References

ACP

Atmospheric Chemistry and Physics

ACP

Atmos. Chem. Phys.

1680-7324

Copernicus Publications

Göttingen, Germany

10.5194/acp-14-7461-2014

Impacts of different plant functional types on ambient ozone predictions in the Seoul Metropolitan Areas (SMAs), Korea

Kim

H.-K.

https://orcid.org/0000-0001-9694-2649

¹ Woo

J.-H.

¹ Park

R. S.

² Song

C. H.

² Kim

J.-H.

³ Ban

S.-J.

⁴ Park

J.-H.

⁴

Konkuk University, Department of Advanced Technology Fusion, Seoul, Korea

Gwangju Institute of Science and Technology, School of Environmental Science and Engineering, Gwangju, Korea

Green-ECOS, Seoul, Korea

National Institute of Environmental Research, Air Quality Research Division, Incheon, Korea

22 07 2014

14 14 7461 7484

2014

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://acp.copernicus.org/articles/14/7461/2014/acp-14-7461-2014.html

The full text article is available as a PDF file from https://acp.copernicus.org/articles/14/7461/2014/acp-14-7461-2014.pdf

Plant functional type (PFT) distributions affect the results of biogenic emission modeling as well as O3 and particulate matter (PM) simulations using chemistry-transport models (CTMs). This paper analyzes the variations of both surface biogenic volatile organic compound (BVOC) emissions and O3 concentrations due to changes in the PFT distributions in the Seoul Metropolitan Areas, Korea. The Fifth-Generation NCAR/Pennsylvania State Meso-scale Model (MM5)/the Model of Emissions of Gases and Aerosols from Nature (MEGAN)/the Sparse Matrix Operator Kernel Emissions (SMOKE)/the Community Multiscale Air Quality (CMAQ) model simulations were implemented over the Seoul Metropolitan Areas in Korea to predict surface O3 concentrations for the period of 1 May to 31 June 2008. Starting from a performance check of CTM predictions, we consecutively assessed the effects of PFT area deviations on the MEGAN BVOC and CTM O3 predictions, and we further considered the basis of geospatial and statistical analyses. The three PFT data sets considered were (1) the Korean PFT, developed with Korea-specific vegetation database; (2) the CDP PFT, adopted from the community data portal (CDP) of US National Center for Atmospheric Research in the United States (NCAR); (3) MODIS PFT, reclassified from the NASA Terra and Aqua combined land cover products. Although the CMAQ performance check reveals that all of the three different PFT data sets are applicable choices for regulatory modeling practice, noticeable primary data (i.e., PFT and Leaf Area Index (LAI)) was observed to be missing in many geographic locations. Based on the assessed effect of such missing data on CMAQ O3 predictions, we found that this missing data can cause spatially increased bias in CMAQ O3. Thus, it must be resolved in the near future to obtain more accurate biogenic emission and chemistry transport modeling results. Comparisons of MEGAN biogenic emission results with the three different PFT data showed that broadleaf trees (BTs) are the most significant contributor, followed by needleleaf trees (NTs), shrub (SB), and herbaceous plants (HBs) to the total BVOCs. In addition, isoprene from BTs and terpene from NTs were recognized as significant primary and secondary BVOC species in terms of BVOC emissions distributions and O3-forming potentials in the study domain. A geographically weighted regression analysis with locally compensated ridge (LCR-GWR) with the different PFT data (δO3 vs. δPFTs) suggests that addition of BT, SB, and NT areas can contribute to O3 increase, whereas addition of an HB area contributes to O3 decrease in the domain. Assessment results of the simulated spatial and temporal changes of O3 distributions with the different PFT scenarios reveal that hourly and local impacts from the different PFT distributions on occasional inter-deviations of O3 are quite noticeable, reaching up to 13 ppb. The simulated maximum 1 h O3 inter-deviations between different PFT scenarios have an asymmetric diurnal distribution pattern (low in the early morning, rising during the day, peaking at 05:00 p.m., and decreasing during the night) in the study domain. Exponentially diverging hourly BVOC emissions and O3 concentrations with increasing ambient temperature suggest that the use of different PFT distribution data requires much caution when modeling (or forecasting) O3 air quality in complicated urban atmospheric conditions in terms of whether uncertainties in O3 prediction results are expected to be mild or severe.

References 1

Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: International symposium on information theory, 2nd, Tsahkadsor, Armenian SS, Budapest: Akademiai Kiado, 267–281, 1973.

