Updating vehicle VOCs emissions characteristics under clean air actions in a tropical city of China.

In the context of clean air actions in China, vehicle emission limits have been continuously tightened, which has facilitated the reduction of volatile organic compounds (VOCs) emissions. However, the characteristics of VOC emissions from vehicles with strict emission limits are poorly understood. This study investigated the VOC emission characteristics from vehicles under the latest standards based on tunnel measurements, and identified future control strategies for vehicle emissions. The results showed that the highest percentage of VOCs from vehicle consisted of alkanes (80.9 %), followed by aromatics (15.8 %) and alkenes (3.1 %). Alkanes had the most significant ozone formation potential due to their high concentrations, in contrast to the aromatics that have been dominant in previous studies. The measured fleet-average VOC emission factor was 71.3 mg·km-1, including tailpipe emissions of 39.6 mg·km-1 and evaporative emissions of 31.7 mg·km-1. The VOC emission factors of the subgroups were obtained. The emission of evaporated VOCs accounted for 44.5 % of the total vehicle VOC emissions, which have increased substantially from previous studies. In addition, the emission characteristics of vehicles that are under the latest emission threshold values have changed significantly, and the mixing ratio of toluene/benzene (T/B) has been updated to 3:1. This study updates the VOCs emission factors of vehicles under clean air actions and highlights the future mitigation policies should focus on reducing evaporative VOC emissions.

PM2.5 pollution modulates the response of ozone formation to VOC emitted from various sources: Insights from machine learning.

Numerous studies have linked ozone (O3) production to its precursors and fine particulate matter (PM2.5), while the complex interaction effects of PM2.5 and volatile organic compounds (VOCs) on O3 remain poorly understood. A systematic approach based on an interpretable machine learning (ML) model was utilized to evaluate the primary driving factors that impact O3 and to elucidate how changes in PM2.5, VOCs from different sources, NOx, and meteorological conditions either promote or inhibit O3 formation through their individual and synergistic effects in a tropical coastal city, Haikou, from 2019 to 2020. The results suggest that under low PM2.5 levels, alongside the linear O3-PM2.5 relationship observed, O3 formation is suppressed by PM2.5 with higher proportions of traffic-derived aerosol. Vehicle VOC emissions contributed maximally to O3 formation at midday, despite the lowest concentration. VOCs from fossil fuel combustion and industry emissions, which have opposing effects on O3, act as inhibitors and promoters by inducing diverse photochemical regimes. As PM2.5 pollution escalates, the impact of these VOCs reverses, becoming more pronounced in shaping O3 variation. Sensitivity analysis reveals that the O3 formation regime is VOC-limited, and effective regional O3 mitigation requires prioritizing substantial VOC reductions to offset enhanced VOC sensitivity induced by the co-reduction in PM2.5, with a focus on industrial and vehicular emissions, and subsequently, fossil fuel combustion once PM2.5 is effectively controlled. This study underscores the potential of the SHAP-based ML approach to decode the intricate O3-NOx-VOCs-PM2.5 interplay, considering both meteorological and atmospheric compositional variations.

[Ozone Pollution Characteristics and Formation Mechanism in a Typical Tropical Seaside City].

To investigate the pollution characteristics and formation mechanism of ambient air ozone(O3) in a typical tropical seaside city, we conducted an observational experiment on O3 and its precursors at an urban site in Haikou, Hainan Province, from June to October 2019. The O3 pollution characteristics were analyzed comprehensively; the O3-NOx-VOCs sensitivities and key precursors were determined, and the control strategies for O3 pollution were carried out. The results were as follows:1 O3 pollution in Haikou mainly occurred in September and October, with daily maximum 8-h O3 concentrations in the range of 39-190 µg·m-3, and the daily variation in O3 was unimodal, peaking at approximately 14:00. 2 The concentrations of NO2 and VOCs were higher during O3 pollution episodes than their respective mean values in Haikou City. The increased O3 precursor concentrations were an important factor leading to O3 pollution, whereas O3 pollution was also influenced by regional transport, with pollutants mainly transported from the northeastern part of Haikou City. 3 O3-NOx-VOCs sensitivity in Haikou City was in the VOCs and NOx transitional regime, and the most sensitive precursors in various months were different. O3 formation in September was sensitive to anthropogenic VOCs the most; however, in October it was sensitive to NOx. 4 In the future, the reduction ratio of VOCs to NOx should be 1:1-4:1 to control O3 pollution effectively in Haikou.