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Predicting urban traffic air pollution: A gis framework
Transpn Res.-D, Vol. 3, No. 5, pp. 329±336, 1998 # 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain 1361-9209/98 $19.00+0.00
Pergamon PII: S1361-9209(98)00011-X
PREDICTING URBAN TRAFFIC AIR POLLUTION: A GIS FRAMEWORK G. GUALTIERI* and M. TARTAGLIA
Applied Meteorology Foundation, via Caproni 8, 50145 Firenze, Italy (Received for publication 30 April 1998) AbstractÐThis paper presents a comprehensive model for the evaluation of air pollution caused by road trac in urban areas, depending on site geometric and morphological conditions. The model is integrated in a Geographic Information System (GIS) that allows the use of spatial co-ordinates to describe the structure of urban areas, road networks, and distribution of pollutants in the atmosphere. # 1998 Elsevier Science Ltd. All rights reserved
1. INTRODUCTION
Air pollution produced by road trac is one of the most serious problems in the management of urban areas. The goal of the work described in this paper is to develop a decision support system to help local administrators reduce the impact of air pollution caused by road trac. Such a simulation planning system consists of a comprehensive air-quality forecasting model based on powerful data management, correct use of mathematical modelling and a friendly interface between numerical data and trac management operators. The system core consists of a number of mathematical submodelsÐbased on current literature or developed through experimental data ®tting made by the authorsÐable to simulate trac, emission and dispersion patterns. Submodels are integrated in a Geographic Information System (GIS), which allows the use of spatial coordinates to describe the structure of urban areas, road networks, and pollutant distribution in the atmosphere. The GIS structure and modelling characteristics are presented here. An application of the GIS-based model to the urban area of Firenze (Florence), Italy, is summarized and main GIS mapping features are described. 2. GIS OVERVIEW
The Geographic Information System was been set up according to the diagram in Fig. 1. The scheme has a three-level structure, including the whole database, a number of mathematical models, and the subsequent results in terms of thematic mappings. The GIS database is provided by road network territorial data, trac demand characteristics, driving cycles and ¯eet composition, and a number of data de®ning an adequate meteoclimatic scenario. GIS-integrating models are aimed at reproducing trac behaviour, emission and dispersion scenarios. They are set up in a cascade fashion, so that modeled trac parameters can be used as input by the emission model, whose subsequential modeled emissions are in their turn necessary for the dispersion model to simulate pollution levels. The GIS database was ®rst organized on the basis of geographical data. Once the road network map of the considered urban area was digitized, each link was integrated with a number of topographic and morphological attributes. In particular, the whole territorial database set up within the GIS was designed according to the details provided in Table 1. *Author for correspondence. Fax: 00 39 55 461701; e-mail: [email protected]®.it
329
330
G. Gualtieri and M. Tartaglia
Trac demand characteristics are expressed in terms of the origin±destination (O±D) matrix of road vehicles. This required de®nition of a suitable zoning of the entire urban context, and an investigation of the corresponding trac demand. Once trac demand data are provided, it is possible to reproduce trac behaviour within each link of the network (Fig. 1), and once trac ¯ows over each link have been assigned by the trac model, it is possible to evaluate their relative mean speed by means of transport data, i.e. ¯ow-speed curves (Table 1). Driving kinematics along each link can thus be calculated; this, together with vehicle ¯eet split, makes it possible to estimate pollutant emissions over the entire road network. A section of the GIS database has been designed to manage meteoclimatic variables, whose values are used as ®xed input by both the emission and the dispersion models. The variables needed for pollution modelling are wind speed and direction, solar radiation, air temperature, and atmospheric stability class. Meteoclimatic parameters are arranged in arrays that represent typical meteoclimatic scenarios, which can be de®ned by the user according to the purpose of the simulation. 3. MODEL DESCRIPTION
As shown in Fig. 1, the GIS model section is made from dierent submodels, aimed at simulating each subprocess involved in the entire air pollution phenomenon. These are trac, emission, and dispersion models, all linked together and using survey input data. Road trac is simulated through a deterministic model designed for solving the equilibrium auto assignment problem with capacity constraints (She, 1985). The basic behavioral assumption of the model is the user optimal principle stated by Wardrop (1952). The problem is solved by using the linear approximation algorithm described by Florian and Nguyen (1976). Such an algorithm allows the estimation of trac ¯ows within each link of the road network starting from the knowledge of the network characteristics and trac demand. In addition to network topology, numerical input data regarding length and number of lanes of each link are provided by the GIS database. Link-related speed±volume and capacity functions are also needed for calculus and stored in the GIS tables. Trac demand is input as the origin±destination matrix of road vehicles.
