339 citations as recorded by crossref.

1. Advantages and challenges of the implementation of a low-cost particulate matter monitoring system as a decision-making tool V. Caquilpán P. et al. 10.1007/s10661-019-7875-4
2. Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a network of low-cost air pollution sensors S. Feinberg et al. 10.1016/j.atmosenv.2019.06.026
3. Generative Adversarial Networks in Imbalanced Gas Samples J. Liu et al. 10.3390/electronics14071346
4. Performance evaluation of ozone and particulate matter sensors H. DeWitt et al. 10.1080/10962247.2020.1713921
5. Demonstration of a Low-Cost Multi-Pollutant Network to Quantify Intra-Urban Spatial Variations in Air Pollutant Source Impacts and to Evaluate Environmental Justice R. Tanzer et al. 10.3390/ijerph16142523
6. Features and Practicability of the Next-Generation Sensors and Monitors for Exposure Assessment to Airborne Pollutants: A Systematic Review G. Fanti et al. 10.3390/s21134513
7. Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. 10.1039/D4EA00086B
8. Detection of presence of Bioactive metabolites in CCFE (Co cultured Cell Free Extracts) of Microbacterium barkeri (LMA4), Corynebacterium argentoratense (LMA5) and Streptomyces shenzhenensis (LMA6) K. Kumar Das et al. 10.52711/0974-360X.2024.00757
9. Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application A. Bigi et al. 10.5194/amt-11-3717-2018
10. Machine Learning for Sensing Applications: A Tutorial V. Shirmohammadli & B. Bahreyni 10.1109/JSEN.2021.3112901
11. The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors J. Kim et al. 10.5194/amt-11-1937-2018
12. Identifying optimal co-location calibration periods for low-cost sensors M. Levy Zamora et al. 10.5194/amt-16-169-2023
13. Smart Cities: Intelligence, Framework and Machine Learning Algorithm . Aditya Gaur 10.48175/IJARSCT-4834
14. Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors A. Cavaliere et al. 10.5194/amt-16-4723-2023
15. Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments T. Zheng et al. 10.5194/amt-11-4823-2018
16. Reliability of Lower-Cost Sensors in the Analysis of Indoor Air Quality on Board Ships O. Schalm et al. 10.3390/atmos13101579
17. 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. 10.1039/D4EA00012A
18. Testing the performance of sensors for ozone pollution monitoring in a citizen science approach A. Ripoll et al. 10.1016/j.scitotenv.2018.09.257
19. A novel application of mobile low-cost sensors for atmospheric particulate matter monitoring in open-pit mines A. Zafra-Pérez et al. 10.1016/j.eti.2022.102974
20. An attention-based domain spatial-temporal meta-learning (ADST-ML) approach for PM2.5 concentration dynamics prediction X. Yang & Z. Zhang 10.1016/j.uclim.2022.101363
21. Review of the Performance of Low-Cost Sensors for Air Quality Monitoring F. Karagulian et al. 10.3390/atmos10090506
22. A Kalman Filter Scheme for the Optimization of Low-Cost Gas Sensor Measurements I. Christakis et al. 10.3390/electronics13010025
23. Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments A. Collier-Oxandale et al. 10.5194/amt-11-3569-2018
24. 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
25. A Variational Bayesian Blind Calibration Approach for Air Quality Sensor Deployments G. Li et al. 10.1109/JSEN.2022.3212009
26. Evaluation of Low-Cost Sensors for Weather and Carbon Dioxide Monitoring in Internet of Things Context T. Araújo et al. 10.3390/iot1020017
27. Non-linear probabilistic calibration of low-cost environmental air pollution sensor networks for neighborhood level spatiotemporal exposure assessment A. Patton et al. 10.1038/s41370-022-00493-y
28. Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning U. Ayvaz et al. 10.32604/cmc.2022.023278
29. IAQ-STL-ML: A novel indoor air quality prediction pipeline using meta-learning framework with STL decomposition H. Yin et al. 10.1016/j.eti.2025.104107
30. Modeling and simulation of temperature drift for ISFET‐based pH sensor and its compensation through machine learning techniques R. Bhardwaj et al. 10.1002/cta.2618
31. Enhancing the reliability of particulate matter sensing by multivariate Tobit model using weather and air quality data W. Won et al. 10.1038/s41598-023-40468-z
32. An improved low-power measurement of ambient NO2 and O3 combining electrochemical sensor clusters and machine learning K. Smith et al. 10.5194/amt-12-1325-2019
33. AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems S. Felici-Castell et al. 10.3390/s23239585
34. Developing a Low-Cost Wearable Personal Exposure Monitor for Studying Respiratory Diseases Using Metal–Oxide Sensors K. Mallires et al. 10.1109/JSEN.2019.2917435
35. Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5 J. Li et al. 10.1016/j.atmosenv.2020.117293
36. ECO4RUPA: 5G-IoT Inclusive and Intelligent Routing Ecosystem with Low-Cost Air Quality Monitoring R. Fayos-Jordan et al. 10.3390/info14080445
37. Bayesian Sensor Calibration M. Berger et al. 10.1109/JSEN.2022.3199485
38. An open‐source tool for evaluating calibration techniques used in low‐cost air pollutant monitors D. Tatsch et al. 10.1049/ell2.12816
39. Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges L. Liang 10.1016/j.envres.2021.111163
40. Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach T. Zheng et al. 10.1016/j.atmosenv.2020.117451
41. A data calibration method for micro air quality detectors based on a LASSO regression and NARX neural network combined model B. Liu et al. 10.1038/s41598-021-00804-7
42. The effect of air purifiers and curtains on aerosol dispersion and removal in multi‐patient hospital rooms S. Rogak et al. 10.1111/ina.13110
43. UNI-CAL: A Universal AI-Driven Model for Air Pollutant Sensor Calibration With Domain-Specific Knowledge Inputs Y. Han et al. 10.1109/ACCESS.2024.3410171
