10.5194/amt-11-1937-2018
85. Bias in PM2.5 measurements using collocated reference-grade and optical instruments M. Kushwaha et al. https://doi.org/10.1007/s10661-022-10293-4
86. Identification of technical problems affecting performance of DustTrak DRX aerosol monitors I. Rivas et al. https://doi.org/10.1016/j.scitotenv.2017.01.129
87. Performance of low-cost monitors to assess household air pollution A. Curto et al. https://doi.org/10.1016/j.envres.2018.01.024
88. Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi T. Zheng et al. https://doi.org/10.5194/amt-12-5161-2019
89. End-user perspective of low-cost sensors for outdoor air pollution monitoring A. Rai et al. https://doi.org/10.1016/j.scitotenv.2017.06.266
90. Response of consumer and research grade indoor air quality monitors to residential sources of fine particles B. Singer & W. Delp https://doi.org/10.1111/ina.12463
91. 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
92. Measurements of PM2.5 with PurpleAir under atmospheric conditions K. Ardon-Dryer et al. https://doi.org/10.5194/amt-13-5441-2020
93. Identifying optimal co-location calibration periods for low-cost sensors M. Levy Zamora et al. https://doi.org/10.5194/amt-16-169-2023
94. Evaluation of low-cost sensors for quantitative personal exposure monitoring S. Mahajan & P. Kumar https://doi.org/10.1016/j.scs.2020.102076
95. Mobile phones as monitors of personal exposure to air pollution: Is this the future? M. Nyarku et al. https://doi.org/10.1371/journal.pone.0193150
96. A new evaluation method of three different low-cost dust sensors using exponentially decaying particle concentrations K. Kang et al. https://doi.org/10.4491/eer.2020.501
97. Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors A. Cavaliere et al. https://doi.org/10.5194/amt-16-4723-2023
98. In Situ Calibration Algorithms for Environmental Sensor Networks: A Review F. Delaine et al. https://doi.org/10.1109/JSEN.2019.2910317
99. Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States W. Jiao et al. https://doi.org/10.5194/amt-9-5281-2016
100. Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments T. Zheng et al. https://doi.org/10.5194/amt-11-4823-2018
101. Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment N. Johnson et al. https://doi.org/10.1016/j.atmosenv.2018.04.019
102. Assessment of seasonal variation in PM2.5 concentration using low-cost sensors: A case study of Jaipur city, India S. Chaudhry et al. https://doi.org/10.1016/j.atmosenv.2025.121067
103. Portable, Ambient PM2.5 Sensor for Human and/or Animal Exposure Studies T. Cao & J. Thompson https://doi.org/10.1080/00032719.2016.1190736
104. Outlier detection and gap filling methodologies for low-cost air quality measurements T. Ottosen & P. Kumar https://doi.org/10.1039/C8EM00593A
105. Effect of micropillars with varying geometry and density on the efficiency of impaction-based quartz crystal microbalance aerosol sensors M. Hajizadehmotlagh & I. Paprotny https://doi.org/10.1063/1.5125895
106. 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
107. Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research J. Huang et al. https://doi.org/10.3390/s22062381
108. Developing a Cloud-Based Air Quality Monitoring Platform Using Low-Cost Sensors A. Samad et al. https://doi.org/10.3390/s24030945
109. Exploring the applicability and limitations of selected optical scattering instruments for PM mass measurement J. Zhang et al. https://doi.org/10.5194/amt-11-2995-2018
110. The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog R. Jayaratne et al. https://doi.org/10.5194/amt-11-4883-2018
111. Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale J. Bi et al. https://doi.org/10.1021/acs.est.9b06046
112. Evaluating the feasibility of a personal particle exposure monitor in outdoor and indoor microenvironments in Shanghai, China K. Wang et al. https://doi.org/10.1080/09603123.2018.1533531
113. Assessment of health impact of the surface ozone on a population residing at Agadir city (Morocco) using the AirQ+ model Y. Bouchriti et al. https://doi.org/10.1051/e3sconf/202336402003
114. Node-to-node field calibration of wireless distributed air pollution sensor network F. Kizel et al. https://doi.org/10.1016/j.envpol.2017.09.042
115. Garbage in, gospel out? – Air quality assessment in the UK planning system A. Mills & S. Peckham https://doi.org/10.1016/j.envsci.2019.06.010
116. An integrated low-cost air quality sensor and a multi-task calibration framework for particulate matter F. Nan et al. https://doi.org/10.1016/j.envint.2025.109981
117. Evaluation of low-cost optical particle counters for monitoring individual indoor aerosol sources P. Salimifard et al. https://doi.org/10.1080/02786826.2019.1697423
