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Record W6888029824 · doi:10.18154/rwth-2022-11764

Pollution in urban environments: levels and profiles of traffic and other organic contaminants in street run-off and atmospheric particles

2022· article· en· W6888029824 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRWTH Publications (RWTH Aachen) · 2022
Typearticle
Languageen
FieldMedicine
TopicMedicinal Plant Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPollutantStormwaterPollutionContaminationSurface runoffParticulatesUrban runoffAir pollutionDeposition (geology)

Abstract

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The urban space is a central point for numerous anthropogenic activities including mobility, settlement, use of resources, and the production of goods, chemicals, waste, wastewater as well as emissions. Through all these activities, the individual compartments making up the urban environment such as air, water, vegetation etc. can all become polluted in different ways.Urban pollutants, including particulate matter and the associated organic pollutants, enter the environment through various pathways. These are then distributed across different environmental matrices where they can potentially cause harm. Possible entry pathways into urban environments include atmospheric emissions (e.g., from fuel combustion) and subsequent deposition processes; abrasion of road surfaces and tires; leaching and release from car components, building material or residential interiors; and various urban land use activities. A significant part of these contaminants eventually ends up in surface runoff which is then collected by street drains. Therefore, during heavy rain events, complex mixtures of pollutants enter the sewage system and if these are not adequately treated in stormwater treatment plants, may significantly contribute to contamination of aquatic environments. Numerous factors, such as climatological conditions, traffic characteristics and the nature of urban structures, influence pollution in urban environments such that these vary in space and time. This then highlights the need for site-specific investigations.This thesis aimed to develop and apply suitable tools and methods to sample and characterize organic pollutants in different environmental compartments within a medium sized town. Pollutant mixtures were sampled from various sites that had been identified as being particularly impacted by traffic. A broad spectrum of organic substances was measured in both airborne particles and in the aqueous and particulate fraction of road runoff. The overall goal was to better understand the sources and nature of this urban pollution, in order to better assess its environmental impact. To study airborne particles, a multidisciplinary approach was applied. First, the spatio-temporal distribution and size distribution of atmospheric particles were detected using a mobile platform. Subsequently, particles of inhalable size (≤2.5 µm; PM2.5) were collected at those sites that were found to be particularly polluted. These were analyzed for an extended set of 48 polycyclic aromatic hydrocarbons (PAHs) using Gas chromatography-mass spectrometry (GC-MS). Measured PM2.5 mass concentrations were relatively low (mean 3.2 μg/m3) but did show spatial variations. They were dominated by small particles that are particularly relevant for human health, due to their ability to travel deep into the lungs or even the bloodstream. At some sites cases exceeded the WHO recommended annual mean value of 5 µg/m3. The broader spectrum of PAHs measured in the atmospheric particles allowed the PAH source to be classified as being predominantly pyrogenic (this was illustrated by the slope-shaped alkylated distribution pattern). It also allowed additional assumptions to be made, e.g. about the combustion source or the age of the air mass. Moreover, it was shown that in urban particulate matter the non-EPA PAHs (in particular the dibenzopyrene isomers and 7H-benzo[c]fluorene) made a high contribution of about 70-80% to the total PAH toxicity. These findings could not have been made based on the more commonly applied analysis of the 16 US-EPA PAHs. Therefore, analysis of a similar, extended set of PAHs should definitely be included in future studies. For the determination of organic contaminants in road runoff, the first step was developing an appropriate sampling trap to locally collect runoff entering road drains at several locations in the study area. This included sampling of the particulate fraction, but also the aqueous fraction via a combination of grab sampling and the exposure of integrative passive samplers composed of silicone sheets. Sampling was done over a period of one month. A novel calibration method for the kinetic passive sampling of dissolved organic contaminants in the water phase was developed. This method is based on measuring the mass ratio of the various contaminants in two passive samplers deployed in parallel with different surface-to-volume ratios. This contaminant mass ratio (CMR) calibration was first tested in the laboratory under controlled conditions, and then confirmed by passive sampling in highway and urban road runoff. It allowed the reliable determination of bioavailable and dissolved concentrations of PAHs covering a hydrophobicity range of log KOW 3.8-7.3. The CMR calibration can be applied as a stand-alone approach, or as a complementary approach to the more common calibration method based on performance reference compounds (PRCs). Application of the trap to sample road runoff provided a comprehensive insight into the presence and concentrations of a broad spectrum of PAHs (via GC-MS analysis) and polar organic pollutants (via LC-HRMS analysis) in the water and particle fractions. Additional characterization of the particulate fraction via microscopic and chemical analyses provided further insights into its characteristics and sources. The road runoff samples contained high proportions of traffic-related substances (high ng/L - low µg/L range in water and high µg/kg dw range in sediment). These included rubber and plastic additives, corrosion inhibitors but also industrial chemicals. Pesticides and perfluorinated alkyl substances were found in the runoff in the low ng/L and µg/kg dw range, respectively. In addition, possible influences of human wastewater on the road runoff were indicated by the presence of pharmaceuticals, stimulants and food ingredients. A total of 62 PAHs were measured in the particulate fraction of the runoff at total concentrations of a few mg/kg dw. The PAH alkylation distribution and the degree of aromatic condensation indicated a predominantly pyrogenic PAH source, but with slight petrogenic influences. Interestingly, the PAH alkylation index negatively correlated with the proportion of tire particles in the sediment as determined by microscopy. Again, non-EPA PAHs accounted for 65-80% of the total PAH toxicity. In this context, it appears valuable to further investigate whether the TEQtotal / TEQEPA ratio of 3-5 that was observed in all samples is typical of urban, predominantly traffic-related particle samples. If this factor is constant, then more accurate estimations of toxicity due to PAH mixtures could be made for those data sets where only 16 EPA PAHs have been measured. A comparison of the organic contaminant concentrations in runoff with various limit values from the European Commission and the Canadian Council of Environment Ministers, showed that dilution factors of up to 50 would be required prior to the release of the stormwater into the inland waters in order to remain below the respective regulatory limits. Overall, the importance of monitoring a broad spectrum of organic substances in different environmental matrices, and of constantly revising and expanding the list of priority substances was evident. This is especially true, because some of the measured pollutants occurred at high concentrations and/or frequencies, or are of particular environmental relevance due to their high toxicity/carcinogenicity, mobility or persistence in aquatic environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.252
Teacher spread0.228 · 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