44. Fleet-based vehicle emission factors using low-cost sensors: Case study in parking garages B. Liu & N. Zimmerman 10.1016/j.trd.2020.102635
45. Use of Machine Learning in Air Pollution Research: A Bibliographic Perspective S. Jain et al. 10.3390/electronics11213621
46. Future Low-Cost Urban Air Quality Monitoring Networks: Insights from the EU’s AirHeritage Project S. De Vito et al. 10.3390/atmos15111351
47. Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O3 and NO2 concentrations in diverse environments R. Sahu et al. 10.5194/amt-14-37-2021
48. Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors E. Villanueva et al. 10.3390/s23073776
49. Development of a Portable and Low-Cost Sensor System for Air Pollution Measurement Z. Wang et al. 10.1007/s41810-025-00284-6
50. STCM: A spatio-temporal calibration model for low-cost air monitoring sensors Y. Zhang et al. 10.1016/j.ins.2023.119307
51. Personal strategies to minimise effects of air pollution on respiratory health: advice for providers, patients and the public C. Carlsten et al. 10.1183/13993003.02056-2019
52. 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
53. A Non-Labeling Technique to Quantify and Visualize Gold Nanoparticle Accumulation at the Single-Cell Level C. Wang et al. 10.2139/ssrn.4065681
54. Improving the short-term prediction of dissolved carbon monoxide using a combination of Light GBM and meta-heuristic algorithms D. Yun et al. 10.1016/j.jece.2024.114043
55. Modelling calibration uncertainty in networks of environmental sensors M. Smith et al. 10.1093/jrsssc/qlad075
56. Recent advancements in low-cost portable sensors for urban and indoor air quality monitoring A. Hernández-Gordillo et al. 10.1007/s11869-021-01067-x
57. A Survey on Sensor Calibration in Air Pollution Monitoring Deployments B. Maag et al. 10.1109/JIOT.2018.2853660
58. Calibration of NO, SO2, and PM using Airify: A low-cost sensor cluster for air quality monitoring M. Ionascu et al. 10.1016/j.atmosenv.2024.120841
59. Impacts of Modifiable Factors on Ambient Air Pollution: A Case Study of COVID-19 Shutdowns R. Tanzer-Gruener et al. 10.1021/acs.estlett.0c00365
60. Application of combined model of stepwise regression analysis and artificial neural network in data calibration of miniature air quality detector B. Liu et al. 10.1038/s41598-021-82871-4
61. Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device E. Bagkis et al. 10.3390/atmos12020251
62. Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview A. Chatterjee et al. 10.3390/s20092734
63. A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features I. Apostolopoulos et al. 10.3390/ai4040041
64. Indoor Air Quality in Urban India: Current Status, Research Gap, and the Way Forward A. Thakur & S. Patel 10.1021/acs.estlett.3c00636
65. Assessment of air quality sensor system performance after relocation S. Zauli-Sajani et al. 10.1016/j.apr.2020.11.010
66. 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
67. Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation C. Malings et al. 10.1080/02786826.2019.1623863
68. 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
69. Development and Calibration of a Low-Cost, Piezoelectric Rainfall Sensor through Machine Learning A. Antonini et al. 10.3390/s22176638
70. Testing the performance of field calibration techniques for low-cost gas sensors in new deployment locations: across a county line and across Colorado J. Casey & M. Hannigan 10.5194/amt-11-6351-2018
71. Correction of Light Scattering-Based Total Suspended Particulate Measurements through Machine Learning Q. Guo et al. 10.3390/atmos11020139
72. Advanced air quality monitoring: PM and ozone with Raspberry Pi and Arduino M. Chandrakala et al. 10.1007/s42452-025-06724-9
73. Integration and calibration of non-dispersive infrared (NDIR) CO2 low-cost sensors and their operation in a sensor network covering Switzerland M. Müller et al. 10.5194/amt-13-3815-2020
74. Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods M. Si et al. 10.5194/amt-13-1693-2020
75. Design and Development of an Electronic Board for Supporting the Operation of Electrochemical Gas Sensors D. Suriano 10.3390/hardware2020009
76. Translating citizen-generated air quality data into evidence for shaping policy S. Mahajan et al. 10.1057/s41599-022-01135-2
77. Preparation of low-concentration H2 test gas mixtures in ambient air for calibration of H2 sensors N. Karbach et al. 10.5194/amt-17-4081-2024
78. Resolving aerosol mixing state increases accuracy of black carbon respiratory deposition estimates J. Ching et al. 10.1016/j.oneear.2020.11.004
79. Spatial variations in urban air pollution: impacts of diesel bus traffic and restaurant cooking at small scales R. Song et al. 10.1007/s11869-021-01078-8
80. Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements M. Badura et al. 10.1007/s42452-019-0630-1
81. Design, calibration, and testing of a mobile sensor system for air pollution and built environment data collection: The urban scanner platform A. Ganji et al. 10.1016/j.envpol.2022.120720
82. A Low-Cost Calibration Method for Temperature, Relative Humidity, and Carbon Dioxide Sensors Used in Air Quality Monitoring Systems R. González Rivero et al. 10.3390/atmos14020191
83. An end-to-end air pollutant concentrations prediction algorithm based on attention mechanism for targeted season: A case study in North China M. Li et al. 10.1016/j.apr.2022.101396
84. In Situ Calibration Algorithms for Environmental Sensor Networks: A Review F. Delaine et al. 10.1109/JSEN.2019.2910317
85. Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration F. Concas et al. 10.1145/3446005
86. Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning S. Koziel et al. 10.1038/s41598-024-59993-6
