Air Quality Part 3 pot

Development and application of a methodology for designing a multi-objective and multi-pollutant air quality monitoring network for urban areas 43 Fig. 13. “Spatial representativeness” for NO 2 monitoring stations (▲). Fig. 14. “Spatial representativeness” for CO monitoring stations (●). Fig. 15. “Spatial representativeness” for PM 10 monitoring stations (▼). 5. Conclusion A multiple objective and multi-pollutant planning procedure for designing an urban air quality monitoring network is presented in this study. The considered monitoring objectives are to maximize the “detection capability” of higher pollutant concentrations and the “protection capability” for areas with higher population density. The design methodology is based on the analysis of air pollutant concentrations estimated by atmospheric dispersion models. It simultaneously considers an exceedance score and a population factor. A statistical analysis is used for optimization. The methodology is applied to design a NO 2 , CO and PM 10 , monitoring network for the city of Buenos Aires considering a spatial resolution (for the emission inventory and model estimations) of 1 x 1km. Air pollutant concentrations in the city have been estimated using the DAUMOD and AERMOD atmospheric dispersion models, that evaluate the contribution of area and point sources, respectively. The optimal alternative of the proposed network can be summarized as: a) seven locations for monitoring NO 2 , CO and PM 10 ; b) two sites for NO 2 and CO; c) one location for CO and PM 10 and d) one station for NO 2 only. The “spatial representativeness” of mean concentrations at monitoring sites varies with each pollutant: a) for NO 2 , between 1-7km 2 ; b) for CO between 2-11km 2 and c) for PM 10 , between 4-12km 2 . It must be noted that the ultimate decision in site selection is left to the air quality monitoring authority. Future studies could be focused on: a) the evaluation of how sensitive is the proposed methodology for air quality network design to slight changes in the input data (e.g. the weighing factors, the spatial resolution) and b) the inclusion of other optimization objectives (e.g. land use, frequency of violations of air quality standards, protect damage to vulnerable receptors as historic and/or artistic valuable property). 6. References Ainslie, B., Reuten, C., Steyn, D.G., Le, N.D. & Zidek, J.V. (2009). Application of an entropy- based Bayesian optimization technique to the redesign of an existing monitoring network for single air pollutants. Journal of Environmental Management, Vol. 90, pp. 2715–2729. Arya S. P. (1999). Air Pollution Meteorology. Oxford University Press. New York Baldauf, R., Wiener, R.W. & Heist, D.K. (2002). Methodology for Siting Ambient Air Monitors at the Neighborhood Scale. Journal of Air & Waste Management Association, Vol. 52, pp. 1433–1442. Bocca, B., Caimi, S., Smichowski, P., Gómez, D. & Cairoli, S. (2006). Monitoring Pt and Rh in urban aerosols from Buenos Aires, Argentina. Science of the Total Environment, Vol. 358, pp. 255-264. Bogo, H., Negri, R.M. & San Román, E. (1999). Continuous measurement of gaseous pollutants in Buenos Aires city. Atmospheric Environment, Vol. 33, pp. 2587-2598. Bogo, H., Gómez, D. R., Reich, S. L., Negri, R. M. & San Román, E. (2001). Traffic pollution in downtown of Buenos Aires City. Atmospheric Environment, Vol. 35, pp. 1717-1727. Bogo, H., Otero, M., Castro, P., Ozafrán, M. J., Kreiner, A., Calvo, E. J. & Negri, R. M. (2003). Study of atmospheric particulate matter in Buenos Aires city. Atmospheric Environment, Vol. 37, pp. 1135-1147. Briggs, G. A. (1993) Plume dispersion in convective boundary layer. Part II: Analysis of CONDORS field experiment data. Journal of Applied Meteorology, Vol. 32, pp. 1388- 1425. Caselton, W.F., Kan, L. & Zidek, J.V. (1992) Quality data networks that minimize entropy. In: Statistics in the Environmental and Earth Sciences. Walden, A. & Guttorp, P. (Eds.), pp. 10-38, Halsted Press, New York. Air Quality44 CERC (2003). ADMS-Urban. An Urban Air Quality Management System. User Guide. Version 2.0 . Cambridge Environmental Research Consultants Ltd., Cambridge. Cimorelli, A. J., Perry, S. G., Venkatram, A., Weil, J. C. , Paine, R. J., Wilson, R. B., Lee, R. F., Peters, W. D. & Brode, R. W. (2005). AERMOD: A dispersion model for industrial source applications Part I: General model formulation and boundary layer characterization. Journal of Applied Meteorology, Vol. 44, pp. 682-693. Derwent, R.G. & Middleton, D.R. (1996). An empirical function for the ratio NO 2 :NO x . Clean Air, Vol. 26, pp. 57-62. Dixon, J., Middleton, D.R. & Derwent, R.G. (2001). Sensitivity of nitrogen dioxide concentrations to oxides of nitrogen controls in the United Kingdom. Atmospheric Environment, Vol. 35, pp. 3715-3728. Egmond, N.D.V. & Onderdelinden, D. (1981). Objective analysis of air pollution monitoring network data: spatial interpolation and network density. Atmospheric Environment, Vol. 15, pp. 1035–1046. Elkamel, A., Fatehifar, E., Taheri, M., Al- Rashidi, M.S. & Lohi, A. (2008). A heuristic optimization approach for Air Quality Monitoring Network design with the simultaneous consideration of multiple pollutants. Environmental Management, Vol. 88, pp. 507-516. Elsom, D.M. (1978). Spatial correlation analysis of air pollution data in an urban area. Atmospheric Environment, Vol. 12, pp. 1103–1107. EMEP/CORINAIR. (2001). Atmospheric Emission Inventory Guidebook, Third Edition, European Environment Agency, Copenhagen. EPA. (1995). Compilation of Air Pollution Emission Factors, AP-42, 5 th ed., United States Environmental Protection Agency, Research Triangle