ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-11-7629-2011 Development of a fast, urban chemistry metamodel for inclusion in global models Cohen J. B. 1 2 Prinn R. G. 1 Joint Program of the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA 02139, USA now at: Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore 01 08 2011 11 15 7629 7656 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/11/7629/2011/acp-11-7629-2011.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/11/7629/2011/acp-11-7629-2011.pdf

A reduced form metamodel has been produced to simulate the effects of physical, chemical, and meteorological processing of highly reactive trace species in urban areas, which is capable of efficiently simulating the urban concentration, surface deposition, and net export flux of these species. A polynomial chaos expansion and the probabilistic collocation method have been used to develop the metamodel, and its coefficients, so that it is applicable under a broad range of present-day and future conditions. The inputs upon which this metamodel have been formed are based on a combination of physical properties (average temperature, diurnal temperature range, date, and latitude), anthropogenic properties (patterns and amounts of emissions), and the nature of the surrounding environment (background concentrations of species). The metamodel development involved using probability distribution functions (PDFs) of the inputs to run a detailed parent chemical and physical model, the Comprehensive Air Quality Model with Extensions (CAMx), thousands of times. Outputs from these runs were used in turn to both determine the coefficients of and test the precision of the metamodel, as compared with the detailed parent model. It was determined that the deviations between the metamodel and the parent mode for many important species (O<sub>3</sub>, CO, NO<sub>x</sub>, and black carbon (BC)) were found to have a weighted RMS error less than 10 % in all cases, with many of the specific cases having a weighted RMS error less than 1 %. Some of the other important species (VOCs, PAN, OC, and sulfate aerosol) usually have their weighted RMS error less than 10 % as well, except for a small number of cases. In these cases, the complexity and non-linearity of the physical, chemical, and meteorological processing is too large for the third order metamodel to give an accurate fit. Finally, sensitivity tests have been performed, to observe the response of the 16 metamodels (4 different meteorologies and 4 different urban types) to a broad set of potential inputs. These results were compared with observations of ozone, CO, formaldehyde, BC, and PM<sub>10</sub> from a few well observed urban areas, and in most of the cases, the output distributions were found to be within ranges of the observations. Overall, a set of efficient and robust metamodels have been generated which are capable of simulating the effects of various physical, chemical, and meteorological processing, and capable of determining the urban concentrations, mole fractions, and fluxes of species, important to human health and the global climate.

 References Badarinath, K., Kharol, S., Reddy, R., Gopal, K., Narasimhulu, K., Reddy, L., and Kumar, K.: Black carbon aerosol mass concentration variation in urban and rural environments of India – a case study, Atmos. Sci. Lett., 10, 29–33, http://dx.doi.org/10.1002/asl.206doi:10.1002/asl.206, 2009. Beegum, S., Moorthy, K., Babu, S., Satheesh, S., Vinoj, V., Badarinath, K., Safai, P., Devara, P., Singh, S., DUC, V., and Pant, P.: Spatial distribution of aerosol black carbon over India during pre-monsoon season, Atmos. Environ., 43, 1071–1078, http://dx.doi.org/10.1016/j.atmosenv.2008.11.042doi:10.1016/j.atmosenv.2008.11.042, 2009. Butler, T. and Lawrence, M.: The influence of megacities on global atmospheric chemistry: a modelling study, Environ. Chem., 6, 219–225, 2009. Calbo, J., Pan, W., Webster, M., Prinn, R., and McRae, G.: Parameterization of urban subgrid scale processes in global atmospheric chemistry models, J. Geophys. Res.