Arneth, A., Schurgers, G., Lathiere, J., Duhl, T., Beerling, D. J., Hewitt, C. N., Martin, M., and Guenther, A.: Global terrestrial isoprene emission models: sensitivity to variability in climate and vegetation, Atmos. Chem. Phys., 11, 8037–8052, <a href="http://dx.doi.org/10.5194/acp-11-8037-2011">https://doi.org/10.5194/acp-11-8037-2011</a>, 2011.

Atkinson, R. and Arey, J.: Gas-phase tropospheric chemistry of biogenic volatile organic compounds: a review, Atmos. Environ., 37 (Suppl. 2), The 1997 Southern California Ozone Study (SCOS97-NARSTO), dedicated to the Memory of Dr. Glen Cass (1947–2001), S197–S219, 2003.

Belsley, D. A., Kuh, E., and Welsch, R. E.: Regression diagnostics: Identifying influential data and sources of collinearity, John Wiley & Sons, Inc., 1980.

Binkowski, F. S. and Roselle, S. J.: Models-3 Community Multi-scale Air Quality (CMAQ) model aerosol components: 1. model description, J. Geophys. Res., 108, 4183, <a href="http://dx.doi.org/10.1029/2001JD001409">https://doi.org/10.1029/2001JD001409</a>, 2003.

Bonan, G. B., Levis, S., Kergoat, L., and Oleson, K. W.: Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models, Global Biogeochem. Cy., 16, 1021, <a href="http://dx.doi.org/10.1029/2000GB001360">https://doi.org/10.1029/2000GB001360</a>, 2002.

Bowman, A.: An Alternative Method of cross-validation for the smoothing of density estimates, Biometrika, 71, 353–360, 1984.

Brunsdon, C., Fotheringham, A. S., and Charlton, M.: Geographically Weighted Regression:A Method for Exploring Spatial Nonstationarity, Geogr. Anal., 28, 281–289, 1996.

Brunsdon, C., Fotheringham, A. S., and Charlton, M.: Geographically weighted regression-Modeling spatial non-stationarity, The Statistician, 47, 431–443, 1998.

Brunsdon, C., Charlton, M., and Harris, P.: Living with Collinearity in Local Regression Models, in: Proceedings of the 10th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Brasil, 2012.

Byun, D. W. and Ching, J. K. S.: Science algorithms of the EPA models-3 Community Multiscale Air Quality (CMAQ) modeling system, EPA/600/R-99/030, US EPA, Research Triangle Park, USA, 1999.

Byun, D. W. and Schere, K. L.: Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, Appl. Mech. Rev., 59, 51–77, 2006.

CARB, Proposed Regulations for Low-Emission Vehicles and Clean Fuels – Staff Report and Technical Support Document, California Air Resources Board, Sacramento, CA, 1993.

Carter, W. P. L.: Implementation of the SAPRC-99 chemical mechanism into the models-3 framework, Report to the United States Environmental Protection Agency, 2000a.

Carter, W. P. L.: Documentation of the SAPRC-99 Chemical Mechanism for VOC Reactivity Assessment," Report to the California Air Resources Board, Contracts 92-329 and 95-308, 8 May, available at: <a href="http://cert.ucr.edu/ carter/absts.htm#saprc99">http://cert.ucr.edu/ carter/absts.htm#saprc99</a> (last access: 21 May 2013), 2000b.

Carter, W. P. L. and Seinfeld, J. H.: Winter ozone formation and VOC incremental reactivities in the Upper Green River Basin of Wyoming, Atmos. Environ., 50, 255–266, 2012.

Cleveland, W. S.: Robust locally weighted regression and smoothing scatterplots, J. Am. Stat. Assoc., 74, 829–836, 1979.

CMAQ v4.6 Operational Guidance Document, <a href="http://www.ie.unc.edu/cempd/products/cmaq/op_guidance_4.6/html">http://www.ie.unc.edu/cempd/products/cmaq/op_guidance_4.6/html</a> (last access: 22 August 2006), 2006.

Colella, P. and Woodward, P. L.: The Piecewise Parabolic Method (PPM) for gas dynamical simulation, J. Comput. Phys., 54, 174–201, 1984.

Finlayson-Pitts, B. J. and Pitts Jr., J. N.: Chemistry of the upper and lower atmosphere, Academic Press, San Diego, CA, 2000.