87. Minimized Training of Machine Learning-Based Calibration Methods for Low-Cost O3 Sensors S. Tondini et al. 10.1109/JSEN.2023.3339202
88. 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. 10.3390/s25051614
89. Retrieval of betalain contents based on the coupling of radiative transfer model and SVM model R. Sawut et al. 10.1016/j.jag.2021.102340
90. Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study S. Diez et al. 10.5194/amt-17-3809-2024
91. 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 10.1080/02786826.2020.1719971
92. Development and evaluation of a robust temperature sensitive algorithm for long term NO2 gas sensor network data correction P. Wei et al. 10.1016/j.atmosenv.2020.117509
93. A Sustainable Method for Publishing Interoperable Open Data on the Web R. Buyle et al. 10.3390/data6080093
94. Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art M. Narayana et al. 10.3390/s22010394
95. Machine Learning for Optical Gas Sensing: A Leaky-Mode Humidity Sensor as Example V. Kornienko et al. 10.1109/JSEN.2020.2978931
96. Estimating the spatial variability of fine particles at the neighborhood scale using a distributed network of particle sensors R. Shafran-Nathan et al. 10.1016/j.atmosenv.2019.117011
97. Particulate air pollution in the Copenhagen metro part 2: Low-cost sensors and micro-environment classification H. Russell et al. 10.1016/j.envint.2022.107645
98. Voice Calibration Using Ambient Sensors J. Chen et al. 10.1142/S0218126623500433
99. Performance-based protocol for selection of economical portable sensor for air quality measurement N. Shukla et al. 10.1007/s10661-023-11438-9
100. Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors M. El Adoui et al. 10.1145/3590956
101. Field calibration of low-cost particulate matter sensors using artificial neural networks and affine response correction S. Koziel et al. 10.1016/j.measurement.2024.114529
102. Yapay Zekâ Tabanlı Hava Kalitesi İyileştirme Stratejilerinin Değerlendirilmesi T. Pekdoğan 10.61512/emobd.1498119
103. Characterization of a commercial lower-cost medium-precision non-dispersive infrared sensor for atmospheric CO2 monitoring in urban areas E. Arzoumanian et al. 10.5194/amt-12-2665-2019
104. Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing S. Koziel et al. 10.1016/j.jestch.2024.101729
105. Machine learning techniques to improve the field performance of low-cost air quality sensors T. Bush et al. 10.5194/amt-15-3261-2022
106. A novel zone-based machine learning approach for the prediction of the performance of industrial flares H. Lou et al. 10.1016/j.compchemeng.2022.107795
107. A Multi-Scale Convolutional Residual Time-Frequency Calibration Method for Low-Accuracy Air Pollution Data J. Liu et al. 10.3390/app15020935
108. Field and laboratory performance evaluations of 28 gas-phase air quality sensors by the AQ-SPEC program A. Collier-Oxandale et al. 10.1016/j.atmosenv.2019.117092
109. Nitrogen Dioxide Monitoring by Means of a Low-Cost Autonomous Platform and Sensor Calibration via Machine Learning with Global Data Correlation Enhancement S. Koziel et al. 10.3390/s25082352
110. Unmanned Aerial Vehicles for Air Pollution Monitoring: A Survey N. Hossein Motlagh et al. 10.1109/JIOT.2023.3290508
111. Restaurant Impacts on Outdoor Air Quality: Elevated Organic Aerosol Mass from Restaurant Cooking with Neighborhood-Scale Plume Extents E. Robinson et al. 10.1021/acs.est.8b02654
112. Combining Radiative Transfer Model and Regression Algorithms for Estimating Aboveground Biomass of Grassland in West Ujimqin, China L. Zhang et al. 10.3390/rs15112918
113. Low-Cost Investigation into Sources of PM2.5 in Kinshasa, Democratic Republic of the Congo D. Westervelt et al. 10.1021/acsestair.3c00024
114. Calibration and validation-based assessment of low-cost air quality sensors J. Dong et al. 10.1016/j.scitotenv.2025.179364
115. Characterizing the Aging of Alphasense NO2 Sensors in Long-Term Field Deployments J. Li et al. 10.1021/acssensors.1c00729
116. Evaluation of high-resolution predictions of fine particulate matter and its composition in an urban area using PMCAMx-v2.0 B. Dinkelacker et al. 10.5194/gmd-15-8899-2022
117. Relevance of Drift Components and Unit-to-Unit Variability in the Predictive Maintenance of Low-Cost Electrochemical Sensor Systems in Air Quality Monitoring G. Tancev 10.3390/s21093298
118. Elucidating Contributions of Anthropogenic Volatile Organic Compounds and Particulate Matter to Ozone Trends over China C. Li et al. 10.1021/acs.est.2c03315
119. An overview of outdoor low-cost gas-phase air quality sensor deployments: current efforts, trends, and limitations K. Okorn & L. Iraci 10.5194/amt-17-6425-2024
120. 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
121. Influence of Concept Drift on Metrological Performance of Low-Cost NO2 Sensors G. D'Elia et al. 10.1109/TIM.2022.3188028
122. Low-Cost Air Quality Sensors: Biases, Corrections and Challenges in Their Comparability I. Hayward et al. 10.3390/atmos15121523
123. Machine Learning on FPGA for Robust ${\rm {Si}}_{3}{\rm {N}}_{4}$-Gate ISFET pH Sensor in Industrial IoT Applications S. Sinha et al. 10.1109/TIA.2021.3117233
124. Comparing Building and Neighborhood-Scale Variability of CO2 and O3 to Inform Deployment Considerations for Low-Cost Sensor System Use A. Collier-Oxandale et al. 10.3390/s18051349
125. Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid Land Use Models S. Jain et al. 10.1021/acs.est.1c02653
126. Inferring Aerosol Sources from Low-Cost Air Quality Sensor Measurements: A Case Study in Delhi, India D. Hagan et al. 10.1021/acs.estlett.9b00393
127. Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana C. McFarlane et al. 10.1021/acsearthspacechem.1c00217