-Atmos., 103, 3437–3451, 1998. Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT): Gridded Population of the World Version 3 (GPWv3): Population Grids. Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University, available at: http://sedac.ciesin.columbia.edu/gpw, 2005. Chang, J. S., Brost, R. A., Isaksen, I. S. A., Madronich, S., Middleton, P., Stockwell, W. R., and Walcek, C. J.: A 3-Dimensional Eulerian Acid Deposition Model – Physical Concepts and Formulation, J. Geophys. Res.-Atmos., 92, 14681–14700, http://dx.doi.org/10.1029/JD092iD12p14681doi:10.1029/JD092iD12p14681, 1987. Cossa, P.: Uncertainty analysis of the cost of climate policies, Master's thesis, Massachusetts Institute of Technology, http://dspace.mit.edu/handle/1721.1/30074, 2004. Ding, A. J., Wang, T., Thouret, V., Cammas, J.-P., and Nédélec, P.: Tropospheric ozone climatology over Beijing: analysis of aircraft data from the MOZAIC program, Atmos. Chem. Phys., 8, 1–13, http://dx.doi.org/10.5194/acp-8-1-2008doi:10.5194/acp-8-1-2008, 2008. Dutkiewicz, V., Alvi, S., Ghauri, B., Choudhary, M., and Husain, L.: Black carbon aerosols in urban air in South Asia, Atmos. Environ., 43, 1737–1744, http://dx.doi.org/10.1016/j.atmosenv.2008.12.043doi:10.1016/j.atmosenv.2008.12.043, 2009. Dutta, C., Som, D., Chatterjee, A., Mukherjee, A., Jana, T., and Sen, S.: Mixing ratios of carbonyls and BTEX in ambient air of Kolkata, India and their associated health risk, Environ. Monit. Assess., 148, 97–107, http://dx.doi.org/10.1007/s10661-007-0142-0doi:10.1007/s10661-007-0142-0, 2009. Gery, M., Whitten, G., Killus, J., and Dodge, M.: A photochemical kinetics mechanism for urban and regional scale computer modeling, J. Geophys. Res.-Atmos., 94, 12925–12956, 1989. Godoy, M., Godoy, J., Roldao, L., Soluri, D., and Donagemma, R.: Coarse and fine aerosol source apportionment in Rio de Janeiro, Brazil, Atmos. Environ., 43, 2366–2374, http://dx.doi.org/10.1016/j.atmosenv.2008.12.046doi:10.1016/j.atmosenv.2008.12.046, 2009. Houweling, S., Dentener, F., and Lelieveld, J.: The impact of nonmethane hydrocarbon compounds on tropospheric photochemistry, J. Geophys. Res.-Atmos., 103, 10673–10696, 1998. Huang, X.-F., He, L.-Y., Hu, M., Canagaratna, M. R., Sun, Y., Zhang, Q., Zhu, T., Xue, L., Zeng, L.-W., Liu, X.-G., Zhang, Y.-H., Jayne, J. T., Ng, N. L., and Worsnop, D. R.: Highly time-resolved chemical characterization of atmospheric submicron particles during 2008 Beijing Olympic Games using an Aerodyne High-Resolution Aerosol Mass Spectrometer, Atmos. Chem. Phys., 10, 8933–8945, http://dx.doi.org/10.5194/acp-10-8933-2010doi:10.5194/acp-10-8933-2010, 2010. Husain, L., Dutkiewicz, V., Khan, A., and Ghauri, B.: Characterization of carbonaceous aerosols in urban air, Atmos. Environ., 41, 6872–6883, http://dx.doi.org/10.1016/j.atmosenv.2007.04.037doi:10.1016/j.atmosenv.2007.04.037, 2007. Jones, P., New, M., Parker, D., Martin, S., and Rigor, I.: Surface air temperature and its changes over the past 150 years, Rev. Gephys., 37, 173–199, 1999. Koo, B. Y., Ansari, A. S., and Pandis, S. N.: Integrated approaches to modeling the organic and inorganic atmospheric aerosol components, Atmos. Environ., 34, 4757–4768, http://dx.doi.org/10.1016/j.atmosenv.2003.08.016doi:10.1016/j.atmosenv.2003.08.016, 2003. Lei, W., Zavala, M., de Foy, B., Volkamer, R., Molina, M. J., and Molina, L. T.: Impact of primary formaldehyde on air pollution in the Mexico City Metropolitan Area, Atmos. Chem. Phys., 9, 2607–2618, http://dx.doi.org/10.5194/acp-9-2607-2009doi:10.5194/acp-9-2607-2009, 2009. Mayer, M., Wang, C., Webster, M., and Prinn, R.: Linking local air pollution to global chemistry and climate, J. Geophys. Res.