A study of high temporal-spatial resolution greenhouse gas emissions inventory for on-road vehicles based on traffic speed-flow model: A case of Beijing

Abstract In order to explore the establishment method of high-resolution vehicle emission inventory and analyze the temporal and spatial variation law of vehicle greenhouse gases (GHG) emissions. This paper proposes a bottom-up method based on road network information and the real-time average interval speed of road segments. A traffic speed-flow model is proposed to predict the hourly traffic flow and the localized Motor Vehicle Emission Simulator (MOVES) is used to simulate the CO2, N2O and CH4 emission factors. A high temporal (1 h × 1 h) and spatial (1 km × 1  km) resolution GHG emission inventory of motor vehicles in Beijing in 2018 is developed by this means. The actual emissions of CO2, N2O and CH4 are 19,864,590, 82 and 511 t, respectively. And the total GHG emission is 19,901,933 tCO2e combined with global warming potential (GWP). The daily GHG emissions on weekday and weekend are 55206.30 and 52817.64 tCO2e, respectively. There are three obvious peak emission periods on the weekday, namely, the morning peak (8:00–9:00), the noon peak (11:00–12:00) and the evening peak (18:00–19:00), which contribute 11.76%, 11.84% and 12.92% of the daily emission, respectively. The 1 h × 1 h emission grid shows the spatial distribution characteristics of emissions. The areas within the Fifth Ring Road (973 km2) are only 5.93% of the total area of the city (16,410 km2) but contribute 41.53% of the total vehicle GHG emissions. This study provides detailed data support for implementing vehicle GHG emission mitigation measures.