High Resolution Urban Air Quality Modeling by Coupling CFD and Mesoscale Models: a Review
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Abhijith, K.V., Kumar, P., Gallagher, J., McNabola, A., Baldauf, R., Pilla, F., Broderick, B., di Sabatino, S., Pulvirenti, B.: Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments – a review. Atmos. Environ. 162, 71–86 (2017). https://doi.org/10.1016/j.atmosenv.2017.05.014
Affum, H.A., Akaho, E.H.K., Niemela, J.J., Armenio, V., Danso, K.A.: Validating the California puff (CALPUFF) modelling system using an industrial area in Accra, Ghana as a Case Study. Open J. Air Pollut. 5, 27–36 (2016)
Anderson, G.: An Evaluation of Dispersion Formulas: Final Report. Travellers Research Corp. (1969)
Azid, A., Juahir, H., Ezani, E., Toriman, M.E., Endut, A., Rahman, M.N.A., Yunus, K., Kamarudin, M.K.A., Hasnam, C.N.C., Saudi, A.S.M., Umar, R.: Identification source of variation on regional impact of air quality pattern using chemometric. Aerosol Air Qual. Res. 15, 1545–1558 (2015). https://doi.org/10.4209/aaqr.2014.04.0073
Baik, J.-J., Park, S.-B., Kim, J.-J.: Urban flow and dispersion simulation using a CFD model coupled to a mesoscale model. J. Appl. Meteorol. Climatol. 48, 1667–1681 (2009). https://doi.org/10.1175/2009JAMC2066.1
Baklanov, A.A., Nuterman, R.B.: Multi-scale atmospheric environment modelling for urban areas. Adv. Sci. Res. 3, 53–57 (2009). https://doi.org/10.5194/asr-3-53-2009
Baklanov, A., Mestayer, P.G., Clappier, A., Zilitinkevich, S., Joffre, S., Mahura, A., Nielsen, N.W.: Towards improving the simulation of meteorological fields in urban areas through updated/advanced surface fluxes description. Atmos. Chem. Phys. 8, 523–543 (2008). https://doi.org/10.5194/acp-8-523-2008
Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S.T., Savage, N., Seigneur, C., Sokhi, R.S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects. Atmos. Chem. Phys. 14, 317–398 (2014). https://doi.org/10.5194/acp-14-317-2014
Baklanov, A., Molina, L.T., Gauss, M.: Megacities, air quality and climate. Atmos. Environ. 126, 235–249 (2016). https://doi.org/10.1016/j.atmosenv.2015.11.059
Banerjee, T., Murari, V., Kumar, M., Raju, M.P.: Source apportionment of airborne particulates through receptor modeling: Indian scenario. Atmos. Res. 164–165, 167–187 (2015). https://doi.org/10.1016/j.atmosres.2015.04.017
Batterman, S., Chambliss, S., Isakov, V.: Spatial resolution requirements for traffic-related air pollutant exposure evaluations. Atmos. Environ. 94, 518–528 (2014). https://doi.org/10.1016/j.atmosenv.2014.05.065
Bechtel, B., Zakšek, K., Oßenbrügge, J., Kaveckis, G., Böhner, J.: Towards a satellite based monitoring of urban air temperatures. Sustain. Cities Soc. 34, 22–31 (2017). https://doi.org/10.1016/j.scs.2017.05.018
Beevers, S.D., Kitwiroon, N., Williams, M.L., Carslaw, D.C.: One way coupling of CMAQ and a road source dispersion model for fine scale air pollution predictions. Atmos. Environ. 59, 47–58 (2012). https://doi.org/10.1016/j.atmosenv.2012.05.034
Bhati, S., Mohan, M.: WRF model evaluation for the urban heat island assessment under varying land use/land cover and reference site conditions. Theor. Appl. Climatol. 126, 385–400 (2016). https://doi.org/10.1007/s00704-015-1589-5
Biljecki, F., Ledoux, H., Stoter, J.: Generating 3D city models without elevation data. Comput. Environ. Urban. Syst. 64, 1–18 (2017)
Blocken, B.: 50 years of computational wind engineering: past, present and future. J. Wind Eng. Ind. Aerodyn. 129, 69–102 (2014). https://doi.org/10.1016/j.jweia.2014.03.008
Blocken, B.: Computational fluid dynamics for urban physics: importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations. Build. Environ. 91, 219–245 (2015). https://doi.org/10.1016/j.buildenv.2015.02.015
Bonner, C.S., Ashley, M.C.B., Cui, X., Feng, L., Gong, X., Lawrence, J.S., Luong-van, D.M., Shang, Z., Storey, J.W.V., Wang, L., Yang, H., Yang, J., Zhou, X., Zhu, Z.: Thickness of the atmospheric boundary layer above dome a, Antarctica, during 2009. Publ. Astron. Soc. Pac. 122, 1122–1131 (2010). https://doi.org/10.1086/656250
Borrego, C., Amorim, J.H., Tchepel, O., Dias, D., Rafael, S., Sá, E., Pimentel, C., Fontes, T., Fernandes, P., Pereira, S.R., Bandeira, J.M., Coelho, M.C.: Urban scale air quality modelling using detailed traffic emissions estimates. Atmos. Environ. 131, 341–351 (2016). https://doi.org/10.1016/j.atmosenv.2016.02.017
Bruse, M., Fleer, H.: Simulating surface–plant–air interactions inside urban environments with a three dimensional numerical model. Environ. Model Softw. 13, 373–384 (1998). https://doi.org/10.1016/S1364-8152(98)00042-5
Byun, D., 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). https://doi.org/10.1115/1.2128636
Carslaw, D.C., Ropkins, K.: Openair — an R package for air quality data analysis. Environ. Model Softw. 27–28, 52–61 (2012). https://doi.org/10.1016/j.envsoft.2011.09.008
Cetin Dogruparmak, S., Pekey, H., Arslanbas, D.: Odor dispersion modeling with CALPUFF: case study of a waste and residue treatment incineration and utilization plant in Kocaeli, Turkey. Environ. Forensic. 19, 79–86 (2018)
Chang, C.-H., Meroney, R.N.: Numerical and physical modeling of bluff body flow and dispersion in urban street canyons. J. Wind Eng. Ind. Aerodyn. 89, 1325–1334 (2001). https://doi.org/10.1016/S0167-6105(01)00129-5
