Modelling Some Heavy Metals Air Concentration in Europe |
| |
Authors: | Ma ángeles González Marta G Vivanco Inmaculada Palomino Juan Luis Garrido Manuel Santiago Bertrand Bessagnet |
| |
Institution: | 1. CIEMAT, Avda. Complutense, 22, Madrid, 28040, Spain 2. INERIS, Institut National de l??Environnement Industriel et des Risques, Parc technologique ALATA, 60550, Verneuil en Halatte, France
|
| |
Abstract: | Metals, as well as other air toxic pollutants, can be responsible for a range of human health effects via inhalation or ingestion. European normatives regulate lead, arsenic, cadmium, mercury and nickel ambient air levels in order to prevent potential damage to human health and ecosystems; annual target levels of 500, 6, 5 and 20?ng/m3 for Pb, As, Cd and Ni are set for these pollutants in directives 2008/50/CE and 2004/107/CE. Air quality models constitute a powerful tool to understand tropospheric dynamic and to assign concentration values to areas where no measurement is available. However, not many models include heavy metals in their code, and mainly results for Pb, Cd and Hg have been published. In this paper, we present preliminary results on modelling Pb, Cd, As, Ni, Cu, Zn, Cr and Se air background concentration in Europe using the CHIMERE model, at a 0.2° resolution and the evaluation of the model performance in order to see its capability to reproduce observed levels. This evaluation was performed by comparing simulated values with observations at the EMEP monitoring sites, as only values at background sites can be captured at the 0.2° model resolution. Important uncertainties mainly related to emissions should be solved in order to obtain an improvement of model performance; more recent annual totals, information on snap activities for each metal, higher spatial resolution and a better knowledge of the temporal emission behaviour is necessary to adequately model these air pollutants. Also a better treatment of these particles considering more realistic metal size distribution, more refined deposition processes or some chemical processes regarding Se could result in better model results. A larger number of stations and a better temporal coverage of observations are also important to carry out a better statistical analysis of model performance. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|