APPLYING THE END FOR LOMBARDY S AIRPORTS

APPLYING THE END FOR LOMBARDY S AIRPORTS Angela Alberici, Maurizio Bassanino, Paolo Deforza, Nadia Fibbiani, Paola Maggi, Mauro Mussin, Valeria Spirolazzi ARPA Lombardia Via Francesco Restelli 3\1, 20124, Milano, Italia a.alberici@arpalombardia.it ABSTRACT The Italian transposition of the Directive 2002/49/EC (END) has been quite fitting in the most important contents. This paper shows different approaches to agglomerations delimitation and also describes noise mapping around airports, in order to evaluate the geometry of noise curves and the population involved. In Lombardy there are three airports that have more than 50,000 flight operations per year: Milano Malpensa, Milano Linate and Bergamo Orio al Serio. The population distribution assessment and the delimitation of agglomerations have been obtained using different dataset: the Land Cover and Land Use Map, which uses the CORINE legend until 4 layer improving significantly the resolution, and the real population data depending on the local authorities dataset available. The shape of noise curves was obtained using INM, but different kinds of inputs and postprocessing of outputs have been made. Differences between simulation and measurement have been taken into account so it is possible to determine which couple of geometry/population distribution has to be considered for the strategic noise mapping. 1

1 INTRODUCTION Directive 2002/49/EC (END) relating to the assessment and management of environmental noise, has the aim to define a common approach to avoid, prevent or reduce the exposure to environmental noise, through noise mapping [1]. The END requires strategic noise maps to be made for large urban areas (agglomerations) and separate noise maps of main roads, rail, airports, ports and industries. The indicators chosen by the END noise mapping are L den and L night : the Italian transposition has adopted a different L den definition, reducing evening period and increasing day period (Day: 6-20, Evening 20-22, Night 22-6). This paper describes the Lombardy case. This territory is situated in the north of Italy and is at the crossroads between the major East-West and the North-South communications routes. It s the fourth largest region in Italy by extension, with a territory that is 47% flatland, 40% mountainous and the remaining 13% are hills. The northern part of Lombardy is almost mountainous and the southern part is agricultural, anyway it s one of the most densely populated regions in Italy. Most of the population live in Milano, or in the hinterland, and in the main cities located in the central part of Lombardy, where the contiguity between big cities, towns and villages is a common characteristic, so that it s not always easy to distinguish administrative boundaries. The methodology of the agglomeration definition here described is based on this territorial contiguity and overcomes the concept of municipality. The road network extends for more than 28,000 km, while the railway system has an extension of 1,875 km. In Lombardy there are two airports that overcome the END threshold of 50,000 flight operations per year: the international hub Milano Malpensa and Milano Linate near the Milan city downtown for domestic and international flights. There is also Bergamo Orio al Serio, an international and cargo airport that is increasing and, for the first time, during last year (2005) had more than 50,000 flight operations [2]. 2 METHODOLOGY 2.1 Agglomerations The END defines the agglomeration as a part of territory delimitated by the Member State, having a population in excess of 100,000 persons and a population density such that the Member State considers it to be an urbanised area [1]. Agglomerations with a population in excess of 250,000 need to be mapped until 30 June 2007. The discussion of WG-AEN (European Commission Working Group Assessment of Exposure to Noise) on the END provides insufficient information to interpret the general term agglomeration. This paper presents different methods suggested by ARPA Lombardia to retrieve the agglomerations. Two main criteria were used to detect agglomerations in Lombardy region: the discrete areas of continuous urban land referred to air pollution directive [3] and the agglomerations worked out using the Corine Land Cover (CLC2000) [4]. Concerning the arguments of air pollution directive, the Regional Lombardy law [4], [6] detects five agglomerations having more than 250,000 inhabitants. These agglomerations were defined by administrative boundaries of contiguous Municipalities ( Fig. 1). 2

