Impacts of vehicle emission on air quality and human health in China

Insights into transportation CO2 emissions with big data and artificial intelligence

The ever-increasing stream of big data offers potential for deep decarbonization in the transportation sector but also presents challenges in extracting interpretable insights due to its complexity and volume. This overview discusses the application of transportation big data to help understand carbon dioxide emissions and introduces how artificial intelligence models, including machine learning (ML) and deep learning (DL), are used to assimilate and understand these data. We suggest using ML to interpret low-dimensional data and DL to enhance the predictability of data with spatial connections across multiple timescales. Overcoming challenges related to algorithms, data, and computation requires interdisciplinary collaboration on both technology and data.

Particulate matter air pollution as a cause of lung cancer: epidemiological and experimental evidence

Air pollution has a significant global impact on human health. Epidemiological evidence strongly suggests that airborne particulate matter (PM), the dust components of polluted air, is associated with increased incidence and mortality of lung cancer. PM2.5 (PM less than 2.5 µm) from various sources carries different toxic substances, such as sulfates, organic compounds, polycyclic aromatic hydrocarbons, and heavy metals, which are considered major carcinogens that increase lung cancer risk. The incidence and mortality of lung cancer caused by PM2.5 exposure may be due to significant geographical differences, and can be influenced by various factors, including local sources of air pollution, socioeconomic conditions, and public health measures. This review aims to provide comprehensive insights into the health implications of air pollution and to inform strategies for lung cancer prevention, by summarising the relationship between exposure to PM2.5 and lung cancer development. We explore the different sources of PM2.5 and relevant carcinogenic mechanisms in the context of epidemiological studies on the development of lung cancer from various geographical regions worldwide.

Emission control status and future perspectives of diesel trucks in China

Chinese diesel trucks are the main contributors to NOx and particulate matter (PM) vehicle emissions. An increase in diesel trucks could aggravate air pollution and damage human health. The Chinese government has recently implemented a series of emission control technologies and measures for air quality improvement. This paper summarizes recent control technologies and measures for diesel truck emissions in China and introduces the comprehensive application of control technologies and measures in Beijing-Tianjin-Hebei and surrounding regions. Remote online monitoring technology has been adopted according to the China VI standard for heavy-duty diesel trucks, and control measures such as transportation structure adjustment and heavy pollution enterprise classification control continue to support the battle action plan for pollution control. Perspectives and suggestions are provided for promoting pollution control and supervision of diesel truck emissions: adhere to the concept of overall management and control, vigorously promote the application of systematic and technological means in emission monitoring, continuously facilitate cargo transportation structure adjustment and promote new energy freight vehicles. This paper aims to accelerate the implementation of control technologies and measures throughout China. China is endeavouring to control diesel truck exhaust pollution. China is willing to cooperate with the world to protect the global ecological environment.

Features of particle-phase PAHs from traffic emissions using tunnel measurement and urban roadside observation in China

Vehicle emissions are recognized as a primary source of particle-phase polycyclic aromatic hydrocarbons (PAHs), significant contributors to the hazardous properties of PM2.5. This study investigates the profiles of PAHs through measurements conducted in a tunnel and an urban roadside environment in 2020. We quantified real-world vehicle emission factors for mixed fleets in the Zhongshu tunnel in Guizhou, southwest China, and found that the total PAHs had an emission factor of 8.15 μg veh-1km-1, with higher factors observed for high-ring PAHs. Additionally, we analyzed concentrations of 15 PAHs at a roadside environment in Hefei, southern China, with an average concentration of 23.81 ng/m3. PAHs with 4, 5, and 6 rings comprised 80% of total PAHs at the roadside and 87% in the tunnel. Gasoline emissions were the predominant source in both the tunnel and roadside environments, supplemented by non-tailpipe emissions, catering, domestic cooking, and asphalt from road surfaces. Notably, while the total toxicity equivalent concentration of Benzo[a]pyrene (BaP) significantly exceeded the World Health Organization (WHO) guidelines of 1 ng/m3, the presence of additional compounds such as Dibenzo[a,h]anthracene (DahA) markedly increased the toxicological impact of PM2.5 from vehicle emissions. Therefore, it is essential to implement targeted pollution control strategies in central urban areas that address not only the overall concentration of PAHs but also their specific toxic contributions Continuous monitoring and assessment of PAHs in the urban environment are indispensable for effectively reducing their health impacts.

Heavy metal emissions from on-road vehicles in Xia-Zhang-Quan metropolitan area in southeastern China from 2015 to 2060: impact of vehicle electrification

Vehicle electrification is an important means of reducing urban air pollution. However, vehicle electrification does not necessarily reduce particulate matter (PM2.5 and PM10) and heavy metals (HM) due to the increase in non-exhaust emissions. In this study, we established the emission inventories of PM2.5, PM10, and their associated heavy metals (PM2.5-HM and PM10-HM) from the on-road vehicles in the Xiamen-Zhangzhou-Quanzhou metropolitan area in southeastern China between 2015 and 2060. In the base year 2021, brake wear emissions account for 66.6% of PM2.5-HM and 76.9% of PM10-HM, much higher than the contributions of exhaust emissions to PM2.5-HM (12.4%) and PM10-HM (6.2%). Light-duty passenger vehicles, heavy-duty trucks, and light-duty trucks are the three main contributors to PM and HM. HM emissions have a high emission density in urban areas. In the business-as-usual (BAU) scenario, HM emissions continue to increase from 2021 to 2060 due to the combined effects of the stricter emission standards and the growth of vehicle population, while the health risk of HM shows an initial decrease and then an increasing trend. Compared with BAU, moderate and aggressive electrification scenarios show a significant reduction in PM2.5-HM emissions between 2030 and 2060, but not in PM10-HM emissions. Further increases in vehicle electrification will bring forward the peak of PM2.5-HM emissions, with the potential to reduce adverse health effects. In the process of vehicle electrification, the reduction of heavy metal emissions from the braking system should be prioritized in order to effectively reduce traffic pollution.

