Traffic Emissions Research Articles

Pyrogenic PAHs Have Different Biogeochemical Fates in the Eastern Indian Ocean.

Understanding the fate of polycyclic aromatic hydrocarbons (PAHs) in the deep ocean is crucial for elucidating the biogeochemical cycle of organic carbon under anthropogenic influences. In this study, surface sediments were collected from the deep sea of the Eastern Indian Ocean (water depth: 2161-4545 m) and analyzed for 29 semivolatile organic compounds (SVOCs), including parent PAHs and their alkylated derivatives, as well as source biomarkers. The target SVOCs (∑29SVOCs: 23.0-183 ng/g, ∑16PAHs: 11.3-93.3 ng/g) were mainly from pyrogenic sources, namely coal combustion, traffic emissions, and wood burning. The contributions from wood burning and coal combustion exhibited distinct trends with increasing total organic carbon contents, suggesting different dominant biogeochemical behaviors. Major fractions of PAHs from wood burning can be biodegraded or photodegraded, leading to a depletion-dominated fate in the water column. Conversely, PAHs from coal combustion showed an accumulation-dominated fate via their sedimentation due to their persistence and hydrophobicity. This study highlights the distinct biogeochemical fates of PAHs from biomass or fossil fuel combustion in deep oceans and has implications for the marine cycle of refractory organic carbon under anthropogenic impacts.

Heavy metals in sediments of the river-lake system in the Dianchi basin, China: Their pollution, sources, and risks

The accumulation of heavy metals in river and lake sediments in basins seriously threatens ecological safety and human health. To manage the basin effectively, it is crucial to understand pollution levels and identify and quantify the sources and risks of heavy metals in rivers and lakes separately for targeted control. In this study, 34 sediment samples were collected from the Dianchi Basin, China, and the pollution, sources, and risks in the river-lake system sediments were systematically analysed for cadmium (Cd), chromium (Cr), arsenic (As), mercury (Hg), lead (Pb), copper (Cu), zinc (Zn), and nickel (Ni). The results showed that at least half of the heavy metals in the lakes and rivers exceeded the local soil background values during the flood and dry seasons. Heavy metal concentrations were generally higher in the lakes, with high concentrations in the lakes and nearby rivers. Through positive matrix factorisation and Geodetector traceability discovery, agricultural activities were found to be the main source of heavy metals in river sediments, whereas urban activities were the main source in lake sediments. Ecological risk assessments indicated that Hg and Cd were the main risk factors, causing pollution in lakes due to atmospheric deposition and traffic emissions and moderate pollution in rivers due to atmospheric deposition and agricultural production. Health risk assessments revealed that As and Ni were the main carcinogenic risks, originating from human and industrial activities in lakes, and from agricultural and natural sources in rivers. Children faced higher carcinogenic risks than adults, possibly because of their behaviour and physiology. Overall, the presence of heavy metals, along with their ecological and health risks, was significantly higher in the lakes than in the rivers. This study provides a comprehensive overview of the pollution, sources, and risks of eight heavy metals in the river-lake system of the Dianchi Basin.

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.

Assessment of the Effect of Vehicular Emission on Air Quality in Uyo

The presence of vehicular traffic in urban city centers due to urban expansion occasioned by rapid population growth has been of immense concern for many years, as emissions from vehicle exhausts pose a major threat to both public health and environmental quality. This trend is expected to continue as ownership of vehicles keeps increasings. In this study, attempts are made to measure the concentration of traffic related emissions, with a view to assess the intricate relationships between vehicular emissions, their effect on surrounding air quality, and their impact on public health by extension, in Uyo metropolis, using USEPA air quality index. Six sampling locations were selected for this analysis. In selecting the study locations, special preference was given to accessibility, availability of open space free from shed, meteorological consideration of upward and downward directions, and areas with minimal local influence from vehicular movement. Also, consideration was given to the sensitivity and stability of the equipment, as well as its capability to reproduce results. The concentrations of the priority pollutants (i.e., CO, H2S, SO2 and O) were measured with highly sensitive digital portable meters (Crowcon Gasman Monitors) in each of the selected locations, at peak traffic periods, and compared with standard air quality ratings. The results of the study showed that locations IRJ, IPJ and NRJ recorded relatively the highest measured concentrations of the pollutants (CO, H2S and SO2) respectively, while IPJ, NRJ and IRJ recorded the lowest measured concentrations of same pollutants respectively. Continuous accumulation of these toxic gases in the air poses greater threat or may add to an already existing health burden of the residents of those locations. Therefore, further and thorough investigation is highly recommended to study the health impact of these contaminants in the residents.

