Articles | Volume 13, issue 3
https://doi.org/10.5194/amt-13-1181-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-1181-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Effect of aerosol composition on the performance of low-cost optical particle counter correction factors
School of Geography, Earth and Environmental Sciences, University of
Birmingham, Birmingham, UK
now at: Department of Chemistry, York University, Toronto, Canada
Ajit Singh
School of Geography, Earth and Environmental Sciences, University of
Birmingham, Birmingham, UK
Louisa J. Kramer
School of Geography, Earth and Environmental Sciences, University of
Birmingham, Birmingham, UK
Marvin D. Shaw
National Centre for Atmospheric Science, Wolfson Atmospheric Chemistry
Laboratories, University of York, York, UK
Mohammed S. Alam
School of Geography, Earth and Environmental Sciences, University of
Birmingham, Birmingham, UK
Joshua S. Apte
Department of Civil, Architectural and Environmental Engineering, The
University of Texas at Austin, Austin, Texas, USA
William J. Bloss
School of Geography, Earth and Environmental Sciences, University of
Birmingham, Birmingham, UK
Lea Hildebrandt Ruiz
Department of Civil, Architectural and Environmental Engineering, The
University of Texas at Austin, Austin, Texas, USA
Pingqing Fu
Institute of Surface-Earth System Science, Tianjin University,
Tianjin, China
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
Weiqi Fu
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
Shahzad Gani
Department of Civil, Architectural and Environmental Engineering, The
University of Texas at Austin, Austin, Texas, USA
Michael Gatari
Institute of Nuclear Science and Technology, University of Nairobi,
Nairobi, Kenya
Evgenia Ilyinskaya
School of Earth and Environment, University of Leeds, Leeds, UK
Alastair C. Lewis
National Centre for Atmospheric Science, Wolfson Atmospheric Chemistry
Laboratories, University of York, York, UK
David Ng'ang'a
Institute of Nuclear Science and Technology, University of Nairobi,
Nairobi, Kenya
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
Rachel C. W. Whitty
School of Earth and Environment, University of Leeds, Leeds, UK
Siyao Yue
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
Stuart Young
National Centre for Atmospheric Science, Wolfson Atmospheric Chemistry
Laboratories, University of York, York, UK
Francis D. Pope
School of Geography, Earth and Environmental Sciences, University of
Birmingham, Birmingham, UK
Viewed
Total article views: 6,705 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 07 Oct 2019)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
5,287 | 1,330 | 88 | 6,705 | 337 | 83 | 100 |
- HTML: 5,287
- PDF: 1,330
- XML: 88
- Total: 6,705
- Supplement: 337
- BibTeX: 83
- EndNote: 100
Total article views: 5,757 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Mar 2020)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
4,748 | 928 | 81 | 5,757 | 219 | 77 | 93 |
- HTML: 4,748
- PDF: 928
- XML: 81
- Total: 5,757
- Supplement: 219
- BibTeX: 77
- EndNote: 93
Total article views: 948 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 07 Oct 2019)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
539 | 402 | 7 | 948 | 118 | 6 | 7 |
- HTML: 539
- PDF: 402
- XML: 7
- Total: 948
- Supplement: 118
- BibTeX: 6
- EndNote: 7
Viewed (geographical distribution)
Total article views: 6,705 (including HTML, PDF, and XML)
Thereof 5,928 with geography defined
and 777 with unknown origin.
Total article views: 5,757 (including HTML, PDF, and XML)
Thereof 5,183 with geography defined
and 574 with unknown origin.
Total article views: 948 (including HTML, PDF, and XML)
Thereof 745 with geography defined
and 203 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
64 citations as recorded by crossref.