Chatani, S., Morikawa, T., Nakatsuka, S., Matsunaga, S., Minoura, H.: Development of a framework for a high-resolution, three-dimensional regional air quality simulation and its application to predicting future air quality over Japan. Atmos. Environ. 45, 1383–1393 (2011). https://doi.org/10.1016/j.atmosenv.2010.12.036
Chavez, M., Hajra, B., Stathopoulos, T., Bahloul, A.: Assessment of near-field pollutant dispersion: effect of upstream buildings. J. Wind Eng. Ind. Aerodyn. 104–106, 509–515 (2012). https://doi.org/10.1016/j.jweia.2012.02.019
Chen, B., Liu, S., Miao, Y., Wang, S., Li, Y.: Construction and validation of an urban area flow and dispersion model on building scales. Acta Meteorologica Sinica. 27, 923–941 (2013)
Ching J, Byun D. Introduction to the Models-3 framework and the Community Multiscale Air Quality model (CMAQ). Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System 1999
Ciarelli, G., Aksoyoglu, S., Haddad, I.E., Bruns, E.A., Crippa, M., Poulain, L., et al.: Modelling winter organic aerosol at the European scale with CAMx: evaluation and source apportionment with a VBS parameterization based on novel wood burning smog chamber experiments. Atmos. Chem. Phys. 17, 7653–7669 (2017)
Coats Jr., C.J.: High-performance algorithms in the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system. In: Proc. Ninth AMS Joint Conference on Applications of Air Pollution Meteorology with A&WMA, Amer. Meteor. Soc., pp. 584–588. Citeseer, Atlanta (1996)
Cochran, L., Derickson, R., Meroney, R.N., Sharp, H.: On what new building project managers need to know about wind engineering. Proceedings of the 17th Australasian Wind Engineering Society Workshop, February 11e13, Wellington (2015)
Coirier, W.J., Fricker, D.M., Furmanczyk, M., Kim, S.: A computational fluid dynamics approach for urban area transport and dispersion modeling. Environ. Fluid Mech. 5, 443–479 (2005). https://doi.org/10.1007/s10652-005-0299-4
Daftry, S., Hoppe, C., Bischof, H.: Building with drones: Accurate 3D facade reconstruction using MAVs. In: IEEE International Conference on Robotics and Automation (ICRA), vol. 2015, pp. 3487–3494. IEEE, Seattle (2015). https://doi.org/10.1109/ICRA.2015.7139681
de la Paz, D., Vedrenne, M., Borge, R., Lumbreras, J., de Andrés, J.M., Pérez, J., et al.: Modelling Saharan dust transport into the Mediterranean basin with CMAQ. Atmos. Environ. 70, 337–350 (2013)
Demographia. Demographia World Urban Areas: 14th Annual Edition:201804. 2018
Easom, G.: Improved turbulence models for computational wind engineering. PhD Thesis. University of Nottingham (2000)
EEA. EMEP/EEA air pollutant emission inventory guidebook. 2009
El Fazziki, A., Benslimane, D., Sadiq, A., Ouarzazi, J., Sadgal, M.: An agent based traffic regulation system for the roadside air quality control. IEEE Access. 5, 13192–13201 (2017). https://doi.org/10.1109/ACCESS.2017.2725984
El-Harbawi, M.: Air quality modelling, simulation, and computational methods: a review. Environ. Rev. 21, 149–179 (2013). https://doi.org/10.1139/er-2012-0056
FAIRMODE. FAIRMODE - The Forum for Air quality Modelling. 2018
Fernandes A, Riffler M, Ferreira J, Wunderle S, Borrego C, Tchepel O. Comparisons of aerosol optical depth provided by seviri satellite observations and CAMx air quality modelling. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2015;XL-7/W3:187–193. https://doi.org/10.5194/isprsarchives-XL-7-W3-187-2015
Fisher, B., Kukkonen, J., Piringer, M., Rotach, M.W., Schatzmann, M.: Meteorology applied to urban air pollution problems: concepts from COST 715. Atmos. Chem. Phys. 6, 555–564 (2006). https://doi.org/10.5194/acp-6-555-2006
Fitch, J.P., Raber, E., Imbro, D.R.: Technology challenges in responding to biological or chemical attacks in the civilian sector. Science. 302, 1350–1354 (2003)
Franzese, P., Huq, P.: Urban dispersion modelling and experiments in the daytime and nighttime atmosphere. Bound.-Layer Meteorol. 139, 395–409 (2011). https://doi.org/10.1007/s10546-011-9593-5
Frueh, C., Zakhor, A.: Constructing 3D city models by merging ground-based and airborne views. Computer Vision and Pattern Recognition, 2003. In: Proceedings. 2003 IEEE Computer Society Conference on, vol. 2, pp. II–562. IEEE (2003)
Georgiou, G.K., Christoudias, T., Proestos, Y., Kushta, J., Hadjinicolaou, P., Lelieveld, J.: Air quality modelling in the summer over the eastern Mediterranean using WRF-Chem: chemistry and aerosol mechanism intercomparison. Atmos. Chem. Phys. 18, 1555–1571 (2018). https://doi.org/10.5194/acp-18-1555-2018
Granier C, Lamarque JF, Mieville A, Muller JF, Olivier J, Orlando J, et al. POET, a database of surface emissions of ozone precursors. 2005
Grell GA, Dudhia J, Stauffer DR. A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) 1994
Grell, G.A., Emeis, S., Stockwell, W.R., Schoenemeyer, T., Forkel, R., Michalakes, J., Knoche, R., Seidl, W.: Application of a multiscale, coupled MM5/chemistry model to the complex terrain of the VOTALP valley campaign. Atmos. Environ. 34, 1435–1453 (2000). https://doi.org/10.1016/S1352-2310(99)00402-1
Grell, G.A., Peckham, S.E., Schmitz, R., McKeen, S.A., Frost, G., Skamarock, W.C., et al.: Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39, 6957–6975 (2005). https://doi.org/10.1016/j.atmosenv.2005.04.027
