May 15, 2024 • Vehicular Emissions

Advancements in the VEIN Model for Urban Emissions

VEIN model visualization
Ibarra-Espinosa, S., Ynoue, R., O'Sullivan, S., Pebesma, E., Andrade, M. D. F., & Osses, M. (2018). VEIN v0.2.2: an R package for bottom–up vehicular emissions inventories. Geoscientific Model Development, 11(6), 2209-2229.
https://doi.org/10.5194/gmd-11-2209-2018

I'm excited to announce significant advancements in the VEIN (Vehicular Emissions INventory) model, an open-source R package designed for estimating vehicular emissions at high spatial and temporal resolution. The latest version brings substantial improvements to computational efficiency and modeling capabilities.

Traffic in São Paulo

Heavy traffic in São Paulo, Brazil - a key study area for the VEIN model

New Features in Version 1.0

  • Enhanced spatial analysis capabilities with improved integration with sf and terra packages
  • New emission factors for latest vehicle technologies in developing countries
  • Improved algorithms for traffic pattern analysis that better capture daily and weekly variations
  • Advanced chemical speciation profiles for VOCs and PM
  • Parallel processing capabilities that reduce computation time by up to 70%
  • Better integration with atmospheric chemistry models like WRF-Chem and CMAQ

Case Studies

  • São Paulo, Brazil: Comprehensive inventory with hourly resolution showing the impact of COVID-19 lockdowns on urban air quality
  • Santiago, Chile: Analysis of electrification scenarios for public transportation
  • Beijing, China: Assessment of policy interventions for reducing particulate matter during winter pollution episodes

VEIN Version History

Origins (2016-2017)

VEIN began as a collection of R scripts called "remIAG" before its official release. The initial paper describing the methodology was published in the Journal of Earth Sciences & Geotechnical Engineering.

Early Versions (0.2.x - 0.3.x)

  • Added support for emissions calculations in various units (kg)
  • Implemented NMHC speciation for industrial and buildings sources
  • Improved wear emissions calculations with ef_wear and emis_wear functions
  • Added age distribution functions with default naming conventions

Middle Versions (0.6.x - 0.7.x)

  • Added split_emis functionality (v0.6.1)
  • Introduced PM characteristics including Active Surface measurements
  • Enhanced emis_grid to support evaluated parsed text operations ("sum", "mean", etc.)

Recent Versions (0.9.x and beyond)

  • Added speciation for liquid fuels (E25, E100, G)
  • Incorporated hybrid gasoline and diesel emission factors for Chinese vehicles
  • Developed specialized projects for various regions (e.g., brazil_bu_chem)

Future Developments

  • Machine learning integration for improved traffic flow prediction
  • Real-time emission modeling capabilities using streaming traffic data
  • Enhanced visualization tools for policy communication