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Record W6983457845

Microplastic particles from roads and traffic : occurrence and concentrations in the environment

2022· dissertation· en· W6983457845 on OpenAlexaboutno aff

Bibliographic record

VenueDuo Research Archive (University of Oslo) · 2022
Typedissertation
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsAbrasion (mechanical)PollutantPollutionRoad dustCadmiumMicroplasticsContaminationRoad traffic
DOInot available

Abstract

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The impact of roads and traffic on the environment have been studied for several decades, and negative impact on both the terrestrial and aquatic environment from pollutants have been demonstrated. Road pollution is typically related to high concentrations of particles, such as mineral particles (quartz, feldspar) and micro- and nanoparticles from the abrasion of tires and road surfaces. In recent years, the research interest for tire and road wear particles have increased substantially due to the increased research interest in micro- and nanoparticle pollution. As tires and some types of road surfaces contain synthetic rubbers, these particles are also included in the plastic pollution terminology as tire wear particles (TWP), tire wear particles combined with mineral particles (TRWP) and road wear particles with polymer-modified bitumen (RWPPMB). Road pollution is also typically high in particle-associated pollutants, such as metals (zinc (Zn), copper (Cu), cadmium (Cd), nickel (Ni), lead (Pb)) and organic micropollutants such as polycyclic aromatic hydrocarbons compounds (PAC), organophosphates compounds (OPC), benzothiazoles, hexa(methoxymethyl)melamine (HMMM) and N-1,3-dimethylbutyl-N 0-phenyl-p-phenylenediamine-quinone (6-PPD-quinone). Especially the acute toxic effect of 6-PPD-quinone and benzothiazoles in the environment have been linked to the release of tire wear particles. Roads and traffic are estimated as the largest source of microplastic particles (MP) from land to the marine environment, and TWP are estimated to be the main microplastic source, with abrasion particles from road markings (RMP) and RWPPMB as the second and third source.\nRoad transport is an essential part of modern society and predictions estimate that the number of vehicles will almost double over the next 30 years. It is therefore crucial for the environment on the planet that road-associated microplastic particles (RAMP) are assessed and mitigated. To reliably assess the levels of RAMP and ensure correct and efficient mitigation measures, there is an urgent need for more environmental data. However, comparisons between current available data on RAMP are hampered by the lack of standardized methods for both sampling and analysis. There are currently several initiatives in the research community and on governmental level to harmonize the assessments of MP, such as Horizon2020-project EUROqCHARM (https://www.euroqcharm.eu/en). There are also efforts made to unify the analytical methods for TWP/TRWP specifically, such as the European TRWP Platform (https://www.csreurope.org/trwp). At the national level, several countries have implemented action plans against plastic pollution. In the National Transport Plan (NTP, 2022-2033), the Norwegian government have incorporated the need to improve the knowledge of microplastic release from roads and traffic and how to reduce the negative impact on the environment. The presented thesis aimed to contribute knowledge on the sources of MPs from roads and traffic, on the occurrence and concentrations of microplastic in different environmental compartments and to assess possible remedial actions for road-associated microplastic particles. \nTo investigate potential new sources of RAMP, the microplastic concentrations in road de-icing salt from both sea salt and rock salt sources were assessed, and the annual release of microplastic particles from road de-icing salt in Scandinavia was estimated (Paper I). The results demonstrated that MPs are present in road de-icing salt in Scandinavia, however the contribution was negligible compared to the other three sources of RAMP previously identified. The annual release of MPs from road de-icing salt was estimated to contribute to less than 0.003% of the total estimated microplastic release, compared to TWP (90%), RM (9%) and RWPPMB (0.5%). However, the results support the need to identify and assess all sources of RAMP in order to evaluate the realistic levels of microplastic pollution and to reduce the negative impact on the environment. Although the work of this thesis focused on the high road salt consumption in Scandinavia, high salt consumption is also observed in several other countries, such as Germany, the United Kingdom, Ireland, the US, Canada and China. As different road de-icing salts are used in different countries, future research should assess the microplastic levels in the salts used locally to realistically address the annual release of microplastic particles from this source.