Carbon emission reduction effects of heterogeneous car travelers under green travel incentive strategies

Encouraging car travelers to switch to public transport is an effective measure to alleviate urban traffic congestion and reduce traffic carbon emissions. This study integrates travel survey data with multi-dimensional individual travel data to focus on incentive strategies. The research identifies key target groups for green travel incentives and quantifies the carbon reduction potential and cost-benefit effectiveness of differentiated incentive strategies for heterogeneous car users. Using Nanjing as a case study, the results show that low-income groups, long-distance commuters, and those with lower car dependency are primary targets users for these incentives. The optimal periods for implementing these strategies are during morning and evening peak commuting times. There is a positive correlation between overall carbon reduction and incentive levels. With a green travel incentive of 0.5 yuan per trip, the target group’s carbon emissions from travel decreased by 27.3%. The highest cost-effectiveness was observed with a 0.1 yuan per trip incentive, resulting in a reduction of approximately 280 yuan per ton of carbon. This study provides crucial insights for designing effective green incentive strategies, enhancing both cost-efficiency and carbon reduction in urban transport.

Heavy metal(loid)s pollution in soils of a typical agricultural and rural area: Source apportionment and derived risk quantification.

In light of the pervasive contamination of soils with heavy metals (HMs) on a global scope, the precise identification and assessment of sources and areas of contamination are crucial for the effective management and utilization of land. This study combines several models to quantitatively apportion possible sources of HMs and environmental risks in agricultural and rural areas of Langfang City, Hebei Province. The findings indicate that the accumulation of HMs is influenced by human activities, with varying degrees of accumulation observed. The Positive Matrix Factorization (PMF) model identified five sources, with the order of magnitude of their contributions to the soil HMs content being as follows: coal combustion source (24.9%), industrial activities source (23.1%), agricultural activities source (19.2%), natural source (16.6%), and traffic emissions source (16.3%). The ecological risks present in the study area are not significant; however, the human health risks exceed the acceptable limits. The primary sources of pollution that pose a risk to human health are industrial, followed by natural source. As, Cr and Ni were responsible for the non-carcinogenic risk, while Cr was the dominant factor in the carcinogenic effect. The risk distribution map indicates that the areas exhibiting the highest risk are situated in the east-central region of the study area. This study had identified the priority control factors (including HMs, sources of pollution and high-risk areas) for the use and management of the land.

Assessment of long-term exposure to traffic-related air pollution: An exposure framework.

Exposure to ambient air pollution is associated with morbidity and mortality, making it an important public health concern. Emissions from motorized traffic are a common source of air pollution but evaluating the contribution of traffic-related air pollution (TRAP) emissions to health risks is challenging because it is difficult to disentangle the contribution of individual air pollution sources to exposure contrasts in an epidemiological study. This paper describes a new framework to identify whether air pollution differences reflect contrasts in TRAP exposures. Because no commonly measured pollutant is entirely specific to on-road motor vehicles, this exposure framework combined information on pollutants, spatial scale (i.e., geographic extent), and exposure assessment methods and their spatial scale to determine whether the estimated effect of air pollution in a given study was related to differences in TRAP. The exposure framework extended beyond the near-road environment to include differences in exposure to TRAP at neighborhood resolution ( ≤ 5 km) across urban, regional, and national scales. It also embedded a stricter set of criteria to identify studies that provided the strongest evidence that exposure contrasts were related to differences in traffic emissions. Application of the framework to the transparent selection of epidemiological studies for a systematic review produced insights on assessing and improving comparability of TRAP exposure measures, particularly for indirect measures such as distances from roads. It also highlighted study design challenges related to the duration of measurements and the structure of epidemiological models. This manuscript describes a new exposure framework to identify studies of traffic-related air pollution, a case study of its application in an HEI systematic review, and its implications for exposure science and air pollution epidemiology experts. It identifies challenges and provides recommendations for the field going forward. It is important to bring this information to the attention of researchers in air pollution exposure science and epidemiology because applying the broader lessons learned will improve the conduct and reporting of studies going forward.