Fig. 1. The GIS structure.
Table 1. Geographic attributes associated to road network links Attributes tipology Topographic Toponomastic Physical Morphological Transport Geometrical (canyon links only)
Network links attributes Nodes UTM co-ordinates, street axis direction, total length Street names Driving directions, Number of lanes Flag: street canyon, inter-section links, open areas, etc. Road tipology by means of ¯ow-speed curves Canyon width, building mean height
Predicting urban trac air pollution: A GIS framework
331
The emission model has been developed on the basis of the results shown by Journard et al. (1992), Eggleston et al. (1991), and Tartaglia (1995). It is able to calculate emission levels of some typical trac-related pollutants such as carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx). Emission factors refer to a vehicle grouping based on the knowledge of vehicle model year, fuel type, gross weight, and power characteristics. The emission factor Eg due to road vehicles belonging to group gÐtravelling in typical road and environmental conditionsÐis expressed as the mass of pollutant per unit length as a function of the average travel speed Vm . Total pollutant emission Q produced by the trac ¯ow f of N vehicular groups is computed using the formula: Q
N c X g Eg Vm f 100 g1
1
where cg is the percentage of vehicular group g with respect to the vehicle ¯eet. The in¯uence of non-typical road and environmental conditionsÐsuch as road grades and cold startsÐis taken into account by means of additional functions. The pollutant dispersion section of the model features a number of options which take into account several geomorphological conditions and pollutant substances. Total pollutant concentrations C are computed by summing the local contribution (Cl , due to the road link nearest the receptor point) and the area contribution (Ca , due to the rest of the road network): C Cl Ca
2
The local contribution to pollutant concentration can be calculated according to two dierent approaches, depending on site characteristics. Whenever road section presents no obstacles to dispersion, pollutant concentration is calculated by means of a Gaussian dispersion model (Zannetti, 1990). A dierent algorithm is used for calculating the local contribution within street canyons, i.e. urban sites where road section is surrounded by tall buildings on each side. As summarized by DePaul and Sheih (1986), under typical conditions a local isolated circulation causes pollutants to be trapped in urban canyons. Pollutant concentrations could consequently be calculated with a sort of empirical local box model. The latter is modal in nature, i.e. it involves the grouping of wind directions into three separate sectors, centered at the road axis. The estimation of pollutant concentrations within a street canyon is provided by means of a modi®cation of the canyon empirical model originally proposed by Hoydysh and Dabberdt (1988) for CO. This modi®cation was carried out by Tartaglia et al. (1995) and Gualtieri and Tartaglia (1997) on the basis of ®eld data measurements in the urban area of Firenze. The pollutants analyzed were CO and NO. A calibration process based on the application of regression analysis was performed in modal terms with relation to wind direction by means of collection of a three-month historical series of data. The survey period was January through March 1994 for CO and May, July and August 1994 for NO, As regards NO, in the calibration process solar radiation was also taken into account, in order to involveÐthough indirectlyÐthe overall chemical and photochemical reactions occurring in the lower atmosphere. The process brought a general improvement of estimations provided by the Hoydysh and Dabberdt CO model, and a comparable degree of precision for the new NOx model (see references for further details). To summarize, the general formula of the street canyon model can be written as: Cl a
X QS di Radi e F bT cH u 0:5 i
3
where: Cl is the local contribution to concentration; Qs is the link mean emission rate expressed in mass per unit length and unit time; F* is a shape factor depending on the speci®c wind direction *The description of F shape factors can be found in the references Tartaglia et al. (1995). Note that coecients b, c, and di are null for the CO model. Mixing height H is calculated as a function of the atmospheric stability class and meteorological parameters measured at the ground, as suggested by Bellasio et al. (1994).
