Representativeness of air quality monitoring networks. (March 2015)

• Record Type: Journal Article
• Authors: Duyzer, Jan; van den Hout, Dick; Zandveld, Peter; van Ratingen, Sjoerd
• Is Part Of: Atmospheric environment. Volume 104(2015)
• Page Start: 88
• Page End: 101
• Publication Date: 2015-03
• Subjects: Air pollution -- Monitoring -- Networks -- Nitrogen dioxide -- Exposure; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2014.12.067
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: The suitability of European networks to check compliance with air quality standards and to assess exposure of the population was investigated. An air quality model (URBIS) was applied to estimate and compare the spatial distribution of the concentration of nitrogen dioxide (NO2 ) in ambient air in four large cities. The concentrations calculated at the location of the monitoring stations, compared well with the concentrations measured at the stations indicating that the models worked well. Therefore the calculated concentration distributions were used as a proxy for the actual concentration distributions across the cities. The distributions of these proxy concentrations across the city populations was determined and cumulative population distribution curves were estimated. The calculated annual mean values at the monitoring network stations were located on the population distribution curves to estimate the fractions of the populations that the monitoring network stations represent. This macro scale procedure is used to evaluate which subgroups of the monitoring stations can be reliably used to decide on compliance or to estimate the concentration the population is exposed to. In addition, the CAR model and Computational Fluid Dynamics (CFD) models are used to investigate the effect of micro scale siting of the monitoring stations within the streets. The following observations were made: - Berlin and London networks cover the distribution of concentrations to which the population is exposed rather well, while Stuttgart and Barcelona have stations at sites with mainly the higher concentrations and the exposure is covered less well. - The networks in London and Berlin, with a substantial number of urban background stations, seem fit to monitor the average population exposure, contrary to those in Stuttgart and Barcelona with only a limited number of these stations. - The concentrations measured at street stations hardly reflect the calculated differences in street pollution between the cities. In Stuttgart the stations are, in line with the EU directive, placed in the most polluted streets, while in other cities there are no stations in the streets with the highest pollution levels. - The concentrations measured at street stations – particularly where buildings inhibit ventilation – are very sensitive to the exact location within the street. Different siting choices may have an effect that for NO2 could reach up to 10 μg/m 3 in realistic conditions. Street stations, representing only a small urban area, are not suitable for characterising the exposure of the general population. It is important to note that epidemiological studies whether investigating short term-effects or those studying long-term effects are potentially affected by the issues raised in the paper. Long-term cumulative exposure estimates that are based rather uncritically on monitoring data may be biased if the stations are not representative. It is recommended to use models to support the interpretation and spatial extrapolation of the results of measurements in existing networks. The use of models also relaxes the need for station relocation in inadequate networks, which often would compromise trend analysis. It also relaxes the importance of exact or detailed, comprehensive, station classifications since all stations can be used in exposure assessments. Highlights: Air quality networks in cities show important difference in design. Differences in design of air quality networks may lead to exposure assessments that are hard to compare. Differences in design of air quality networks may lead to unbalanced checking of compliance. Models are needed to support data evaluation network evaluation.