@article{rastogi_frey_2021, title={Characterizing Fuel Use and Emission Hotspots for a Diesel-Operated Passenger Rail Service}, volume={55}, ISSN={["1520-5851"]}, url={https://doi.org/10.1021/acs.est.1c00273}, DOI={10.1021/acs.est.1c00273}, abstractNote={Spatially varying diesel locomotive fuel use and emission rates (FUERs) are needed to accurately quantify local emission hotspots and their health impacts. However, existing locomotive FUER data are typically not spatially resolved or representative of real-world locomotive operation. Therefore, existing data are of limited use in quantifying the spatial variability in real-world FUERs. The objectives of this work are to quantify spatial variability in locomotive FUERs and identify factors differentiating hotspots from non-hotspots. FUERs were measured based on real-world measurements conducted for the Piedmont passenger rail service using a portable emission measurement system. FUERs were quantified based on 0.25 mile track segments on the Piedmont route. Hotspots were defined as segments in the top quintile of segment-average FUERs. On average, hotspots contributed 40-50% to trip fuel use and emissions. Hotspots were typically associated with low-to-medium speed, and high acceleration and grade. In contrast, non-hotspots were associated with high speed, and low acceleration and grade. Hotspots were typically located near populated areas and, thus, may exacerbate air pollutant exposure. The method demonstrated here can be applied to other passenger train services to assess key trends in hotspot locations and factors that explain the occurrence of hotspots.}, number={15}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, publisher={American Chemical Society (ACS)}, author={Rastogi, Nikhil and Frey, H. Christopher}, year={2021}, month={Aug}, pages={10633–10644} }
 @article{yuan_frey_wei_rastogi_vandergriend_miller_mattison_2019, title={Comparison of real-world vehicle fuel use and tailpipe emissions for gasoline-ethanol fuel blends}, volume={249}, ISSN={["1873-7153"]}, url={https://doi.org/10.1016/j.fuel.2019.03.115}, DOI={10.1016/j.fuel.2019.03.115}, abstractNote={Differences in fuel use and emission rates of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC), nitrogen oxide (NOx), and particulate matter (PM) were quantified for three gasoline-ethanol blends and neat gasoline measured for one flexible-fuel vehicle (FFV) and four non-FFVs using a portable emission measurement system (PEMS). The purpose was to determine if non-FFVs can adapt to a mid-level blend and to compare the fuel use and emission rates among the fuels. Each vehicle was measured on neat gasoline (E0), 10% ethanol by volume (E10) “regular” (E10R) and “premium” (E10P), and 27% ethanol by volume (E27). Four real-world cycles were repeated for each vehicle with each fuel. Second-by-second fuel use and emission rates were binned into Vehicle Specific Power (VSP) modes. The modes were weighted according to real-world standard driving cycles. All vehicles, including the non-FFVs, were able to adapt to E27. Octane-induced efficiency gain was observed for higher octane fuels (E10P and E27) versus lower octane fuels (E0 and E10R). E27 tends to lower PM emission rates compared to E10R and E10P and CO emission rates compared to the other three fuels. HC emission rates for E27 were comparable to those of E10R and E10P. No significant difference was found in NOx emission rates for E27 versus the other fuels. Intervehicle variability in fuel use and emission rates was observed. Lessons learned regarding study design, vehicle selection, and sample size, and their implications are discussed.}, journal={FUEL}, publisher={Elsevier BV}, author={Yuan, Weichang and Frey, H. Christopher and Wei, Tongchuan and Rastogi, Nikhil and VanderGriend, Steven and Miller, David and Mattison, Lawrence}, year={2019}, month={Aug}, pages={352–364} }
 @article{yuan_frey_rastogi_2019, title={Quantification of Energy Saving Potential for A Passenger Train Based on Inter-Run Variability in Speed Trajectories}, volume={2673}, ISSN={["2169-4052"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85063956284&partnerID=MN8TOARS}, DOI={10.1177/0361198119838516}, abstractNote={ Passenger train energy consumption is dependent on speed trajectories. The variability of passenger train energy consumption owing to the variability in speed trajectories can help identify ways to reduce train energy use via improved operations. Empirical fuel use data from a portable measurement emission measurement system (PEMS) and empirical speed trajectories measured using a global positioning system (GPS) receiver were used to verify and quantify real-world energy consumption variability and the variability in empirical speed trajectories, respectively. To identify potential realistic speed trajectories that can lead to energy saving (i.e., eco-driving), a Markov chain based speed trajectory simulator was used to simulate inter-run variability in speed trajectories. An energy index model (EIM) was used to compare energy consumption among different speed trajectories. The results show inter-run variability in fuel use associated with inter-run variability in the empirical speed trajectories. There is also inter-segment variability in fuel use related to the segment length and grade. The Markov chain based speed trajectory simulator can simulate realistic inter-run variability in speed trajectories based on calibration using empirical speed trajectories. The number of empirical speed trajectories used for simulator calibration affects the range of simulated inter-run variability. The EIM provides an accurate estimation of the empirical fuel use. Eco-driving, such as reducing the peak speed, can reduce energy consumption without compromising travel time. The methodology shown in this study is not system-specific and can be applied to other passenger train systems. }, number={5}, journal={TRANSPORTATION RESEARCH RECORD}, author={Yuan, Weichang and Frey, H. Christopher and Rastogi, Nikhil}, year={2019}, month={May}, pages={153–165} }