AIMC Topic: Vehicle Emissions

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Machine learning based prediction of the performance and emission characteristics of CRDI diesel engine using diethyl ether and carbon nanotube additives with Spirulina platensis as a third-generation biofuel.

Scientific reports
Alternative fuels are required to provide the world's energy demands due to excessive fossil fuel use, harmful petrol emissions, environmental pollution, growing demand, rising costs, and fossil fuel degradation. The additives are utilized in biodies...

Particle number emissions on mountainous roads: machine learning insights from on-road testing.

Environmental research
Mountainous roads pose unique challenges for controlling vehicular fine particulate number (PN) emissions, a critical pollutant impacting air quality and public health. This study integrates on-road testing with interpretable machine learning to anal...

Air transportation carbon dioxide emission forecasting: An improved back propagation neural network.

PloS one
To address the challenges of increasing carbon dioxide (CO2) emissions and climate change caused by the growth of air traffic, accurate prediction of CO2 emissions in civil aviation has become crucial. This study proposes a CO2 emission prediction me...

Analysis of interactions of particle-associated oxidative potential sources using multilayer perceptron neural networks: A case study in Shenyang, China.

Environmental pollution (Barking, Essex : 1987)
The oxidative potential (OP) of particulate matter (PM) is a possible indicator for assessing the oxidative-imbalance risk caused by PM exposure. The OP contributions of different PM sources exhibit nonlinear relationships, and the specific patterns ...

Using open data to derive parsimonious data-driven models for uncovering the influence of local traffic and meteorology on air quality: The case of Madrid.

Environmental pollution (Barking, Essex : 1987)
Air pollution remains a critical public health and environmental challenge, particularly in urban areas where traffic emissions and meteorological conditions strongly influence air quality. While Machine Learning (ML) techniques have been increasingl...

A method for delineating traffic low emission control zone based on deep learning and multi-objective optimization.

Environmental monitoring and assessment
Current methods for defining traffic low emission control zones (TLEZ) often face limitations that hinder their widespread implementation and effectiveness. This study addresses these challenges by employing a comprehensive approach to analyze PM con...

Assessing the impact of traffic restriction interventions on school air quality: a citizen science-based modelling study.

Environmental research
Air pollution poses a significant threat to human health, especially for the vulnerable groups such as children. Given that schools are central to their daily lives, ensuring good air quality in these environments is crucial. This study evaluates the...

Explainable AI analysis for smog rating prediction.

Scientific reports
Smog poses a direct threat to human health and the environment. Addressing this issue requires understanding how smog is formed. While major contributors include industries, fossil fuels, crop burning, and ammonia from fertilizers, vehicles play a si...

An ML-Enhanced Laser-Based Methane Slip Sensor Using Wavelength Modulation Spectroscopy.

ACS sensors
Natural gas (NG) is a promising alternative to diesel for sustainable transport, potentially reducing GHG and air quality emissions significantly. However, the GHG benefits hinge on managing methane slip, the unburned methane in the exhaust of NG eng...

Machine learning helps reveal key factors affecting tire wear particulate matter emissions.

Environment international
Tire wear particles (TWPs) are generated with every rotation of the tire. However, obtaining TWPs under real driving conditions and revealing key factors affecting TWPs are challenging. In this study, we obtained a TWPs dataset by simulating tire wea...