Numerical case study of spatial-temporal variations of surface nitrogen dioxide and ozone concentrations over Tehran using WRF-Chem model

Document Type : Research Article

Authors

1 Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.

2 Atmospheric Science Research Center, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran.

Abstract

This study aimed to evaluate the performance of the WRF-Chem model in estimating the amount of NO2 and O3 in the Tehran region during recent summers (2019 to 2021). First, by investigating the NO2 and O3 concentrations (including hourly and daily averages) from the Air Quality Control stations in Tehran city, the days of maximum ozone concentration in summer were selected for which run the Weather Research and Forecasting Chemistry (WRF-Chem) model. The simulations were performed for 36-hours, and the first 12 hours were considered spin-up times. The simulations were conducted using two nests with 30 and 10 km resolutions, respectively, so that the second domain covered the Tehran region. Also, in the model settings, 35 levels were considered in the vertical direction, and the pressure at the highest level was 50 hPa. In these simulations, the Global Forecast System (GFS) data with a spatial resolution of 0.5 degrees and 6-hours time step was used as the initial and boundary conditions. Physical parameterization schemes that performed well in previous studies in simulating atmospheric pollutants dispersion were used in the model configuration. The Rapid Radiation Transfer Model (RRTM) and the Goddard Shortwave schemes were also used to simulate the long-and short-wave radiation, respectively. The Monin-Obukhov scheme was used to simulate surface layer fluxes and the Yonsei University (YSU) PBL scheme was also used to simulate boundary layer fluxes. The land surface fluxes were obtained from the NOAH model. In addition, the Grell and Devenyi ensemble scheme was used to parameterize moist convection and the WRF-Single-Moment-Microphysics 5-class scheme was used to parameterize microphysical processes. The RADM2 chemical mechanism was also used in this configuration. The PREP–CHEM–SRC emissions preprocessor (version 1.5) was used to produce anthropogenic, biogenic and biomass burning gridded emission over the user-specified simulation domain. The global emission data comes from RETRO and GOCART background emission data. Anthropogenic emissions of greenhouse gases and air pollutants including CO, NH3, NOx, SO2, NMVOC and CH4, were derived from the EDGAR_HTAP v5.0 Emissions Database with 0.1 horizontal resolution. Primary anthropogenic aerosol emissions of BC, OC and DMS from GOCART model databases were also used. Biogenic emissions were calculated using the MEGAN model. Also, 3BEM fire emissions which are prepared using PREP-CHEM-SRC are used. Evaluation of the results of WRF-Chem model simulations was performed by two methods of horizontal distribution and station evaluation. The evaluation results of the simulated horizontal distribution of NO2 and the one is taken from the OMI satellite data showed that considering the slight spatial displacement in the model results, the WRF-Chem model had good performance in simulating the maximum surface NO2 in all cases. Considering the spatial distribution of O3 in days with maximum ozone pollution, the model has simulated areas of maximum ozone in the Tehran. However, in most cases, no comment can be made on the accuracy of the simulated maximum areas because of the not precisely coinciding of the results maps with satellite transits. The station evaluation showed an overestimate of ozone concentration and a high underestimate of NO2 concentration by the WRF-Chem model.

