ACTION 3: Spatial Variability of Ozone Impacts


Description

The following action is organized across Italy (except islands) and South-eastern France at forest plots level. The chemical transport model CHIMERE, coupled with the meteorological model WRF, and geostatistical approaches are used to model ozone concentrations and map different ozone risk indicators for forests. If climate change and ozone are global, impacts on vegetation vary strongly at regional scale.

This action consists in developing maps of potential ozone risk indicators for forests to estimate the risk of direct air pollution effects on forest ecosystems and establish a system to detect and evaluate changes in forests health in Europe. In addition, this action will allow, in fine, the identification of ozone-sensitive communities of vegetation and the refinement of criteria and suggestion of validated thresholds as a decision-support tool for national and European authorities.

Ecological indicators are needed to improve understanding and monitoring of the effects of air pollutants on ecosystems and to scientifically assess the effectiveness of air pollution control strategies. With information in hand, policymakers can make informed decisions about proposed changes to legislation and associated activities.


Meteorological and photochemical modelling

The Weather Research Forecasting (WRF) model developed by the National Center for Atmospheric Research, the National Oceanic and Atmospheric Administration, the Air Force Weather Agency, the Naval Research Laboratory, the Oklahoma University and the Federal Aviation Administration. The WRF Model is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a 3-dimensional variational data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers. WRF allows researchers the ability to conduct simulations reflecting either real data or idealized configurations.

The CHIMERE multi-scale model, developed by the Pierre-Simon Laplace Institute (IPSL/CNRS) in 1996, is primarily designed to produce daily forecasts of ozone, aerosols and other pollutants and make long-term simulations for emission control scenarios. CHIMERE runs over a range of spatial scales from the regional scale (several thousand kilometers) to the urban scale (1-2 Km). It can run with several vertical resolutions, and with a wide range of complexity. It can run with several chemical mechanisms, simplified or more complete, with or without aerosols. As regards the gaseous chemistry, CHIMERE leans on the chemical mechanism MELCHIOR: in operational configuration 44 chemical species and 120 reactions are taken into account, including O3, NOx, VOC and fine particles.

Temporal coverage:

Hourly data
Spatial coverage: [4°42’ - 7°37’°E; 42°57’ - 43°50’°N] at 2*2Km
[4° - 18°E; 41 - 51°N’] at 6*6Km

 

Figure1: Principle of the Chemical-Transport Model

 

Geostatistical approach

Geographical Information System (GIS) is a computer system for capturing, storing, checking, integrating, manipulating, analysing and displaying data related to positions on the Earth's surface. Typically, a GIS is used for handling maps of one kind or another.

Spatial distributions of plant responses (growth reduction and leaf injury), O3 indices (AOT40 and stomatal flux) and meteorological parameters are developed within the ArcGIS 9.2 software (ESRI, Redlands, CA). The spatial data are transformed to the Transverse Mercator projection for calculations, geostatistical modelling, and analysis. Ordinary kriging usually provides the best estimate of the interpolated value at the unsampled locations, creating a continuous layer of information. Layers are developed for plant responses, exposure-based indices, as well as mean diurnal variables for flux calculation.

The geostatistical approach leads to a map with a pixel resolution. The potential of the geostatistical approach is very high but it depends by the distribution and the number of the data used like input.