\nOne of the challenges with describing the environmental impact of MPs, including TWP and RWPPMB, is knowing what the relevant environmental concentrations are. Thus, reliable and comparable quantification methods must be developed so that these levels can be assessed across different studies in time and space, and between different environmental compartments. Current available literature presents several different analytical methods for analysing RAMP, mostly focused on TWP. For single-particle analysis, methods such as Fourier Transform Infrared Spectroscopy (FTIR) and Raman Spectroscopy, Scanning Electron Microscopy with Energy Dispersive X-Ray Analysis (SEM-EDX) and Micro-X-ray Fluorescence (µXRF) have been utilized. For mass concentration analysis, Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Liquid-Chromatography Mass Spectrometry (LC-MS/MS), Thermal Desorption Gas Chromatography Mass Spectroscopy (TED-GC/MS) and Pyrolysis Gas Chromatography Mass Spectroscopy (PYR-GC/MS) are the most used methods in current literature. For mass-based analysis, the challenge is finding suitable marker compounds that are reliable for both reference material and environmental samples, stable in different types of matrices and accessible for a high throughput of samples in order to establish environmental concentration levels across different types of samples. For PYR-GC/MS, the International Organization for Standardization (ISO) has published two technical specifications for quantifying TWP/TRWP in soil/sediment and air samples. However, these methods are currently not adjusted for the presence of synthetic rubbers in the road surface wear layer (RWPPMB), such as styrene butadiene styrene rubbers (SBS) or scrap tires, which as applied in many countries for roads with high traffic volume. As SBS is currently the only rubber added to RWPPMB on state and county roads as well as some municipality roads of Norway, the current thesis aimed to improved quantification methods for TWP/TRWP and RWPPMB. \nThe improved method (Paper II) proposed in this thesis utilizes multiple pyrolysis markers for the quantification of styrene butadiene rubber (SBR) and butadiene rubber (BR) from tires and SBS from the road surface in environmental samples. The suggested markers were benzene (mz 78), α-methylstyrene (mz 117), ethylstyrene (mz 118) and butadiene trimer (mz 91). The proposed markers substantially lowered the standard deviation of the results to 40% s.d. compared to 62% (4-VCH), 77% (SB dimer) and 85% (SBB trimer) for the single marker compounds proposed in previous studies. The multiple pyrolysis markers also demonstrated good recoveries in complex road matrices (88–104%), which further validated the strength of the method. The proposed method also included an improved calculation step from the measured rubber concentration to the mass of TWP and RWPPMB. This step included the use of local emission factors, traffic data and locally relevant reference tires. The calculations were performed with Monte Carlo simulation. The use of Monte Carlo simulation also enabled the uncertainties related to these calculations to be reported. Incorporating assessments of the uncertainty is important, as the rubber concentration in commercial tires are highly variable. The average percentage of SBR+BR rubbers in personal and heavy vehicles tires reported in the current thesis were 31% (of total tire tread) and 33%, respectively. However, for personal vehicles and heavy vehicles, the variation between different tire types and brands were large. These results differ substantially from previous studies were 40-50% SBR+BR have been assumed for all personal vehicle tires and 50% natural rubber (NR) have been proposed for truck tires. Thus, the use of locally relevant reference tires will improve the quantification of TWP in environmental samples. As TWP in the environment are exposed to other road particles on the road surface, tire wear particles are often reported as agglomerate particles mixed with mineral particles from the road, defined as tire and road wear particles (TRWP). Based on a limited number of studies, previous quantification methods assume that all TRWP particles contain TWP and minerals in a 1:1 ratio. In the present thesis, we propose an improved method for calculating TRWP based on the concentration of TWP and the current data available on mineral content for TRWP. The calculations for TRWP are also performed with Monte Carlo simulation. Even though the proposed method is hampered by the limited knowledge on mineral content of TRWP, it demonstrates the possibility to optimize quantification methods for locally relevant data, such as different road surfaces, different driving patterns or other variables, as future publications contribute with improved data. \nThe improved quantification methods for TWP, RWPMB and TRWP were further used to analyse the concentration levels in roadside snow (Paper III) and in different compartments of a road tunnel (Paper IV). The TWP concentrations in roadside snow (76.0–14 500 mg/L meltwater; 222–109 000 mg/m2 mass loads) far exceeded concentration levels reported for snow and road runoff in previous studies, as well as the concentrations reported for tunnel wash water (TWW) in the present thesis (untreated: 14.5-47.8 mg/L; treated: 6.78-29.4 mg/L). As concentrations of RWPMB had not been assessed in previous studies, only comparison between the roads

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.238
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
Has abstractyes

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