Park, NC. EPA. (2004). User’s Guide for the AMS/EPA Regulatory Model-AERMOD, EPA-454/B-03-001. United States Environmental Protection Agency, Research Triangle Park, NC. Fagundez, L. A., Fernández V. L., Marino T. H., Martín I., Persano D. A., Rivarola y Benítez M., Sadañiowski I. V., Codnia J. & Zalts A. (2001). Preliminary air pollution monitoring in San Miguel, Buenos Aires. Environmental Monitoring and Assessment, Vol. 71, pp. 61-70. Gifford, F.A. (1970). Atmospheric Diffusion in an Urban Area, NOAA Research Lab. Nº 33. Oak Ridge, N. C. Gifford, F.A. & Hanna, S.R. (1973). Modelling urban air pollution. Atmospheric Environment, Vol. 7, pp. 131-136. Graves, R.J., Lee, T.D. & McGinnis, L.F.J. (1981). Air Monitoring Network Design: case study. Journal of Environmental Engineering ASCE, Vol. 107, pp. 941-955. Gryning, S.E., Footslog, A.A.M., Irwin, J.S. & Sivertsen, B. (1987). Applied dispersion modelling based on meteorological scaling parameters. Atmospheric Environment, Vol. 21, pp. 79-89. Handscombe, C.M. & Elsom, D.M. (1982). Rationalisation of the National Survey of Air Pollution Monitoring Network of the United Kingdom using spatial correlation analysis: a case study of the Greater London area. Atmospheric Environment, Vol. 16, pp. 1061-1070. Hougland, E.S. & Stephens, N.T. (1976). Air Pollutant Monitor Siting by Analytical Techniques. Journal of the Air Pollution Control Association, Vol. 26, pp. 51-53. Husain, T. & Khan, S.M. (1983). Air Monitoring Network Design Using Fisher’s Information Measures—A Case Study. Atmospheric Environment, Vol. 17, pp. 2591–2598. Hwang, J.S. & Chan, Ch.Ch. (1997). Redundant measurements on urban air monitoring networks in air quality reporting. Journal of Air & Waste Management Association, Vol. 47, pp. 614-619. INDEC (2008). Estimaciones de población total por departamento y año calendario. Período 2001- 2010. Serie Análisis Demográfico. Nº 34. Instituto Nacional de Estadística y Censos. (www.indec.gov.ar) (in Spanish) Kainuma, Y., Shiozawa, K, & Okamoto, S. (1990). Study of the Optimal Allocation of Ambient Air Monitoring Stations. Atmospheric Environment, Vol. 24B, pp. 395–406. Koda, M. & Seinfeld, J.H. (1978). Air monitoring siting by objective. EPA-600/4-7-036, United States Environmental Protection Agency, Las Vegas, Nevada. Langstaff, J., Seigneur, C. & Liu, M.K. (1987) Design of an optimum air monitoring network for exposure assessment. Atmospheric Environment, Vol. 21, pp. 1393-1410. Le, N.D. & Zidek, J.V. (2006). Statistical Analysis of Environmental Space–Time Processes. Springer, New York. Liu, M. K., Avrin, J., Pollack, R. I., Behar, J. V., & McElory J.L. (1986) Methodology for designing air quality monitoring networks. Environmental Monitoring and Assessment, Vol. 6, pp. 1-11. Mazzeo, N. A. & Venegas, L. E. (1991). Air pollution model for an urban area. Atmospheric Research , Vol. 26, pp. 165-179. Mazzeo, N. A. & Venegas, L. E. (2000). Practical use of the ISCST3 model to select monitoring site locations for air pollution control. International Journal of Environment and Pollution , Vol. 14, pp. 246-259. Mazzeo, N. A. & Venegas, L. E. (2002). Estimation of cumulative frequency distribution for carbon monoxide concentration from wind-speed data, in Buenos Aires (Argentina). Water, Air and Soil Pollution, Focus, Vol. 2, pp. 419-432. Mazzeo, N. A. & Venegas, L. E. (2003). Carbon monoxide and nitrogen oxides emission inventory for Buenos Aires City (Argentina). Proceedings of the Fourth International Conference on Urban Air Quality – Measurement, Modelling and Management, pp. 159- 162. Prague, Czech Republic, March 2003. University of Hertfordshire, Hatfield. Mazzeo, N. A. & Venegas, L. E. (2004). Some aspects of air pollution in Buenos Aires city. I nternational Journal of Environment and Pollution, Vol. 22, pp. 365-378. Mazzeo, N. A., Venegas, L. E. & Choren, H. (2005). Analysis of NO, NO 2 , O 3 and NO x concentrations measured at a green area of Buenos Aires City during wintertime, Atmospheric Environment, Vol. 39, pp. 3055-3068. Mazzeo, N.A. & Venegas, L.E. (2008). Design of an air quality surveillance system for Buenos Aires city integrated by a NO x monitoring network and atmospheric dispersion models. Environmental Modeling and Assessment, Vol. 13, pp. 349-356. McElroy, J.L., Behar, J.V., Meyers, T.C. & Liu, M.K. (1986). Methodology for designing Air Quality Monitoring Networks: II. Application to Las Vegas, Nevada, for carbon monoxide. Environmental Monitoring and Assessment, Vol. 6, pp. 13-34. Middleton, D.R., Jones, A.R., Redington, A.L., Thomson, D.J., Sokhi, R.S., Luhana, L. & Fisher, B.E.A. (2008). Lagrangian modelling of plume chemistry for secondary pollutants in large industrial plumes. Atmospheric Environment, Vol. 42, pp. 415-427. Development and application of a methodology for designing a multi-objective and multi-pollutant air quality monitoring network for urban areas 45 CERC (2003). ADMS-Urban. An Urban Air Quality Management System. User Guide. Version 2.0 . Cambridge Environmental Research Consultants Ltd., Cambridge. Cimorelli, A. J., Perry, S. G., Venkatram, A., Weil, J. C. , Paine, R. J., Wilson, R. B., Lee, R. F., Peters, W. D. & Brode, R. W. (2005). AERMOD: A dispersion model for industrial source applications Part I: General model formulation and boundary layer characterization. Journal of Applied Meteorology, Vol. 44, pp. 682-693. Derwent, R.G. & Middleton, D.R. (1996). An empirical function for the ratio NO 2 :NO x . Clean Air, Vol. 26, pp. 57-62. Dixon, J., Middleton, D.R. & Derwent, R.G. (2001). Sensitivity of nitrogen dioxide concentrations to oxides of nitrogen controls in the United Kingdom. Atmospheric