Quantifying factors affecting contributions of roadway exhaust and non-exhaust emissions to ambient PM10-2.5 and PM2.5-0.2 particles

Mechanisms involved in pro-inflammatory responses to traffic-derived particulate matter (PM) in THP-1 macrophages compared to HBEC3-KT bronchial epithelial cells

The pro-inflammatory responses in THP-1-derived macrophages and human bronchial epithelial cells (HBEC3-KT) were examined after exposure to size-fractioned particulate matter (PM) sampled in two road tunnels. All tunnel PM samples induced release and expression of CXCL8 and IL-1β in THP-1 macrophages (50 µg/mL) and HBEC3-KT cells (100 µg/mL), but the potency of the samples differed between the cell types. The road tunnel PM induced pro-inflammatory responses in the macrophages to a much higher extent than diesel exhaust particles (DEP) and particles derived from the stone materials used in the asphalt. Tunnel PM induced a markedly higher increase in cytochrome P450 (CYP)1A1 expression in HBEC3-KT cells than in THP-1 macrophages. The content of organic carbon (OC) in PM correlated to the release of CXCL8 in HBEC3-KT cells, but not in THP-1 macrophages. Moreover, the aryl hydrocarbon receptor (AhR)-inhibitor CH223191 and the antioxidant N-acetyl cysteine (NAC) reduced the PM-induced cytokine release in the macrophages to a lower extent than in HBEC3-KT. In contrast, a toll-like receptor (TLR)2 antibody markedly reduced the PM-induced responses in THP-1 macrophages, but not in HBEC3-KT. A TLR4 antibody was without effect in both cell types. The levels of the microbial TLR2-ligand β-glucan in the PM samples were in a range that might be sufficient to induce pro-inflammatory responses. However, a microbial-independent mechanism could also be involved. In support of such a mechanism, the pro-inflammatory responses to a sample of α-quartz (Min-U-Sil 5), with low levels of β-glucan, were reduced by anti-TLR2. In conclusion, our results indicate that traffic-derived PM exert pro-inflammatory responses in THP-1 macrophages and HBEC3-KT cells via different PM constituents and mechanisms. OC/AhR-dependent mechanisms appeared to be important for PM-induced CXCL8 responses in HBEC3-KT cells, while the cytokine responses in THP-1 macrophages seemed to involve TLR2-mediated activation, either via β-glucan-dependent or -independent mechanisms.

Laboratory measurement and machine learning-based analysis of driving factors for brake wear particle emissions from light-duty electric vehicles and heavy-duty vehicles

This study investigates brake wear particle (BWP) emissions from light-duty electric vehicles (EVs) and heavy-duty vehicles (HDVs) using a self-developed whole-vehicle testing system and a modified brake dynamometer. The results show that regenerative braking significantly reduces emissions: weak and strong regenerative braking modes reduce brake wear PM2.5 by 75 % and 87 %, and brake wear PM10 by 90 % and 95 %, respectively. HDVs with drum brakes produce lower emissions and higher PM2.5/PM10 ratios than those with disc brakes. A machine learning model (XGBoost) was developed to analyze the relationship between BWP emissions and factors (11 for light-duty EVs and 8 for HDVs, based on kinematic, vehicle, and braking parameters). SHapley Additive exPlanations (SHAP) were used for model interpretation. For light-duty EVs, reducing high kinetic energy losses (Ike > 6500 J) and initial speeds (V > 45 km/h) braking events significantly lowers emissions. Additionally, the emission reduction effect of regenerative braking intensity (BI) stabilizes when BI exceeds 900 J. For HDVs, controlling braking temperature (Avg.T < 200°C) and initial speed (V < 50 km/h) effectively reduces emissions. Our findings provide new insights into the emission characteristics and control strategies for BWPs. SYNOPSIS: The construction and interpretation of a machine learning based model of brake wear emissions provides new insights into the refined assessment and control of non-exhaust emissions.

