Influence of a High-Resolution Land Cover Classification on Air Quality Modelling

Poor air quality is one of the main environmental causes of premature deaths worldwide, and mainly in cities, where the majority of the population lives. It is a consequence of successive land cover (LC) and use changes, as a result of the intensification of human activities. Knowing these landscape modifications in a comprehensive spatiotemporal dimension is, therefore, essential for understanding variations in air pollutant concentrations. In this sense, the use of air quality models is very useful to simulate the physical and chemical processes that affect the dispersion and reaction of chemical species into the atmosphere. However, the modelling performance should always be evaluated since the resolution of the input datasets largely dictates the reliability of the air quality outcomes. Among these data, the updated LC is an important parameter to be considered in atmospheric models, since it takes into account the Earth’s surface changes due to natural and anthropic actions, and regulates the exchanges of fluxes (emissions, heat, moisture, etc.) between the soil and the air. This work aims to evaluate the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), when different LC classifications are used as an input. The influence of two LC classifications was tested: i) the 24-classes USGS (United States Geological Survey) LC database included by default in the model, and the ii) CLC (Corine Land Cover) and specific high-resolution LC data for Portugal, reclassified according to the new USGS nomenclature (33-classes). Two distinct WRF-Chem simulations were carried out to assess the influence of the LC on air quality over Europe and Portugal, as a case study, for the year 2015, using the nesting technique over three simulation domains (25 km², 5 km² and 1 km² horizontal resolution). Based on the 33-classes LC approach, particular emphasis was attributed to Portugal, given the detail and higher LC spatial resolution (100 m x 100 m) than the CLC data (5000 m x 5000 m). As regards to the air quality, only the LC impacts on tropospheric ozone concentrations were evaluated, because ozone pollution episodes typically occur in Portugal, in particular during the spring/summer, and there are few research works relating to this pollutant with LC changes. The WRF-Chem results were validated by season and station typology using background measurements from the Portuguese air quality monitoring network. As expected, a better model performance was achieved in rural stations: moderate correlation (0.4 – 0.7), BIAS (10 – 21µg.m^-3) and RMSE (20 – 30 µg.m^-3), and where higher average ozone concentrations were estimated. Comparing both simulations, small differences grounded on the Leaf Area Index and air temperature values were found, although the high-resolution LC approach shows a slight enhancement in the model evaluation. This highlights the role of the LC on the exchange of atmospheric fluxes, and stresses the need to consider a high-resolution LC characterization combined with other detailed model inputs, such as the emission inventory, to improve air quality assessment.

Authors:

• C. Silveira
• A. Ascenso
• J. Ferreira
• A. I. Miranda
• P. Tuccella
• G. Curci

Keywords:

• Land cover
• tropospheric ozone
• WRF-Chem
• air quality assessment.

References:

