Project description
Identifying high-emitting vehicles without number plate data
Vehicle emissions continue to be an important factor concerning public health. The problem is linked to high-emitting vehicles and non-exhaust emission sources such as brakes and tires. Point sampling is an affordable method for determining emissions, but it may raise privacy issues because of number plates. Supported by the Marie Skłodowska-Curie Actions programme, the RHive-ML project will distinguish combustion emissions from non-exhaust emissions by correlating particle metrics (PN, BC) with CO2 levels. This correlation will help identify vehicles that emit only non-exhaust particles, such as electric vehicles. Machine learning models will analyse combustion-based emissions to determine fuel type and emission standards without the need for number plate readings. The project aims to create a privacy-respecting framework for real-time detection of high emitters.
Objective
Air pollution remains a leading environmental health risk, with road traffic a major contributor. Although strict regulations have reduced emissions from new vehicles, two persistent challenges undermine progress: a minority of high emitters caused by malfunctioning or tampered aftertreatment systems, and the rising contribution of non-exhaust emissions from brakes, tyres, and road dust. Point Sampling (PS) provides a cost-effective, accurate approach to capture real-world emissions, but its use is constrained by the need to distinguish combustion-based from non-exhaust particles and by reliance on number plate data, which raises privacy concerns. This project will overcome these limitations by separating combustion from non-exhaust emissions through correlation of particle metrics (PN, BC) with CO2, where strong correlation signals combustion-related sources. Based on this separation, vehicles that only emit non-exhaust particles can be identified as electric vehicles (EVs). Machine learning (ML) models will then be developed on combustion-based emissions to infer fuel type and emission standard without number plate readings, and to translate PS snapshots into representative average emissions to identify high emitters while distinguishing them from Diesel Particulate Filter regeneration events. The models will be trained on detailed emission profiles derived from chassis dynamometer and Portable Emission Measurement System data and applied to PS datasets from European field campaigns, with robustness and interpretability ensured through state-of-the-art ML algorithms and interpretable ML methods. The outcome will be a privacy-respecting, PS-based framework for large-scale, real-time detection of high emitters, enabling stronger enforcement of emission standards, effective mitigation of traffic-related air pollution, reliable identification of EVs from non-exhaust-only signatures, and future-proofing of European emission control policies.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences social geography transport electric vehicles
- engineering and technology environmental engineering energy and fuels
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2025-PF
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8010 Graz
Austria
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