Research article
| Highlight paper
15 Jan 2018
Research article
| Highlight paper
| 15 Jan 2018
A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
Naomi Zimmerman et al.
Viewed
Total article views: 12,556 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 Aug 2017)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
7,676 | 4,654 | 226 | 12,556 | 870 | 147 | 188 |
- HTML: 7,676
- PDF: 4,654
- XML: 226
- Total: 12,556
- Supplement: 870
- BibTeX: 147
- EndNote: 188
Total article views: 10,652 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Jan 2018)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
6,641 | 3,796 | 215 | 10,652 | 692 | 141 | 167 |
- HTML: 6,641
- PDF: 3,796
- XML: 215
- Total: 10,652
- Supplement: 692
- BibTeX: 141
- EndNote: 167
Total article views: 1,904 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 Aug 2017)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
1,035 | 858 | 11 | 1,904 | 178 | 6 | 21 |
- HTML: 1,035
- PDF: 858
- XML: 11
- Total: 1,904
- Supplement: 178
- BibTeX: 6
- EndNote: 21
Viewed (geographical distribution)
Total article views: 12,556 (including HTML, PDF, and XML)
Thereof 11,848 with geography defined
and 708 with unknown origin.
Total article views: 10,652 (including HTML, PDF, and XML)
Thereof 9,976 with geography defined
and 676 with unknown origin.
Total article views: 1,904 (including HTML, PDF, and XML)
Thereof 1,872 with geography defined
and 32 with unknown origin.
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Cited
165 citations as recorded by crossref.
- 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
- 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
- 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
- Addressing the Global Air Pollution Crisis: Chemistry’s Role V. McNeill 10.1016/j.trechm.2019.01.005
- Performance evaluation of ozone and particulate matter sensors H. DeWitt et al. 10.1080/10962247.2020.1713921
- Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry A. Adams et al. 10.1002/cplu.202100418
- 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
- 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
- RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning L. Monti et al. 10.3390/s20195583
- 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
- Evaluation of low-cost sensors for quantitative personal exposure monitoring S. Mahajan & P. Kumar 10.1016/j.scs.2020.102076
- On the robustness of field calibration for smart air quality monitors S. De Vito et al. 10.1016/j.snb.2020.127869
- 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
- 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
- Development and Implementation of a Platform for Public Information on Air Quality, Sensor Measurements, and Citizen Science . Wesseling et al. 10.3390/atmos10080445
- Spatial calibration and PM2.5 mapping of low-cost air quality sensors H. Chu et al. 10.1038/s41598-020-79064-w
- Tackling Data Quality When Using Low-Cost Air Quality Sensors in Citizen Science Projects Å. Watne et al. 10.3389/fenvs.2021.733634
- 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
- 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
- Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments T. Zheng et al. 10.5194/amt-11-4823-2018
- Prediction of Mineralization Prospects Based on Geological Semantic Model and Mobile Computer Machine Learning Z. An et al. 10.1155/2021/7734080
- 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
- From air quality sensors to sensor networks: Things we need to learn Y. Li et al. 10.1016/j.snb.2021.130958
- 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
- Review of the Performance of Low-Cost Sensors for Air Quality Monitoring F. Karagulian et al. 10.3390/atmos10090506
- 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
- Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization A. Angulo et al. 10.1039/D1DD00027F
- Evaluation of Low-Cost Sensors for Weather and Carbon Dioxide Monitoring in Internet of Things Context T. Araújo et al. 10.3390/iot1020017
- Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2 P. Han et al. 10.3390/s21010256
- Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies E. Yoo et al. 10.1016/j.atmosenv.2019.117091
- 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
- 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
- Long-term calibration models to estimate ozone concentrations with a metal oxide sensor T. Sayahi et al. 10.1016/j.envpol.2020.115363
- Distributed Multi-Scale Calibration of Low-Cost Ozone Sensors in Wireless Sensor Networks J. Barcelo-Ordinas et al. 10.3390/s19112503
- 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
- 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
- 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
- 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
- Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore A. Datta et al. 10.1016/j.atmosenv.2020.117761
- Study on the accuracy of photoacoustic spectroscopy system based on multiple linear regression correction algorithm H. Jin & P. Luo 10.1063/5.0060595
- Evaluating uncertainty in sensor networks for urban air pollution insights D. Peters et al. 10.5194/amt-15-321-2022
- A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2019.2929594
- Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network W. Wang et al. 10.3390/s20175002
- Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges L. Liang 10.1016/j.envres.2021.111163
- 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
- First-Principles Algorithm for Air Quality Electrochemical Gas Sensors B. Ouyang 10.1021/acssensors.0c01129
- 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
- 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
- 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
- Air Quality Sensors and Data Adjustment Algorithms: When Is It No Longer a Measurement? G. Hagler et al. 10.1021/acs.est.8b01826
- 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
- 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
- Fleet-based vehicle emission factors using low-cost sensors: Case study in parking garages B. Liu & N. Zimmerman 10.1016/j.trd.2020.102635
