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Environmental local agencies have to enforce European directives that impose a land classification, according to air quality status, to distinguish zones needing further actions from those needing only maintenance. This paper presents a land classification in zones featured by different criticality levels of atmospheric pollution, considering pollutant time series as functional data: we call this proposal “Functional Zoning”. Our proposal is articulated in order to also meet two specific requirements: upscaling pollutant concentration data to the municipality scale, since municipalities are the reference territorial administrative units for undertaking actions; aggregating different pollutants in order to provide a multi-pollutant zoning outcome reflecting the air quality status. Specifically, we present three different alternatives to upscale data from a regular grid to the municipality scale. Then, to aggregate by pollutant, we evaluate two strategies summarizing time series: the assessment of an air quality index and the use of the Multivariate Functional Principal Component Analysis (MFPCA). The partition of municipalities is obtained by clustering air quality time series and MFPCA scores. In particular, the proposed functional zoning is carried out for Piemonte (Northern Italy), considering the hourly concentration fields of the main pollutants. We obtain six classifications of the same land and we propose a comparison study of the different strategies’ results, by mapping and analyzing the differences between clusters’ labels. By taking into account the comparison study’s findings, we finally suggest an analysis strategy to environmental agencies and policy makers to obtain an easily interpretable outcome at a very reasonable computational cost.
