Estimating gaseous pollutants from bus emissions: A hybrid model based on GRU and XGBoost

Highlights: • On-road bus emissions are highly correlated with stop-and-go driving patterns. • The real circumstances of bus stations are more complex than other road types. • In most cases, the emission factors produced from intersections are the highest. • Weather conditions are of vital importance in terms of bus emissions modeling. In urban areas, traffic-related contamination is one of the main contributors to environmental deterioration, and the pollution from public transit buses is a major component. To mitigate these impacts, it is essential to estimate bus emissions and analyze their characteristics. This paper proposes a hybrid model based on gated recurrent unit (GRU) and extreme gradient boosting (XGBoost), termed GRU-XGB, to predict gaseous pollutants from bus emissions (CO, CO₂, HC, NO_X) under real conditions. On-road experimental data collected from CNG-fueled and diesel-powered buses in Zhenjiang was used as a case study to verify the model's effectiveness. A comparison between the proposed and other state-of-the-art models reveals that GRU-XGB performs best for all evaluation metrics on both microscopic and aggregative levels, with an average correlation coefficient above 0.98 and an average MAPE lower than 9%. Moreover, the results of estimation errors analysis suggest that the real conditions of bus stations are more complicated than those of intersections and road sections. In most cases, however, the emission factors produced from intersections are proven to be the highest. Furthermore, operating patterns are shown to be the most significant factors, with relative importance equal to 45.09% and 71.68% for CNG and diesel buses, respectively. Besides, the results also indicate that humidity has little impact on this issue, while the influence of temperature is obvious, with relative importance equal to 17.56% and 9.41% for CNG and diesel buses, separately. Such findings can provide theoretical guidance for both emission estimation and environmental protection. Also, it is applicable for the management of accurate monitoring from an urban-level and can be integrated into emission simulation tools.