Evaluating Hourly Sulphur Dioxide and Ground Ozone Simulated with the Air Quality Model in Lima, Peru
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32807
Evaluating Hourly Sulphur Dioxide and Ground Ozone Simulated with the Air Quality Model in Lima, Peru

Authors: Odón R. Sánchez-Ccoyllo, Elizabeth Ayma-Choque, Alan Llacza

Abstract:

Sulphur dioxide (SO₂) and surface-ozone (O₃) concentrations are associated with diseases. The objective of this research is to evaluate the effectiveness of the air-quality Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model with a horizontal resolution of 5 km x 5 km. For this purpose, the measurements of the hourly SO₂ and O₃ concentrations available in three air quality monitoring stations in Lima, Peru were used for the purpose of validating the simulations of the SO₂ and O₃ concentrations obtained with the WRF-Chem model in February 2018. For the quantitative evaluation of the simulations of these gases, statistical techniques were implemented, such as the average of the simulations; the average of the measurements; the Mean Bias (MeB); the Mean Error (MeE); and the Root Mean Square Error (RMSE). The results of these statistical metrics indicated that the simulated SO₂ and O₃ values over-predicted the SO₂ and O₃ measurements. For the SO₂ concentration, the MeB values varied from 0.58 to 26.35 µg/m³; the MeE values varied from 8.75 to 26.5 µg/m³; the RMSE values varied from 13.3 to 31.79 µg/m³; while for O₃ concentrations the statistical values of the MeB varied from 37.52 to 56.29 µg/m³; the MeE values varied from 37.54 to 56.70 µg/m³; the RMSE values varied from 43.05 to 69.56 µg/m³.

Keywords: Ground-ozone, Lima, Sulphur dioxide, WRF-Chem.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 290

References:


[1] E. Oikonomou, G. Lazaros, and D. Tousilis, “The association of air pollutants exposure with subclinical inflammation and carotid atherosclerosis,” International Journal of Cardiology, vol. 342, pp. 108–114, Nov. 2021, doi: 10.1016/j.ijcard.2021.07.056.
[2] Y. Kim, Y. H. Choi, M. K. Kim, H. J. Paik, and D. H. Kim, “Different adverse effects of air pollutants on dry eye disease: Ozone, PM2.5, and PM10,” Environmental Pollution, vol. 265, Oct. 2020, doi: 10.1016/j.envpol.2020.115039.
[3] J. Ngarambe, S. J. Joen, C. H. Han, and G. Y. Yun, “Exploring the relationship between particulate matter, CO, SO₂, NO2, O₃ and urban heat island in Seoul, Korea,” Journal of Hazardous Materials, vol. 403, Feb. 2021, doi: 10.1016/j.jhazmat.2020.123615.
[4] Z. He, P. Liu, X. Zhao, X. He, J. Liu, and Y. Mu, “Responses of surface O₃ and PM2.5 trends to changes of anthropogenic emissions in summer over Beijing during 2014–2019: A study based on multiple linear regression and WRF-Chem,” Science of the Total Environment, vol. 807, Feb. 2022, doi: 10.1016/j.scitotenv.2021.150792.
[5] T. Sha, X. Ma, and J. Wang, “Improvement of inorganic aerosol component in PM2.5 by constraining aqueous-phase formation of sulfate in cloud with satellite retrievals: WRF-Chem simulations,” Science of the Total Environment, vol. 804, Jan. 2022, doi: 10.1016/j.scitotenv.2021.150229.
[6] P. Wang, P. Wang, K. Chen, J. Du, and H. Zhang, “Ground-level ozone simulation using ensemble WRF/Chem predictions over the Southeast United States,” Chemosphere, vol. 287, Jan. 2022, doi: 10.1016/j.chemosphere.2021.132428.
[7] G. A. Grell, S.E. Peckham, R. Schmitz, S. McKeen, G. Frost, W. C. Skamarock, and B. Eder, “Fully coupled ‘online’ chemistry within the WRF model,” Atmospheric Environment, vol. 39, no. 37, pp. 6957–6975, 2005, doi: 10.1016/j.atmosenv.2005.04.027.
[8] NCEP, “National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, https://doi.org/10.5065/D6M043C6, 2000. Accessed 07 July 2021.
[9] O. R. Sánchez-Ccoyllo, C. G. Ordoñez-Aquino, Á G. Muñoz, A. Llacza, M. F. Andrade, Y. Liu, W. Reátegui-Romero, and G Brasseur. “Modeling Study of the Particulate Matter in Lima with the WRF-Chem Model: Case Study of April 2016,” International Journal of Applied Engineering Research, vol. 13, no. 11, p. 10129, 2018, doi: 10.37622/ijaer/13.11.2018.10129-10141.
[10] M. T. Chuang, C. F. Wu, C. Y. Lin, W. C. Lin, C. C. K Chou, Lee, and S. S. K. Kong, “Simulating nitrate formation mechanisms during PM2.5 events in Taiwan and their implications for the controlling direction,” Atmospheric Environment, vol. 269, Jan. 2022, doi: 10.1016/j.atmosenv.2021.118856.
[11] C. Abdallah, C. Afif, N. el Masri, F. Öztürk, M. Keleş, and K. Sartelet, “A first annual assessment of air quality modeling over Lebanon using WRF/Polyphemus,” Atmospheric Pollution Research, vol. 9, no. 4. Elsevier B.V., pp. 643–654, Jul. 01, 2018. doi: 10.1016/j.apr.2018.01.003.