Chapter 47
Hierarchical Clustering for Optimizing
Air Quality Monitoring Networks
Joana Soares, Paul Makar, Yayne-Abeba Aklilu and Ayodeji Akingunola
Abstract Hierarchical clustering (HC) analysis groups datasets into clusters based
on their (dis)similarity, and can be used to assess air-quality monitoring networks
representability. The methodology describe here is a new approach to designing optimized air-quality monitoring networks by combining Kolmogorov-Zurbenko filtering (KZ) and HC of observed and modelled time series. Here we present the optimization of the air quality network in the province of Alberta, Canada, for NO 2 , SO 2 ,
PM 2.5 and O 3. The study suggests that network optimization will vary depending on
chemical species due to different emissions sources and/or the results of secondary
chemistry. Making use of hourly and time-filtered time series allows identifying
emission sources, with much of the signal identifying sources emissions residing in
shorter time scales (hourly to daily) due to short-term variation of concentrations, and
background concentrations can be identified by larger time scales (monthly or over).
The methodology is also capable of generating maps of sub-regions within which a
single station will represent the entire sub-region, to a given level of dissimilarity,
when applied to gridded datasets such as chemical transport modelling output.
47.1 Introduction
Canada is home to one of the largest deposits of oil sands in the world. The Governments of Canada and Alberta have set-up the Oil Sands Monitoring (OSM) Plan
to integrate the existing monitoring networks across the Province, for better understanding the air-quality within and downwind of the oil sands region. A potentially
powerful tool for assessing the consistency and spatial representativeness of the existJ. Soares (B) · P. Makar · A. Akingunola
Air Quality Modelling and Integration Section, Air Quality Research Division, Environment and
Climate Change, Toronto, ON M3H 5T4, Canada
e-mail: joana.soares@canada.ca
Y.-A. Aklilu
Environmental Monitoring and Science Division, Alberta Environment and Parks, Edmonton, AL
T5J 5C6, Canada
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_47
299
Hierarchical Clustering for Optimizing
Air Quality Monitoring Networks
Joana Soares, Paul Makar, Yayne-Abeba Aklilu and Ayodeji Akingunola
Abstract Hierarchical clustering (HC) analysis groups datasets into clusters based
on their (dis)similarity, and can be used to assess air-quality monitoring networks
representability. The methodology describe here is a new approach to designing optimized air-quality monitoring networks by combining Kolmogorov-Zurbenko filtering (KZ) and HC of observed and modelled time series. Here we present the optimization of the air quality network in the province of Alberta, Canada, for NO 2 , SO 2 ,
PM 2.5 and O 3. The study suggests that network optimization will vary depending on
chemical species due to different emissions sources and/or the results of secondary
chemistry. Making use of hourly and time-filtered time series allows identifying
emission sources, with much of the signal identifying sources emissions residing in
shorter time scales (hourly to daily) due to short-term variation of concentrations, and
background concentrations can be identified by larger time scales (monthly or over).
The methodology is also capable of generating maps of sub-regions within which a
single station will represent the entire sub-region, to a given level of dissimilarity,
when applied to gridded datasets such as chemical transport modelling output.
47.1 Introduction
Canada is home to one of the largest deposits of oil sands in the world. The Governments of Canada and Alberta have set-up the Oil Sands Monitoring (OSM) Plan
to integrate the existing monitoring networks across the Province, for better understanding the air-quality within and downwind of the oil sands region. A potentially
powerful tool for assessing the consistency and spatial representativeness of the existJ. Soares (B) · P. Makar · A. Akingunola
Air Quality Modelling and Integration Section, Air Quality Research Division, Environment and
Climate Change, Toronto, ON M3H 5T4, Canada
e-mail: joana.soares@canada.ca
Y.-A. Aklilu
Environmental Monitoring and Science Division, Alberta Environment and Parks, Edmonton, AL
T5J 5C6, Canada
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_47
299