Geographical information systems for air quality management planning C. Trozzi, R. Vaccaro, R. Trobbiani & P. Digiovandomenico

Transcription

1 Geographical information systems for air quality management planning C. Trozzi, R. Vaccaro, R. Trobbiani & P. Digiovandomenico Abstract The paper describes the use of Geographical Information Systems in the framework of the realization of regional air quality management plans. The criteria for represented area, point and line emissions sources are briefly resumed. Particular emphasis is place on methodology for spatial distribution of area emissions on 1 km x 1 km grid, particularly on the use of the official EC cartography related to land use. For area emissions, the municipal level seems to be the most reliable defined to estimate emissions. Inside the municipality a square grid mesh is built-up to evaluate emissions for the application of diffusion models. To disaggregate pollutant emissions from the municipal level to the mesh level, the methodology of proxy variables is used. When the emissions which has to be distributed depends on an extensive variable (a variable proportional to level of covering of a single mesh as for example forests) the level of covering is utilized as proxy variable. In this case, through the maps of land use, developed within the EC CORINE Land Cover, it is possible, for every typology of classification, to calculate the covering on every single mesh. Finally, in the paper the representation of pollutant concentration data coming from air quality monitoring network and grid representation of air quality dispersion models data is resumed. The methodology has been applied to prepare emission inventories in areas with large urban and/or industrial agglomerates (Venice, Florence, Piombino, Rossano Calabro, and Roma) and now is in progress within regional air quality management plans (Trento, Toscana, Bolzano and Liguria).

2 24 GIS Technologies and their Environmental Applications Air quality management plans The paper describes the criteria for realizing Geographical Information System for use in Italy Regional Air Quality Management Planning'. The criteria have been applied previously to prepare emission inventories in areas with large urban and/or industrial agglomerates (Province of Venice, Province of Florence, industrial area of Piombino, power plant area of Rossano Calabro, Province of Roma) and now to regional emissions inventories, air quality data interpretation and air pollution modeling as basic tools for air quality management plans. Several experiences of air quality management plans have been realized or are in progress (Autonomous Province of Trento, Toscana Region, Autonomous Province of Bolzano, and Liguria Region). Italian legislation, through a Republic President legislative decree (no. 203, dated May 24, 1988), has delegated provincial administrations to realize air pollutant emissions inventories. A subsequent law, on "Criteria for elaboration of regional air quality plan" (dated May 20, 1991), has established that all the regional administrations have to draft the air quality management plan. Emission inventories are the basic instrument for the realization of air quality management plans, but at today only few regions have really absolved to these laws. According to the law, the air quality management plan is the instrument of programming, co-ordination and control of human activities with atmospheric emissions, having as the primary aim the protection of human health and of environment. The predisposition of plans involves: # the individuation of areas or of productive sectors, through the analysis of general characteristics of the territory and the elaboration of information available, for the evaluation of impact on population and environment. the indication, in every area and/or productive or civil sector, of interventions for air quality management planning. the development, setting up and audit of control instruments for the carrying out of plan. Information systems for air quality management plans The complex nature of air quality management planning involves a complex architecture of information system to support their realization.

3 GIS Technologies and their Environmental Applications 25 In fig. 1 a general picture of the information system is reported. 1 socioeconomic md emission data r- - _- ^ emissions estimate models ^ẇ ^w n air quality monitoring data base 1 1 air quality monitoring network ^- J- meteo monitoring network ^- _- Figure 1. Information system structure. land use data The information system contains: knowledge tools: emissions inventory^, model to estimate emissions in particular topics (road transport, airports, forests, ports and navigation lines, forest fires), air quality monitoring data base, meteorological data base, land use geographical information system; evaluation tools: statistical package for air quality and meteo data analysis, air quality dispersion models, air quality photochemical models,

4 26 GIS Technologies and their Environmental Applications geographical information systems to show data and to evaluate impact; planning tools: model for projection of emissions, model to evaluate measures on emissions. The information system has been implemented on a network of Pentium computers with large extend of RAM running Windows NT, ORACLE or Access, ArcView or Mapinfo, and Statistica. GIS for emissions inventory data The preparation of air pollutants emissions inventories in urban areas allows to characterize the different role played by the various emission sources and consequently represents a basic tool to define criteria for air quality management plans. An inventory may be also of great importance for planning an air quality-monitoring network. A fundamental aspect in preparing inventories is the definition of the main activities to evaluate pollutant emissions. The nomenclature used at local level follows the guidelines coming from the European Commission CORINAIR working group, which is the base of CORINAIR 1990 inventory^. CORINAIR nomenclature includes about 200 activities grouped in 11 macro-sectors (public power plants, cogeneration and district heating; combustion in commercial, residential and public administration; industrial combustion; production processes; fuels extraction and distribution; solvent use; road transport; other mobile sources; waste treatment and disposal; agriculture; nature). This nomenclature may be suitably enlarged to take in account locally relevant activities. Local inventories generally refer to five main air pollutants, namely nitrogen oxides (NO:), sulfur oxides (SO%, SO]), non metanic organic compounds (NMVOC), carbon monoxide (CO) and suspended particles (particularly PMjo). Regarding NMVOC the single compounds must be considered, in order to permit the aggregation into species for using dispersion photochemical models. Other pollutants, which are included in the inventories, are: heavy metals, carbon dioxide (CO]), and ammonia (NH%). The sources are generally splitted in four categories: point & localized sources, distributed or area sources & linear sources. Emissions are shown on geographical information systems as thematic maps; in fig. 2 is reported an example of point and localized sources, in fig, 3 is reported an example of linear sources and in fig. 4 is reported an example of distributed sources. In fig. 5 an alternative representation (dot representation) of distributed sources is reported. Similar maps can be obtained for projection data instead of actual data.

