Traceable determination of metal composition of tyres using tandem ICP-MS and benchmarking of emissions inventories.

Non-exhaust emissions are becoming of increasing significance with respect to total particulate matter (PM) concentrations in ambient air. Of particular interest is the metal content of this PM since metallic compounds are well known to have toxic effects on human health and the environment. In this study, 'bottom-up' annual tyre wear emission rates were estimated and compared to top-down' emissions declared by the UK; it was calculated that between 14 and 25 tonnes of Zn entered the atmosphere in PM10 in 2020. The emission rates were estimated using a cost-effective, simple but robust validated method for analysis of the metals in tyres using tandem inductively coupled plasma mass spectrometry (ICP-MS/MS) for the first time, involving minimal offline sample preparation. This method was applied to five different tyre makes and brands, all available for sale in the UK, and the uncertainty of each measurement was determined. Traceability was ensured in all methods and novel validation techniques were applied due to lack of available reference materials. Zn was found to be the largest metal component in all tyres with a mass fraction of approximately 10 mg g-1. The mean mass fractions of metals in the tyres decreased in the order of Zn > Al > Fe > Mg > Ti > Pb > Cu > Ba > Ni. Significant differences in composition were found between the five tyres. The relative expanded uncertainties of the metals measurements ranged from 4 to 21%, with elements of higher mass fraction resulting in lower uncertainties. These findings will contribute to assessing current and future air quality challenges and will help to inform regulation surrounding non-exhaust emissions.

Multi-element analysis of tyre rubber for metal tracers.

The purpose of this study was to identify a characteristic elemental tyre fingerprint that can be utilised in atmospheric source apportionment calculations. Currently zinc is widely used as a single element tracer to quantify tyre wear, however several authors have highlighted issues with this approach. To overcome this, tyre rubber tread was digested and has been analysed for 25 elements by ICP-MS to generate a multielement profile. Additionally, to estimate the percentage of the tyre made up of inert fillers, thermogravimetric analysis was performed on a subset. Comparisons were made between passenger car and heavy goods vehicle tyre composition, and a subset of tyres had both tread and sidewall sampled for further comparison. 19 of the 25 elements were detected in the analysis. The mean mass fraction of zinc detected was 11.17 g/kg, consistent with previous estimates of 1% of the tyre mass. Aluminium, iron, and magnesium were found to be the next most abundant elements. Only one source profile for tyre wear exists in both the US and EU air pollution species profile databases, highlighting the need for more recent data with better coverage of tyre makes and models. This study provides data on new tyres which are currently operating on-road in Europe and is therefore relevant for ongoing atmospheric studies assessing the levels of tyre wear particles in urban areas.

Identifying trends in ultrafine particle infiltration and carbon dioxide ventilation in 92 vehicle models.

There has been ongoing research aimed at reducing pollution concentrations in vehicles due to the high exposure which occurs in this setting. These studies have found using recirculate (RC) settings substantially reduces in-cabin traffic-related pollution concentrations but possibly leads to an adverse accumulation of carbon dioxide (CO2) from driver respiration. The aim of this study was to highlight how vehicle models and ventilation settings affect in-cabin concentrations to ultrafine particles (UFP) and CO2 in real-world conditions. We assessed the ability of different vehicles to balance reductions in UFP against the build-up of in-cabin CO2 concentrations by measuring these pollutants concurrently both inside and outside the vehicle to derive an in/out ratio. When ventilation settings were set to RC, UFP concentrations inside the vehicles (median: 3205 pt./cm3) were 86% lower compared to outside air (OA) (23,496 pt./cm3) across a 30-min real-world driving route. However, CO2 concentrations demonstrated a rapid linear increase under RC settings, at times exceeding 2500 ppm. These concentrations have previously been associated with decreased cognitive performance. Our study did not find an effect of gasoline fuelled vehicles affecting in-cabin UFP levels compared to hybrid or electric vehicles, suggesting that self-pollution was not an issue. We also found that certain vehicle models were better at reducing both in-cabin UFP and CO2 concentrations. The results suggest that under RC settings in/out CO2 ratios are largely determined by the leakiness of the vehicle cabin, whereas in/out UFP ratios are primarily determined by the efficacy of the in-built air filter in the vehicles ventilation system.

Taxi drivers' exposure to black carbon and nitrogen dioxide in electric and diesel vehicles: A case study in London.

