Updating On-Road Vehicle Emissions for China: Spatial Patterns, Temporal Trends, and Mitigation Drivers.

Environmental science & technology(2023)

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摘要
Vehicle emissions in China have been decoupled from rapid motorization owing to comprehensive control strategies. China's increasingly ambitious goals for better air quality are calling for deep emission mitigation, posing a need to develop an up-to-date emission inventory that can reflect the fast-developing policies on vehicle emission control. Herein, large-sample vehicle emission measurements were collected to update the vehicle emission inventory. For instance, ambient temperature correction modules were developed to depict the remarkable regional and seasonal emission variations, showing that the monthly emission disparities for total hydrocarbon (THC) and nitrogen oxide (NO) in January and July could be up to 1.7 times in northern China. Thus, the emission ratios of THC and NO can vary dramatically among various seasons and provinces, which have not been considered well by previous simulations regarding the nonlinear atmospheric chemistry of ozone (O) and fine particulate matter (PM) formation. The new emission results indicate that vehicular carbon monoxide (CO), THC, and PM emissions decreased by 69, 51, and 61%, respectively, during 2010-2019. However, the controls of NO and ammonia (NH) emissions were not as efficient as other pollutants. Under the most likely future scenario (PC [1]), CO, THC, NO, PM and NH emissions were anticipated to reduce by 35, 36, 35, 45, and 4%, respectively, from 2019 to 2025. These reductions will be expedited with expected decreases of 56, 58, 74, 53, and 51% from 2025 to 2035, which are substantially promoted by the massive deployment of new energy vehicles and more stringent emission standards. The updated vehicle emission inventory can serve as an important tool to develop season- and location-specific mitigation strategies of vehicular emission precursors to alleviate haze and O problems.
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关键词
vehicle emissions, emission inventory, spatial-temporalpatterns, scenario projections, mitigation drivers
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