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Abstract: Ambient air-quality data contain information about air-pollution sources that is currently under-exploited. This information could be used to assess trends in the emissions performance of specific sources, and to check at an early stage if policies or controls to reduce air-quality impacts from particular sources are working. Previous techniques for extracting such information have tended to adopt complex analyses and to rely on data from monitoring networks with many sites, thus limiting their applicability to non-specialist users and to networks with few sites. This paper describes simple techniques for ‘conditionally’ selecting data from one or two monitors, and for analysing and interpreting concentrations in terms of source performance or policy progress. Our techniques minimise the effects of variations in meteorology and source activity, so that the selected data give a more consistent indication of individual source performance. We demonstrate our techniques with a case study, in which we track the source performance of road traffic on the M4 motorway in London and show how impacts per vehicle have changed over time under different conditions of traffic flow and fleet composition. [Copyright &y& Elsevier]

Abstract: Recent acknowledgement of the sensitivity of air quality to changes in climate has initiated a closer examination of the relationships between meteorology and air quality. This paper presents the estimated response of daily air pollutant concentrations in Melbourne, Australia to local-scale meteorology. The meteorological-pollutant relationships have been assessed after controlling for long-term trends, seasonality, weekly emissions, spatial variation, and temporal persistence using the framework of generalized additive models (GAMs). Overall, results found that the aggregate impact of meteorological variables in the models explained 26.3% of the variance in O3, 21.1% in PM10, and 26.7% in NO2. This indicates that meteorology – at the local-scale, is a relatively strong driver of air quality in Melbourne. Analysis of partial residuals plots found that changes in temperature, particularly when above 35 °C, resulted in the strongest positive response for O3 (150%), PM10 (150%) and NO2 (120%). Other variables (boundary layer height, winds, water vapor pressure, radiation, precipitation, and mean sea-level pressure) displayed some importance for one or more of the pollutants, but their impact in the models was less pronounced. In sum, we provide results that form a solid foundation for understanding the importance of local-scale meteorology as a driver of regional air pollution. Additionally, these results can be used to provide a window into how changes in climate may affect air quality. [Copyright &y& Elsevier]

Abstract: 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 maximum 8 hourly levels of O3 and daily mean 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. Apart from a lower explained variance due to intrinsic limitations of the regression approach itself, a lower variability of the meteorological predictors (resolution effect) and model deficiencies, this synoptic-regression-based tool is generally 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 the approach using only meteorology data was found to be insufficient to explain the observed PM10 variability using the meteorological variables considered in this study. [Copyright &y& Elsevier]