283 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. Performance evaluation of ozone and particulate matter sensors H. DeWitt et al. 10.1080/10962247.2020.1713921
4. 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
5. 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
6. Performance of NO, NO<sub>2</sub> low cost sensors and three calibration approaches within a real world application A. Bigi et al. 10.5194/amt-11-3717-2018
7. Machine Learning for Sensing Applications: A Tutorial V. Shirmohammadli & B. Bahreyni 10.1109/JSEN.2021.3112901
8. The BErkeley Atmospheric CO<sub>2</sub> Observation Network: field calibration and evaluation of low-cost air quality sensors J. Kim et al. 10.5194/amt-11-1937-2018
9. Identifying optimal co-location calibration periods for low-cost sensors M. Levy Zamora et al. 10.5194/amt-16-169-2023
10. Smart Cities: Intelligence, Framework and Machine Learning Algorithm . Aditya Gaur 10.48175/IJARSCT-4834
11. 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
12. Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments T. Zheng et al. 10.5194/amt-11-4823-2018
13. Reliability of Lower-Cost Sensors in the Analysis of Indoor Air Quality on Board Ships O. Schalm et al. 10.3390/atmos13101579
14. 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
15. 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
16. 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
17. Review of the Performance of Low-Cost Sensors for Air Quality Monitoring F. Karagulian et al. 10.3390/atmos10090506
18. A Kalman Filter Scheme for the Optimization of Low-Cost Gas Sensor Measurements I. Christakis et al. 10.3390/electronics13010025
19. 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
20. A Variational Bayesian Blind Calibration Approach for Air Quality Sensor Deployments G. Li et al. 10.1109/JSEN.2022.3212009
21. Evaluation of Low-Cost Sensors for Weather and Carbon Dioxide Monitoring in Internet of Things Context T. Araújo et al. 10.3390/iot1020017
22. 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
23. Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning U. Ayvaz et al. 10.32604/cmc.2022.023278
24. 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
25. 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
26. An improved low-power measurement of ambient NO<sub>2</sub> and O<sub>3</sub> combining electrochemical sensor clusters and machine learning K. Smith et al. 10.5194/amt-12-1325-2019
27. AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems S. Felici-Castell et al. 10.3390/s23239585
28. 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
29. 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
30. ECO4RUPA: 5G-IoT Inclusive and Intelligent Routing Ecosystem with Low-Cost Air Quality Monitoring R. Fayos-Jordan et al. 10.3390/info14080445
31. Bayesian Sensor Calibration M. Berger et al. 10.1109/JSEN.2022.3199485
32. An open‐source tool for evaluating calibration techniques used in low‐cost air pollutant monitors D. Tatsch et al. 10.1049/ell2.12816
33. Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges L. Liang 10.1016/j.envres.2021.111163
34. 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
35. 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
36. 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
37. Fleet-based vehicle emission factors using low-cost sensors: Case study in parking garages B. Liu & N. Zimmerman 10.1016/j.trd.2020.102635
38. Use of Machine Learning in Air Pollution Research: A Bibliographic Perspective S. Jain et al. 10.3390/electronics11213621
39. Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments R. Sahu et al. 10.5194/amt-14-37-2021
40. Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors E. Villanueva et al. 10.3390/s23073776
41. STCM: A spatio-temporal calibration model for low-cost air monitoring sensors Y. Zhang et al. 10.1016/j.ins.2023.119307
42. 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
43. 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
44. A Non-Labeling Technique to Quantify and Visualize Gold Nanoparticle Accumulation at the Single-Cell Level C. Wang et al. 10.2139/ssrn.4065681
45. Modelling calibration uncertainty in networks of environmental sensors M. Smith et al. 10.1093/jrsssc/qlad075
46. 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
47. A Survey on Sensor Calibration in Air Pollution Monitoring Deployments B. Maag et al. 10.1109/JIOT.2018.2853660
48. 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
49. 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
50. Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device E. Bagkis et al. 10.3390/atmos12020251
51. Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview A. Chatterjee et al. 10.3390/s20092734
52. A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features I. Apostolopoulos et al. 10.3390/ai4040041
53. Indoor Air Quality in Urban India: Current Status, Research Gap, and the Way Forward A. Thakur & S. Patel 10.1021/acs.estlett.3c00636
54. Assessment of air quality sensor system performance after relocation S. Zauli-Sajani et al. 10.1016/j.apr.2020.11.010
55. 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
56. Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation C. Malings et al. 10.1080/02786826.2019.1623863
57. 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