Evaluation of emission factors for resuspended tire-wear particles in urban road dust using empirical model-based methods

Tire and road wear particles, major contributors to non-exhaust particulate matter emissions, are frequently resuspended into the atmosphere from road dust, posing significant environmental and health challenges. Conventional approaches to estimating emission factors (EFs) often rely on variables such as road dust loading, vehicle types, and road classifications; however, these methods typically neglect the critical influence of wind speed on resuspension dynamics. This study introduces a methodology that incorporates wind speed as a fundamental parameter to improve the accuracy of EF estimations for resuspended tire-wear particles (TWPs). Our approach utilizes particle size analysis, pyrolysis-gas chromatography-mass spectrometry for quantifying TWP content, and a wind-speed depended weighting factor (WFTWP) that accounts for the resuspension potential of particles. The average TWP content in road dust was determined to be 23,495 mg/kg (2.4 wt%), aligning with findings from previous urban studies. At a near-ground wind speed of 1.5 m/s, resuspended TWPs accounted for 2.6 % of the total resuspended dust mass, closely reflecting the original TWP proportion in road dust. Using modified EPA and Amato methods, calculated EF values ranged from 2.02 to 7.22 mg/vkm, with the Amato method's EF value (3.35 ± 2.21 mg/vkm) comparable to the EPA-derived EF for passenger cars (2.02 ± 0.55 mg/vkm) but showing significant variation for buses (7.22 ± 1.97 mg/vkm). Furthermore, the study found that as wind speed increased, the WFTWP also increased proportionally, directly impacting EF values. The results indicate the importance of incorporating wind dynamics into EF calculations to more accurately represent real-world resuspension behaviors. This methodology provides a practical tool for estimating the resuspension of TWPs under varying wind conditions and aids in refining emission inventories.

Beyond the tailpipe: Review of non-exhaust airborne nanoparticles from road vehicles

With the electrification of road vehicles leading to a reduction in tailpipe emissions, the relative contribution of non-exhaust emissions (NEEs) has become increasingly prominent. NEEs, particularly nanoparticles smaller than 100 nm in aerodynamic diameter (PM0.1), present significant health and environmental risks. A comprehensive understanding and strategic management of these emissions are urgently required to mitigate their impact. This article reviews existing studies and reveals that nanoparticles in NEEs are generated from brake and tyre wear under critical temperature conditions, while road wear and resuspension do not directly produce nanoparticles but contribute to larger particles. Common methodologies in studying these emissions include laboratory experiments (with brake dynamometers, tyre dynamometers, chassis dynamometers, and simulators), field tests (tunnel and real road emission tests), and source apportionments. The emission rate of PM0.1, calculated based on particle number concentration, ranges from 1.2% to 98.9%, depending on driving conditions. Extreme driving conditions result in high nanoparticle generation. Emission inventories reveal that PM0.1 emission levels have remained stable since 2020, without an observable reduction. Moreover, emissions attributable to brake wear are found to surpass those from tyre wear. Current mitigation strategies focus on material improvements for brake pads and tyres, better road maintenance, and regulatory measures. Mitigating the environmental and health impacts of nanoscale particulate matter requires additional research and regulations to control it at the source.

Genetic evidence for the causal effects of air pollution on the risk of respiratory diseases

BACKGROUND

Epidemiological studies have consistently demonstrated a robust association between long-term exposure to air pollutants and respiratory diseases. However, establishing causal relationships remains challenging due to residual confounding in observational studies. In this study, Mendelian randomization (MR) analysis was used to explore the causal and epigenetic relationships between various air pollutants and common respiratory diseases.

METHODS

We utilized a two-sample Mendelian randomization (TSMR) approach to explore the impact of PM2.5, PM2.5-10, PM10, NO2, and NOX on the incidence of nine respiratory diseases using data from large-scale European GWAS datasets (N = 423,796-456,380 for exposures; N = 162,962-486,484 for outcomes). The primary analytical method was inverse variance weighting (IVW), which explored the exposure-outcome relationship using single nucleotide polymorphisms (SNPs) associated with air pollution. Sensitivity analyses, including MR-Egger regression and leave-one-out analyses, were employed to ensure result consistency. Multivariate MR (MVMR) was performed to adjust for potential smoking-related confounders, such as cigarettes per day, household smoking, exposure to tobacco smoke at home, ever smoked, second-hand smoke, smoking initiation, and age at smoking initiation, as well as the independent effects of each air pollutant. Additionally, methylation and enrichment analyses were conducted to further elucidate the potential effects of air pollution on respiratory diseases.

