Land use to characterize spatial representativeness of air quality monitoring stations and its relevance for model validation

Janssen, Stijn; Dumont, Gerwin; Fierens, Frans; Deutsch, Felix; Maiheu, Bino; Celis, D.; Trimpeneers, Elke; Mensink, Clemens

doi:10.1016/j.atmosenv.2012.05.028

Cited by 37 publications

(21 citation statements)

References 13 publications

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“…SR assessment can provide objective criteria for stations classification (Joly and Peuch, 2012) and it is useful for supporting network design and optimization (Mofarrah and Husain, 2010;Malherbe et al, 2013) in order to maximize spatial coverage and to avoid stations redundancy (Martin et al, 2014). Moreover, any use of measurements data for modelling purposes benefits from SR assessment of the monitoring sites: validation and data assimilation procedures may be improved by selecting the monitoring sites that are representative of geographical areas related to the spatial resolution of the modelling system (Janssen et al, 2012;Lefebvre et al, 2013).…”

Section: Introductionmentioning

confidence: 98%

“…Indeed, different approaches can be based on various sources of information including: additional air pollutant measurements from specific experimental campaigns (Vardoulakis et al, 2005;Parra et al, 2009) or dense monitoring network (Blanchard et al, 1999;Chow et al, 2006), modelled air pollutant concentrations (Diegmann et al, 2013;Santiago et al, 2013;Martin et al, 2014;Piersanti et al, 2015a;Duyzer et al, 2015), surrogate spatial data e.g. land use (Janssen et al, 2012;Piersanti et al, 2015b) or emission data (Henne et al, 2010;Righini et al, 2014). When air quality model results are used to assess SR of monitoring sites, depending on the type of the station, concentration fields are needed at the proper spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

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A Lagrangian modelling approach to assess the representativeness area of an industrial air quality monitoring station

Vitali

Morabito

Adani

et al. 2016

Atmospheric Pollution Research

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A Lagrangian modelling approach to assess the representativeness area of an industrial air quality monitoring station

Vitali

Morabito

Adani

et al. 2016

Atmospheric Pollution Research

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“…The first one is based on specific physical models; for example, the footprint model is used to select the evapotranspiration product pixels corresponding to the effective range of ground station observation for the validation purpose [47]. The second type of method directly compares the station observations to the average value of the corresponding area [48]. The third type of method combines multitemporal observations from multiple stations to compute the average difference between a given station and nearby stations and thus to determine the observational representativeness [49].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products

Liu

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Continuous leaf area index (LAI) observations from global ground stations are an important reference dataset for the validation of remotely sensed LAI products. In this study, a pragmatic approach is presented for evaluating the spatial representativeness of station-observed LAI dataset in the product pixel grid. Three evaluation indicators, including dominant vegetation type percent (DVTP), relative absolute error (RAE) and coefficient of sill (CS), were established to quantify different levels of spatial representativeness. The DVTP was used to evaluate whether the station-observed vegetation type was the dominant one in the pixel grid, and the RAE and CS were applied to quantify the point-toarea consistency for a given station observation and the spatial heterogeneity caused by the different density of vegetation within the pixel, respectively. The proposed approach was applied to 25 stations from the Chinese Ecosystem Research Network, and results show significant differences of representativeness errors at different levels. The spatial representativeness for different stations varied seasonally with different vegetation growth stages due to temporal changes in heterogeneity, but the spatial representativeness remained consistent at interannual timeframes due to the relatively stable vegetation structure and pattern between adjacent years. A large error can occur in MOD15A2 product validation when the representativeness level of station LAI observations is low. This approach can effectively distinguish various levels of spatial representativeness for the station-observed LAI dataset at the pixel grid scale, which can consequently improve the reliability of LAI product validation by selecting LAI observations with a high degree of representativeness.

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“…2) Use of some surrogate indicators or proxies related to emission sources distribution, land use data, etc. With these data, knowledge of the pollutant spatial distribution is obtained in spite of the effect of transport and dispersion of pollutants is not directly computed (Janssen et al, 2012). 3) Use of air quality modelling to estimate detailed maps of the distribution of air pollutants around the monitoring site.…”

Section: Física De La Tierramentioning

confidence: 99%

Uso de modelos CFD para estimar la representatividad espacial de estaciones de medida de la contaminación atmosférica urbana y la idoneidad de sus ubicaciones

Santiago

Martín

2016

Fís. Tierra

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A methodology to estimate the spatial representativeness of air pollution monitoring sites is applied to two urban districts. This methodology is based on high resolution maps of air pollution computed by using Computational Fluid Dynamics (CFD) modelling tools. Traffic-emitted NO 2 dispersion is simulated for several meteorological conditions taking into account the effect of the buildings on air flow and pollutant dispersion and using a steady state CFD-RANS approach. From these results, maps of average pollutant concentrations for January-May 2011 are computed as a combination of the simulated scenarios. Two urban districts of Madrid City were simulated. Spatial representativeness areas for 32 different sites within the same district (including the site of the operative air quality stations) have been estimated by computing the portion of the domains with average NO 2 concentration differing less than a 20% of the concentration at each candidate monitoring site. New parameters such as the ratio AR between the representativeness area and the whole domain area or the representativeness index (IR) has been proposed to discuss and compare the representativeness areas. Significant differences between the spatial representativeness of the candidate sites of both studied districts have been found. The sites of the Escuelas Aguirre district have generally smaller representativeness areas than those of the Plaza de Castilla. More stations are needed to cover the Escuelas Aguirre district than for the Plaza de Castilla one. The operative air quality station of the Escuelas Aguirre district is less representative than the station of the Plaza de Castilla district. The cause of these differences seems to be the differences in urban structure of both districts prompting different ventilation. Key words: Air quality, CFD modelling, monitoring stations, spatial representativeness Uso de modelos CFD para estimar la representatividad espacial de estaciones de medida de la contaminación atmosférica urbana y la idoneidad de sus ubicaciones ResumenSe ha aplicado una metodología para estimar la representatividad espacial de sitios de monitoreo de contaminación atmosférica en dos distritos urbanos. Esta metodología se basa en mapas de alta resolución de la contaminación del aire calculados mediante modelos CFD (Computational Fluid Dynamics). Se ha simulado la dispersión de NO 2 emitido por el tráfico para varias condiciones meteorológicas, teniendo en cuenta el efecto de los edificios en los flujos de aire y la dispersión de contaminantes y utilizando simulaciones CFD-RANS en estado estacionario. A partir de estos resultados se calcularon mapas de las concentraciones de contaminantes promedio para el periodo Enero-Mayo de 2011 como una combinación de los escenarios simulados. Se simularon dos distritos urbanos de la ciudad de Madrid. Se han estimado las zonas de representatividad espacial para 32 ubicaciones diferentes Santiago and Martin Use of CFD modeling for estimating spatial representativeness…Física de la Tierra Vol...

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Land use to characterize spatial representativeness of air quality monitoring stations and its relevance for model validation

Cited by 37 publications

References 13 publications

A Lagrangian modelling approach to assess the representativeness area of an industrial air quality monitoring station

A Lagrangian modelling approach to assess the representativeness area of an industrial air quality monitoring station

Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products

Uso de modelos CFD para estimar la representatividad espacial de estaciones de medida de la contaminación atmosférica urbana y la idoneidad de sus ubicaciones

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