- 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
- Leveraging machine learning algorithms to advance low-cost air sensor calibration in stationary and mobile settings A. Wang et al. 10.1016/j.atmosenv.2023.119692
- Development and testing a low-cost device for airborne PM monitoring in highly anthropized areas: The international maritime hub of Civitavecchia (Rome, Italy) F. Lucci et al. 10.1016/j.geogeo.2022.100120
- Evaluation of a low-cost dryer for a low-cost optical particle counter M. Chacón-Mateos et al. 10.5194/amt-15-7395-2022
- Visibility as a proxy for air quality in East Africa A. Singh et al. 10.1088/1748-9326/ab8b12
- Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment D. Bousiotis et al. 10.1016/j.envint.2024.109069
- Opportunistic mobile air quality mapping using sensors on postal service vehicles: from point clouds to actionable insights J. Hofman et al. 10.3389/fenvh.2023.1232867
- Effect of relative humidity on the performance of five cost-effective PM sensors P. Wang et al. 10.1080/02786826.2021.1910136
- Design and Implementation of a Crowdsensing-Based Air Quality Monitoring Open and FAIR Data Infrastructure P. Diviacco et al. 10.3390/pr11071881
- Hyperlocal air pollution in an urban environment - measured with low-cost sensors L. Frederickson et al. 10.1016/j.uclim.2023.101684
- Evaluation of optical particulate matter sensors under realistic conditions of strong and mild urban pollution A. Masic et al. 10.5194/amt-13-6427-2020
- Machine learning techniques to improve the field performance of low-cost air quality sensors T. Bush et al. 10.5194/amt-15-3261-2022
- Vertical variation of atmospheric particulate matter under different pollution levels in the suburbs of Tianjin based on unmanned aerial vehicle S. Li et al. 10.1080/10962247.2022.2134231
- Field Calibration and Evaluation of an Internet-of-Things-Based Particulate Matter Sensor N. Cowell et al. 10.3389/fenvs.2021.798485
- Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability J. Kang & K. Choi 10.3390/s24103023
- Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests S. Mills et al. 10.1016/j.scitotenv.2023.161969
- Degradation mechanism of measuring performance of optical particle counter under temperature-pressure coupling effect L. Lu et al. 10.1088/1361-6501/ad3e1d
- Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India M. Campmier et al. 10.5194/amt-16-4357-2023
- Improving Aerosol Characterization Using an Optical Particle Counter Coupled with a Quartz Crystal Microbalance with an Integrated Microheater E. Zampetti et al. 10.3390/s24082500
- 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
- Light painting photography makes particulate matter air pollution visible F. Pope et al. 10.1038/s43247-024-01409-4
- Schools’ air quality monitoring for health and education: Methods and protocols of the SAMHE initiative and project L. Chatzidiakou et al. 10.1016/j.dibe.2023.100266
- Calibration Method for Particulate Matter Low-Cost Sensors Used in Ambient Air Quality Monitoring and Research J. Venkatraman Jagatha et al. 10.3390/s21123960
- Mass concentration measurements of autumn bioaerosol using low-cost sensors in a mature temperate woodland free-air carbon dioxide enrichment (FACE) experiment: investigating the role of meteorology and carbon dioxide levels A. Baird et al. 10.5194/bg-19-2653-2022
- Impact of nylon and teflon filter media on the sampling of inorganic aerosols over a high altitude site L. Yang et al. 10.1016/j.envadv.2023.100373
- Development of a physics-based method for calibration of low-cost particulate matter sensors and comparison with machine learning models B. Prajapati et al. 10.1016/j.jaerosci.2023.106284
- PM2.5 exposure differences between children and adults L. Harr et al. 10.1016/j.uclim.2022.101198
- Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter M. Aix et al. 10.1016/j.scitotenv.2023.164063
- MitH: A framework for Mitigating Hygroscopicity in low-cost PM sensors M. Casari & L. Po 10.1016/j.envsoft.2024.105955
- Simultaneous measurement of spatially resolved particle emissions in a pilot plant scale baghouse filter applying distributed low-cost particulate matter sensors P. Bächler et al. 10.1016/j.jaerosci.2020.105644
- A case study evaluating the performance of a cost-effective optical particle counter coupled with a humidity compensation approach for ambient air monitoring of particulate matter T. Dinh et al. 10.1007/s44273-023-00017-6
- Improving PM10 sensor accuracy in urban areas through calibration in Timișoara R. Blaga & S. Gautam 10.1038/s41612-024-00812-0