RESULTS

TSMR analysis revealed that exposure to PM2.5 increased the risk of early-onset chronic obstructive pulmonary disease (COPD), pneumonia, pulmonary embolism and lung cancer. PM2.5-10 exposure was associated with an increased risk of lung cancer, while PM10 exposure increased the risk of pneumonia and bronchiectasis. NO2 exposure was associated with increased risks of lung cancer and adult asthma. Importantly, these associations remained robust even after controlling for potential tobacco-related confounders in the MVMR analyses. In the MVMR analysis adjusting for other pollutants, significant associations persisted between PM2.5 and early-onset COPD, and between PM10 and pneumonia. Genetic co-localization analyses confirmed that methylation of PM2.5-associated CpG loci (cg11386376 near c1orf175, cg11846064 near rfx2, cg18612040 near rptor, and cg19765378 near c7orf50) was associated with an increased risk of early-onset COPD. Finally, SNPs significantly associated with exposure and outcome were selected for enrichment analysis.

CONCLUSIONS

Our findings suggest that exposure to air pollutants may play a causal role in the development of respiratory diseases, with a potential role of epigenomic modifications emphasized. Strengthening comprehensive air pollution regulations by relevant authorities could potentially mitigate the risk of these diseases.

Machine learning helps reveal key factors affecting tire wear particulate matter emissions

Tire wear particles (TWPs) are generated with every rotation of the tire. However, obtaining TWPs under real driving conditions and revealing key factors affecting TWPs are challenging. In this study, we obtained a TWPs dataset by simulating tire wear process under real driving conditions using a tire wear simulator and custom-designed test conditions. This study shows that tire wear PM2.5 accounts for about 65 % of PM10. The response relationship between TWP emissions (both PM2.5 and PM2.5-10) and factors (the radial force, the lateral force, the tangential force, speed, driving torque, tire contact area, total contour length and tire tread temperature) was obtained by machine learning (ML) method. The random forest (RF) model was developed and displayed good prediction performance with an R2 of 0.84 and 0.78 for PM2.5 and PM2.5-10 on the test set, respectively. Model-related (similarity network graph) and model-unrelated (partial dependence plots and centered-individual conditional expectation plots) explainability methods were used to break the black box of ML. Model explainability results show that the feature parameters-emission response relationships for tire wear PM2.5 and PM2.5-10 are different. Avoiding strenuous driving behaviors (TTF < 400 N, TLF < 400 N), reducing tread temperature (T < 45℃), and minimizing the number of small tread patterns are feasible ways to reduce TWPs.

Mobile monitoring reveals the importance of non-vehicular particulate matter sources in London

This study uses mobile monitoring to gain a better understanding of particulate matter (PM) sources in two areas of Central and Outer London, UK. We find that, unlike emissions of nitrogen oxides (NO + NO2 = NOx), which are elevated in Central London due to the high number of diesel vehicles and congestion, fine particulate matter (PM2.5) emissions are well-controlled. This finding provides evidence for the effectiveness of vehicle particulate filters, supporting the view that their widespread adoption has mitigated PM2.5 emissions, even in the highly dieselized area of Central London. However, mobile monitoring also reveals infrequent elevated PM2.5 concentrations caused by malfunctioning vehicles. These events were confirmed through simultaneous measurements of PM2.5 and sulfur dioxide (SO2), the latter being a strong tracer of engine lubricant combustion. A single event from a gasoline car, representing just 0.15% of the driving distance in Outer London, was responsible for 7.4% of the ΔPM2.5 concentration above background levels, highlighting the ongoing importance of addressing high-emission vehicles. In a novel application of mobile monitoring, we demonstrate the ability to identify and quantify non-vehicular sources of PM. Among the sources unambiguously identified are construction activities, which result in elevated concentrations of coarse particulate matter (PMcoarse = PM10 - PM2.5). The mobile measurements clearly highlight the spatial extent of the influence of such sources, which would otherwise be difficult to determine. Furthermore, these sources are shown to be weather-dependent, with PMcoarse concentrations reduced by 62.1% during wet conditions compared to dry ones.

P-phenylenediamines (PPDs) and 6PPD-quinone in tunnel PM2.5: From the perspective of characterization, emission factors, and health risks

