Articles | Volume 13, issue 8
https://doi.org/10.5194/amt-13-4601-2020
© 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-13-4601-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Aug 2020
Research article |
| 26 Aug 2020
| 26 Aug 2020
High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam
Royal Netherlands Meteorological Institute (KNMI), Postbus 201, 3730
AE, De Bilt, the Netherlands
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Cited
24 citations as recorded by crossref.
- Biodiversity modeling to manage urban ecosystems for people and nature J. Casanelles-Abella et al. https://doi.org/10.1038/s44284-025-00263-5
- Short-term NO2 exposure and cognitive and mental health: A panel study based on a citizen science project in Barcelona, Spain F. Gignac et al. https://doi.org/10.1016/j.envint.2022.107284
- Deployment and Evaluation of a Network of Open Low-Cost Air Quality Sensor Systems P. Schneider et al. https://doi.org/10.3390/atmos14030540
- Building-resolving simulations of anthropogenic and biospheric CO2 in the city of Zurich with GRAMM/GRAL D. Brunner et al. https://doi.org/10.5194/acp-25-14387-2025
- Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy? K. Bai et al. https://doi.org/10.1016/j.isprsjprs.2021.12.002
- Integration of Data and Predictive Models for the Evaluation of Air Quality and Noise in Urban Environments J. Govea et al. https://doi.org/10.3390/s24020311
- Data Assimilation Algorithms for Atmospheric Chemistry Models A. Penenko et al. https://doi.org/10.1134/S0001433825700719
- Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems A. Penenko et al. https://doi.org/10.3390/atmos12121697
- A machine learning-based approach for fusing measurements from standard sites, low-cost sensors, and satellite retrievals: Application to NO2 pollution hotspot identification J. Fu et al. https://doi.org/10.1016/j.atmosenv.2023.119756
- Empowering communities: Advancements in air quality monitoring and citizen engagement H. Relvas et al. https://doi.org/10.1016/j.uclim.2025.102344
- Irregular Area Cartograms for Local-Level Presentation of Selected SDGs Indicators Based on Earth Observation Data A. Markowska & D. Dukaczewski https://doi.org/10.3390/ijgi14120500
- Towards integration of LOTOS-EUROS high resolution simulations and heterogenous low-cost sensor observations I. Skoulidou et al. https://doi.org/10.1016/j.atmosenv.2024.120652
- Data fusion for enhancing urban air quality modeling using large-scale citizen science data A. O'Regan et al. https://doi.org/10.1016/j.scs.2024.105896
- Health burden and inequities of urban environmental stressors in Sofia, Bulgaria S. Khomenko et al. https://doi.org/10.1016/j.envres.2025.121782
- Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0 A. Criado et al. https://doi.org/10.5194/gmd-16-2193-2023
- High-resolution mapping of urban NO2 concentrations using Retina v2: a case study on data assimilation of surface and satellite observations in Madrid B. Mijling et al. https://doi.org/10.5194/gmd-18-6439-2025
- Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention J. Lepioufle et al. https://doi.org/10.1017/eds.2024.18
- Street- and census-level NO2 data for Barcelona with uncertainty and exceedance probability mapping A. Criado et al. https://doi.org/10.1038/s41597-026-06592-x
- High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam B. Mijling https://doi.org/10.5194/amt-13-4601-2020
- Design and Implementation of a Low-Cost Air Quality Network for the Aburra Valley Surrounding Mountains A. Yarce Botero et al. https://doi.org/10.3390/pollutants3010012
- Satellite-based assessment of national carbon monoxide concentrations for air quality reporting in Finland T. Karppinen et al. https://doi.org/10.1016/j.rsase.2023.101120
- Agent-based modelling: A stochastic approach to assessing personal exposure to environmental pollutants – Insights from the URBANOME project A. Karakoltzidis et al. https://doi.org/10.1016/j.scitotenv.2025.178804
- Evolving trends in application of low-cost air quality sensor networks: challenges and future directions E. Bagkis et al. https://doi.org/10.1038/s41612-025-01216-4
- High-resolution ground NO2 estimation at hyperlocal level using deep learning with Sentinel-2 and Sentinel-5P data S. Khan et al. https://doi.org/10.1016/j.rsase.2025.101819
24 citations as recorded by crossref.
