Articles | Volume 14, issue 6
https://doi.org/10.5194/amt-14-4139-2021
© Author(s) 2021. 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-14-4139-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jun 2021
Research article |
| 07 Jun 2021
| 07 Jun 2021
Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study
Dimitrios Bousiotis
School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
Ajit Singh
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
David C. S. Beddows
School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
National Centre for Atmospheric Science, School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
Sebastián Diez
Wolfson Atmospheric Chemistry Laboratories, Department of
Chemistry, University of York, Heslington, York YO10 5DD, United Kingdom
Killian L. Murphy
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
School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
Department of Environmental Sciences, Center of Excellence in Environmental Studies, King Abdulaziz University, P.O. Box 80203, Jeddah, 21589, Saudi Arabia
School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
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Cited
23 citations as recorded by crossref.
- Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment D. Bousiotis et al. 10.1016/j.envint.2024.109069
- Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area R. Shah et al. 10.1021/acs.est.2c08096
- 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. 10.1016/j.buildenv.2023.110330
- Highly local sources and large spatial variations in PM2.5 across a city: evidence from a city-wide sensor network in Cork, Ireland R. Byrne et al. 10.1039/D2EA00177B
- A Novel Apportionment Method Utilizing Particle Mass Size Distribution across Multiple Particle Size Ranges P. Wang et al. 10.3390/atmos15080955
- Towards comprehensive air quality management using low-cost sensors for pollution source apportionment D. Bousiotis et al. 10.1038/s41612-023-00424-0
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. 10.1039/D4EA00086B
- A trait-based investigation into evergreen woody plants for traffic-related air pollution mitigation over time Y. Barwise et al. 10.1016/j.scitotenv.2023.169713
- Street-scale black carbon modelling over the West Midlands, United Kingdom: Sensitivity test of traffic emission factor adjustments J. Zhong et al. 10.1016/j.envint.2025.109265
- Inteligentny system identyfikacji zanieczyszczenia powietrza = Intelligent air pollution identification system M. Szwed 10.7163/PrzG.2025.1.3
- Differentiating Semi-Volatile and Solid Particle Events Using Low-Cost Lung-Deposited Surface Area and Black Carbon Sensors M. Haugen et al. 10.3390/atmos13050747
- 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
- 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
- Modelling the dispersion of particle number concentrations in the West Midlands, UK using the ADMS-Urban model J. Zhong et al. 10.1016/j.envint.2023.108273
- Indoor air quality monitoring and source apportionment using low-cost sensors C. Higgins et al. 10.1088/2515-7620/ad1cad
- Aerosol sources characterization and apportionment from low-cost particle sensors in an urban environment V. Kumar et al. 10.1016/j.aeaoa.2024.100271
- 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
- Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors D. Bousiotis et al. 10.1016/j.envint.2023.107907
- 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. 10.1155/2024/5544298
- Deep learning for air pollution detection: Analyzing scots pine needles with SEM/EDS M. Szwed et al. 10.1016/j.dwt.2025.101149
- Performance evaluation of the Alphasense OPC-N3 and Plantower PMS5003 sensor in measuring dust events in the Salt Lake Valley, Utah K. Kaur & K. Kelly 10.5194/amt-16-2455-2023
- Carbon based sensors for air quality monitoring networks; middle east perspective I. Shahid et al. 10.3389/fchem.2024.1391409
- Real-Time Source Apportionment of Particulate Matter from Low-Cost Particle Sensors Using Machine Learning V. Kumar et al. 10.1007/s41810-024-00271-3
23 citations as recorded by crossref.
- Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment D. Bousiotis et al. 10.1016/j.envint.2024.109069
- Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area R. Shah et al. 10.1021/acs.est.2c08096
- 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. 10.1016/j.buildenv.2023.110330
- Highly local sources and large spatial variations in PM2.5 across a city: evidence from a city-wide sensor network in Cork, Ireland R. Byrne et al. 10.1039/D2EA00177B
- A Novel Apportionment Method Utilizing Particle Mass Size Distribution across Multiple Particle Size Ranges P. Wang et al. 10.3390/atmos15080955
- Towards comprehensive air quality management using low-cost sensors for pollution source apportionment D. Bousiotis et al. 10.1038/s41612-023-00424-0
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. 10.1039/D4EA00086B
- A trait-based investigation into evergreen woody plants for traffic-related air pollution mitigation over time Y. Barwise et al. 10.1016/j.scitotenv.2023.169713
- Street-scale black carbon modelling over the West Midlands, United Kingdom: Sensitivity test of traffic emission factor adjustments J. Zhong et al. 10.1016/j.envint.2025.109265
- Inteligentny system identyfikacji zanieczyszczenia powietrza = Intelligent air pollution identification system M. Szwed 10.7163/PrzG.2025.1.3
- Differentiating Semi-Volatile and Solid Particle Events Using Low-Cost Lung-Deposited Surface Area and Black Carbon Sensors M. Haugen et al. 10.3390/atmos13050747
- 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
- 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
- Modelling the dispersion of particle number concentrations in the West Midlands, UK using the ADMS-Urban model J. Zhong et al. 10.1016/j.envint.2023.108273
- Indoor air quality monitoring and source apportionment using low-cost sensors C. Higgins et al. 10.1088/2515-7620/ad1cad
- Aerosol sources characterization and apportionment from low-cost particle sensors in an urban environment V. Kumar et al. 10.1016/j.aeaoa.2024.100271
- 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
- Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors D. Bousiotis et al. 10.1016/j.envint.2023.107907
- 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. 10.1155/2024/5544298
- Deep learning for air pollution detection: Analyzing scots pine needles with SEM/EDS M. Szwed et al. 10.1016/j.dwt.2025.101149
- Performance evaluation of the Alphasense OPC-N3 and Plantower PMS5003 sensor in measuring dust events in the Salt Lake Valley, Utah K. Kaur & K. Kelly 10.5194/amt-16-2455-2023
- Carbon based sensors for air quality monitoring networks; middle east perspective I. Shahid et al. 10.3389/fchem.2024.1391409
- Real-Time Source Apportionment of Particulate Matter from Low-Cost Particle Sensors Using Machine Learning V. Kumar et al. 10.1007/s41810-024-00271-3
Latest update: 23 Apr 2025
Short summary
Measurement and source apportionment of atmospheric pollutants are crucial for the assessment of air quality and the implementation of policies for their improvement. This study highlights the current capability of low-cost sensors in source identification and differentiation using clustering approaches. Future directions towards particulate matter source apportionment using low-cost OPCs are highlighted.
Measurement and source apportionment of atmospheric pollutants are crucial for the assessment of...
