A 1D CNN-based emulator of CMAQ: Predicting NO2 concentration over the most populated urban regions in Texas

Abstract

In this study, we developed an emulator of the Community Multiscale Air Quality (CMAQ) model by employing a 1-dimensional Convolutional Neural Network (CNN) algorithm to predict hourly surface nitrogen dioxide (NO2) concentrations over the most densely populated urban regions in Texas. The inputs for the emulator were the same as those for the CMAQ model, which includes emission, meteorology, and land use land cover data. We trained the model over June, July, and August (JJA) of 2011 and 2014 and then tested it on JJA of 2017, achieving an Index of Agreement (IOA) of 0.95 and a correlation of 0.90. We also employed temporal 3-fold cross-validation to evaluate the model’s performance, ensuring the robustness and generalizability of the results. To gain deeper insights and understand the factors influencing the model’s surface NO2 predictions, we conducted a Shapley Additive Explanations analysis. The results revealed solar radiation reaching the surface, Planetary Boundary Layer height, and NOx (NO + NO2) emissions are key variables driving the model’s predictions. These findings highlight the emulator’s ability to capture the individual impact of each variable on the model’s NO2 predictions. Furthermore, our emulator outperformed the CMAQ model in terms of computational efficiency, being more than 900 times faster in predicting NO2 concentrations, enabling the rapid assessment of various pollution management scenarios. This work offers a valuable resource for air pollution mitigation efforts, not just in Texas, but with appropriate regional data training, its utility could be extended to other regions and pollutants as well.