Fifteen Pathways between Electric Vehicles and Public Health: A Transportation-Health Conceptual Framework

The health impact of electric vehicles (EVs) is complex and multifaceted, encompassing reductions in air pollutants, improvements in road safety, and implications for social equity. However, existing studies often provide fragmented insights, lacking a unified framework to comprehensively assess these public health implications. This paper develops a comprehensive framework to summarize the health outcomes of EVs in urban areas, where the health impacts are more pronounced due to higher levels of traffic congestion and air pollution. Building on previous conceptual work that identified pathways linking general transportation and health, our model illustrates how the characteristics of EVs influence public health through various pathways compared to traditional transportation systems. Additionally, we address socioeconomic factors that introduce variability in EV-related health outcomes, emphasizing the need to consider potential health disparities in policy and intervention development. This comprehensive approach aims to inform holistic policies that account for the complex interplay between transportation, environment, and public health.

Key factors in epidemiological exposure and insights for environmental management: Evidence from meta-analysis

In recent years, the precision of exposure assessment methods has been rapidly improved and more widely adopted in epidemiological studies. However, such methodological advancement has introduced additional heterogeneity among studies. The precision of exposure assessment has become a potential confounding factors in meta-analyses, whose impacts on effect calculation remain unclear. To explore, we conducted a meta-analysis to integrate the long- and short-term exposure effects of PM2.5, NO2, and O3 on all-cause, cardiovascular, and respiratory mortality in the Chinese population. Literature was identified through Web of Science, PubMed, Scopus, and China National Knowledge Infrastructure before August 28, 2023. Sub-group analyses were performed to quantify the impact of exposure assessment precisions and pollution levels on the estimated risk. Studies achieving merely city-level resolution and population exposure are classified as using traditional assessment methods, while those achieving sub-kilometer simulations and individual exposure are considered finer assessment methods. Using finer assessment methods, the RR (under 10 μg/m3 increment, with 95% confidence intervals) for long-term NO2 exposure to all-cause mortality was 1.13 (1.05-1.23), significantly higher (p-value = 0.01) than the traditional assessment result of 1.02 (1.00-1.03). Similar trends were observed for long-term PM2.5 and short-term NO2 exposure. A decrease in short-term PM2.5 levels led to an increase in the RR for all-cause and cardiovascular mortality, from 1.0035 (1.0016-1.0053) and 1.0051 (1.0021-1.0081) to 1.0055 (1.0035-1.0075) and 1.0086 (1.0061-1.0111), with weak between-group significance (p-value = 0.13 and 0.09), respectively. Based on the quantitative analysis and literature information, we summarized four key factors influencing exposure assessment precision under a conceptualized framework: pollution simulation resolution, subject granularity, micro-environment classification, and pollution levels. Our meta-analysis highlighted the urgency to improve pollution simulation resolution, and we provide insights for researchers, policy-makers and the public. By integrating the most up-to-date epidemiological research, our study has the potential to provide systematic evidence and motivation for environmental management.

Impact of Anthropogenic Factors on the Diversity of Microbial Communities of PM10 Air and PM100 of Tilia L. Phylloplane in an Urban Ecosystem

Identifying the relationship between the microbiomes of urban dust particles from different biotopes is important because the state of microorganisms can be used to assess the quality of the environment. The aim of this work was to determine the distribution and interaction patterns of microorganisms of dust particles in the air and on leaf surfaces. Metabarcoding of bacterial and fungal communities, PAH, and metal content analyses and electron microscopy were used in this work. The results obtained allowed us to characterise the biological and chemical components of the dust particles. Some bacterial and fungal genera were correlated with benzanthracene, fluoranthene, and Cu, Ni, Co, Zn, and Mn contents. Bacterial communities were found to be more sensitive to all the pollutants studied. PM10 microbial communities circulated between biotopes and study areas due to air flows, as evidenced by the presence of similar ASVs in fungi and bacteria. The results could help to understand the effects of climate change and anthropogenic activities.

Low-condensation diesel use contributes to winter haze in cold regions of China

The application of low-condensation diesel in cold regions with extremely low ambient temperatures (-14 to -29 °C) has enabled the operation of diesel vehicles. Still, it may contribute to heavy haze pollution in cold regions during winter. Here we examine pollutant emissions from low-condensation diesel in China. We measure the emissions of elemental carbon (EC), organic carbon (OC), and elements, including heavy metals such as arsenic (As). Our results show that low-condensation diesel increased EC and OC emissions by 2.5 and 2.6 times compared to normal diesel fuel, respectively. Indicators of vehicular sources, including EC, As, lead (Pb), cadmium (Cd), chromium (Cr), nickel (Ni), and manganese (Mn), increased by approximately 20.2-162.5% when using low-condensation diesel. Seasonal variation of vehicular source indicators, observed at road site ambient environments revealed the enhancement of PM2.5 pollution by the application of low-condensation diesel in winter. These findings suggest that -35# diesel, a low-cetane index diesel, may enhance air pollution in winter, according to a dynamometer test conducted in laboratory. It raises questions about whether higher emissions are released if -35# diesel is applied to running vehicles in real-world cold ambient environments.