Environment, Vol. 35, pp. 3715-3728. Egmond, N.D.V. & Onderdelinden, D. (1981). Objective analysis of air pollution monitoring network data: spatial interpolation and network density. Atmospheric Environment, Vol. 15, pp. 1035–1046. Elkamel, A., Fatehifar, E., Taheri, M., Al- Rashidi, M.S. & Lohi, A. (2008). A heuristic optimization approach for Air Quality Monitoring Network design with the simultaneous consideration of multiple pollutants. Environmental Management, Vol. 88, pp. 507-516. Elsom, D.M. (1978). Spatial correlation analysis of air pollution data in an urban area. Atmospheric Environment, Vol. 12, pp. 1103–1107. EMEP/CORINAIR. (2001). Atmospheric Emission Inventory Guidebook, Third Edition, European Environment Agency, Copenhagen. EPA. (1995). Compilation of Air Pollution Emission Factors, AP-42, 5 th ed., United States Environmental Protection Agency, Research Triangle Park, NC. EPA. (2004). User’s Guide for the AMS/EPA Regulatory Model-AERMOD, EPA-454/B-03-001. United States Environmental Protection Agency, Research Triangle Park, NC. Fagundez, L. A., Fernández V. L., Marino T. H., Martín I., Persano D. A., Rivarola y Benítez M., Sadañiowski I. V., Codnia J. & Zalts A. (2001). Preliminary air pollution monitoring in San Miguel, Buenos Aires. Environmental Monitoring and Assessment, Vol. 71, pp. 61-70. Gifford, F.A. (1970). Atmospheric Diffusion in an Urban Area, NOAA Research Lab. Nº 33. Oak Ridge, N. C. Gifford, F.A. & Hanna, S.R. (1973). Modelling urban air pollution. Atmospheric Environment, Vol. 7, pp. 131-136. Graves, R.J., Lee, T.D. & McGinnis, L.F.J. (1981). Air Monitoring Network Design: case study. Journal of Environmental Engineering ASCE, Vol. 107, pp. 941-955. Gryning, S.E., Footslog, A.A.M., Irwin, J.S. & Sivertsen, B. (1987). Applied dispersion modelling based on meteorological scaling parameters. Atmospheric Environment, Vol. 21, pp. 79-89. Handscombe, C.M. & Elsom, D.M. (1982). Rationalisation of the National Survey of Air Pollution Monitoring Network of the United Kingdom using spatial correlation analysis: a case study of the Greater London area. Atmospheric Environment, Vol. 16, pp. 1061-1070. Hougland, E.S. & Stephens, N.T. (1976). Air Pollutant Monitor Siting by Analytical Techniques. Journal of the Air Pollution Control Association, Vol. 26, pp. 51-53. Husain, T. & Khan, S.M. (1983). Air Monitoring Network Design Using Fisher’s Information Measures—A Case Study. Atmospheric Environment, Vol. 17, pp. 2591–2598. Hwang, J.S. & Chan, Ch.Ch. (1997). Redundant measurements on urban air monitoring networks in air quality reporting. Journal of Air & Waste Management Association, Vol. 47, pp. 614-619. INDEC (2008). Estimaciones de población total por departamento y año calendario. Período 2001- 2010. Serie Análisis Demográfico. Nº 34. Instituto Nacional de Estadística y Censos. (www.indec.gov.ar) (in Spanish) Kainuma, Y., Shiozawa, K, & Okamoto, S. (1990). Study of the Optimal Allocation of Ambient Air Monitoring Stations. Atmospheric Environment, Vol. 24B, pp. 395–406. Koda, M. & Seinfeld, J.H. (1978). Air monitoring siting by objective. EPA-600/4-7-036, United States Environmental Protection Agency, Las Vegas, Nevada. Langstaff, J., Seigneur, C. & Liu, M.K. (1987) Design of an optimum air monitoring network for exposure assessment. Atmospheric Environment, Vol. 21, pp. 1393-1410. Le, N.D. & Zidek, J.V. (2006). Statistical Analysis of Environmental Space–Time Processes. Springer, New York. Liu, M. K., Avrin, J., Pollack, R. I., Behar, J. V., & McElory J.L. (1986) Methodology for designing air quality monitoring networks. Environmental Monitoring and Assessment, Vol. 6, pp. 1-11. Mazzeo, N. A. & Venegas, L. E. (1991). Air pollution model for an urban area. Atmospheric Research , Vol. 26, pp. 165-179. Mazzeo, N. A. & Venegas, L. E. (2000). Practical use of the ISCST3 model to select monitoring site locations for air pollution control. International Journal of Environment and Pollution , Vol. 14, pp. 246-259. Mazzeo, N. A. & Venegas, L. E. (2002). Estimation of cumulative frequency distribution for carbon monoxide concentration from wind-speed data, in Buenos Aires (Argentina). Water, Air and Soil Pollution, Focus, Vol. 2, pp. 419-432. Mazzeo, N. A. & Venegas, L. E. (2003). Carbon monoxide and nitrogen oxides emission inventory for Buenos Aires City (Argentina). Proceedings of the Fourth International Conference on Urban Air Quality – Measurement, Modelling and Management, pp. 159- 162. Prague, Czech Republic, March 2003. University of Hertfordshire, Hatfield. Mazzeo, N. A. & Venegas, L. E. (2004). Some aspects of air pollution in Buenos Aires city. I nternational Journal of Environment and Pollution, Vol. 22, pp. 365-378. Mazzeo, N. A., Venegas, L. E. & Choren, H. (2005). Analysis of NO, NO 2 , O 3 and NO x concentrations measured at a green area of Buenos Aires City during wintertime, Atmospheric Environment, Vol. 39, pp. 3055-3068. Mazzeo, N.A. & Venegas, L.E. (2008). Design of an air quality surveillance system for Buenos Aires city integrated by a NO x monitoring network and atmospheric dispersion models. Environmental Modeling and Assessment, Vol. 13, pp. 349-356. McElroy, J.L., Behar, J.V., Meyers, T.C. & Liu, M.K. (1986). Methodology for designing Air Quality Monitoring Networks: II. Application to Las Vegas, Nevada, for carbon monoxide. Environmental Monitoring and Assessment, Vol. 6, pp. 13-34. Middleton, D.R., Jones, A.R., Redington, A.L., Thomson, D.J., Sokhi, R.S., Luhana, L. & Fisher, B.E.A. (2008). Lagrangian modelling of plume chemistry for secondary pollutants in large industrial plumes. Atmospheric Environment, Vol. 42, pp. 415-427. Air Quality46 