Claudio Carnevale

An integrated assessment tool to define effective air quality policies at regional scale

In this paper, the Integrated Assessment of air quality is dealt with at regional scale. First the paper describes the main challenges to tackle current air pollution control, including economic aspects. Then it proposes a novel approach to manage the problem, presenting its mathematical formalization and describing its practical implementation into the Regional Integrated Assessment Tool (RIAT). The main features of the software system are described and some preliminary results on a domain in Northern Italy are illustrated. The novel features in RIAT are then compared to the state-of-the-art in integrated assessment of air quality, for example the ability to handle nonlinearities (instead of the usual linear approach) and the multi-objective framework (alternative to cost-effectiveness and scenario analysis). Then the lessons learned during the RIAT implementation are discussed, focusing on the locality, flexibility and openness of the tool. Finally the areas for further development of air quality integrated assessment are highlighted, with a focus on sensitivity analysis, structural and non technical measures, and the application of parallel computing concepts.

Forecasting peak air pollution levels using NARX models

Air pollution has a negative impact on human health. For this reason, it is important to correctly forecast over-threshold events to give timely warnings to the population. Nonlinear models of the nonlinear autoregressive with exogenous variable (NARX) class have been extensively used to forecast air pollution time series, mainly using artificial neural networks (NNs) to model the nonlinearities. This work discusses the possible advantages of using polynomial NARX instead, in combination with suitable model structure selection methods. Furthermore, a suitably weighted mean square error (MSE) (one-step-ahead prediction) cost function is used in the identification/learning process to enhance the model performance in peak estimation, which is the final purpose of this application. The proposed approach is applied to ground-level ozone concentration time series. An extended simulation analysis is provided to compare the two classes of models on a selected case study (Milan metropolitan area) and to investigate the effect of different weighting functions in the identification performance index. Results show that polynomial NARX are able to correctly reconstruct ozone concentrations, with performances similar to NN-based NARX models, but providing additional information, as, e.g., the best set of regressors to describe the studied phenomena. The simulation analysis also demonstrates the potential benefits of using the weighted cost function, especially in increasing the reliability in peak estimation.

POEM-PM: an emission model for secondary pollution control scenarios

The paper describes the POEM-PM (POllutant Emission Model for gas and Particulate Matter) emission model design. The model, providing actual and alternative emission scenarios, represents a decision support tool to evaluate emission control strategy effectiveness. It estimates emissions at local and mesoscale level and implements a combined top-down and bottom-up approach. The POEM-PM emission fields answer the GAMES (Gas Aerosol Modelling Evaluation System) multiphase modelling system requirements: grid distribution, hourly time modulation, NMVOC split and lumping, PM10 granulometric and chemical description. The emission model has been validated performing episodic and seasonal simulations over Northern Italy by GAMES. POEM-PM is at present the emission model for air quality chemical and transport applications recommended by the Italian National Environmental Agency.

Surrogate models to compute optimal air quality planning policies at a regional scale

Secondary pollutants (such as PM10) 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 essential tools to help Environmental Authorities to plan air quality policies that fulfill EU Directive 2008/50 requirements in a cost-efficient way. To implement these DSSs the common approach is to describe the air quality indices using linear models, derived through model reduction techniques starting from deterministic Chemical Transport Model simulations. This linear approach limits the applicability of these surrogate models, and while these may work properly at coarse spatial resolutions (continental/national), where average values over large areas are of interest, they often prove inadequate at sub national scales, where the impact of non linearities on air quality are usually higher. The objective of this work is to identify air quality models able to properly describe the relation between emissions and air quality indices, at a sub national scale. In this context, artificial neural networks, identified processing long-term simulation output of a 3D deterministic multi-phase modelling system, are used to describe the non-linear relations between the control variables (precursor emissions reduction) and a pollution index. These models can then be used with a reasonable computing effort to solve a multi-objective (air quality and emission reduction costs) optimization problem, that requires thousands of model runs and thus would be unfeasible using the original process-based model. A case study of Northern Italy is presented.