The second methodology used to detect agglomerations is based on CLC2000, a European Land Cover Map with a 1:25,000 scale, deriving from ETM+ Landsat 7 satellite, providing both multi-spectral (25 m) and panchromatic data (12.5 m). To define the agglomerations the first class (Artificial areas 1 ) has been considered. The most extended part of Lombardy territory is classified as Urban fabric (77%), that for 73% is discontinuous urban fabric. This methodology has been developed using a standard Geographical Information System (GIS). After having selected all the polygons characterised by CLC2000 Artificial areas attribute, the contiguous polygons have been merged in 1686 main polygons characterised by homogeneous urban network. Then the population living in the main polygons has been estimated and the ones with more than 100,000 inhabitants have been defined agglomerations (Fig. 1). 2.2 Population Two methods were carried out to evaluate the population in the CLC2000 agglomerations: the first one based on the CLC2000 database, the second one based on the DUSAF 2 [2]. DUSAF has a 1:10,000 scale and its urban classes are consistent with Corine legend, developing, in addition, the 4 th and 5 th thematic level. Both methods assume two basic hypotheses: people live in areas classified as residential and not in agricultural or vegetated ones, so that the total inhabitants of a municipality can be assigned to the built-up areas within the municipality; moreover, people are distributed on the built-up areas not equally, but areas with dense urban fabric are more densely populated then areas with discontinuous urban fabric. So, in order to distribute population, each urban area is weighted depending on its residential stuff. 2.2.1 Using CLC2000 database Starting from the first assumption mentioned above, with the aim to evaluate the population of the agglomerations, the subclasses 1.1.1 e 1.1.2 (Continuous urban fabric and Discontinuous urban fabric) have been select from the CLC2000 dataset. The procedure is applied at the level of the Commune (LAU2): for each Commune are selected the urban fabric polygons and population of the Commune is assigned, weighting each area according to its urban fabric (second assumption). The total inhabitants of the Commune is obtained from the data of the ISTAT (National Statistical Institute of Italy) [7]. For each municipality a weighted area (Sp) is defined: S p = m p n i i = 1 j = 1 s ij where : s ij = area of j polygon of the i urban class pi = weight of i CLC2000 class m = number of CLC2000 urban class in the Commune (m=1,2) (1) Inhabitants Ab ij into j CLC2000 polygon of the i urban CLC class are calculated as (2): 1 The Artificial areas include: Urban Fabric; Industrial, Commercial and Transport units; Mine, Dump and Construction sites; Artificial non-agricultural Vegetated areas. 2 Destinazione d Uso dei Suoli Agricoli e Forestali Land Use and Land Cover Map of Lombardy Region. 3

Euronoise 2006, Tampere, Finland pi Abtot sij Abij = Sp where : Abtot = number of total inhabitants in the municipality (ISTAT 2001 Census) for other terms, see (1) (2) The set of weights arises from a previous experimentation [8] using DUSAF dataset to determine the population distribution in specified areas. Among the possible sets of weights, it has been chosen the one that minimize the difference between the real and the calculated population for a sample of polygons. In equation (2) weights must be normalized, in order to ensure that the total population of each commune matches census data. Using GIS functions, population has been estimated summing the inhabitants of the CLC2000 urban polygons falling into each agglomeration. 2.2.2 Using DUSAF database In order to verify the results obtained with CLC2000 database and improve the accuracy of population assessment, the same method has been applied to DUSAF database. For each Commune, expression (2) is applied to calculate population distribution assessment in DUSAF polygons. Agglomeration MI (Milano) VA (Sempione) CO (Como) BS (Brescia) BG (Bergamo) Inhabitants 2 382 497 464 687 458 811 368 244 300 792 2 Agglomeration MI (Milano) VA (Varese) BG (Bergamo) BS (Brescia) CO (Como) VA (Varese2) Area (km ) 580.61 241.19 236.05 397.35 192.27 Inhabitants 2 712 486 566 813 341 772 299 754 164 017 106 013 2 Area (km ) 438.92 189.61 81.13 71.82 44.11 24.40 Fig. 1 Agglomerations from Regional Lombardy air quality law(left) and CLC2000 (right) 3 AIRPORT NOISE CURVES As it s recommended in the Annex II of the END (Assessment methods for the noise indicators), airport noise has to be estimated using the ECAC Document 29 Report on standard method of computing noise contours around civil airports. 4