Modak, P.M. & Lohani, B.N. (1985a). Optimization of ambient Air Quality Monitoring Networks: Part I. Environmental Monitoring and Assessment, Vol. 5, pp. 1–19. Modak, P.M. & Lohani, B.N. (1985b). Optimization of ambient Air Quality Monitoring Networks: Part II. Environmental Monitoring and Assessment, Vol. 5, pp. 21–38. Modak, P.M. & Lohani, B.N. (1985c). Optimization of ambient Air Quality Monitoring Networks: Part III. Environmental Monitoring and Assessment, Vol. 5, pp. 39–53. Mofarrah A. & Husain T. (2010). A Holistic Approach for optimal design of Air Quality Monitoring Network Expansion in an Urban Area. Atmospheric Environment, Vol. 44, pp. 432-440. Munn, R.E. (1975). Suspended particulate concentrations: Spatial correlations in the Detroit- Windsor area. Tellus, Vol. XXVII, pp. 397–405 Munn, R.E. (1981). The Design of Air Quality Monitoring Networks. MacMillan, London. Nakamori, Y. & Sawaragi, Y. (1984) Interactive Design of Urban Level Air Quality Monitoring Network. 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Suspended particulate concentrations: Spatial correlations in the Detroit- Windsor area. Tellus, Vol. XXVII, pp. 397–405 Munn, R.E. (1981). The Design of Air Quality Monitoring Networks. MacMillan, London. Nakamori, Y. & Sawaragi, Y. (1984) Interactive Design of Urban Level Air Quality Monitoring Network. Atmospheric Environment, Vol. 18, pp. 793–799. Noll, K.E. & Mitsutome, S. (1983) Design methodology for optimum dosage air monitoring site selection. Atmospheric Environment, Vol. 17, pp. 2583–2590. Pasquill, F. & Smith, F.B. (1983). Atmospheric Diffusion, John Wiley & Sons, New York. Pickett, E.E. & Whiting, R.G. (1981). The design of cost-effective Air Quality Monitoring Networks. Environmental Monitoring and Assessment, Vol. 1, pp. 59-74. Pineda Rojas, A.L., Venegas, L.E. & Mazzeo, N.A. (2007). Emission inventory of carbon monoxide and nitrogen oxides for area sources at Buenos Aires Metropolitan Area (Argentina). Proceedings of the Sixth International Conference on Urban Air Quality, Cyprus, March 2007, University of Hertfordshire, Hatfield. Pires, J.C.M., Pereira, M.C., Alvim-Ferraz, M.C.M. & Martins, F.G. (2009). Identification of redundant air quality measurements through the use of principal component analysis. Atmospheric Environment, Vol. 43, pp. 3837–3842. Rao, S. T. (2009). Environmental Monitoring and Modeling Needs in the 21st Century, EM, October, pp. 3-4. Rideout, G., Gourley, D. & Walker, J. (2005). Measurement of in-service vehicle emissions in Sao Paulo, Santiago and Buenos Aires. ARPEL Environmental Report Nº25, Environmental Services Association of Alberta, Edmonton. Romano, D., Gaudioso, D. & De Lauretis, R. (1999). Aircraft emissions: a comparison of methodologies based on different data availability. Environmental Monitoring and Assessment, Vol. 56, pp. 51-74. Sarigiannis, D.A. & Saisana, M. (2008). Multi-objective optimization of air quality monitoring. 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Venegas, L.E. & Martin, P.B. (2004). Particulate Matter Concentrations in the City of Buenos Aires, Proceedings of the 14 th Congreso Argentino de Saneamiento y Medio Ambiente, Buenos Aires, November 2004, AIDIS-Argentina, Buenos Aires (in Spanish). Venegas, L.E. & Mazzeo, N.A. (2000). Carbon monoxide concentrations in a street canyon at Buenos Aires City (Argentina). Environmental Monitoring & Assessment, Vol. 65, pp. 417-424. Venegas, L.E. & Mazzeo, N.A. (2002). An evaluation of DAUMOD model in estimating urban background concentration. Water, Air and Soil Pollution: Focus, Vol. 2, pp. 433- 443. Venegas, L.E. & Mazzeo, N.A. (2003a). Design methodology for background air pollution monitoring site selection in an urban area. International Journal of Environment and Pollution, Vol. 20, pp. 185-195. Venegas, L.E. & Mazzeo, N.A. (2003b). 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An entropy based review of selected NADP/NTN network sites for 1983-86. Atmospheric Environment, Vol. 26A, pp. 2089-2103. Air Quality48 Optimization of the design of air quality monitoring networks and its application to NO2 and O3 in Seville, Spain 49 Optimization of the design of air quality monitoring networks and its application to NO2 and O3 in Seville, Spain Antonio Lozano, José Usero, Eva Vanderlinden, Juan Raez, Juan Contreras, Benito Navarrete and Hicham El Bakouri X Optimization of the design of air quality monitoring networks and its application to NO 2 and O 3 in Seville, Spain Antonio Lozano a , José Usero b , Eva Vanderlinden b , Juan Raez a , Juan Contreras c , Benito Navarrete b and Hicham El Bakouri b a The Environmental Management Company (EGMASA), Seville, Spain b Department of Chemical and Environmental Engineering, University of Seville, Spain c Environmental Council of the Junta de Andalucía, Seville, Spain 1. Introduction Air quality monitoring networks are used in order to obtain objective, reliable and comparable information on the air quality of a specific area. This makes it possible to take the requisite measures to protect the environment, to assess the results of such actions and to ensure that the public is properly informed about the state of the air quality. The approval and publication of Council Directive 1996/62/EC (1996) on ambient air quality assessment and management and its daughter directives, 1999/30/EC (1999), 2000/69/EC (2000), 2002/3/EC (2002) and 2004/107/EC (2005), gave rise to an important change in air quality monitoring systems in Europe. Recently, in the interests of clarity, simplification and administrative efficiency, the above-mentioned European directives were replaced by the single Directive 2008/50/EC (2008) on ambient air quality and cleaner air for Europe with no change to existing air quality objectives for nitrogen dioxide (NO 2 ) and ozone (O 3 ). With the aim of being as up-to-date as possible, references to the law will be made to Directive 2008/50/EC (2008). The present work describes a new method to design or optimize air quality networks, particularly to monitor nitrogen dioxide and ozone in compliance with the legislation. The proposed method consists of four steps for choosing the best locations for the monitoring stations: (1) preliminary evaluation; (2) sampling campaigns with passive diffusion samplers; (3) spatial interpolation; (4) selection of best locations for the monitoring stations. The first step in the optimization process is the preliminary evaluation of air quality based on historical data. This evaluation makes it possible to establish the minimum number and characteristics of the stations needed in each zone as set forth in Directive 2008/50/EC (2008). The location of the monitoring stations depends on the distribution of the contamination levels of pollutants, as the stations need to record representative levels for the entire zone. The second step of the method consists of sampling campaigns with a large number of diffusive samplers to determine the concentration of nitrogen dioxide and ozone in the 3 Air Quality50 studied area. In a diffusion sampler, the gas molecules are transported only by molecular diffusion, which is a function of air temperature and pressure. This independence allows the time-weighted average ambient concentration to be calculated using Fick’s laws of diffusion (UNEP, 2004). Diffusive sampling has been increasingly used for the assessment of environmental exposure to criteria pollutants, such as O 3 , NO 2 , SO 2 , NH 3 and COV (Hangartner et al., 1989; Koutrakis et al., 1993; Liu et al., 1995; Krupa & Legge, 2000 and Thöni et al., 2003). The benefits of passive sampling devices include simplicity of sampling, low operating costs, high correlation results as compared to continuous monitors and deployment in areas where there is no electricity. A large number of units can be used simultaneously, gathering information on the spatial distribution of the pollutants. Diffusive sampling can be used if the average, instead of the real-time, and pollutant concentration is adequate for the purpose of monitoring (Krupa & Legge, 2000 and De Santis et al., 2003). To assign a contamination value to every point in the zone, spatial interpolations (step 3) of the information obtained in the sampling campaign are made by use of Geographical Information Systems (GIS), which are becoming increasingly popular to estimate the distribution of environmental phenomena (Spokas et al., 2000 and Duc et al., 2000). Also Directive 2008/50/EC (2008) states that modelling techniques should be applied where possible to enable point data to be interpreted in terms of geographical distribution of concentration. The result map obtained by GIS is used to define the best sites for placing the control stations of the air quality monitoring network. In this last step for the design or optimization of the monitoring network a selection of the best locations for the sampling stations is made, obtaining a spatial distribution that ensures compliance with the micro- and macroscale location criteria established in the legislation. Every few years, new sampling campaigns are carried out to verify the improvement of the optimized network and to make sure that the chosen locations for the stations are still representative of the air quality in the area. The method proposed in this article for optimization of the design of air quality monitoring networks and its application to NO 2 and O 3 was carried out in Seville, a city located in Andalucia, southern Spain. The area considered in this study is Seville city and the most densely populated part of its metropolitan area. Seville city has a population of 703 206 inhabitants, and covers a superficies of 140.8 km². Its metropolitan area is composed by 46 municipals and includes a population of About 1 500 000 inhabitants, occupying a superficies of 4900 km². Traffic is the most important source of air pollution in Seville, followed by households. The mining industry of Seville area is the principal source of SO 2 pollution. The sunny climate in the study area favours the photochemical reactions that originate smog. 2. Materials and methods The method developed in this study consists of four steps that make it possible to choose the best locations for the stations of the monitoring network, in compliance with the legislation. Additionally, a fifth step is included for verification of the optimized monitoring network. 2.1. Preliminary evaluation This first step for optimising or designing an air monitoring network includes zonification, classification of the zones and determination of the minimum number of control stations needed. The zonification of the study area consists in subdividing the territory into different zones with similar air quality. The division is based on studies of topography, population, economic activities, weather, land use, situation of nature parks and emission into the atmosphere. A zone with a population in excess of 250 000 inhabitants is considered an agglomeration. The possible types of zones are city (agglomeration), industrial or rural area (Annex XV of Directive 2008/50/EC, 2008). Each zone is classified in terms of the level of recorded pollutants. The upper and lower assessment thresholds (UAT and LAT) for nitrogen dioxide (NO 2 ) are determined in Annex II of Directive 2008/50/EC (2008) (Table 1). The zones