58. Development and Calibration of a Low-Cost, Piezoelectric Rainfall Sensor through Machine Learning A. Antonini et al. 10.3390/s22176638
59. 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
60. Correction of Light Scattering-Based Total Suspended Particulate Measurements through Machine Learning Q. Guo et al. 10.3390/atmos11020139
61. Integration and calibration of non-dispersive infrared (NDIR) CO<sub>2</sub> low-cost sensors and their operation in a sensor network covering Switzerland M. Müller et al. 10.5194/amt-13-3815-2020
62. 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
63. Translating citizen-generated air quality data into evidence for shaping policy S. Mahajan et al. 10.1057/s41599-022-01135-2
64. Resolving aerosol mixing state increases accuracy of black carbon respiratory deposition estimates J. Ching et al. 10.1016/j.oneear.2020.11.004
65. 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
66. Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements M. Badura et al. 10.1007/s42452-019-0630-1
67. 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
68. 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
69. 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
70. In Situ Calibration Algorithms for Environmental Sensor Networks: A Review F. Delaine et al. 10.1109/JSEN.2019.2910317
71. Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration F. Concas et al. 10.1145/3446005
72. Minimized Training of Machine Learning-Based Calibration Methods for Low-Cost O3 Sensors S. Tondini et al. 10.1109/JSEN.2023.3339202
73. 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
74. 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
75. 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
76. A Sustainable Method for Publishing Interoperable Open Data on the Web R. Buyle et al. 10.3390/data6080093
77. Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art M. Narayana et al. 10.3390/s22010394
78. Machine Learning for Optical Gas Sensing: A Leaky-Mode Humidity Sensor as Example V. Kornienko et al. 10.1109/JSEN.2020.2978931
79. 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
80. 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
81. Voice Calibration Using Ambient Sensors J. Chen et al. 10.1142/S0218126623500433
82. Performance-based protocol for selection of economical portable sensor for air quality measurement N. Shukla et al. 10.1007/s10661-023-11438-9
83. Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors M. Adoui et al. 10.1145/3590956
84. 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
85. Characterization of a commercial lower-cost medium-precision non-dispersive infrared sensor for atmospheric CO<sub>2</sub> monitoring in urban areas E. Arzoumanian et al. 10.5194/amt-12-2665-2019
86. Machine learning techniques to improve the field performance of low-cost air quality sensors T. Bush et al. 10.5194/amt-15-3261-2022
87. 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
88. 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
89. Unmanned Aerial Vehicles for Air Pollution Monitoring: A Survey N. Hossein Motlagh et al. 10.1109/JIOT.2023.3290508
90. 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
91. Combining Radiative Transfer Model and Regression Algorithms for Estimating Aboveground Biomass of Grassland in West Ujimqin, China L. Zhang et al. 10.3390/rs15112918
92. Low-Cost Investigation into Sources of PM2.5 in Kinshasa, Democratic Republic of the Congo D. Westervelt et al. 10.1021/acsestair.3c00024
93. Characterizing the Aging of Alphasense NO2 Sensors in Long-Term Field Deployments J. Li et al. 10.1021/acssensors.1c00729
94. 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
95. 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
96. Elucidating Contributions of Anthropogenic Volatile Organic Compounds and Particulate Matter to Ozone Trends over China C. Li et al. 10.1021/acs.est.2c03315
97. 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
98. Influence of Concept Drift on Metrological Performance of Low-Cost NO2 Sensors G. D'Elia et al. 10.1109/TIM.2022.3188028
99. 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
100. 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
101. 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
102. 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
103. 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
104. A practical approach for high-resolution air quality mapping using IoT mobile devices A. Santos et al. 10.1007/s13762-022-04513-0
105. Variational Bayesian calibration of low-cost gas sensor systems in air quality monitoring G. Tancev & F. Toro 10.1016/j.measen.2021.100365
106. Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References G. Tancev & F. Toro 10.1109/ACCESS.2022.3145945
107. A Correction Method of Environmental Meteorological Model Based on Long‐Short‐Term Memory Neural Network Y. Dai et al. 10.1029/2019EA000641
108. Machine learning calibration of low-cost NO<sub>2</sub> and PM<sub>10</sub> sensors: non-linear algorithms and their impact on site transferability P. Nowack et al. 10.5194/amt-14-5637-2021
109. 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
110. Outlier detection and gap filling methodologies for low-cost air quality measurements T. Ottosen & P. Kumar 10.1039/C8EM00593A
111. 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
112. 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