- 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
- 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
- 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
- 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
- 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
- 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
- Analysis of fine particle pollution data measured at 29 US diplomatic posts worldwide R. Dhammapala 10.1016/j.atmosenv.2019.05.070
- 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
- A Survey on Sensor Calibration in Air Pollution Monitoring Deployments B. Maag et al. 10.1109/JIOT.2018.2853660
- 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
- Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources B. Duncan et al. 10.1029/2021GH000451
- Using low-cost sensors to monitor indoor, outdoor, and personal ozone concentrations in Beijing, China M. Liu et al. 10.1039/C9EM00377K
- 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
- 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
- Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device E. Bagkis et al. 10.3390/atmos12020251
- Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview A. Chatterjee et al. 10.3390/s20092734
- Field and Laboratory Evaluations of the Low-Cost Plantower Particulate Matter Sensor M. Levy Zamora et al. 10.1021/acs.est.8b05174
- Assessment of air quality sensor system performance after relocation S. Zauli-Sajani et al. 10.1016/j.apr.2020.11.010
- 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
- Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation C. Malings et al. 10.1080/02786826.2019.1623863
- The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias G. Tancev & C. Pascale 10.3390/s20216198
- 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
- Correction of Light Scattering-Based Total Suspended Particulate Measurements through Machine Learning Q. Guo et al. 10.3390/atmos11020139
- Self-calibration methods for uncontrolled environments in sensor networks: A reference survey J. Barcelo-Ordinas et al. 10.1016/j.adhoc.2019.01.008
- 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
- 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
- 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
- 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
- Resolving aerosol mixing state increases accuracy of black carbon respiratory deposition estimates J. Ching et al. 10.1016/j.oneear.2020.11.004
- 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
- Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado S. Feinberg et al. 10.5194/amt-11-4605-2018
- Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements M. Badura et al. 10.1007/s42452-019-0630-1
- 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
- In Situ Calibration Algorithms for Environmental Sensor Networks: A Review F. Delaine et al. 10.1109/JSEN.2019.2910317
- Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration F. Concas et al. 10.1145/3446005
- Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. 10.1007/s11270-021-04989-5
- Low-Cost Air Quality Sensing towards Smart Homes H. Omidvarborna et al. 10.3390/atmos12040453
- 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
- Understanding the ability of low-cost MOx sensors to quantify ambient VOCs A. Collier-Oxandale et al. 10.5194/amt-12-1441-2019
- 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
- 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
- A Sustainable Method for Publishing Interoperable Open Data on the Web R. Buyle et al. 10.3390/data6080093
- Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art M. Narayana et al. 10.3390/s22010394
- Machine Learning for Optical Gas Sensing: A Leaky-Mode Humidity Sensor as Example V. Kornienko et al. 10.1109/JSEN.2020.2978931
- 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
- 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
- Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning I. Vajs et al. 10.3390/s21103338
- Performance optimization of shape memory epoxy polymers based on machine learning B. Liu et al. 10.1002/pat.5595
- Performance characteristics of the low-cost Plantower PMS optical sensor M. He et al. 10.1080/02786826.2019.1696015
- 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
- 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
- Environment-Adaptive Calibration System for Outdoor Low-Cost Electrochemical Gas Sensors B. Tian et al. 10.1109/ACCESS.2019.2916826
- Performance evaluation of low-cost air quality sensors: A review Y. Kang et al. 10.1016/j.scitotenv.2021.151769
- 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
- Data Driven Concept for Sensor Data Adaptation of Electrochemical Sensors for Mobile Air Quality Measurements E. Esatbeyoglu et al. 10.1149/1945-7111/ab74bd
- 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
- 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
- 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
- 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
- Improving data reliability: A quality control practice for low-cost PM2.5 sensor network X. Qiao et al. 10.1016/j.scitotenv.2021.146381
- 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
- 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
- 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
- A Category-Based Calibration Approach With Fault Tolerance for Air Monitoring Sensors R. Wang et al. 10.1109/JSEN.2020.2994645
- Characterizing the Aging of Alphasense NO2 Sensors in Long-Term Field Deployments J. Li et al. 10.1021/acssensors.1c00729
- Missing Data Imputation on IoT Sensor Networks: Implications for on-Site Sensor Calibration N. Okafor & D. Delaney 10.1109/JSEN.2021.3105442
- 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
- Editorial Overview: Sensors and Biosensors: New sense for electrochemical sensors P. Ugo 10.1016/j.coelec.2019.08.003
- 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
- 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
- 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
