Rencheng Zhu

The 2022 Olympic Winter Games (OWG) and Paralympic Winter Games (PWG) marked the first time the Beijing–Tianjin–Hebei (BTH) region implemented joint air pollution prevention and control measures for a winter mega-event. Notably, while Beijing maintained clean air (daily PM2.5 concentration ≤ 75 μg/m3, according to the Ambient Air Quality Standards in effect at the time) during the OWG, a heavy-pollution episode occurred during the PWG, with the 24-h average PM2.5 concentration peaking at 155 μg/m3. Combining field observations, dynamic emission characterization, and The Comprehensive Air quality Model with extensions (CAMx) simulations, we conducted a closed-loop study to investigate the underlying causes of this episode. Results show that while the industrial sector effectively regulated emissions, the mobile-source activity levels recovered rapidly after the Chinese New Year, outweighing the control measures implemented for mobile sources. This recovery increased total NOx emissions by 13% in the BTH region, driving nitrate-dominated PM2.5 pollution. During the study period, the PM2.5 nitrate concentration (normalized by the carbon monoxide concentration) was found to increase by 3 × 10–4 and 1 × 10–5 for each one-ton increase in NOx emissions in Beijing and the BTH region, respectively. To eliminate heavy PM2.5 pollution under the meteorological conditions observed during the PWG, NOx emissions would need to be further reduced by ≥1% in Beijing and ≥7% in the BTH region. This work underscores the urgent need to mitigate emissions from diesel trucks and nonroad machinery, especially under extremely adverse meteorological conditions in the BTH region.

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.

The increase in the number of motor vehicles has intensified the impact of traffic sources on air quality. Our aim was to illustrate the characteristics of PM_2.5 emissions from vehicles fueled with E10 (a blend of 10% ethanol and 90% gasoline). A 21-day PM_2.5 sampling in a fully enclosed urban tunnel and the component analysis were completed, and the characteristics, sources, and health risks of tunnel PM_2.5 were studied. Moreover, the PM_2.5 pH and its sensitivity were investigated by the thermodynamic model (ISORROPIA-II). In addition, exposure models were used to assess the health risks of different heavy metals in PM_2.5 to humans through respiratory pathways. The two-point Cu/Sb ratio (entrance: 4.0 ± 1.4; exit: 4.4 ± 1.7) was close to the diagnostic criteria indicating a significant impact from brake wear. NO₃⁻, NH₄⁺, and SO₄²⁻ constituted the main components of water-soluble ions in PM_2.5 of the tunnel, accounting for 83.0–84.6% of the total concentration of inorganic ions. The organic carbon/elemental carbon ratio of the tunnel was greater than 2, indicating that the contribution of gasoline vehicle exhaust was significant. The average emission factors of PM_2.5 in the fleet was 31.4 ± 16.6 mg/(veh·km). The pH value of PM_2.5 in a tunnel environment (4.6 ± 0.3) was more acidic than that in an urban environment (4.9 ± 0.6). The main sensitive factors of PM_2.5 pH in the urban atmosphere and tunnel environment were total ammonia (sum of gas and aerosol, NH₃) and temperature, respectively. The results of the health risk assessment showed that Pb posed a potential carcinogenic risk, while As and Cd presented unacceptable risks for tunnel workers. The non-carcinogenic risk index of heavy metals of PM_2.5 in the tunnel environment exceeded the safety threshold.

Children’s respiratory health is vulnerable to air pollution. Based on data collected from June 2019 to June 2022 at a children’s hospital in Zhengzhou, China, this study utilized Spearman correlation analysis and a generalized additive model (GAM) to examine the relationship between daily visits for common respiratory issues in children and air pollutant concentrations. Results show that the number of upper respiratory tract infection (URTI), pneumonia (PNMN), bronchitis (BCT), and bronchiolitis (BCLT) visits in children showed a positive correlation with PM_2.5, PM₁₀, NO₂, SO₂, and CO while exhibiting a negative correlation with temperature and relative humidity. The highest increases in PNMN visits in children were observed at lag 07 for NO₂, SO₂, and CO. A rise of 10 μg/m³ in NO₂, 1 μg/m³ in SO₂, and 0.1 mg/m³ in CO corresponded to an increase of 9.7%, 2.91%, and 5.16% in PNMN visits, respectively. The effects of air pollutants on the number of BCT and BCLT visits were more pronounced in boys compared to girls, whereas no significant differences were observed in the number of URTI and PNMN visits based on sex. Overall, air pollutants significantly affect the prevalence of respiratory diseases in children, and it is crucial to improve air quality to protect the children’s respiratory health.

The detergency of motor gasoline is closely related to vehicle exhaust emissions and fuel economy. This paper proposed an improved method for the rapid detection of gasoline detergency based on the deposit images of test gasoline on aluminum plates produced by a multichannel gasoline detergency simulation test (MGST). The detection algorithm system was structured to recognize the deposit plate images by computer vision based on the convolutional neural networks (CNNs). Compared with the traditional simulation test, the improved MGST method resulted in significant reductions in fuel consumption, cost, and test time. The performance of three transfer learning models (Inception-ResNet-V2, Inception-V3, and ResNet50-V2) and a customized CNN was evaluated in the detection algorithm system, and their detection accuracies reached 94, 94, 88, and 82%. Inception-RsNet-V2 was selected due to its higher accuracy and better robustness. Based on the model interpretation, it is evident that the model undergoes feature extraction from the sediment deposits on the deposit plate. Subsequently, it employed the acquired deposit features to accurately detect gasoline samples that failed to meet detergency standards. This approach was proved to be effective in enhancing the detection process and ensuring reliable results for gasoline detergency evaluation. It is beneficial to environmental protection regulators for managing market gasoline detergency and urban mobile source pollution. In addition, a deposit plate image database should be established to further improve the detection model performance during the environmental regulation.

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.