title: Functional exploratory data analysis for high-resolution measurements of urban particulate matter.
abstract: In this work we propose the use of functional data analysis (FDA) to deal with a very large dataset of atmospheric aerosol size distribution resolved in both space and time. Data come from a mobile measurement platform in the town of Perugia (Central Italy). An OPC (Optical Particle Counter) is integrated on a cabin of the Minimetrò, an urban transportation system, that moves along a monorail on a line transect of the town. The OPC takes a sample of air every six seconds and counts the number of particles of urban aerosols with a diameter between 0.28 μm and 10 μm and classifies such particles into 21 size bins according to their diameter. Here, we adopt a 2D functional data representation for each of the 21 spatiotemporal series. In fact, space is unidimensional since it is measured as the distance on the monorail from the base station of the Minimetrò. FDA allows for a reduction of the dimensionality of each dataset and accounts for the high space-time resolution of the data. Functional cluster analysis is then performed to search for similarities among the 21 size channels in terms of their spatiotemporal pattern. Results provide a good classification of the 21 size bins into a relatively small number of groups (between three and four) according to the season of the year. Groups including coarser particles have more similar patterns, while those including finer particles show a more different behavior according to the period of the year. Such features are consistent with the physics of atmospheric aerosol and the highlighted patterns provide a very useful ground for prospective model-based studies.
authors.affiliation: Dipartimento di Scienze Politiche, Università degli Studi di Perugia, Via Pascoli, 20, 06123, Perugia, Italy. giovanna.ranalli@unipg.it.
authors.forename: M Giovanna
authors.initials: MG
authors.lastname: Ranalli
authors.affiliation: Dipartimento di Scienze Statistiche, Università di Roma "La Sapienza", P.le Aldo Moro n. 5, 00185, Roma, Italy.
authors.forename: Giorgia
authors.initials: G
authors.lastname: Rocco
authors.affiliation: Dipartimento di Scienze Statistiche, Università di Roma "La Sapienza", P.le Aldo Moro n. 5, 00185, Roma, Italy.
authors.forename: Giovanna
authors.initials: G
authors.lastname: Jona Lasinio
authors.affiliation: Centro di Eccellenza SMAArt, Università degli Studi di Perugia, Via Elce di Sotto, 8, 06123, Perugia, Italy.
authors.forename: Beatrice
authors.initials: B
authors.lastname: Moroni
authors.affiliation: Dipartimento di Chimica, Biologia e Biotecnologie, Università degli Studi di Perugia, Via Elce di Sotto, 8, 06123, Perugia, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Castellini
authors.affiliation: Dipartimento di Scienze Politiche, Università degli Studi di Perugia, Via Pascoli, 20, 06123, Perugia, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Crocchianti
authors.affiliation: Dipartimento di Scienze Politiche, Università degli Studi di Perugia, Via Pascoli, 20, 06123, Perugia, Italy.
authors.forename: David
authors.initials: D
authors.lastname: Cappelletti
mesh_terms: D000336:Aerosols / Q000032:analysis; D000393:Air Pollutants / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D007558:Italy; D008962:Models, Theoretical; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D011446:Prospective Studies; D012621:Seasons
chemical_list: D000336:Aerosols; D000393:Air Pollutants; D052638:Particulate Matter
keywords: Air quality; Classification; Functional data analysis; High-frequency data; Penalized splines; Sensor data