Air Quality Integrated Assessment pp 85-104
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+.
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 is the Brussels Capital Region in Belgium and the other 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+.
5.2 The RIAT+ System
Precursor emissions of local and surrounding sources;
Abatement measures (technical and non-technical) described per activity sector and technology with information on application rates, emission removal efficiency and cost;
The effect of meteorology and prevailing chemical regimes through the use of site-specific source/receptor functions.
The system runs as a stand-alone desktop application and can be downloaded from the OPERA project website (http://www.operatool.eu/download/). The package is distributed with a personal, non-exclusive and royalty-free license and has been applied in various regions, such as Emilia-Romagna (Carnevale et al. 2012) and in Alsace (Carnevale et al. 2014).
Scenario analysis, where emission reduction measures are selected on the basis of expert judgment or Source Apportionment and then tested through simulations of an air pollution model.
Optimisation, where the set of cost effective measures for air quality improvement are automatically selected by solving a multi-objective optimization problem.
The development of the surrogate models thus means: first, the definition of the input variables and of the form of the so-called “activation function” φ, generally a strongly nonlinear function of its argument, which is in turn a weighted sum of the input values; second, the determination of all the model parameters (namely, the weights wij and the threshold θj).
This second step (training) is performed by imposing that the surrogate model represent, as much as possible, a set of CTM calculations that are representative of the range of emissions/AQI that may be entailed by the plan to be developed. The process of selecting such configurations to be simulated by the CTM is usually referred to as the ‘Design of Experiment’. On the one hand, these simulations have to be limited in number due to their computational time, but, on the other hand, they must be able to represent as closely as possible the cause-effect relationship between precursor emissions and the considered AQIs.
RIAT+ IAM system has been used in support of air quality planning in Brussels Capital Region (Belgium) and in the Great Porto Area (Portugal). The results of such applications are briefly sketched in the next sections.
5.3 Brussels Capital Region
For the BCR, Brussels Environment, BIM (http://www.ibgebim.be) is responsible for the study, monitoring and management of air, water, soil, waste, noise and nature (green space and biodiversity). BIM proposed a list of 13 measures to improve air quality, approved by the Brussels authorities and consisting of nine measures related to vehicle traffic and four to domestic heating. For these abatement measures, BIM provided order-of-magnitude estimations of the costs and emission reductions. These were screened to determine the effect of the different measures using RIAT+ in the scenario mode.
5.3.1 Proposed Abatement Measures
The introduction of a low emission zone (LEZ) extended to the entire capital region or only the inner part of Brussels municipality. Two possibilities were tested: a restriction only for Heavy Duty Vehicles (HDV) with emission standard prior to Euro 5; or a restriction also for passenger cars with diesel engine before Euro 5 and gasoline before Euro 2. Emission and cost data related to these cases were derived from a TM-Leuven (2011) study. Reduction entailed by these plans ranged, for instance for PM2.5, between 10 and 40 % with respect to the CLE scenario.
Reduction of the car parking lots available in Brussels by 25,000. This measure is assumed to reduce the number of commuters entering Brussels every day and discourage the local inhabitants from using cars to drive to work. The estimate of BIM/IBGE (2012) is that 140,000 commuters enter Brussels in every weekday and 225,000 residents use their vehicle to get to work. Given that the estimated distance travelled is about 9 km for residents and 13 km for the others, and assuming that the reduction of parking places entails a corresponding reduction of trips, this measure would mean a reduction of 129 M km a year with a 1.5 % reduction of the traffic emissions.
The implementation of mobility plans encouraging public transport for all the sites hosting more than 1000 people and all events involving more than 1000 participants. This is assumed to equal a 3.7 % reduction of the traffic sector, with a correspondent reduction, for instance, of 2.6 % of PM10 emissions.
A modal shift from car to bike for commuting. This follows existent plans to move from the current 1.9 % of commuting trips by bike to 20 % by 2018. An English study indeed showed that each new cyclist corresponds to a 500 € gain per year for society, mainly through the reduction of costs in health care (Cycling England 2007). This would correspond to a further reduction of commuting private traffic by 4.8 %.
The introduction of a urban toll. This can be implemented according to different schemes: a toll of 12 €/day within BCR; one of 3 €/day in the larger Brussels Regional Express Network (RER); a price of 7 c€/km in the RER zone. The first scheme is estimated to reduce NOx emission by 18 % with respect to CLE 2018, the second by 11 %, the third by 9 %. According to a STRATEC (2014) study, the net present value of the costs for implementing these scheme ranges from 250 M€ for the first two, to about 2500 M€ for the third.