128. A practical approach for high-resolution air quality mapping using IoT mobile devices A. Santos et al. 10.1007/s13762-022-04513-0
129. Variational Bayesian calibration of low-cost gas sensor systems in air quality monitoring G. Tancev & F. Toro 10.1016/j.measen.2021.100365
130. Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References G. Tancev & F. Toro 10.1109/ACCESS.2022.3145945
131. A Correction Method of Environmental Meteorological Model Based on Long‐Short‐Term Memory Neural Network Y. Dai et al. 10.1029/2019EA000641
132. Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability P. Nowack et al. 10.5194/amt-14-5637-2021
133. On Memory-Based Precise Calibration of Cost-Efficient NO2 Sensor Using Artificial Intelligence and Global Response Correction S. Koziel et al. 10.1016/j.knosys.2024.111564
134. Outlier detection and gap filling methodologies for low-cost air quality measurements T. Ottosen & P. Kumar 10.1039/C8EM00593A
135. Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa C. Malings et al. 10.5194/amt-13-3873-2020
136. Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia G. Mulugeta et al. 10.1186/s12911-023-02185-5
137. Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring C. Malings et al. 10.5194/amt-12-903-2019
138. Improved deep bidirectional recurrent neural network for learning the cross-sensitivity rules of gas sensor array Z. Wang et al. 10.1016/j.snb.2023.134996
139. Aerial monitoring of atmospheric particulate matter produced by open-pit mining using low-cost airborne sensors A. Zafra-Pérez et al. 10.1016/j.scitotenv.2023.166743
140. A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN B. Montrucchio et al. 10.1109/TVT.2020.3035554
141. Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network S. Ali et al. 10.3390/s23020854
142. Surface distributions and vertical profiles of trace gases (CO, O3, NO, NO2) in the Arctic wintertime boundary layer using low-cost sensors during ALPACA-2022 B. Barret et al. 10.5194/amt-18-1163-2025
143. Mapping pollution exposure and chemistry during an extreme air quality event (the 2018 Kīlauea eruption) using a low-cost sensor network B. Crawford et al. 10.1073/pnas.2025540118
144. Impact of broader ecological and socio‐environmental components on Aedes mosquito population dynamics: a spatial–temporal longitudinal study Y. Wang et al. 10.1002/ps.8478
145. Performance validation and calibration conditions for novel dynamic baseline tracking air sensors in long-term field monitoring H. Mei et al. 10.5194/amt-18-1771-2025
146. Machine Learning for Improving Accuracy and Utility of Low-Cost Environmental Air Pollution Sensor Networks for Probabilistic Spatial Exposure Assessment A. Patton et al. 10.2139/ssrn.4001163
147. Field calibration of a low-cost sensors network to assess traffic-related air pollution along the Brenner highway A. Bisignano et al. 10.1016/j.atmosenv.2022.119008
148. HypeAIR: A novel framework for real-time low-cost sensor calibration for air quality monitoring in smart cities C. Bachechi et al. 10.1016/j.ecoinf.2024.102568
149. 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
150. Application of RR-XGBoost combined model in data calibration of micro air quality detector B. Liu et al. 10.1038/s41598-021-95027-1
151. A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data E. Arnaudo et al. 10.3390/app10134587
152. Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration C. Buehler et al. 10.5194/amt-14-995-2021
153. Embedded machine learning of IoT streams to promote early detection of unsafe environments E. Fernández et al. 10.1016/j.iot.2024.101128
154. Efficient and Automated Generation of Orthogonal Atmospheres for the Characterization of Low-Cost Gas Sensor Systems in Air Quality Monitoring G. Tancev et al. 10.1109/TIM.2022.3198747
155. Revisiting Atmospheric Oxidation Kinetics of Nitrogen Oxides: The Use of Low-Cost Electrochemical Sensors to Measure Reaction Kinetics S. Owen et al. 10.3390/reactions5040040
156. 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
157. Updating Indoor Air Quality (IAQ) Assessment Screening Levels with Machine Learning Models L. Wong et al. 10.3390/ijerph19095724
158. Multisensor Data Fusion Calibration in IoT Air Pollution Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2020.2965283
159. Tuning the structural and NO2 gas sensing properties of SnO2 films via In doping A. Addie et al. 10.1016/j.tsf.2025.140669
160. Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants A. Baruah et al. 10.1039/D2EA00165A
161. Using a network of lower-cost monitors to identify the influence of modifiable factors driving spatial patterns in fine particulate matter concentrations in an urban environment S. Rose Eilenberg et al. 10.1038/s41370-020-0255-x
162. Addressing the Global Air Pollution Crisis: Chemistry’s Role V. McNeill 10.1016/j.trechm.2019.01.005
163. Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry A. Adams et al. 10.1002/cplu.202100418
164. Investigation of aerosol dispersion and air purifier performance in a hospital patient room using CFD and measurements M. Chan et al. 10.1051/e3sconf/202339602029
165. Environmental Effects on NDIR-Based CH4 Monitoring: Characterization and Correction W. Dong et al. 10.1021/acs.est.4c11110
166. RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning L. Monti et al. 10.3390/s20195583
167. Evaluation of low-cost sensors for quantitative personal exposure monitoring S. Mahajan & P. Kumar 10.1016/j.scs.2020.102076
168. On the robustness of field calibration for smart air quality monitors S. De Vito et al. 10.1016/j.snb.2020.127869
169. Calibration Assessment of Low-Cost Carbon Dioxide Sensors Using the Extremely Randomized Trees Algorithm T. Araújo et al. 10.3390/s23136153