Grimmond, C.S.B., Blackett, M., Best, M.J., Barlow, J., Baik, J.-J., Belcher, S.E., Bohnenstengel, S.I., Calmet, I., Chen, F., Dandou, A., Fortuniak, K., Gouvea, M.L., Hamdi, R., Hendry, M., Kawai, T., Kawamoto, Y., Kondo, H., Krayenhoff, E.S., Lee, S.H., Loridan, T., Martilli, A., Masson, V., Miao, S., Oleson, K., Pigeon, G., Porson, A., Ryu, Y.H., Salamanca, F., Shashua-Bar, L., Steeneveld, G.J., Tombrou, M., Voogt, J., Young, D., Zhang, N.: The international urban energy balance models comparison project: first results from phase 1. J Appl Meteor Climatol. 49, 1268–1292 (2010). https://doi.org/10.1175/2010JAMC2354.1
Grimmond, C.S.B., Blackett, M., Best, M.J., Baik, J.-J., Belcher, S.E., Beringer, J., Bohnenstengel, S.I., Calmet, I., Chen, F., Coutts, A., Dandou, A., Fortuniak, K., Gouvea, M.L., Hamdi, R., Hendry, M., Kanda, M., Kawai, T., Kawamoto, Y., Kondo, H., Krayenhoff, E.S., Lee, S.H., Loridan, T., Martilli, A., Masson, V., Miao, S., Oleson, K., Ooka, R., Pigeon, G., Porson, A., Ryu, Y.H., Salamanca, F., Steeneveld, G.J., Tombrou, M., Voogt, J.A., Young, D.T., Zhang, N.: Initial results from phase 2 of the international urban energy balance model comparison. Int. J. Climatol. 31, 244–272 (2011). https://doi.org/10.1002/joc.2227
Gromke, C., Blocken, B.: Influence of avenue-trees on air quality at the urban neighborhood scale. Part I: quality assurance studies and turbulent Schmidt number analysis for RANS CFD simulations. Environ. Pollut. 196, 214–223 (2015). https://doi.org/10.1016/j.envpol.2014.10.016
Gros, A., Bozonnet, E., Inard, C.: Cool materials impact at district scale—coupling building energy and microclimate models. Sustain. Cities Soc. 13, 254–266 (2014). https://doi.org/10.1016/j.scs.2014.02.002
Gsella, A., de Meij, A., Kerschbaumer, A., Reimer, E., Thunis, P., Cuvelier, C.: Evaluation of MM5, WRF and TRAMPER meteorology over the complex terrain of the Po Valley, Italy. Atmos. Environ. 89, 797–806 (2014). https://doi.org/10.1016/j.atmosenv.2014.03.019
Gunwani, P., Mohan, M.: Sensitivity of WRF model estimates to various PBL parameterizations in different climatic zones over India. Atmos. Res. 194, 43–65 (2017). https://doi.org/10.1016/j.atmosres.2017.04.026
Gutschow, J., Jeffery, L., Gieseke, R., Gebel, R., Stevens, D., Krapp, M., et al.: The PRIMAP-hist national historical emissions time series (1850-2014). GFZ Data Services. (2016). https://doi.org/10.2904/EDGARv4.2
Hennemuth, B., Lammert, A.: Determination of the atmospheric boundary layer height from radiosonde and Lidar backscatter. Bound.-Layer Meteorol. 120, 181–200 (2006). https://doi.org/10.1007/s10546-005-9035-3
Hirschmuller, H.: Stereo processing by Semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30, 328–341 (2008). https://doi.org/10.1109/TPAMI.2007.1166
Holben, B.N., Tanré, D., Smirnov, A., Eck, T.F., Slutsker, I., Abuhassan, N., Newcomb, W.W., Schafer, J.S., Chatenet, B., Lavenu, F., Kaufman, Y.J., Castle, J.V., Setzer, A., Markham, B., Clark, D., Frouin, R., Halthore, R., Karneli, A., O'Neill, N.T., Pietras, C., Pinker, R.T., Voss, K., Zibordi, G.: An emerging ground-based aerosol climatology: aerosol optical depth from AERONET. J. Geophys. Res. Atmos. 106, 12067–12097 (2001). https://doi.org/10.1029/2001JD900014
Hu, X.-M., Nielsen-Gammon, J.W., Zhang, F.: Evaluation of three planetary boundary layer schemes in the WRF model. J. Appl. Meteorol. Climatol. 49, 1831–1844 (2010). https://doi.org/10.1175/2010JAMC2432.1
Huq, P., Franzese, P.: Measurements of turbulence and dispersion in three idealized urban canopies with different aspect ratios and comparisons with a Gaussian plume model. Bound.-Layer Meteorol. 147, 103–121 (2013). https://doi.org/10.1007/s10546-012-9780-z
Isakov, V., Arunachalam, S., Batterman, S., Bereznicki, S., Burke, J., Dionisio, K., Garcia, V., Heist, D., Perry, S., Snyder, M., Vette, A.: Air quality modeling in support of the near-road exposures and effects of urban air pollutants study (NEXUS). Int. J. Environ. Res. Public Health. 11, 8777–8793 (2014). https://doi.org/10.3390/ijerph110908777
Jacobson, M.Z.: GATOR-GCMM: a global- through urban-scale air pollution and weather forecast model: 1. Model design and treatment of subgrid soil, vegetation, roads, rooftops, water, sea ice, and snow. Journal of Geophysical Research: Atmospheres. 106, 5385–5401 (2001). https://doi.org/10.1029/2000JD900560
Janhäll, S.: Review on urban vegetation and particle air pollution – deposition and dispersion. Atmos. Environ. 105, 130–137 (2015). https://doi.org/10.1016/j.atmosenv.2015.01.052
Janicke. Austal2000-Program Documentation of Version 2.4. 2011
Jeanjean, A.P.R., Monks, P.S., Leigh, R.J.: Modelling the effectiveness of urban trees and grass on PM2.5 reduction via dispersion and deposition at a city scale. Atmos. Environ. 147, 1–10 (2016). https://doi.org/10.1016/j.atmosenv.2016.09.033
Jeanjean, A.P.R., Buccolieri, R., Eddy, J., Monks, P.S., Leigh, R.J.: Air quality affected by trees in real street canyons: the case of Marylebone neighbourhood in Central London. Urban For. Urban Green. 22, 41–53 (2017). https://doi.org/10.1016/j.ufug.2017.01.009
Jensen, S.S., Ketzel, M., Becker, T., Christensen, J., Brandt, J., Plejdrup, M., Winther, M., Nielsen, O.K., Hertel, O., Ellermann, T.: High resolution multi-scale air quality modelling for all streets in Denmark. Transp. Res. Part D: Transp. Environ. 52, 322–339 (2017). https://doi.org/10.1016/j.trd.2017.02.019
Jeon, B.-G., Lee, S.-H.: A Impact Analysis of Air Quality by Air Pollution Control Facilities Improvement on Point Source Pollution. Journal of the Korea Academia-Industrial cooperation Society. 16, 2876–2882 (2015).
