Traffic contributes to fine particulate matter (PM₂.₅) in the atmosphere through engine exhaust emissions and road dust generation. However, the evolution of traffic related PM₂.₅ emission over recent years remains unclear, especially when various efforts to reduce emission e.g., aftertreatment technologies and high emission standards from China IV to China V, have been implemented. In this study, hourly elemental carbon (EC), a marker of primary engine exhaust emissions, and trace element of calcium (Ca), a marker of road dust, were measured at a nearby highway sampling site in Shanghai from 2016 to 2019. A random forest-based machine learning algorithm was applied to decouple the influences of meteorological variables on the measured EC and Ca, revealing the deweathered trend in exhaust emissions and road dust. After meteorological normalization, we showed that non-exhaust emissions, i.e., road dust from traffic, increased their fractional contribution to PM₂.₅ over recent years. In particular, road dust was found to be more important, as revealed by the deweathered trend of Ca fraction in PM₂.₅, increasing at 6.1% year⁻¹, more than twice that of EC (2.9% year⁻¹). This study suggests that while various efforts have been successful in reducing vehicular exhaust emissions, road dust will not abate at a similar rate. The results of this study provide insights into the trend of traffic-related emissions over recent years based on high temporal resolution monitoring data, with important implications for policymaking.

Unplanned urbanization and heavy automobile use by the rapidly growing population contribute to a variety of environmental issues. Roadside plants can mitigate air pollution by modifying their enzymatic activity, physiological and anatomical traits. Plant enzymes, physiological and anatomical traits play an important role in adaptation and mitigation mechanisms against vehicular emissions. There is a significant gap in understanding of how plant enzymes and anatomical traits respond or how they participate in modulating the effect of vehicular emissions/air pollution. Modulation of leaf anatomical traits is also useful in regulating plant physiological behavior. Hence, the present study was conducted to evaluate the effects of vehicular pollution on the enzymatic activity, physiological, and anatomical traits of plant species that grow in forests (S1) and alongside roads (S2-1 km away from the S1 site) during different seasons. The present study examines four commonly found roadside tree species i.e. Grevillea robusta, Cassia fistula, Quercus leucotrichophora and Cornus oblonga. The study found that the activities of catalase and phenylalanine ammonium enzymes were higher in G. robusta species of roadside than control site (S1). Non-enzymatic antioxidants such as flavonoid and phenol were also found in higher concentrations in roadside tree species during the summer season. However, the measured values of physiological traits were higher in Q. leucotrichophora tree species of S1 during the summer season. When compared to the other species along the roadside, Q. leucotrichophora had the highest number of stomata and epidermal cells during the summer season. Hence, we found that tree species grown along the roadside adapted towards vehicular emissions by modulating their enzymatic, physiological, and anatomical traits to mitigate the effect of air pollution.

Size-fractionated particulate matters (SPMs) in a range of 9.0 to 0.43 μm, classified based on aerodynamic diameter (dₐₑ) as fine PMs (0.43 μm ≤ dₐₑ < 2.1 μm) and coarse PMs (2.1 μm ≤ dₐₑ < 9.0 μm) were collected by cascade impactors (7 fractions) during smoke haze (SH) and non-smoke haze (NSH) seasons in urban and rural areas of Chiang Mai, Thailand. Their polycyclic aromatic hydrocarbons (PAHs) compositions were determined for respiratory health risk assessment. During SH episode, concentrations of SPMs and PAHs in the rural area were approximately two times higher than in the urban area and about 62–68% of the SPMs were fine particles. Conversely, during NSH season the concentrations in the urban area were higher due to traffic emission. The finest particle sizes (0.65–0.43 μm) contained the highest PAHs concentrations among the other PM sizes. Benzo[b]fluoranthene was a main PAH component found during SH season suggesting biomass burning is a major pollutant source. High molecular weight (5–6 rings) PAHs with high carcinogenicity were likely to concentrate in fine particles. Distribution patterns of SPMs and PAHs during SH season were bimodal with the highest peak at a fine size range (0.65–0.43 μm) and a small peak at a coarse size range (5.8–4.7 μm). Respiratory health risk was estimated based on toxicity equivalent concentrations of PAHs bound-SPMs and inhalation cancer risk (ICR). Relatively high ICR values (1.14 × 10⁻⁴ (rural) and 6.80 × 10⁻⁵ (urban)) were found during SH season in both areas, in which fine particles played an important role. It revealed that high concentration of fine particles in ambient air is related to high respiratory health risk due to high content of carcinogenic substances.