118. A versatile low-cost sensing device for assessing PM2.5 spatiotemporal variation and quantifying source contribution S. Lung et al. https://doi.org/10.1016/j.scitotenv.2020.137145
119. Calibration of Portable Particulate Matter–Monitoring Device using Web Query and Machine Learning B. Loh & G. Choi https://doi.org/10.1016/j.shaw.2019.08.002
120. Low-Cost Indoor Sensor Deployment for Predicting PM2.5 Exposure S. Tsameret et al. https://doi.org/10.1021/acsestair.3c00105
121. Mathematical Modelling and Parameter Optimization of Fine Particle Sensors based on Laser Light Scattering W. Shao et al. https://doi.org/10.1109/JSEN.2017.2747083
122. Representativeness of the particulate matter pollution assessed by an official monitoring station of air quality in Santiago, Chile: projection to human health M. Préndez et al. https://doi.org/10.1007/s10653-022-01390-x
123. Performance evaluation of ozone and particulate matter sensors H. DeWitt et al. https://doi.org/10.1080/10962247.2020.1713921
124. A methodology for the characterization of portable sensors for air quality measure with the goal of deployment in citizen science B. Languille et al. https://doi.org/10.1016/j.scitotenv.2019.134698
125. Low Cost Sensor With IoT LoRaWAN Connectivity and Machine Learning-Based Calibration for Air Pollution Monitoring S. Ali et al. https://doi.org/10.1109/TIM.2020.3034109
126. Investigating a Low-Cost Dryer Designed for Low-Cost PM Sensors Measuring Ambient Air Quality A. Samad et al. https://doi.org/10.3390/s21030804
127. 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
128. Assessment of particulate matter (PM10, PM5, PM2.5, and PM1) concentrations at significant intersections in Douala-Cameroon city using low-cost sensors Y. Ngangmo & C. Adiang https://doi.org/10.1007/s12517-025-12239-9
129. Assessing the accuracy of low-cost optical particle sensors using a physics-based approach D. Hagan & J. Kroll https://doi.org/10.5194/amt-13-6343-2020
130. Effects of aerosol particle size on the measurement of airborne PM2.5 with a low-cost particulate matter sensor (LCPMS) in a laboratory chamber T. Oluwadairo et al. https://doi.org/10.1007/s10661-021-09715-6
131. The evaluation and optimization of calibration methods for low-cost particulate matter sensors: Inter-comparison between fixed and mobile methods X. Qin et al. https://doi.org/10.1016/j.scitotenv.2020.136791
132. Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements E. Cross et al. https://doi.org/10.5194/amt-10-3575-2017
133. A Survey on Sensor Calibration in Air Pollution Monitoring Deployments B. Maag et al. https://doi.org/10.1109/JIOT.2018.2853660
134. Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors A. Cavaliere et al. https://doi.org/10.3390/s18092843
135. Evaluation of two collocated federal equivalent method PM2.5 instruments over a wide range of concentrations in Sarajevo, Bosnia and Herzegovina G. Hagler et al. https://doi.org/10.1016/j.apr.2022.101374
136. In-car particulate matter exposure across ten global cities P. Kumar et al. https://doi.org/10.1016/j.scitotenv.2020.141395
137. Data Verification Tools for Minimizing Management Costs of Dense Air-Quality Monitoring Networks G. Miskell et al. https://doi.org/10.1021/acs.est.5b04421
138. 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
139. New Methods for Personal Exposure Monitoring for Airborne Particles K. Koehler & T. Peters https://doi.org/10.1007/s40572-015-0070-z
140. Evaluation of two low-cost PM monitors under different laboratory and indoor conditions R. He et al. https://doi.org/10.1080/02786826.2020.1851649
141. Enhancing the reliability of particulate matter sensing by multivariate Tobit model using weather and air quality data W. Won et al. https://doi.org/10.1038/s41598-023-40468-z
142. Ambient and laboratory evaluation of a low-cost particulate matter sensor K. Kelly et al. https://doi.org/10.1016/j.envpol.2016.12.039
143. Anti-perspirant deodorant particulate matter temporal concentrations during home usage V. Seller et al. https://doi.org/10.1016/j.buildenv.2021.107738
144. 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
145. The Effects of Climate Therapy on Cardiorespiratory Fitness and Exercise-Induced Bronchoconstriction in Children with Asthma C. De Pieri et al. https://doi.org/10.3390/atmos12111486
146. A Statistical Calibration Framework for Improving Non-Reference Method Particulate Matter Reporting: A Focus on Community Air Monitoring Settings S. Commodore et al. https://doi.org/10.3390/atmos11080807
147. Annual NO2 as a Predictor of Hourly NO2 Variability: Do Defra UK’s Heuristics Make Sense? A. Mills & S. Peckham https://doi.org/10.3390/atmos12030385
148. Extensive evaluation and classification of low‐cost dust sensors in laboratory using a newly developed test method K. Ahn et al. https://doi.org/10.1111/ina.12615