Light-duty gasoline vehicle emission deterioration insights from large-scale inspection/maintenance data: The synergistic impact of usage characteristics

Accurately estimating vehicle emissions is crucial for effective air quality management. As key data for emission inventory construction, emission factors (EFs) are influenced by vehicle usage characteristics and experience deterioration. Current deterioration models often employ single-factor approaches based on vehicle age or accumulated mileage, which fail to capture the effects of varying usage intensities within the same mileage or age intervals. This study addressed this limitation by developing a novel emission deterioration model that incorporates multi-dimensional usage characteristics and that utilizes a large-scale inspection and maintenance (I/M) dataset for light-duty gasoline vehicles (LDGVs). The modeling results reveal distinct deterioration patterns for different pollutants and highlight the synergistic effects of the usage duration and intensity: natural aging significantly impacts HC and NOx emissions, while CO emissions are more strongly affected by intensive use. Specifically, China V LDGVs that were driven 4 × 104 km/yr exhibited HC, CO, and NOx deterioration rates per mile that were approximately 4.1 % lower, 10.3 % higher, and 1.1 % higher, respectively, than those of vehicles driven 2 × 104 km/yr as the mileage increased from 5 × 104 km to 10 × 104 km. By leveraging timely emission data and explicitly accounting for usage intensity, this study corrected biases in local emission estimates by 5-85 % with respect to estimates from commonly used models. This framework enables the development of more effective control strategies and refinements to policy evaluations in megacities with I/M programs.

Failure of the three-way catalyst (TWC) introduces "super emitters"

Vehicle exhaust is one of the major organic sources in urban areas. Old taxis equipped with failed three-way catalysts (TWCs) have been regarded as "super emitters". Compressed natural gas (CNG) is a regular substitution fuel for gasoline in taxis. The relative effect of fuel substitution and TWC failure has not been thoroughly investigated. In this work, vehicle exhausts from gasoline and CNG taxis with optimally functioning and malfunctioning TWCs are sampled by Tenax TA tubes and then analyzed by a comprehensive two-dimensional gas chromatography-mass spectrometer (GC×GC-MS). A total of 216 organics are quantified, including 80 volatile organic compounds (VOCs) and 132 intermediate volatility organic compounds (IVOCs). Failure of TWC introduces super emitters with 30 - 70 times emission factors (EFs), 60 - 112 times ozone formation potentials (OFPs), and 34 - 92 times secondary organic aerosols (SOAs) more than normal vehicles. Specifically, for the taxi with failed TWC, the total organic EF of CNG is 16 times that of gasoline, indicating that the failure of TWC exceeds the emission reduction achieved by CNG-gasoline substitution. A significant but unbalanced reduction of ozone and SOA is observed after TWC, whereas a notable "enrichment" in IVOCs was observed. Naphthalene is a typical IVOC component strongly associated with CNG-gasoline substitution and TWC failure, which is lacking in current VOC measurement. We especially emphasize that there is an urgent need to scrap vehicles with failed TWCs in order to significantly reduce air pollution.

VOC and IVOC emission features and inventory of motorcycles in China

How did the motorcycle emissions evolve during the economic development in China? To address data gaps, this study firstly measured the volatile organic compound (VOC) and intermediate-volatility organic compound (IVOC) emissions from motorcycles. The results confirmed that the emission control of motorcycles, especially small-displacement motorcycles, significantly lagged behind other gasoline-powered vehicles. For the China IV motorcycles, the average VOC and IVOC emission factors (EFs) were 2.74 and 7.78 times higher than the China V-VI light-duty gasoline vehicles, respectively. The notable high IVOC emissions were attributed to a dual influence from gasoline and lubricating oil. Furthermore, based on the complete EF dataset and economy-related activity data, a county-level emission inventory was developed in China. Motorcycle VOC and IVOC emissions changed from 2536.48 Gg and 197.19 Gg in 2006 to 594.21 Gg and 12.66 Gg in 2020, respectively. The absence of motorcycle IVOC emissions in the existed vehicular inventories led to an underestimation of up to 20%. Across the 15 years, the motorcycle VOC and IVOC emission hotspots were concentrated in the undeveloped regions, with the rural emissions reaching 5.81-10.14 times those of the urban emissions. This study provides the first-hand and close-to-realistic data to support motorcycle emission management and accurate air quality simulations.

Short-term association of particulate matter and cardiovascular disease mortality in Shanghai, China between 2003 and 2020

Objective

Evidence regarding the effects of particulate matter (PM) pollutants on cardiovascular disease (CVD) mortality remains limited in Shanghai, China. Our objective was to thoroughly evaluate associations between PM pollutants and CVD mortality.

Methods

Daily data on CVD mortality, PM (PM10 and PM2.5) pollutants, and meteorological variables in Shanghai, China were gathered from 2003 to 2020. We utilized a time-series design with the generalized additive model to assess associations between PM pollutants and CVD mortality. Additionally, we conducted stratified analyses based on sex, age, education, and seasons using the same model.

Results

We found that PM pollutants had a significant association with CVD mortality during the study period. Specifically, there was a 0.29% (95%CI: 0.14, 0.44) increase in CVD mortality for every 10 μg/m3 rise in a 2-day average (lag01) concentration of PM10. A 0.28% (95% CI: 0.07, 0.49) increase in CVD mortality was associated with every 10 μg/m3 rise in PM2.5 concentration at lag01. Overall, the estimated effects of PM10 and PM2.5 were larger in the warm period compared with the cold period. Furthermore, males and the older adult exhibited greater susceptibility to PM10 and PM2.5 exposure, and individuals with lower education levels experienced more significant effects from PM10 and PM2.5 than those with higher education levels.

