Temporal and spatial distribution characteristics of no_x emissions of city buses on real road based on spatial autocorrelation

To characterize the spatial and temporal distribution of NOx exhausted by urban buses, we measured real-world on-road NOx emissions from these vehicles in the city of Kunming, China, using an onboard monitoring platform. To fill the data gaps and produce a complete data set, we combined Bayesian network modeling and probabilistic inference. The complete data set was then used to generate an NOx emission heat map, and spatial autocorrelation was applied to evaluate the distribution characteristics. The results show that our method for filling in the missing data provides highly accurate values, with spatial autocorrelation indices of 0.648, 0.836, 0.935, and 0.798 for the morning, midday, afternoon, and evening, respectively. The NOx emissions showed spatial correlation during all four periods, whereas the pollutive emissions showed spatial aggregation. According to the heat map, the NOx concentrations peaked during the midday and the afternoon. Furthermore, regardless of the period, the largest emissions accumulated in Road Sections 1–3 and 6–9, and the highest as well as the fastest-growing emission intensity occurred in Road Sections 5–9.