A multi-model comparison of meteorological drivers of surface ozone over Europe

The implementation of European emission abatement strategies has led to a significant reduction in the emissions of ozone precursors during the last decade. Ground-level ozone is also influenced by meteorological factors such as temperature, which exhibit interannual variability and are expected to change in the future. The impacts of climate change on air quality are usually investigated through air-quality models that simulate interactions between emissions, meteorology and chemistry. Within a multi-model assessment, this study aims to better understand how air-quality models represent the relationship between meteorological variables and surface ozone concentrations over Europe. A multiple linear regression (MLR) approach is applied to observed and modelled time series across 10 European regions in springtime and summertime for the period of 2000–2010 for both models and observations. Overall, the air-quality models are in better agreement with observations in summertime than in springtime and particularly in certain regions, such as France, central Europe or eastern Europe, where local meteorological variables show a strong influence on surface ozone concentrations. Larger discrepancies are found for the southern regions, such as the Balkans, the Iberian Peninsula and the Mediterranean basin, especially in springtime. We show that the air-quality models do not properly reproduce the sensitivity of surface ozone to some of the main meteorological drivers, such as maximum temperature, relative humidity and surface solar radiation. Specifically, all air-quality models show more limitations in capturing the strength of the ozone–relative-humidity relationship detected in the observed time series in most of the regions, for both seasons. Here, we speculate that dry-deposition schemes in the air-quality models might play an essential role in capturing this relationship. We further quantify the relationship between ozone and maximum temperature (mo3 − T, climate penalty) in observations and air-quality models. In summertime, most of the air-quality models are able to reproduce the observed climate penalty reasonably well in certain regions such as France, central Europe and northern Italy. However, larger discrepancies are found in springtime, where air-quality models tend to overestimate the magnitude of the observed climate penalty

Validity and Accuracy of Atmospheric Air Quality Models

Effective evaluation of air pollution control strategies requires the use of validated and reliable mathematical models that can relate pollutant emissions to atmospheric air quality. The derivation and use of such models, at least for inert and linearly decaying pollutants such as CO and SO_2, has received a great deal of attention. Much less work has been devoted to assessing how the model predictions are related to actual atmospheric concentrations. The objectives of this paper are to formulate the concepts of validity and accuracy and to suggest and describe some experiments that can be performed to assess these features

Evolution of perturbations in 3D air quality models

The deterministic approach of sensitivity analysis is applied on the solution vector of an Air Quality Model. In particular, the photochemical CAMx code is augmented with derivatives utilising the automatic differentiation software ADIFOR. The enhanced with derivatives version of the model is then adopted in a study of the effect of perturbations at the boundary conditions on the predicted ozone concentrations. The calculated derivative matrix provides valuable information e.g., on the ordering of the infl uential factors or the localisation of highly affected regions. Two fundamentally different domains of the Auto-Oil II programme were used as test cases for the simulations, namely Athens and Milan. The results suggest that ozone concentration be highly affected by its own boundary conditions and subsequently, with an order of magnitude less, by the boundary conditions of NOX and VOC

Assimilation of Satellite Data in Regional Air Quality Models

In terms of important uncertainty in regional-scale air-pollution models, probably no other aspect ranks any higher than the current ability to specify clouds and soil moisture on the regional scale. Because clouds in models are highly parameterized, the ability of models to predict the correct spatial and radiative characteristics is highly suspect and subject to large error. The poor representation of cloud fields from point measurements at National Weather Services stations and the almost total absence of surface moisture availability observations has made assimilation of these variables difficult to impossible. Yet, the correct inclusion of clouds and surface moisture are of first-order importance in regional-scale photochemistry

A revised parameterization for gaseous dry deposition in air-quality models

International audienceA parameterization scheme for calculating gaseous dry deposition velocities in air-quality models is revised based on recent study results on non-stomatal uptake of O3 and SO2 over 5 different vegetation types. Non-stomatal resistance, which includes in-canopy aerodynamic resistance, soil resistance and cuticle resistance, for SO2 and O3 is parameterized as a function of friction velocity, relative humidity, leaf area index, and canopy wetness. Non-stomatal resistance for all other species is scaled to those of SO2 and O3 based on their chemical and physical characteristics. Stomatal resistance is calculated using a leaf-stomatal-resistance model for all gaseous species of interest. The improvements in the present model compared to its earlier version include a newly developed non-stomatal resistance formulation, a realistic treatment of cuticle and ground resistance in winter and the handling of seasonally-dependent input parameters. Model evaluation shows that the revised parameterization can provide more realistic deposition velocities for both O3 and SO2, especially for wet canopies. Example model output shows that the parameterization provides reasonable estimates of dry deposition velocities for different gaseous species, land types and diurnal and seasonal variations. Maximum deposition velocities from model output are close to reported measurement values for different land types. The current parameterization can be easily adopted into different air-quality models that require inclusion of dry deposition processes

a review of airq models and their applications for forecasting the air pollution health outcomes

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects

Indicators to support the dynamic evaluation of air quality models

Air quality models are useful tools for the assessment and forecast of pollutant concentrations in the atmosphere. Most of the evaluation process relies on the “operational phase” or in other words the comparison of model results with available measurements which provides insight on the model capability to reproduce measured concentrations for a given application. But one of the key advantages of air quality models lies in their ability to assess the impact of precursor emission reductions on air quality levels. Models are then used in a dynamic mode (i.e. response to a change in a given model input data) for which evaluation of the model performances becomes a challenge. The objective of this work is to propose common indicators and diagrams to facilitate the understanding of model responses to emission changes when models are to be used for policy support. These indicators are shown to be useful to retrieve information on the magnitude of the locally produced impacts of emission reductions on concentrations with respect to the “external to the domain” contribution but also to identify, distinguish and quantify impacts arising from different factors (different precursors). In addition information about the robustness of the model results is provided. As such these indicators might reveal useful as first screening methodology to identify the feasibility of a given action as well as to prioritize the factors on which to act for an increased efficiency. Finally all indicators are made dimensionless to facilitate the comparison of results obtained with different models, different resolutions, or on different geographical areas