Jones, W., Launder, B.: The prediction of laminarization with a two-equation model of turbulence. Int. J. Heat Mass Transf. 15, 301–314 (1972). https://doi.org/10.1016/0017-9310(72)90076-2
Jose, R.S., Pérez, J.L., González, R.M., Pecci, J., Palacios, M.: Improving air quality modelling systems by using on-line wild land fire forecasting tools coupled into WRF/Chem simulations over Europe. Urban Climate. 22, 2–18 (2017). https://doi.org/10.1016/j.uclim.2016.09.001
Kadaverugu, R.: Framework for mathematical modeling of Soil-Tree system. Model. Earth Syst. Environ. 1, 17 (2015). https://doi.org/10.1007/s40808-015-0017-2
Kadaverugu, R.: Modeling of subsurface horizontal flow constructed wetlands using OpenFOAM®. Model. Earth Syst. Environ. 2, 55 (2016). https://doi.org/10.1007/s40808-016-0111-0
Kang, Y.-H., Song, S.-K., Hwang, M.-K., Jeong, J.-H., Kim, Y.-K.: Impacts of detailed land-use types and urban heat in an urban canopy model on local meteorology and ozone levels for air quality modeling in a Coastal City, Korea. Terr. Atmos. Ocean. Sci. 27, 877–891 (2016). https://doi.org/10.3319/TAO.2016.01.13.01(A)
Kariminia, S., Ahmad, S.S., Saberi, A.: Microclimatic conditions of an Urban Square: role of built environment and geometry. Procedia – Soc. Behav. Sci. 170, 718–727 (2015). https://doi.org/10.1016/j.sbspro.2015.01.074
Khaniabadi, Y.O., Sicard, P., Taiwo, A.M., De Marco, A., Esmaeili, S., Rashidi, R.: Modeling of particulate matter dispersion from a cement plant: upwind-downwind case study. J. Environ. Chem. Eng. 6, 3104–3110 (2018)
Kim, S.-W., Heckel, A., McKeen, S.A., Frost, G.J., Hsie, E.-Y., Trainer, M.K., et al.: Satellite-observed U.S. power plant NO x$ emission reductions and their impact on air quality. Geophys. Res. Lett. 33, 33 (2006). https://doi.org/10.1029/2006GL027749
Kocaman, S., Zhang, L., Gruen, A., Poli, D.: 3D City Modeling from High-Resolution Satellite Images, pp. 14–16. Proceedings of ISPRS Workshop on Topographic Mapping from Space, Ankara (2006)
Kondo, H., Genchi, Y., Kikegawa, Y., Ohashi, Y., Yoshikado, H., Komiyama, H.: Development of a multi-layer urban canopy model for the analysis of energy consumption in a big City: structure of the urban canopy model and its basic performance. Bound.-Layer Meteorol. 116, 395–421 (2005). https://doi.org/10.1007/s10546-005-0905-5
Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO – Simulation of Urban Mobility. International Journal On Advances in Systems and Measurements, 5, 128–135 (2012)
Kraus, K.: Photogrammetry: Geometry from Images and Laser Scans. Walter de Gruyter (2011)
Kuik, F., Lauer, A., Churkina, G., van der Gon HAC, D., Fenner, D., Mar, K.A., et al.: Air quality modelling in the Berlin–Brandenburg region using WRF-Chem v3.7.1: sensitivity to resolution of model grid and input data. Geosci. Model Dev. 9, 4339–4363 (2016). https://doi.org/10.5194/gmd-9-4339-2016
Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Di Sabatino, S., et al.: The rise of low-cost sensing for managing air pollution in cities. Environ. Int. 75, 199–205 (2015). https://doi.org/10.1016/j.envint.2014.11.019
Kurokawa, J., Ohara, T., Morikawa, T., Hanayama, S., Janssens-Maenhout, G., Fukui, T., Kawashima, K., Akimoto, H.: Emissions of air pollutants and greenhouse gases over Asian regions during 2000–2008: regional emission inventory in ASia (REAS) version 2. Atmos. Chem. Phys. 13, 11019–11058 (2013). https://doi.org/10.5194/acp-13-11019-2013
Kusaka, H., Kondo, H., Kikegawa, Y., Kimura, F.: A simple single-layer urban canopy model for atmospheric models: comparison with multi-layer and slab models. Bound.-Layer Meteorol. 101, 329–358 (2001). https://doi.org/10.1023/A:1019207923078
Kwak, K.-H., Baik, J.-J., Ryu, Y.-H., Lee, S.-H.: Urban air quality simulation in a high-rise building area using a CFD model coupled with mesoscale meteorological and chemistry-transport models. Atmos. Environ. 100, 167–177 (2015). https://doi.org/10.1016/j.atmosenv.2014.10.059
Kwak, K.-H., Woo, S.H., Kim, K.H., Lee, S.-B., Bae, G.-N., Ma, Y.-I., Sunwoo, Y., Baik, J.J.: On-road air quality associated with traffic composition and street-canyon ventilation: Mobile monitoring and CFD modeling. Atmosphere. 9, 92 (2018). https://doi.org/10.3390/atmos9030092
Lahoz, W., Khattatov, B., Ménard, R.: Data Assimilation and Information, pp. 3–12. Springer, Data Assimilation (2010)
Lamarque, J.-F., Bond, T.C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M.G., Shindell, D., Smith, S.J., Stehfest, E., van Aardenne, J., Cooper, O.R., Kainuma, M., Mahowald, N., McConnell, J.R., Naik, V., Riahi, K., van Vuuren, D.P.: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application. Atmos. Chem. Phys. 10, 7017–7039 (2010). https://doi.org/10.5194/acp-10-7017-2010
Lateb, M., Meroney, R.N., Yataghene, M., Fellouah, H., Saleh, F., Boufadel, M.C.: On the use of numerical modelling for near-field pollutant dispersion in urban environments − a review. Environ. Pollut. 208, 271–283 (2016). https://doi.org/10.1016/j.envpol.2015.07.039
Lee, H.-D., Yoo, J.-W., Kang, M.-K., Kang, J.-S., Jung, J.-H., Oh, K.-J.: Evaluation of concentrations and source contribution of PM10 and SO2 emitted from industrial complexes in Ulsan, Korea: interfacing of the WRF–CALPUFF modeling tools. Atmos. Pollut. Res. 5, 664–676 (2014)