Carbonaceous particles are an important radiative forcing agent in the atmosphere, with large temporal and spatial variations in their concentrations and compositions, especially in remote regions. This study reported the Δ¹⁴C and δ¹³C of total carbon (TC) and water-insoluble particulate carbon (IPC) of the total suspended particles (TSP) and PM₂.₅ at a remote site of the eastern Tibetan Plateau (TP), a region that is influenced by heavy air pollution from Southwest China. The average organic carbon and elemental carbon concentrations of TSP samples in this study were 3.20 ± 2.38 μg m⁻³ and 0.68 ± 0.67 μg m⁻³, respectively, with low and high values in summer and winter, respectively. The fossil fuel contributions of TC in TSP and PM₂.₅ samples were 18.91 ± 7.22% and 23.13 ± 12.52%, respectively, both of which were far lower than that in Southwest China, indicating the importance of non-fossil contributions from local sources. The δ¹³C of TC in TSP samples of the study site was −27.06 ± 0.96‰, which is between the values of long-range transported sources (e.g., Southwest China) and local biomass combustion emissions. Therefore, despite the contribution from the long-range transport of particles, aerosols emitted from local biomass combustion also have an important influence on carbonaceous particles at the study site. The findings of this work can be applied to other remote sites on the eastern TP and should be considered in related research in the future.

Lung cancer is the most common cancer in China and second worldwide, of which the incidence of lung adenocarcinoma is rising. As an independent factor, air pollution has drawn the attention of the public. An increasing body of studies has focused on the effect of PM₂.₅ on lung adenocarcinoma; however, the mechanism remains unclear. We collected the PM₂.₅ in two megacities, Beijing (BPM) and Shijiazhuang (SPM), located in the capital of China, and compared the different components and sources of PM₂.₅ in the two cities. Vehicle emissions are the primary sources of BPM, whereas SPM is industrial emissions. We found that chronic exposure to PM₂.₅ promotes the tumorigenesis and metastasis of lung adenocarcinoma in patient-derived xenograft (PDX) models, as well as the migration and invasion of lung adenocarcinoma cell lines. SPM has more severe effects in vivo and in vitro. The underlying mechanisms are related to the stem cell properties of cancer cells, the epithelial-mesenchymal transition (EMT) process, and the corresponding miRNAs. It is hopeful to provide a theoretical basis for improving air pollution in China, especially in the capital area, and is of the significance of long-term survival of lung cancer patients.

It is enlightening to determine the discrepancies and potential reasons for the degree of impact from the COVID-19 control measures on air quality as well as the associated health and economic impacts. Analysis of air quality, socio-economic factors, and meteorological data from 447 cities in 46 countries indicated that the COVID-19 control measures had significant impacts on the PM₂.₅ (particulate matter with an aerodynamic diameter less than 2.5 μm) concentrations in 20 (reduced PM₂.₅ concentrations of −7.4–29.1 μg m⁻³) of the selected 46 countries. In these 20 countries, the robustly distinguished changes in the PM₂.₅ concentrations caused by the control measures differed between the developed (95% confidence interval (CI): −2.7–5.5 μg m⁻³) and developing countries (95% CI: 8.3–23.2 μg m⁻³). As a result, the COVID-19 lockdown reduced death and hospital admissions change from the decreased PM₂.₅ concentrations by 7909 and 82,025 cases in the 12 developing countries, and by 78 and 1214 cases in the eight developed countries. The COVID-19 lockdown reduced the economic cost from the PM₂.₅ related health burden by 54.0 million dollars in the 12 developing countries and by 8.3 million dollars in the eight developed countries. The disparity was related to the different chemical compositions of PM₂.₅. In particular, the concentrations of primary PM₂.₅ (e.g., BC) in cities of developing countries were 3–45 times higher than those in developed countries, so the mass concentration of PM₂.₅ was more sensitive to the reduced local emissions in developing countries during the COVID-19 control period. The mass fractions of secondary PM₂.₅ in developed countries were generally higher than those in developing countries. As a result, these countries were more sensitive to the secondary atmospheric processing that may have been enhanced due to reduced local emissions.

Air pollution is a leading public health concern around the world. Assessing the public's knowledge about air quality is critical to calibrate public health interventions. However, previous efforts to measure knowledge about air quality (AQIQ) have not relied on consistent and validated measures, thus precluding cross-country comparisons. We aimed to develop a robust scale to assess AQIQ and tested it in multiple countries. To evaluate the psychometric properties and select the best performing items out of 10 AQIQ questions, we used methods from classical test theory and item response theory. We evaluated the scales using several scalability measures, including the Kuder-Richardson Formula 20 (KR-20), Loevinger's H, as well as trace lines. Volunteers from the United States (US, n = 400), India (n = 403), and China (n = 443) were recruited to validate the scale. Multiple linear regression was used to estimate the association between demographic factors and AQIQ. We found that participants from India had the highest AQIQ. In addition, not all questions performed well in each country. The scale was pruned and shorter subscales were validated. In the US, we obtained a 4-item scale (KR20 = 0.53, Loevinger's H = 0.34). In India, we obtained a 6-item scale (KR20 = 0.56; Loevinger's H = 0.48 for just 2 items). In China, we obtained a 5-item scale (KR20 = 0.39; Loevinger's H = 0.41 for just 2 items). Compared to the 10-item scale, the pruned scales showed stronger associations between measures of socioeconomic status and AQIQ. The results were robust to the scale used. Overall, general knowledge questions measured AQIQ more effectively in the US and India whereas knowledge of the air quality index better measured AQIQ in China. The findings suggest that careful measurement and validation are essential to develop knowledge scales for use in public health and environmental research.