- 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
- 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
- Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection L. Grbčić et al. 10.3390/s21041157
- MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity S. Zhang et al. 10.1109/ACCESS.2020.2979833
- 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
- Variational Bayesian calibration of low-cost gas sensor systems in air quality monitoring G. Tancev & F. Toro 10.1016/j.measen.2021.100365
- Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References G. Tancev & F. Toro 10.1109/ACCESS.2022.3145945
- 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
- A Correction Method of Environmental Meteorological Model Based on Long‐Short‐Term Memory Neural Network Y. Dai et al. 10.1029/2019EA000641
- 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
- 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
- Outlier detection and gap filling methodologies for low-cost air quality measurements T. Ottosen & P. Kumar 10.1039/C8EM00593A
- 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
- 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
- A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN B. Montrucchio et al. 10.1109/TVT.2020.3035554
- Real‐Time and Image‐Based AQI Estimation Based on Deep Learning Q. Zhang et al. 10.1002/adts.202100628
- 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
- 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
- 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
- A Smart Rig for Calibration of Gas Sensor Nodes M. Benammar et al. 10.3390/s20082341
- 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
- Application of RR-XGBoost combined model in data calibration of micro air quality detector B. Liu et al. 10.1038/s41598-021-95027-1
- 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
- A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data E. Arnaudo et al. 10.3390/app10134587
- 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
- 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
- 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
- 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
- Design and Evaluation of a Reliable Low-Cost Atmospheric Pollution Station in Urban Environment G. Astudillo et al. 10.1109/ACCESS.2020.2980736
- Multisensor Data Fusion Calibration in IoT Air Pollution Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2020.2965283
- 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
- Using depolarization to quantify ice nucleating particle concentrations: a new method J. Zenker et al. 10.5194/amt-10-4639-2017
- 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
- Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities S. Ameer et al. 10.1109/ACCESS.2019.2925082
- Reliability of Low-Cost, Sensor-Based Fine Dust Measurement Devices for Monitoring Atmospheric Particulate Matter Concentrations E. Cho et al. 10.3390/ijerph16081430
- 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
- Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary) A. Clements et al. 10.3390/s17112478
- 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
- Evaluation of low-cost optical particle counters for monitoring individual indoor aerosol sources P. Salimifard et al. 10.1080/02786826.2019.1697423
- 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
- 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
155 citations as recorded by crossref.
- 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
- 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
- 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
- Addressing the Global Air Pollution Crisis: Chemistry’s Role V. McNeill 10.1016/j.trechm.2019.01.005
- Performance evaluation of ozone and particulate matter sensors H. DeWitt et al. 10.1080/10962247.2020.1713921
- Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry A. Adams et al. 10.1002/cplu.202100418
- 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
- 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
- RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning L. Monti et al. 10.3390/s20195583
- 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
- Evaluation of low-cost sensors for quantitative personal exposure monitoring S. Mahajan & P. Kumar 10.1016/j.scs.2020.102076
- On the robustness of field calibration for smart air quality monitors S. De Vito et al. 10.1016/j.snb.2020.127869
- 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
- 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
- Development and Implementation of a Platform for Public Information on Air Quality, Sensor Measurements, and Citizen Science . Wesseling et al. 10.3390/atmos10080445
- Spatial calibration and PM2.5 mapping of low-cost air quality sensors H. Chu et al. 10.1038/s41598-020-79064-w
- Tackling Data Quality When Using Low-Cost Air Quality Sensors in Citizen Science Projects Å. Watne et al. 10.3389/fenvs.2021.733634
- 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
- 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
- Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments T. Zheng et al. 10.5194/amt-11-4823-2018
- Prediction of Mineralization Prospects Based on Geological Semantic Model and Mobile Computer Machine Learning Z. An et al. 10.1155/2021/7734080
- 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
- From air quality sensors to sensor networks: Things we need to learn Y. Li et al. 10.1016/j.snb.2021.130958
- 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
- Review of the Performance of Low-Cost Sensors for Air Quality Monitoring F. Karagulian et al. 10.3390/atmos10090506
- 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
- Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization A. Angulo et al. 10.1039/D1DD00027F
- Evaluation of Low-Cost Sensors for Weather and Carbon Dioxide Monitoring in Internet of Things Context T. Araújo et al. 10.3390/iot1020017
- Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2 P. Han et al. 10.3390/s21010256
- Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies E. Yoo et al. 10.1016/j.atmosenv.2019.117091
- 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
- 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
- Long-term calibration models to estimate ozone concentrations with a metal oxide sensor T. Sayahi et al. 10.1016/j.envpol.2020.115363
- Distributed Multi-Scale Calibration of Low-Cost Ozone Sensors in Wireless Sensor Networks J. Barcelo-Ordinas et al. 10.3390/s19112503
- 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
- 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
- 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
- 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
- Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore A. Datta et al. 10.1016/j.atmosenv.2020.117761
- Study on the accuracy of photoacoustic spectroscopy system based on multiple linear regression correction algorithm H. Jin & P. Luo 10.1063/5.0060595
- Evaluating uncertainty in sensor networks for urban air pollution insights D. Peters et al. 10.5194/amt-15-321-2022
- A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2019.2929594
- Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network W. Wang et al. 10.3390/s20175002
- Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges L. Liang 10.1016/j.envres.2021.111163
- 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
- First-Principles Algorithm for Air Quality Electrochemical Gas Sensors B. Ouyang 10.1021/acssensors.0c01129
- 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
- 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
- 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
- Air Quality Sensors and Data Adjustment Algorithms: When Is It No Longer a Measurement? G. Hagler et al. 10.1021/acs.est.8b01826
- 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
- 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
- Fleet-based vehicle emission factors using low-cost sensors: Case study in parking garages B. Liu & N. Zimmerman 10.1016/j.trd.2020.102635
- 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
- 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
- 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
- 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
- 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
- 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
- Analysis of fine particle pollution data measured at 29 US diplomatic posts worldwide R. Dhammapala 10.1016/j.atmosenv.2019.05.070
- 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
- A Survey on Sensor Calibration in Air Pollution Monitoring Deployments B. Maag et al. 10.1109/JIOT.2018.2853660
- 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
- Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources B. Duncan et al. 10.1029/2021GH000451
- Using low-cost sensors to monitor indoor, outdoor, and personal ozone concentrations in Beijing, China M. Liu et al. 10.1039/C9EM00377K
- 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
- 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
- Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device E. Bagkis et al. 10.3390/atmos12020251
- Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview A. Chatterjee et al. 10.3390/s20092734
- Field and Laboratory Evaluations of the Low-Cost Plantower Particulate Matter Sensor M. Levy Zamora et al. 10.1021/acs.est.8b05174
- Assessment of air quality sensor system performance after relocation S. Zauli-Sajani et al. 10.1016/j.apr.2020.11.010
- 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
- Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation C. Malings et al. 10.1080/02786826.2019.1623863
- The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias G. Tancev & C. Pascale 10.3390/s20216198
- 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
- Correction of Light Scattering-Based Total Suspended Particulate Measurements through Machine Learning Q. Guo et al. 10.3390/atmos11020139
- Self-calibration methods for uncontrolled environments in sensor networks: A reference survey J. Barcelo-Ordinas et al. 10.1016/j.adhoc.2019.01.008
- 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
- 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
- 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
- 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
- Resolving aerosol mixing state increases accuracy of black carbon respiratory deposition estimates J. Ching et al. 10.1016/j.oneear.2020.11.004
- 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
- Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado S. Feinberg et al. 10.5194/amt-11-4605-2018
- Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements M. Badura et al. 10.1007/s42452-019-0630-1
- 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
- In Situ Calibration Algorithms for Environmental Sensor Networks: A Review F. Delaine et al. 10.1109/JSEN.2019.2910317
- Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration F. Concas et al. 10.1145/3446005
- Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. 10.1007/s11270-021-04989-5
- Low-Cost Air Quality Sensing towards Smart Homes H. Omidvarborna et al. 10.3390/atmos12040453
- 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
- Understanding the ability of low-cost MOx sensors to quantify ambient VOCs A. Collier-Oxandale et al. 10.5194/amt-12-1441-2019
- 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
- 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
- A Sustainable Method for Publishing Interoperable Open Data on the Web R. Buyle et al. 10.3390/data6080093
- Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art M. Narayana et al. 10.3390/s22010394
- Machine Learning for Optical Gas Sensing: A Leaky-Mode Humidity Sensor as Example V. Kornienko et al. 10.1109/JSEN.2020.2978931
- 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
- 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
- Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning I. Vajs et al. 10.3390/s21103338
- Performance optimization of shape memory epoxy polymers based on machine learning B. Liu et al. 10.1002/pat.5595
