Situation Model of the Transport, Transport Emissions and Meteorological Conditions

• URL: http://arxiv.org/abs/2509.10541v1
• Date: Sat, 06 Sep 2025 11:02:02 GMT
• Title: Situation Model of the Transport, Transport Emissions and Meteorological Conditions
• Authors: V. Benes, M. Svitek, A. Michalikova, M. Melicherik,
• Abstract summary: This paper focuses on a systemic approach to traffic emissions with their relation to meteorological conditions.<n>The proposed model is based on traffic, meteorology and emission data measured in Prague, Czech Republic.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Air pollution in cities and the possibilities of reducing this pollution represents one of the most important factors that today's society has to deal with. This paper focuses on a systemic approach to traffic emissions with their relation to meteorological conditions, analyzing the effect of weather on the quantity and dispersion of traffic emissions in a city. Using fuzzy inference systems (FIS) the model for prediction of changes in emissions depending on various conditions is developed. The proposed model is based on traffic, meteorology and emission data measured in Prague, Czech Republic. The main objective of the work is to provide insight into how urban planners and policymakers can plan and manage urban transport more effectively with environmental protection in mind.

Related papers

• Tackling air quality with SAPIENS [30.5491665195957]
  Air pollution is a chronic problem in large cities worldwide.<n>Vehicular traffic has been identified as a major contributor to poor air quality.<n>We present an in-depth study of pollution sensor measurements combined with traffic data from Mexico City.
  arXiv  Detail & Related papers  (2026-01-30T17:41:38Z)
• Macroscopic Emission Modeling of Urban Traffic Using Probe Vehicle Data: A Machine Learning Approach [1.2338647942124328]
  Urban congestions cause inefficient movement of vehicles and exacerbate greenhouse gas emissions and urban air pollution.<n>This study is the first to apply machine learning methods to predict the network wide emission rate to traffic relationship in U.S. urban areas at a large scale.
  arXiv  Detail & Related papers  (2025-11-11T19:29:41Z)
• Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling [50.3911487821783]
  Air pollution remains a leading global health and environmental risk, particularly in regions vulnerable to episodic air pollution spikes due to wildfires, urban haze and dust storms.<n>Here, we present SynCast, a high-resolution neural forecasting model that integrates meteorological and air composition data to improve predictions of both average and extreme pollution levels.
  arXiv  Detail & Related papers  (2025-10-28T01:18:00Z)
• Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network [47.699409089023696]
  Air quality prediction plays a crucial role in public health and environmental protection.<n>Existing works underestimate the critical role atmospheric conditions in air quality prediction.<n> MDSTNet is an encoder framework explicitly that captures atmosphere-pollution dependencies for prediction.<n>ChinaAirNet is the first dataset combining air quality records with multi-pressure-level meteorological observations.
  arXiv  Detail & Related papers  (2025-04-14T09:18:11Z)
• AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment [46.56288727659417]
  Air pollution remains a leading global health risk, exacerbated by rapid industrialization and urbanization.<n>We introduce AirCast, a novel multi-variable air pollution forecasting model.<n>AirCast employs a multi-task head architecture that simultaneously forecasts atmospheric conditions and pollutant concentrations.
  arXiv  Detail & Related papers  (2025-02-25T07:34:18Z)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates [49.93577170464313]
  We deal with air quality observations in a city-wide network of ground monitoring stations. We propose a conditioning block that embeds past and future covariates into the current observations. We find that conditioning on future weather information has a greater impact than considering past traffic conditions.
  arXiv  Detail & Related papers  (2024-04-08T09:13:16Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• IMPACT: Integrated Bottom-Up Greenhouse Gas Emission Pathways for Cities [0.0]
  IMPACT pathways integrate technology adoption policies with zoning policies, climate change, and grid decarbonization scenarios. We identify an emission premium for sprawling and show that adverse policy combinations exist that can exhibit rebounding emissions over time.
  arXiv  Detail & Related papers  (2022-01-31T02:30:39Z)
• Deciphering Environmental Air Pollution with Large Scale City Data [0.0]
  Various factors ranging from emissions from traffic and power plants, household emissions, natural causes are known to be primary causal agents or influencers behind rising air pollution levels. We introduce a large scale city-wise dataset for exploring the relationships among these agents over a long period of time. Also, we provide a set of benchmarks for the problem of estimating or forecasting pollutant levels with a set of diverse models and methodologies.
  arXiv  Detail & Related papers  (2021-09-09T22:00:51Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality [5.273501657421094]
  COVID-19 related lockdown measures offer a unique opportunity to understand how changes in economic activity and traffic affect ambient air quality. We estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring stations. We show that our models achieve state-of-the-art performance on the data from air pollution measurement stations in Switzerland and in China.
  arXiv  Detail & Related papers  (2020-11-19T23:09:30Z)
• Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution [0.8122270502556374]
  We propose to train models able to generate synthetic nitrogen dioxide daily time series according to a given classification. The proposed approach is able to generate accurate and diverse pollution daily time series, while requiring reduced computational time.
  arXiv  Detail & Related papers  (2020-10-05T18:01:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.