332
G. Gualtieri and M. Tartaglia
Table 2. Scenario_W: condition and experimental measurements for each variable (1994 historical series for morning peak hour) Variable
Condition
Wind direction (WD) Wind speed (WS) Solar radiation (SR) Air temperature (AT) Pasquill Stability Class (SC)
Mode Models sensitivity lower threshold Mode Lower 5±95 percentile Upper 95±5 percentile
Variable
WD ( N) WS (m/S) SR (Wlm2) AT ( C) SC
Measurements by scenario Annual
Spring
Summer
Autumn
Winter
116 1.50 3 2.50 D
116 1.50 31 4.6 D
116 1.50 239 17 D
116 1.50 4 3.5 D
120 1.50 3 ÿ0.8 D
Table 3. Scenario_M: condition and experimental measurements for each variable (1994 historical series for morning peak hour) Variable
Condition
Wind direction (WD) Wind speed (WS) Solar radiation (SR) Air temperature (AT) Pasquill Stability Class (SC)
Mode Mean Mean Mean Mean
Variable
WD ( N) WS (m/s) SR (W/m2) AT ( C) SC
Measurements by scenario Annual
Spring
Summer
Autumn
Winter
116 1.94 95.91 12.88 D
116 1.76 154.17 12.73 D
116 1.78 161.57 21.15 C
116 1.78 48.82 10.60 D
120 2.00 18.06 6.62 D
sector; u is wind speed at roof level; T is ambient temperature; H is mixing height in the atmosphere; Radi is solar radiation at time i expressed in power per unit area; the sum is extended over the previous 23 h; a, b, c, di , and e are the regression coecients. The area contribution can be calculated by using a simpli®ed Gaussian line source dispersion model or a Eulerian box model. The Gaussian model is based on the algorithm described by Simmon et al. (1986). It calculates pollutant concentrations assuming the existence of a number of virtual area sources whose overall emission rate matches the contribution due to road links. The subsequent pollutant distribution in the urban atmosphere is assumed to follow a Gaussian law. Dispersion formulas take into account variables such as wind speed and Pasquill stability class. The box model has been developed by the authors on the basis of the principle of pollutant mass conservation. The average emission rate is assumed to be constant in the whole simulation area, so that the relative average concentration rate can be determined. The model takes advantage from the same algorithm for determining mixing height used in the street canyon model. 4. GIS MAPPING FEATURES
In order to represent some main GIS thematic capabilities, a sample number of graphics outputs produced by the GIS-integrating models are presented in the following ®gures. As illustrated in Fig. 1, these describe trac ¯ow behaviour, vehicular emission rates and concentration levels. Simulations refer speci®cally to the urban area of Firenze.
Predicting urban trac air pollution: A GIS framework
333
4.1. Input data description A mathematical scheme of the Firenze road networkÐmade up of about 800 linksÐhas been integrated into the GIS database. Each link is characterized by trac parameters and ¯ow-speed plus capacity functions. Trac demand refers to data resulting from direct experimental surveys. O±D matrices are updated to 1991 and referred to the morning peak hour (7:15±8:15). It is important to notice that, since trac modelling was carried out by using O±D data referring to the hours 7:15±8:15, this time period was used as the reference period for both the emission and dispersion models as well. In particular, in order to simulate pollution levels, the reference time period 7:15±8:15 has made it necessary to set up an adequate meteorological scenario. The meteoclimatic scenario was set up according to two dierent options: a `worst' scenario, enabling the highest air pollution condition to be reached, and a `mean' scenario, representing the most frequent meteoclimatic conditions occurring in the study area. Both scenarios (named Scenario_W and Scenario_M) have been de®ned on the basis of a historical data series collected by the Ximeniano observatory meteo station in Firenze, which covers the entire 1994 year. In addition, a further option of choosing between an annual or a seasonal scenario is oered. Scenario_W, describing the worst meteorological conditions with respect to air quality, required a previous study of the role played by all main atmospheric parameters in aecting pollutant dispersion caused by vehicular trac in an urban area. In particular, the mode±i.e. the most frequent valueÐwas chosen for wind direction and solar radiation, whereas to obtain reliable concentration estimations performed by the dispersion model, a lower thresholdÐequal to 1.5 m/sÐwas chosen for wind speed (Hanna et al., 1982). How the scenario was set up is summarized in Table 2, where the worst meteoclimatic conditions and experimental measurements are given, for both the annual and the seasonal options.
Fig. 2. An example of trac ¯ows mapping over the city of Firenze.
Fig. 3. An example of pollutant emission mapping due to all trac volumes over the city of Firenze: case concerning HC emission rates per unit length.
334
G. Gualtieri and M. Tartaglia
Fig. 4. An example of pollutant emission mapping due to all trac volumes over the city of Firenze: case concerning NOx emission rates per unit length according to a 1.5 Km wide grid representation.
Fig. 5. An example of pollutant dispersion mapping: case concerning CO concentrations within all canyon links of road network in a worst winter scenario.
