Constructing representative air quality indicators with measures of uncertainty

Air quality indicators (AQI) summarise overall pollution concentrations for an urban area, and are calculated from routine monitoring data comprising numerous pollutants measured at many locations. The indicator is usually constructed by aggregating these data over space and pollutants, typically using the sample mean, median, or maximum. In this project we proposed an alternative approach based on geostatistical modelling, which allows uncertainty intervals to be calculated for the spatial aggregation stage, and hence for the final indicator. We then extended our geostatistical  model by allowing for the fact that the locations chosen for the  pollution monitors may depend on the hypothesised concentrations at these locations, a phenomenon known as preferential sampling. We assessed the effectiveness of our methods by simulation, and used them to construct an air quality indicator for Greater London, England, for the month of August 2006.

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