Relationship Between Road Traffic Composition and Noise Emission: an Investigation Based on Hungarian National Public Road Traffic Data

Different vehicles contribute to the noise emission of the total road traffic in different ways and degrees. Therefore, emission calculation methods classify road vehicles based on their typical noise emission parameters. This article investigated the role of the four vehicle categories of CNOSSOS-EU method (light motor vehicles, medium heavy vehicles, heavy vehicles, and powered two-wheelers) in generating road traffic noise emissions. Based on traffic data available for national public roads in Hungary, the proportion of each vehicle category in the total road traffic was determined, providing the average distribution and other statistical parameters for each road category. The impact of changes in the traffic composition on noise emission was analysed based on the statistical parameters. The results show the dominance of light vehicles for all road categories (76–89% on average). The average proportion of heavy vehicles is also significant on controlled-access highways (motorways: 21%, expressways: 15%) and main roads (9%). The deviation from the average traffic composition can cause a noticeable difference in road traffic noise emission (up to nearly 3 dB). It was found that the impact of changing the proportion of heavy and light vehicles on noise emission is higher on roads inside built-up areas.

A spatial source-oriented and probability-based risk-assessment framework for heavy metal and PAH contamination of urban soils in Guangzhou, China

The identification and quantification of high-risk hotspots for soils contaminated by heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) remains a challenge due to their various sources and heterogeneous sink properties in urban soil systems. In this study of 221 soil samples from Guangzhou, China, a novel framework combining Bivariate local Moran’s I (BLMI), positive matrix factorization (PMF), human health risk (HHR) assessment, Monte Carlo simulation (MCS), and a newly developed spatial risk model were proposed to conduct probabilistic source-oriented HHR assessment, high-risk hotspot quantification, and risk formation mechanism elaboration. Study results indicate that traffic emissions are the largest contributor of HMs (47.6 %) and PAHs (40.2 %), but not always the largest contributor of HHR. Agricultural or urban green-space management activities of HM, and mixed source of PAH, are the largest contributors of non-carcinogenic risk (NCR, 48.7 % and 51.1 %, respectively), while mixed source of HM and traffic emissions of PAH are the largest contributors of carcinogenic risk (CR, 53.9 % and 71.2 %, respectively). The probability of risk exceeding safe threshold levels is < 5.0 % for NCR and > 90.0 % for CR. High-risk hotspots were identified in the mid-west and south of the city, making up 15.0 % of the total Guangzhou area. Risk mechanisms were deduced from the spatial heterogeneity and inter-dependence of emission sources and soil sink, based on source–sink theory. Our findings provide a new framework for precisely identifying risk sources and target areas, thereby alleviating HHR associated with co-occurring HMs and PAHs in urban soil systems.

Urban form and seasonal PM2.5 dynamics: Enhancing air quality prediction using interpretable machine learning and IoT sensor data

This study investigates the critical issue of how urban form characteristics influence PM2.5 concentrations, a key concern for public health in densely populated cities. Traditional monitoring methods have faced data gaps and methodological limitations. To address this, we employed interpretable machine learning (ML) models with data from 1,069 Internet-of-Things (IoT) sensors across Seoul, South Korea (September 2020–August 2023). Over 80 urban form variables—including density, transportation, road design, building morphology, and land use—were analyzed using Recursive Feature Elimination to identify key factors affecting PM2.5 concentrations within three buffer zones (300-m, 500-m, 1-km). The random forest model demonstrated the highest accuracy, with an R² of 95 % for autumn and 96 % for spring. Our findings show higher PM2.5 levels in colder months, driven by road width and building density in autumn and traffic and industrial activity in winter. In summer, green spaces and meteorological conditions were primary factors, while spring air quality was notably impacted by localized traffic emissions around highways and bus stops. This study offers robust predictions and actionable insights for urban planning and air quality management. Future research could integrate additional environmental variables and expand sensor coverage to further refine predictive models.