Conclusion

Our findings suggested that PM pollutants have a substantial impact on increasing CVD mortality in Shanghai, China. Moreover, the impacts of air pollution on health may be altered by factors such as season, sex, age, and educational levels.

Are electric vehicles really the optimal option for the transportation sector in China to approach pollution reduction and carbon neutrality goals?

Profound worldwide fleet electrification is thought to be the primary route for achieving the target of carbon neutrality. However, when and how electrification can help mitigate environmental impacts and carbon emissions in the transport sector remains unclear. Herein, the overall life-cycle environmental impacts and carbon saving range of two typical A-class vehicles in China, including electric vehicle (EV) and internal combustion engine vehicle (ICEV), were quantified by the life cycle assessment model for endpoint damage with localization parameters. The results showed that the EV outperformed the ICEV for the total environment impact after a travel distance of 39,153 km and for carbon emissions after 32,292 km. The ICEV was more carbon-friendly only when the driving distance was less than 3229 km/a. Considering a full lifespan travel distance of 150,000 km, the whole life-cycle average environmental impacts of EV and ICEV were calculated as 8.6 and 17.5 mPt/km, respectively, but the EV had 2.3 times higher impacts than the ICEV in the production phase. In addition, the EV unit carbon emission was 140 g/km, 46.8% lower than that of the ICEV. Finally, three potential reduction scenarios were considered: cleaner power mix, energy efficiency improvement and composite scenario. These scenarios contributed 19.1%, 13.0% and 32.1% reductions, respectively. However, achieving carbon peak and neutrality goals in China remains a great challenge unless fossil fuels are replaced by renewable energy. The research can provide scientific reference for the method and practice of emission reduction link identification, eco-driving choice and emission reduction path formulation.

Exploring particle concentrations and inside-to-outside ratios in vehicles: A real-time road test study

In transportation microenvironments, humans exposed to particulate matter (PM) inside vehicles can experience higher levels of daily exposure. To make inside-vehicle PM exposure measurements more feasible and easy under real driving conditions, and to quantify the relationship between the concentrations and influencing factors, we assessed PM1, PM2.5, and PM10. levels. Additionally, we collected key influencing factors to develop predictive models. The measurements of PM1, PM2.5, and PM10 concentrations showed that the ventilation setting was a significant influencing factor. The concentrations decreased significantly under the recirculation setting (RC) compared to the outside air setting (OA). The inside-to-outside (I/O) ratios of PM were 1.69 to 1.93-fold higher than those of RC under OA conditions. However, a substantial reduction in the I/O ratios was observed when RC was employed. Although both the concentrations and I/O ratios exhibited significant differences, they demonstrated strong potential relationships. PM2.5 I/O ratios accounted for over 85 % of the variation in the PM1 and PM10 I/O ratios. The developed models for the I/O ratios of PM accounted for >40 and 60 % of the variation in the measured I/O ratios for RC and OA, respectively. We used the vehicle age, vehicle interior volume, speed, cabin temperature, cabin humidity, and their higher-order terms as predictive variables. It is important to note that the influential predictive feature importance differed under RC and OA, and considering the vehicle characteristics between vehicles of the same type may be necessary when using RC. Overall, these findings indicate that the inside-vehicle PM exposure can be measured more easily under real driving conditions by considering the key influencing factors and utilizing the developed predictive models.

Fugitive road dust particulate matter emission inventory for India: A field campaign in 32 Indian cities

This research delves into the pivotal issue of road dust emissions and their profound ramifications on air quality across diverse regions of India. In pursuit of this objective, the study initiated a comprehensive field campaign to estimate silt loading (sL) values and evaluate the distribution of vehicles at 259 locations spanning 32 Indian cities. Remarkable disparities in sL values were observed across different road types and states. Notably, sites in Rajasthan, characterized by its arid Aravalli range and industrial activities, emerged as stark outliers, exhibiting significantly elevated sL values (up to 137 g/m2) compared to their counterparts. The regional analysis goes further to elucidate the relation between climatic conditions, topography, and silt loading. As a broader trend, roads in North India have higher sL values in contrast to those in South India. Further, a comprehensive particulate matter road dust emission inventory for the entire India in the year 2022 was developed using the vehicle registration data from 1352 road transport offices nationwide, in conjunction with the data from the field campaign concerning sL values and vehicle counts. Specific states such as Rajasthan, Uttar Pradesh, Maharashtra, Karnataka, and Gujarat emerged as the predominant contributors to road dust emissions. These states not only exhibit elevated sL values, but also account for a substantial proportion of the total registered vehicles in India, thereby underscoring the pressing imperative for effective mitigation measures. Weather Research and Forecasting coupled with chemistry (WRF-Chem) simulations, using this emission inventory, reveal that PM2.5 concentrations stemming from road dust exceed the World Health Organization guidelines in 55 % of the states across India. Further analysis delineates that more than 10,000 lives are annually lost due to PM2.5 pollution attributable to road dust in India, with the potential to salvage 10 % of these lives by paving all roads throughout the country.