Fotheringham, A. S., Brunsdon, C., and Charlton, M.: Geographically Weighted Regression: the Analysis of Spatially Varying Relationships, Wiley, Chichester, 2002.

Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C.: Global Land Cover Mapping from MODIS: Algorithms and Early Results, Remote Sens. Environ., 83, 287–302, 2002.

Gollini, I., Lu, B., Charlton, M., Brunsdon, C., and Harris, P.: GWmodel: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models. In arXiv:1306.0413 [stat.AP], available at: <a href="http://arxiv.org/pdf/1306.0413v2.pdf">http://arxiv.org/pdf/1306.0413v2.pdf</a> (last access: 13 January 2014), 2013.

Grell, G. A., Dudhia, J., and Stauffer, D. R.: A description of the fifth-generation Penn State/NCAR mesoscale model (MM5), NCAR Technical Note, NCAR/TN-398+STR, 117 pp., 1994.

Guenther, A. and Sakulyanontvittaya, T.: Improved Biogenic Emission Inventories across the West, Technical Analysis Report for Western Governors' Association, Project Number: 06-27369, Alex Guenther, Colorado and ENVIRON International Corporation, California, available at: <a href="http://www.wrapair2.org/pdf/WGA_Task1_TechnicalAnalysisReport_ImprovedBiogenicEmissionInventories.pdf">http://www.wrapair2.org/pdf/WGA_Task1_TechnicalAnalysisReport_ImprovedBiogenicEmissionInventories.pdf</a> (last access: 5 February 2012), 2011.

Guenther, A., Hewitt, C. N., Erickson, D., Fall, R., Geron, C., Graedel, T., Harley, P., Klinger, L., Lerdau, M., Mckay, W. A., Pierce, T., Scholes, B., Steinbrecher, R., Tallamraju, R., Taylor, J., and Zimmerman, P. A.: Global-Model of Natural Volatile Organic-Compound Emissions, J. Geophys. Res.-Atmos., 100, 8873–8892, 1995.

Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, <a href="http://dx.doi.org/10.5194/acp-6-3181-2006">https://doi.org/10.5194/acp-6-3181-2006</a>, 2006.

Hoaglin, D. C. and Welsch, R. E.: The hat matrix in regression and ANOVA, The American Statistician, 32, 17–22, 1978.

Hoerl, A. E.: Application of ridge analysis to regression problems, Chemical Engineering Progress, 58, 54–59, 1962.

Hoerl, A. E. and Kennard, R. W.: Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, 12, 55–67, 1970.

Hogrefe, C., Isukapalli, S., Tang, X., Georgopoulos, P., He, S., Zalewsky, E., Hao, W., Ku, J., Key, T., and Sistla, G.: Impact of biogenic emission uncertainties on the simulated response of ozone and fine Particulate Matter to anthropogenic emission reductions, J. Air Waste Manage., 61, 92–108, 2011.

Hong, S.-Y. and Pan, H.-L.: Nonlocal boundary layer vertical diffusion in a medium-range forecast model, Mon. Weather Rev., 124, 2322–2339, 1996.

Hurvich, C. M., Simonoff, J. S., and Tsai, C. L.: Smoothing parameter selection in non-parametric regression using an improved Akaike information criterion, Journal of Real State Society, Series B (Statistic Methodology), 60, 271–293, 1998.

IPCC Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, UK and New York, NY, USA, 996 pp., 2007.

Ji, M., Cohan, D. S., and Bell, M. L.: Meta-analysis of the association between short-term exposure to ambient ozone and respiratory hospital admissions, Environ. Res. Lett., 6, 024006, <a href="http://dx.doi.org/10.1088/1748-9326/6/2/024006">https://doi.org/10.1088/1748-9326/6/2/024006</a>, 2011.

Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S. K., Hnilo, J. J., Fiorino, M., and Potter, G. L.: NCEP-DOE AMIP-II reanalysis (R-2), B. Am. Meteor. Soc., 83, 1631–1643, 2002.

Loader, C. R.: Bandwidth selection: classical or plug-in?, The Annals of Statistics, 27, 415–438, 1999.

Lu, B., Harris, P., Charlton, M., Brunsdon, C., Nakaya, T., Gollini, I., and Lu, M. B.: Package "GWmodel" Version 1.2-2, available at: <a href="http://cran.r-project.org/src/contrib/Archive/GWmodel/">http://cran.r-project.org/src/contrib/Archive/GWmodel/</a> (last access: 13 January 2014), 2013.

