Abstract
Abstract. Air pollution is a major health hazard, and while air quality overall has been improving in industrialized nations, pollution is still a major economic and public health issue, with some species, such as ozone (O3), still exceeding the standards set by governing agencies. Chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution both at local and regional scales. In this study, the Polair3D v1.11 CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model's capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales. The simulation by the model included three nested domains, at horizontal resolutions of 9 km by 9 km and 3 km by 3 km, as well as two 1 km by 1 km domains covering the cities of Montréal and Québec. We find that the model captures the spatial variability and seasonal effects and, to a lesser extent, the hour-by-hour or day-to-day temporal variability for a fixed location. The model at both the 3 km and the 1 km resolution struggled to capture high-frequency temporal variability and showed large variabilities in correlation and bias from site to site. When comparing the biases and correlation at a site-wide scale, the 3 km domain showed slightly higher correlation for carbon monoxide (CO), nitrogen dioxide (NO2), and nitric oxide (NO), while ozone (O3), sulfur dioxide (SO2), and PM2.5 showed slight increases in correlation at the 1 km domain. The performance of the Polair3D model was in line with other models over Canada and comparable to Polair3D's performance over Europe.
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