Spatiotemporal patterns of surface ozone exposure inequality in China

Rising surface ozone (O3) levels in China are increasingly emphasizing the potential threats to public health, ecological balance, and economic sustainability. Using a 1 km × 1 km dataset of O3 concentrations, this research employs subpopulation demographic data combined with a population-weighted quality model. Its aim is to evaluate quantitatively the differences in O3 exposure among various subpopulations within China, both at a provincial and urban cluster level. Additionally, an exposure disparity indicator was devised to establish unambiguous exposure risks among significant urban agglomerations at varying O3 concentration levels. The findings reveal that as of 2018, the population-weighted average concentration of O3 for all subgroups has experienced a significant uptick, surpassing the average O3 concentration (118 μg/m3). Notably, the middle-aged demographic exhibited the highest O3 exposure level at 135.7 μg/m3, which is significantly elevated compared to other age brackets. Concurrently, there exists a prominent positive correlation between educational attainment and O3 exposure levels, with the medium-income bracket showing the greatest susceptibility to O3 exposure risks. From an industrial vantage point, the secondary sector demographic is the most adversely impacted by O3 exposure. In terms of urban-rural structure, urban groups in all regions had higher levels of exposure to O3 than rural areas, with North and East China having the most significant levels of exposure. These findings not only emphasize the intricate interplay between public health and environmental justice but further highlight the indispensability of segmented subgroup strategies in environmental health risk assessment. Moreover, this research furnishes invaluable scientific groundwork for crafting targeted public health interventions and sustainable air quality management policies.

Advancing shipping NOx pollution estimation through a satellite-based approach

Estimating shipping nitrogen oxides (NOx) emissions and their associated ambient NO2 impacts is a complex and time-consuming task. In this study, a satellite-based ship pollution estimation model (SAT-SHIP) is developed to estimate regional shipping NOx emissions and their contribution to ambient NO2 concentrations in China. Unlike the traditional bottom-up approach, SAT-SHIP employs satellite observations with varying wind patterns to improve the top-down emission inversion methods for individual sectors amidst irregular emission plume signals. Through SAT-SHIP, shipping NOx emissions for 17 ports in China are estimated. The results show that SAT-SHIP performed comparably with the bottom-up approach, with an R2 value of 0.8. Additionally, SAT-SHIP reveals that the shipping sector in port areas contributes ∼21 and 11% to NO2 concentrations in the Yangtze River Delta and Pearl River Delta areas of China, respectively, which is consistent with the results from chemical transportation model simulations. This approach has practical implications for policymakers seeking to identify pollution sources and develop effective strategies to mitigate air pollution.

Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing

With the decreasing regional-transported levels, the health risk assessment derived from fine particulate matter (PM2.5) has become insufficient to reflect the contribution of local source heterogeneity to the exposure differences. Here, we combined the both ultra-high-resolution PM2.5 concentration with population distribution to provide the personal daily PM2.5 internal dose considering the indoor/outdoor exposure difference. A 30-m PM2.5 assimilating method was developed fusing multiple auxiliary predictors, achieving higher accuracy (R2 = 0.78-0.82) than the chemical transport model outputs without any post-simulation data-oriented enhancement (R2 = 0.31-0.64). Weekly difference was identified from hourly mobile signaling data in 30-m resolution population distribution. The population-weighted ambient PM2.5 concentrations range among districts but fail to reflect exposure differences. Derived from the indoor/outdoor ratio, the average indoor PM2.5 concentration was 26.5 μg/m3. The internal dose based on the assimilated indoor/outdoor PM2.5 concentration shows high exposure diversity among sub-groups, and the attributed mortality increased by 24.0% than the coarser unassimilated model.

A novel approach for the prediction and analysis of daily concentrations of particulate matter using machine learning

Traditional air quality analysis and prediction methods depend on the statistical and numerical analyses of historical air quality data with more information related to a specific region; therefore, the results are unsatisfactory. In particular, fine particulate matter (PM2.5, PM10) in the atmosphere is a major concern for human health. The modelling (analysis and prediction) of particulate matter concentrations remains unsatisfactory owing to the rapid increase in urbanization and industrialization. In the present study, we reconstructed a prediction model for both PM2.5 and PM10 with varying meteorological conditions (windspeed, temperature, precipitation, specific humidity, and air pressure) in a specific region. In this study, a prediction model was developed for the two observation stations in the study region. The analysis of particulate matter shows that seasonal variation is a primary factor that highly influences air pollutant concentrations in urban regions. Based on historical data, the maximum number of days (92 days in 2019) during the winter season exceeded the maximum permissible level of particulate matter (PM2.5 = 15 μg/m3) concentration in air. The prediction results showed better performance of the Gaussian process regression model, with comparatively larger R2 values and smaller errors than the other models. Based on the analysis and prediction, these novel methods may enhance the accuracy of particulate matter prediction and influence policy- and decision-makers among pollution control authorities to protect air quality.

Assessment of health burden due to the emissions of fine particulate matter from motor vehicles: A case of Nakhon Ratchasima province, Thailand

Air pollution, owing to the ever-increasing transport vehicle fleet, and adverse health effects are increasing in provinces of Thailand. The study estimated that the vehicle fleet size of Nakhon Ratchasima (NR) province of Thailand will grow to 2 million vehicles by 2030, which was 1.36 million in 2021. In NR, the PM2.5 and PM10 concentrations already surpassed both WHO and NAAQS guidelines in 2019-2021. Using Pollution Control Department (PCD) approved Tier I and II Methodology of EMEP/EEA, this research estimated that the total tailpipe emission load will be 1039 tons of PM2.5, 16,630 tons of NO₂, 20,623 tons of CO, 195 tons NH₃, and 249 tons of SO₂ in NR during 2030. The emission load will increase to 1752 tons of PM2.5, 21,126 tons of NO2, 25,559 tons of CO, 361 tons of NH3 and 9344 tons of SO₂ during 2030 if upstream emissions are considered. This study has developed five control scenarios in line with the directives of PCD to mitigate the adverse health from vehicle-led air pollution in NR and implementation during 2024-2030. According to the study, different control scenarios to be implemented during 2024-2030, will be able to keep the fleet size of vehicles in the NR under control. The results show that the control scenarios will keep the annual tailpipe emission of PM2.5 at 604 tons in 2030, a 42 % reduction over the 2030 Business-As-Usual scenario (BAU). The health damage in the range of 6941 to 11,625 disability-adjusted life years (DALYs) under the 2030 BAU scenario in NR due to tailpipe and upstream emissions can be reduced to 4162-7318 DALYs with the implementation of different control scenarios. The control scenarios will also provide significant economic benefits ranging from 4465 to 6718 million THB during 2024-2030 through reduced DALYs and associated costs.