O'Brien, R.: A caution regarding rules of thumb for variance inflation factors, Qual. Quant., 41, 673–690, 2007.

Pfister, G. G., Emmons, L. K., Hess, P. G., Lamarque, J. F., Orlando, J. J., Walters, S., Guenther, A., Palmer, P. I., and Lawrence, P. J.: Contribution of isoprene to chemical budgets: A model tracer study with the NCAR CTM MOZART-4, J. Geophys. Res., 113, D05308, <a href="http://dx.doi.org/10.1029/2007JD008948">https://doi.org/10.1029/2007JD008948</a>, 2008.

Pleim, J. E. and Chang, J. S.: A non-local closure model in the convective boundary layer, Atmos. Environ. A-Gen., 26, 965–981, 1992.

Reisner, J., Rasmussen, R. J., and Bruintjes, R. T.: Explicit forecasting of supercooled liquid water in winter storms using MM5 mesoscale model, Q. J. Roy. Meteor. Soc., 124B, 1071–1107, 1998.

Russell, A. G. and Dennis, R.: NARSTO critical review of photochemical models and modeling. Atmos. Environ., 34, 2284–2324, 2000.

Smith, T. M., Shugart, H. H., and Woodward, F. I. (Eds.): Plant functional types: Their relevance to ecosystem properties and global change, International geosphere-biosphere programme book series 1, Cambridge University Press, Cambridge, 1997.

Strahler, A., Muchoney, D., Borak, J., Friedl, M. A., Gopal, S., Lambin, E., and Moody, A.: MODIS land cover product algorithm theoretical basis document (ATBD) (Version 5.0), available at: <a href="http://modis.gsfc.nasa.gov/data/atbd/atbd_mod12.pdf">http://modis.gsfc.nasa.gov/data/atbd/atbd_mod12.pdf</a>(last access: 3 July 2009), 1999.

Streets, D. G., Bond, T. C., Carmichael, G. R., Fernandes, S. D., Fu, Q., He, D., Klimont, Z., Nelson, S. M., Tsai, N. Y., Wang, M. Q., Woo, J.-H., and Yarber, K. F.: An inventory of gaseous and primary aerosol emissions in Asia in the year 2000, J. Geophys. Res., 108, 8809, <a href="http://dx.doi.org/10.1029/2002JD003093">https://doi.org/10.1029/2002JD003093</a>, 2003.

Sun, W., Liang, S., Xu, G., Fang, H., and Dickinson, R.: Mapping plant functional types from MODIS data using multisource evidential reasoning, Remote Sens. Environ., 112, 1010–1024, 2008.

Tao, Z., Larson, S. M., Wuebbles, D. J., Williams, A., and Caughey, M.: A Summer Simulation of Biogenic Contributions to Ground-Level Ozone over the Continental United States. J. Geophys. Res., 108, 4404, <a href="http://dx.doi.org/10.1029/2002JD002945">https://doi.org/10.1029/2002JD002945</a>, 2003.

Woo, J.-H., Choi, K.-C., Kim, H.-K., Baek, B. H., Jang, M. D., Eum, J.-H., Song, C. H., Ma, Y.-I., Young Sunwoo, Y., Chang, L.-S., and Yoo, Y. H.: Development of an anthropogenic emissions processing system for Asia using SMOKE, Atmos. Environ., 58, 5–13, 2012.

Xie, X., Shao, M., Liu, Y., Lu, S. H., Chang, C. C., and Chen, Z. M.: Estimate of initial isoprene contribution to ozone formation potential in Beijing, China, Atmos. Environ., 42, 6000–6010, 2008.

Zhang, Q., Streets, D. G., Carmichael, G. R., He, K. B., Huo, H., Kannari, A., Klimont, Z., Park, I. S., Reddy, S., Fu, J. S., Chen, D., Duan, L., Lei, Y., Wang, L. T., and Yao, Z. L.: Asian emissions in 2006 for the NASA INTEX-B mission, Atmos. Chem. Phys., 9, 5131–5153, <a href="http://dx.doi.org/10.5194/acp-9-5131-2009">https://doi.org/10.5194/acp-9-5131-2009</a>, 2009.

Zheng, J. Y., Shao, M., Che, W. W., Zhang, L. J., Zhong, L. J., Zhang, Y. H., and Streets, D. G.: Speciated VOC emission inventory and spatial patterns of ozone formation potential in the Pearl River Delta, China, Environ. Sci. Technol., 43, 8580–8586, 2009.