Nitrogen dioxide (NO2) and black carbon (BC) concentrations were measured inside London taxicabs across 40 work shifts in a real-world occupational study. The shifts were measured across five plug-in hybrid range-extender electric taxicabs (TXe City) and five diesel taxicabs (TX4 Diesel). The aim of this study was to characterise the impact of fuel and cabin design on professional drivers' air pollution exposures. Personal exposure was monitored using portable BC, NO2 and GPS devices. A controlled study replicating a typical taxi drivers' route in central London was conducted. Simultaneous inside and outside BC concentrations were measured to assess infiltration rates. The drivers were instructed to keep the BC devices with them at all times, providing a comparison of exposures at work and outside of work. The driver's average BC and NO2 exposure while working was nearly twice as high for diesel taxicab drivers (6.8 ± 7.0 µg/m³, 101.9 ± 87.8 µg/m³) compared with electric drivers (3.6 ± 4.9 µg/m³, 55.3 ± 53.0 µg/m³, respectively). The exposure to BC while not working was 1.6 µg/m³ for diesel drivers and 1.1 µg/m³ for electric drivers, highlighting the very high exposures experienced by this occupational sector. The analysis of vehicle type on BC concentrations showed that the airtight cabin design and presence of an in-built filter in the electric TXe City reduced the exposure to BC substantially; indoor to outdoor ratios being 0.63 on the electric taxi compared to 0.99 on the diesel taxi with recirculate ventilation mode off and 0.07 to 0.44 with recirculate on. These findings provide important evidence for occupational health of professional drivers through exposure reduction measures in vehicle design.

Modelling of instantaneous emissions from diesel vehicles with a special focus on NOx: Insights from machine learning techniques.

Accurate instantaneous vehicle emissions models are vital for evaluating the impacts of road transport on air pollution at high temporal and spatial resolution. In this study, we apply machine learning techniques to a dataset of 70 diesel vehicles tested in real-world driving conditions to: (i) cluster vehicles with similar emissions performance, and (ii) model instantaneous emissions. The application of dynamic time warping and clustering analysis by NOx emissions resulted in 17 clusters capturing 88% of trips in the dataset. We show that clustering effectively groups vehicles with similar emissions profiles, however no significant correlation between emissions and vehicle characteristics (i.e. engine size, vehicle weight) were found. For each cluster, we evaluate three instantaneous emissions models: a look-up table (LT) approach, a non-linear regression (NLR) model and a neural network multi-layer perceptron (MLP) model. The NLR model provides accurate instantaneous NOx predictions, on par with the MLP: relative errors in prediction of emission factors are below 20% for both models, average fractional biases are -0.01 (s.d. 0.02) and -0.0003 (s.d. 0.04), and average normalised mean squared errors are 0.25 (s.d. 0.14) and 0.29 (s.d. 0.16), for the NLR and MLP models respectively. However, neural networks are better able to deal with vehicles not belonging to a specific cluster. The new models that we present rely on simple inputs of vehicle speed and acceleration, which could be extracted from existing sources including traffic cameras and vehicle tracking devices, and can therefore be deployed immediately to enable fast and accurate prediction of vehicle NOx emissions. The speed and the ease of use of these new models make them an ideal operational tool for policy makers aiming to build emission inventories or evaluate emissions mitigation strategies.

Real world CO2 and NOx emissions from 149 Euro 5 and 6 diesel, gasoline and hybrid passenger cars.

In this study CO2 and NOx emissions from 149 Euro 5 and 6 diesel, gasoline and hybrid passenger cars were compared using a Portable Emissions Measurement System (PEMS). The models sampled accounted for 56% of all passenger cars sold in Europe in 2016. We found gasoline vehicles had CO2 emissions 13-66% higher than diesel. During urban driving, the average CO2 emission factor was 210.5 (sd. 47) gkm-1 for gasoline and 170.2 (sd. 34) gkm-1 for diesel. Half the gasoline vehicles tested were Gasoline Direct Injection (GDI). Euro 6 GDI engines <1.4ℓ delivered ~17% CO2 reduction compared to Port Fuel Injection (PFI). Gasoline vehicles delivered an 86-96% reduction in NOx emissions compared to diesel cars. The average urban NOx emission from Euro 6 diesel vehicles 0.44 (sd. 0.44) gkm-1 was 11 times higher than for gasoline 0.04 (sd. 0.04) gkm-1. We also analysed two gasoline-electric hybrids which out-performed both gasoline and diesel for NOx and CO2. We conclude action is required to mitigate the public health risk created by excessive NOx emissions from modern diesel vehicles. Replacing diesel with gasoline would incur a substantial CO2 penalty, however greater uptake of hybrid vehicles would likely reduce both CO2 and NOx emissions. Discrimination of vehicles on the basis of Euro standard is arbitrary and incentives should promote vehicles with the lowest real-world emissions of both NOx and CO2.