Emission and influences of non-road mobile sources on air quality in China, 2000-2019

Non-road mobile sources (NRMS) are potential important contributors to air pollution in China. However, their extreme impact on air quality had been seldom studied. In this study, the emission inventory of NRMS in mainland China during 2000-2019 was established. Then, the validated WRF-CAMx-PSAT model was applied to simulate the contribution to the atmospheric PM_2.5, NO₃^-, and NOx. Results showed that emissions increased rapidly since 2000 and reached a peak in 2014-2015, with an annual average change rate (AACR) of 8.7-10.0%; after then, the emissions were relatively stable (AACR, -1.4-1.5%). The modeling results indicated that NRMS has become a crucial contributor to the air quality in China: from 2000 to 2019, the contribution to PM_2.5, NOx, and NO₃^- significantly increased by 131.1%, 43.9%, and 61.7%; and for NOx, the contribution ratio in 2019 reached 24.1%. Further analysis showed that the reduction (-0.8% and -0.5%) of the NOx and NO₃^- contribution ratios was much lower than that (-4.8%) of NOx emissions from 2015 to 2019, implying that the control of NRMS lagged behind the national overall pollution control level. The contribution ratio of agricultural machinery (AM) and construction machinery (CM) to PM_2.5, NOx, NO₃^- in 2019 was 2.6%, 11.3%, 8.3% and 2.5%, 12.6%, 6.8%, respectively. Although the contribution was much lower, the contribution ratio of civil aircraft had the fastest growth (202-447%). Moreover, an interesting phenomenon was that AM and CM had opposite contribution sensitivity characteristics for air pollutants: CM had a higher Contribution Sensitivity Index (CSI) for primary pollutants (e.g., NOx), ∼1.1 times that of AM; while AM had a higher CSI for secondary pollutants (e.g., NO₃^-), ∼1.5 times that of CM. This work can provide a deeper understanding for the environmental impact of NRMS emissions and for the control strategy formulation of NRMS.

Updating emission inventories for vehicular organic gases: Indications from cold-start and temperature effects on advanced technology cars

How would the organic gas emission inventories of future urban vehicles change with new features of advanced technology cars? Here, volatile organic compounds (VOCs) and intermediate volatile organic compounds (IVOCs) from a fleet of Chinese light-duty gasoline vehicles (LDGVs) were characterized by chassis dynamometer experiments to grasp the key factors affecting future inventory accuracy. Subsequently, the VOC and IVOC emissions of LDGVs in Beijing, China, from 2020 to 2035, were calculated and the spatial-temporal variations were recognized under a scenario of fleet renewal. With the tightening of emission standards (ESs), cold start contributed a larger fraction of the total unified cycle VOC emissions due to the imbalanced emission reductions between operating conditions. It took 757.47 ± 337.75 km of hot running to equal one cold-start VOC emission for the latest certified vehicles. Therefore, the future tailpipe VOC emissions would be highly dependent on discrete cold start events rather than traffic flows. By contrast, the equivalent distance was shorter and more stable for IVOCs, with an average of 8.69 ± 4.59 km across the ESs, suggesting insufficient controls. Furthermore, there were log-linear relationships between temperatures and cold-start emissions, and the gasoline direct-injection vehicles performed better adaptability under low temperatures. In the updated emission inventories, the VOC emissions were more effectively reduced than the IVOC emissions. The start emissions of VOCs were estimated to be increasingly dominant, especially in wintertime. By winter 2035, the contribution of VOC start emissions could reach 98.98 % in Beijing, while the fraction of IVOC start emissions would decrease to 59.23 %. Spatially allocation showed that the high emission regions of tailpipe organic gases from LDGVs have transferred from road networks to regions of intense human activities. Our results provide new insights into tailpipe organic gas emissions of gasoline vehicles, and can support future emission inventories and refined assessment of air quality and human health risk.

Altitude-dependent gaseous emissions from freight trucks along the China-Pakistan Economic Corridor in Pakistan

Recent increases in emissions from freight transport have caused strong concerns about air quality in Pakistan, following the rapid development of projects related to the China-Pakistan Economic Corridor (CPEC). This study reported the first measurements of on-road truck emissions in Pakistan and investigated their dependence on altitude along CPEC routes. Emissions from 70 trucks were measured on CPEC highways located in Islamabad (540 m above sea level), Sost (2800 m above sea level), and at the Khunjerab Pass (4693 m above sea level). Calculated emission factors for carbon monoxide, hydrocarbons, and nitrogen oxides from heavy-duty trucks in Islamabad were 12.94 ± 1.46, 15.21 ± 1.67, and 10.69 ± 1.34 g km^-1 (95% confidence level), respectively, for pre-Pak-II trucks, and 12.75 ± 2.80, 14.24 ± 3.53, and 10.24 ± 2.34 g km^-1 (95% confidence level), respectively, for Pak-II trucks, representing 2-20 times higher values than the emission standards in Pakistan and India. An altitude increase of approximately 4000 m, with the associated changes in meteorology and fleet characteristics, induced an average increase of 103.6%, 86.3%, 124.5%, and 133.6% in the emission factors of carbon monoxide, hydrocarbons, nitrogen oxides, and carbon dioxide, respectively. Moreover, on-road emissions along the CPEC were mainly influenced by truck types. This study will support the budget evaluation of transport emissions from the CPEC trade fleet.