are classified as follows: - The level of the pollutant is higher than the UAT; - The level is between the LAT and the UAT; - The level is lower than the LAT. Hourly limit value for the protection of human health (NO 2 ) Annual limit value for the protection of human health (NO 2 ) Annual critical level for the protection of vegetation and natural ecosystems (NO X ) Upper assessment threshold 70 % of limit value (140 μg/m 3 , not to be exceeded more than 18 times in any calendar year) 80 % of limit value (32 μg/m 3 ) 80 % of critical level (24 μg/m 3 ) Lower assessment threshold 50 % of limit value (100 μg/m 3 , not to be exceeded more than 18 times in any calendar year) 65 % of limit value (26 μg/m 3 ) 65 % of critical level (19.5 μg/m 3 ) Table 1. Upper and lower assessment thresholds for nitrogen dioxide and oxides of nitrogen as expressed in Annex II of Directive 2008/50/EC. The classification of each zone or agglomeration in relation to the assessment thresholds must be reviewed at least every five years. Classification must be reviewed earlier in the event of significant changes in activities relevant to ambient concentrations (Directive, 2008). The minimum number of sampling points for the fixed measurement of NO 2 concentration in ambient air is given in annex V of Council Directive 2008/50/EC (2008) and depends on the classification of the zone. The minimum number of sampling points for fixed continuous measurements of ozone (O 3 ) concentration to assess air quality for compliance with the target values, long-term objectives and information and alert thresholds where continuous measurement is the sole source of information is indicated in Annex IX of Directive 2008/50/EC (2008). Table 2 resumes the minimum number of sampling points needed for NO 2 and O 3 . Optimization of the design of air quality monitoring networks and its application to NO2 and O3 in Seville, Spain 51 studied area. In a diffusion sampler, the gas molecules are transported only by molecular diffusion, which is a function of air temperature and pressure. This independence allows the time-weighted average ambient concentration to be calculated using Fick’s laws of diffusion (UNEP, 2004). Diffusive sampling has been increasingly used for the assessment of environmental exposure to criteria pollutants, such as O 3 , NO 2 , SO 2 , NH 3 and COV (Hangartner et al., 1989; Koutrakis et al., 1993; Liu et al., 1995; Krupa & Legge, 2000 and Thöni et al., 2003). The benefits of passive sampling devices include simplicity of sampling, low operating costs, high correlation results as compared to continuous monitors and deployment in areas where there is no electricity. A large number of units can be used simultaneously, gathering information on the spatial distribution of the pollutants. Diffusive sampling can be used if the average, instead of the real-time, and pollutant concentration is adequate for the purpose of monitoring (Krupa & Legge, 2000 and De Santis et al., 2003). To assign a contamination value to every point in the zone, spatial interpolations (step 3) of the information obtained in the sampling campaign are made by use of Geographical Information Systems (GIS), which are becoming increasingly popular to estimate the distribution of environmental phenomena (Spokas et al., 2000 and Duc et al., 2000). Also Directive 2008/50/EC (2008) states that modelling techniques should be applied where possible to enable point data to be interpreted in terms of geographical distribution of concentration. The result map obtained by GIS is used to define the best sites for placing the control stations of the air quality monitoring network. In this last step for the design or optimization of the monitoring network a selection of the best locations for the sampling stations is made, obtaining a spatial distribution that ensures compliance with the micro- and macroscale location criteria established in the legislation. Every few years, new sampling campaigns are carried out to verify the improvement of the optimized network and to make sure that the chosen locations for the stations are still representative of the air quality in the area. The method proposed in this article for optimization of the design of air quality monitoring networks and its application to NO 2 and O 3 was carried out in Seville, a city located in Andalucia, southern Spain. The area considered in this study is Seville city and the most densely populated part of its metropolitan area. Seville city has a population of 703 206 inhabitants, and covers a superficies of 140.8 km². Its metropolitan area is composed by 46 municipals and includes a population of About 1 500 000 inhabitants, occupying a superficies of 4900 km². Traffic is the most important source of air pollution in Seville, followed by households. The mining industry of Seville area is the principal source of SO 2 pollution. The sunny climate in the study area favours the photochemical reactions that originate smog. 2. Materials and methods The method developed in this study consists of four steps that make it possible to choose the best locations for the stations of the monitoring network, in compliance with the legislation. Additionally, a fifth step is included for verification of the optimized monitoring network. 