- Machine learning methods for low-cost pollen monitoring – Model optimisation and interpretability S. Mills et al. 10.1016/j.scitotenv.2023.165853
- EEATC: A Novel Calibration Approach for Low-Cost Sensors M. Narayana et al. 10.1109/JSEN.2023.3304366
- Local spatiotemporal dynamics of particulate matter and oak pollen measured by machine learning aided optical particle counters S. Mills et al. 10.1016/j.scitotenv.2024.173450
- Particulate matter in a lockdown home: evaluation, calibration, results and health risk from an IoT enabled low-cost sensor network for residential air quality monitoring N. Cowell et al. 10.1039/D2EA00124A
- Air Quality Sensor Networks for Evidence-Based Policy Making: Best Practices for Actionable Insights J. Hofman et al. 10.3390/atmos13060944
- The Aerosol Research Observation Station (AEROS) K. Ardon-Dryer et al. 10.5194/amt-15-2345-2022
- Evaluation of calibration performance of a low-cost particulate matter sensor using collocated and distant NO2 K. Ko et al. 10.5194/amt-17-3303-2024
- Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study D. Bousiotis et al. 10.5194/amt-14-4139-2021
- Spatial Distribution of PM2.5 Mass and Number Concentrations in Paris (France) from the Pollutrack Network of Mobile Sensors during 2018–2022 J. Renard et al. 10.3390/s23208560
- A study on the performance of low-cost sensors for source apportionment at an urban background site D. Bousiotis et al. 10.5194/amt-15-4047-2022
- Seasonal Changes in Urban PM2.5 Hotspots and Sources from Low-Cost Sensors L. Harr et al. 10.3390/atmos13050694
- Quantification of within-vehicle exposure to NOx and particles: Variation with outside air quality, route choice and ventilation options V. Matthaios et al. 10.1016/j.atmosenv.2020.117810
- Air quality assessment in three East African cities using calibrated low-cost sensors with a focus on road-based hotspots A. Singh et al. 10.1088/2515-7620/ac0e0a
- Evaluation of low-cost formaldehyde sensors calibration M. Justo Alonso et al. 10.1016/j.buildenv.2022.109380
- Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance F. Bulot et al. 10.1016/j.heliyon.2023.e15943
- Long-term airborne measurements of pollutants over the United Kingdom to support air quality model development and evaluation A. Mynard et al. 10.5194/amt-16-4229-2023
- Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science J. Hofman et al. 10.3390/s24175653
- Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors D. Bousiotis et al. 10.1016/j.envint.2023.107907
- Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges L. Liang 10.1016/j.envres.2021.111163
- Assessment of the Feasibility of a Future Integrated Larger-Scale Epidemiological Study to Evaluate Health Risks of Air Pollution Episodes in Children S. Nauwelaerts et al. 10.3390/ijerph19148531
- Testing a New “Decrypted” Algorithm for Plantower Sensors Measuring PM2.5: Comparison with an Alternative Algorithm L. Wallace 10.3390/a16080392
- Performance evaluation of MOMA (MOment MAtching) – a remote network calibration technique for PM2.5 and PM10 sensors L. Weissert et al. 10.5194/amt-16-4709-2023
- Distant calibration of low-cost PM and NO2 sensors; evidence from multiple sensor testbeds J. Hofman et al. 10.1016/j.apr.2021.101246
- Potential of low-cost PM monitoring sensors to fill monitoring gaps in areas of Sub-Saharan Africa G. Gualtieri et al. 10.1016/j.apr.2024.102158
- Impacts of daily household activities on indoor particulate and NO2 concentrations; a case study from oxford UK A. Singh et al. 10.1016/j.heliyon.2024.e34210
- Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information E. Considine et al. 10.1021/acs.est.2c06626
- Towards comprehensive air quality management using low-cost sensors for pollution source apportionment D. Bousiotis et al. 10.1038/s41612-023-00424-0
- Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany J. Venkatraman Jagatha et al. 10.3390/s24134193
- SensEURCity: A multi-city air quality dataset collected for 2020/2021 using open low-cost sensor systems M. Van Poppel et al. 10.1038/s41597-023-02135-w
- Particle generation and dispersion from high-speed dental drilling M. Kumar et al. 10.1007/s00784-023-05163-3
- Spatial calibration and PM2.5 mapping of low-cost air quality sensors H. Chu et al. 10.1038/s41598-020-79064-w
- Deployment of networked low-cost sensors and comparison to real-time stationary monitors in New Delhi J. Prakash et al. 10.1080/10962247.2021.1890276
63 citations as recorded by crossref.