How does vehicle emission control policy affect air pollution emissions? Evidence from Hainan Province, China

Vehicular emissions have become important sources of air pollution in China. Regarding the environmental impacts of vehicle emission control policies (VECPs), changes in air pollutants and CO₂ emissions have attracted more attention. Hainan is the first province in China declared to ban the sale of fuel-powered cars by 2030, aiming to accelerate cutting down the local air pollution emissions. However, there is no previous study examining how these VECPs would affect air pollutants in Hainan. Further, research on whether the controls would lead to a real carbon reduction is limited. Therefore, this paper quantitatively assesses the emission changes of primary air pollutants (including NO_x, CO, VOCs, PM_2.5, PM₁₀, and PM_TSP) and greenhouse gases (CO₂, CH₄, and N₂O) in the transportation sector with regard to different VECPs in Hainan. The results reveal that (1) VECPs would lead to significant increases in vehicular population by 21 %-65 % in 2025-2050. Specifically, light-duty cars and buses with 4-stroke engines (LD4Cs) is the largest contributor and banning sales of fuel-powered vehicles would lead to a larger increase of 1914.6 thousand (64 %) in 2030; (2) for air pollutant emissions, the policy scenario would bring notable reduction effects, decreasing by 1.0 %-16.0 % and 16.7 %-38.7 % in 2030 and 2050 (PM excluding), respectively, suggesting VECPs play important roles in alleviating environmental pollution; (3) conversely, for CO₂ emissions, the policy scenario would cause increases of 0.8 Mt. (17.8 %) and 0.3 Mt. (6.1 %) in 2035 and 2050, respectively, indicating promoting new energy vehicles (NEVs) would increase carbon emissions. Meanwhile, it suggests that CO₂ emission in the transportation sector of Hainan peaked in 2020. This research highlights that VECPs would be a double-edged sword, leading to air pollutants reductions but not necessarily decline CO₂ emissions. This fact would further accelerate mechanism and technological innovation in transport to alleviate air pollution and carbon emissions simultaneously.

A dual attention-based fusion network for long- and short-term multivariate vehicle exhaust emission prediction

The increasing number of vehicles is one main cause of atmospheric environment pollution problems. Timely and accurate long- and short-term (LST) prediction of the on-road vehicle exhaust emission could contribute to atmospheric pollution prevention, public health protection, and government decision-making for environmental management. Vehicle exhaust emission has strong non-stationary and nonlinear characteristics due to the inherent randomness and imbalance nature of meteorological factors and traffic flow. Therefore accurate LST vehicle exhaust emission prediction encounters many challenges, such as the LST temporal dependencies and complicated nonlinear correlation on various emission gases, including carbon monoxide (CO), hydrocarbon (HC), and nitric oxide (NO), and external influence factors. To resolve these challenging issues, we propose a novel hybrid deep learning framework, namely Dual Attention-based Fusion Network (DAFNet), to effectively predict LST multivariate vehicle exhaust emission with the temporal convolutional network, convolutional neural network, long short term memory (LSTM)-skip based on recurrent neural network, dual attention mechanism, and autoregressive decomposition model. The proposed DAFNet consists of three major parts: 1) a nonlinear component to effectively capture the dynamic LST temporal dependency of multivariate gas by the temporal convolutional network, convolutional neural network, and LSTM-skip. Moreover, the above two networks employ an attention mechanism to model the internal relevance of the LST temporal patterns and multivariate gas, respectively. 2) a linear component to tackle the scale-insensitive problem of the neural network model by an autoregressive decomposition model. 3) the external components are taken to compensate the impact of external factors on vehicle exhaust emission by the multilayer perceptron model. Finally, the proposed DAFNet is evaluated on two real-world vehicle emission datasets in Zibo and Hefei, China. Experimental results demonstrate that the proposed DAFNet is a powerful tool to provide highly accurate prediction for LST multivariate vehicle exhaust emission in the field of vehicle environmental management.

Incorporating Health Cobenefits into Province-Driven Climate Policy: A Case of Banning New Internal Combustion Engine Vehicle Sales in China

Incorporating health cobenefits from coabated air pollution into carbon mitigation policy making is particularly important for developing countries to boost policy efficiency. For sectors that highly depend on electrification for decarbonization, it remains unclear how the increased electricity demand and consequent health impacts from sectoral mitigation policy in one province would change the scale and the regional and sectoral distributions of the overall health impacts in the whole country. This study chooses the banning of new sales of internal combustion engine vehicles in the private vehicle sector in China as a case. The results show that, without carbon neutrality and air pollution control goals in electricity generation, 53% of CO2 reduction and 65% of health benefits from the private vehicle sector would be offset by increased electricity demand. The regional distributions of CO2 reduction and health benefits due to a province-driven ban policy are greatly uneven, as the top five provinces take up over one-third of the total impact in China. Health benefits per ton of carbon reduction (H/C) may vary by up to 8 times across provinces. Finally, the provinces in southeast China and the Sichuan Basin, with their stably high H/C values, are suggested to enact the province-driven ban policy first.