149. A fast calibration algorithm for Non-Dispersive Infrared single channel carbon dioxide sensor based on deep learning K. Mao et al. https://doi.org/10.1016/j.comcom.2021.08.003
150. Evaluation of PM2.5 measured in an urban setting using a low-cost optical particle counter and a Federal Equivalent Method Beta Attenuation Monitor B. Magi et al. https://doi.org/10.1080/02786826.2019.1619915
151. Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges L. Liang https://doi.org/10.1016/j.envres.2021.111163
152. Monitoring of Indoor Air Quality in a Classroom Combining a Low-Cost Sensor System and Machine Learning I. Apostolopoulos et al. https://doi.org/10.3390/chemosensors13040148
153. 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
154. Personal air pollution exposure: Monitoring methods and the emerging role of big data and machine learning D. Wang et al. https://doi.org/10.1016/j.eti.2025.104593
155. 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
156. Compositional Analysis of Adsorbed Organic Aerosol on a Microresonator Mass Sensor A. Zielinski et al. https://doi.org/10.1007/s41810-018-0029-1
157. Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning Y. Lv et al. https://doi.org/10.1016/j.scitotenv.2022.159339
158. Calibrating networks of low-cost air quality sensors P. deSouza et al. https://doi.org/10.5194/amt-15-6309-2022
159. Field testing a low-cost passive aerosol sampler for long-term measurement of ambient PM2.5 concentrations and particle composition M. Castillo et al. https://doi.org/10.1016/j.atmosenv.2019.116905
160. Improving data reliability: A quality control practice for low-cost PM2.5 sensor network X. Qiao et al. https://doi.org/10.1016/j.scitotenv.2021.146381
161. Predicting airborne pollutant concentrations and events in a commercial building using low-cost pollutant sensors and machine learning: A case study A. Mohammadshirazi et al. https://doi.org/10.1016/j.buildenv.2022.108833
162. Real-time particle monitoring of pesticide drift from an axial fan airblast orchard sprayer M. Blanco et al. https://doi.org/10.1038/s41370-018-0090-5
163. Calibration of low-cost particulate matter sensors: Model development for a multi-city epidemiological study M. Zusman et al. https://doi.org/10.1016/j.envint.2019.105329
164. Investigation of Low-Cost and Optical Particulate Matter Sensors for Ambient Monitoring M. Rogulski & A. Badyda https://doi.org/10.3390/atmos11101040
165. Ambient characterisation of PurpleAir particulate matter monitors for measurements to be considered as indicative A. Caseiro et al. https://doi.org/10.1039/D2EA00085G
166. Quantifying the impacts of PM2.5 constituents and relative humidity on visibility impairment in a suburban area of eastern Asia using long-term in-situ measurements Y. Ting et al. https://doi.org/10.1016/j.scitotenv.2021.151759
167. 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
168. Laboratory evaluation of low-cost PurpleAir PM monitors and in-field correction using co-located portable filter samplers J. Tryner et al. https://doi.org/10.1016/j.atmosenv.2019.117067
169. Fusion of Environmental Sensing on PM2.5 and Deep Learning on Vehicle Detecting for Acquiring Roadside PM2.5 Concentration Increments W. Wang et al. https://doi.org/10.3390/s20174679
170. Particulate matter pollution in an informal settlement in Nairobi: Using citizen science to make the invisible visible S. West et al. https://doi.org/10.1016/j.apgeog.2019.102133
171. Quantifying the dynamic characteristics of indoor air pollution using real-time sensors: Current status and future implication J. Wang et al. https://doi.org/10.1016/j.envint.2023.107934
172. Evaluating Indoor Air Quality in Residential Environments: A Study of PM2.5 and CO2 Dynamics Using Low-Cost Sensors K. Shah et al. https://doi.org/10.3390/environments11110237
173. Measuring Micrometers of Matter and Inventing Indices: Entangling Social Perception within Discrete and Continuous Measurements of Air Quality E. Schmitt https://doi.org/10.3390/socsci8020048
174. Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado S. Feinberg et al. https://doi.org/10.5194/amt-11-4605-2018
175. Low-cost PM monitors as an opportunity to increase the spatiotemporal resolution of measurements of air quality M. Rogulski https://doi.org/10.1016/j.egypro.2017.09.026
176. Evaluation of Low-cost Air Quality Sensor Calibration Models K. Aula et al. https://doi.org/10.1145/3512889
177. Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment S. Koziel et al. https://doi.org/10.1016/j.knosys.2024.111879
178. Development and Performance Evaluation of a Mixed-Sensor System for Fine Particles and Road Traffic Noise C. Wu et al. https://doi.org/10.2139/ssrn.4020749