Keywords

Main Subjects

References

کریمی، ص. (۱۳۹۳). تحلیل همدیدی تغییرات غلظت ازون وردسپهری کلانشهر تهران، فصلنامه تحقیقات کاربردی علوم جغرافیایی، 14(32)، ۷-۲۶.
نیک‌فال، ا.، و رنجبر، ع. (1395). برآورد غلظت آلاینده‌های معیار جوی با استفاده از مدل WRF-Chem و داده‌های جهانی انتشار-مطالعه موردی تهران. فصلنامه علوم محیطی، 14(3)، 123-130.
Anand, J. S., & Monks, P. S. (2017). Estimating daily surface NO2 concentrations from satellite data–a case study over Hong Kong using land use regression models. Atmospheric Chemistry and Physics, 17(13), 8211-8230.
Ackermann, I. J., Hass, H., Memmesheimer, M., Ebel, A., Binkowski, F. S., & Shankar, U. M. A. (1998). Modal aerosol dynamics model for Europe: Development and first applications. Atmospheric environment, 32(17), 2981-2999.
Ainsworth, E. A., Yendrek, C. R., Sitch, S., Collins, W. J., & Emberson, L. D. (2012). The effects of tropospheric ozone on net primary productivity and implications for climate change. Annual review of plant biology, 63, 637-661.
Chen, F., & Dudhia, J. (2001). Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly weather review, 129(4), 569-585.
Chin, M., Rood, R. B., Lin, S. J., Müller, J. F., & Thompson, A. M. (2000). Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties. Journal of Geophysical Research: Atmospheres, 105(D20), 24671-24687.
Chou, M. D., & Suarez, M. J. (1994). An efficient thermal infrared radiation parameterization for use in general circulation models.
Freitas, S. R. D., Longo, K. M., Alonso, M. A., Pirre, M., Marecal, V., Grell, G., Stockler, R., Mello, R. F., & Sánchez Gácita, M. (2011). PREP-CHEM-SRC–1.0: a preprocessor of trace gas and aerosol emission fields for regional and global atmospheric chemistry models. Geoscientific Model Development, 4(2), 419-433.
Georgiou, G. K., Christoudias, T., Proestos, Y., Kushta, J., Hadjinicolaou, P., & Lelieveld, J. (2018). Air quality modelling in the summer over the eastern Mediterranean using WRF-Chem: chemistry and aerosol mechanism intercomparison. Atmospheric Chemistry and Physics, 18(3), 1555-1571.
Grell, G. A., & Dévényi, D. (2002). A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophysical Research Letters, 29(14), 38-1.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., & Eder, B. (2005). Fully coupled “online” chemistry within the WRF model. Atmospheric Environment, 39(37), 6957-6975.
Gu, Y., Li, K., Xu, J., Liao, H., & Zhou, G. (2020). Observed dependence of surface ozone on increasing temperature in Shanghai, China. Atmospheric Environment, 221, 117108.
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., & Geron, C. (2006). Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmospheric Chemistry and Physics, (6(11, 3181-3210.
Hong, S. Y. (2010). A new stable boundary‐layer mixing scheme and its impact on the simulated East Asian summer monsoon. Quarterly Journal of the Royal Meteorological Society, 136(651), 1481-1496.
Hong, S. Y., Dudhia, J., & Chen, S. H. (2004). A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Monthly weather review, 132(1), 103-120.
Hong, S. Y., Noh, Y., & Dudhia, J. (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly weather review, 134(9), 2318-2341.
IPCC (2013). Climate Change 2013, the Physical Science Basis. Working Group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge.
Janjic, Z. I. (1996). The Mellor-Yamada level 2.5 turbulence closure scheme in the NCEP Eta Model. World Meteorological Organization-Publications-WMO TD, 4-14.
Jiang, F., Liu, Q., Huang, X., Wang, T., Zhuang, B., & Xie, M. (2012). Regional modeling of secondary organic aerosol over China using WRF/Chem. Journal of aerosol science, 43(1), 57-73.
Kumar, R., Naja, M., Pfister, G. G., Barth, M. C., Wiedinmyer, C., & Brasseur, G. P. (2012). Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem): chemistry evaluation and initial results. Geoscientific Model Development, 5(3), 619-648.
