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Abstract:

A simple urban air quality model [MODelo de Dispersión Atmosférica Ubana – Generic Reaction Set (DAUMOD-GRS)] was recently developed. One-hour peak O3 concentrations in the Metropolitan Area of Buenos Aires (MABA) during the summer estimated with the DAUMOD-GRS model have shown values lower than 20 ppb (the regional background concentration) in the urban area and levels greater than 40 ppb in its surroundings. Due to the lack of measurements outside the MABA, these relatively high ozone modelled concentrations constitute the only estimate for the area. In this work, a methodology based on the Monte Carlo analysis is implemented to evaluate the uncertainty in these modelled concentrations associated to possible errors of the model input data. Results show that the larger 1-h peak O3 levels in the MABA during the summer present larger uncertainties (up to 47 ppb). On the other hand, multiple linear regression analysis is applied at selected receptors in order to identify the variables explaining most of the obtained variance. Although their relative contributions vary spatially, the uncertainty of the regional background O3 concentration dominates at all the analysed receptors (34.4–97.6%), indicating that their estimations could be improved to enhance the ability of the model to simulate peak O3 concentrations in the MABA. © 2016 Elsevier Ltd

Registro:

Documento: Artículo
Título:Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis
Autor:Pineda Rojas, A.L.; Venegas, L.E.; Mazzeo, N.A.
Filiación:Centro de Investigaciones del Mar y la Atmósfera (CIMA/CONICET-UBA), DCAO/FCEN, UMI-IFAECI/CNRS, Ciudad Universitaria, Pabellón II, Piso 2. 1428, Buenos Aires, Argentina
Department of Chemical Engineering, Avellaneda Regional Faculty, National Technological University, CONICET, Av. Ramón Franco 5050, 1874, Avellaneda, Buenos Aires, Argentina
Palabras clave:Air quality; Model uncertainty; Monte Carlo analysis; Ozone; Sensitivity; Air quality; Input output programs; Linear regression; Monte Carlo methods; Ozone; Quality control; Regression analysis; Background concentration; Generic reaction set; Model uncertainties; Monte carlo analysis; Multiple linear regression analysis; Relative contribution; Sensitivity; Urban air quality; Uncertainty analysis; nitrogen oxide; oxygen; ozone; volatile organic compound; air quality; concentration (composition); data assimilation; error analysis; metropolitan area; Monte Carlo analysis; ozone; uncertainty analysis; air quality; air temperature; Article; controlled study; Monte Carlo method; multiple linear regression analysis; photolysis; priority journal; sensitivity analysis; solar radiation; titrimetry; urban area; wind; Argentina; Buenos Aires [Argentina]
Año:2016
Volumen:141
Página de inicio:422
Página de fin:429
DOI: http://dx.doi.org/10.1016/j.atmosenv.2016.07.020
Título revista:Atmospheric Environment
Título revista abreviado:Atmos. Environ.
ISSN:13522310
CODEN:AENVE
CAS:nitrogen oxide, 11104-93-1; oxygen, 7782-44-7; ozone, 10028-15-6
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_13522310_v141_n_p422_PinedaRojas

Referencias:

  • Athanassiadou, M., Baker, J., Carruthers, D., Collins, W., Girnary, S., Hassell, D., Hort, M., Witham, C., An assessment of the impact of climate change on air quality at two UK sites (2010) Atmos. Environ., 44, pp. 1877-1886
  • Azzi, M., Johnson, G., Cope, M., An introduction to the generic reaction set photochemical smog model (1992) Proc. 11th Int. Clean. Air Conf., pp. 451-462
  • Borrego, C., Monteiro, A., Ferreira, J., Miranda, A.I., Costa, A.M., Carvalho, A.C., Lopes, M., Procedures for estimation of modelling uncertainty in air quality assessment (2008) Environ. Int., 34, pp. 613-620
  • Bergin, M.S., Noblet, G.S., Petrini, K., Dhieux, J.R., Milford, J.B., Harley, R.A., Formal uncertainty analysis of a Lagrangian photochemical air pollution model (1999) Environ. Sci. Technol., 33, pp. 1116-1126
  • Bogo, H., Negri, R.M., San Román, E., Continuous measurement of gaseous pollutants in Buenos Aires city (1999) Atmos. Environ., 33, pp. 2587-2598
  • Calfapietra, C., Fares, S., Manes, F., Morani, A., Sgrigna, G., Loreto, F., Role of Biogenic Volatile Organic Compounds (BVOC) emitted by urban trees on ozone concentration in cities: a review (2013) Environ. Pollut., 183, pp. 71-80
  • Chang, J.C., Hanna, S.R., Technical Descriptions and User's Guide for the BOOT Statistical Model Evaluation Software Package, Version 2.0 (2005), p. 64. , http://www.harmo.org/Kit/Download/BOOT_UG.pdf, Available on; Chen, S., Brune, W., Global sensitivity analysis of ozone production and O3-NOx-VOC limitation based on field data (2012) Atmos. Environ., 55, pp. 288-296
  • Clapp, L.J., Jenkin, M.E., Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOx in UK (2001) Atmos. Environ., 35, pp. 6391-6405
  • Derwent, D., Fraser, A., Abbott, J., Jenkin, M., Willis, P., Murrells, T., Evaluating the Performance of Air Quality Models. DEFRA Report. Issue 3/June 2010 (2010), http://www.airquality.co.uk/reports/cat05/1006241607_100608_MIP_Final_Version.pdf, Available on; Derwent, R.G., Murrells, T.P., Impact of policy-relevant scenarios on ozone in southern England: influence of chemical mechanism choice (2013) Atmos. Environ., 72, pp. 89-96
  • Dunker, A.M., Yarwood, G., Ortmann, P., Wilson, G.M., The decoupled direct method for sensitivity analysis in a three-dimensional air quality model - implementation, accuracy, and efficiency (2002) Environ. Sci. Technol., 36, pp. 2965-2976
  • Hanna, S.R., Chang, J.C., Fernau, M.E., Monte Carlo estimates of uncertainties in predictions by a photochemical grid model (UAM-IV) due to uncertainties in input variables (1998) Atmos. Environ., 32 (21), pp. 3619-3628
  • Hanna, S.R., Paine, R., Heinold, D., Kintigh, E., Baker, D., Uncertainties in air toxics calculated by the dispersion models AERMOD and ISCST3 in the houston ship channel area (2007) J. Appl. Meteorol. Climatol., 46, pp. 1372-1382
  • Hanna, S.R., Russell, A.G., Wilkinson, J.G., Vukovich, J., Hansen, D.A., Monte Carlo estimation of uncertainties in BEIS3 emission outputs and their effects on uncertainties in chemical transport model predictions (2005) J. Geophys. Res., 110, p. D01302
  • Hurley, P.J., Physick, W.L., Luhar, A.K., TAPM: a practical approach to prognostic meteorological and air pollution modelling (2005) Environ. Model. Softw., 20, pp. 737-752
  • Kim, C.-H., Park, S.-U., Song, C.-K., A simple semi-empirical photochemical model for the simulation of ozone concentration in the Seoul metropolitan area in Korea (2005) Atmos. Environ., 39, pp. 5597-5607
  • Kioutsioukis, I., Melas, D., Zerefos, C., Ziomas, I., Efficient sensitivity computations in 3D air quality models (2005) Comput. Phys. Commun., 167, pp. 23-33
  • Lee, Y.C., Shindell, D.T., Faluvegi, G., Wenig, M., Lam, Y.F., Ning, Z., Hao, S., Lai, C.S., Increase of ozone concentrations, its temperature sensitivity and the precursor factor in South China (2014) Tellus B, 66, p. 23455. , http://dx.doi.org/10.3402/tellusb.v66.23455
  • Mazzeo, N.A., Venegas, L.E., Air pollution model for an urban area (1991) Atmos. Res., 26, pp. 165-179
  • Mazzeo, N.A., Venegas, L.E., Design of an air-quality surveillance system for Buenos Aires city integrated by a NOx monitoring network and atmospheric dispersion models (2008) Environ. Model. Assess., 13, pp. 349-356
  • Mazzeo, N.A., Venegas, L.E., Choren, H., Analysis of NO, NO2, O3 and NOx concentrations measured at a green area of Buenos Aires City during wintertime (2005) Atmos. Environ., 39, pp. 3055-3068
  • Moore, G.E., Londergan, R.J., Sampled Monte Carlo uncertainty analysis for photochemical grid models (2001) Atmos. Environ., 35, pp. 4863-4876