Lefebvre, W., Vercauteren, J., Schrooten, L., Janssen, S., Degraeuwe, B., Maenhaut, W., de Vlieger, I., Vankerkom, J., Cosemans, G., Mensink, C., Veldeman, N., Deutsch, F., van Looy, S., Peelaerts, W., Lefebre, F.: Validation of the MIMOSA-AURORA-IFDM model chain for policy support: modeling concentrations of elemental carbon in Flanders. Atmos. Environ. 45, 6705–6713 (2011). https://doi.org/10.1016/j.atmosenv.2011.08.033
Lefebvre, W., Van Poppel, M., Maiheu, B., Janssen, S., Dons, E.: Evaluation of the RIO-IFDM-street canyon model chain. Atmos. Environ. 77, 325–337 (2013). https://doi.org/10.1016/j.atmosenv.2013.05.026
Li, X.-B., Lu, Q.-C., Lu, S.-J., He, H.-D., Peng, Z.-R., Gao, Y., Wang, Z.Y.: The impacts of roadside vegetation barriers on the dispersion of gaseous traffic pollution in urban street canyons. Urban For. Urban Green. 17, 80–91 (2016). https://doi.org/10.1016/j.ufug.2016.03.006
Liao, J., Wang, T., Jiang, Z., Zhuang, B., Xie, M., Yin, C., Wang, X., Zhu, J., Fu, Y., Zhang, Y.: WRF/Chem modeling of the impacts of urban expansion on regional climate and air pollutants in Yangtze River Delta, China. Atmos. Environ. 106, 204–214 (2015). https://doi.org/10.1016/j.atmosenv.2015.01.059
Liu, Y.S., Miao, S.G., Zhang, C.L., Cui, G.X., Zhang, Z.S.: Study on micro-atmospheric environment by coupling large eddy simulation with mesoscale model. J. Wind Eng. Ind. Aerodyn. 107–108, 106–117 (2012). https://doi.org/10.1016/j.jweia.2012.03.033
Liu, G., Liu, J., Tarasick, D.W., Fioletov, V.E., Jin, J.J., Moeini, O., Liu, X., Sioris, C.E., Osman, M.: A global tropospheric ozone climatology from trajectory-mapped ozone soundings. Atmos. Chem. Phys. 13, 10659–10675 (2013). https://doi.org/10.5194/acp-13-10659-2013
Maggiotto, G., Buccolieri, R., Santo, M.A., Leo, L.S., Di Sabatino, S.: Validation of temperature-perturbation and CFD-based modelling for the prediction of the thermal urban environment: the Lecce (IT) case study. Environ. Model Softw. 60, 69–83 (2014). https://doi.org/10.1016/j.envsoft.2014.06.001
Malys, L., Musy, M., Inard, C.: Microclimate and building energy consumption: study of different coupling methods. Advances in Building Energy Research. 9, 151–174 (2015). https://doi.org/10.1080/17512549.2015.1043643
Martilli, A.: An idealized study of city structure, urban climate, energy consumption, and air quality. Urban Climate. 10, 430–446 (2014). https://doi.org/10.1016/j.uclim.2014.03.003
Masson, V.: Urban surface modeling and the meso-scale impact of cities. Theor. Appl. Climatol. 84, 35–45 (2006). https://doi.org/10.1007/s00704-005-0142-3
Matthias, V., Arndt, J.A., Aulinger, A., Bieser, J., van der, G.H.D., Kranenburg, R., et al.: Modeling emissions for three-dimensional atmospheric chemistry transport models. J. Air Waste Manage. Assoc. 68, 763–800 (2018). https://doi.org/10.1080/10962247.2018.1424057
Mead, M.I., Popoola, O.A.M., Stewart, G.B., Landshoff, P., Calleja, M., Hayes, M., Baldovi, J.J., McLeod, M.W., Hodgson, T.F., Dicks, J., Lewis, A., Cohen, J., Baron, R., Saffell, J.R., Jones, R.L.: The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmos. Environ. 70, 186–203 (2013). https://doi.org/10.1016/j.atmosenv.2012.11.060
Miao, Y., Liu, S., Chen, B., Zhang, B., Wang, S., Li, S.: Simulating urban flow and dispersion in Beijing by coupling a CFD model with the WRF model. Adv. Atmos. Sci. 30, 1663–1678 (2013). https://doi.org/10.1007/s00376-013-2234-9
Middel, A., Häb, K., Brazel, A.J., Martin, C.A., Guhathakurta, S.: Impact of urban form and design on mid-afternoon microclimate in Phoenix local climate zones. Landsc. Urban Plan. 122, 16–28 (2014). https://doi.org/10.1016/j.landurbplan.2013.11.004
Mills, G.: Urban climatology: history, status and prospects. Urban Climate. 10, 479–489 (2014). https://doi.org/10.1016/j.uclim.2014.06.004
Mirzaei, P.A.: Recent challenges in modeling of urban heat island. Sustain. Cities Soc. 19, 200–206 (2015). https://doi.org/10.1016/j.scs.2015.04.001
Mirzaei, P.A., Haghighat, F.: Approaches to study urban Heat Island – abilities and limitations. Build. Environ. 45, 2192–2201 (2010). https://doi.org/10.1016/j.buildenv.2010.04.001
Montazeri, H., Blocken, B.: CFD simulation of wind-induced pressure coefficients on buildings with and without balconies: validation and sensitivity analysis. Build. Environ. 60, 137–149 (2013). https://doi.org/10.1016/j.buildenv.2012.11.012
Musy, M., Malys, L., Morille, B., Inard, C.: The use of SOLENE-microclimat model to assess adaptation strategies at the district scale. Urban Climate. 14, 213–223 (2015). https://doi.org/10.1016/j.uclim.2015.07.004
Nopmongcol, U., Liu, Z., Stoeckenius, T., Yarwood, G.: Modeling inter-continental transport of ozone in North America with CAMx for the air quality model evaluation international Initiative (AQMEII) phase 3. Atmos. Chem. Phys. Discuss. 1–25 (2017). https://doi.org/10.5194/acp-2017-194
OECD: The Economic Consequences of Outdoor Air Pollution. OECD Publishing (2016). https://doi.org/10.1787/9789264257474-en
Okaze, T., Ono, A., Mochida, A., Kannuki, Y., Watanabe, S.: Evaluation of turbulent length scale within urban canopy layer based on LES data. J. Wind Eng. Ind. Aerodyn. 144, 79–83 (2015). https://doi.org/10.1016/j.jweia.2014.11.016
Ortiz, S.T., Friedrich, R.: A modelling approach for estimating background pollutant concentrations in urban areas. Atmos. Pollut. Res. 4, 147–156 (2013)