Analysis of Decoupling Effects and Influence Factors in Transportation: Evidence from Guangdong Province, China

In recent years, global environmental issues have become increasingly prominent. The transportation industry, as the fundamental sector of national economic development, is also characterized by high energy consumption and carbon emissions. Therefore, it is imperative to conduct research on the carbon emission problem within this industry. In light of the Tapio decoupling model, an analysis of the correlation between traffic carbon emissions and economic development in Guangdong province during 1999–2019 was carried out. With the aim of encouraging Guangdong province’s low-carbon transportation development, the factors affecting the transportation industry are analyzed utilizing the generalized Divisia index model (GDIM). We also introduced passenger and freight turnover as an influencing factor for analysis. The findings indicate that (1) Guangdong province’s traffic carbon emissions increased from 1999 to 2019; (2) the traffic carbon emissions’ decoupling effect is mainly “weakly decoupled”, and the overall decoupling effect is not strong in Guangdong province; (3) among the traffic carbon emissions’ factors, the effects of the production value of traffic and the turnover volume are at the forefront, and the effect of turnover volume has gradually exceeded the production value of traffic in recent years. The suppression of the intensity of carbon emissions is relatively large, while the suppression of the intensity of energy consumption and transport is relatively weak. Based on this, strategies were proposed to promote a cleaner energy mix, improve energy use efficiency, create energy savings, develop green technologies, and foster the restructuring of transportation.

Advancing source apportionment of soil potentially toxic elements using a hybrid model: a case study in urban parks, Beijing, China.

Identifying the source-specific health risks of potentially toxic elements (PTE) in urban park soils is essential for human health protection. However, previous studies have mostly focused on the deterministic source-specific health risks, ignoring the health risk assessment from a probabilistic perspective. To fill this gap, we developed a hybrid model that incorporated machine learning (ML) interpretability into positive matrix factorization (PMF) and probability health risk assessment (PHRA) based on the Monte Carlo simulation. The results indicated that concentrations of soil PTEs except for Mn and Sb were significantly higher than their corresponding background values. Random forest (RF) was regarded as the best ML model to identify key drivers for As, Cd, Cr, Cu, Ni, Pb, and Zn, with R2 > 0.60, but was less effective for other soil PTEs (R2 < 0.49). Specifically, the contributions of the four potential pollutionsources were mixed sources, traffic emission, fuel combustion, and building materials, with contribution rate of 24.88%, 30.56%, 28.99%, and 15.56%, respectively. Fuel combustion contributed the most to non-carcinogenic for children (39.45%), male (43.84%), and female (43.76%), and the non-carcinogenic risk could be considered negligiblefor human. However, building materials was the major contributor to carcinogenic risk for children (36.1%), male (44.9%), and female (43.2%). The integration of the RF model with PMF and PHRA improved the accuracy of the results by identifying and quantifying the specific sources of each soil PTE using the relative importance analysis from the RF model. The results of this study assisted in providing efficient strategies for risk management and control of soil PTEs in Beijing parks.

Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing

Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterise the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems.

Modeling traffic pollutants in a street canyon by CFD: Idealized line sources versus multiple realistic car sources