2.1. Preliminary evaluation This first step for optimising or designing an air monitoring network includes zonification, classification of the zones and determination of the minimum number of control stations needed. The zonification of the study area consists in subdividing the territory into different zones with similar air quality. The division is based on studies of topography, population, economic activities, weather, land use, situation of nature parks and emission into the atmosphere. A zone with a population in excess of 250 000 inhabitants is considered an agglomeration. The possible types of zones are city (agglomeration), industrial or rural area (Annex XV of Directive 2008/50/EC, 2008). Each zone is classified in terms of the level of recorded pollutants. The upper and lower assessment thresholds (UAT and LAT) for nitrogen dioxide (NO 2 ) are determined in Annex II of Directive 2008/50/EC (2008) (Table 1). The zones are classified as follows: - The level of the pollutant is higher than the UAT; - The level is between the LAT and the UAT; - The level is lower than the LAT. Hourly limit value for the protection of human health (NO 2 ) Annual limit value for the protection of human health (NO 2 ) Annual critical level for the protection of vegetation and natural ecosystems (NO X ) Upper assessment threshold 70 % of limit value (140 μg/m 3 , not to be exceeded more than 18 times in any calendar year) 80 % of limit value (32 μg/m 3 ) 80 % of critical level (24 μg/m 3 ) Lower assessment threshold 50 % of limit value (100 μg/m 3 , not to be exceeded more than 18 times in any calendar year) 65 % of limit value (26 μg/m 3 ) 65 % of critical level (19.5 μg/m 3 ) Table 1. Upper and lower assessment thresholds for nitrogen dioxide and oxides of nitrogen as expressed in Annex II of Directive 2008/50/EC. The classification of each zone or agglomeration in relation to the assessment thresholds must be reviewed at least every five years. Classification must be reviewed earlier in the event of significant changes in activities relevant to ambient concentrations (Directive, 2008). The minimum number of sampling points for the fixed measurement of NO 2 concentration in ambient air is given in annex V of Council Directive 2008/50/EC (2008) and depends on the classification of the zone. The minimum number of sampling points for fixed continuous measurements of ozone (O 3 ) concentration to assess air quality for compliance with the target values, long-term objectives and information and alert thresholds where continuous measurement is the sole source of information is indicated in Annex IX of Directive 2008/50/EC (2008). Table 2 resumes the minimum number of sampling points needed for NO 2 and O 3 . Air Quality52 Population of agglomeration or zone (thousands) NO 2 O 3 Maximum concentrations exceed UAT Maximum concentrations between UAT and LAT agglomeration Other zones (urban and suburban) 0-249 1 1 1 250-499 2 1 1 2 500-749 2 1 2 2 750-999 3 1 2 2 1000-1499 4 2 3 3 1500-1999 5 2 3 4 2000-2749 6 3 4 5 2750-3749 7 3 5 6 3750-4749 8 4 1 additional station per 2 million inhabitants 4750-5999 9 4 >6000 10 5 Table 2. Minimum number of sampling points (for fixed measurement) needed for NO 2 and O 3 depending on the classification of the zone 2.2. Sampling campaigns with passive diffusion samplers Once the evaluation requirements are known, the most appropriate sites for placement must be determined. Areas with high pollution levels but representative of the zone must be ascertained. In the proposed method, sampling campaigns with passive diffusion samplers are planned in order to determine the spatial distribution of the concentrations and to find the locations within each zone that have the best characteristics for continuous monitoring of air quality. For purposes of taking into account the influence of weather conditions on the contamination levels of nitrogen oxide, two sampling campaigns are carried out, one in winter and one in summer. As the formation of ozone is a photochemical reaction, a large difference in ozone concentrations could be expected between winter and summer, with higher ozone values in summer. Therefore, this pollutant is only measured during a summer campaign (Guicherit & Van Dop, 1977 and Beck et al., 1998). Each sampling campaign consisted of a series of biweekly sampling periods. The average of the periods determines the campaign value. The annual behaviour of the pollutants is estimated from the values of the winter and summer campaigns. In accordance with Annex I of Directive 2008/50/EC (2008), the indicative measurement of nitrogen dioxide needs a minimum time coverage of 14% which means one measurement a week at random, evenly distributed over the year, or eight weeks evenly distributed over the year. For ozone, the minimum time coverage for indicative measurements should be more than 10% during summer. To determine the best siting for the air quality monitoring stations in Seville, two NO 2 campaigns were carried out, one in winter (December 1999-April 2000) and one in summer (June 2000-October 2000). Both campaigns included eight biweekly sampling periods. For O 3 , a summer campaign of 7 biweekly periods was carried out from June 2000 until September 2000. Different kind of passive samplers can be used to determine the studied pollutants. In this study, Ogawa badges were used. They consist of a cylindrical Teflon surface, whose approximate dimensions are 19 mm in external diameter and 30 mm in length. The cylinder is comprised of two chambers separated by a solid segment. The following components are placed in each chamber of the Ogawa tub, beginning at the innermost part: a solid pad, a pad-retaining ring, a stainless steel grid, a fibre-glass filter impregnated with the absorbent reagent, another grid of stainless steel and the diffuser cap at the outer end (Ogawa, 1998 and Ogawa, 2001). The diffusive sampling technique is based on the principle that the pollutant is absorbed into a specific sorbent at a rate controlled by molecular diffusion of the