Vehicle mileage is one of the key parameters for accurately evaluating vehicle emissions and energy consumption. With the support of the national annual vehicle emission inspection networked platform in China, this study used big data methods to analyze the activity level characteristics of the light-duty passenger vehicle fleet with the highest ownership proportion. We found that the annual mileage of vehicles does not decay significantly with the increase in vehicle age, and the mileage of vehicles is relatively low in the first few years due to the run-in period, among other reasons. This study indicated that the average mileage of the private passenger car fleet is 10,300 km/yr and that of the taxi fleet was 80,000 km/yr in China in 2019, and the annual mileage dropped by 22% in 2020 due to the pandemic. Based on the vehicle mileage characteristics, the emission inventory of major pollutants from light-duty passenger vehicles in China for 2010–2020 was able to be updated, which will provide important data support for more accurate environmental and climate benefit assessments in the future.

Accurate and comprehensive estimation of mobile source (MS) emissions is essential for air quality management and atmospheric research. However, existing emission inventories often lack sufficient coverage of MS categories, emerging pollutants, and newly developed model parameterizations. Here, we present the Gridded Mobile-source Emission Dataset (GMED), a model-ready emission inventory with detailed source classification and multi-pollutant coverage. GMED provides monthly emissions from 2011 to 2020 at a spatial resolution of 36 km × 36 km, covering eight MS categories and including both tailpipe and non-exhaust emissions. It incorporates several methodological improvements and integrates localized measurements from multiple sources, including emission factors, organic compound speciation, spatiotemporal proxies, and activity data. Validation involves comparisons with established inventories and cross-checks of key model parameters, both showing good agreement across multiple metrics. GMED outputs also align well with high-resolution emission inventories derived from big traffic data. As a validated, long-term dataset, GMED supports emission assessments, air quality modelling, and policy evaluation, helping to fill a critical gap in differentiated, multi-year MS emission inventories.

In the present work, volatile organic compounds (VOCs) from vehicle exhaust and cooking fumes were investigated via simulation experiments, which covered engine emissions produced during gasoline direct injection (GDI) using two kinds of fuels and cooking emissions produced by preparing three domestic dishes. The distinct characteristics of VOCs emitted during the two processes were identified. Alkanes (73% mass fraction on average) and aromatics (15% on average) dominated the vehicle VOCs, while oxygenated VOCs (49%) and alkanes (29%) dominated the cooking VOCs. Isopentane (22%) was the most abundant species among the vehicle VOCs. N-hexanal (20%) dominated the cooking VOCs. The n-hexanal-to-n-pentanal ratio (3.68 ± 0.64) was utilized to identify cooking VOCs in ambient air. The ozone formation potential produced by cooking VOCs was from 1.39 to 1.93 times higher than that produced by vehicle VOCs, which indicates the significant potential contribution of cooking VOCs to atmospheric ozone. With the equivalent photochemical age increasing from 0 h to 72 h, the secondary organic aerosol formation by vehicle VOCs was from 3% to 38% higher than that of cooking VOCs. Controlling cooking emissions can reduce SOA pollution in a short time due to its higher SOA formation rate than that of vehicle VOCs within the first 30 h. However, after 30 h of oxidation, the amount of SOAs formed by vehicle exhaust emissions exceeded the amount of SOAs produced by cooking activities, implying that reducing vehicle emissions will benefit particle pollution for a longer time. Our results highlight the importance of VOCs produced by cooking fumes, which has not been given much attention before. Further, our study suggested that more research on semi-volatile organic compounds produced by cooking emissions should be conducted in the future.

Greenhouse gas (GHG) emissions represent a pivotal driver of global climate change, with vehicular emissions, particularly from light-duty vehicles, emerging as a prominent source of GHGs. Despite extensive research on gaseous pollutants, studies on GHG emissions within the framework of carbon neutrality remain scarce. This study delves into the emission characteristics of three primary GHGs (carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)) from various light-duty vehicles, encompassing conventional gasoline and hybrid vehicles and bi-fuel taxis. As anticipated, with advancements in emission standards and powertrains, vehicular GHG emissions have significantly decreased. However, our findings also revealed surprising trends. While engine technology upgrades reduced CO₂, they unexpectedly increased CH₄ and N₂O emissions. Additionally, hot starts, beneficial for reducing CO₂ and CH₄ emissions, caused heightened N₂O emissions, which is noteworthy under operating conditions with frequent start–stop events. Intriguingly, compressed natural gas (CNG), generally perceived as cleaner, increased CH₄ emissions. Regarding the impact of three-way-catalyst (TWC) converters on GHG emissions, under “TWC deteriorated” conditions, N₂O emissions from CNG-powered vehicles were approximately 3 times higher than those under “TWC worked” conditions, which can be attributed to the significant increase in nitrogen oxides (NO_x). Considering the global warming potential (GWP), the “TWC deteriorated” scenario paradoxically decreased GWP values, highlighting the complex interaction between emission control technologies and their environmental impacts. This study provides crucial insights into vehicular GHG emissions, which are essential for developing effective strategies aimed at mitigating emissions and enhancing the efficiency of emission control systems.