Functional zoning for air quality

Ignaccolo, Rosaria; Ghigo, Stefania; Bande, Stefano

doi:10.1007/s10651-012-0210-7

Cited by 30 publications

(15 citation statements)

References 25 publications

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“…In particular, they analyzed the daily time series of CO and NO emissions in Rome. Ignaccolo et al [68] proposed to partition a land in zones characterized by different criticality levels of atmospheric pollution considering pollutant time series as functional data (Functional Zoning). Specifically, they considered air pollutant time series of Piemonte (Northern Italy) provided by a deterministic air quality model on a regular grid, and preprocessed by assimilating observations, as functional data.…”

Section: Literature Of Time Series Clustering/classification In Envirmentioning

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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Section: Literature Of Time Series Clustering/classification In Envirmentioning

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 the last decade these methods have been increasingly developed and used in various scientific areas and especially in life and environment observation. For example, Ruiz-Medina and Espejo (2012) proposed spatial interpolation of functional ocean surface temperature and Ignaccolo et al (2013) proposed regional zoning according to functional air quality data. Moreover, Sangalli et al (2013) proposed functional regression for complex spatial configurations which are important, for example, in the study of hemodynamic forces, see Ettinger et al (2013).…”

Section: A Fassò Et Al: Collocation Uncertainty In Atmospheric Profmentioning

confidence: 99%

Statistical modelling of collocation uncertainty in atmospheric thermodynamic profiles

et al. 2014

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Abstract. The quantification of measurement uncertainty of atmospheric parameters is a key factor in assessing the uncertainty of global change estimates given by numerical prediction models. One of the critical contributions to the uncertainty budget is related to the collocation mismatch in space and time among observations made at different locations. This is particularly important for vertical atmospheric profiles obtained by radiosondes or lidar.In this paper we propose a statistical modelling approach capable of explaining the relationship between collocation uncertainty and a set of environmental factors, height and distance between imperfectly collocated trajectories. The new statistical approach is based on the heteroskedastic functional regression (HFR) model which extends the standard functional regression approach and allows a natural definition of uncertainty profiles. Along this line, a five-fold decomposition of the total collocation uncertainty is proposed, giving both a profile budget and an integrated column budget.HFR is a data-driven approach valid for any atmospheric parameter, which can be assumed smooth. It is illustrated here by means of the collocation uncertainty analysis of relative humidity from two stations involved in the GCOS reference upper-air network (GRUAN). In this case, 85 % of the total collocation uncertainty is ascribed to reducible environmental error, 11 % to irreducible environmental error, 3.4 % to adjustable bias, 0.1 % to sampling error and 0.2 % to measurement error.

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“…; Ignaccolo et al. ; Ignaccolo et al. ), there are two major challenges for clustering streams based on the water–air temperature relationship.…”

Section: Introductionmentioning

confidence: 99%

Bivariate functional data clustering: grouping streams based on a varying coefficient model of the stream water and air temperature relationship

Deng

Dolloff

et al. 2015

Environmetrics

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A novel clustering method for bivariate functional data is proposed to group streams based on their water–air temperature relationship. A distance measure is developed for bivariate curves by using a time‐varying coefficient model and a weighting scheme. This distance is also adjusted by spatial correlation of streams via the variogram. Therefore, the proposed distance not only measures the difference among the streams with respect to their water–air temperature relationship but also accounts for spatial correlation among the streams. The proposed clustering method is applied to 62 streams in Southeast US that have paired air–water temperature measured over a ten‐month period. The results show that streams in the same cluster reflect common characteristics such as solar radiation, percent forest and elevation. Copyright © 2015 John Wiley & Sons, Ltd.

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Functional zoning for air quality

Cited by 30 publications

References 25 publications

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

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

Statistical modelling of collocation uncertainty in atmospheric thermodynamic profiles

Bivariate functional data clustering: grouping streams based on a varying coefficient model of the stream water and air temperature relationship

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