Idealized sources are commonly used to reproduce the traffic emission in street canyons in experimental and numerical investigations. However, it remains unclear whether idealized sources can accurately reproduce the pollutant dispersion compared to more realistic sources. The goal of this paper is to investigate the impact of idealized and realistic sources on traffic-induced pollutant concentration in a street canyon by numerical simulation with Computational Fluid Dynamics (CFD). First, the scale-adaptive simulation (SAS) results of mean velocity and concentration are compared with wind-tunnel (WT) data for idealized line sources (ILS) and a satisfactory agreement is found between the SAS and WT results. Next, SAS are performed to investigate the impact on mean velocity and mean pollutant concentration of ILS versus multiple realistic car sources (MRCS) cases, designed to mimic a configuration of traffic jam-like conditions (i.e. rows of idling cars) in a street canyon. Different levels of geometrical simplifications of sources and total emission rates are considered. Although this research study focuses on these aforementioned specific set of conditions and conclusions relate solely to these circumstances, the SAS results show that the overall-averaged concentration associated with MRCS and the simplified source-geometry cases is about 1.18–6.22 times larger than that by ILS. Thus, ILS may be inadequate to reproduce the pollutant concentration in real scenarios. This study can contribute to enhancing the pollution prediction accuracy and reduce the potential risk of human exposure.

Accessibility Measures to Evaluate Public Transport Competitiveness: The Case of Rome and Turin

The transport sector worldwide relies heavily on oil products, and private cars account for the largest share of passenger mobility in several countries. Public transport could represent an interesting alternative under many perspectives, including a decrease in traffic, pollutants, and climate emissions. However, for public transport to succeed, it should be attractive for final users, representing a viable alternative to private mobility. In this work, we analyse the spatial distribution of public transport service provision within two metropolitan cities, considering the three key dimensions of mobility, competitiveness, and accessibility of public transport. The results show that private car performs better than public transport in all scopes considered, and that performance indicators are highly variable among city areas, indicating inequalities in social and environmental sustainability in urban systems. The outcomes of the analysis provide interesting insights for policy makers and researchers that deal with similar topics, and can also be extended to other cities and countries.

Changes in source contribution to particle number concentrations during the 2022 COVID-19 Lockdown on southern edge of North China Plain

The COVID-19 lockdown was a typical example of extreme emission reduction, providing an opportunity to study the impact of lockdown measures on air pollution. Particle number concentrations (PNC) originate from direct emissions or through new particle formation events. However, their variations during the lockdown period are under investigation. This study focuses on Luohe, a city on the southern edge of the North China Plain, analyzing the changes in PNC and its sources before, during, and after the COVID-19 lockdown. From March 25th to May 31st, 2022, real-time PNC measurements were conducted using a Scanning Mobility Particle Sizer for particle size. Results showed an 11.2 % decrease in PNC during the lockdown compared to pre-lockdown and a 3.6 % decrease compared to post-lockdown, indicating reduced local emissions and weakened regional transportation during the lockdown. Positive Matrix Factorization analysis identified six sources contributing to the total PNC, including photochemical nucleation, aged photochemical nucleation, gasoline vehicle emissions, diesel vehicle emissions, coal and biomass combustion, and secondary aerosols. The significant changes in source emissions indicate a substantially reduced traffic volume after the implementation of lockdown measures (2644.8 #/cm³, 2202.2 #/cm³, 2792.7#/cm³). Concurrently, photochemical nucleation (310.1 #/cm³, 306.3 #/cm³, 393.1 #/cm³) and photochemical nucleation aging (592.8 #/cm³, 744.1 #/cm³, 810.7 #/cm³) exhibited increasing trends, while coal/biomass combustion (1656.6 #/cm³, 1586.2 #/cm³, 980.0 #/cm³) and secondary sources (999.4 #/cm³, 791.1 #/cm³, 804.1 #/cm³) showed decreasing trends. In summary, the contributions of traffic emissions to PNC highlight the potential for targeted traffic management strategies to improve urban air quality.

Examining the structural properties of hydrophilic and hydrophobic organic aerosols using 1H NMR: diurnal variations and source apportionment.

