Evaluating Two-Dimensional Horizontal Grids in a Deterministic Air Pollution Model: Estimating the Impact on Outputs, Inputs, and ANN-CHIMERE

Abstract

Air pollution, especially from ozone and particulate matter, significantly contributes to environmental and health problems. Monitoring complexity and high costs have made modeling a parallel approach to surveillance. Models such as CHIMERE are essential in combining weather conditions, emissions, boundaries, and various physical processes, including transport and spatial resolution. This study introduces three methodologies to assess the influence of a two-dimensional horizontal grid in the Eulerian atmospheric transport model. It explores the impact on outputs, inputs, and model fitting. The first approach analyzes the resolution effect on CHIMERE outputs, focusing on O3. The second approach investigates various effects on inputs, including temperature (T), wind speed (WS), Planetary Boundary Layer Height (PBLH), Land Use and Land Cover (LULC), and Emissions (E). The third approach created a straightforward, strong, and precise ANN-CHIMERE adjustment model to evaluate the impact of spatial resolution in this context. The outcomes are verified through ozone measurement data collected in Agadir and Casablanca, Morocco, during various periods in 2016 and 2021.The results show that a higher horizontal grid improves the probability of good predictions. The impact of input data accuracy on high resolution can be concluded, providing insights into the limitations of CHIMERE in predicting output data. The new ANN-CHIMERE adjustment model delivers superior outcomes at high resolution, exhibiting an enhanced correlation coefficient and a significant reduction in the RMSE. In the future, the proposed approaches can be applied for spatial resolution optimization, maintaining the accuracy of the results and the computation time. It can also be adopted as an adjustment process of the inputs and outputs of deterministic air pollution models.