Analysis of the spatial representativeness of rural background monitoring stations in Spain

Developmental Biology

Fernando Martin¹  Juan L. Garrido¹  Marta G. Vivanco¹  Lorenzo Fileni¹  Inmaculada Palomino¹ 

[1] Atmospheric Pollution Division, CIEMAT, Avda. Complutense 40, Ed. 3, 28040–Madrid, Spain$$

关键词: Air quality assessment;    station representativeness;    air quality modeling;    air quality network analysis;   

DOI  :  10.5094/APR.2014.087

学科分类：农业科学（综合）

来源: Dokuz Eylul Universitesi * Department of Environmental Engineering

【 摘 要 】

The spatial representativeness of rural background air quality stations was estimated using the spatial distribution of air pollutants computed by the combinations of the results of annual WRF–CHIMERE model simulations and data measured at stations of the Iberian Peninsula in 2008, 2009 and 2010 for NO₂, SO₂, O₃ and PM₁₀. The advantage of using validated models combined with measurements is that effects of the emission sources distribution and atmospheric pollutant processes are both taken into account and that the model bias and errors are corrected. This methodology provides a considerably realistic spatial view of air pollutant concentration distribution around the rural background stations. The criteria for delimiting the representativeness area are based on the assumptions that: (1) concentration does not differ by more than a certain percentage from the concentration at the station; and (2) the air quality in the station and in the representativeness area should have the same status regarding the legal standard. The results showed that there is a large variability in the size and shape of the representativeness area of rural background stations in Spain, also depending on the pollutant and the limit or target value. In addition, the interannual variability of the representativeness areas, station redundancy and network coverage have been analyzed. A high interannual variability of spatial representativeness areas was found, except for daily and hourly SO₂, hourly O₃ and annual NO₂. Roughly 50% of rural background stations measured O₃ overlap with other stations in at least 80% of their spatial representativeness area, denoting a high percentage of station redundancy. Concerning network coverage, there are zones that are not covered by stations, the worst coverage being for PM₁₀. The proposed methodology seems to be useful for determining the spatial representativeness of air quality stations.

【 授权许可】

Unknown   

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