Eco-driving. To make eco-driving the standard on roads, it should be first taught during the various formations of the road users (driving license, taxi driver permit, training of bus and truck drivers, etc.); but we must also regularly sensitize drivers by information and awareness tools, particularly within enterprise transport plans. Following AIRPARIF (2012), it is assumed that about 25 % of all drivers are susceptible to a more eco-driving style, implying 7 % less fuel use, and hence a 1.7 % reduction of emissions. Assuming a full scale eco-driving campaign similar to that implemented in the Netherlands (ECODRIVEN 2008) will result in a rough estimate of 180 k€ annually. This implies a net present value, discounted over a time period of 6 years (e.g. 2014–2020) on the basis of a 5.7 % interest rate, of about 1 M€.
Stimulating the use of Compressed Natural Gas (CNG) as car fuel. While this is a more technical measure, it seems that in Belgium is more a psychological problem than a lack of infrastructure. It is necessary to implement incentives and information campaigns and to increase the number of points of sale sufficiently to make CNG a viable alternative as in many other countries. With respect to the 2010 situation (FEBIAC 2013), it was assumed that 540,000 (10 % of the fleet) could run on CNG in 2020, substituting 6.3 % of current diesel cars and 3.7 % of gasoline cars. Since the average mileage is 15,500 km/year, this would mean a decrease of 7.6 % of NOx emitted by cars.
Maintenance of residential heating appliances. This measure consists of a periodic inspection of boilers, according to the requirements listed in the PEB (Performance Energétique des Bâtiments) guidelines. In particular, in this study, the measure was only applied to residential boilers, with a power in excess of 20 kW, which corresponds, to 95 % of all boilers in the residential sector. Specifically, the periodic inspection of boilers consists of cleaning all components of the boiler and flue system, the burner setting and compliance verification requirements. Oil-fired boilers should be checked annually while natural gas boilers should be checked every three years. The adoption of this measure is assumed to reduce NOx emissions by 72 ton, SOx by 33 ton, VOC by 9.3 ton, and PM2.5 by 3 ton in 2020. According to the estimate of VITO (2011) the adoption of this action may cost around 18 M€/year.
Improving building isolation. This measure aims to stimulate the construction and building renovation programs by demonstrating that it is possible to achieve excellent energy and environmental performance while opting for economically justifiable solutions and promoting high architectural quality. It provides building owners the opportunity to be ambitious, and allows to generate a number of exemplary buildings that have a lasting effect on the Brussels construction market through the experience obtained. Between 2007 and 2013, 520,000 m2 of buildings were renovated with an improved isolation in the Brussels area.
Local plan for energy management and energy audits. This measure is mainly constituted by an analysis of existing or renovated building owned by large real estate companies to define where energy-saving maintenance is needed, and is mandatory under city government regulations. It is estimated to reduce NOx emission by about 24 ton in 2020, VOC emissions by slightly more than 3 tons and only by 0.6 ton PM2.5. The cost has not being evaluated since they are part of CLE 2020.
5.3.2 The 2010 Scenario
Emissions (ton/year) in the BCR for the base scenario
Emissions reduction (%) wrt the base scenario
Emission reduction (%)
Energy efficiency large bldgs
The air quality modelling system AURORA (Mensink et al. 2001; Lauwaet et al. 2013) was used in Brussels capital region to simulate the transport, chemical transformations and deposition of atmospheric constituents at the urban to regional scale. It consists of several modules. The emission generator calculates hourly pollutant emissions at the desired resolution, based on available emission data and proxy data to allow for proper downscaling of coarse data. The actual CTM then uses hourly meteorological input data and emission data to predict the dynamic behaviour of air pollutants over the study area. This results in hourly three-dimensional concentration and two-dimensional deposition fields for all species of interest. For the BCR, AURORA was set up for a domain of 49 × 49 grid cells at 1 km resolution. For the vertical discretisation, 20 layers were used for a domain extending up to 5 km. The layer thickness increases from 27 m for the bottom layer to 743 m for the top layer. For the boundary conditions, the results of an AURORA run were used for a domain covering Belgium at a resolution of 4 km. The same boundary conditions were used in all runs. For the meteorological inputs, the ECMWF ERA INTERIM data with a resolution of 0.25° were used and interpolated to the model grid. The emissions are based on the CORINAIR emission inventory, which were spatially disaggregated using the Emission MAPping tool (E-MAP) developed by VITO (Maes et al. 2009). This tool downscales national emission inventories using a set of proxy data, such as land use information or the road network. The carbon bond CB05 chemical mechanism (Yarwood et al. 2005) was used.
