Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Assessment of the Madrid region air quality zoning based on mesoscale modelling and k-means clustering
https://orcid.org/0000-0003-3390-6709
Abstract According to the Air Quality (AQ) Directive (2008/50/EC), European Member States should establish (and revise every five years) their own AQ zones, where the air quality and population exposure are homogeneous. In Madrid, there are currently seven AQ zones, which were determined based on administrative geographic and land use criteria in 2014. However, so far, there has been no standardized methodology to define an objective AQ zoning. In this study, a new methodology is applied to revise the AQ zones of the Madrid region by using the WRF-CMAQ modelling system with 1 km2 spatial resolution. All the relevant legal indicators of the main pollutants (NO2, O3, PM10 and PM2.5) were computed from an annual 1-h temporal resolution model run and aggregated at municipality level. Then, seven basic statistics (mean, interquartile range, etc.) are computed for each air quality indicator within each of the 179 municipalities of the Madrid region. A Principal Components Analysis (PCA) is applied to identify the most relevant clustering variables from all these statistics to subsequently apply a k-mean cluster analysis. The definition of the number air quality zones (clusters) is based on three methods: Elbow, Silhouette, and Gap statistic. Following this methodology, five zones for NO2, PM10, and PM2.5 and four zones for O3 are proposed. To assess the resulting zoning and to compare it with the current one, concentration distributions in each zone are visualized through boxplots. In addition, in order to confirm significant differences among the zones of both zonings, they are examined by two statistical tests: the Kruskal-Wallis and Dunn tests. Finally, the coverage and potential redundancy areas of the 47 existing air quality monitoring stations in the region are analysed for the two alternatives, confirming the suitability of the new air quality zoning proposed.
Graphical abstract Display Omitted
Highlights A methodology to define air quality zones is presented and applied to Madrid Region. CMAQ outputs combined with k-means clustering to group homogeneous municipalities. Two separate zoning schemes: one for NO2, PM10 and PM2.5 and another one for O3. The proposed zoning presents better statistical results than the current one. New zoning compatible with the current air quality monitoring network in the region.
Assessment of the Madrid region air quality zoning based on mesoscale modelling and k-means clustering
https://orcid.org/0000-0003-3390-6709
Abstract According to the Air Quality (AQ) Directive (2008/50/EC), European Member States should establish (and revise every five years) their own AQ zones, where the air quality and population exposure are homogeneous. In Madrid, there are currently seven AQ zones, which were determined based on administrative geographic and land use criteria in 2014. However, so far, there has been no standardized methodology to define an objective AQ zoning. In this study, a new methodology is applied to revise the AQ zones of the Madrid region by using the WRF-CMAQ modelling system with 1 km2 spatial resolution. All the relevant legal indicators of the main pollutants (NO2, O3, PM10 and PM2.5) were computed from an annual 1-h temporal resolution model run and aggregated at municipality level. Then, seven basic statistics (mean, interquartile range, etc.) are computed for each air quality indicator within each of the 179 municipalities of the Madrid region. A Principal Components Analysis (PCA) is applied to identify the most relevant clustering variables from all these statistics to subsequently apply a k-mean cluster analysis. The definition of the number air quality zones (clusters) is based on three methods: Elbow, Silhouette, and Gap statistic. Following this methodology, five zones for NO2, PM10, and PM2.5 and four zones for O3 are proposed. To assess the resulting zoning and to compare it with the current one, concentration distributions in each zone are visualized through boxplots. In addition, in order to confirm significant differences among the zones of both zonings, they are examined by two statistical tests: the Kruskal-Wallis and Dunn tests. Finally, the coverage and potential redundancy areas of the 47 existing air quality monitoring stations in the region are analysed for the two alternatives, confirming the suitability of the new air quality zoning proposed.
Graphical abstract Display Omitted
Highlights A methodology to define air quality zones is presented and applied to Madrid Region. CMAQ outputs combined with k-means clustering to group homogeneous municipalities. Two separate zoning schemes: one for NO2, PM10 and PM2.5 and another one for O3. The proposed zoning presents better statistical results than the current one. New zoning compatible with the current air quality monitoring network in the region.
Assessment of the Madrid region air quality zoning based on mesoscale modelling and k-means clustering
https://orcid.org/0000-0003-3390-6709
Abstract According to the Air Quality (AQ) Directive (2008/50/EC), European Member States should establish (and revise every five years) their own AQ zones, where the air quality and population exposure are homogeneous. In Madrid, there are currently seven AQ zones, which were determined based on administrative geographic and land use criteria in 2014. However, so far, there has been no standardized methodology to define an objective AQ zoning. In this study, a new methodology is applied to revise the AQ zones of the Madrid region by using the WRF-CMAQ modelling system with 1 km2 spatial resolution. All the relevant legal indicators of the main pollutants (NO2, O3, PM10 and PM2.5) were computed from an annual 1-h temporal resolution model run and aggregated at municipality level. Then, seven basic statistics (mean, interquartile range, etc.) are computed for each air quality indicator within each of the 179 municipalities of the Madrid region. A Principal Components Analysis (PCA) is applied to identify the most relevant clustering variables from all these statistics to subsequently apply a k-mean cluster analysis. The definition of the number air quality zones (clusters) is based on three methods: Elbow, Silhouette, and Gap statistic. Following this methodology, five zones for NO2, PM10, and PM2.5 and four zones for O3 are proposed. To assess the resulting zoning and to compare it with the current one, concentration distributions in each zone are visualized through boxplots. In addition, in order to confirm significant differences among the zones of both zonings, they are examined by two statistical tests: the Kruskal-Wallis and Dunn tests. Finally, the coverage and potential redundancy areas of the 47 existing air quality monitoring stations in the region are analysed for the two alternatives, confirming the suitability of the new air quality zoning proposed.
Graphical abstract Display Omitted
Highlights A methodology to define air quality zones is presented and applied to Madrid Region. CMAQ outputs combined with k-means clustering to group homogeneous municipalities. Two separate zoning schemes: one for NO2, PM10 and PM2.5 and another one for O3. The proposed zoning presents better statistical results than the current one. New zoning compatible with the current air quality monitoring network in the region.
Assessment of the Madrid region air quality zoning based on mesoscale modelling and k-means clustering
Borge, Rafael (Autor:in) / Jung, Daeun (Autor:in) / Lejarraga, Iciar (Autor:in) / de la Paz, David (Autor:in) / Cordero, José María (Autor:in)
Atmospheric Environment ; 287
27.06.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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