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.

A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood

“…For these reasons, we think that medoid-based algorithms are more appropriate for mHealth data than centroid-based ones. In the literature, several studies in the biological and medical domains involve the use of kMedFD [13,36,37]. All these applications are carried out following the classical approach to clustering.…”

“…FDA has often been applied in the biological and medical domains (see, for a review, [11]). Recent examples can be found in, e.g., [12][13][14][15].…”

Particulate matter exposure predicts residence in high-risk areas for community acquired pneumonia among hospitalized children

“…High-resolution 1 km by 1 km PM 2.5 is emerging as a more sensitive measure for assessing PM 2.5 effects on several health outcomes. [14][15][16][17][18] Biologic and clinical risk factors for pediatric pneumonia have been well studied; [19][20][21][22] however, few studies have investigated how geospatial patterns affect community-acquired pneumonia (CAP) risk in children [23][24][25][26][27][28] and this has not directly been examined in U.S. children. Geographical Information Systems (GIS) provide the opportunity to examine associations between clinical factors, environmental factors, and spatial distribution of disease.…”

Geostatistics: Unde venis et quo vadis?

“…The construction of the functional data from the observed point data (the first step in FK) is usually carried out using cubic B-splines (Peng et al, 2006;Franco-Villoria and Ignaccolo, 2015). Another alternative pioneered by Eilers and Marx (1996) is penalized splines (Ranalli et al, 2016;Aguilera-Morillo et al, 2017). This approach is recommended because when including a penalization, the subjectivism induced by the number of knots used and where they are located disappears.…”