Articles | Volume 15, issue 13
https://doi.org/10.5194/amt-15-4047-2022
© Author(s) 2022. 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-15-4047-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jul 2022
Research article |
| 08 Jul 2022
| 08 Jul 2022
A study on the performance of low-cost sensors for source apportionment at an urban background site
Dimitrios Bousiotis
CORRESPONDING AUTHOR
Division of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences University of Birmingham,
Edgbaston, Birmingham, B15 2TT, United Kingdom
David C. S. Beddows
Division of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences University of Birmingham,
Edgbaston, Birmingham, B15 2TT, United Kingdom
Ajit Singh
Division of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences University of Birmingham,
Edgbaston, Birmingham, B15 2TT, United Kingdom
Molly Haugen
Department of Engineering, University of Cambridge, Trumpington
Street, Cambridge, CB2 1PZ, United Kingdom
Sebastián Diez
Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, Heslington, York, YO10 5DD, United Kingdom
Pete M. Edwards
Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, Heslington, York, YO10 5DD, United Kingdom
Adam Boies
Department of Engineering, University of Cambridge, Trumpington
Street, Cambridge, CB2 1PZ, United Kingdom
Roy M. Harrison
Division of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences University of Birmingham,
Edgbaston, Birmingham, B15 2TT, United Kingdom
Francis D. Pope
Division of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences University of Birmingham,
Edgbaston, Birmingham, B15 2TT, United Kingdom
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Cited
29 citations as recorded by crossref.
- Indoor air quality monitoring and source apportionment using low-cost sensors C. Higgins et al. https://doi.org/10.1088/2515-7620/ad1cad
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. https://doi.org/10.1039/D4EA00086B
- Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors D. Bousiotis et al. https://doi.org/10.1016/j.envint.2023.107907
- Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain G. Kosmopoulos et al. https://doi.org/10.3390/s23146541
- Attributing PM sources within a swine production facility via PMF modeling of particle size distribution data P. Kumar et al. https://doi.org/10.1016/j.scitotenv.2026.181519
- A Novel Apportionment Method Utilizing Particle Mass Size Distribution across Multiple Particle Size Ranges P. Wang et al. https://doi.org/10.3390/atmos15080955
- Fundamental understanding of low-cost sensor response S. Kookkal & S. Dhaniyala https://doi.org/10.1080/02786826.2026.2649803
- Indoor and outdoor particulate matter source apportionment in a household in Tetovo, North Macedonia: A pilot study using low-cost sensors L. Shaqiri et al. https://doi.org/10.1016/j.apr.2026.103065
- Calibrating low-cost sensors using MERRA-2 reconstructed PM2.5 mass concentration as a proxy V. Malyan et al. https://doi.org/10.1016/j.apr.2023.102027
- The impact of urban mobility on air pollution in Kampala, an exemplar sub-Saharan African city O. Ghaffarpasand et al. https://doi.org/10.1016/j.apr.2024.102057
- Low-Cost Source Apportionment (LoCoSA) of air pollution - literature review of the state of the art D. Bousiotis et al. https://doi.org/10.1016/j.scitotenv.2025.180257
- Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey Ö. Akyürek https://doi.org/10.21324/dacd.1599855
- Condensation particle counters: Exploring the limits of miniaturisation S. Balendra et al. https://doi.org/10.1016/j.jaerosci.2023.106266
- Air exchange rates in a naturally ventilated apartment: a multi-box modelling approach using occupant-generated CO2 decay measurements Y. Su et al. https://doi.org/10.1016/j.rineng.2026.110563
- Investigating Indoor Air Pollution Sources and Student’s Exposure Within School Classrooms: Using a Low‐Cost Sensor and Source Apportionment Approach O. Rose et al. https://doi.org/10.1155/2024/5544298
- Real-Time Source Apportionment of Particulate Matter from Low-Cost Particle Sensors Using Machine Learning V. Kumar et al. https://doi.org/10.1007/s41810-024-00271-3
- Low-Cost Investigation into Sources of PM2.5 in Kinshasa, Democratic Republic of the Congo D. Westervelt et al. https://doi.org/10.1021/acsestair.3c00024
- Using low-cost sensors to assess common air pollution sources across multiple residences C. Rathbone et al. https://doi.org/10.1038/s41598-025-85985-1
- Deriving ozone and PM pollution vertical profiles using lightweight, cost-effective sensors and deep learning D. Nissenbaum et al. https://doi.org/10.1038/s41612-025-01155-0
- Particulate matter monitoring and source apportionment inside and outside schools in a Global South metropolis V. Nwokorie et al. https://doi.org/10.1039/D5EA00096C
- A study on the performance of low-cost sensors for source apportionment at an urban background site D. Bousiotis et al. https://doi.org/10.5194/amt-15-4047-2022
- Towards comprehensive air quality management using low-cost sensors for pollution source apportionment D. Bousiotis et al. https://doi.org/10.1038/s41612-023-00424-0
- Assessing the spatial transferability of calibration models across a low-cost sensors network V. Malyan et al. https://doi.org/10.1016/j.jaerosci.2024.106437
- Aerosol sources characterization and apportionment from low-cost particle sensors in an urban environment V. Kumar et al. https://doi.org/10.1016/j.aeaoa.2024.100271
- Source identification with high-temporal resolution data from low-cost sensors using bivariate polar plots in urban areas of Ghana C. Hodoli et al. https://doi.org/10.1016/j.envpol.2022.120448
- The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK T. Bush et al. https://doi.org/10.1016/j.buildenv.2023.110330
- Design and Evaluation of a Calibration Chamber for Low-Cost PM Sensors–Part 1: Number Concentration-Based Assessment V. Malyan et al. https://doi.org/10.1021/acsestair.5c00262
- Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment D. Bousiotis et al. https://doi.org/10.1016/j.envint.2024.109069
- Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests S. Mills et al. https://doi.org/10.1016/j.scitotenv.2023.161969
29 citations as recorded by crossref.
