A methodology for quantifying areas of spatial representativeness of air quality monitoring station is here proposed, exploiting the wide spatial and temporal coverage of chemical transport models results. The method is based on the analysis of time series of model concentrations, extracted at monitoring sites and around, by means of a Concentration Similarity Function (CSF). The method was tested on AMSMINNI model results, covering Italy and three reference years (2003, 2005, 2007), for assessing the spatial representativeness of PM2.5 and O3 rural background monitoring stations. The CSF methodology shows good performances in describing both the extension and the shape of representativeness areas, taking into account the difference between pollutants and the dependence on averaging time and temporal interval of concentration data. Results show a large variability in the size and shape of the selected stations in Italy, ranging from 220 to 4500 km2. This confirms the importance of carrying out adhoc analyses on monitoring stations, as general a priori classifications and qualitative assessments of spatial representativeness are not able to fully capture the complexity of different territorial contexts. © 2015 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.

Spatial representativeness of air quality monitoring stations: A grid model based approach

Ciancarella, L.;Cremona, G.;Righini, G.;Vitali, L.;Piersanti, A.
2015

Abstract

A methodology for quantifying areas of spatial representativeness of air quality monitoring station is here proposed, exploiting the wide spatial and temporal coverage of chemical transport models results. The method is based on the analysis of time series of model concentrations, extracted at monitoring sites and around, by means of a Concentration Similarity Function (CSF). The method was tested on AMSMINNI model results, covering Italy and three reference years (2003, 2005, 2007), for assessing the spatial representativeness of PM2.5 and O3 rural background monitoring stations. The CSF methodology shows good performances in describing both the extension and the shape of representativeness areas, taking into account the difference between pollutants and the dependence on averaging time and temporal interval of concentration data. Results show a large variability in the size and shape of the selected stations in Italy, ranging from 220 to 4500 km2. This confirms the importance of carrying out adhoc analyses on monitoring stations, as general a priori classifications and qualitative assessments of spatial representativeness are not able to fully capture the complexity of different territorial contexts. © 2015 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Chemical transport model;Rural background;Monitoring networks;Spatial representativeness
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/2397
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