Urban air quality: The challenge of traffic non-exhaust emissions

Amato, Fúlvio; Cassee, Flemming R.; Gon, Hugo Denier van der; Gehrig, Robert; Gustafsson, Mats; Hafner, Wolfgang; Harrison, Roy M.; Jozwicka, Magdalena; Kelly, Frank J.; Moreno, Teresa; Prévôt, Andrê S. H.; Schaap, Martijn; Sunyer, Jordi; Querol, Xavier

doi:10.1016/j.jhazmat.2014.04.053

Cited by 343 publications

(148 citation statements)

References 63 publications

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“…These are mostly heavy metals which are often used as tracers for determining the amount of brake wear in PM. The reason for this is that brake wear is a major source for some metals, while tyre wear contributes the least of the non-exhaust sources [50]. An important source of non-exhaust PM, which is not addressed in this paper, is resuspended road dust.…”

Section: Non-exhaust Emissionsmentioning

confidence: 99%

“…The reason for its exclusion is that it is difficult to quantify and is submissive to many external factors such as season, precipitation and road moisture content. For the same reason the resuspension part is not dealt with in emission inventory reporting in the framework of the Convention on Long-Range Transboundary Air Pollution (CLRTAP), although it dominates PM emissions in some countries [50]. Typically, brake wear particles account for 16%-55% by mass of the generated PM10 emissions by passenger cars in urban environments and normally represents a unimodal size distribution of PM10 with peaks varying from 2 to 6 μm.…”

Section: Non-exhaust Emissionsmentioning

confidence: 99%

“…No such conclusions have been made for the non-exhausted share of traffic-related PM, although it contributes as much and often more than tailpipe exhaust to the ambient air PM concentrations in cities [50]. Regarding the health impact of PM, the most relevant properties of the particles are (1) the size distribution; (2) the surface area; (3) the chemical composition; and (4) the agglomeration state.…”

Section: Health Impact Of Pmmentioning

confidence: 99%

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Environmental Analysis of Petrol, Diesel and Electric Passenger Cars in a Belgian Urban Setting

et al. 2016

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Abstract:The combustion of fossil fuels in the transport sector leads to an aggravation of the air quality along city roads and highways. Urban air quality is a serious problem nowadays as the number of vehicles increases on a yearly basis. With stricter Euro emission regulations, vehicle manufacturers are not meeting the imposed limits and are also disregarding the non-exhaust emissions. This paper highlights the relevance of non-exhaust emissions of passenger vehicles, both conventional (diesel and petrol) or electric vehicles (EV), on air quality levels in an urban environment in Belgium. An environmental life cycle assessment was carried out based on a real-world emission model for passenger cars and fuel refinery data. A cut-off was applied to the models to highlight what emissions, both from the refinery to the exhaust and electricity production for EV, do actually occur within Belgium's borders. Results show that not much progress has been made from Euro 4 to 6 for conventional vehicles. Electric vehicles pose the best alternative solution as a more environmentally friendly means of transportation. The analysis results target policy makers with the intention that regulations and policies would be developed in the future and target the characterization of non-exhaust emissions from vehicles. These results indicate that EVs offer a valid solution for addressing the urban air quality issue and that non-exhaust emissions should be addressed in future regulatory steps as they dominate the impact spectrum.

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Section: Non-exhaust Emissionsmentioning

confidence: 99%

Section: Non-exhaust Emissionsmentioning

confidence: 99%

Section: Health Impact Of Pmmentioning

confidence: 99%

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Environmental Analysis of Petrol, Diesel and Electric Passenger Cars in a Belgian Urban Setting

et al. 2016

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“…In general, motor traffic emits both secondary and primary aerosols [8,61,62]. However, particles are underlying several aging processes, like e.g., the processes of coagulation or impaction [42], and therefore accrue over time.…”

Section: Interpretation Of Ann Model Resultsmentioning

confidence: 99%

Modelling of Urban Near-Road Atmospheric PM Concentrations Using an Artificial Neural Network Approach with Acoustic Data Input

et al. 2017

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Air quality assessment is an important task for local authorities due to several adverse health effects that are associated with exposure to e.g., urban particle concentrations throughout the world. Based on the consumption of costs and time related to the experimental works required for standardized measurements of particle concentration in the atmosphere, other methods such as modelling arise as integrative options, on condition that model performance reaches certain quality standards. This study presents an Artificial Neural Network (ANN) approach to predict atmospheric concentrations of particle mass considering particles with an aerodynamic diameter of 0.25-1 μm (PM(0.25-1)), 0.25-2.5 μm (PM(0.25-2.5)), 0.25-10 μm (PM(0.25-10)) as well as particle number concentrations of particles with an aerodynamic diameter of 0.25-2.5 μm (PNC(0.25-2.5)). ANN model input variables were defined using data of local sound measurements, concentrations of background particle transport and standard meteorological data. A methodology including input variable selection, data splitting and an evaluation of their performance is proposed. The ANN models were developed and tested by the use of a data set that was collected in a street canyon. The ANN models were applied furthermore to a research site featuring an inner-city park to test the ability of the approach to gather spatial information of aerosol concentrations. It was observed that ANN model predictions of PM(0.25-10) and PNC(0.25-2.5) within the street canyon case as well as predictions of PM(0.25-2.5), PM(0.25-10) and PNC(0.25-2.5) within the case study of the park area show good agreement to observations and meet quality standards proposed by the European Commission regarding mean value prediction. Results indicate that the ANN models proposed can be a fairly accurate tool for assessment in predicting particle concentrations not only in time but also in space.

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“…Road traffic is an important source of atmospheric particulate matter in urban and industrialized areas (e.g., Thorpe and Harrison, 2008;Wik and Dave, 2009;Franco et al, 2013;Kumar et al, 2013;Pant and Harrison, 2013;Amato et al, 2014;Grigoratos and Martini, 2015;Suvarapu and Baek, 2016;Timmers and Achten, 2016). It includes exhaust and non-exhaust emissions.…”

Section: Introductionmentioning

confidence: 99%

Characterization and preliminary risk assessment of road dust collected in Venice airport (Italy)

Valotto

Zannoni

Rampazzo

et al. 2018

Journal of Geochemical Exploration

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Road dust is a non-exhaust source that can significantly contribute to atmospheric particulate by resuspension. Beside the issue of the overcoming of guidelines limits for the air quality, the characterization of this matrix is of crucially high interest for the inherent toxicity of resuspended particles, that can act as carriers of heavy metals and toxic-carcinogenic components.In this paper, road dust collected in the Venice international airport is characterized with a multi-technique approach in order to identify the main pollutant sources and to highlight the differences between airside/ landside places. The most polluted sites are identified and a preliminary risk assessment is performed taking into account ingestion, dermal intake and inhalation of fugitive particulate pathways. Specifically, the main sources are related to construction activities, anti-icing safety procedures, and brake, tire and road wear; streets are the most polluted landside places; PAHs, Na, Al, Cu, Zn, Ag, Cd are more concentrated in airside area; as regards the risk assessment As, BaP, Cr, Sb, BaA, and BbF are the most critical pollutants.

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Urban air quality: The challenge of traffic non-exhaust emissions

Cited by 343 publications

References 63 publications

Environmental Analysis of Petrol, Diesel and Electric Passenger Cars in a Belgian Urban Setting

Environmental Analysis of Petrol, Diesel and Electric Passenger Cars in a Belgian Urban Setting

Modelling of Urban Near-Road Atmospheric PM Concentrations Using an Artificial Neural Network Approach with Acoustic Data Input

Characterization and preliminary risk assessment of road dust collected in Venice airport (Italy)

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