Project Summary
Rail freight supports California as a leading gateway for goods. However, rail freight relies mostly on diesel engines, which emit substantial amounts of diesel particulate matter, fine particulate matter, oxides of nitrogen, and greenhouse gases. These emissions lead to adverse health effects, particularly in communities near railroads. Current train-level emissions estimation method oversimply the rail operation by assuming fixed train composition and uniform railcar weight. Such assumptions fail to capture the real-world freight movements operations and might lead to imprecise rail emission environmental impacts. To address these limitations, this project develops a more accurate and operationally representative methodology for freight rail emissions estimation. This study leverages the Rail Activity Monitoring System developed by UC Irvine, which can capture the train activity by identifying locomotives and railcars across 25 categories. This information is integrated with Rail Waybill dataset for more accurate weight estimation to associate rail emissions. Finally, a pilot investigation explores the feasibility of correlating model-based emission estimates with pollutant concentration collected from air quality sensors installed by this study, which provide validations and support model calibration. The fine-grained rail emission estimation approach enhances the ability to inform data-driven statewide policy planning.