5 GIS Technologies and their Environmental Applications 27 Figure 2. Toscana Nitrogen Oxides point sources emissions (t). Figure 3. Trento Nitrogen oxides linear sources emissions (kg/km).

6 28 GIS Technologies and their Environmental Applications Figure 4. Toscana Volatile organic compounds emissions in domestic use of paint (t). Figure 5. Toscana Volatile organic compounds emissions in domestic use of paint (t) - dot representation.

7 GIS Technologies and their Environmental Applications 29 GIS for grid representation of emissions data The municipale level seems to be the most reliable defined to estimate emissions (see fig. 4). Inside the municipality a square grid mesh is builtup to represent pollutants emissions to allow the application of diffusion models. To disaggregate pollutant emissions from the municipale level to the mesh level, the methodology of proxy variables is used. Proxy variables allow obtaining information on a certain spatial resolution assuming that it is known for larger spatial resolutions. When municipale value is known, it is distributed on the square grid mesh according to the two following cases: the data to be distributed is an extensive variable and it is proportional to level of covering of a thematism on single mesh (for example forests); in this case the level of covering is utilized and the municipal value is related to this level; + the data to be distributed is an intensive variable and depends on the presence of the activity estimated at municipal level on the single mesh; in this case the weight of the mesh on the total municipal value, obtained through the "surrogate" variable is utilized. In the first case, through the maps of land use (for example the EC CORINE Land Cove/) it is possible to calculate the covering on every single mesh. The emissions on mesh k are obtained as: Ek = Zj%.,(E jpkij/zkpkij) (1) where: i, activity; j, municipality; k, mesh; 1, variable proxy (land cover thematism), Ey, total emission of activity i in the municipality j, and Pkij cover of proxy (thematism) 1 on the mesh k in the municipality j. Table 1 indicates the variables used for the disaggregation on grid. Table 2 shows the correspondence between proxy and activity. Table 1. Examples of proxy coming from land use and sources. Proxy Spatial Source Urban zones Agricultural zones Industrial and commercial zones Modeled artificial zones Permanent crops Arable land crops Permanent meadows Deciduous forests Coniferous forests CORINE Land Cover 1.1 CORINE Land Cover 2 CORINE Land Cover CORINE Land Cover 1 CORINE Land Cover 2.2 CORINE Land Cover 2.1 CORINE Land Cover 2.3 CORINE Land Cover CORINE Land Cover 3.1.2

8 30 GIS Technologies and their Environmental Applications Table 2. Example of proxy used by activity. Activity Domestic combustion plant < 50 MWth Agriculture - Combustion plants <50 MWth Industry - Combustion plants <50 MWth Service stations Paint appl.: construction & domestic; other domestic solvent use Paint appl.: car repairing, boat building & other industrial use Transport - Urban driving & Evaporative emissions Off roads vehicles and machinery - Agriculture Off roads vehicles and machinery - Industry Permanent crops with fertilizers & Stubble burning Arable land crops with fertilizers Meadows with fertilizers Deciduous forests non-isoprene emitters Coniferous forests Proxy Figure 6 reports the graph of the distributed VOC emissions coming from the Trento inventory. Similar maps can be obtained for projection data instead of actual data HOOO Figure 6. Trento Volatile Organic Compounds diffuse emissions on square grid mesh (kg/km).

9 GIS Technologies and their Environmental Applications 31 GIS for air quality data Air quality monitoring data are generally historical time series of several thousands of hourly concentration values on several sites of a geographical network. Statistical parameters parameters (median, percentiles, average, maximum values) are deduced from original data series to evaluated standard compliance. To compare different sites thematic maps of statistical are produced within information system. Figure 7 reports the graph of NO* maximum hourly concentration value coming from the Genoa network. P.za Masnata Gastaldi C.so Firenze Acquasola V. XX settembre ffijuarto Figure 7. Genova network Nitrogen oxides maximum hourly concentration value ( ig/nf). GIS for air quality models data Air quality models generate in output computed values of pollutants concentrations in specific points or on a grid systems. These concentration can be represented as thematic maps on a grid base or can be elaborated to obtain curve level maps.

10 32 GIS Technologies and their Environmental Applications Figure 8 reports a thematic map on a grid base of NO% concentration computed by United States Environmental Protection Agency ISC model on Trento areas. Similar maps can be obtained for model application on projection data instead of actual data. Figure 8. Trento annual average Nitrogen oxides concentration computed by US EPA ISC model References [1] Trozzi, C & Vaccaro, R. Emission inventories in Italy regional air quality management planning; Proc. of /'' Int. Conf. on Emission Inventory, October 28-30, 1997, Research Triangle Park, USA. [2] Trozzi, C., Vaccaro, R., Nicolo, L., Trobbiani R. & Digiovandomenico, P. APEX: Air Pollutants Emissions inventory Computer Systems, In Software per l'ambiente\ Patron, Eds.; Bologna, 1995 pp [3] European Commission, CORINAIR Technical Annexes, Volume 1 Nomenclature and software, European Commission, DGXI, Luxembourg, EUR 12586/1EN, [4] European Communities, COR1NE Land Cover - Guide technique European Commission, DGXI, Luxembourg, CD-NA FR-C. [5] United States Environmental Protection Agency User's guide for the Industrial Source Complex (ISC3) dispersion models EPA- 454/B a - September 1995.