P-phenylenediamines (PPDs) and a quinone derivative (6PPD-Q), as antioxidants added to tires, can inevitably enter into the environment during tire wear emission, posing potential health and ecological risks. However, investigation on their pollution characteristics in PM2.5 is still lacking, especially for high-pollution scenarios, such as tunnels. Herein, we investigated the pollution characteristics and emission factors, as well as the correlation analysis and daily intakes of PM2.5-bound PPDs and 6PPD-Q in tunnel. The results indicated heavy PPDs and 6PPD-Q pollution were observed in tunnel PM2.5, with the concentration at the two tunnel sites being 3.83 and 8.73 times higher than those at the urban site, respectively. PPDs were negatively correlated to relative humidity and positively to temperature. Emission factors of 6PPD and 6PPD-Q were 3013.54 and 1466.67 ng·veh-1·km-1 for large vehicles. PPDs and 6PPD-Q were most harmful to children, and annual exposure dosages at the tunnel sites were 4.64 times higher than those at the urban site. This study conducted a comparison of PPDs and 6PPD-Q in urban and tunnel environments for the first time. Our findings clarified the key factors to reduce the pollution of PPDs in tunnel and supported policy-making for emission reduction of PPDs and 6PPD-Q.

Quantification and occurrence of 39 tire-related chemicals in urban and rural aerosol from Saxony, Germany

Tire and road wear particles (TRWP) are a major contributor to non-exhaust traffic emissions, but their contribution to and dynamics in urban aerosol is not well known. Urban particulate matter (PM) in the size fraction below 10 µm (PM10) from two German cities was collected over 2 weeks and analysed for 39 tire-related chemicals, including amines, guanidines, ureas, benzothiazoles, p-phenylenediamines, quinolines and several transformation products (TPs). Of these, 37 compounds were determined in PM10 at median concentrations of 212 pg/m3 for 1,3-diphenylguanidine (DPG) and 132 pg/m3 for benzothiazole-2-sulfonic acid (BTSA); 10 of the compounds have not been reported in urban aerosol before. Median concentrations of N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6-PPD), 6-PPD quinone (6-PPDQ), and 1,2-dihydro-2,2,4-trimethylquinoline (TMQ) were 1.0, 4.1, and 8.1 pg/m3, respectively. Some parent compounds showed positive correlation with their TPs, e.g. 6-PPD with 4-aminodiphenylamine (4-ADPA), N,N'-diphenyl-p-phenylenediamine (DPPD) with DPPD quinone (DPPDQ), and DPG with phenylguanidine (PG). The concentration pattern of the compounds in PM10 did not agree to the pattern found for cryo-milled tire tread (CMTTmix), likely reflecting transformation processes in tires or the aerosol and the influence from other sources than TRWP. Concentrations in PM2.5 were determined from one of the sites and were by a factor of 4 - 10 lower than in PM10-2.5 for 9 compounds, but a few others, mostly benzothiazoles showed similar or higher concentrations. Many of the tire-related chemicals were also determined in PM10 of one rural site, although at median concentrations up to two orders of magnitude lower. A large number of tire chemicals with a wide concentration range is present in urban PM10 and PM2.5 aerosol and requires scrutiny with respect to its relevance for human exposure.

Tire Wear Chemicals in the Urban Atmosphere: Significant Contributions of Tire Wear Particles to PM2.5

Tire wear particles (TWPs) containing tire wear chemicals (TWCs) are of global concern due to their large emissions and potential toxicity. However, TWP contributions to urban fine particles are poorly understood. Here, 72 paired gas-phase and PM2.5 samples were collected in the urban air of the Pearl River Delta, China. The concentrations of 54 compounds were determined, and 28 TWCs were detected with total concentrations of 3130-317,000 pg/m3. Most p-phenylenediamines (PPDs) were unstable in solvent, likely leading to their low detection rates. The TWCs were mainly (73 ± 26%) in the gas phase. 2-OH-benzothiazole contributed 82 ± 21% of the gas-phase TWCs and benzothiazole-2-sulfonic acid contributed 74 ± 18% of the TWCs in PM2.5. Guangzhou and Foshan were "hotspots" for atmospheric TWCs. Most TWC concentrations significantly correlated with the road length nearby. More particulate TWCs were observed than model predictions, probably due to the impacts of nonexchangeable portion and sampling artifacts. Source apportionment combined with characteristic molecular markers indicated that TWPs contributed 13 ± 7% of urban PM2.5. Our study demonstrates that TWPs are important contributors to urban air pollution that could pose risks to humans. There is an urgent need to develop strategies to decrease TWP emissions, along with broader urban air quality improvement strategies.

The effects of urban green space and road proximity to indoor traffic-related PM2.5, NO2, and BC exposure in inner-city schools

BACKGROUND

Since there are known adverse health impacts of traffic-related air pollution, while at the same time there are potential health benefits from greenness, it is important to examine more closely the impacts of these factors on indoor air quality in urban schools.

