Emission rates of C8-C15 VOCs from seaweed and sand in the inter-tidal zone at Mace Head, Ireland.

Emission fluxes for a range of C8–C15 volatile organic compounds (VOCs) were determined from the seaweed Fucus spiralis (spiral wrack) and an adjacent sand surface during low tide on the coastline of Mace Head, Ireland. These two surface types, assessed using dynamic flux chamber systems, are typical of the Mace Head inter-tidal zone. A range of n-alkanes and oxygenates were routinely identified in the measurement of chamber air. Examination of the odd/even n-alkane ratios and use of the carbon preference index (CPI) suggested a biogenic source for these compounds (CPIs >2 in for all samples). Fluxes of n-pentadecane, the most predominant n-alkane, ranged from 0.2 to 5.1 μg m−2 h−1 (0.9–24 nmol m−2 h−1), while oxygenates such as nonanal and decanal had fluxes ranging from <0.1 to 4.4 μg m−2 h−1 (<0.1–31 nmol m−2 h−1) and <0.1 to 4.6 μg m−2 h−1 (<0.1–30 nmol m−2 h−1), respectively. Seaweed emission rates for n-pentadecane were correlated with photosynthetically active radiation (PAR) (rs=0.94) while emissions from sand showed correlation with temperature (rs=0.85). This suggests a possible biochemical route controlling the release of n-pentadecane from spiral wrack, and temperature-driven volatilisation from sand. Volatilisation from residual seawater trapped in the sand may explain the comparable flux of both n-alkanes and oxygenates from this surface. Unlike the n-alkanes, oxygenate fluxes from sand correlate with PAR, suggesting a photodependent production from organic carbon residues present in seawater. Comparison with previous flux estimates from coastal seawater indicates that the two source types (Fucus spiralis and bare sand) are significant but not dominant sources of these VOCs

A rigorous inter-comparison of ground-level ozone predictions

Novel statistical approaches to prediction have recently been shown to perform well in several scientific fields but have not, until now, been comprehensively evaluated for predicting air pollution. In this paper we report on a model inter-comparison exercise in which 15 different statistical techniques for ozone forecasting were applied to ten data sets representing different meteorological and emission conditions throughout Europe. We also attempt to compare the performance of the statistical techniques with a deterministic chemical trajectory model. Likewise, our exercise includes comparisons of sites, performance indices, forecasting horizons, etc. The comparative evaluation of forecasting performance (benchmarking) produced 1340 yearly time series of daily predictions and the results are described in terms of predefined performance indices. Through analysing associations between the performance indices, we found that the success index is of outstanding significance. For models that are excellent in predicting threshold exceedances and have a high success index, we also observe high performance in the overall goodness of fit. The 8-h average ozone concentration forecast accuracy was found to be superior to the 1-h mean ozone concentration forecast, which makes the former very significant for operational forecasting. The best forecasts were achieved for sites located in rural and suburban areas in Central Europe unaffected by extreme emissions (e.g. from industries). Our results demonstrate that a particular technique is often excellent in some respects but poor in others. For most situations, we recommend neural network and generalised additive models as the best compromise, as these can handle nonlinear associations and can be easily adapted to site specific conditions. In contrast, nonlinear modelling of the dynamical development of univariate ozone time-series was not profitable