Scenario_M representing the ensemble of meteorological conditions typically occurring in the area, was set up according to the values reported in Table 3. Because of its signi®cance, the mode value was chosen for wind direction. 4.2. Graphical outputs description Some graphical outputs provided by GIS models on the basis of previously mentioned input data are presented below. In Fig. 2 an example of vehicular ¯ows distribution over the entire urban area of Firenze is illustrated. In particular, for each link, trac ¯ows are assigned by the trac model in both directions. An estimation of pollutant rates released by all the above trac ¯ows is given in Fig. 3, where in particular HC emissions per unit length are mapped. A further option in describing modeled pollutant emissions is reported in Fig. 4, where NOx emissions per unit length over the whole urban area are mapped according to a 1.5 km wide grid representation. Predicted air pollution levels due to vehicular trac are mapped in Fig. 5. In particular, CO concentrations within all street canyon links simulated by the street canyon model are presented. Estimations refer to a winter `worst' meteo scenario. For each street canyon, concentrations are calculated at 3 m over the ground level and 1 m from building walls and assumed not changing along street axis. Mapped concentrations are averaged between both canyon sides. As far as pollutant dispersion modelling is also concerned, a `microscale' diagram of NOx concentration patterns within a sample urban canyon is given in Fig. 6. Predicted NOx pro®les are modeled according to both the canyon cross section (3 m over ground level) and the opposite building walls (1 m away).
Predicting urban trac air pollution: A GIS framework
335
Fig. 6. An example of pollutant dispersion mapping: case concerning NOx concentration patterns within a sample street canyon in a worst summer scenario.
5. CONCLUSIONS
A comprehensive GIS-based simulation model for predicting and evaluating air pollution due to road trac in urban areas was set up. The GIS features of the model make it suitable for use by the local administration in order to forecast alert pollutant levels in urban atmosphere, integrate existing monitoring network measurements, and estimate the sensitivity of pollution levels to such variables as trac ¯ow distribution and atmospheric conditions. REFERENCES Bellasio, R., Lanzani, G., Tamponi, M. and Tirabassi, T. (1994) Boundary layer parametrization for atmospheric diusion models by meteorological measurements at ground level. R Nuovo Cimento 2, 163±174. DePaul, F. T. and She, C. M. (1986) Measurements of wind velocities in a street canyon. Atmospheric Environment 3, 455±459. Eggleston, H. S., Gaudioso, D., Gorissen, N., Joumard, R., Rijkeboer, R. C., Sameres, Z. and Zierock, K. H. (1991) CORINAIR Working Group on Emission Factors for Calculating 1990 Emissions from Road Trac. Vol. I., Methodology and Emission Factors. Final Report, Contract n.B4/3045(91)IOPH, Directorate Generale XI. European Environmental Agency Task Force, Commission of European Communities, Bruxelles, Belgium. Florian, M. and Nguyen, S. (1976) An application and validation of equilibrium trip assignment methods. Transportation Science 10, 374±389. Gualtieri, G. and Tartaglia, M. (1997) A street canyon model for estimating NOx concentrations due to road trac. In Measurements and Modelling in Environmental Pollution, ed. C. A. Brebbia. Computational Mechanics Publications, Southampton. Hanna, R. S., Briggs, A. G. and Hosker Jr, P. R. (1982) Handbook on Atmospheric Diusion. Technical Information Center, Blacksburg, Virginia, U.S.A. Hoydysh, G. W. and Dabberdt, F. W. (1988) Kinematics and dispersion characteristics of ¯ows in asymmetric street canyons. Atmospheric Environment 22, 2677±2689. Joumard, R., Hickmann, L., Nemerlin, J. and Hassel, D. (1992) Model of Exhaust and Noise Emissions and Fuel Consumption of Trac in Urban Areas. INRETS, Lion-Bron, France.
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She, Y. (1985) Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. PrenticeHall, Englewood Clis, NJ. Simmon, P. B., Patterson, R. M., Ludwig, F. L. and Jones, L. B. (1981) The APRAC3/MOBILE1 Emissions and Diusion Modeling Package. U.S. Environmental Protection Agency, Washington, DC. Tartaglia, M. (1995) Relazione fra emissions inquinanti e velocit{ dei veicoli stradali. Ingegneria Ferroviaria 5, 337±346. Tartaglia, M., Giannone, A., Gualtieri, G. and Barbaro, A. (1995) Development and validation of an urban street canyon model based on carbon-monoxide experimental data measured in Firenze. In Urban Transport and the Environment for the 21st Century, ed. L. J. Sucharov. Computational Mechanics Publications, Southampton. Wardrop, J. G. (1952) Some theoretical aspects of roda trac research. Proc. Inst. Civ. Eng. 2, 325±378. Zannetti, P. (1990) Air pollution modeling. Van Nostrand Reinhold, New York.
