Anna Pederzoli

A tool to evaluate air quality model performances in regulatory applications

This paper describes the details of the DELTA Tool and Benchmarking service for air quality models, recently developed in the framework of FAIRMODE (Forum for Air Quality Modelling in Europe). One of the main objectives of the FAIRMODE activities is the development of a procedure for the evaluation and benchmarking of air quality modelling applications for regulatory purposes. The DELTA Tool is a specific software which provides summary statistics (i.e. BIAS, RMSE, correlation coefficient) as well as scatter-plots, time series plots, Taylor, Target and other diagrams providing an overview of the quality of model results with respect to monitored data. Moreover, the benchmarking service implemented in DELTA produces summary reports containing performance indicators related to a given model application in the frame of the EU Air Quality Directive (AQD, 2008). This work describes the structure of the DELTA tool and template for reporting model performances. Some examples of application are also briefly presented.

Exploring trade-offs between air pollutants through an Integrated Assessment Model.

When designing air pollution reduction policies, regional decision makers face a limited budget to choose the most efficient measures which will have impacts on several pollutants in different ways. RIAT+ is a regional integrated assessment tool that supports the policy maker in this selection of the optimal emission reduction technologies, to improve air quality at minimum costs. In this paper, this tool is formalized and applied to the specific case of a French region (Alsace), to illustrate how focusing on one single pollutant may exacerbate problems related to other pollutants, on top of conflicts related to budget allocation. In our case, results are shown for possible trade-offs between NO2 and O3 control policies. The paper suggests an approach to prioritize policy maker objectives when planning air pollution policies at regional scale.

Lazy Learning based surrogate models for air quality planning

Air pollution in atmosphere derives from complex non-linear relationships, involving anthropogenic and biogenic precursor emissions. Due to this complexity, Decision Support Systems (DSSs) are important tools to help Environmental Authorities to control/improve air quality, reducing human and ecosystems pollution impacts. DSSs implementing cost-effective or multi-objective methodologies require fast air quality models, able to properly describe the relations between emissions and air quality indexes. These, namely surrogate models (SM), are identified processing deterministic model simulation data. In this work, the Lazy Learning technique has been applied to reproduce the relations linking precursor emissions and pollutant concentrations. Since computational time has to be minimized without losing precision and accuracy, tests aimed at reducing the amount of input data have been performed on a case study over Lombardia Region in Northern Italy. The modellisation of PM10 concentration and emission precursors performed through simplified, computational efficient models based on Lazy Learning technique.Good performances both in terms of computational time and models evaluation.Comparison between Lazy Learning and Artificial Neural Network surrogate models.

Applying the delta tool to support the Air Quality Directive: evaluation of the TCAM chemical transport model

This paper presents an application of the DELTA evaluation tool V3.2 to support the EU Air Quality Directive (AQD 2008). This software, designed in the frame of the FAIRMODE project (Forum for Air Quality Modelling in Europe, http://fairmode.ew.eea.europa.eu/), is currently used as support to working groups of modelers across Europe in the diagnostics and assessment of air quality model performances under the AQD (2008). The skills of the DELTA tool V3.2 are tested by looking at the results of a 1-year (2005) simulation performed using the transport chemical aerosol model (Carnevale et al. 2008) at 6 × 6-km2 resolution over the Po Valley. The modeled daily PM10 concentrations at surface level are compared to observations provided by approximately 50 stations distributed across the domain. The main statistical parameters (i.e., bias, root mean square error, correlation coefficient, standard deviation) as well as different types of diagrams (scatter plots, time series plots, Taylor and target plots) have been produced. A representation of the observation uncertainty in the target plot, used to derive model performance criteria for the main statistical indicators, is also presented and discussed.