5.3.3 Design of Experiments and Source/Receptor Models
The B emission level corresponds to the CLE2020 emissions, increased by 20 %.
The H level is obtained by projecting the 2009 regional emission inventory to 2020 with the projected application rates of all technologies (as predicted by the GAINS inventory).
The L level (low emission reductions) is obtained as the average of B and H.
The emission levels for the model grid cells outside the BCR were also changed according to the changes inside the BCR, while for the boundary conditions of the 49 × 49 km domain, the average emission variation from the 2009 inventory projected to CLE2020 of the BCR domain cells was applied to the emission inventory covering all Belgium.
To determine the emission reduction scenarios for the ANN training, the three levels B, H, L were combined to produce 14 different emission sets.
The selected Air Quality Indexes (AQIs) were: yearly average of PM10 concentrations and yearly average of NO2 concentrations. The emissions surrounding individual model grid cells were aggregated according to the quadrants in Fig. 5.1 with a dimension of 14 cells for PM10 and of 20 cells for NO2.
The ANN is well capable of reproducing the CTM behaviour for NO2 but has more difficulties with reproducing the PM10 concentration changes. This is especially true for a model run in which only the NOx emissions are changed (represented by small triangles in the figure). For this last case, the average normalised bias amounts to 3.6 % with extreme values of up to 33 % whereas for all the other scenarios the average normalised bias is less than 0.25 %.
The application of RIAT+ allows to quickly compute the impacts of any combination of measures. In particular, for BCR, the concentration patterns were examined, together with the distribution of YOLLs assuming a uniform population density in the area.
Looking at individual measures, the ‘toll’ seems the most effective, while other traffic measures, such as LEZ, seem less relevant because a large part of old EURO vehicles will already be replaced by newer types in 2020.
5.4 Great Porto Area
This region of Portugal is one of the several EU zones that had to develop and implement an air quality plan (AQP) to reduce PM10. AQP was initially designed using a scenario approach considering the implementation of an a priori defined set of abatement measures (Borrego et al. 2011, 2012). This allowed to identify the most relevant emission sectors: industrial combustion, residential combustion and road traffic.
5.4.1 Proposed Abatement Measures
A list of possible abatement measures, including costs and emissions effects was compiled using the GAINS database for Portugal. This database includes: activity details (unabated emission factor, activity level…) and technology details (removal efficiency, CLE and potential application rate, unit cost) for the years 2010, 2015, 2020 and 2025. The reference scenario for March 2013 was considered and 103 «triplets» (sector-activity-technology) were associated to the emission inventory. They are related to: Combustion in energy and transformation industries (SNAP 1) (20 measures); Non-industrial combustion (SNAP 2) (4 measures); Combustion in manufacturing industry (SNAP 3) (23 measures); Production processes (SNAP 4) (7 measures); Solvent and other product use (SNAP 6) (10 measures); Road Transport (SNAP 7) (25 measures); Other mobile sources and machinery (SNAP 8) (14 measures). They are basically all end-of-pipe measures. Technologies for food and drink industry production processes and for construction activities were not included as they are not present in the GAINS database.
5.4.2 The Chemical Transport Model
The Air Pollution Model (TAPM) (Hurley et al. 2005) was used for the simulation of different mitigation scenarios. It is a 3-D Eulerian model with nesting capabilities, which predicts meteorology and air pollution concentrations. It simulates the transport, dispersion and chemistry of atmospheric pollutants, at both local and regional scale, and it is suitable for long term simulations (e.g. a full year) since it is not strongly time-demanding in terms of computational efforts. Point, line and area/volume source emissions are considered. The model has two components: the meteorological prognostic and the air pollution concentrations component. The meteorological module of TAPM is an incompressible, optionally non-hydrostatic, primitive equation model with terrain-following coordinates for 3D simulations. The results from the meteorological module are one of the inputs to the air pollution component. The gas-phase chemistry mode of TAPM was used, which is based on the semi-empirical mechanism called Generic Reaction Set (GRS), including also the reactions of SO2 and PM, having 10 reactions for 13 species. The TAPM model was applied to the Great Porto Area (150 km × 150 km) for one entire reference year (2012) with a 2 km by 2 km spatial resolution (see Fig. 5.9) using disaggregated emissions from the Portuguese 2009 emission inventory, which is the most recent available. Its results were compared to the measured values at the monitoring stations inside the model domain. As in Brussels case, we used the methodology proposed by FAIRMODE for the validation.