Pergamon PII: S1361-9209(98)00011-X
PREDICTING URBAN TRAFFIC AIR POLLUTION: A GIS FRAMEWORK G. GUALTIERI* and M. TARTAGLIA
Applied Meteorology Foundation, via Caproni 8, 50145 Firenze, Italy (Received for publication 30 April 1998) AbstractÐThis paper presents a comprehensive model for the evaluation of air pollution caused by road trac in urban areas, depending on site geometric and morphological conditions. The model is integrated in a Geographic Information System (GIS) that allows the use of spatial co-ordinates to describe the structure of urban areas, road networks, and distribution of pollutants in the atmosphere. # 1998 Elsevier Science Ltd. All rights reserved
1. INTRODUCTION
Air pollution produced by road trac is one of the most serious problems in the management of urban areas. The goal of the work described in this paper is to develop a decision support system to help local administrators reduce the impact of air pollution caused by road trac. Such a simulation planning system consists of a comprehensive air-quality forecasting model based on powerful data management, correct use of mathematical modelling and a friendly interface between numerical data and trac management operators. The system core consists of a number of mathematical submodelsÐbased on current literature or developed through experimental data ®tting made by the authorsÐable to simulate trac, emission and dispersion patterns. Submodels are integrated in a Geographic Information System (GIS), which allows the use of spatial coordinates to describe the structure of urban areas, road networks, and pollutant distribution in the atmosphere. The GIS structure and modelling characteristics are presented here. An application of the GIS-based model to the urban area of Firenze (Florence), Italy, is summarized and main GIS mapping features are described. 2. GIS OVERVIEW
The Geographic Information System was been set up according to the diagram in Fig. 1. The scheme has a three-level structure, including the whole database, a number of mathematical models, and the subsequent results in terms of thematic mappings. The GIS database is provided by road network territorial data, trac demand characteristics, driving cycles and ¯eet composition, and a number of data de®ning an adequate meteoclimatic scenario. GIS-integrating models are aimed at reproducing trac behaviour, emission and dispersion scenarios. They are set up in a cascade fashion, so that modeled trac parameters can be used as input by the emission model, whose subsequential modeled emissions are in their turn necessary for the dispersion model to simulate pollution levels. The GIS database was ®rst organized on the basis of geographical data. Once the road network map of the considered urban area was digitized, each link was integrated with a number of topographic and morphological attributes. In particular, the whole territorial database set up within the GIS was designed according to the details provided in Table 1. *Author for correspondence. Fax: 00 39 55 461701; e-mail: [email protected]®.it
329
330
G. Gualtieri and M. Tartaglia
Trac demand characteristics are expressed in terms of the origin±destination (O±D) matrix of road vehicles. This required de®nition of a suitable zoning of the entire urban context, and an investigation of the corresponding trac demand. Once trac demand data are provided, it is possible to reproduce trac behaviour within each link of the network (Fig. 1), and once trac ¯ows over each link have been assigned by the trac model, it is possible to evaluate their relative mean speed by means of transport data, i.e. ¯ow-speed curves (Table 1). Driving kinematics along each link can thus be calculated; this, together with vehicle ¯eet split, makes it possible to estimate pollutant emissions over the entire road network. A section of the GIS database has been designed to manage meteoclimatic variables, whose values are used as ®xed input by both the emission and the dispersion models. The variables needed for pollution modelling are wind speed and direction, solar radiation, air temperature, and atmospheric stability class. Meteoclimatic parameters are arranged in arrays that represent typical meteoclimatic scenarios, which can be de®ned by the user according to the purpose of the simulation. 3. MODEL DESCRIPTION
As shown in Fig. 1, the GIS model section is made from dierent submodels, aimed at simulating each subprocess involved in the entire air pollution phenomenon. These are trac, emission, and dispersion models, all linked together and using survey input data. Road trac is simulated through a deterministic model designed for solving the equilibrium auto assignment problem with capacity constraints (She, 1985). The basic behavioral assumption of the model is the user optimal principle stated by Wardrop (1952). The problem is solved by using the linear approximation algorithm described by Florian and Nguyen (1976). Such an algorithm allows the estimation of trac ¯ows within each link of the road network starting from the knowledge of the network characteristics and trac demand. In addition to network topology, numerical input data regarding length and number of lanes of each link are provided by the GIS database. Link-related speed±volume and capacity functions are also needed for calculus and stored in the GIS tables. Trac demand is input as the origin±destination matrix of road vehicles.