Sensitivity of CTM simulations to meteorological input

The objective of this work is to investigate the sensitivity of an Eulerian Chemical Tranport Model to the reconstruction of the wind field. In the framework of City-Delta exercise, three 14-day simulations have been performed, using the same model (CAMx) and different meteorological inputs; these were built combining large-scale analysis, local observations and the output of a high resolution meteorological model (Aladin). When Aladin output is used, stronger surface winds lead to enhanced nighttime mixing, resulting in higher ozone and lower PM10 concentrations in urban areas; on the other hand, the stronger advection associated with Aladin wind seems to help the model reproduce high ozone concentrations during day. The use of local wind observations appears beneficial to model performance.

A multi-objective nonlinear optimization approach to designing effective air quality control policies

This paper presents the implementation of a two-objective optimization methodology to select effective tropospheric ozone pollution control strategies on a mesoscale domain. The objectives considered are (a) the emission reduction cost and (b) the Air Quality Index. The control variables are the precursor emission reductions due to available technologies. The nonlinear relationship linking air quality objective and precursor emissions is described by artificial neural networks, identified by processing deterministic Chemical Transport Modeling system simulations. Pareto optimal solutions are calculated with the Weighted Sum Strategy. The two-objective problem has been applied to a complex domain in Northern Italy, including the Milan metropolitan area, a region characterized by frequent and persistent ozone episodes.

A cokriging based approach to reconstruct air pollution maps, processing measurement station concentrations and deterministic model simulations

One of the aims of regional Environmental Authorities is to provide citizens information about the quality of the atmosphere over a certain region. To reach this objective Environmental Authorities need suitable tools to interpolate the data coming from monitoring networks to domain locations where no measures are available. In this work a spatial interpolation system has been developed to estimate 8-h mean daily maximum ozone concentrations and daily mean PM10 concentrations over a domain, starting from measured concentration values. The presented approach is based on a cokriging technique, using the results of a deterministic Chemical Transport Model (CTM) simulation as secondary variable. The developed methodology has been tested over a 60 x 60 km^2 domain located in Northern Italy, including Milan metropolitan area, one of the most polluted areas in Europe.

A non-linear analysis to detect the origin of PM10 concentrations in Northern Italy.

This work presents the formalization and the application of the factor separation technique in order to investigate the impact of precursor emission and their nonlinear interaction (in particulate matter accumulation processes). By processing the simulations of a 3D multiphase modeling system, the factor separation methodology can support the Environmental Authority in quantifying the impact of precursor emissions on PM10 production and consequently in assessing the feasible efficiency of different emission control strategies over a considered domain. The case study proposed by this paper focuses on the Po Valley region (Northern Italy), characterized by critical PM10 levels claiming for sound emission reduction policies. The results show the heavy nonlinearities and the strong seasonal dependence in the formation of PM10, over the study domain. Furthermore the results highlight that peak PM10 concentrations are mainly related to primary PM emissions in urban areas, and gas emissions (mainly NOx and NH3) in rural areas.

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.

A comparison of reanalysis techniques: Applying optimal interpolation and Ensemble Kalman Filtering to improve air quality monitoring at mesoscale

To fulfill the requirements of the 2008/50 Directive, which allows member states and regional authorities to use a combination of measurement and modeling to monitor air pollution concentration, a key approach to be properly developed and tested is the data assimilation one. In this paper, with a focus on regional domains, a comparison between optimal interpolation and Ensemble Kalman Filter is shown, to stress pros and drawbacks of the two techniques. These approaches can be used to implement a more accurate monitoring of the long-term pollution trends on a geographical domain, through an optimal combination of all the available sources of data. The two approaches are formalized and applied for a regional domain located in Northern Italy, where the PM10 level which is often higher than EU standard limits is measured.