The four non-complying stations have high values of BIAS and Root Mean Square Error (RMSE), which could be related to an overestimation of background values. The target diagram for NO2 shows that 69 % of the values comply with the unit circle criterion, while values are similar to PM10 for the other performance indicators.
5.4.3 Design of Experiments and Source/Receptor Models
Ten emission sets were defined to train the RIAT+ Artificial Neural Networks for the Great Porto Area. These scenarios have to contain all possible relationships between precursor emissions and the various air quality indices. Ideally, the number of scenarios is determined by checking the incremental improvements to the ANN results of adding additional scenarios to the training dataset.
Starting from the 2009 Portuguese emission inventory, three different emission levels were considered: B (base case), L (low emission reductions) and H (high emission reductions).
The B (base) case considers the evolution of 2009 emissions taking into account the fulfilment of CLE2020 increased by 15 %. The H (high reduction) case is associated to the Maximum Feasible Reduction of emissions in 2020 (MFR2020), further decreased by 15 %. These bounds guarantee that the optimal plausible reductions will lie within those present in the training dataset. The L (low reduction) scenario results from averaging B and H emission values.
ANN best parameters for PM10 annual mean index
Nodes in the input layer
Hidden layer transfer function
Nodes of the hidden layer
Output layer transfer function
Nodes in the output layer
Radius of influence (n° of cells)
Training set (n° of cells)
Validation set (n° of cells)
Further analyses confirmed this conclusion. For instance, the average correlation between TAPN and the ANN surrogate model in terms of AQI variations with respect to the base scenario is about 0.93.
The solution corresponding to point C of the curve, for instance, would be reached mainly acting on non-industrial sector activities (SNAP 2). Road transport (SNAP 7) and other mobile sources and machinery (SNAP 8) could also contribute to the required reduction of PM concentrations. More precisely, the major investment should be in measures related to new and improved fireplaces. These results are consistent with the ones obtained by Borrego et al. (2012): in Portugal, 18 % of PM10 emissions are due to residential wood combustion, which may deeply impact the PM10 levels in the atmosphere. According to the Portuguese emission inventory, this macro sector is the second most important in terms of PM10 emissions, after macro sector 4 (industrial processes), in the Great Porto area.
The analysis of RIAT+ results for the selected solution, which implies annual costs around 7.6 M€, shows that some areas can still be expected to exceed the PM10 annual limit value (40 µg/m3).
From the experiences of application of a comprehensive IAM system (RIAT+) to the test cases of Brussels Capital Region and the Great Porto area a number of conclusions can be drawn.
The list of options for abatement measures is restricted not only by what is technically and economically feasible but possibly even more by political and social acceptance. IAM tools should therefore be further extended to take into account the implications of political and social acceptance at an early stage of the decision process (see also Laniak et al. 2013).
Existing tools can be practically applied in an integrated assessment of air quality not only to consider compliance to the concentration limits but also to efficiently take into account internal and external costs (e.g. health impact) of different available abatement options.
The biggest task when implementing such a comprehensive IAM is—as is also the case in regular air quality modelling applications—to obtain high quality input data on local emissions and the cost and effectiveness of possible abatement measures. When such data is lacking, one can still rely on existing European inventories and databases with data on abatement measures such as EMEP and GAINS well keeping in mind the assumed validity of such data for the region of interest and the implications for the results obtained using the IAM.
If an IAM system uses S/R relationships (artificial neural networks, linear regression, …) to relate emission changes to air quality changes, such relationships should be carefully tested to ensure that they not only correctly replicate the concentration values obtained through more complex modelling tools (e.g. CTMs) but also capture the dynamics i.e. the concentration changes calculated by the model for which they are a surrogate.
In the Brussels case, a lot of effort was put into defining and evaluating specific measures while the impact on air quality of these measures is rather limited due to the dimension of the area selected. A first screening step such as a simple scenario to check the importance of the impacts should be done before using a complex methodology, as the latter has limited added value in such cases.
In the Porto case, a list of available technologies from an existing database was used and the main sectors were selected and identified. Nevertheless, a more local list of measures needs to be decided and discussed with stakeholders and policy makers. With the optimization approach, it was possible to quickly identify the sectors and the entity of optimal investment costs to achieve a given air quality objective and the corresponding benefits.
This chapter is partly taken from APPRAISAL Deliverable D4.3 (downloadable from the project website http://www.appraisal-fp7.eu/site/documentation/deliverables.html).
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