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., & Pozzer, A. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525(7569), 367-371.
Liu, Y., Chen, F., Warner, T., & Basara, J. (2006). Verification of a mesoscale data-assimilation and forecasting system for the Oklahoma City area during the Joint Urban 2003 field project. Journal of applied meteorology and climatology, 45(7), 912-929.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated‐k model for the longwave. Journal of Geophysical Research: Atmospheres, 102(D14), 16663-16682.
NASA: OMNO2d, available at: https://disc.gsfc.nasa.gov/datasets/OMNO2d_003/summary, last access: 30 June 2019.
Nikfal, A. H., Ranjbar Saadat Abadi, A., Rahnama, M., Tajbakhsh Mosalman, S., & Moradi, M. (2022). Contribution of source emissions in the air pollution modeling-a WRF/Chem case study. Journal of the Earth and Space Physics, 47(4), 143-156.‎
Olivier, J. G., Van Aardenne, J. A., Dentener, F. J., Pagliari, V., Ganzeveld, L. N., & Peters, J. A. (2005). Recent trends in global greenhouse gas emissions: regional trends 1970–2000 and spatial distributionof key sources in 2000. Environmental Sciences, 2(2-3), 81-99.
Pickering, K. E., Bucsela, E., Allen, D., Ring, A., Holzworth, R., & Krotkov, N. (2016). Estimates of lightning NOx production based on OMI NO2 observations over the Gulf of Mexico. Journal of Geophysical Research: Atmospheres, 121(14), 8668-8691.
Price, C., & Rind, D. (1993). What determines the cloud‐to‐ground lightning fraction in thunderstorms?. Geophysical Research Letters, 20(6), 463-466.
Schell, B., Ackermann, I. J., Hass, H., Binkowski, F. S., & Ebel, A. (2001). Modeling the formation of secondary organic aerosol within a comprehensive air quality model system. Journal of Geophysical Research: Atmospheres, 106(D22), 28275-28293.
Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric Chemistry and Physics. Wiley.
Sitch, S., Cox, P. M., Collins, W. J., & Huntingford, C. (2007). Indirect radiative forcing of climate change through ozone effects on the land-carbon sink. Nature, 448(7155), 791-794.
Stockwell, W. R., Middleton, P., Chang, J. S., & Tang, X. (1990). The second generation regional acid deposition model chemical mechanism for regional air quality modeling. Journal of Geophysical Research: Atmospheres, 95(D10), 16343-16367.
Terrenoire, E., Bessagnet, B., Rouil, L., Tognet, F., Pirovano, G., Letinois, L., Beauchamp, M., Colette, A., Thunis, P., Amann, M., & Menut, L. (2015). High-resolution air quality simulation over Europe with the chemistry transport model CHIMERE. Geoscientific Model Development, 8(1), 21-42.
Travis, K. R., Jacob, D. J., Fisher, J. A., Kim, P. S., Marais, E. A., Zhu, L., Yu, K., Miller, C. C., Yantosca, R. M., Sulprizio, M. P., & Thompson, A. M. (2016). Why do models overestimate surface ozone in the Southeast United States?. Atmospheric Chemistry and Physics, 16(21), 13561-13577.
U.S. EPA. Integrated Science Assessment (ISA) for Ozone and Related Photochemical Oxidants (Final Report, Apr 2020). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-20/012, 2020.
Visser, A. J., Boersma, K. F., Ganzeveld, L. N., & Krol, M. C. (2019). European NO x emissions in WRF-Chem derived from OMI: impacts on summertime surface ozone. Atmospheric Chemistry and Physics, 19(18), 11821-11841.
Wong, J., Barth, M. C., & Noone, D. (2013). Evaluating a lightning parameterization based on cloud-top height for mesoscale numerical model simulations. Geoscientific Model Development, 6(2), 429-443.
Yerramilli, A., Challa, V. S., Dodla, V. B. R., Myles, L., Pendergrass, W. R., Vogel, C. A., Tuluri, F., Baham, J. M., Hughes, R., Patrick, C. & Young, J. (2012). Simulation of surface ozone pollution in the Central Gulf Coast region during summer synoptic condition using WRF/Chem air quality model. Atmospheric Pollution Research, 3(1), 55-71.
Journal of the Earth and Space Physics
Volume 49, Issue 2
online publication date: 30 Aug 2023
August 2023
Pages 471-490

Files

Share

How to cite

Statistics