- 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
- Leveraging machine learning algorithms to advance low-cost air sensor calibration in stationary and mobile settings A. Wang et al. 10.1016/j.atmosenv.2023.119692
- Development and testing a low-cost device for airborne PM monitoring in highly anthropized areas: The international maritime hub of Civitavecchia (Rome, Italy) F. Lucci et al. 10.1016/j.geogeo.2022.100120
- Evaluation of a low-cost dryer for a low-cost optical particle counter M. Chacón-Mateos et al. 10.5194/amt-15-7395-2022
- Visibility as a proxy for air quality in East Africa A. Singh et al. 10.1088/1748-9326/ab8b12
- Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment D. Bousiotis et al. 10.1016/j.envint.2024.109069
- Opportunistic mobile air quality mapping using sensors on postal service vehicles: from point clouds to actionable insights J. Hofman et al. 10.3389/fenvh.2023.1232867
- Effect of relative humidity on the performance of five cost-effective PM sensors P. Wang et al. 10.1080/02786826.2021.1910136
- Design and Implementation of a Crowdsensing-Based Air Quality Monitoring Open and FAIR Data Infrastructure P. Diviacco et al. 10.3390/pr11071881
- Hyperlocal air pollution in an urban environment - measured with low-cost sensors L. Frederickson et al. 10.1016/j.uclim.2023.101684
- Evaluation of optical particulate matter sensors under realistic conditions of strong and mild urban pollution A. Masic et al. 10.5194/amt-13-6427-2020
- Machine learning techniques to improve the field performance of low-cost air quality sensors T. Bush et al. 10.5194/amt-15-3261-2022
- Vertical variation of atmospheric particulate matter under different pollution levels in the suburbs of Tianjin based on unmanned aerial vehicle S. Li et al. 10.1080/10962247.2022.2134231
- Field Calibration and Evaluation of an Internet-of-Things-Based Particulate Matter Sensor N. Cowell et al. 10.3389/fenvs.2021.798485
- Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability J. Kang & K. Choi 10.3390/s24103023
- Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests S. Mills et al. 10.1016/j.scitotenv.2023.161969
- Degradation mechanism of measuring performance of optical particle counter under temperature-pressure coupling effect L. Lu et al. 10.1088/1361-6501/ad3e1d
- Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India M. Campmier et al. 10.5194/amt-16-4357-2023
- Improving Aerosol Characterization Using an Optical Particle Counter Coupled with a Quartz Crystal Microbalance with an Integrated Microheater E. Zampetti et al. 10.3390/s24082500
- 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
- Light painting photography makes particulate matter air pollution visible F. Pope et al. 10.1038/s43247-024-01409-4
- Schools’ air quality monitoring for health and education: Methods and protocols of the SAMHE initiative and project L. Chatzidiakou et al. 10.1016/j.dibe.2023.100266
- Calibration Method for Particulate Matter Low-Cost Sensors Used in Ambient Air Quality Monitoring and Research J. Venkatraman Jagatha et al. 10.3390/s21123960
- Mass concentration measurements of autumn bioaerosol using low-cost sensors in a mature temperate woodland free-air carbon dioxide enrichment (FACE) experiment: investigating the role of meteorology and carbon dioxide levels A. Baird et al. 10.5194/bg-19-2653-2022
- Impact of nylon and teflon filter media on the sampling of inorganic aerosols over a high altitude site L. Yang et al. 10.1016/j.envadv.2023.100373
- Development of a physics-based method for calibration of low-cost particulate matter sensors and comparison with machine learning models B. Prajapati et al. 10.1016/j.jaerosci.2023.106284
- PM2.5 exposure differences between children and adults L. Harr et al. 10.1016/j.uclim.2022.101198
- Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter M. Aix et al. 10.1016/j.scitotenv.2023.164063
- MitH: A framework for Mitigating Hygroscopicity in low-cost PM sensors M. Casari & L. Po 10.1016/j.envsoft.2024.105955
- Simultaneous measurement of spatially resolved particle emissions in a pilot plant scale baghouse filter applying distributed low-cost particulate matter sensors P. Bächler et al. 10.1016/j.jaerosci.2020.105644
- A case study evaluating the performance of a cost-effective optical particle counter coupled with a humidity compensation approach for ambient air monitoring of particulate matter T. Dinh et al. 10.1007/s44273-023-00017-6
- Improving PM10 sensor accuracy in urban areas through calibration in Timișoara R. Blaga & S. Gautam 10.1038/s41612-024-00812-0
- Machine learning methods for low-cost pollen monitoring – Model optimisation and interpretability S. Mills et al. 10.1016/j.scitotenv.2023.165853