Recent advances and perspectives towards emission inventories of mobile sources: Compilation approaches, data acquisition methods, and case studies

In recent years, great efforts have been devoted to reducing emissions from mobile sources with the dramatic growth of motor vehicle and nonroad mobile source populations. Compilation of a mobile source emission inventory is conducive to the analysis of pollution emission characteristics and the formulation of emission reduction policies. This study summarizes the latest compilation approaches and data acquisition methods for mobile source emission inventories. For motor vehicles, a high-resolution emission inventory can be developed based on a bottom-up approach with a refined traffic flow model and real-world speed-coupled emission factors. The top-down approach has advantages when dealing with macroscale vehicle emission estimation without substantial traffic flow infrastructure. For nonroad mobile sources, nonroad machinery, inland river ships, locomotives, and civil aviation aircraft, a top-down approach based on fuel consumption or power is adopted. For ocean-going ships, a bottom-up approach based on automatic identification system (AIS) data is adopted. Three typical cases are studied, including emission reduction potential, a cost-benefit model, and marine shipping emission control. Outlooks and suggestions are given on future research directions for emission inventories for mobile sources: building localized emission models and factor databases, improving the dynamic updating capability of emission inventories, establishing a database of emission factors of unconventional pollutants and greenhouse gas from mobile sources, and establishing an urban high temporal-spatial resolution volatile organic compound (VOC) evaporation emission inventory.

Comprehensive Assessment for the Impacts of S/IVOC Emissions from Mobile Sources on SOA Formation in China

Semivolatile/intermediate-volatility organic compounds (S/IVOCs) from mobile sources are essential SOA contributors. However, few studies have comprehensively evaluated the SOA contributions of S/IVOCs by simultaneously comparing different parameterization schemes. This study used three SOA schemes in the CMAQ model with a measurement-based emission inventory to quantify the mobile source S/IVOC-induced SOA (MS-SI-SOA) for 2018 in China. Among different SOA schemes, SOA predicted by the 2D-VBS scheme was in the best agreement with observations, but there were still large deviations in a few regions. Three SOA schemes showed the peak value of annual average MS-SI-SOA was up to 0.6 ± 0.3 μg/m³. High concentrations of MS-SI-SOA were detected in autumn, while the notable relative contribution of MS-SI-SOA to total SOA was predicted in the coastal areas in summer, with a regional average contribution up to 20 ± 10% in Shanghai. MS-SI-SOA concentrations varied by up to 2 times among three SOA schemes, mainly due to the discrepancy in SOA precursor emissions and chemical reactions, suggesting that the differences between SOA schemes should also be considered in modeling studies. These findings identify the hotspot areas and periods for MS-SI-SOA, highlighting the importance of S/IVOC emission control in the future upgrading of emission standards.

Estimation of exposure and premature mortality from near-roadway fine particulate matter concentrations emitted by heavy-duty diesel trucks in Beijing

Traffic exhaust is a main source of fine particulate matter (PM_2.5) in cities. Heavy-duty diesel trucks (HDDTs), the primary mode of freight transport, contribute significantly to PM_2.5, posing a great threat to public health. However, existing research based on dispersion models to simulate pollutant concentrations lacks high-spatiotemporal-resolution emission inventories of HDDTs as input data, and the public health effects of such emissions in different populations have not been thoroughly assessed. To fill this gap, we focused on Beijing as the research area and developed a high-resolution PM_2.5 emission inventory for HDDTs based on Global Navigation Satellite System-equipped vehicle trajectory data. We then simulated the fine-scale spatial distribution of diesel-related PM_2.5 and assessed the population exposure by integrating the dispersion model and population distributions. Further, we quantified the mortality attributable to noncommunicable diseases (NCDs) plus lower respiratory infections (LRIs) related to PM_2.5 emissions from HDDTs. Results showed that 3.3% of Beijing people lived in areas with high PM_2.5 HDDT emissions, which were near intercity highways. Furthermore, the estimated number of NCD + LRI annual premature deaths attributed to PM_2.5 HDDT emissions in Beijing was 339 (95% CI: 276-401). The NCD + LRI mortality increased with age, and deaths were more frequent in males than females. Our results aid the identification of HDDT PM_2.5 emission exposure hotspots for the formulation of effective mitigation measures and provide important insights into the adverse health impacts of HDDT emissions.

Primary organic gas emissions in vehicle cold start events: Rates, compositions and temperature effects

Identification of air toxics emitted from light-duty gasoline vehicles (LDGVs) is expected to better protect human health. Here, the volatile organic compound (VOC) and intermediate VOC (IVOC) emissions in the high-emitted start stages were measured on a chassis dynamometer under normal and extreme temperatures for China 6 LDGVs. Low temperature enhanced the emission rates (ERs) of both VOCs and IVOCs. The VOC ERs were averaged 5.19 ± 2.74 times higher when the temperature dropped from 23 °C to 0 °C, and IVOCs were less sensitive to temperature change with an enlargement of 2.27 ± 0.19 times. Aromatics (46.75 ± 2.83%) and alkanes (18.46 ± 1.21%) dominated the cold start VOC emissions under normal temperature, which was quite different from hot running emission profiles. From the perspective of emission inventories, changes in the speciated composition of VOCs and IVOCs were less important than that in the actual magnitude of ERs under cold conditions. However, changes in the ERs and emission profiles were equally important at high temperatures. Furthermore, high time-resolved measurements revealed that low temperature enhanced both the emission peak and peak duration of fuel components and incomplete combustion products during cold start, while high temperature only increased the peak concentration of fuel components.