Performance criteria for the benchmarking of air quality model regulatory applications: the ‘target’ approach

The definition of appropriate performance criteria is one of the key issues for the benchmarking of air quality models in regulatory applications. As part of the FAIRMODE benchmarking activities (Thunis et al., 2010), suitable criteria for air quality modelling in the frame of the EU air quality directive (AQD) 2008 are proposed and tested. The suggested approach builds on the target indicator (Jolliff et al., 2009) as support to the relative directive error, the current official statistical parameter as defined in the AQD (EEA, 2011), for quantitatively estimating model performances in air quality modelling applications. This study describes the advantages of using the target compared to the actual limitations of RDE and addresses the main links between the target and some ‘traditional’ statistical indicators (MFB, R, FAC2, σ ). It also describes the application of this methodology to NO 2 , O 3 and PM 10 concentrations on three different model-observations datasets. Among these datasets two focus on the urban areas of Madrid and London and include modelled results provided by the air quality models CMAQ and ADMS-Urban for years 2007 and 2008 respectively. One other dataset (POMI) covering the Po valley and including multiple model results has also been tested for year 2005.

Evaluation of WRF model performance in different European regions with the DELTA-FAIRMODE evaluation tool

One-year (2006) WRF model simulations performed at a European scale and ECMWF-IFS forecasts are compared with 10 m wind speed and 2 m temperature observations from around 1,200 surface stations. A statistical evaluation on the modelled meteorological fields is performed using the DELTA software, developed in the framework of FAIRMODE, the forum for air quality modellers relevant to the application of the European Air Quality Directive. In terms of wind speed, ECMWF forecasts are pretty good over most of the domain, while WRF model simulations are less skillful, e.g., they show a larger bias and RMSE. Regarding 2 m temperature, performance criteria are better satisfied by both modelling systems. Finally, the models’ statistics are exemplified in a couple of specific areas: near Berlin, one of the urban areas showing a better model performance, and in the Alpine region, where the model skill is very poor.

Two Illustrative Examples: Brussels and Porto

To evaluate in practice how IAM can be used to formulate and improve current air quality plans, this chapter reports on the application of one of the existing IAM tools, to two test cases: one for the Brussels Capital Region in Belgium and the other to the region of Porto in the North of Portugal. The two cases are representative for the two options that are available for the decision pathway in the IAM framework as presented in Chap. 2: the scenario evaluation and the optimisation. Before presenting the peculiarities and the results obtained for the two test cases, this chapter briefly describes the specific features of the IAM tool used, namely RIAT+.

An Integrated Data-Driven/Data Assimilation Approach for the Forecast of PM10 Levels in Northern Italy

The EU Air Quality Directive 2008/50/EC recommends member states to ensure that timely information about actual and forecasted levels of pollutant concentrations are provided to the public. In order to follow these guidelines, prevent critical episodes and inform the public, environmental authorities need fast and reliable real time alarm systems. In this work, a performance comparison of different data driven model families has been performed using information provided by more than 100 monitoring stations in Northern Italy. The different models include linear (auto-regressive), non-linear (neural network), time variant (lazy learning) methods and their ensemble. Moreover, their inability to perform forecast where no monitoring stations are available is known as one of the main limitations related to this kind of models. To address this issue, an optimal interpolation technique has been introduced to integrate daily PM10 forecasted concentrations with the results of a deterministic chemical transport model, extending the forecast from the monitoring network sites to the whole area of interest. The validation shows very good performances for all stations, with high agreement in both mean value and 95th percentile computed over the whole year, a correlation coefficient usually higher than 0.7 and small values of root mean square error.

Scenario Analysis And Optimization Approach In Air Quality Planning: A Case Study In Northern Italy.

Secondary pollution derives from complex non-linear reactions involving precursor emissions, namely VOC, NOx, NH3, primary PM and SO2. Due to difficulty to cope with this complexity, Decision Support Systems (DSSs) are key tools to support Environmental Authorities in planning cost-effective air quality policies that fulfill EU Directive 2008/50 requirements. The objective of this work is to formalize and compare the scenario analysis and the multi-objective optimization approach for air quality planning purposes. A case study of Northern Italy is presented.