170. New understanding of miniaturized VOCs monitoring device: PID-type sensors performance evaluations in ambient air W. Xu et al. 10.1016/j.snb.2020.129285
171. Assessment of seasonal variation in PM2.5 concentration using low-cost sensors: A case study of Jaipur city, India S. Chaudhry et al. 10.1016/j.atmosenv.2025.121067
172. A Scalable Calibration Method for Enhanced Accuracy in Dense Air Quality Monitoring Networks A. Winter et al. 10.1021/acs.est.4c08855
173. Development and Implementation of a Platform for Public Information on Air Quality, Sensor Measurements, and Citizen Science J. Wesseling et al. 10.3390/atmos10080445
174. A method for calibrating measurement data of a micro air quality monitor based on MLR-BRT-ARIMA combined model B. Liu & P. Jiang 10.1039/D3RA02408C
175. Spatial calibration and PM2.5 mapping of low-cost air quality sensors H. Chu et al. 10.1038/s41598-020-79064-w
176. Tackling Data Quality When Using Low-Cost Air Quality Sensors in Citizen Science Projects Å. Watne et al. 10.3389/fenvs.2021.733634
177. Evaluation of the Performance of Low-Cost Air Quality Sensors at a High Mountain Station with Complex Meteorological Conditions H. Li et al. 10.3390/atmos11020212
178. Review of low-cost sensors for indoor air quality: Features and applications M. Ródenas García et al. 10.1080/05704928.2022.2085734
179. Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory Z. Ebrahimi-Khusfi et al. 10.1016/j.ecolind.2021.108287
180. Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors A. Abu-Hani et al. 10.5194/amt-17-3917-2024
181. Modelling PM2.5 for Data-Scarce Zone of Northwestern India using Multi Linear Regression and Random Forest Approaches V. Sharma et al. 10.1080/19475683.2023.2183523
182. Prediction of Mineralization Prospects Based on Geological Semantic Model and Mobile Computer Machine Learning Z. An et al. 10.1155/2021/7734080
183. Field evaluation of low-cost electrochemical air quality gas sensors under extreme temperature and relative humidity conditions R. Papaconstantinou et al. 10.5194/amt-16-3313-2023
184. From air quality sensors to sensor networks: Things we need to learn Y. Li et al. 10.1016/j.snb.2021.130958
185. Bayesian Sensor Calibration of a CMOS-Integrated Hall Sensor Against Thermomechanical Cross-Sensitivities M. Berger et al. 10.1109/JSEN.2023.3243783
186. Probabilistic Machine Learning with Low-Cost Sensor Networks for Occupational Exposure Assessment and Industrial Hygiene Decision Making A. Patton et al. 10.1093/annweh/wxab105
187. Remote sensing and machine learning method to support sea surface pCO2 estimation in the Yellow Sea W. Li et al. 10.3389/fmars.2023.1181095
188. Analyzing the impact of COVID-19 on the electricity demand in Austin, TX using an ensemble-model based counterfactual and 400,000 smart meters T. Dai et al. 10.1007/s43762-023-00095-w
189. Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization A. Angulo et al. 10.1039/D1DD00027F
190. Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2 P. Han et al. 10.3390/s21010256
191. Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies E. Yoo et al. 10.1016/j.atmosenv.2019.117091
192. A Study on Evaporation Calculations of Agricultural Reservoirs in Hyper-Arid Areas C. Yin et al. 10.3390/agriculture12050612
193. Using spatiotemporal prediction models to quantify PM2.5 exposure due to daily movement S. Jain et al. 10.1039/D3EA00051F
194. Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors T. Sayahi et al. 10.1016/j.envpol.2019.113131
195. Long-term calibration models to estimate ozone concentrations with a metal oxide sensor T. Sayahi et al. 10.1016/j.envpol.2020.115363
196. Indoor Air Sensing: A Study in Cost, Energy, Reliability and Fidelity in Sensing P. Sharma et al. 10.1007/s11220-023-00412-x
197. EEATC: A Novel Calibration Approach for Low-Cost Sensors M. Narayana et al. 10.1109/JSEN.2023.3304366
198. Distributed Multi-Scale Calibration of Low-Cost Ozone Sensors in Wireless Sensor Networks J. Barcelo-Ordinas et al. 10.3390/s19112503
199. Reliability Validation of a Low-Cost Particulate Matter IoT Sensor in Indoor and Outdoor Environments Using a Reference Sampler S. Trilles et al. 10.3390/su11247220
200. Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore A. Datta et al. 10.1016/j.atmosenv.2020.117761
201. Study on the accuracy of photoacoustic spectroscopy system based on multiple linear regression correction algorithm H. Jin & P. Luo 10.1063/5.0060595
202. Evaluating uncertainty in sensor networks for urban air pollution insights D. Peters et al. 10.5194/amt-15-321-2022
203. A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2019.2929594
204. The application of machine learning to air pollution research: A bibliometric analysis Y. Li et al. 10.1016/j.ecoenv.2023.114911
205. Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network W. Wang et al. 10.3390/s20175002
206. Particulate Air Pollution in the Copenhagen Metro Part 2: Low-Cost Sensors and Micro-Environment Classification H. Russell et al. 10.2139/ssrn.4143147
207. First-Principles Algorithm for Air Quality Electrochemical Gas Sensors B. Ouyang 10.1021/acssensors.0c01129
208. Emissions and exposure to NOx, CO, CO2 and PM2.5 from a gas stove using reference and low-cost sensors D. Jaffe & A. Creekmore 10.1016/j.atmosenv.2024.120564
209. Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site L. Yang et al. 10.1021/acs.est.1c07005
210. Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model B. Liu et al. 10.1038/s41598-020-79462-0
211. 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
212. Air Quality Sensors and Data Adjustment Algorithms: When Is It No Longer a Measurement? G. Hagler et al. 10.1021/acs.est.8b01826
213. Research on Data Correction Method of Micro Air Quality Detector Based on Combination of Partial Least Squares and Random Forest Regression B. Liu et al. 10.1109/ACCESS.2021.3096216
