Air quality monitoring using heterogeneous networks

Fassò, Alessandro; Cameletti, Michela; Nicolis, Orietta

doi:10.1002/env.837

Cited by 32 publications

(25 citation statements)

References 18 publications

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“…Such observations tend to agree with the findings of other studies conducted in the region on fine particulate (Cocchi et al 2006;Fassó et al 2007) and as such confirm that the area may be perceived, for both geographical, meteorological and anthropic reasons, as a part of a unique metropolitan area covering the entire Po Valley.…”

Section: Discussionsupporting

confidence: 81%

A simple non-separable, non-stationary spatiotemporal model for ozone

2008

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The past two decades have witnessed an increasing interest in the use of space-time models for a wide range of environmental problems. The fundamental tool used to embody both the temporal and spatial components of the phenomenon in question is the covariance model. The empirical estimation of space-time covariance models can prove highly complex if simplifying assumptions are not employed. For this reason, many studies assume both spatiotemporal stationarity, and the separability of spatial and temporal components. This second assumption is often unrealistic from the empirical point of view. This paper proposes the use of a model in which non-separability arises from temporal non-stationarity. The model is used to analyze tropospheric ozone data from the Emilia-Romagna Region of Italy.

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Section: Discussionsupporting

confidence: 81%

A simple non-separable, non-stationary spatiotemporal model for ozone

2008

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“…The methods, as presented in this paper, represent an alternative to a universal kriging-based approach, such as applied by Fassó et al (2007) (air quality) and Baume et al (2009) (radiation). The difference between the two approaches appears to be minor for estimation of biases for overlapping regions, something we also verified with simulations (results are not shown).…”

Section: Discussionmentioning

confidence: 98%

“…Hence, there is an increased interest in merging the observations from local observation networks into larger databases. Some examples can be found in forestry (Köhl et al, 2000), soil quality (Wagner et al, 2001), air quality (Fassó et al, 2007) reported observations from Belgium might be systematically higher than the observations in the neighbouring countries. The Dutch network uses Bitt proportional counter RS03 with a self-effect of 2 nSv/h, whereas the Belgium network uses Berthold LB6360 with self-effect 11 nSv/h (Bossew et al, 2008).…”

Section: Introductionmentioning

confidence: 89%

Identifying and removing heterogeneities between monitoring networks

et al. 2009

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SUMMARYThere is an increased interest in merging observations from local networks into larger national and international databases. However, the observations from different networks have typically been made using different equipment and applying different post-processing of the values. These heterogeneities in recorded values between networks can lead to inconsistencies between different networks, and to discontinuities at the borders between regions if the observations are used as a source for interpolated maps of the process. Such discontinuities are undesirable, and could create difficulties in interpreting the maps by decision makers.In this paper, we present two variants of a method that can be used to identify and quantify differences between networks. The first variant deals with networks sharing the same region (usually multiple networks within a country) while the second variant deals with networks in neighbouring regions (usually networks in different countries). The estimated differences can be used to estimate individual biases for each network, which can be subtracted as a harmonization procedure.The method was applied to European gamma dose rate (GDR) measurements from May 2008 from the European Radiological Data Exchange Platform (EURDEP) database. Data from the Slovenian GDR network are used for an application of the first variant of the method whereas the complete dataset is used to illustrate the second variant. The results indicate that these two variants are able to identify and quantify biases reliably, and the interpolated maps after subtraction of the estimated biases appear more reliable than maps created on the basis of the recorded data.

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“…It was originally applied to predict air concentrations or to deal with error measurements in instruments (Fasso et al, 2007). This package uses the function Stem.Estimation to carry out the iterations of the EM algorithm until convergence.…”

Section: The State (S)-space Modelmentioning

confidence: 99%

Interpolation of daily rainfall using spatiotemporal models and clustering

et al. 2014

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Accumulated daily rainfall in non-observed locations on a particular day is frequently required as input to decision-making tools in precision agriculture or for hydrological or meteorological studies. Various solutions and estimation procedures have been proposed in the literature depending on the auxiliary information and the availability of data, but most such solutions are oriented to interpolating spatial data without incorporating temporal dependence. When data are available in space and time, spatiotemporal models usually provide better solutions. Here, we analyse the performance of three spatiotemporal models fitted to the whole sampled set and to clusters within the sampled set. The data consists of daily observations collected from 87 manual rainfall gauges from 1990 to 2010 in Navarre, Spain. The accuracy and precision of the interpolated data are compared with real data from 33 automated rainfall gauges in the same region, but placed in different locations than the manual rainfall gauges. Root mean squared error by months and by year are also provided. To illustrate these models, we also map interpolated daily precipitations and standard errors on a 1 km 2 grid in the whole region.

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Air quality monitoring using heterogeneous networks

Cited by 32 publications

References 18 publications

A simple non-separable, non-stationary spatiotemporal model for ozone

A simple non-separable, non-stationary spatiotemporal model for ozone

Identifying and removing heterogeneities between monitoring networks

Interpolation of daily rainfall using spatiotemporal models and clustering

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