Fig. 1. The GIS structure.
Table 1. Geographic attributes associated to road network links Attributes tipology Topographic Toponomastic Physical Morphological Transport Geometrical (canyon links only)
Network links attributes Nodes UTM co-ordinates, street axis direction, total length Street names Driving directions, Number of lanes Flag: street canyon, inter-section links, open areas, etc. Road tipology by means of ¯ow-speed curves Canyon width, building mean height
Predicting urban trac air pollution: A GIS framework
331
The emission model has been developed on the basis of the results shown by Journard et al. (1992), Eggleston et al. (1991), and Tartaglia (1995). It is able to calculate emission levels of some typical trac-related pollutants such as carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx). Emission factors refer to a vehicle grouping based on the knowledge of vehicle model year, fuel type, gross weight, and power characteristics. The emission factor Eg due to road vehicles belonging to group gÐtravelling in typical road and environmental conditionsÐis expressed as the mass of pollutant per unit length as a function of the average travel speed Vm . Total pollutant emission Q produced by the trac ¯ow f of N vehicular groups is computed using the formula: Q
N c X g Eg Vm f 100 g1
1
where cg is the percentage of vehicular group g with respect to the vehicle ¯eet. The in¯uence of non-typical road and environmental conditionsÐsuch as road grades and cold startsÐis taken into account by means of additional functions. The pollutant dispersion section of the model features a number of options which take into account several geomorphological conditions and pollutant substances. Total pollutant concentrations C are computed by summing the local contribution (Cl , due to the road link nearest the receptor point) and the area contribution (Ca , due to the rest of the road network): C Cl Ca
2
The local contribution to pollutant concentration can be calculated according to two dierent approaches, depending on site characteristics. Whenever road section presents no obstacles to dispersion, pollutant concentration is calculated by means of a Gaussian dispersion model (Zannetti, 1990). A dierent algorithm is used for calculating the local contribution within street canyons, i.e. urban sites where road section is surrounded by tall buildings on each side. As summarized by DePaul and Sheih (1986), under typical conditions a local isolated circulation causes pollutants to be trapped in urban canyons. Pollutant concentrations could consequently be calculated with a sort of empirical local box model. The latter is modal in nature, i.e. it involves the grouping of wind directions into three separate sectors, centered at the road axis. The estimation of pollutant concentrations within a street canyon is provided by means of a modi®cation of the canyon empirical model originally proposed by Hoydysh and Dabberdt (1988) for CO. This modi®cation was carried out by Tartaglia et al. (1995) and Gualtieri and Tartaglia (1997) on the basis of ®eld data measurements in the urban area of Firenze. The pollutants analyzed were CO and NO. A calibration process based on the application of regression analysis was performed in modal terms with relation to wind direction by means of collection of a three-month historical series of data. The survey period was January through March 1994 for CO and May, July and August 1994 for NO, As regards NO, in the calibration process solar radiation was also taken into account, in order to involveÐthough indirectlyÐthe overall chemical and photochemical reactions occurring in the lower atmosphere. The process brought a general improvement of estimations provided by the Hoydysh and Dabberdt CO model, and a comparable degree of precision for the new NOx model (see references for further details). To summarize, the general formula of the street canyon model can be written as: Cl a
X QS di Radi e F bT cH u 0:5 i
3
where: Cl is the local contribution to concentration; Qs is the link mean emission rate expressed in mass per unit length and unit time; F* is a shape factor depending on the speci®c wind direction *The description of F shape factors can be found in the references Tartaglia et al. (1995). Note that coecients b, c, and di are null for the CO model. Mixing height H is calculated as a function of the atmospheric stability class and meteorological parameters measured at the ground, as suggested by Bellasio et al. (1994).