gaseous pollutant in the air. The theoretical rate at which the diffusive sampler collects the pollutant from the atmosphere is described by Fick’s first law of diffusion (Perkauskas & Mikelinskiene, 1998). The concentration C of the pollutant is given by C=m/(U·t), where m is the collected mass of pollutant, t is the averaging time and U is the uptake rate. For the adsorption of NO 2 , the filters are impregnated with triethanolamine (TEA) (Palmes et al., 1976 and Atkins et al., 1986). Many chemicals can be used in diffusive sampling badges for the determination of O 3 concentrations, although studies have shown that sodium nitrite is a better one (Zhou & Smith, 1997). Nitrite impregnated filters were used in this work. Research has shown that when using passive samplers to determine ozone concentrations, measurements are not affected by temperature and humidity and, under ambient conditions, co-pollutant interference is negligible (Koutrakis et al., 1993). The passive diffusion samplers are placed in such a manner that the measurements represent the concentrations of their environment. Geographic Information Systems (GIS) are used to select the sampling sites. The siting criteria established in the legislation are implied in these systems to obtain those sites susceptible to get a diffusion sampler. Annexes III and VIII of Directive 2008/50/EC (2008) list the macroscale and microscale siting criteria to consider for sampling of NO 2 and O 3 respectively. To minimize the effect of wind, rain and direct solar radiation, the tubes were protected by rain shields. Different models are available, ceramic rain shields giving the best results in this study. This protection was attached to wooden blocks (5 cm), fastened to posts and placed between 1.5 and 2.5 m from the ground, using urban furniture. Duplicates (10% of the exposed tubes) were used to determine reproducibility, and field blanks (10% of the exposed tubes) were placed to determine the background reagent contamination and interference during the analytical process. Samplers were sent to and from the field in sealed plastic recipients. A large number of diffusive samplers were used in this study, taking advantage of low operating costs and ease of use. They were located at 139 sites, representing a total area of 1109.3 km², which makes the average radius of representativeness per sampler 1.59 km. [...]... was done for Alcalá de Guadaira, Dos Hermanas and Aljarafe region, the most populated areas of the metropolitan area (Table 3) nº of stations necessary area Superficies (km²) NO2 O3 Existing stations before Seville 141 7 03 206 2 2 8 7 Alcalá de Guadaira 285 70 155 1 1 2 1 Dos Hermanas 159 122 9 43 1 1 0 1 Aljarafe 1 136 33 8 532 1 1 0 1 inhabitants Existing stations after Table 3 Number of assessment stations... only Alcalá de Guadaira counted with air quality assessment stations After optimization, one stations is located in each area, assessing both NO2 and O3 Optimization of the design of air quality monitoring networks and its application to NO2 and O3 in Seville, Spain 57 3. 2 Sampling campaigns with passive diffusion samplers To determine the best siting for the stations of the air quality monitoring... at 139 sites, representing a total area of 1109 .3 km², which makes the average radius of representativeness per sampler 1.59 km 54 Air Quality The municipals included in the study area are: Seville ( 53 sites), Alcalá de Guadaira, Dos Hermanas, La Rinconada, Coria del Río, Bormujos, Santiponce, La Algaba, Gelves, Mairena del Alcor, Mairena del Aljarafe, Camas, Carmona, Castilleja de Guzmán, Espartinas,... the chosen locations for the 56 Air Quality stations were still representative of the air quality in the area The method used was the same as the one described in sections 2.1 to 2 .3 If it seems necessary to relocate some stations, the method described in section 2.4 should be used 3 Results and discussion 3. 1 Preliminary evaluation Before the optimization of the air quality assessment network, Seville... present NO2 values between 21 and 30 μg/m3 Dos Hermanas is characterized by values between 21 and 25 μg/m3 while Alcalá de Guadaira presents values between 16 and 20 μg/m3 and some higher values in the western part of the community The distribution of the ozone concentrations measured in the summer campaign can be seen in Figure 3 Fig 3 The average concentrations for O3 in the summer campaigns in Seville... (20 03) Ozone monitoring in the polar troposphere using a new diffusive sampler Physics and Chemistry of the Earth, Vol 28 (28 -30 ) 12 13- 1216 Directive 2000/69/EC of the European Parliament and of the Council of 16 November 2000 relating to limit values for benzene and carbon monoxide in ambient air, Official Journal of the European Union, L 31 3, 13/ 12/2000, 12-21 Optimization of the design of air quality. .. eastern and western part of the studied area recorded the highest concentrations (>61 μg/m3, locally >71 Optimization of the design of air quality monitoring networks and its application to NO2 and O3 in Seville, Spain 59 μg/m3), while the city centre of Seville showed lower ozone concentrations ( . stations after NO 2 O 3 Seville 141 7 03 206 2 2 8 7 Alcalá de Guadaira 285 70 155 1 1 2 1 Dos Hermanas 159 122 9 43 1 1 0 1 Aljarafe 1 136 33 8 532 1 1 0 1 Table 3. Number of assessment. stations after NO 2 O 3 Seville 141 7 03 206 2 2 8 7 Alcalá de Guadaira 285 70 155 1 1 2 1 Dos Hermanas 159 122 9 43 1 1 0 1 Aljarafe 1 136 33 8 532 1 1 0 1 Table 3. Number of assessment. 1 2 500-749 2 1 2 2 750-999 3 1 2 2 1000-1499 4 2 3 3 1500-1999 5 2 3 4 2000-2749 6 3 4 5 2750 -37 49 7 3 5 6 37 50-4749 8 4 1 additional station