179. Particulate Matter Sensors Mounted on a Robot for Environmental Aerosol Measurements A. Cashikar et al. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001569
180. Low-Cost Air Quality Sensing towards Smart Homes H. Omidvarborna et al. https://doi.org/10.3390/atmos12040453
181. Review of the Performance of Low-Cost Sensors for Air Quality Monitoring F. Karagulian et al. https://doi.org/10.3390/atmos10090506
182. Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA J. Bi et al. https://doi.org/10.1016/j.envres.2019.108810
183. 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
184. A feasible experimental framework for field calibration of portable light-scattering aerosol monitors: Case of TSI DustTrak Z. Li et al. https://doi.org/10.1016/j.envpol.2019.113136
185. 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
186. Review of smoke taint in wine: smoke-derived volatile phenols and their glycosidic metabolites in grapes and vines as biomarkers for smoke exposure and their role in the sensory perception of smoke taint M. Krstic et al. https://doi.org/10.1111/ajgw.12183
187. Smart homes and the control of indoor air quality A. Schieweck et al. https://doi.org/10.1016/j.rser.2018.05.057
188. Flow rate-independent electrical aerosol sensor A. Rostedt & J. Keskinen https://doi.org/10.1080/02786826.2018.1498586
189. Response of eight low-cost particle sensors and consumer devices to typical indoor emission events in a real home (ASHRAE 1756-RP) Y. Zou et al. https://doi.org/10.1080/23744731.2019.1676094
190. Enhanced performance of pocket-sized nanoparticle exposure monitor for healthy indoor environment H. Wasisto et al. https://doi.org/10.1016/j.buildenv.2015.09.013
191. FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor M. Budde et al. https://doi.org/10.3390/s19030749
192. Evaluation of new low-cost particle monitors for PM2.5 concentrations measurements N. Zikova et al. https://doi.org/10.1016/j.jaerosci.2016.11.010
193. Improving usability of sensor-based measurements for community monitoring: Long-term performance and classification of data quality Y. Luo & A. Stuart https://doi.org/10.1016/j.atmosenv.2026.121993
194. Assessment of PM2.5 monitoring using MicroPEM: A validation study in a city with elevated PM2.5 levels Y. Du et al. https://doi.org/10.1016/j.ecoenv.2019.01.002
195. Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise C. Borrego et al. https://doi.org/10.1016/j.atmosenv.2016.09.050
196. Development and evaluation of a palm-sized optical PM2.5sensor T. Nakayama et al. https://doi.org/10.1080/02786826.2017.1375078
197. An efficient spatiotemporal data calibration approach for the low-cost PM2.5 sensing network: A case study in Taiwan C. Lee et al. https://doi.org/10.1016/j.envint.2019.05.032
198. A systematic investigation on the effects of temperature and relative humidity on the performance of eight low-cost particle sensors and devices Y. Zou et al. https://doi.org/10.1016/j.jaerosci.2020.105715
199. Performance calibration of low-cost and portable particular matter (PM) sensors D. Liu et al. https://doi.org/10.1016/j.jaerosci.2017.05.011
200. Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5 J. Li et al. https://doi.org/10.1016/j.atmosenv.2020.117293
201. Real-Time Monitoring of Spray Drift from Three Different Orchard Sprayers M. Blanco et al. https://doi.org/10.1016/j.chemosphere.2019.01.092
202. Hourly land-use regression models based on low-cost PM monitor data M. Masiol et al. https://doi.org/10.1016/j.envres.2018.06.052
203. Measurement of PM10 concentration using hybrid cyclone separator and particle counter R. Purbakawaca et al. https://doi.org/10.1088/1755-1315/622/1/012025
204. The Exposome in the Era of the Quantified Self X. Zhang et al. https://doi.org/10.1146/annurev-biodatasci-012721-122807
205. Effect of relative humidity on the performance of five cost-effective PM sensors P. Wang et al. https://doi.org/10.1080/02786826.2021.1910136
206. Lightweight IoT-edge framework with attention-enhanced GRU for closed-loop real-time indoor environmental monitoring and prediction J. Yousaf et al. https://doi.org/10.1016/j.measen.2026.101994
207. Unveiling the Limits of Existing Correction Factors for a Low-Cost PM2.5 Sensor in Cold Environments and Development of a Tailored Solution J. Leenose et al. https://doi.org/10.1021/acsestair.5c00018
208. High-Resolution Modeling of Particulate Matter: Comparing SEMARA AOD and MAIAC AOD Models M. Bagherinia & S. Boudaghpour https://doi.org/10.1007/s00024-025-03847-3
209. Improving Urban Air Quality Monitoring in Delhi, India: Reflections on Low-Cost Air Quality Sensors (LCAQS) and Participatory Engagement A. Kumar & S. Chaudhuri https://doi.org/10.1177/09754253221122752
210. Aerosol Health Effects from Molecular to Global Scales M. Shiraiwa et al. https://doi.org/10.1021/acs.est.7b04417