332
G. Gualtieri and M. Tartaglia
Table 2. Scenario_W: condition and experimental measurements for each variable (1994 historical series for morning peak hour) Variable
Condition
Wind direction (WD) Wind speed (WS) Solar radiation (SR) Air temperature (AT) Pasquill Stability Class (SC)
Mode Models sensitivity lower threshold Mode Lower 5±95 percentile Upper 95±5 percentile
Variable
WD ( N) WS (m/S) SR (Wlm2) AT ( C) SC
Measurements by scenario Annual
Spring
Summer
Autumn
Winter
116 1.50 3 2.50 D
116 1.50 31 4.6 D
116 1.50 239 17 D
116 1.50 4 3.5 D
120 1.50 3 ÿ0.8 D
Table 3. Scenario_M: condition and experimental measurements for each variable (1994 historical series for morning peak hour) Variable
Condition
Wind direction (WD) Wind speed (WS) Solar radiation (SR) Air temperature (AT) Pasquill Stability Class (SC)
Mode Mean Mean Mean Mean
Variable
WD ( N) WS (m/s) SR (W/m2) AT ( C) SC
Measurements by scenario Annual
Spring
Summer
Autumn
Winter
116 1.94 95.91 12.88 D
116 1.76 154.17 12.73 D
116 1.78 161.57 21.15 C
116 1.78 48.82 10.60 D
120 2.00 18.06 6.62 D
sector; u is wind speed at roof level; T is ambient temperature; H is mixing height in the atmosphere; Radi is solar radiation at time i expressed in power per unit area; the sum is extended over the previous 23 h; a, b, c, di , and e are the regression coecients. The area contribution can be calculated by using a simpli®ed Gaussian line source dispersion model or a Eulerian box model. The Gaussian model is based on the algorithm described by Simmon et al. (1986). It calculates pollutant concentrations assuming the existence of a number of virtual area sources whose overall emission rate matches the contribution due to road links. The subsequent pollutant distribution in the urban atmosphere is assumed to follow a Gaussian law. Dispersion formulas take into account variables such as wind speed and Pasquill stability class. The box model has been developed by the authors on the basis of the principle of pollutant mass conservation. The average emission rate is assumed to be constant in the whole simulation area, so that the relative average concentration rate can be determined. The model takes advantage from the same algorithm for determining mixing height used in the street canyon model. 4. GIS MAPPING FEATURES
In order to represent some main GIS thematic capabilities, a sample number of graphics outputs produced by the GIS-integrating models are presented in the following ®gures. As illustrated in Fig. 1, these describe trac ¯ow behaviour, vehicular emission rates and concentration levels. Simulations refer speci®cally to the urban area of Firenze.
Predicting urban trac air pollution: A GIS framework
333
4.1. Input data description A mathematical scheme of the Firenze road networkÐmade up of about 800 linksÐhas been integrated into the GIS database. Each link is characterized by trac parameters and ¯ow-speed plus capacity functions. Trac demand refers to data resulting from direct experimental surveys. O±D matrices are updated to 1991 and referred to the morning peak hour (7:15±8:15). It is important to notice that, since trac modelling was carried out by using O±D data referring to the hours 7:15±8:15, this time period was used as the reference period for both the emission and dispersion models as well. In particular, in order to simulate pollution levels, the reference time period 7:15±8:15 has made it necessary to set up an adequate meteorological scenario. The meteoclimatic scenario was set up according to two dierent options: a `worst' scenario, enabling the highest air pollution condition to be reached, and a `mean' scenario, representing the most frequent meteoclimatic conditions occurring in the study area. Both scenarios (named Scenario_W and Scenario_M) have been de®ned on the basis of a historical data series collected by the Ximeniano observatory meteo station in Firenze, which covers the entire 1994 year. In addition, a further option of choosing between an annual or a seasonal scenario is oered. Scenario_W, describing the worst meteorological conditions with respect to air quality, required a previous study of the role played by all main atmospheric parameters in aecting pollutant dispersion caused by vehicular trac in an urban area. In particular, the mode±i.e. the most frequent valueÐwas chosen for wind direction and solar radiation, whereas to obtain reliable concentration estimations performed by the dispersion model, a lower thresholdÐequal to 1.5 m/sÐwas chosen for wind speed (Hanna et al., 1982). How the scenario was set up is summarized in Table 2, where the worst meteoclimatic conditions and experimental measurements are given, for both the annual and the seasonal options.
Fig. 2. An example of trac ¯ows mapping over the city of Firenze.
Fig. 3. An example of pollutant emission mapping due to all trac volumes over the city of Firenze: case concerning HC emission rates per unit length.
334
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Fig. 4. An example of pollutant emission mapping due to all trac volumes over the city of Firenze: case concerning NOx emission rates per unit length according to a 1.5 Km wide grid representation.
Fig. 5. An example of pollutant dispersion mapping: case concerning CO concentrations within all canyon links of road network in a worst winter scenario.