Ortolani, C., Vitale, M.: The importance of local scale for assessing, monitoring and predicting of air quality in urban areas. Sustain. Cities Soc. 26, 150–160 (2016). https://doi.org/10.1016/j.scs.2016.06.001
Over, M., Schilling, A., Neubauer, S., Zipf, A.: Generating web-based 3D city models from OpenStreetMap: the current situation in Germany. Comput. Environ. Urban. Syst. 34, 496–507 (2010). https://doi.org/10.1016/j.compenvurbsys.2010.05.001
Paas, B., Schneider, C.: A comparison of model performance between ENVI-met and Austal2000 for particulate matter. Atmos. Environ. 145, 392–404 (2016). https://doi.org/10.1016/j.atmosenv.2016.09.031
Padro-Martinez, L.T., Patton, A.P., Trull, J.B., Zamore, W., Brugge, D., Durant, J.L.: Mobile monitoring of particle number concentration and other traffic-related air pollutants in a near-highway neighborhood over the course of a year. Atmos. Environ. 61, 253–264 (2012). https://doi.org/10.1016/j.atmosenv.2012.06.088
Pasquill, F.: The estimation of the dispersion of windborne material. Met. Mag. 90, 33 (1961)
Pasquill, F.: Atmospheric Diffusion, 2nd edn. Ellis Horwood Ltd, Chichester (1974)
Pasquill, F., Smith, B.: Study of the Disperion of windborne material Etc. In: Ellis Horwood (1983)
Pepe, N., Pirovano, G., Lonati, G., Balzarini, A., Toppetti, A., Riva, G.M., Bedogni, M.: Development and application of a high resolution hybrid modelling system for the evaluation of urban air quality. Atmos. Environ. 141, 297–311 (2016). https://doi.org/10.1016/j.atmosenv.2016.06.071
Pernigotti, D., Belis, C.A., Spanò, L.: SPECIEUROPE: the European data base for PM source profiles. Atmos. Pollut. Res. 7, 307–314 (2016). https://doi.org/10.1016/j.apr.2015.10.007
Piringer, M., Joffre, S., Baklanov, A., Christen, A., Deserti, M., Ridder, K.D., et al.: The surface energy balance and the mixing height in urban areas—activities and recommendations of COST-action 715. Bound.-Layer Meteorol. 124, 3–24 (2007). https://doi.org/10.1007/s10546-007-9170-0
Pournazeri, S., Princevac, M., Venkatram, A.: Scaling of building affected plume rise and dispersion in water channels and wind tunnels—revisit of an old problem. J. Wind Eng. Ind. Aerodyn. 103, 16–30 (2012). https://doi.org/10.1016/j.jweia.2012.01.006
Price, C., Penner, J., Prather, M.: NO2 from lightning: 1. Global distribution based on lightning physics. J. Geophys. Res. Atmos. 102, 5929–5941 (1997). https://doi.org/10.1029/96JD03504
Pugh, T.A.M., MacKenzie, A.R., Whyatt, J.D., Hewitt, C.N.: Effectiveness of green infrastructure for improvement of air quality in urban street canyons. Environ Sci Technol. 46, 7692–7699 (2012). https://doi.org/10.1021/es300826w
Pul, W.A.J.V., Holtslag, A.: a. M, swart DPJ. A comparison of ABL heights inferred routinely from lidar and radiosondes at noontime. Bound.-Layer Meteorol. 68, 173–191 (1994). https://doi.org/10.1007/BF00712670
Rakowska, A., Wong, K.C., Townsend, T., Chan, K.L., Westerdahl, D., Ng, S., Močnik, G., Drinovec, L., Ning, Z.: Impact of traffic volume and composition on the air quality and pedestrian exposure in urban street canyon. Atmospheric Environment. 98, 260–270 (2014). https://doi.org/10.1016/j.atmosenv.2014.08.073
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2017)
Robitu, M., Musy, M., Inard, C., Groleau, D.: Modeling the influence of vegetation and water pond on urban microclimate. Solar Energy, Urban Ventilation. 80, 435–447 (2006). https://doi.org/10.1016/j.solener.2005.06.015
Rottensteiner, F., Sohn, G., Gerke, M., Wegner, J.D., Breitkopf, U., Jung, J.: Results of the ISPRS benchmark on urban object detection and 3D building reconstruction. ISPRS J. Photogramm. Remote Sens. 93, 256–271 (2014)
Santamouris, M., Xirafi, F., Gaitani, N., Spanou, A., Saliari, M., Vassilakopoulou, K.: Improving the microclimate in a dense urban area using experimental and theoretical techniques - the case of Marousi, Athens. Int. J. Vent. 11, 1–16 (2012). https://doi.org/10.1080/14733315.2012.11683966
Santiago, J.L., Martilli, A.: A dynamic urban canopy parameterization for mesoscale models based on computational fluid dynamics Reynolds-averaged Navier–stokes microscale simulations. Bound.-Layer Meteorol. 137, 417–439 (2010). https://doi.org/10.1007/s10546-010-9538-4
Santiago, J.L., Sanchez, B., Martilli, A.: Microscale modeling of effects of realistic surface heat fluxes on pollutant distribution within a simplified urban configuration. In: 9th International Conference on Urban Climate Jointly with 12th Symposium on the Urban Environment Toulouse (2015)
Schultz, M.G., Heil, A., Hoelzemann, J.J., Spessa, A., Thonicke, K., Goldammer, J.G., et al.: Global wildland FIRE emissions from 1960 to 2000: GLOBAL FIRE EMISSIONS 1960-2000. Glob. Biogeochem. Cycles. 22, n/a–n/a (2008). https://doi.org/10.1029/2007GB003031
Searcy, C., Dean, K., Stringer, W.: PUFF: a high-resolution volcanic ash tracking model. J. Volcanol. Geotherm. Res. 80, 1–16 (1998)
Shahbazi, H., Ganjiazad, R., Hosseini, V., Hamedi, M.: Investigating the influence of traffic emission reduction plans on Tehran air quality using WRF/CAMx modeling tools. Transp. Res. Part D: Transp. Environ. 57, 484–495 (2017)
Sharma, S., Chatani, S., Mahtta, R., Goel, A., Kumar, A.: Sensitivity analysis of ground level ozone in India using WRF-CMAQ models. Atmos. Environ. 131, 29–40 (2016). https://doi.org/10.1016/j.atmosenv.2016.01.036
