SO2 and NO2 simulation and validation in Metropolitan Lima using WRF-chem model

Ana Luna, Hector Navarro, Aldo Moya

Research output: Contribution to journalArticle in a journalpeer-review


In this research, we used the chemical-meteorological model Weather Research and Forecasting coupled with Chemical (WRF-Chem v3.8) to predict pollution scenarios. We studied and analyzed three 2017 months of summer (January, February and March) and three months of winter (July, August and September) to evaluate and forecast two pollutants concentration, sulfur dioxide (SO2) and nitrogen dioxide (NO2) over the city of Lima. We also considered the meteorological variables such as the wind speed and its direction, average temperature, relative humidity and atmospheric pressure. Besides, we used fixed industrial sources inventory as emission data and the Global Forecast System (GFS) as border data for the meteorological components. Within the WRF-Chem model, we implemented the Grell-Freitas parameterization of convection to represent the clouds; we used RRTMG for the shortwave/longwave radiation scheme, and the Monin-Obukhov for the processes in the surface layer, among others. On the other hand, for the gas phase chemistry, we used the RADM2 scheme, for the aerosol module we utilized the MADE-SORGAM, and finally, we employed the Fast-j photolysis scheme. We finally compared the results with the data provided by the ten monitoring stations that belong to the National Service of Meteorology and Hydrology (SENAMHI) which are located in strategic zones in Lima. Lastly, we showed that the variables studied are within the environmental quality standard authorized by the Ministry of the Environment, and we also demonstrated that the simulations given by the model are, in general, overlapping the values measured experimentally in all of the monitoring stations evaluated.
Original languageEnglish
Pages (from-to)135-147
Number of pages13
JournalInternational Journal of Computational Methods and Experimental Measurements
Issue number2
StatePublished - 4 Jul 2020

Bibliographical note

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  • Air pollution
  • Forecasting
  • Industrial emissions
  • WRF-Chem


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