- Biodiversity modeling to manage urban ecosystems for people and nature J. Casanelles-Abella et al. https://doi.org/10.1038/s44284-025-00263-5
- Short-term NO2 exposure and cognitive and mental health: A panel study based on a citizen science project in Barcelona, Spain F. Gignac et al. https://doi.org/10.1016/j.envint.2022.107284
- Deployment and Evaluation of a Network of Open Low-Cost Air Quality Sensor Systems P. Schneider et al. https://doi.org/10.3390/atmos14030540
- Building-resolving simulations of anthropogenic and biospheric CO2 in the city of Zurich with GRAMM/GRAL D. Brunner et al. https://doi.org/10.5194/acp-25-14387-2025
- Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy? K. Bai et al. https://doi.org/10.1016/j.isprsjprs.2021.12.002
- Integration of Data and Predictive Models for the Evaluation of Air Quality and Noise in Urban Environments J. Govea et al. https://doi.org/10.3390/s24020311
- Data Assimilation Algorithms for Atmospheric Chemistry Models A. Penenko et al. https://doi.org/10.1134/S0001433825700719
- Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems A. Penenko et al. https://doi.org/10.3390/atmos12121697
- A machine learning-based approach for fusing measurements from standard sites, low-cost sensors, and satellite retrievals: Application to NO2 pollution hotspot identification J. Fu et al. https://doi.org/10.1016/j.atmosenv.2023.119756
- Empowering communities: Advancements in air quality monitoring and citizen engagement H. Relvas et al. https://doi.org/10.1016/j.uclim.2025.102344
- Irregular Area Cartograms for Local-Level Presentation of Selected SDGs Indicators Based on Earth Observation Data A. Markowska & D. Dukaczewski https://doi.org/10.3390/ijgi14120500
- Towards integration of LOTOS-EUROS high resolution simulations and heterogenous low-cost sensor observations I. Skoulidou et al. https://doi.org/10.1016/j.atmosenv.2024.120652
- Data fusion for enhancing urban air quality modeling using large-scale citizen science data A. O'Regan et al. https://doi.org/10.1016/j.scs.2024.105896
- Health burden and inequities of urban environmental stressors in Sofia, Bulgaria S. Khomenko et al. https://doi.org/10.1016/j.envres.2025.121782
- Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0 A. Criado et al. https://doi.org/10.5194/gmd-16-2193-2023
- High-resolution mapping of urban NO2 concentrations using Retina v2: a case study on data assimilation of surface and satellite observations in Madrid B. Mijling et al. https://doi.org/10.5194/gmd-18-6439-2025
- Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention J. Lepioufle et al. https://doi.org/10.1017/eds.2024.18
- Street- and census-level NO2 data for Barcelona with uncertainty and exceedance probability mapping A. Criado et al. https://doi.org/10.1038/s41597-026-06592-x
- High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam B. Mijling https://doi.org/10.5194/amt-13-4601-2020
- Design and Implementation of a Low-Cost Air Quality Network for the Aburra Valley Surrounding Mountains A. Yarce Botero et al. https://doi.org/10.3390/pollutants3010012
- Satellite-based assessment of national carbon monoxide concentrations for air quality reporting in Finland T. Karppinen et al. https://doi.org/10.1016/j.rsase.2023.101120
- Agent-based modelling: A stochastic approach to assessing personal exposure to environmental pollutants – Insights from the URBANOME project A. Karakoltzidis et al. https://doi.org/10.1016/j.scitotenv.2025.178804
- Evolving trends in application of low-cost air quality sensor networks: challenges and future directions E. Bagkis et al. https://doi.org/10.1038/s41612-025-01216-4
- High-resolution ground NO2 estimation at hyperlocal level using deep learning with Sentinel-2 and Sentinel-5P data S. Khan et al. https://doi.org/10.1016/j.rsase.2025.101819
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
Many cities are experimenting with networks of low-cost sensors, complementary to their reference stations. Often the observations are published as dots on a map, as spatial interpolation is far from trivial. A new methodology to assimilate observations of different accuracy in a generic urban-air-quality model is introduced. It can be used for mapping local air quality based on reference measurements only or as a framework to integrate low-cost measurements next to official measurements.
Many cities are experimenting with networks of low-cost sensors, complementary to their...