- EEATC: A Novel Calibration Approach for Low-Cost Sensors M. Narayana et al. 10.1109/JSEN.2023.3304366
- Local spatiotemporal dynamics of particulate matter and oak pollen measured by machine learning aided optical particle counters S. Mills et al. 10.1016/j.scitotenv.2024.173450
- Particulate matter in a lockdown home: evaluation, calibration, results and health risk from an IoT enabled low-cost sensor network for residential air quality monitoring N. Cowell et al. 10.1039/D2EA00124A
- Air Quality Sensor Networks for Evidence-Based Policy Making: Best Practices for Actionable Insights J. Hofman et al. 10.3390/atmos13060944
- The Aerosol Research Observation Station (AEROS) K. Ardon-Dryer et al. 10.5194/amt-15-2345-2022
- Evaluation of calibration performance of a low-cost particulate matter sensor using collocated and distant NO2 K. Ko et al. 10.5194/amt-17-3303-2024
- Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study D. Bousiotis et al. 10.5194/amt-14-4139-2021
- Spatial Distribution of PM2.5 Mass and Number Concentrations in Paris (France) from the Pollutrack Network of Mobile Sensors during 2018–2022 J. Renard et al. 10.3390/s23208560
- A study on the performance of low-cost sensors for source apportionment at an urban background site D. Bousiotis et al. 10.5194/amt-15-4047-2022
- Seasonal Changes in Urban PM2.5 Hotspots and Sources from Low-Cost Sensors L. Harr et al. 10.3390/atmos13050694
- Quantification of within-vehicle exposure to NOx and particles: Variation with outside air quality, route choice and ventilation options V. Matthaios et al. 10.1016/j.atmosenv.2020.117810
- Air quality assessment in three East African cities using calibrated low-cost sensors with a focus on road-based hotspots A. Singh et al. 10.1088/2515-7620/ac0e0a
- Evaluation of low-cost formaldehyde sensors calibration M. Justo Alonso et al. 10.1016/j.buildenv.2022.109380
- Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance F. Bulot et al. 10.1016/j.heliyon.2023.e15943
- Long-term airborne measurements of pollutants over the United Kingdom to support air quality model development and evaluation A. Mynard et al. 10.5194/amt-16-4229-2023
- Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science J. Hofman et al. 10.3390/s24175653
- Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors D. Bousiotis et al. 10.1016/j.envint.2023.107907
- Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges L. Liang 10.1016/j.envres.2021.111163
- Assessment of the Feasibility of a Future Integrated Larger-Scale Epidemiological Study to Evaluate Health Risks of Air Pollution Episodes in Children S. Nauwelaerts et al. 10.3390/ijerph19148531
- Testing a New “Decrypted” Algorithm for Plantower Sensors Measuring PM2.5: Comparison with an Alternative Algorithm L. Wallace 10.3390/a16080392
- Performance evaluation of MOMA (MOment MAtching) – a remote network calibration technique for PM2.5 and PM10 sensors L. Weissert et al. 10.5194/amt-16-4709-2023
- Distant calibration of low-cost PM and NO2 sensors; evidence from multiple sensor testbeds J. Hofman et al. 10.1016/j.apr.2021.101246
- Potential of low-cost PM monitoring sensors to fill monitoring gaps in areas of Sub-Saharan Africa G. Gualtieri et al. 10.1016/j.apr.2024.102158
- Impacts of daily household activities on indoor particulate and NO2 concentrations; a case study from oxford UK A. Singh et al. 10.1016/j.heliyon.2024.e34210
- Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information E. Considine et al. 10.1021/acs.est.2c06626
- Towards comprehensive air quality management using low-cost sensors for pollution source apportionment D. Bousiotis et al. 10.1038/s41612-023-00424-0
- Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany J. Venkatraman Jagatha et al. 10.3390/s24134193
- SensEURCity: A multi-city air quality dataset collected for 2020/2021 using open low-cost sensor systems M. Van Poppel et al. 10.1038/s41597-023-02135-w
- Particle generation and dispersion from high-speed dental drilling M. Kumar et al. 10.1007/s00784-023-05163-3
- Spatial calibration and PM2.5 mapping of low-cost air quality sensors H. Chu et al. 10.1038/s41598-020-79064-w
Latest update: 19 Nov 2024
Short summary
There is considerable interest in using low-cost optical particle counters (OPCs) for particle mass measurements; however, there is no agreed upon method with respect to calibration. Here we exploit a number of datasets globally to demonstrate that particle composition and relative humidity are the key factors affecting measured concentrations from a low-cost OPC, and we present a simple correction methodology that corrects for this influence.
There is considerable interest in using low-cost optical particle counters (OPCs) for particle...