Articles | Volume 22, issue 21
https://doi.org/10.5194/acp-22-13949-2022
© 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/acp-22-13949-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Nov 2022
Research article |
| 01 Nov 2022
| 01 Nov 2022
Are dense networks of low-cost nodes really useful for monitoring air pollution? A case study in Staffordshire
Louise Bøge Frederickson
Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
AirLabs Denmark, Nannasgade 28, 2200 Copenhagen N, Denmark
Ruta Sidaraviciute
AirLabs Denmark, Nannasgade 28, 2200 Copenhagen N, Denmark
Johan Albrecht Schmidt
AirLabs Denmark, Nannasgade 28, 2200 Copenhagen N, Denmark
Ole Hertel
Department of Ecoscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
AirLabs Denmark, Nannasgade 28, 2200 Copenhagen N, Denmark
Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark
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Cited
18 citations as recorded by crossref.
- Spatial analysis of PM2.5 using a concentration similarity index applied to air quality sensor networks R. Byrne et al. 10.5194/amt-17-5129-2024
- Data‐Driven Placement of PM2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice M. Kelp et al. 10.1029/2023GH000834
- Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al. 10.3390/atmos15050594
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. 10.1039/D4EA00086B
- Citizen scientists filling knowledge gaps of phosphate pollution dynamics in rural areas S. Loiselle et al. 10.1007/s10661-024-12389-5
- Urban vertical air pollution gradient and dynamics investigated with low-cost sensors and large-eddy simulations L. Frederickson et al. 10.1016/j.atmosenv.2023.120162
- Removal of volatile organic compounds by mobile air cleaners: Dynamics, limitations, and possible side effects S. Sørensen et al. 10.1016/j.buildenv.2023.110541
- Integrating Cost-Effective Measurements and CFD Modeling for Accurate Air Quality Assessment G. Ioannidis et al. 10.3390/atmos15091056
- Hyperlocal air pollution in an urban environment - measured with low-cost sensors L. Frederickson et al. 10.1016/j.uclim.2023.101684
- Challenges and opportunities of low-cost sensors in capturing the impacts of construction activities on neighborhood air quality W. Jaafar et al. 10.1016/j.buildenv.2024.111363
- Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution—Part B—Particle Number Concentrations F. Bulot et al. 10.3390/s23177657
- Estimation of Surface-Level NO2 Using Satellite Remote Sensing and Machine Learning: A review M. Siddique et al. 10.1109/MGRS.2024.3398434
- Factors influencing particle resuspension and dispersion: An experimental study using test-track measurements B. Obeid et al. 10.1016/j.trd.2024.104321
- Hyperlocal Air Pollution in London: Validating Low-Cost Sensors for Mobile Measurements from Vehicles H. Russell et al. 10.1021/acsestair.3c00043
- The Deployment Modeling of Low-Cost Sensors for Urban Particulate Matter Monitoring: A Case Study for PM2.5 Monitoring in Tehran City S. Ghomi et al. 10.1007/s41742-024-00659-6
- Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment D. Bousiotis et al. 10.1016/j.envint.2024.109069
- RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London M. Bogaert et al. 10.1016/j.scitotenv.2024.171522
- Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network J. Pan et al. 10.3390/toxics12100760
18 citations as recorded by crossref.
- Spatial analysis of PM2.5 using a concentration similarity index applied to air quality sensor networks R. Byrne et al. 10.5194/amt-17-5129-2024
- Data‐Driven Placement of PM2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice M. Kelp et al. 10.1029/2023GH000834
- Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al. 10.3390/atmos15050594
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. 10.1039/D4EA00086B
- Citizen scientists filling knowledge gaps of phosphate pollution dynamics in rural areas S. Loiselle et al. 10.1007/s10661-024-12389-5
- Urban vertical air pollution gradient and dynamics investigated with low-cost sensors and large-eddy simulations L. Frederickson et al. 10.1016/j.atmosenv.2023.120162
- Removal of volatile organic compounds by mobile air cleaners: Dynamics, limitations, and possible side effects S. Sørensen et al. 10.1016/j.buildenv.2023.110541
- Integrating Cost-Effective Measurements and CFD Modeling for Accurate Air Quality Assessment G. Ioannidis et al. 10.3390/atmos15091056
- Hyperlocal air pollution in an urban environment - measured with low-cost sensors L. Frederickson et al. 10.1016/j.uclim.2023.101684
- Challenges and opportunities of low-cost sensors in capturing the impacts of construction activities on neighborhood air quality W. Jaafar et al. 10.1016/j.buildenv.2024.111363
- Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution—Part B—Particle Number Concentrations F. Bulot et al. 10.3390/s23177657
- Estimation of Surface-Level NO2 Using Satellite Remote Sensing and Machine Learning: A review M. Siddique et al. 10.1109/MGRS.2024.3398434
- Factors influencing particle resuspension and dispersion: An experimental study using test-track measurements B. Obeid et al. 10.1016/j.trd.2024.104321
- Hyperlocal Air Pollution in London: Validating Low-Cost Sensors for Mobile Measurements from Vehicles H. Russell et al. 10.1021/acsestair.3c00043
- The Deployment Modeling of Low-Cost Sensors for Urban Particulate Matter Monitoring: A Case Study for PM2.5 Monitoring in Tehran City S. Ghomi et al. 10.1007/s41742-024-00659-6
- Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment D. Bousiotis et al. 10.1016/j.envint.2024.109069
- RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London M. Bogaert et al. 10.1016/j.scitotenv.2024.171522
- Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network J. Pan et al. 10.3390/toxics12100760
Latest update: 08 May 2025
Short summary
Low-cost sensors see additional pollution that is not seen with traditional regional air quality monitoring stations. This additional local pollution is sufficient to cause exceedance of the World Health Organization exposure thresholds. Analysis shows that a significant amount of the NO2 pollution we observe is local, mainly due to road traffic. This article demonstrates how networks of nodes containing low-cost pollution sensors can powerfully extend existing monitoring programmes.
Low-cost sensors see additional pollution that is not seen with traditional regional air quality...
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