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This study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years'' (2000--06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality--related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047--53) and present (2000--06) simulation results driven by PCM data and is presented in a separate paper. [ABSTRACT FROM AUTHOR]

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]

This paper represents, for the first time, scientific data as a part of monitoring air quality in the city of Split. The city has a problem with the air pollution mostly caused by anthropogenic activities and partly by natural origin activities. This study presents results of monthly and seasonal variations of NO2, SO2 and black-smoke as well as the influence of meteorological parameters on observed concentration levels. Average seasonal values for SO2 ranged from 22.54 to 54.81 gg m-3, for NO2 from 48.24 to 56.38 gg m-3 and for black-smoke from 4.15 to 5.64/µg m-3. The results obtained for SO2 and black-smoke were below, while for NO2 were above the limit value of pollutants in the air recommended by Croatian Government (Air Protection Act (OG 178/04); Regulation on limit values of pollutants in air (OG 133/05)). As the result of stable meteorological conditions (high temperatures and drought), the increase in SO2 concentration was significant in summer period, while the concentrations of NO2 and black-smoke were not significantly changed during year and did not depend on other parameters. [ABSTRACT FROM AUTHOR]

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]

This paper investigates seasonal patterns and annual trends of PM10 concentrations in the small town of Mosgiel located in Otago, southern New Zealand. A second aim is to explore the relationship between PM10 concentrations and concomitant meteorological factors. PM10 data were collected intermittently over a seven-year period (1998-2004) using both gravimetric Minovol and Hivol methods. Meteorological data were gathered over the same time period from two sites in the region. Although there has been an increase in annual PM10 concentrations over the period 2001-2004, the small dataset and the high variability precludes drawing firm conclusions. Monthly PM10 levels were at their highest in the winter months of June and July on days with low minimum daily temperatures, low wind speeds and little or no rainfall. the analysis shows that, based on limited data, PM10 exceeded the new national 24-hour PM10 standard of 50µg m-3 to ten times each winter. Given more frequent sampling, it is expected the number of exceedances could be significantly higher, and the Mosgiel area is unlikely to meet the new national standard, which allows only one exceedance of the standard each year. Analysis of meteorological data shows that minimum daily temperature and logarithmically transformed average daily wind-speed and total daily rainfall are all significantly correlated with PM10 concentration, but the correlations are not strong. The study highlights the need for regional authorities to undertake continuous sampling of particulate and more detailed meteorological monitoring close tot the pollution sources in order to better understand the air pollution meteorology of this small town. [ABSTRACT FROM AUTHOR]