On-road carbon dioxide (CO₂) emissions from light-duty diesel trucks (LDDTs) are greatly affected by driving conditions, which may be better predicted with the sequence deep learning model as compared to traditional models. In this study, two typical LDDTs were selected to investigate the on-road CO₂ emission characteristics with a portable emission measurement system (PEMS) and a global position system (GPS). A deep learning-based LDDT CO₂ emission model (DL-DTCEM) was developed based on the long short-term memory network (LSTM) and trained by the measured data with the PEMS. Results show that the vehicle speed, acceleration, VSP, and road slope had obvious impacts on the transient CO₂ emission rates. There was a rough positive correlation between the vehicle speed, road slope, and CO₂ emission rates. The CO₂ emission rate increased significantly when the speed was >5 m/s, especially at high acceleration. The correlation coefficient (R²) and the root mean square error (RMSE) between the monitored CO₂ emissions with PEMS and the predicted values with the DL-DTCEM were 0.986–0.990 and 0.165–0.167, respectively. The results proved that the model proposed in this study can predict very well the on-road CO₂ emissions from LDDTs.

Volatile organic compounds (VOCs) are ubiquitous in the atmosphere, posing significant adverse impacts on air quality and human health. However, current research on atmospheric VOCs mainly focuses on specific regions or industries, without comprehensive national-level analysis. In this study, a total of 99 articles on atmospheric VOCs in China published from 2015 to 2024 were screened, and data on their concentrations, source apportionment, and health risks were extracted and summarized. The results revealed that the annual average concentrations of TVOCs and their groups in China generally increased and then decreased between 2011 and 2022, peaking in 2018–2019. A distinct seasonal pattern was observed, with the highest concentrations occurring in winter, followed by autumn, spring, and summer. TVOC emissions were highly concentrated in northern and eastern China, mainly contributed by alkanes and alkenes. Source apportionment of VOCs indicated that vehicle sources (32.9% ± 14.3%), industrial emissions (18.0% ± 12.8%), and other combustion sources (13.0% ± 13.0%) were the primary sources of VOCs in China. There was a significant positive correlation (p < 0.05) between the annual mean VOC concentration and population size, and a notable negative correlation (p < 0.05) with GDP per capita. Atmospheric VOCs had no non-carcinogenic risk (HI = 0.5) but exhibited a probable carcinogenic risk (7.5 × 10⁻⁵), with relatively high values for 1,2-dibromoethane, 1,2-dichloroethane, and naphthalene. The health risk was predominantly driven by halocarbons. These findings are essential for a better understanding of atmospheric VOCs and for developing more targeted VOC control measures.

Enhancing gasoline detergency is pivotal for enhancing fuel efficiency and mitigating exhaust emissions in gasoline vehicles. This study investigated gasoline vehicle emission characteristics with different gasoline detergency, explored synergistic emission reduction potentials, and developed versatile emission prediction models. The results indicate that improved fuel detergency leads to a reduction of 5.1% in fuel consumption, along with decreases of 3.2% in total CO₂, 55.4% in CO, and 15.4% in HC emissions. However, during low-speed driving, CO₂ and CO emissions reductions are limited, and HC emissions worsen. A synergistic emission reduction was observed, particularly with CO exhibiting a pronounced reduction compared to HC. The developed deep-learning-based vehicle emission model for different gasoline detergency (DPVEM-DGD) enables accurate emission predictions under various fuel detergency conditions. The Pearson correlation coefficients (Pearson’s r) between predicted and measured values of CO₂, CO, and HC emissions before and after adding detergency agents are 0.913 and 0.934, 0.895 and 0.915, and 0.931 and 0.969, respectively. The predictive performance improves due to reduced peak emissions resulting from improved fuel detergency. Elevated gasoline detergency not only reduces exhaust emissions but also facilitates more refined emission management to a certain extent.

Coal combustion for winter heating is a major source of heavy atmospheric pollution in China, while its impacts on black carbon (BC) are not yet clear. A dual-spot Aethalometer was selected to monitor the atmospheric BC concentration in Zhengzhou, China, during the heating season, which is from 15 November through 15 March of the following year, and the non-heating season (days other than heating season). The characteristics and sources of BC were analyzed, and a concentration weight trajectory (CWT) analysis was conducted. The results showed that the BC concentrations in the heating season were generally higher than those in the non-heating season. The diurnal variation in BC concentrations during heating season was bimodal, and that during the non-heating season was unimodal. The α-values in the heating and non-heating seasons indicated that combustion of coal and biomass and vehicle emissions were the major BC sources for the heating season and non-heating season, respectively. BC concentrations were positively correlated with PM_2.5, PM₁₀, CO, and NO_X. There was a strong negative correlation between wind speed and BC concentrations, and that for relative humidity was the opposite. BC concentration during heating season was mainly influenced by the northwestern areas of China and the eastern part of Henan, and that in the non-heating season was mainly from the northeastern areas of China and southern Henan.

Microplastics (MPs) are receiving increasing attention because of their potential harm to the environment and human health. This research aims to summarize the abundance, toxicological effects, and analysis methods of MPs, as well as present their current status and trends in scientific research. Bibliometric analysis confirmed a substantial rise in annual research papers on MPs, predominantly over the previous nine years. The central research areas relating to MPs include distribution, sources, toxic effects, analytical approaches, and adsorption of MPs with other pollutants. Airborne MPs are a primary source of microplastic pollution in remote areas. Humans may inhale and ingest MPs, leading to the accumulation of these particles in their bodies. Additionally, microplastics can have biological toxicity that poses a potential threat to human health. Standard procedures for sampling and both qualitative and quantitative analysis of microplastics in various environmental media must be established urgently to enable effective comparison of experimental conclusions.