-Atmos., 105, 22869–22896, 2000. Nenes, A., Pandis, S. N., and Pilinis, C.: Continued development and testing of a new thermodynamic aerosol module for urban and regional air quality models, Atmos. Environ., 33, 1553–1560, http://dx.doi.org/10.1016/S1352-2310(98)00352-5doi:10.1016/S1352-2310(98)00352-5, 1999. Paltsev, S., Reilly, J., Jacoby, H., Eckaus, R., McFarland, J., Sarofim, M., Asadoorian, M., and Babiker, M.: The MIT emissions prediction and policy analysis (EPPA) Model: Version 4, The MIT Joint Program on the Science and Policy of Global Change Report Series 125, Massachusetts Institute of Technology, http://globalchange.mit.edu/files/document/MITJPSPGC_Rpt125.pdf, 2005. Prather, M., Ehhalt, D., Dentener, F., Derwent, R., Dlugokencky, E., Holland, E., Isaksen, I., Katima, J., Kirchhoff, V., Matson, P., Midglet, P., and Wang, M.: Atmospheric chemistry and greenhouse gases, in: Climate change 2001: The scientific basis. Contribution of working group I to the third assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 2001. Qian, Y., Gustafson Jr., W. I., and Fast, J. D.: An investigation of the sub-grid variability of trace gases and aerosols for global climate modeling, Atmos. Chem. Phys., 10, 6917–6946, http://dx.doi.org/10.5194/acp-10-6917-2010doi:10.5194/acp-10-6917-2010, 2010. Ramirez, S., Hermes, U., Andrade, G., Maria, D., Bejaran, R., Garcia, G., Mario, E., Wallo, V., Pompa, T., Ana, C., and delaTorreVillasenor, O.: The spatial-temporal distribution of the atmospheric polluting agents during the period 2000–2005 in the Urban Area of Guadalajara, Jalisco, Mexico, J. Hazard. Mater., 165, 1128–1141, http://dx.doi.org/10.1016/j.jhazmat.2008.10.127doi:10.1016/j.jhazmat.2008.10.127, 2009. Russell, M. and Allen, D.: Predicting secondary organic aerosol formation rates in southeast Texas, J. Geophys. Res.-Atmos., 110, 7S17–7S30, 2005. Sarwar, G., Luecken, D., Yarwood, G., Whitten, G., and Carter, W.: Impact of an updated carbon bond mechanism on predictions from the CMAQ modeling system: Preliminary assessment, J. Appl. Meteorol. Clim., 47, 3–14, http://dx.doi.org/10.1175/2007JAMC1393.1doi:10.1175/2007JAMC1393.1, 2008. See, S. W., Balasubramanian, R., and Wang, W.: A study of the physical, chemical, and optical properties of ambient aerosol particles in Southeast Asia during hazy and nonhazy days, J. Geophys. Res.-Atmos., 111, 0148–0227, http://dx.doi.org/10.1029/2005JD006180doi:10.1029/2005JD006180, 2006. Sokolov, A., Schlosser, A., Dutkiewicz, S., Paltsev, S., Kicklighter, D., Jacoby, H., Prinn, R., Forest, C., Reilly, J., Wang, C., Felzer, B., Sarofim, M., Scott, J., Stone, P., Melillo, J., and Cohen, J.: MIT integrated global system model (IGSM) version 2: Model description and baseline evaluation, The MIT Joint Program on the Science and Policy of Global Change Report Series 124, Massachusetts Institute of Technology, 2005. Strader, R., Lurmann, F., and Pandis, S. N.: Evaluation of secondary organic aerosol formation in winter, Atmos. Environ., 33, 4849–4863, http://dx.doi.org/10.1016/S1352-2310(99)00310-6doi:10.1016/S1352-2310(99)00310-6, 1999. Tatang, M., Pan, W., Prinn, R., and McRae, G.: An efficient method for parametric uncertainty analysis of numerical geophysical models, J. Geophys. Res.-Atmos., 102, 21925–21932, 1997. Vukovich, F.: PAMS data analysis: An investigation of local meteorological effects on ozone during the OTAG 1995 episode and the weekday/weekend differences in the Northeast Corridor, Tech. rep., United States Environmental Protection Agency, http://capita.wustl.edu/otag/reports/pamsinv2/pamsinv2.html, 1997. Wild, O. and Prather, M.: Global tropospheric ozone modeling: Quantifying errors due to grid resolution, J. Geophys. Res.-Atmos., 111, 11305–11318, http://dx.doi.org/10.1029/2005JD006605doi:10.1029/2005JD006605, 2006. Yang, K., Ting, C., Wang, J., Wingenter, O., and Chan, C.: Diurnal and seasonal cycles of ozone precursors observed from continuous measurement at an urban site in Taiwan, Atmos. Environ., 39, 3221–3230, http://dx.doi.org/10.1016/j.atmosenv.2005.02.003doi:10.1016/j.atmosenv.2005.02.003, 2005. Yokouchi, Y.: Seasonal and diurnal variation of isoprene and its reaction products in a semirural area, Atmos. Environ., 28, 2651–2658, 1994.