Scenario_M representing the ensemble of meteorological conditions typically occurring in the area, was set up according to the values reported in Table 3. Because of its signi®cance, the mode value was chosen for wind direction. 4.2. Graphical outputs description Some graphical outputs provided by GIS models on the basis of previously mentioned input data are presented below. In Fig. 2 an example of vehicular ¯ows distribution over the entire urban area of Firenze is illustrated. In particular, for each link, trac ¯ows are assigned by the trac model in both directions. An estimation of pollutant rates released by all the above trac ¯ows is given in Fig. 3, where in particular HC emissions per unit length are mapped. A further option in describing modeled pollutant emissions is reported in Fig. 4, where NOx emissions per unit length over the whole urban area are mapped according to a 1.5 km wide grid representation. Predicted air pollution levels due to vehicular trac are mapped in Fig. 5. In particular, CO concentrations within all street canyon links simulated by the street canyon model are presented. Estimations refer to a winter `worst' meteo scenario. For each street canyon, concentrations are calculated at 3 m over the ground level and 1 m from building walls and assumed not changing along street axis. Mapped concentrations are averaged between both canyon sides. As far as pollutant dispersion modelling is also concerned, a `microscale' diagram of NOx concentration patterns within a sample urban canyon is given in Fig. 6. Predicted NOx pro®les are modeled according to both the canyon cross section (3 m over ground level) and the opposite building walls (1 m away).
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Fig. 6. An example of pollutant dispersion mapping: case concerning NOx concentration patterns within a sample street canyon in a worst summer scenario.
5. CONCLUSIONS
A comprehensive GIS-based simulation model for predicting and evaluating air pollution due to road trac in urban areas was set up. The GIS features of the model make it suitable for use by the local administration in order to forecast alert pollutant levels in urban atmosphere, integrate existing monitoring network measurements, and estimate the sensitivity of pollution levels to such variables as trac ¯ow distribution and atmospheric conditions. REFERENCES Bellasio, R., Lanzani, G., Tamponi, M. and Tirabassi, T. (1994) Boundary layer parametrization for atmospheric diusion models by meteorological measurements at ground level. R Nuovo Cimento 2, 163±174. DePaul, F. T. and She, C. M. (1986) Measurements of wind velocities in a street canyon. Atmospheric Environment 3, 455±459. Eggleston, H. S., Gaudioso, D., Gorissen, N., Joumard, R., Rijkeboer, R. C., Sameres, Z. and Zierock, K. H. (1991) CORINAIR Working Group on Emission Factors for Calculating 1990 Emissions from Road Trac. Vol. I., Methodology and Emission Factors. Final Report, Contract n.B4/3045(91)IOPH, Directorate Generale XI. European Environmental Agency Task Force, Commission of European Communities, Bruxelles, Belgium. Florian, M. and Nguyen, S. (1976) An application and validation of equilibrium trip assignment methods. Transportation Science 10, 374±389. Gualtieri, G. and Tartaglia, M. (1997) A street canyon model for estimating NOx concentrations due to road trac. In Measurements and Modelling in Environmental Pollution, ed. C. A. Brebbia. Computational Mechanics Publications, Southampton. Hanna, R. S., Briggs, A. G. and Hosker Jr, P. R. (1982) Handbook on Atmospheric Diusion. Technical Information Center, Blacksburg, Virginia, U.S.A. Hoydysh, G. W. and Dabberdt, F. W. (1988) Kinematics and dispersion characteristics of ¯ows in asymmetric street canyons. Atmospheric Environment 22, 2677±2689. Joumard, R., Hickmann, L., Nemerlin, J. and Hassel, D. (1992) Model of Exhaust and Noise Emissions and Fuel Consumption of Trac in Urban Areas. INRETS, Lion-Bron, France.
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She, Y. (1985) Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. PrenticeHall, Englewood Clis, NJ. Simmon, P. B., Patterson, R. M., Ludwig, F. L. and Jones, L. B. (1981) The APRAC3/MOBILE1 Emissions and Diusion Modeling Package. U.S. Environmental Protection Agency, Washington, DC. Tartaglia, M. (1995) Relazione fra emissions inquinanti e velocit{ dei veicoli stradali. Ingegneria Ferroviaria 5, 337±346. Tartaglia, M., Giannone, A., Gualtieri, G. and Barbaro, A. (1995) Development and validation of an urban street canyon model based on carbon-monoxide experimental data measured in Firenze. In Urban Transport and the Environment for the 21st Century, ed. L. J. Sucharov. Computational Mechanics Publications, Southampton. Wardrop, J. G. (1952) Some theoretical aspects of roda trac research. Proc. Inst. Civ. Eng. 2, 325±378. Zannetti, P. (1990) Air pollution modeling. Van Nostrand Reinhold, New York.