Taxonomy of factors which influence heavy metal build-up on urban road surfaces

Liu, An; Gunawardana, Chandima; Gunawardena, Janaka; Egodawatta, Prasanna; Ayoko, Godwin A.; Goonetilleke, Ashantha

doi:10.1016/j.jhazmat.2016.02.026

Cited by 58 publications

(9 citation statements)

References 30 publications

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“…Several factors have been pointed out as responsible of road dust build-up (i.e. emission potential): pavement characteristics (texture, mineralogy, age (Amato et al, 2013;Denby et al, 2013b;China and James, 2012;Berger and Denby, 2011;Gehrig et al, 2010;Gustafsson et al, 209;Raisanen et al, 2005), traffic intensity and speed, fleet composition (Bukowiecki et al, 2010;Liu et al, 2016), proximity to traffic lights, but also the presence of external sources (e.g. construction dust, unpaved areas, African dust deposition, etc.…”

Section: Introductionmentioning

confidence: 99%

An empirical model to predict road dust emissions based on pavement and traffic characteristics

Padoan

Ajmone-Marsan

Querol

et al. 2018

Environmental Pollution

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The relative impact of non-exhaust sources (i.e. road dust, tire wear, road wear and brake wear particles) on urban air quality is increasing. Among them, road dust resuspension has generally the highest impact on PM concentrations but its spatio-temporal variability has been rarely studied and modeled. Some recent studies attempted to observe and describe the time-variability but, as it is driven by traffic and meteorology, uncertainty remains on the seasonality of emissions. The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pavement characteristics, traffic intensity and speed, fleet composition, proximity to traffic lights, but also the presence of external sources. However, no parameterization is available as a function of these variables. We investigated mobile road dust smaller than 10 μm (MF10) in two cities with different climatic and traffic conditions (Barcelona and Turin), to explore MF10 seasonal variability and the relationship between MF10 and site characteristics (pavement macrotexture, traffic intensity and proximity to braking zone). Moreover, we provide the first estimates of emission factors in the Po Valley both in summer and winter conditions. Our results showed a good inverse relationship between MF10 and macro-texture, traffic intensity and distance from the nearest braking zone. We also found a clear seasonal effect of road dust emissions, with higher emission in summer, likely due to the lower pavement moisture. These results allowed building a simple empirical mode, predicting maximal dust loadings and, consequently, emission potential, based on the aforementioned data. This model will need to be scaled for meteorological effect, using methods accounting for weather and pavement moisture. This can significantly improve bottom-up emission inventory for spatial allocation of emissions and air quality management, to select those roads with higher emissions for mitigation measures.

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Section: Introductionmentioning

confidence: 99%

An empirical model to predict road dust emissions based on pavement and traffic characteristics

Padoan

Ajmone-Marsan

Querol

et al. 2018

Environmental Pollution

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“…In the Cu-Zn-Cd-Sb-Pb group, previous studies reported that Cu and Zn were not only related to industrial emissions, but also to agricultural fertilizers [ 53 , 54 ]. The elements Cd and Pb may primarily come from traffic pollutants and accumulate dust [ 55 , 56 ]. In our results, the source of Pb was different from other heavy metals along different urbanization gradients, from which it could be inferred that the source of Pb was always transportation.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Heavy Metal Sources in the Soil of Riverbanks Across an Urbanization Gradient

Zuo

Dai

et al. 2018

IJERPH

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Regional soil quality issues arising from rapid urbanization have received extensive attention. The riverbank that runs through a city is representative of urbanization gradient transformation. Thirty soil samples in the Yangtze River Delta urban agglomeration were collected and analyzed for the concentrations of seven analytes. Correlation, principle component analysis, cluster analysis and GeoDetector models suggested that the four groups (Cr-Ni-Cu, Cu-Zn-As-Sb, Cd and Pb) shared the same sources in the core urban region; five groups (Cr-Ni-Cu-Zn, As, Cd, Sb and Pb) in the suburbs and three groups (Cr-Ni, Cu-Zn-Cd-Sb-Pb and As) in the exurbs. GeoDetector methods not only validated the results of the three other methods, but also provided more possible impact factors. Besides the direct influences, the interaction effects among factors were quantified. Interactive combination with strong nonlinear increment changed from between-two-weak factors in the central region to between-strong-and-weak factors in the suburbs. In the exurbs, the stronger interaction effects were observed between strong and weak factors. Therefore, the GeoDetector model, which provided more detailed information of artificial sources could be used as a tool for identifying the potential factors of toxic elements and offering scientific basis for the development of subsequent pollution reduction strategies.

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“…The deposition fluxes on traffic areas are strongly influenced by site-specific factors [69]. For example, the absence of curbs, hard shoulders, and noise barriers are site-specific influences that have a positive effect on reducing runoff concentrations by decreasing the deposition rates and the subsequent wash-off [41,70].…”

Section: Annual Variations and Climatic Site-specific Influencesmentioning

confidence: 99%

“…Section 3.2.2). Nevertheless, not all traffic related emissions are found in the runoff because of wind turbulences, vehicles (traffic volume and vehicle speed), antecedent dry periods, and other site-specific factors (e.g., particle size distribution, land use, road surface roughness, or elevated configuration of the traffic area; [69]). For example, Hallberg et al [90] found out that splashing corresponds with the average vehicle speed during storms.…”

Section: Transportation Of Traffic Related Heavy Metalsmentioning

confidence: 99%

Stormwater Management: Calculation of Traffic Area Runoff Loads and Traffic Related Emissions

Huber¹

2016

Water

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Metals such as antimony, cadmium, chromium, copper, lead, nickel, and zinc can be highly relevant pollutants in stormwater runoff from traffic areas because of their occurrence, toxicity, and non-degradability. Long-term measurements of their concentrations, the corresponding water volumes, the catchment areas, and the traffic volumes can be used to calculate specific emission loads and annual runoff loads that are necessary for mass balances. In the literature, the annual runoff loads are often specified by a distinct catchment area (e.g., g/ha). These loads were summarized and discussed in this paper for all seven metals and three types of traffic areas (highways, parking lots, and roads; 45 sites). For example, the calculated median annual runoff loads of all sites are 355 g/ha for copper, 110 g/ha for lead (only data of the 21st century), and 1960 g/ha for zinc. In addition, historical trends, annual variations, and site-specific factors were evaluated for the runoff loads. For Germany, mass balances of traffic related emissions and annual heavy metal runoff loads from highways and total traffic areas were calculated. The influences on the mass fluxes of the heavy metal emissions and the runoff pollution were discussed. However, a statistical analysis of the annual traffic related metal fluxes, in particular for different traffic area categories and land uses, is currently not possible because of a lack of monitoring data.

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Taxonomy of factors which influence heavy metal build-up on urban road surfaces

Cited by 58 publications

References 30 publications

An empirical model to predict road dust emissions based on pavement and traffic characteristics

An empirical model to predict road dust emissions based on pavement and traffic characteristics

Analysis of Heavy Metal Sources in the Soil of Riverbanks Across an Urbanization Gradient

Stormwater Management: Calculation of Traffic Area Runoff Loads and Traffic Related Emissions

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