The behaviour of the carbonaceous aerosols during the rainy season and the diurnal variations in their structural groups have not been thoroughly examined. The present study aims to understand the structural composition of hydrophilic and hydrophobic organic aerosols (OA) at an urban background location in Mumbai, India. The carbonaceous fractions, i.e., Elemental (EC) and Organic (OC) Carbon, accounted for 14-34% of the total PM10 (Particulate matter with aerodynamic diameter ≤ 10 μm). The PM10 and EC were maximum in the morning, while OC was the highest in the evening. The aliphatic structural groups were more concentrated in the total fraction, contributing 53-62% of the total resonances. The total concentrations of the structural groups in both hydrophilic (29.2±9.8 μmol/m3) and total (197.8±154.3 μmol/m3) fractions were highest in the morning. Traffic emissions impacted the morning and evening aerosols, as suggested by the broad aliphatic and sharp aromatic resonances observed in the total fraction. This is further corroborated by the variability in EC and OC, their significant correlations with Volatile OC and Nitrogen oxides, and their contribution to regression models and principal components. The afternoon aerosols demonstrated characteristics of Secondary OA. Our work extends the present understanding of the diurnal variability and the heterogeneity of the hydrophilic and hydrophobic structural groups in organic aerosols.

Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

Determining the priority control sources of heavy metals in the roadside soils in a typical industrial city of North China

Heavy metals (HMs) in roadside soils (RS) are closely related to urban ecosystem and human health, but priority control sources and eco-health risks of HMs remain unclear. We explored pollution sources of HMs using positive matrix factorization (PMF), scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), and random forest (RF), and assessed their source-specific eco-health risks. The combination of PMF, SEM-EDS and RF indicated that pollution sources of HMs were coal combustion (27.99 %), construction materials (30.15 %), and traffic emissions (41.86 %). SEM-EDS revealed RS particles were identified by a combination of physical (spherical, porous rounded, crustal flake, crustal rounded and irregular particles) and elemental surface characteristics (O, C, Si, Al, Fe, Ca, Mg, K, Zn, Cu, and Mn). RF highlighted road network density, residential density, and industrial density had significant importance influence on pollution sources. Approximately 72.35 % of soil samples were at a low ecological risk with traffic emissions being the major contributor. Non-carcinogenic risks had a minimal effect, but carcinogenic risks were at a cautionary level with coal combustion being the highest contributor. Overall, coal combustion and traffic emissions were regarded as priority control sources of HMs. These findings provided effective guidance for soil pollution prevention and risk control.

Rewiring the nexus between urban traffic pollution-derived polycyclic aromatic hydrocarbon exposure and DNA injury via urinary metabolomics

Urban road traffic environmental stress impacts outdoor population health, with oxidative damage serving as an early indicator of xenobiotic exposure. Polycyclic aromatic hydrocarbons (PAHs) as priority carcinogens pose significant public health burden, yet knowledge remains limited regarding the endogenous metabolic alternations associated with oxidative DNA injury. This cross-sectional study focused on the cohort consisting of 109 sanitation workers (“traffic exposure group”) and 112 demographics-matched common residents (“controls”) in South China. The goal was to elucidate the occurrence of internal exposure to nine hydroxyl PAHs, and the interrelations with oxidative DNA damage (indicated by 8-hydroxy-2′-deoxyguanosine, 8-OHdG) by linear mixed-effect regression model. T-test and orthogonal partial least squares discriminant analysis were used to determine differential metabolites in non-targeted metabolomics. Results revealed outdoor workers suffered from the heavier PAH exposure burden and exhibited a stronger dose-dependent correlation with 8-OHdG, evidenced by the higher regression coefficient (0.244, 95% CI: 0.154–0.334) than controls (0.203, 95% CI: 0.079–0.328). In total 42 differential endogenous metabolites witnessed significant expression under traffic emission scenario, mainly implicated in phenylalanine, tyrosine and tryptophan biosynthesis. The down-expressed uric acid was the unique metabolite that inversely correlated with the increased intake of ∑8PAH especially in cases. Partially attributed to the traffic-derived PAHs, the dysregulated amino acid, nicotinamide, purine, and steroid hormones metabolic pathways encompassing 11 metabolites were determined as underlying biomarkers in mediating DNA damage. Notably, our findings proposed uric acid may act as a potential antioxidant, as evidenced by the negative correlation with 8-OHdG. The study illustrates outcomes of metabolomics can collaboratively indicate DNA oxidative damage caused by PAHs linked to urban traffic exposure, which holds significant implications for future toxicological research.