OBJECTIVE

This study investigates the association of road proximity and urban greenness to indoor traffic-related fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) in inner-city schools.

METHODS

PM2.5, NO2, and BC were measured indoors at 74 schools and outdoors at a central urban over a 10-year period. Seasonal urban greenness was estimated using the Normalized Difference Vegetation Index (NDVI) with 270 and 1230 m buffers. The associations between indoor traffic-related air pollution and road proximity and greenness were investigated with mixed-effects models.

RESULTS

The analysis showed linear decays of indoor traffic-related PM2.5, NO2, and BC by 60%, 35%, and 22%, respectively for schools located at a greater distance from major roads. The results further showed that surrounding school greenness at 270 m buffer was significantly associated (p < 0.05) with lower indoor traffic-related PM2.5: -0.068 (95% CI: -0.124, -0.013), NO2: -0.139 (95% CI: -0.185, -0.092), and BC: -0.060 (95% CI: -0.115, -0.005). These associations were stronger for surrounding greenness at a greater distance from the schools (buffer 1230 m) PM2.5: -0.101 (95% CI: -0.156, -0.046) NO2: -0.122 (95% CI: -0.169, -0.075) BC: -0.080 (95% CI: -0.136, -0.026). These inverse associations were stronger after fully adjusting for regional pollution and meteorological conditions.

IMPACT STATEMENT

More than 90% of children under the age of 15 worldwide are exposed to elevated air pollution levels exceeding the WHO's guidelines. The study investigates the impact that urban infrastructure and greenness, in particular green areas and road proximity, have on indoor exposures to traffic-related PM2.5, NO2, and BC in inner-city schools. By examining a 10-year period the study provides insights for air quality management, into how road proximity and greenness at different buffers from the school locations can affect indoor exposure.

Evaluating exposure to vehicle pollutants using physics-informed immersive reality models

Major health risks and chronic diseases are caused by exposure to unregulated particle pollutants from road, tyre and brake sources. Here, we use large-eddy simulations to identify local exposure to these harmful pollutants and build a physics-informed immersive reality experience to communicate outcomes with the general public for health guidance. Our analysis reveals that exposure to non-exhaust pollution is greatest at the end of braking phases, when deceleration rates are above 3 m s-2, diminishes to background levels for pedestrians located 1.5 m away from a car, and is reasonably insensitive to the car type. We show that by using immersive reality models to visualize pollution data in a human-centric format, people could identify pollutant sources and health risks, and understand how to navigate urban spaces for reduced exposure. This was achieved without any prerequisite knowledge and with minimal dependency on educational background, suggesting the approach can support public health guidance, policymakers and urban planners towards improving air quality in urban environments.

Decomposing PM2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions

Vehicle-emitted fine particulate matter (PM2.5) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM2.5 using a novel factorization framework. Specifically, co-measured nitrogen oxides (NOx) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012-2019) PM2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM2.5 concentrations at an AQM station from the first location showed close agreement (R2=0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7-11 % of the observed PM2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60-78 % of the traffic-related PM2.5 concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement.

Pilot analysis of tire tread characteristics and associated tire-wear particles in vehicles produced across distinct time periods

Owing to stringent vehicle emission regulations and the shifting automotive landscape towards clean-energy vehicles, the emission of non-exhaust tire-wear particles and its implications for microplastic contamination have garnered substantial attention, emerging as a focal point of research interest. Unlike traditional source apportionment methods involving direct environmental sampling, this study focuses on the physical and chemical attributes of tire treads, the tread temperature changes, and the tire-wear particle emissions of three light-duty vehicles manufactured between 2011 and 2021. This study advances the understanding of the effects of tire properties on particle emissions, which provides preliminary information on low-wear tires. The results show that tire-wear particle emissions, mainly composed of ultrafine particles in terms of number, heavily depend on the elevated tread temperatures. The change in tread temperature is influenced not only by the initial tread temperature but also by tread pyrolysis characteristics. Ca, Mg, and Zn are abundantly contained in the tire tread and tire-wear particles.

