|Title||GIS based assessment of the spatial representativeness of air quality monitoring stations using pollutant emissions data|
|Publication Type||Articolo su Rivista peer-reviewed|
|Year of Publication||2014|
|Authors||Righini, Gaia, Cappelletti Andrea, Ciucci A., Cremona G., Piersanti Antonio, Vitali Lina, and Ciancarella Luisella|
|Keywords||air monitoring, air pollutant, Air pollution, Air quality, Air quality modeling, Air quality monitoring stations, algorithm, article, concentration (composition), concentration (parameters), environmental quality, geographic information system, geographic information systems, GIS, Gis-based, histogram, Horizontal variability, hydrography, Image resolution, industrial area, Italy, Long term exposure, long-term change, methodology, monitoring, Monitoring network, Monitoring stations, neighborhood, parameterization, Particulate emissions, particulate matter, Pollutant emission, Pollutants emissions, Pollution, pollution exposure, pollution monitoring, polycyclic aromatic hydrocarbon, population density, priority journal, rural area, spatial analysis, spatial distribution, Spatial representativeness, spatial resolution, statistical distribution, territory, urban area|
Spatial representativeness of air quality monitoring stations is a critical parameter when choosing location of sites and assessing effects on population to long term exposure to air pollution. According to literature, the spatial representativeness of a monitoring site is related to the variability of pollutants concentrations around the site.As the spatial distribution of primary pollutants concentration is strongly correlated to the allocation of corresponding emissions, in this work a methodology is presented to preliminarily assess spatial representativeness of a monitoring site by analysing the spatial variation of emissions around it. An analysis of horizontal variability of several pollutants emissions was carried out by means of Geographic Information System using a neighbourhood statistic function; the rationale is that if the variability of emissions around a site is low, the spatial representativeness of this site is high consequently.The methodology was applied to detect spatial representativeness of selected Italian monitoring stations, located in Northern and Central Italy and classified as urban background or rural background. Spatialized emission data produced by the national air quality model MINNI, covering entire Italian territory at spatial resolution of 4×4km2, were processed and analysed.The methodology has shown significant capability for quick detection of areas with highest emission variability. This approach could be useful to plan new monitoring networks and to approximately estimate horizontal spatial representativeness of existing monitoring sites. Major constraints arise from the limited spatial resolution of the analysis, controlled by the resolution of the emission input data, cell size of 4×4km2, and from the applicability to primary pollutants only. © 2014 Elsevier Ltd.
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