113. 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
114. 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
115. 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
116. A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN B. Montrucchio et al. 10.1109/TVT.2020.3035554
117. Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network S. Ali et al. 10.3390/s23020854
118. 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
119. 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
120. 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
121. 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
122. 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
123. Application of RR-XGBoost combined model in data calibration of micro air quality detector B. Liu et al. 10.1038/s41598-021-95027-1
124. A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data E. Arnaudo et al. 10.3390/app10134587
125. 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
126. Embedded machine learning of IoT streams to promote early detection of unsafe environments E. Fernández et al. 10.1016/j.iot.2024.101128
127. 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
128. Updating Indoor Air Quality (IAQ) Assessment Screening Levels with Machine Learning Models L. Wong et al. 10.3390/ijerph19095724
129. Multisensor Data Fusion Calibration in IoT Air Pollution Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2020.2965283
130. Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants A. Baruah et al. 10.1039/D2EA00165A
131. 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
132. Addressing the Global Air Pollution Crisis: Chemistry’s Role V. McNeill 10.1016/j.trechm.2019.01.005
133. Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry A. Adams et al. 10.1002/cplu.202100418
134. 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
135. RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning L. Monti et al. 10.3390/s20195583
136. Evaluation of low-cost sensors for quantitative personal exposure monitoring S. Mahajan & P. Kumar 10.1016/j.scs.2020.102076
137. On the robustness of field calibration for smart air quality monitors S. De Vito et al. 10.1016/j.snb.2020.127869
138. Calibration Assessment of Low-Cost Carbon Dioxide Sensors Using the Extremely Randomized Trees Algorithm T. Araújo et al. 10.3390/s23136153
139. 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
140. Development and Implementation of a Platform for Public Information on Air Quality, Sensor Measurements, and Citizen Science . Wesseling et al. 10.3390/atmos10080445
141. 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
142. Spatial calibration and PM2.5 mapping of low-cost air quality sensors H. Chu et al. 10.1038/s41598-020-79064-w
143. Tackling Data Quality When Using Low-Cost Air Quality Sensors in Citizen Science Projects Å. Watne et al. 10.3389/fenvs.2021.733634
144. 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
145. Review of low-cost sensors for indoor air quality: Features and applications M. Ródenas García et al. 10.1080/05704928.2022.2085734
146. 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
147. 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
148. Prediction of Mineralization Prospects Based on Geological Semantic Model and Mobile Computer Machine Learning Z. An et al. 10.1155/2021/7734080
149. 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
150. From air quality sensors to sensor networks: Things we need to learn Y. Li et al. 10.1016/j.snb.2021.130958
151. Bayesian Sensor Calibration of a CMOS-Integrated Hall Sensor Against Thermomechanical Cross-Sensitivities M. Berger et al. 10.1109/JSEN.2023.3243783
152. 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
153. 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
154. 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
155. Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization A. Angulo et al. 10.1039/D1DD00027F
156. Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2 P. Han et al. 10.3390/s21010256
157. Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies E. Yoo et al. 10.1016/j.atmosenv.2019.117091
158. A Study on Evaporation Calculations of Agricultural Reservoirs in Hyper-Arid Areas C. Yin et al. 10.3390/agriculture12050612
159. Using spatiotemporal prediction models to quantify PM2.5 exposure due to daily movement S. Jain et al. 10.1039/D3EA00051F
160. 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
161. Long-term calibration models to estimate ozone concentrations with a metal oxide sensor T. Sayahi et al. 10.1016/j.envpol.2020.115363
162. Indoor Air Sensing: A Study in Cost, Energy, Reliability and Fidelity in Sensing P. Sharma et al. 10.1007/s11220-023-00412-x
163. EEATC: A Novel Calibration Approach for Low-Cost Sensors M. Narayana et al. 10.1109/JSEN.2023.3304366
164. Distributed Multi-Scale Calibration of Low-Cost Ozone Sensors in Wireless Sensor Networks J. Barcelo-Ordinas et al. 10.3390/s19112503
165. 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
166. Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore A. Datta et al. 10.1016/j.atmosenv.2020.117761
167. Study on the accuracy of photoacoustic spectroscopy system based on multiple linear regression correction algorithm H. Jin & P. Luo 10.1063/5.0060595
168. Evaluating uncertainty in sensor networks for urban air pollution insights D. Peters et al. 10.5194/amt-15-321-2022
