Articles | Volume 15, issue 13
https://doi.org/10.5194/amt-15-4091-2022
© 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-15-4091-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jul 2022
Research article |
| 13 Jul 2022
| 13 Jul 2022
Air pollution measurement errors: is your data fit for purpose?
Wolfson Atmospheric Chemistry Laboratories, University of York, York YO10 5DD, UK
Stuart E. Lacy
Wolfson Atmospheric Chemistry Laboratories, University of York, York YO10 5DD, UK
Thomas J. Bannan
Department of Earth and Environmental Science, Centre for Atmospheric Science, School of Natural Sciences, The University of Manchester, Manchester M13 9PL, UK
Michael Flynn
Department of Earth and Environmental Science, Centre for Atmospheric Science, School of Natural Sciences, The University of Manchester, Manchester M13 9PL, UK
Tom Gardiner
National Physical Laboratory, Teddington TW11 0LW, UK
David Harrison
Bureau Veritas UK, London E1 8HG, UK
Nicholas Marsden
Department of Earth and Environmental Science, Centre for Atmospheric Science, School of Natural Sciences, The University of Manchester, Manchester M13 9PL, UK
Nicholas A. Martin
National Physical Laboratory, Teddington TW11 0LW, UK
Katie Read
Wolfson Atmospheric Chemistry Laboratories, University of York, York YO10 5DD, UK
National Centre for Atmospheric Science, University of York, York YO10 5DD, UK
Wolfson Atmospheric Chemistry Laboratories, University of York, York YO10 5DD, UK
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Cited
22 citations as recorded by crossref.
- A neural network model for identification and recovery of dissociated data based on adaptive resonance L. Tan et al.
- Evolving trends in application of low-cost air quality sensor networks: challenges and future directions E. Bagkis et al.
- Community based air pollution measurements in Europe: Opportunities and challenges A. Viitanen et al.
- High-resolution PM2.5 mapping and evaluation in Tehran using mobile monitoring, land-use regression, and explainable hybrid models A. Shakerdonyavi & B. Yeganeh
- Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India A. Feldman et al.
- Gaussian processes and sensor network calibration J. Sousa & A. Forbes
- Enhancing anthropogenic NMVOC emission speciation for European air quality modelling K. Oliveira et al.
- Study of the Suitability of a Personal Exposure Monitor to Assess Air Quality H. Aljofi et al.
- Evaluation of urban PM2.5 concentrations over 73 major cities and their association with satellite Aerosol Optical Depth: A global analysis of ambient air pollution D. Stratoulias et al.
- Understanding Responses of Atmospheric Pollution and its Variability to Contradicting Nexus of Urbanization–Industrial Emission Control in Haldia, an Industrial City of West Bengal N. Yadav et al.
- Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study S. Diez et al.
- QUANT: a long-term multi-city commercial air sensor dataset for performance evaluation S. Diez et al.
- Evaluating low-cost sensors for particle mass concentrations, personal exposure and internal dose characterization at Eastern Mediterranean sites: Can they stand as efficient alternatives? S. Chatoutsidou et al.
- Recalibration of low-cost O3 and PM2.5 sensors: linking practices to recent air sensor test protocols P. Gäbel & E. Hertig
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al.
- Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona A. Domínguez et al.
- Indoor air quality in UK future standard homes: Experimental evaluation of mitigation and ventilation strategies N. Thamban et al.
- A framework for advancing independent air quality sensor measurements via transparent data generating process classification S. Diez et al.
- Spatial analysis of PM2.5 using a concentration similarity index applied to air quality sensor networks R. Byrne et al.
- Mapping trends and analyzing key themes in low-cost sensors for air quality monitoring K. Alhasa et al.
- Filling gaps in PM2.5 time series: A broad evaluation from statistical to advanced neural network models R. Safarov et al.
- Assessing the impact of data fusion and data quality on spatiotemporal characteristics of exposure models and probability maps - a case study for hazardous mineral fibres D. Zelman-Fahm et al.
22 citations as recorded by crossref.
- A neural network model for identification and recovery of dissociated data based on adaptive resonance L. Tan et al.
- Evolving trends in application of low-cost air quality sensor networks: challenges and future directions E. Bagkis et al.
- Community based air pollution measurements in Europe: Opportunities and challenges A. Viitanen et al.
- High-resolution PM2.5 mapping and evaluation in Tehran using mobile monitoring, land-use regression, and explainable hybrid models A. Shakerdonyavi & B. Yeganeh
- Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India A. Feldman et al.
- Gaussian processes and sensor network calibration J. Sousa & A. Forbes
- Enhancing anthropogenic NMVOC emission speciation for European air quality modelling K. Oliveira et al.
- Study of the Suitability of a Personal Exposure Monitor to Assess Air Quality H. Aljofi et al.
- Evaluation of urban PM2.5 concentrations over 73 major cities and their association with satellite Aerosol Optical Depth: A global analysis of ambient air pollution D. Stratoulias et al.
- Understanding Responses of Atmospheric Pollution and its Variability to Contradicting Nexus of Urbanization–Industrial Emission Control in Haldia, an Industrial City of West Bengal N. Yadav et al.
- Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study S. Diez et al.
- QUANT: a long-term multi-city commercial air sensor dataset for performance evaluation S. Diez et al.
- Evaluating low-cost sensors for particle mass concentrations, personal exposure and internal dose characterization at Eastern Mediterranean sites: Can they stand as efficient alternatives? S. Chatoutsidou et al.
- Recalibration of low-cost O3 and PM2.5 sensors: linking practices to recent air sensor test protocols P. Gäbel & E. Hertig
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al.
- Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona A. Domínguez et al.
- Indoor air quality in UK future standard homes: Experimental evaluation of mitigation and ventilation strategies N. Thamban et al.
- A framework for advancing independent air quality sensor measurements via transparent data generating process classification S. Diez et al.
- Spatial analysis of PM2.5 using a concentration similarity index applied to air quality sensor networks R. Byrne et al.
- Mapping trends and analyzing key themes in low-cost sensors for air quality monitoring K. Alhasa et al.
- Filling gaps in PM2.5 time series: A broad evaluation from statistical to advanced neural network models R. Safarov et al.
- Assessing the impact of data fusion and data quality on spatiotemporal characteristics of exposure models and probability maps - a case study for hazardous mineral fibres D. Zelman-Fahm et al.
Saved (final revised paper)
Latest update: 11 May 2026
Short summary
Regardless of the cost of the measuring instrument, there are no perfect measurements. For this reason, we compare the quality of the information provided by cheap devices when they are used to measure air pollutants and we try to emphasise that before judging the potential usefulness of the devices, the user must specify his own needs. Since commonly used performance indices/metrics can be misleading in qualifying this, we propose complementary visual analysis to the more commonly used metrics.
Regardless of the cost of the measuring instrument, there are no perfect measurements. For this...
