High-resolution vehicle emission inventory and emission reduction effect evaluation in Pingdingshan City

Abstract

The COPERT model and ArcGIS were utilized to construct a high-resolution vehicle emission inventory of 1 km × 1 km in Pingdingshan City in 2021, evaluating the emission reduction effects under various measures. According to the findings, Pingdingshan City's vehicle emissions in 2021 were 1.668, 10.267, 0.023, 0.455, and 3735.940 Gg of VOCs, NO_x, SO₂, PM_2.5, and CO₂. Among them, the biggest contributors of VOCs, SO₂, and CO₂ were LDPVs (69.2%, 49.63%, and 50.78%, respectively); the greatest sources of NO_x and PM_2.5, on the other hand, were HDTs (71.23% and 43.83%, respectively). China III vehicles were the primary sources of VOCs, NO_x, and PM_2.5 emissions, and the emissions of China II and below vehicles were also significant. Gasoline and diesel vehicles exhibited similar emission characteristics as light and heavy vehicles, respectively. NEVs achieved almost zero emissions. Heavy emission intensity regions were primarily found in places with a dense road network, while the temporal distribution was mainly influenced by the frequency of residential trips. Between 2018 and 2021, SO₂ and CO₂ emissions continued to rise, but at a gradually slower pace. In addition, an assessment of several emission reduction measures revealed that the government needs to adopt diversified control strategies to maximize emission reductions because the effectiveness of single measures to reduce emissions is limited. The core of future pollution control lies in optimizing the structure of road traffic models, especially in increasing the market share of new energy and strict emission standard vehicles.