214. Air Quality in Puerto Rico in the Aftermath of Hurricane Maria: A Case Study on the Use of Lower Cost Air Quality Monitors R. Subramanian et al. 10.1021/acsearthspacechem.8b00079
215. The Development of a Low-Cost Particulate Matter 2.5 Sensor Calibration Model in Daycare Centers Using Long Short-Term Memory Algorithms H. Jeon et al. 10.3390/atmos14081228
216. Calibration-free pH long-time measurement method based on electrode potential drift dynamic compensation-applying self-adaptive dynamic optimization exponential smoothing method X. Zhao et al. 10.1088/1361-6501/ac94eb
217. A Simple and Effective Random Forest Refit to Map the Spatial Distribution of NO2 Concentrations Y. Chi & Y. Zhan 10.3390/atmos13111832
218. A critical review on odor measurement and prediction Y. Wang et al. 10.1016/j.jenvman.2023.117651
219. Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation S. De Vito et al. 10.3390/s21155219
220. HUM: A review of hydrochemical analysis using ultraviolet-visible absorption spectroscopy and machine learning J. Carter et al. 10.1016/j.scitotenv.2023.165826
221. Advancing air quality prediction models in urban India: a deep learning approach integrating DCNN and LSTM architectures for AQI time-series classification A. Barthwal & A. Goel 10.1007/s40808-023-01934-9
222. High-resolution urban air pollution mapping J. Apte & C. Manchanda 10.1126/science.adq3678
223. Quantile regression with a metal oxide sensors array for methane prediction over a municipal solid waste treatment plant E. Taguem et al. 10.1016/j.snb.2021.129590
224. Assessment of Urban air pollution by PM10 and NO2 and associated impacts and risks through computational analysis in Kigali, Rwanda E. Irankunda & A. Ozunu 10.1007/s40974-024-00333-9
225. Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks S. Takahama et al. 10.5194/amt-12-525-2019
226. Analysis of fine particle pollution data measured at 29 US diplomatic posts worldwide R. Dhammapala 10.1016/j.atmosenv.2019.05.070
227. Air quality measurement, prediction and warning using transfer learning based IOT system for ambient assisted living S. Sonawani & K. Patil 10.1108/IJPCC-07-2022-0271
228. A method for using stationary networks to observe long-term trends of on-road emission factors of primary aerosol from heavy-duty vehicles H. Fitzmaurice & R. Cohen 10.5194/acp-22-15403-2022
229. Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors H. Li et al. 10.1016/j.aeaoa.2019.100012
230. Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources B. Duncan et al. 10.1029/2021GH000451
231. Using low-cost sensors to monitor indoor, outdoor, and personal ozone concentrations in Beijing, China M. Liu et al. 10.1039/C9EM00377K
232. Field and Laboratory Evaluations of the Low-Cost Plantower Particulate Matter Sensor M. Levy Zamora et al. 10.1021/acs.est.8b05174
233. Evaluation of a low-cost dryer for a low-cost optical particle counter M. Chacón-Mateos et al. 10.5194/amt-15-7395-2022
234. Opportunities and challenges for filling the air quality data gap in low- and middle-income countries R. Pinder et al. 10.1016/j.atmosenv.2019.06.032
235. Optimizing Air Quality Monitoring: Comparative Analysis of Linear Regression and Machine Learning in Low-Cost Sensor Calibration R. Fang et al. 10.1007/s44408-025-00009-x
236. A label-free technique to quantify and visualize gold nanoparticle accumulation at the single-cell level C. Wang et al. 10.1016/j.chemosphere.2022.134857
237. The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias G. Tancev & C. Pascale 10.3390/s20216198
238. Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model B. Liu & Y. Zhang 10.1038/s41598-022-13531-4
239. Low-cost system application for policy assessment: a case study from Berlin A. Caseiro et al. 10.1088/2752-5309/ad56bb
240. Prototyping low-cost automatic weather stations for natural disaster monitoring G. Bernardes et al. 10.1016/j.dcan.2022.05.002
241. GAMMA: A universal model for calibrating sensory data of multiple low-cost air monitoring devices A. Nguyen et al. 10.1016/j.engappai.2023.107591
242. Self-calibration methods for uncontrolled environments in sensor networks: A reference survey J. Barcelo-Ordinas et al. 10.1016/j.adhoc.2019.01.008
243. Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors I. Christakis et al. 10.3390/signals5010004
244. Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources J. Thorson et al. 10.3390/s19173723
245. Calibrating low-cost sensors to measure vertical and horizontal gradients of NO2 and O3 pollution in three street canyons in Berlin S. Schmitz et al. 10.1016/j.atmosenv.2023.119830
246. Improving Data Quality of Low-cost IoT Sensors in Environmental Monitoring Networks Using Data Fusion and Machine Learning Approach N. Okafor et al. 10.1016/j.icte.2020.06.004
247. Design of a Decision Support System to Operate a NO2 Gas Sensor Using Machine Learning, Sensitive Analysis and Conceptual Control Process Modelling M. Gheibi et al. 10.3390/chemosensors11020126
248. Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado S. Feinberg et al. 10.5194/amt-11-4605-2018
249. Low-Cost Nitric Oxide Sensors: Assessment of Temperature and Humidity Effects S. Owen et al. 10.3390/s22229013
250. 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
251. Reconstruction of the disturbance history of a temperate coniferous forest through stand-level analysis of airborne LiDAR data N. Sanchez-Lopez et al. 10.1093/forestry/cpz048
252. 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
253. Long-term behavior and stability of calibration models for NO and NO2 low-cost sensors H. Kim et al. 10.5194/amt-15-2979-2022
254. Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment S. Koziel et al. 10.1016/j.knosys.2024.111879
255. Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. 10.1007/s11270-021-04989-5
256. High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling S. Koziel et al. 10.1038/s41598-024-77214-y
257. Low-Cost Air Quality Sensing towards Smart Homes H. Omidvarborna et al. 10.3390/atmos12040453
258. Evaluation and calibration of low‐cost off‐the‐shelf particulate matter sensors using machine learning techniques M. Ghamari et al. 10.1049/wss2.12043
259. Intelligent Wireless Sensor Network for Gas Classification Using Machine Learning N. Zaeri & R. Qasim 10.1109/JSYST.2023.3238357
260. Understanding the ability of low-cost MOx sensors to quantify ambient VOCs A. Collier-Oxandale et al. 10.5194/amt-12-1441-2019
261. Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO2 and CH4 in air R. Biagi et al. 10.1016/j.heliyon.2024.e29772
262. Low-Cost CO2 NDIR Sensors: Performance Evaluation and Calibration Using Machine Learning Techniques R. Dubey et al. 10.3390/s24175675
263. Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring B. Chauhan et al. 10.3390/atmos16040359
264. A study on extending the use of air quality monitor data via deep learning techniques N. Liu et al. 10.1016/j.jclepro.2020.122956
265. Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning I. Vajs et al. 10.3390/s21103338
266. Estimating Black Carbon Levels With Proxy Variables and Low-Cost Sensors X. Liu et al. 10.1109/JIOT.2024.3361977
267. Monitoring of Indoor Air Quality in a Classroom Combining a Low-Cost Sensor System and Machine Learning I. Apostolopoulos et al. 10.3390/chemosensors13040148
268. Performance optimization of shape memory epoxy polymers based on machine learning B. Liu et al. 10.1002/pat.5595
269. Performance characteristics of the low-cost Plantower PMS optical sensor M. He et al. 10.1080/02786826.2019.1696015
270. Laboratory Evaluations of Correction Equations with Multiple Choices for Seed Low-Cost Particle Sensing Devices in Sensor Networks W. Wang et al. 10.3390/s20133661
271. An enhanced approach for predicting air pollution using quantum support vector machine O. Farooq et al. 10.1038/s41598-024-69663-2
272. Environment-Adaptive Calibration System for Outdoor Low-Cost Electrochemical Gas Sensors B. Tian et al. 10.1109/ACCESS.2019.2916826
273. Performance evaluation of low-cost air quality sensors: A review Y. Kang et al. 10.1016/j.scitotenv.2021.151769
274. A comprehensive review on advancements in sensors for air pollution applications T. Seesaard et al. 10.1016/j.scitotenv.2024.175696
275. Data Driven Concept for Sensor Data Adaptation of Electrochemical Sensors for Mobile Air Quality Measurements E. Esatbeyoglu et al. 10.1149/1945-7111/ab74bd
276. Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study C. Zuidema et al. 10.3390/s21124214
277. Sensor-based Wireless Air Quality Monitoring Network (SWAQMN) - A smart tool for urban air quality management S. Gulia et al. 10.1016/j.apr.2020.06.016
278. The U.S. EPA wildland fire sensor challenge: Performance and evaluation of solver submitted multi-pollutant sensor systems M. Landis et al. 10.1016/j.atmosenv.2020.118165
279. Improving data reliability: A quality control practice for low-cost PM2.5 sensor network X. Qiao et al. 10.1016/j.scitotenv.2021.146381
280. 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
281. Real-time air quality prediction using traffic videos and machine learning L. Deveer & L. Minet 10.1016/j.trd.2025.104688
282. Quantifying high-resolution spatial variations and local source impacts of urban ultrafine particle concentrations P. Saha et al. 10.1016/j.scitotenv.2018.11.197
283. Temperature and temporal drift compensation for Al2O3-gate ISFET-based pH sensor using machine learning techniques S. Sinha et al. 10.1016/j.mejo.2020.104710
284. A Category-Based Calibration Approach With Fault Tolerance for Air Monitoring Sensors R. Wang et al. 10.1109/JSEN.2020.2994645
285. Missing Data Imputation on IoT Sensor Networks: Implications for on-Site Sensor Calibration N. Okafor & D. Delaney 10.1109/JSEN.2021.3105442
286. Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors S. Ali et al. 10.3390/s24092930
287. Editorial Overview: Sensors and Biosensors: New sense for electrochemical sensors P. Ugo 10.1016/j.coelec.2019.08.003
288. Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2022.3196154
289. 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
290. Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights D. Topalović et al. 10.1007/s10661-024-13069-0
291. Low-cost electrochemical gas sensing of vertical differences in wintertime air composition (CO, NO, NO2, O3) in Fairbanks, Alaska T. Roberts et al. 10.1039/D4FD00177J
292. Research of low-cost air quality monitoring models with different machine learning algorithms G. Wang et al. 10.5194/amt-17-181-2024
293. Portable Deep Learning-Driven Ion-Sensitive Field-Effect Transistor Scheme for Measurement of Carbaryl Pesticide N. Houngkamhang & P. Phasukkit 10.3390/s22093543
294. Application of low-cost particulate matter sensors for air quality monitoring and exposure assessment in underground mines: A review N. Amoah et al. 10.1007/s12613-021-2378-z
295. 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
296. Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection L. Grbčić et al. 10.3390/s21041157
297. MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity S. Zhang et al. 10.1109/ACCESS.2020.2979833
298. Statistical Study of Sensor Data and Investigation of ML-Based Calibration Algorithms for Inexpensive Sensor Modules: Experiments From Cape Point T. Barrett & A. Mishra 10.1109/TIM.2024.3372211
299. Performance characterization of low-cost air quality sensors for off-grid deployment in rural Malawi A. Bittner et al. 10.5194/amt-15-3353-2022