  • Paoletti, E., Manning, W.J., Toward a biologically significant and usable standard for ozone that will also protect plants. A Review (2007) Environ. Pollut., 150, pp. 85-95
  • Paoletti, E., De Marco, A., Beddows, D., Harrison, R., Manning, W.J., Ozone levels in European and USA cities are increasing more than at rural sites, while peak values are decreasing (2014) Environ. Pollut., 192, pp. 295-299
  • Pineda Rojas, A.L., Simple atmospheric dispersion model to estimate hourly ground-level nitrogen dioxide and ozone concentrations at urban scale (2014) Environ. Model. Softw., 59, pp. 127-134
  • Pineda Rojas, A.L., Venegas, L.E., Atmospheric deposition of nitrogen emitted in the metropolitan area of Buenos Aires to coastal waters of de la Plata River (2009) Atmos. Environ., 43, pp. 1339-1348
  • Pineda Rojas, A.L., Venegas, L.E., Upgrade of the DAUMOD atmospheric dispersion model to estimate urban background NO2 concentrations (2013) Atmos. Res., 120-121, pp. 147-154
  • Pineda Rojas, A.L., Venegas, L.E., Spatial distribution of ground-level urban background O3 concentrations in the Metropolitan Area of Buenos Aires, Argentina (2013) Environ. Pollut., 183, pp. 159-165
  • Refsgaard, J.C., van der Sluijs, J.P., Højberg, A.L., Vanrolleghem, P.A., Uncertainty in the environmental modelling process e A framework and guidance (2007) Environ. Model. Softw., 22, pp. 1543-1556
  • Rodriguez, M.A., Bouwer, J., Samuelsen, G.S., Dabdub, D., Air quality impacts of distributed power generation in the South Coast Air Basin of California 2: model uncertainty and sensitivity analysis (2007) Atmos. Environ., 41, pp. 5618-5635
  • Russell, A., Dennis, R., NARSTO critical review of photochemical models and modelling (2000) Atmos. Environ., 34 (12-14), pp. 2283-2324
  • Sillman, S., Samson, P.J., Impact of temperature on oxidant photochemistry in urban, polluted rural and remote environment (1995) J. Geophys. Res., 100, pp. 11497-11508
  • Strong, J., Whyatt, J.D., Metcalfe, S.E., Derwent, R.G., Hewitt, C.N., Investigating the impacts of anthropogenic and biogenic VOC emissions and elevated temperatures during the 2003 ozone episode in the Uk (2013) Atmos. Environ., 74, pp. 393-401
  • Tan, Y., Robinson, A.L., Presto, A.A., Quantifying uncertainties in pollutant mapping studies using the Monte Carlo method (2014) Atmos. Environ., 99, pp. 333-340
  • Tang, X., Wang, Z., Zhu, J., Gbaguidi, A.E., Wu, Q., Li, J., Zhu, T., Sensitivity of ozone to precursor emissions in urban Beijing with a Monte Carlo scheme (2010) Atmos. Environ., 44, pp. 3833-3842
  • Venegas, L.E., Mazzeo, N.A., Modelling of urban background pollution in Buenos Aires city (Argentina) (2006) Environ. Model. Softw., 21 (4), pp. 577-586
  • Venegas, L.E., Mazzeo, N.A., Pineda Rojas, A.L., Chapter 14: evaluation of an emission inventory and air pollution in the metropolitan area of Buenos Aires (2011) Air Quality-models and Applications, Editorial In-tech, pp. 261-288. , D. Popovic
  • Venkatram, A., Karamchandani, P., Pai, P., Goldstein, R., The development and application of a simplified ozone modelling system (SOMS) (1994) Atmos. Environ., 28 (22), pp. 3665-3678
  • Wang, Y., Konopka, P., Liu, Y., Chen, H., Müller, R., Plöger, F., Riese, M., Lü, D., Tropospheric ozone trend over Beijing from 2002–2010: ozonesonde measurements and modeling analysis (2012) Atmos. Chem. Phys., 12, pp. 8389-8399
  • WHO, World Health Organization Media Centre, Air Quality and Health, Fact Sheet N.313. Updated March 2014 (2014), http://www.who.int/mediacentre/factsheets/fs313/es/, available online on

Citas:

---------- APA ----------
Pineda Rojas, A.L., Venegas, L.E. & Mazzeo, N.A. (2016) . Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis. Atmospheric Environment, 141, 422-429.
http://dx.doi.org/10.1016/j.atmosenv.2016.07.020
---------- CHICAGO ----------
Pineda Rojas, A.L., Venegas, L.E., Mazzeo, N.A. "Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis" . Atmospheric Environment 141 (2016) : 422-429.
http://dx.doi.org/10.1016/j.atmosenv.2016.07.020
---------- MLA ----------
Pineda Rojas, A.L., Venegas, L.E., Mazzeo, N.A. "Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis" . Atmospheric Environment, vol. 141, 2016, pp. 422-429.
http://dx.doi.org/10.1016/j.atmosenv.2016.07.020
---------- VANCOUVER ----------
Pineda Rojas, A.L., Venegas, L.E., Mazzeo, N.A. Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis. Atmos. Environ. 2016;141:422-429.
http://dx.doi.org/10.1016/j.atmosenv.2016.07.020