- Indoor air quality monitoring and source apportionment using low-cost sensors C. Higgins et al. https://doi.org/10.1088/2515-7620/ad1cad
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. https://doi.org/10.1039/D4EA00086B
- Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors D. Bousiotis et al. https://doi.org/10.1016/j.envint.2023.107907
- Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain G. Kosmopoulos et al. https://doi.org/10.3390/s23146541
- Attributing PM sources within a swine production facility via PMF modeling of particle size distribution data P. Kumar et al. https://doi.org/10.1016/j.scitotenv.2026.181519
- A Novel Apportionment Method Utilizing Particle Mass Size Distribution across Multiple Particle Size Ranges P. Wang et al. https://doi.org/10.3390/atmos15080955
- Fundamental understanding of low-cost sensor response S. Kookkal & S. Dhaniyala https://doi.org/10.1080/02786826.2026.2649803
- Indoor and outdoor particulate matter source apportionment in a household in Tetovo, North Macedonia: A pilot study using low-cost sensors L. Shaqiri et al. https://doi.org/10.1016/j.apr.2026.103065
- Calibrating low-cost sensors using MERRA-2 reconstructed PM2.5 mass concentration as a proxy V. Malyan et al. https://doi.org/10.1016/j.apr.2023.102027
- The impact of urban mobility on air pollution in Kampala, an exemplar sub-Saharan African city O. Ghaffarpasand et al. https://doi.org/10.1016/j.apr.2024.102057
- Low-Cost Source Apportionment (LoCoSA) of air pollution - literature review of the state of the art D. Bousiotis et al. https://doi.org/10.1016/j.scitotenv.2025.180257
- Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey Ö. Akyürek https://doi.org/10.21324/dacd.1599855
- Condensation particle counters: Exploring the limits of miniaturisation S. Balendra et al. https://doi.org/10.1016/j.jaerosci.2023.106266
- Air exchange rates in a naturally ventilated apartment: a multi-box modelling approach using occupant-generated CO2 decay measurements Y. Su et al. https://doi.org/10.1016/j.rineng.2026.110563
- Investigating Indoor Air Pollution Sources and Student’s Exposure Within School Classrooms: Using a Low‐Cost Sensor and Source Apportionment Approach O. Rose et al. https://doi.org/10.1155/2024/5544298
- Real-Time Source Apportionment of Particulate Matter from Low-Cost Particle Sensors Using Machine Learning V. Kumar et al. https://doi.org/10.1007/s41810-024-00271-3
- Low-Cost Investigation into Sources of PM2.5 in Kinshasa, Democratic Republic of the Congo D. Westervelt et al. https://doi.org/10.1021/acsestair.3c00024
- Using low-cost sensors to assess common air pollution sources across multiple residences C. Rathbone et al. https://doi.org/10.1038/s41598-025-85985-1
- Deriving ozone and PM pollution vertical profiles using lightweight, cost-effective sensors and deep learning D. Nissenbaum et al. https://doi.org/10.1038/s41612-025-01155-0
- Particulate matter monitoring and source apportionment inside and outside schools in a Global South metropolis V. Nwokorie et al. https://doi.org/10.1039/D5EA00096C
- A study on the performance of low-cost sensors for source apportionment at an urban background site D. Bousiotis et al. https://doi.org/10.5194/amt-15-4047-2022
- Towards comprehensive air quality management using low-cost sensors for pollution source apportionment D. Bousiotis et al. https://doi.org/10.1038/s41612-023-00424-0
- Assessing the spatial transferability of calibration models across a low-cost sensors network V. Malyan et al. https://doi.org/10.1016/j.jaerosci.2024.106437
- Aerosol sources characterization and apportionment from low-cost particle sensors in an urban environment V. Kumar et al. https://doi.org/10.1016/j.aeaoa.2024.100271
- Source identification with high-temporal resolution data from low-cost sensors using bivariate polar plots in urban areas of Ghana C. Hodoli et al. https://doi.org/10.1016/j.envpol.2022.120448
- The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK T. Bush et al. https://doi.org/10.1016/j.buildenv.2023.110330
- Design and Evaluation of a Calibration Chamber for Low-Cost PM Sensors–Part 1: Number Concentration-Based Assessment V. Malyan et al. https://doi.org/10.1021/acsestair.5c00262
- Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment D. Bousiotis et al. https://doi.org/10.1016/j.envint.2024.109069
- Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests S. Mills et al. https://doi.org/10.1016/j.scitotenv.2023.161969
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
In the last decade, low-cost sensors have revolutionised the field of air quality monitoring. This paper extends the ability of low-cost sensors to not only measure air pollution, but also to understand where the pollution comes from. This "source apportionment" is a critical step in air quality management to allow for the mitigation of air pollution. The techniques developed in this paper have the potential for great impact in both research and industrial applications.
In the last decade, low-cost sensors have revolutionised the field of air quality monitoring....