300. HELIOS-Stack: A Novel Hybrid Ensemble Learning Approach for Precise Joint Roughness Coefficient Prediction in Rock Discontinuity Analysis I. Umar et al. 10.3390/ma18081807
301. Evaluation of Low-cost Air Quality Sensor Calibration Models K. Aula et al. 10.1145/3512889
302. Smart Air Quality Monitoring IoT-Based Infrastructure for Industrial Environments L. García et al. 10.3390/s22239221
303. SmartAirQ: A Big Data Governance Framework for Urban Air Quality Management in Smart Cities A. Kaginalkar et al. 10.3389/fenvs.2022.785129
304. The comparison between in-situ monitored data and modelled results of nitrogen dioxide (NO2): case-study, road networks of Kigali city, Rwanda E. Irankunda et al. 10.1016/j.heliyon.2022.e12390
305. Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment C. Zuidema et al. 10.1038/s41370-024-00667-w
306. The Spatial and Temporal Variability of the Indoor Environmental Quality during Three Simulated Office Studies at a Living Lab N. Clements et al. 10.3390/buildings9030062
307. Cylindrical-Shaped Waterflow Vector Sensor Using Multiple Absolute-Pressure Sensors and Polynomial-Regression Models K. Shimada et al. 10.1109/JSEN.2024.3480118
308. 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
309. Short-Term Field Evaluation of Low-Cost Sensors Operated by the “AirSensEUR” Platform A. Pichlhöfer & A. Korjenic 10.3390/en15155688
310. Response of low-cost sensors to high PM 2.5 concentrations during bushfire and haze events X. Liu et al. 10.1080/02786826.2024.2368733
311. A Global Multiunit Calibration as a Method for Large-Scale IoT Particulate Matter Monitoring Systems Deployments S. De Vito et al. 10.1109/TIM.2023.3331428
312. Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data K. Barkjohn et al. 10.1021/acsestair.4c00125
313. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring N. Zimmerman et al. 10.5194/amt-11-291-2018
314. Real‐Time and Image‐Based AQI Estimation Based on Deep Learning Q. Zhang et al. 10.1002/adts.202100628
315. Evaluating and improving the reliability of gas-phase sensor system calibrations across new locations for ambient measurements and personal exposure monitoring S. Vikram et al. 10.5194/amt-12-4211-2019
316. MAIC: Metalearning-Based Adaptive In-Field Calibration for IoT Air Quality Monitoring System N. Liu et al. 10.1109/JIOT.2022.3150849
317. Cost-Efficient measurement platform and machine-learning-based sensor calibration for precise NO2 pollution monitoring A. Pietrenko-Dabrowska et al. 10.1016/j.measurement.2024.115168
318. In search of an optimal in-field calibration method of low-cost gas sensors for ambient air pollutants: Comparison of linear, multilinear and artificial neural network approaches D. Topalović et al. 10.1016/j.atmosenv.2019.06.028
319. A Smart Rig for Calibration of Gas Sensor Nodes M. Benammar et al. 10.3390/s20082341
320. Calibrating networks of low-cost air quality sensors P. deSouza et al. 10.5194/amt-15-6309-2022
321. A Low-Temperature Micro Hotplate Gas Sensor Based on AlN Ceramic for Effective Detection of Low Concentration NO2 W. Zhao et al. 10.3390/s19173719
322. Electrochemical sensors on board a Zeppelin NT: in-flight evaluation of low-cost trace gas measurements T. Schuldt et al. 10.5194/amt-16-373-2023
323. Measuring Spatial and Temporal PM2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost Sensors A. Mukherjee et al. 10.3390/s19214701
324. Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors S. Schmitz et al. 10.5194/amt-14-7221-2021
325. Assessing the accuracy of commercially available gas sensors for the measurement of ambient ozone and nitrogen dioxide K. Isiugo et al. 10.1080/15459624.2018.1513135
326. Design and Evaluation of a Reliable Low-Cost Atmospheric Pollution Station in Urban Environment G. Astudillo et al. 10.1109/ACCESS.2020.2980736
327. Air Pollution Monitoring Using Cost-Effective Devices Enhanced by Machine Learning Y. Colléaux et al. 10.3390/s25051423
328. Field Calibration of a Low-Cost Air Quality Monitoring Device in an Urban Background Site Using Machine Learning Models I. Apostolopoulos et al. 10.3390/atmos14020368
329. Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning I. Apostolopoulos et al. 10.3390/s24134110
330. Design and testing of a low-cost sensor and sampling platform for indoor air quality J. Tryner et al. 10.1016/j.buildenv.2021.108398
331. Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities S. Ameer et al. 10.1109/ACCESS.2019.2925082
332. Reliability of Low-Cost, Sensor-Based Fine Dust Measurement Devices for Monitoring Atmospheric Particulate Matter Concentrations E. Cho et al. 10.3390/ijerph16081430
333. Relevance and Reliability of Outdoor SO2 Monitoring in Low-Income Countries Using Low-Cost Sensors R. González Rivero et al. 10.3390/atmos14060912
334. Comparative Analysis of Machine Learning Techniques in Air Quality Index (AQI) prediction in smart cities G. Sharma et al. 10.1007/s13198-024-02315-w
335. Optimizing Sensor Calibration in Open Environments: A Bayesian Approach for Non-Specific Multisensory Systems M. Dumon et al. 10.5802/smai-jcm.114
336. Evaluation of low-cost optical particle counters for monitoring individual indoor aerosol sources P. Salimifard et al. 10.1080/02786826.2019.1697423
337. Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework R. Gardner-Frolick et al. 10.1021/acs.est.1c01739
338. The social costs of health- and climate-related on-road vehicle emissions in the continental United States from 2008 to 2017 S. Zelasky & J. Buonocore 10.1088/1748-9326/ac00e3
339. Investigating the mechanisms driving the seasonal variations in surface PM_2.5 concentrations over East Africa with the WRF-Chem model N. Idrissa et al. 10.52396/JUSTC-2022-0142