- Performance characteristics of the low-cost Plantower PMS optical sensor M. He et al. 10.1080/02786826.2019.1696015
- 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
- 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
- Environment-Adaptive Calibration System for Outdoor Low-Cost Electrochemical Gas Sensors B. Tian et al. 10.1109/ACCESS.2019.2916826
- Performance evaluation of low-cost air quality sensors: A review Y. Kang et al. 10.1016/j.scitotenv.2021.151769
- 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
- Data Driven Concept for Sensor Data Adaptation of Electrochemical Sensors for Mobile Air Quality Measurements E. Esatbeyoglu et al. 10.1149/1945-7111/ab74bd
- 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
- 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
- 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
- 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
- Improving data reliability: A quality control practice for low-cost PM2.5 sensor network X. Qiao et al. 10.1016/j.scitotenv.2021.146381
- 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
- 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
- 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
- A Category-Based Calibration Approach With Fault Tolerance for Air Monitoring Sensors R. Wang et al. 10.1109/JSEN.2020.2994645
- Characterizing the Aging of Alphasense NO2 Sensors in Long-Term Field Deployments J. Li et al. 10.1021/acssensors.1c00729
- Missing Data Imputation on IoT Sensor Networks: Implications for on-Site Sensor Calibration N. Okafor & D. Delaney 10.1109/JSEN.2021.3105442
- 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
- Editorial Overview: Sensors and Biosensors: New sense for electrochemical sensors P. Ugo 10.1016/j.coelec.2019.08.003
- 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
- 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
- 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
- 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
- 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
- Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection L. Grbčić et al. 10.3390/s21041157
- MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity S. Zhang et al. 10.1109/ACCESS.2020.2979833
- 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
- Variational Bayesian calibration of low-cost gas sensor systems in air quality monitoring G. Tancev & F. Toro 10.1016/j.measen.2021.100365
- Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References G. Tancev & F. Toro 10.1109/ACCESS.2022.3145945
- 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
- A Correction Method of Environmental Meteorological Model Based on Long‐Short‐Term Memory Neural Network Y. Dai et al. 10.1029/2019EA000641
- 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
- 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
- Outlier detection and gap filling methodologies for low-cost air quality measurements T. Ottosen & P. Kumar 10.1039/C8EM00593A
- 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
- 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
- A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN B. Montrucchio et al. 10.1109/TVT.2020.3035554
- Real‐Time and Image‐Based AQI Estimation Based on Deep Learning Q. Zhang et al. 10.1002/adts.202100628
- 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
- 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
- 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
- A Smart Rig for Calibration of Gas Sensor Nodes M. Benammar et al. 10.3390/s20082341
- 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
- Application of RR-XGBoost combined model in data calibration of micro air quality detector B. Liu et al. 10.1038/s41598-021-95027-1
- 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
- A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data E. Arnaudo et al. 10.3390/app10134587
- 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
- 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
- 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
- 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
- Design and Evaluation of a Reliable Low-Cost Atmospheric Pollution Station in Urban Environment G. Astudillo et al. 10.1109/ACCESS.2020.2980736
- Multisensor Data Fusion Calibration in IoT Air Pollution Platforms P. Ferrer-Cid et al. 10.1109/JIOT.2020.2965283
- 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
10 citations as recorded by crossref.
- Using depolarization to quantify ice nucleating particle concentrations: a new method J. Zenker et al. 10.5194/amt-10-4639-2017
- 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
- Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities S. Ameer et al. 10.1109/ACCESS.2019.2925082
- Reliability of Low-Cost, Sensor-Based Fine Dust Measurement Devices for Monitoring Atmospheric Particulate Matter Concentrations E. Cho et al. 10.3390/ijerph16081430
- 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
- Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary) A. Clements et al. 10.3390/s17112478
- 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
- Evaluation of low-cost optical particle counters for monitoring individual indoor aerosol sources P. Salimifard et al. 10.1080/02786826.2019.1697423
- 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
- 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
Discussed (final revised paper)
Discussed (preprint)
Latest update: 28 Jan 2023
Short summary
Low-cost sensors promise neighborhood-scale air quality monitoring but have been plagued by inconsistent performance for precision, accuracy, and drift. CMU and SenSevere collaborated to develop the RAMP, which uses electrochemical sensors. We present a machine learning algorithm that overcomes previous performance issues and meets US EPA's data quality recommendations for personal exposure for NO2 and tougher "supplemental monitoring" standards for CO & ozone across 19 RAMPs for several months.
Low-cost sensors promise neighborhood-scale air quality monitoring but have been plagued by...