[1] NOAA (National Oceanic and Atmospheric Administration), “What is the difference between land cover and land use?,” Available at: https://oceanservice.noaa.gov/facts/lclu.html, Last update: 25 Jun. 2018, Accessed on 19 Aug. 2018.
[2] D. Lu, W. Mao, D. Yang, J. Zhao, and J. Xu, “Effects of land use and landscape pattern on PM2.5 in Yangtze River Delta, China, “Atmospheric Pollution Research, vol. 9, pp. 705-713, Feb. 2018.
[3] B. Zou, S. Xu, T. Sternberg, and X. Fang, “Effect of Land Use and Cover Change on Air Quality in Urban Sprawl, “Sustainability, vol. 8:677, pp. 1-14, Jul. 2016.
[4] S. Wu, L. J. Mickley, J. O. Kaplan, and D. J. Jacob, “Impacts of changes in land use and land cover on atmospheric chemistry and air quality over the 21st century, “Atmospheric Chemistry and Physics, vol, 12, pp. 1597-1609, Feb. 2012.
[5] L. Sun, J. Wei, D. H. Duan, Y. M. Guo, D. X. Yang, C. Jia, and X. T. Mi, “Impact of Land-Use and Land-Cover Change on urban air quality in representative cities of China,” Journal of Atmospheric and Solar-Terrestrial Physics, vol. 142, pp. 43-54, May 2016.
[6] B. Jiménez-Esteve, M. Udina, M. R. Soler, N. Pepin, and J. R. Miró, “Land use and topography influence in a complex terrain area: A high resolution mesoscale modelling study over the Eastern Pyrenees using the WRF model, “Atmospheric Research, vol. 202, pp. 49-62, Nov. 2017.
[7] G. Xu, L. Jiao, S. Zhao, M. Yuan, X. Li, Y. Han, B. Zhang, and T. Dong, “Examining the Impacts of Land Use on Air Quality from a Spatio-Temporal Perspective in Wuhan, China, “Atmosphere, vol. 7:62, pp. 1-18, Apr. 2016.
[8] C. L. Heald, and D. V. Spracklen, “Land Use Change Impacts on Air Quality and Climate,” Chem. Rev. 115, no. 10, pp. 4476-4496, May 2015.
[9] E. C. McDonald-Buller, A. Webb, K. M. Kockelman, and B. Zhou, “Air quality impacts of transportation and land use policies: a case study in Austin, Texas,” Transportation Research Record No. 2158, pp. 28-35, Jan.2010.
[10] E. Kalnay, and M. Cai, “Impact of urbanization and land-use change on climate,” Nature, vol. 423, pp. 528-531, May 2003.
[11] F. Kuik, A. Lauer, G. Churkina, H. A. C. Denier van der Gon, D. Fenner, K. A. Mar, and T. M. Butler, “Air quality modelling in the Berlin–Brandenburg region using WRF-Chem v3.7.1: sensitivity to resolution of model grid and input data, “Geoscientific Model Development, vol. 9, 4339-4363, Dec. 2016.
[12] J. Fallmann, Numerical simulations to assess the effect of urban heat island mitigation strategies on regional air quality (PhD Thesis). Munich, Germany, 2014, pp. 137.
[13] S. Salata, S. Ronchi, and A. Arcidiacono, “Mapping air filtering in urban areas. A Land Use Regression model for Ecosystem Services assessment in planning, ” Ecosystem Services, vol. 28, pp.341-350, Oct. 2017.
[14] H. Yang, W. Chen, and Z. Liang, “Impact of Land Use on PM2.5 Pollution in a Representative City of Middle China, “International Journal of Environmental Research and Public Health, vol. 14:462, pp. 1-14, Apr. 2017.
[15] K. Civerolo, C. Hogrefe, B. Lynn, J. Rosenthal, J.-Y. Ku, W. Solecki, J. Cox, C. Small, C. Rosenzweig, R. Goldberg, K. Knowlton, and P. Kinney, “Estimating the effects of increased urbanization on surface meteorology and ozone concentrations in the New York City metropolitan region, “Atmospheric Environment, vol. 41, pp. 1803-1818, Mar. 2007.
[16] K. A. Mar, N. Ojha, A. Pozzer, and T. M. Butler, “Ozone air quality simulations with WRF-Chem (v3.5.1) over Europe: model evaluation and chemical mechanism comparison”, Geoscientific Model Development, vol. 9, 3699-3728, Oct 2016.
[17] R. Žabkar, L. Honzak, G. Skok, R. Forkel, J. Rakovec, A. Ceglar, and N. Žagar, “Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions, “Geoscientific Model Development, vol. 8, pp. 2119-2137, Jul. 2015.
[18] K. Md H. Al Razi, and M. Hiroshi, “Numerical simulation for regional ozone concentrations: A case study by weather research and forecasting/chemistry (WRF/Chem) model, “International Journal of Energy and Environment, vol. 4:6, pp. 933-954, 2013.
[19] G. A. Grell, S. E. Peckham, R. Schmitz, S. A. McKeen, G. Frost, W. C. Skamarock, and B. Eder, “Fully coupled “online” chemistry within the WRF model. “Atmospheric Environment, vol. 39, pp. 6957-6975, Mar. 2005.
[20] UCAR & NCAR (University Corporation & National Center for Atmospheric Research), “WRF Source Codes and Graphics Software Download Page,” Available at: http://www2.mmm.ucar.edu/wrf/users/download/get_sources.html, Last update: 14 Aug. 2018, Accessed on 19 Aug. 2018.
[21] P. Tuccella, G. Curci, G. A. Grell, G. Visconti, S. Crumeyrolle, A. Schwarzenboeck, and A. A. Mensah, “A new chemistry option in WRF-Chem v. 3.4 for the simulation of direct and indirect aerosol effects using VBS: evaluation against IMPACT-EUCAARI data, “Geoscientific Model Development, vol. 8, pp. 2749-2776, Sep. 2015.
[22] F. Chen, and J. Dudhia, “Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity,“ Monthly Weather Review, vol. 129:4, pp. 569-585, 2001.
[23] DGT (Directorate-General for the Territorial Development), “A Land Cover/Use Map of Mainland Portugal for 2010, ” Lisbon, Portugal, 2017.
[24] N. Pineda, O. Jorba, J. Jorge, and J.M. Baldasano, “Using NOAA AVHRR and SPOT VGT data to estimate surface parameters: application to a mesoscale meteorological model, “International Journal of Remote Sensing, vol. 25, pp. 129-143, Jan. 2004.
[25] EMEP (European Monitoring and Evaluation Programme), “Grid emissions in 0.1° x 0.1° (long-lat) resolution,” Available at: http://www.ceip.at/ms/ceip_home1/ceip_home/webdab_emepdatabase, Last update: 3 Jul. 2018, Accessed on 19 Aug. 2018.
[26] P. Tuccella, G. Curci, G. Visconti, B. Bessagnet, L. Menut, and R.J. Park, “Modeling of gas and aerosol with WRF/Chem over Europe: Evaluation and sensitivity study, “Journal of Geophysical Research, vol. 117, D03303, pp. 1-15, Feb. 2012.
[27] NCAR & ACOM (National Center for Atmospheric Research & Atmospheric Chemistry Observations & Modeling), “WRF-Chem Tools for the Community,” Available at: https://www2.acom.ucar.edu/wrf-chem/wrf-chem-tools-community, Accessed on 19 Aug. 2018.
[28] A. Guenther, T. Karl, P. Harley, C. Wiedinmyer, P.I. Palmer, and C. Geron, “Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), “Atmospheric Chemistry and Physics, vol. 6, pp. 3181-3210, Aug. 2006.
[29] ECMWF (European Centre for Medium-Range Weather Forecasts), “ERA Interim, Daily,” Available at: http://apps.ecmwf.int/datasets/data/interim-full-daily, Accessed on 19 Aug. 2018.
[30] NCAR & ACOM (National Center for Atmospheric Research & Atmospheric Chemistry Observations & Modeling), “MOZART Download,” Available at: http://www.acom.ucar.edu/wrf-chem/mozart.shtml, Accessed on 19 Aug. 2018.