Enrico Turrini

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.

Impact of reduced mass of light commercial vehicles on fuel consumption, CO2 emissions, air quality, and socio-economic costs

This study presents a modelling system to evaluate the impact of weight reduction in light commercial vehicles with diesel engines on air quality and greenhouse gas emissions. The PROPS model assesses the emissions of one vehicle in the aforementioned category and its corresponding reduced-weight version. The results serve as an input to the RIAT+ tool, an air quality integrated assessment modelling system. This paper applies the tools in a case study in the Lombardy region (Italy) and discusses the input data pre-processing, the PROPS-RIAT+ modelling system runs, and the results.

Uncertainty analysis in air quality control systems

Air pollution in the atmosphere derives from complex non-linear relationships, involving anthropogenic and biogenic precursor emissions. Due to this complexity, Integrated Assessment Modelling systems (IAMs) can be used, to help Environmental Authorities to control air quality reducing human and ecosystems pollution exposure effects in a cost efficient way. In this context, the literature suggests control modeling systems solving multi-objective optimization problems. Such approach requires descriptive models linking the control variables to the objectives. As they are assessed thousands and thousands of times by the optimization algorithms, they have to be on one hand no time consuming and on the other hand enough robust. It follows that one of the main aspects to be taken into account assessing the control policies is the impact of uncertainties, in the descriptive models itself and in the optimization control problem results. In this work the application of the general probabilistic framework (GPF) for uncertainty and sensitivity analysis has been applied to assess the sensitivity of the descriptive models in a PM10 exposure control problem over Northern Italy, an area often characterized by high pollution levels.

A non-linear optimization programming model for air quality planning including co-benefits for GHG emissions

This paper introduces the MAQ (Multi-dimensional Air Quality) model aimed at defining cost-effective air quality plans at different scales (urban to national) and assessing the co-benefits for GHG emissions. The model implements and solves a non-linear multi-objective, multi-pollutant decision problem where the decision variables are the application levels of emission abatement measures allowing the reduction of energy consumption, end-of pipe technologies and fuel switch options. The objectives of the decision problem are the minimization of tropospheric secondary pollution exposure and of internal costs. The model assesses CO2 equivalent emissions in order to support decision makers in the selection of win-win policies. The methodology is tested on Lombardy region, a heavily polluted area in northern Italy.

A Framework for Integrated Assessment Modelling

“Air quality plans” according to Air Quality Directive 2008/50/EC Art. 23 are the strategic element to be developed, with the aim to reliably meet ambient air quality standards in a cost-effective way. This chapter provides a general framework to develop and assess such plans along the lines of the European Commission’s basic ideas to implement effective emission reduction measures at local, region, and national level. This methodological point of view also allows to analyse the existing integrated approaches.

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+.

Air Quality Modelling to Support Decision-Making: Scenario and Optimization Approaches

In this work a multi-objective approach to define air quality policies is proposed based on the RIAT+ (Regional Integrated Assessment Modelling Tool) system. The solutions of the decision problem represent cost-effective policies at the sectorial level. The methodology is being applied to the Porto urban area, one of the most polluted areas in Portugal, and optimal control policies up to 2020 will be selected.

Application of a Comprehensive Integrated Assessment Tool for the Brussels Capital Region

While in general air quality has improved in Europe over the past decades, there are still problems with exceedances of ambient air quality limit values in many urban areas. To design efficient Air Quality Plans to face these problems, methodologies and tools are required to assess the effects of possible abatement measures on local air quality. One such tool is RIAT+ (http://www.riatplus.eu) which was designed to help regional decision makers select air pollution reduction policies that will improve the air quality at minimal costs. In this contribution to ITM we present the results obtained as well as the lessons learned for an application of the RIAT+ tool to the Brussels Capital Region. RIAT+ has been previously applied successfully to regions in the Po Valley in Italy and to the Alsace region in France. The application to the BCR however poses specific challenges due to the fact that both the area on which the abatement measures can be applied as the emissions are more limited than in previous cases. Inside the BCR, emissions of nitrogen oxide and particulate matter are mainly from non-industrial combustion and traffic. For these two source categories a list of possible air quality abatement measures was provided by the Brussels Environmental agency. To allow RIAT+ to determine the optimal combination of abatement measures with minimal cost, information was collected on both the emission reduction efficiency and the costs of each of these measures. RIAT+ efficiently calculates concentration changes from emission changes using a receptor model based on an artificial neural network. Input for this receptor model was obtained from the results of a validated AURORA chemical transport model setup for the BCR. Once the receptor model was validated, RIAT+ was then used to calculate the effect of the different proposed abatement measures on air quality.