Abstract: In the realm of air quality modeling, accurately representing the planetary boundary layer height (PBLH) is pivotal for assessing pollutant levels with precision. Given the limited availability of PBLH data, meteorological modeling emerges as a viable alternative. However, this approach introduces errors and modeling bias, and the extent of their influence on CMAQ (Community Multiscale Air Quality) results remains to be determined. Our study quantifies the impact of propagated errors from WRF (Weather Research and Forecasting) PBLH simulations on CMAQ air quality modeling. We calculated biases between WRF PBLH outputs and meteorological balloon data, as well as biases between CMAQ ozone (O3) concentration simulations and ground measurements in the São Paulo metropolitan region, Brazil. Employing the Spearman correlation index, we examined the relationship between these biases to unravel the intricate interplay between the two models. Our results reveal a significant relationship, shedding light on the substantial influence of modeling errors in WRF on CMAQ concentration outcomes. These results underscore the critical role of the planetary boundary layer’s height in shaping the dispersion of pollutants. These findings not only contribute to advancing our understanding of air quality modeling intricacies but also hold implications for refining future modeling approaches in pollution assessment.

Keywords: WRF, CMAQ, PBLH, AIR QUALITY