Hybrid Deep Learning Framework for Forecasting Ground-Level Ozone in a North Texas Urban Region

Abstract

Ground-level ozone is a critical secondary air pollutant and greenhouse gas, especially in urban oil and gas regions, where it poses severe public health and environmental risks. Urban areas in North Texas have experienced persistently elevated ozone levels over the past two decades despite emission control efforts, highlighting the need for advanced forecasting tools. This study presents a hybrid recurrent neural network (RNN) model that combines Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures to predict 8 h average ground-level ozone concentrations over a full annual cycle. The model leverages one-hour lagged ozone precursor pollutants (VOC and NOx) and seven meteorological variables, using a novel framework designed to capture complex temporal dependencies and spatiotemporal variability in environmental data. Trained and validated on multi-year datasets from two distinctly different urban air quality monitoring sites, the model achieved high predictive accuracy (R2 ≈ 0.97, IoA > 0.96), outperforming standalone LSTM and Random Forest models by 6–12%. Beyond statistical performance, the model incorporates Shapley Additive exPlanation (SHAP) analysis to provide mechanistic interpretability, revealing the dominant roles of relative humidity, temperature, solar radiation, and precursor concentrations in modulating ozone levels. These findings demonstrate the model’s effectiveness in learning the nonlinear dynamics of ozone formation, outperforming traditional statistical models, and offering a reliable tool for long-term ozone forecasting and regional air quality management.