169. A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2019.2929594
170. The application of machine learning to air pollution research: A bibliometric analysis Y. Li et al. 10.1016/j.ecoenv.2023.114911
171. Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network W. Wang et al. 10.3390/s20175002
172. Particulate Air Pollution in the Copenhagen Metro Part 2: Low-Cost Sensors and Micro-Environment Classification H. Russell et al. 10.2139/ssrn.4143147
173. First-Principles Algorithm for Air Quality Electrochemical Gas Sensors B. Ouyang 10.1021/acssensors.0c01129
174. 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
175. 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
176. 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
177. Air Quality Sensors and Data Adjustment Algorithms: When Is It No Longer a Measurement? G. Hagler et al. 10.1021/acs.est.8b01826
178. 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
179. 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
180. 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
181. 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
182. A Simple and Effective Random Forest Refit to Map the Spatial Distribution of NO2 Concentrations Y. Chi & Y. Zhan 10.3390/atmos13111832
183. A critical review on odor measurement and prediction Y. Wang et al. 10.1016/j.jenvman.2023.117651
184. 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
185. HUM: A review of hydrochemical analysis using ultraviolet-visible absorption spectroscopy and machine learning J. Carter et al. 10.1016/j.scitotenv.2023.165826
186. 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
187. 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
188. 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
189. Analysis of fine particle pollution data measured at 29 US diplomatic posts worldwide R. Dhammapala 10.1016/j.atmosenv.2019.05.070
190. 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
191. 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
192. 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
193. Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources B. Duncan et al. 10.1029/2021GH000451
194. Using low-cost sensors to monitor indoor, outdoor, and personal ozone concentrations in Beijing, China M. Liu et al. 10.1039/C9EM00377K
195. Field and Laboratory Evaluations of the Low-Cost Plantower Particulate Matter Sensor M. Levy Zamora et al. 10.1021/acs.est.8b05174
196. Evaluation of a low-cost dryer for a low-cost optical particle counter M. Chacón-Mateos et al. 10.5194/amt-15-7395-2022
197. 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
198. 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
199. The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias G. Tancev & C. Pascale 10.3390/s20216198
200. Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model B. Liu & Y. Zhang 10.1038/s41598-022-13531-4
201. Prototyping low-cost automatic weather stations for natural disaster monitoring G. Bernardes et al. 10.1016/j.dcan.2022.05.002
202. 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
203. Self-calibration methods for uncontrolled environments in sensor networks: A reference survey J. Barcelo-Ordinas et al. 10.1016/j.adhoc.2019.01.008
204. Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors I. Christakis et al. 10.3390/signals5010004
205. 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
206. 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
207. 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
208. 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
209. Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado S. Feinberg et al. 10.5194/amt-11-4605-2018
210. Low-Cost Nitric Oxide Sensors: Assessment of Temperature and Humidity Effects S. Owen et al. 10.3390/s22229013
211. 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
212. 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
213. 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
214. Long-term behavior and stability of calibration models for NO and NO<sub>2</sub> low-cost sensors H. Kim et al. 10.5194/amt-15-2979-2022
215. Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. 10.1007/s11270-021-04989-5
216. Low-Cost Air Quality Sensing towards Smart Homes H. Omidvarborna et al. 10.3390/atmos12040453
217. Evaluation and calibration of low‐cost off‐the‐shelf particulate matter sensors using machine learning techniques M. Ghamari et al. 10.1049/wss2.12043
218. Intelligent Wireless Sensor Network for Gas Classification Using Machine Learning N. Zaeri & R. Qasim 10.1109/JSYST.2023.3238357
219. Understanding the ability of low-cost MOx sensors to quantify ambient VOCs A. Collier-Oxandale et al. 10.5194/amt-12-1441-2019
220. 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
221. Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning I. Vajs et al. 10.3390/s21103338
222. Performance optimization of shape memory epoxy polymers based on machine learning B. Liu et al. 10.1002/pat.5595
223. Performance characteristics of the low-cost Plantower PMS optical sensor M. He et al. 10.1080/02786826.2019.1696015
224. 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
225. Environment-Adaptive Calibration System for Outdoor Low-Cost Electrochemical Gas Sensors B. Tian et al. 10.1109/ACCESS.2019.2916826
