A new method to estimate air-quality levels using a synoptic-regression approach. Part I: Present-day O-3 and PM10 analysis

To assess regional air quality, a high spatial and temporal resolution of data is necessary in order to develop strategies that follow the European Directives on Air Quality. Moreover, in order to make projections for future air quality levels, a robust methodology is needed that succeeds in reconstructing present-day air quality levels. At present, climate projections for meteorological variables are available from Atmospheric-Ocean Coupled Global Climate Models (AOGCMs) but the temporal and spatial resolution is insufficient for air quality assessment. Therefore, a variety of methods are tested in this paper in their ability to hindcast daily mean levels of O3 and PM10 from observed meteorological data. The methods are based on a multiple linear regression technique combined with the automated Lamb weather classification. Moreover, we studied whether the above-mentioned multiple regression analysis still holds when driven by operational ECMWF (European Center for Medium-Range Weather Forecast) meteorological data. The main results show that a weather type classification prior to the regression analysis is superior to a simple linear regression approach. In contrast to PM10 downscaling, seasonal characteristics should be taken into account during the downscaling of O3 time series. Furthermore, our analysis validates the strength of the observation-based regression to hindcast O3 concentrations even when applied on low-resolution gridded data as available from AOGCMs. Although part of the explained variance is lost due to a lower variability of the meteorological predictors and model deficiencies, this regression-circulation pattern tool is able to reproduce the relevant statistical properties of the observed O3 distributions important in terms of European air quality Directives and air quality mitigation strategies. For PM10, the situation is different as a regression-based approach using only meteorology data was found to be insufficient to explain the observed PM10 variability.status: publishe