Articles | Volume 18, issue 9
https://doi.org/10.5194/acp-18-6543-2018
© Author(s) 2018. 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-18-6543-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The use of hierarchical clustering for the design of optimized monitoring networks
Joana Soares
CORRESPONDING AUTHOR
Air Quality Modelling and Integration Section, Air Quality Research
Division, Environment and Climate Change, Toronto, ON, M3H 5T4, Canada
Paul Andrew Makar
Air Quality Modelling and Integration Section, Air Quality Research
Division, Environment and Climate Change, Toronto, ON, M3H 5T4, Canada
Yayne Aklilu
Environmental Monitoring and Science Division, Alberta Environment
and Parks, Edmonton, AL, T5J 5C6, Canada
Ayodeji Akingunola
Air Quality Modelling and Integration Section, Air Quality Research
Division, Environment and Climate Change, Toronto, ON, M3H 5T4, Canada
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Cited
15 citations as recorded by crossref.
- Real-time estimation of PM2.5 concentrations at high spatial resolution in Busan by fusing observational data with chemical transport model outputs E. Jang et al. 10.1016/j.apr.2021.101277
- Hybridization of hierarchical clustering with persistent homology in assessing haze episodes between air quality monitoring stations N. Zulkepli et al. 10.1016/j.jenvman.2022.114434
- Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019) P. Govender & V. Sivakumar 10.1016/j.apr.2019.09.009
- Long-term performance of air quality networks: implications in health and environmental management D. Galán-Madruga et al. 10.1007/s13762-025-06526-x
- Assessment of air quality monitoring networks using an ensemble clustering method in the three major metropolitan areas of Mexico T. Stolz et al. 10.1016/j.apr.2020.05.005
- Robust optimization for designing air quality monitoring network in coal ports under uncertainty B. Liu et al. 10.1016/j.atmosenv.2023.119792
- Optimal design of air quality monitoring networks: A systematic review S. Verghese & A. Nema 10.1007/s00477-022-02187-1
- Performance assessment of air quality monitoring networks. A specific case study and methodological approach D. Galán-Madruga et al. 10.1007/s11869-022-01254-4
- Critical Analysis of the Results of a Network System for Nitrogen Dioxide Monitoring M. Caselli 10.3390/su17062738
- Stable sulfur isotope measurements to trace the fate of SO2 in the Athabasca oil sands region N. Amiri et al. 10.5194/acp-18-7757-2018
- A multi-pollutant methodology to locate a single air quality monitoring station in small and medium-size urban areas M. Miñarro et al. 10.1016/j.envpol.2020.115279
- Measurement optimization for determining concentrations and emission rates of particulate matter and ammonia from a large size dairy building Y. Li et al. 10.1016/j.biosystemseng.2025.104230
- Wind variability over a large lake with complex topography: Lake of the Woods D. Brunet et al. 10.1016/j.jglr.2022.08.019
- A cluster analysis approach to sampling domestic properties for sensor deployment T. Menneer et al. 10.1016/j.buildenv.2023.110032
- Local weather phenomenon Galerna influences daily radon concentrations in northern Iberian Peninsula M. Hernández-Ceballos et al. 10.1016/j.jenvrad.2024.107494
15 citations as recorded by crossref.
- Real-time estimation of PM2.5 concentrations at high spatial resolution in Busan by fusing observational data with chemical transport model outputs E. Jang et al. 10.1016/j.apr.2021.101277
- Hybridization of hierarchical clustering with persistent homology in assessing haze episodes between air quality monitoring stations N. Zulkepli et al. 10.1016/j.jenvman.2022.114434
- Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019) P. Govender & V. Sivakumar 10.1016/j.apr.2019.09.009
- Long-term performance of air quality networks: implications in health and environmental management D. Galán-Madruga et al. 10.1007/s13762-025-06526-x
- Assessment of air quality monitoring networks using an ensemble clustering method in the three major metropolitan areas of Mexico T. Stolz et al. 10.1016/j.apr.2020.05.005
- Robust optimization for designing air quality monitoring network in coal ports under uncertainty B. Liu et al. 10.1016/j.atmosenv.2023.119792
- Optimal design of air quality monitoring networks: A systematic review S. Verghese & A. Nema 10.1007/s00477-022-02187-1
- Performance assessment of air quality monitoring networks. A specific case study and methodological approach D. Galán-Madruga et al. 10.1007/s11869-022-01254-4
- Critical Analysis of the Results of a Network System for Nitrogen Dioxide Monitoring M. Caselli 10.3390/su17062738
- Stable sulfur isotope measurements to trace the fate of SO2 in the Athabasca oil sands region N. Amiri et al. 10.5194/acp-18-7757-2018
- A multi-pollutant methodology to locate a single air quality monitoring station in small and medium-size urban areas M. Miñarro et al. 10.1016/j.envpol.2020.115279
- Measurement optimization for determining concentrations and emission rates of particulate matter and ammonia from a large size dairy building Y. Li et al. 10.1016/j.biosystemseng.2025.104230
- Wind variability over a large lake with complex topography: Lake of the Woods D. Brunet et al. 10.1016/j.jglr.2022.08.019
- A cluster analysis approach to sampling domestic properties for sensor deployment T. Menneer et al. 10.1016/j.buildenv.2023.110032
- Local weather phenomenon Galerna influences daily radon concentrations in northern Iberian Peninsula M. Hernández-Ceballos et al. 10.1016/j.jenvrad.2024.107494
Latest update: 08 Aug 2025
Short summary
Grouping data on the basis of (dis)similarity can be used to assess the efficacy of monitoring networks. The data are cross-compared in terms of temporal variation and magnitude of concentrations, and sites are ranked according to their level of potential redundancy. The methodology can be applied to measurement data, helping to identify sites with different measuring technologies or data flaws, and to model output, generating maps of areas of spatial representativeness of a monitoring site.
Grouping data on the basis of (dis)similarity can be used to assess the efficacy of monitoring...
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