Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows

Abstract. VERT (Vehicular Emissions from Road Traffic) is an R package developed to estimate traffic emissions of a wide range of pollutants and greenhouse gases based on traffic estimates and vehicle fleet composition data, following the EMEP/EEA methodology. Compared to other tools available in the literature, VERT is characterised by its ease of use and rapid configuration, while it maintains great flexibility in user input. It is capable of estimating exhaust, non-exhaust, resuspension, and evaporative emissions and is designed to accommodate future updates of available emission factors. In this paper, case studies conducted at both urban and regional scales demonstrate VERT's ability to accurately assess transport emissions. In an urban setting, VERT is integrated with the Lagrangian dispersion model GRAMM–GRAL and provides NOx concentrations in line with observed trends at monitoring stations, especially near traffic hotspots. On a regional scale, VERT simulations provide emission estimates that are highly consistent with the reference inventories for the Emilia-Romagna region (Italy). These findings make VERT a valuable tool for air quality management and traffic emission scenario assessment.

Authors

Institutions

Publication Details

Journal
Geoscientific model development
Published
2024-08-30
DOI
https://doi.org/10.5194/gmd-17-6465-2024
Citations
3
Primary Topic
Vehicle emissions and performance
Type
article
Field-Weighted Citation Impact
0.61
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows

Grazia Ghermandi, Giorgio Veratti, Sergio Teggi, Alessandro Bigi
3 citations
Geoscientific model development
Vehicle emissions and performance
0.61
article

Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows

Grazia Ghermandi, Giorgio Veratti, Sergio Teggi, Alessandro Bigi
article en
3 citations

Abstract

Abstract. VERT (Vehicular Emissions from Road Traffic) is an R package developed to estimate traffic emissions of a wide range of pollutants and greenhouse gases based on traffic estimates and vehicle fleet composition data, following the EMEP/EEA methodology. Compared to other tools available in the literature, VERT is characterised by its ease of use and rapid configuration, while it maintains great flexibility in user input. It is capable of estimating exhaust, non-exhaust, resuspension, and evaporative emissions and is designed to accommodate future updates of available emission factors. In this paper, case studies conducted at both urban and regional scales demonstrate VERT's ability to accurately assess transport emissions. In an urban setting, VERT is integrated with the Lagrangian dispersion model GRAMM–GRAL and provides NOx concentrations in line with observed trends at monitoring stations, especially near traffic hotspots. On a regional scale, VERT simulations provide emission estimates that are highly consistent with the reference inventories for the Emilia-Romagna region (Italy). These findings make VERT a valuable tool for air quality management and traffic emission scenario assessment.

Geoscientific model developmentVol. 17(16)
University of Modena and Reggio Emilia (IT), Ferrari (Italy) (IT)
Openalex Percentile: Top 34%
Vehicle emissions and performance
0.61
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.