Spatial assignment of emissions using a new locomotive emissions model.

Area of Science:

• Environmental Science
• Transportation Engineering
• Atmospheric Chemistry

Background:

• Current freight rail emission estimates use outdated, aggregate methods lacking spatial detail.
• Private industry data limitations hinder accurate contemporary air quality assessments.
• Accurate spatial data is crucial for understanding localized freight rail impacts.

Purpose of the Study:

• To develop a new spatially resolved model for estimating air pollutant emissions from locomotives.
• To provide more accurate and granular insights into freight rail's impact on air quality.
• To compare model outputs with existing regional and federal estimates.

Main Methods:

• Utilized track segment-level data: track grade, train traffic type (bulk, intermodal, manifest).
• Incorporated local locomotive fleet data: EPA tier certification and fuel efficiency.
• Modeled the California Class I freight rail system for analysis.

Main Results:

• The new model's emission estimates varied considerably from California Air Resources Board and EPA guidance-based estimates.
• Significant differences in fuel and emission intensity were observed for individual rail corridors.
• Spatially resolved data revealed localized variations in air pollutant emissions.

Conclusions:

• The developed spatially resolved model offers a more accurate representation of freight rail emissions compared to traditional methods.
• Findings underscore the need for granular data to address specific air quality challenges posed by freight rail.
• The model provides a robust framework for future air quality management and policy development in transportation sectors.