Since 2003 ARPA Lombardia has been working out more than 20 different INM3 simulations about Lombardy airports, testing the model sensitivity to different kinds of inputs and post-processing of outputs. For each airport, INM output depends strongly from the aircraft tracks; in fact there are three different ways to create ground tracks for an INM study: Using established AIP tracks, with ECAC dispersion; Using the real medium track for each AIP track (backbone tracks from radar); Using the real tracks for each aircraft (tracks from radar). There are many differences between these three possibilities, due to pilot s capability to follow AIP routes. The real situation is well represented by radar data, so the best way to model the real situation is setting INM tracks as radar tracks. Using data from radar to build up real tracks might be a long difficult procedure if there are many flight operations. ARPA Lombardia has worked out an automatic procedure to build up real tracks and to assign to each track the right flight model, profile and stage. The shape of noise curves obtained using INM real tracks is useful to the noise mapping activity, in order to determine real exposure of population. Fig. 2 Comparison between simulations with real tracks and AIP tracks. 4 POPULATION ASSESSMENT FOR NOISE CURVES Population data of a region can be available at different degree of accuracy. The total number of inhabitants of the Commune can be available (Municipality level). At a more detailed degree, population can be known at street or house number level, so that the inhabitants of each street-house number is ready. Depending on the local authorities dataset available, a methodology to estimate the population involved in airport noise curves can be found out. Municipality level - The total number of inhabitants can be disaggregated over Commune area, according to the methods described in par. 2.2. Using GIS tools, the CLC2000/DUSAF urban polygons are intersected with the noise curves, the number of inhabitants of the intersected polygons is re-calculated, multiplying population density by polygon area, and the result is summed for each noise range. Street level - Assuming that the population of a street are evenly spread over it, the linear density of inhabitants is calculated for each arc of street. To estimate people into noise curves, the street network is intersected with the noise curves, the number of inhabitants of the 3 Integrated Noise Model (Federal Aviation Administration USA) is an acoustic model compliant with recommendations of ECAC Document 29. Despite of the major aim of the model is an evaluation of projected noise exposure, it may be used in order to evaluate noise assessment 5

intersected polyline is re-calculated, multiplying population density by length, and the result is summed for each noise range. If the georeferenced road network is not available from the local authority, any commercial street dataset can be used as geographic layer. ARPA Lombardia has adopted TeleAtlas database, which guarantees a complete and uniform street map at Lombardy level. Moreover, as it s shown in the next topic, TeleAtlas is a useful database in order to georeference house number inhabitants. House number level - In this case, to estimate people included in noise curves, points representing house number are detected within each curve and the inhabitants are summed for each noise range. Unfortunately the georeferenced house number file is not always available from the local authority. To overcome this problem, a software tool has been developed to georeference addresses. The routine uses as input TeleAtlas dataset and an alphanumeric file of addresses and produces an output file, containing the locations of house number along the TeleAtlas polyline. This can be done as each TeleAtlas arc has, as descriptive data, the house number range inside. 5 CONCLUSIONS These approaches could be used to detect the agglomerations for the strategic noise mapping that has to be done before 2007. As a first assessment the selection of CLC2000 method has the advantage of maintaining consistency with previous government work and policies such as the implementation of the Air Quality Directive transposition, but additional checks are necessary for sites with different kinds of population settlement. This study could be improved by acquiring information on building heights (3D models, cartography ) and using an own and more detailed Land Use/Cover data set. One major point of concern is the identification of Local Authority, because the agglomeration definition goes over the administrative boundaries and considers the whole contiguous urban area. REFERENCES [1] EC, Directive 2002/49/EC relating to the assessment and management of environmental noise, 2002. [2] Regione Lombardia, www.regione.lombardia.it, Checked 2006. [3] EC, Directive 1999/30/EC relating to limit values for sulphur dioxide, nitrogen dioxide and oxides of nitrogen, particulate matter and lead in ambient air, 1999. [4] EEA, CORINE Land Cover; technical guide Addendum 2000, Technical report n 40, 2000. [5] Regione Lombardia, D.G.R. n. VII/6501, 19/10/2001, 2001. [6] Regione Lombardia, D.G.R. n. VII/11485, 6/12/2002, 2002. [7] ISTAT, www.istat.it, Census 2001, Checked 2006. [8] ARPA Lombardia, Metodo di stima della popolazione in un area definita, Prot. N 151041, 2004. 6