226. Performance evaluation of low-cost air quality sensors: A review Y. Kang et al. 10.1016/j.scitotenv.2021.151769
227. Data Driven Concept for Sensor Data Adaptation of Electrochemical Sensors for Mobile Air Quality Measurements E. Esatbeyoglu et al. 10.1149/1945-7111/ab74bd
228. 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
229. 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
230. 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
231. Improving data reliability: A quality control practice for low-cost PM2.5 sensor network X. Qiao et al. 10.1016/j.scitotenv.2021.146381
232. 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
233. 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
234. 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
235. A Category-Based Calibration Approach With Fault Tolerance for Air Monitoring Sensors R. Wang et al. 10.1109/JSEN.2020.2994645
236. Missing Data Imputation on IoT Sensor Networks: Implications for on-Site Sensor Calibration N. Okafor & D. Delaney 10.1109/JSEN.2021.3105442
237. Editorial Overview: Sensors and Biosensors: New sense for electrochemical sensors P. Ugo 10.1016/j.coelec.2019.08.003
238. Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2022.3196154
239. 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
240. Research of low-cost air quality monitoring models with different machine learning algorithms G. Wang et al. 10.5194/amt-17-181-2024
241. Portable Deep Learning-Driven Ion-Sensitive Field-Effect Transistor Scheme for Measurement of Carbaryl Pesticide N. Houngkamhang & P. Phasukkit 10.3390/s22093543
242. 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
243. Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection L. Grbčić et al. 10.3390/s21041157
244. MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity S. Zhang et al. 10.1109/ACCESS.2020.2979833
245. 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
246. 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
247. Evaluation of Low-cost Air Quality Sensor Calibration Models K. Aula et al. 10.1145/3512889
248. Smart Air Quality Monitoring IoT-Based Infrastructure for Industrial Environments L. García et al. 10.3390/s22239221
249. SmartAirQ: A Big Data Governance Framework for Urban Air Quality Management in Smart Cities A. Kaginalkar et al. 10.3389/fenvs.2022.785129
250. 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
251. Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment C. Zuidema et al. 10.1038/s41370-024-00667-w
252. 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
253. 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
254. Short-Term Field Evaluation of Low-Cost Sensors Operated by the “AirSensEUR” Platform A. Pichlhöfer & A. Korjenic 10.3390/en15155688
255. 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
256. Real‐Time and Image‐Based AQI Estimation Based on Deep Learning Q. Zhang et al. 10.1002/adts.202100628
257. 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
258. MAIC: Metalearning-Based Adaptive In-Field Calibration for IoT Air Quality Monitoring System N. Liu et al. 10.1109/JIOT.2022.3150849
259. 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
260. A Smart Rig for Calibration of Gas Sensor Nodes M. Benammar et al. 10.3390/s20082341
261. Calibrating networks of low-cost air quality sensors P. deSouza et al. 10.5194/amt-15-6309-2022
262. 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
263. 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
264. 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
265. 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
266. 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
267. Design and Evaluation of a Reliable Low-Cost Atmospheric Pollution Station in Urban Environment G. Astudillo et al. 10.1109/ACCESS.2020.2980736
268. 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
269. 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
270. Using depolarization to quantify ice nucleating particle concentrations: a new method J. Zenker et al. 10.5194/amt-10-4639-2017
271. Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities S. Ameer et al. 10.1109/ACCESS.2019.2925082
272. Reliability of Low-Cost, Sensor-Based Fine Dust Measurement Devices for Monitoring Atmospheric Particulate Matter Concentrations E. Cho et al. 10.3390/ijerph16081430
273. Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements E. Cross et al. 10.5194/amt-10-3575-2017
274. 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
275. Evaluation of low-cost optical particle counters for monitoring individual indoor aerosol sources P. Salimifard et al. 10.1080/02786826.2019.1697423
276. 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
277. 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
278. Short-Term Field Evaluation of Low-Cost Sensors Operated by the “AirSensEUR” Platform A. Pichlhöfer & A. Korjenic 10.3390/en15155688
279. Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments D. Hagan et al. 10.5194/amt-11-315-2018
280. Relevance and Reliability of Outdoor SO2 Monitoring in Low-Income Countries Using Low-Cost Sensors R. González Rivero et al. 10.3390/atmos14060912
281. 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
282. Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary) A. Clements et al. 10.3390/s17112478
283. 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