Shih, T.-H., Liou, W.W., Shabbir, A., Yang, Z., Zhu, J.: A new k-ϵ eddy viscosity model for high Reynolds number turbulent flows. Comput. Fluids. 24, 227–238 (1995). https://doi.org/10.1016/0045-7930(94)00032-T
Sicard, P., Agathokleous, E., Araminiene, V., Carrari, E., Hoshika, Y., De Marco, A., et al.: Should we see urban trees as effective solutions to reduce increasing ozone levels in cities? Environ. Pollut. 243, 163–176 (2018). https://doi.org/10.1016/j.envpol.2018.08.049
Simon, H., Beck, L., Bhave, P.V., Divita, F., Hsu, Y., Luecken, D., Mobley, J.D., Pouliot, G.A., Reff, A., Sarwar, G., Strum, M.: The development and uses of EPA’s SPECIATE database. Atmos. Pollut. Res. 1, 196–206 (2010). https://doi.org/10.5094/APR.2010.026
Simon, H., Baker, K.R., Phillips, S.: Compilation and interpretation of photochemical model performance statistics published between 2006 and 2012. Atmos. Environ. 61, 124–139 (2012). https://doi.org/10.1016/j.atmosenv.2012.07.012
Sindelarova, K., Granier, C., Bouarar, I., Guenther, A., Tilmes, S., Stavrakou, T., Müller, J.F., Kuhn, U., Stefani, P., Knorr, W.: Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years. Atmos. Chem. Phys. 14, 9317–9341 (2014). https://doi.org/10.5194/acp-14-9317-2014
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Wang, W., et al.: A description of the advanced research WRF version 3. UCAR/NCAR. (2008). https://doi.org/10.5065/D68S4MVH
Steensen, T., Stuefer, M., Webley, P., Grell, G., Freitas, S.: Qualitative comparison of mount redoubt 2009 volcanic clouds using the PUFF and WRF-Chem dispersion models and satellite remote sensing data. J. Volcanol. Geotherm. Res. 259, 235–247 (2013). https://doi.org/10.1016/j.jvolgeores.2012.02.018
Stein, A.F., Isakov, V., Godowitch, J., Draxler, R.R.: A hybrid modeling approach to resolve pollutant concentrations in an urban area. Atmos. Environ. 41, 9410–9426 (2007). https://doi.org/10.1016/j.atmosenv.2007.09.004
Streets, D.G., Canty, T., Carmichael, G.R., de Foy, B., Dickerson, R.R., Duncan, B.N., Edwards, D.P., Haynes, J.A., Henze, D.K., Houyoux, M.R., Jacob, D.J., Krotkov, N.A., Lamsal, L.N., Liu, Y., Lu, Z., Martin, R.V., Pfister, G.G., Pinder, R.W., Salawitch, R.J., Wecht, K.J.: Emissions estimation from satellite retrievals: a review of current capability. Atmos. Environ. 77, 1011–1042 (2013)
Sutton, O.G.: Micrometeorology. McGraw-Hill, NewYork (1953)
Synnefa, A., Saliari, M., Santamouris, M.: Experimental and numerical assessment of the impact of increased roof reflectance on a school building in Athens. Energy and Buildings. 55, 7–15 (2012)
Taylor, G.: Turbulence. Q.J.R. Meteorol. Soc. 53 (1927)
Temel, O., Bricteux, L., van Beeck, J.: Coupled WRF-OpenFOAM study of wind flow over complex terrain. J. Wind Eng. Ind. Aerodyn. 174, 152–169 (2018). https://doi.org/10.1016/j.jweia.2018.01.002
Tewari, M., Chen, F., Kusaka, H., Miao, S.: Coupled WRF/unified Noah/urban-canopy modeling system. Ncar WRF Documentation, NCAR, Boulder. 122, (2007)
Tewari, M., Kusaka, H., Chen, F., Coirier, W.J., Kim, S., Wyszogrodzki, A.A., Warner, T.T.: Impact of coupling a microscale computational fluid dynamics model with a mesoscale model on urban scale contaminant transport and dispersion. Atmos. Res. 96, 656–664 (2010). https://doi.org/10.1016/j.atmosres.2010.01.006
Thunis, P., Miranda, A., Baldasano, J.M., Blond, N., Douros, J., Graff, A., Janssen, S., Juda-Rezler, K., Karvosenoja, N., Maffeis, G., Martilli, A., Rasoloharimahefa, M., Real, E., Viaene, P., Volta, M., White, L.: Overview of current regional and local scale air quality modelling practices: assessment and planning tools in the EU. Environ. Sci. Pol. 65, 13–21 (2016). https://doi.org/10.1016/j.envsci.2016.03.013
Toja-Silva, F., Chen, J., Hachinger, S., Hase, F.: CFD simulation of CO 2 dispersion from urban thermal power plant: analysis of turbulent Schmidt number and comparison with Gaussian plume model and measurements. J. Wind Eng. Ind. Aerodyn. 169, 177–193 (2017)
Tominaga, Y., Mochida, A., Yoshie, R., Kataoka, H., Nozu, T., Yoshikawa, M., Shirasawa, T.: AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings. J. Wind Eng. Ind. Aerodyn. 96, 1749–1761 (2008). https://doi.org/10.1016/j.jweia.2008.02.058
Toparlar, Y., Blocken, B., Maiheu, B., van Heijst, G.J.F.: A review on the CFD analysis of urban microclimate. Renew. Sust. Energ. Rev. 80, 1613–1640 (2017). https://doi.org/10.1016/j.rser.2017.05.248
Trombetti M., Pisoni E., Lavalle C.: Downscaling methodology to produce a high resolution gridded emission inventory to support local/city level air quality policies, EUR 28428 EN (2017). https://doi.org/10.2760/51058
Tsakovski, S.L., Simeonova, P.A., Simeonov, V.D.: Statistical modeling of air pollution. J. Environ. Sci. Health A. 47, 31–43 (2012). https://doi.org/10.1080/10934529.2012.629576
Turner DB. Workbook on atmospheric dispersion estimates. Office of air Programs pub. No. AP-26, Enviornmental protection agency, USA; 1970
UN. World Urbanization Prospects: The 2018 Revision. 2018
UNDESA. UN Department of Economic and Social Affairs. 2018
Vennam, L.P., Vizuete, W., Talgo, K., Omary, M., Binkowski, F.S., Xing, J., Mathur, R., Arunachalam, S.: Modeled full-flight aircraft emissions impacts on air quality and their sensitivity to grid resolution: aircraft emissions impacts on surface AQ. J. Geophys. Res. Atmos. 122, 13,472–13,494 (2017). https://doi.org/10.1002/2017JD026598