The consequence of the lockdowns implemented to address the COVID-19 pandemic on human health damage due to air pollution and other environmental issues must be better understood. This paper analyses the effect of reducing energy demand on the evolution of environmental impacts during the occurrence of 2020-lockdown periods in Italy, with a specific focus on life expectancy. An energy metabolism analysis is conducted based on the life cycle assessment (LCA) of all monthly energy consumptions, by sector, category and province area in Italy between January 2015 to December 2020. Results show a general decrease (by ∼5% on average) of the LCA midpoint impact categories (global warming, stratospheric ozone depletion, fine particulate matter formation, etc.) over the entire year 2020 when compared to past years. These avoided impacts, mainly due to reductions in fossil energy consumptions, are meaningful during the first lockdown phase between March and May 2020 (by ∼21% on average). Regarding the LCA endpoint damage on human health, ∼66 Disability Adjusted Life Years (DALYs) per 100,000 inhabitants are estimated to be saved. The analysis shows that the magnitude of the officially recorded casualties is substantially larger than the estimated gains in human lives due to the environmental impact reductions. Future research could therefore investigate the complex cause-effect relationships between the deaths occurred in 2020 imputed to COVID-19 disease and co-factors other than the SARS-CoV-2 virus.

Air pollution exposure is a public health emergency, which attributes globally to an estimated seven million deaths on a yearly basis We are all exposed to air pollutants, varying from ambient air pollution hanging over cities to dust inside the home. It is a mixture of airborne particulate matter and gases that can be subdivided into three categories based on particle diameter. The smallest category called PM₀.₁ is the most abundant. A fraction of the particles included in this category might enter the blood stream spreading to other parts of the body. As air pollutants can enter the body via the lungs and gut, growing evidence links its exposure to gastrointestinal and respiratory impairments and diseases, like asthma, rhinitis, respiratory tract infections, Crohn's disease, ulcerative colitis, and abdominal pain. It has become evident that there exists a crosstalk between the respiratory and gastrointestinal tracts, commonly referred to as the gut-lung axis. Via microbial secretions, metabolites, immune mediators and lipid profiles, these two separate organ systems can influence each other. Well-known immunomodulators and gut health stimulators are probiotics, prebiotics, together called synbiotics. They might combat air pollution-induced systemic inflammation and oxidative stress by optimizing the microbiota composition and microbial metabolites, thereby stimulating anti-inflammatory pathways and strengthening mucosal and epithelial barriers. Although clinical studies investigating the role of probiotics, prebiotics, and synbiotics in an air pollution setting are lacking, these interventions show promising health promoting effects by affecting the gastrointestinal- and respiratory tract. This review summarizes the current data on how air pollution can affect the gut-lung axis and might impact gut and lung health. It will further elaborate on the potential role of probiotics, prebiotics and synbiotics on the gut-lung axis, and gut and lung health.

Mitigation of ambient ozone (O₃) pollution is a great challenge because it depends heavily on the background O₃ which has been poorly evaluated in many regions, including in China. By establishing the relationship between O₃ and air temperature near the surface, the mean background O₃ mixing ratios in the clean and polluted seasons were determined to be 35–40 and 50–55 ppbv in China during 2013–2019, respectively. Simulations using the chemical transport model (i.e., the Weather Research and Forecasting coupled with Chemistry model, WRF/Chem) suggested that biogenic volatile organic compounds (VOC) emissions were the primary contributor to the increase in the background O₃ in the polluted season (BOP) compared to the background O₃ in the clean season (BOC), ranging from 8 ppbv to 16 ppbv. More importantly, the BOP continuously increased at a rate of 0.6–8.0 ppbv yr⁻¹ during 2013–2019, while the non-BOP stopped increasing after 2017. Consequently, an additional 2%–16% reduction in anthropogenic VOC emissions is required to reverse the current O₃ back to that measured in the period from 2013 to 2017. The results of this study emphasize the importance of the relative contribution of the background O₃ to the observed total O₃ concentration in the design of anthropogenic precursor emission control strategies for the attainment of O₃ standards.