Speed limits and their effect on air pollution in Mexico City: A quasi-experimental study

Speed limits are an evidence-based intervention to prevent traffic collisions and deaths, yet their impact on air pollution in cities is understudied. The objective of this study was to investigate the association between lower speed limits and air pollution. We leverage the introduction of a new road safety policy in Mexico City in December 2015 which lowered speed limits, increased fines, and installed speed radars to enforce compliance. We tested whether the policy had an impact on particulate matter (PM2.5) and nitrogen dioxide (NO2) at the city level, and whether air-quality monitoring stations' proximity to speed radars moderated this effect due to more acceleration and deceleration around radars. NO2 and PM2.5 concentrations from January 2014 to December 2018 were obtained from the National System of Air Quality Information. Air-quality monitoring stations were classified as in close-proximity or far-from-speed radars. Interrupted time series analyses were conducted for each outcome separately, using linear mixed models and adjusting for seasonality and time-varying confounders: registered vehicles, temperature, wind-speed and relative humidity. The results suggest improvement in both contaminants after the speed limits policy. For NO2, the pre-policy trend was flat, while the post-policy trend showed a decline in concentrations of 0.04 ppb/week. For PM2.5, concentrations were increasing pre-policy by 0.08 μg/m3 per week, then this trend flattened in the post-policy period to a weekly, non-significant, increase of 0.03 μg/m3 (p = 0.08). Air-quality monitors' proximity to speed radars did not moderate the effect of the policy on either of the pollutants. In conclusion, the speed limits policy implemented in Mexico City in 2015 was associated with improvements in air pollution.

In-vehicle exposure to NO2 and PM2.5: A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure

Vehicles are the third most occupied microenvironment, other than home and workplace, in developed urban areas. Vehicle cabins are confined spaces where occupants can mitigate their exposure to on-road nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations. Understanding which parameters exert the greatest influence on in-vehicle exposure underpins advice to drivers and vehicle occupants in general. This study assessed the in-vehicle NO2 and PM2.5 levels and developed stepwise general additive mixed models (sGAMM) to investigate comprehensively the combined and individual influences of factors that influence the in-vehicle exposures. The mean in-vehicle levels were 19 ± 18 and 6.4 ± 2.7 μg/m3 for NO2 and PM2.5, respectively. sGAMM model identified significant factors explaining a large fraction of in-vehicle NO2 and PM2.5 variability, R2 = 0.645 and 0.723, respectively. From the model's explained variability on-road air pollution was the most important predictor accounting for 22.3 and 30 % of NO2 and PM2.5 variability, respectively. Vehicle-based predictors included manufacturing year, cabin size, odometer reading, type of cabin filter, ventilation fan speed power, window setting, and use of air recirculation, and together explained 48.7 % and 61.3 % of NO2 and PM2.5 variability, respectively, with 41.4 % and 51.9 %, related to ventilation preference and type of filtration media, respectively. Driving-based parameters included driving speed, traffic conditions, traffic lights, roundabouts, and following high emitters and accounted for 22 and 7.4 % of in-vehicle NO2 and PM2.5 exposure variability, respectively. Vehicle occupants can significantly reduce their in-vehicle exposure by moderating vehicle ventilation settings and by choosing an appropriate cabin air filter.

Physical and chemical characteristics of particles emitted by a passenger vehicle at the tire-road contact

Non-exhaust emissions are now recognized as a significant source of atmospheric particulate matter and the trend towards a reduction of conventionally fueled internal combustion engine vehicles on the road is increasing their contribution to air pollution due to lower exhaust emissions. These particles include brake wear particles (BWP) and tire-road contact particles (TRCP), which are composed of tire wear particles (TWP), road wear particles (RWP) and resuspended road dust (RRD). The goal of this study has therefore been to design an original experimental approach to provide insight into the chemical composition of particles emitted at the tire-road contact, focusing on the micron (PM10-1μm) and submicron (PM1-0.1μm) fractions. Through this characterization, an examination of the different TRCP generated by different materials (tire, road surface, brake system) was conducted. To achieve this, TRCP were collected at the rear of the wheel of an instrumented vehicle during road and track tests, and a SEM-EDX analysis was performed. Our experimental conditions have allowed us to demonstrate that, at the individual particle scale, TRCP are consistently associated with road dust materials and particles solely composed of tire or road materials are practically non-existent. The contribution of BWP to TRCP is marked by the emission of Fe-rich particles, including heavy metals like Ba, Mn and Cr. TWP, which result from rubber abrasion, consist of C-rich particles abundant in Si, Zn, and S. RWP, mainly composed of Al, Si, Fe, and Ca, can be either part of RRD or internally mixed with emitted TWP. The findings of this study highlight the substantial role of RRD to TRCP emissions under real driving conditions. Consequently, it underscores the importance of examining them simultaneously to achieve a more accurate estimation of on-road traffic emissions beyond the vehicle exhaust.