Wang, M., Zhu, T., Zheng, J., Zhang, R.Y., Zhang, S.Q., Xie, X.X., Han, Y.Q., Li, Y.: Use of a mobile laboratory to evaluate changes in on-road air pollutants during the Beijing 2008 summer Olympics. Atmos. Chem. Phys. 9, 8247–8263 (2009). https://doi.org/10.5194/acp-9-8247-2009
Wang, Y., Bakker, F., de Groot, R., Wortche, H., Leemans, R.: Effects of urban trees on local outdoor microclimate: synthesizing field measurements by numerical modelling. Urban Ecosystems. 18, 1305–1331 (2015). https://doi.org/10.1007/s11252-015-0447-7
Wen, D., Lin, J.C., Millet, D.B., Stein, A.F., Draxler, R.R.: A backward-time stochastic Lagrangian air quality model. Atmospheric Environment. 54, 373–386 (2012). https://doi.org/10.1016/j.atmosenv.2012.02.042
WHO. WHO Global Ambient Air Quality Database (update 2018). 2018
Wilkerson, J.T., Jacobson, M.Z., Malwitz, A., Balasubramanian, S., Wayson, R., Fleming, G., Naiman, A.D., Lele, S.K.: Analysis of emission data from global commercial aviation: 2004 and 2006. Atmos. Chem. Phys. 10, 6391–6408 (2010)
Woody, M.C., West, J.J., Jathar, S.H., Robinson, A.L., Arunachalam, S.: Estimates of non-traditional secondary organic aerosols from aircraft SVOC and IVOC emissions using CMAQ. Atmos. Chem. Phys. 15, 6929–6942 (2015). https://doi.org/10.5194/acp-15-6929-2015
Xia, Q., Niu, J., Liu, X.: Dispersion of air pollutants around buildings: a review of past studies and their methodologies. Indoor and Built Environ. 23, 201–224 (2014). https://doi.org/10.1177/1420326X12464585
Xiao, X.D., Dong, L., Yan, H., Yang, N., Xiong, Y.: The influence of the spatial characteristics of urban green space on the urban heat island effect in Suzhou Industrial Park. Sustain. Cities Soc. 40, 428–439 (2018). https://doi.org/10.1016/j.scs.2018.04.002
Xie, B., Fung, J.C., Chan, A., Lau, A.: Evaluation of nonlocal and local planetary boundary layer schemes in the WRF model. J. Geophys. Res. Atmos. 117, (2012)
Yahya, K., Wang, K., Campbell, P., Chen, Y., Glotfelty, T., He, J., Pirhalla, M., Zhang, Y.: Decadal application of WRF/Chem for regional air quality and climate modeling over the U.S. under the representative concentration pathways scenarios. Part 1: model evaluation and impact of downscaling. Atmos. Environ. 152, 562–583 (2017). https://doi.org/10.1016/j.atmosenv.2016.12.029
Yakhot, V., Orszag, S.A.: Renormalization group analysis of turbulence. I. Basic theory. J. Sci. Comput. 1, 3–51 (1986). https://doi.org/10.1007/BF01061452
Yamada, T., Mellor, G.: A simulation of the Wangara atmospheric boundary layer data. J. Atmos. Sci. 32, 2309–2329 (1975). https://doi.org/10.1175/1520-0469(1975)032<2309:ASOTWA>2.0.CO;2
Yan, W.Y., Shaker, A., El-Ashmawy, N.: Urban land cover classification using airborne LiDAR data: a review. Remote Sens. Environ. 158, 295–310 (2015). https://doi.org/10.1016/j.rse.2014.11.001
Yang, Z.: Prediction of air pollutant from poultry houses by a modified Gaussian plume model. In: PhD Thesis (2017)
Yang, X., Zhao, L., Bruse, M., Meng, Q.: An integrated simulation method for building energy performance assessment in urban environments. Energy and Buildings. 54, 243–251 (2012). https://doi.org/10.1016/j.enbuild.2012.07.042
Yao, Z., Nagel, C., Kunde, F., Hudra, G., Willkomm, P., Donaubauer, A., Adolphi, T., Kolbe, T.H.: 3DCityDB - a 3D geodatabase solution for the management, analysis, and visualization of semantic 3D city models based on CityGML. Open geospatial data, softw. stand. 3, 5 (2018). https://doi.org/10.1186/s40965-018-0046-7
Yotova, G.I., Tsitouridou, R., Tsakovski, S.L., Simeonov, V.D.: Urban air quality assessment using monitoring data of fractionized aerosol samples, chemometrics and meteorological conditions. J. Environ. Sci. Health A. 51, 544–552 (2016)
Yu, S., Mathur, R., Pleim, J., Pouliot, G., Wong, D., Eder, B., Schere, K., Gilliam, R., Rao, S.T.: Comparative evaluation of the impact of WRF/NMM and WRF/ARW meteorology on CMAQ simulations for PM2.5 and its related precursors during the 2006 TexAQS GoMACCS study. Atmos. Chem. Phys. 12, 4091–4106 (2012). https://doi.org/10.5194/acp-12-4091-2012
Zannetti, P.: Gaussian Models. In: Air Pollution Modeling, pp. 141–183. Springer US, Boston, MA (1990). https://doi.org/10.1007/978-1-4757-4465-1_7
Zhang, Y., Chen, Y., Sarwar, G., Schere, K.: Impact of gas-phase mechanisms on weather research forecasting model with chemistry (WRF/Chem) predictions: mechanism implementation and comparative evaluation. J. Geophys. Res. Atmos. 117, (2012). https://doi.org/10.1029/2011JD015775
Zhang, Y., Zhang, X., Wang, L., Zhang, Q., Duan, F., He, K.: Application of WRF/Chem over East Asia: part I. Model evaluation and intercomparison with MM5/CMAQ. Atmos. Environ. 124, 285–300 (2016). https://doi.org/10.1016/j.atmosenv.2015.07.022
Zheng, Y., Miao, Y., Liu, S., Chen, B., Zheng, H., Wang, S.: Simulating flow and dispersion by using WRF-CFD coupled model in a built-up area of Shenyang, China. Adv. Meteorol. 2015, 1–15 (2015). https://doi.org/10.1155/2015/528618
Zhong, M., Saikawa, E., Liu, Y., Naik, V., Horowitz, L.W., Takigawa, M., Zhao, Y., Lin, N.H., Stone, E.A.: Air quality modeling with WRF-Chem v3.5 in East Asia: sensitivity to emissions and evaluation of simulated air quality. Geosci. Model Dev. 9, 1201–1218 (2016). https://doi.org/10.5194/gmd-9-1201-2016