The ozone patterns in the aerological basin of Milan (Italy)

Lavecchia, Cristina; Angelino, Elisabetta; Bedogni, Marco; Bravetti, E.; Gualdi, R.; Lanzani, G.; Musitelli, A.; Valentini, M.

doi:10.1016/s0266-9838(96)00017-2

Cited by 21 publications

(11 citation statements)

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“…A hierarchical clustering (complete linkage) procedure is employed by Lavecchia et al (1996) on the monitoring network in Lombardia (Northern Italy) to evaluate similarities among ozone monitoring sites in terms of concentration levels and temporal trends. A similar approach is employed by Gabusi and Volta (2005) for the classification of Northern Italy measurement stations, in order to identify the representative ones for subsequent analysis of ozone concentration.…”

Section: Introductionmentioning

confidence: 99%

Analysis of air quality monitoring networks by functional clustering

2008

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SUMMARYAir quality monitoring networks are important tools in management and evaluation of air quality. Classifying monitoring stations via homogeneous clusters allows identification of similarities in pollution, of representative sites, and of spatial patterns. Instead of summaries by statistical indicators, we propose to consider the air pollutant concentrations as functional data. We then classify using functional cluster analysis, where Partitioning Around Medoids (PAM) algorithm is embedded. The proposed data analysis approach is applied to the air quality monitoring network in Piemonte (Northern Italy); we consider the three more critical pollutants: NO 2 , PM 10 , and O 3 .

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Section: Introductionmentioning

confidence: 99%

Analysis of air quality monitoring networks by functional clustering

2008

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“…Ludwig et al [41] used cluster analysis for studying the daily ozone maxima in California. Lavecchia et al [42] employed a complete linkage-based procedure on the monitoring network in Lombardia (Northern Italy) to evaluate similarities among ozone monitoring sites in terms of concentration levels and temporal trends. The authors compared the ozone patterns by using the Euclidean distance and the correlation coefficient in the clustering procedure.…”

Section: Literature Of Time Series Clustering/classification In Envirmentioning

confidence: 99%

“…Finally, we compare AR-FCMdC-Exp model with some partitioning procedures suggested in the literature on air pollution monitoring which analyze the observed data set directly and not a proper modelbased parametric representation of the data: non hierarchical clustering (k-means clustering) [34] and hierarchical agglomerative cluster analysis, i.e. Ward [31], single linkage [46,59], average linkage [46,56,57] and complete linkage [42,46,58]. For each method, the optimal partition is detected by means of the silhouette criterion.…”

Section: Insert Figure 5 About Herementioning

confidence: 99%

Time series clustering by a robust autoregressive metric with application to air pollution

D’Urso

Giovanni

Massari

2015

Chemometrics and Intelligent Laboratory Systems

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In this paper, following a fuzzy approach and adopting an autoregressive parameterization, we propose a robust clustering model for classifying time series. In particular, by adopting a fuzzy\ud partitioning around medoids approach, the suggested clustering model is able to define the so-called medoid time series, which is a representative time series of each cluster, and the membership degrees of each time series to the different clusters. The robustness of the proposed clustering model is guaranteed by the adoption of a suitable robust metric for time series, i.e. the so-called exponential distance measure. In this way, the clustering model is able to tolerate the presence of outlier time series in the clustering process. In particular, it is capable of neutralizing and smoothing the disruptive effect of outlier time series, preserving the original clustering structure of the dataset, by assigning to outlier time series approximately the same membership degrees across clusters. To illustrate the usefulness and effectiveness of the suggested time series clustering model, a simulation study and an application to air pollution time series are carried out. Comparison with some existing clustering procedures suggested in the literature shows several advantages of the proposed model

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“…Cluster analysis technique is used for classifying patterns (Lavecchia, C. et al, 1996, Ludwig, F.L. et al, 1995.…”

Section: Clustering Processmentioning

confidence: 99%

“…In this paper the variables considered are the concentration data, registered in different monitoring sites, geographical spread in the investigated area. As for the methodology (1) the set of concentration measured data for a specific time period is considered to be a point in a space of many dimensions (this space has many dimensions as there are variables under studies); (2) points classified "close together", referring to a specific distance, are grouped into the same category; the distance measure can highlights similarities in a quantitative point of view, as the Euclidean distance, or focalises similarities among temporal trend phases (Lavecchia, C. et al, 1996).…”

Section: Clustering Processmentioning

confidence: 99%