Role of different mechanisms in pro-inflammatory responses triggered by traffic-derived particulate matter in human bronchiolar epithelial cells

BACKGROUND

Traffic-derived particles are important contributors to the adverse health effects of ambient particulate matter (PM). In Nordic countries, mineral particles from road pavement and diesel exhaust particles (DEP) are important constituents of traffic-derived PM. In the present study we compared the pro-inflammatory responses of mineral particles and DEP to PM from two road tunnels, and examined the mechanisms involved.

METHODS

The pro-inflammatory potential of 100 µg/mL coarse (PM10-2.5), fine (PM2.5-0.18) and ultrafine PM (PM0.18) sampled in two road tunnels paved with different stone materials was assessed in human bronchial epithelial cells (HBEC3-KT), and compared to DEP and particles derived from the respective stone materials. Release of pro-inflammatory cytokines (CXCL8, IL-1α, IL-1β) was measured by ELISA, while the expression of genes related to inflammation (COX2, CXCL8, IL-1α, IL-1β, TNF-α), redox responses (HO-1) and metabolism (CYP1A1, CYP1B1, PAI-2) was determined by qPCR. The roles of the aryl hydrocarbon receptor (AhR) and reactive oxygen species (ROS) were examined by treatment with the AhR-inhibitor CH223191 and the anti-oxidant N-acetyl cysteine (NAC).

RESULTS

Road tunnel PM caused time-dependent increases in expression of CXCL8, COX2, IL-1α, IL-1β, TNF-α, COX2, PAI-2, CYP1A1, CYP1B1 and HO-1, with fine PM as more potent than coarse PM at early time-points. The stone particle samples and DEP induced lower cytokine release than all size-fractionated PM samples for one tunnel, and versus fine PM for the other tunnel. CH223191 partially reduced release and expression of IL-1α and CXCL8, and expression of COX2, for fine and coarse PM, depending on tunnel, response and time-point. Whereas expression of CYP1A1 was markedly reduced by CH223191, HO-1 expression was not affected. NAC reduced the release and expression of IL-1α and CXCL8, and COX2 expression, but augmented expression of CYP1A1 and HO-1.

CONCLUSIONS

The results indicate that the pro-inflammatory responses of road tunnel PM in HBEC3-KT cells are not attributed to the mineral particles or DEP alone. The pro-inflammatory responses seem to involve AhR-dependent mechanisms, suggesting a role for organic constituents. ROS-mediated mechanisms were also involved, probably through AhR-independent pathways. DEP may be a contributor to the AhR-dependent responses, although other sources may be of importance.

Measurement of road traffic brake and tyre dust emissions using both particle composition and size distribution data

Estimates of tyre and brake wear emission factors are presented, derived from data collected from roadside and urban background sites on the premises of the University of Birmingham, located in the UK's second largest city. Size-fractionated particulate matter samples were collected at both sites concurrently in the spring/summer of 2019 and analysed for elemental concentrations and magnetic properties. Using Positive Matrix Factorisation (PMF), three sources were identified in the roadside mass increment of the 1.0-9.9 μm stages of MOUDI impactors located at both sites, namely: brake dust (7.1%); tyre dust (9.6%); and crustal (83%). The large fraction of the mass apportioned to crustal material was suspected to be mainly from a nearby construction site rather than resuspension of road dust. By using Ba and Zn as elemental tracers, brake and tyre wear emission factors were estimated as 7.4 mg/veh.km and 9.9 mg/veh.km, respectively, compared with the PMF-derived equivalent values of 4.4 mg/veh.km and 11 mg/veh.km. Based on the magnetic measurements, an emission factor can be estimated independently for brake dust of 4.7 mg/veh.km. A further analysis was carried out on the concurrently measured roadside increment in the particle number size distribution (10 nm-10 μm). Four factors were identified in the hourly measurements: traffic exhaust nucleation; traffic exhaust solid particles; windblown dust; and an unknown source. The high increment of the windblown dust factor, 3.2 μg/m³, was comparable in magnitude to the crustal factor measured using the MOUDI samples (3.5 μg/m³). The latter's polar plot indicated that this factor was dominated by a large neighbouring construction site. The number emission factors of the exhaust solid particle and exhaust nucleation factors were estimated as 2.8 and 1.9 x 10¹²/veh.km, respectively.