The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

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The Environmental Protection Agency

Several hybrid regression models that predict hot stabilized vehicle fuel consumption and emission rates for light-duty vehicles and light-duty trucks are presented in this paper. Key input variables to these models are instantaneous vehicle speed and acceleration measurements. The energy and emission models described in this paper utilize data collected at the Oak Ridge National Laboratory (ORNL) that included fuel consumption and emission rate measurements (CO, HC, and NOx) for five light-duty vehicles and three light-duty trucks as a function of the vehicle’s instantaneous speed and acceleration levels. The fuel consumption and emission models are found to be highly accurate as compared to the ORNL data, with coefficients of determination ranging from 0.92 to 0.99. Given that the models utilize the vehicle’s instantaneous speed and acceleration levels as independent variables, these models are capable of evaluating the environmental impacts of operational-level projects including intelligent transporta...

Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels

Under the Connected Vehicles (CV) environment, it is possible to create a Cooperative Vehicle Intersection Control (CVIC) system that enables cooperation between vehicles and infrastructure for effective intersection operations and management when all vehicles are fully automated. Assuming such a CVIC environment, this paper proposed a CVIC algorithm that does not require a traffic signal. The CVIC algorithm was designed to manipulate individual vehicles' maneuvers so that vehicles can safely cross the intersection without colliding with other vehicles. By eliminating the potential overlaps of vehicular trajectories coming from all conflicting approaches at the intersection, the CVIC algorithm seeks a safe maneuver for every vehicle approaching the intersection and manipulates each of them. An additional algorithm was designed to deal with the system failure cases resulting from inevitable trajectory overlaps at the intersection and infeasible solutions. A simulation-based case study implemented on a hypothetical four-way single-lane approach intersection under varying congestion conditions showed that the CVIC algorithm significantly improved intersection performance compared with conventional actuated intersection control: 99% and 33% of stop delay and total travel time reductions, respectively, were achieved. In addition, the CVIC algorithm significantly improved air quality and energy savings: 44% reductions of CO2 and 44% savings of fuel consumption.

Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm Under the Connected Vehicles Environment

Commercialization of Lithium Battery Technologies for Electric Vehicles

https://pure.tue.nl/ws/files/3887204/760250.pdf

A review of recent research on green road freight transportation

The paper applies a framework for developing microscopic emission models (VT-Micro model version 2.0) for assessing the environmental impacts of transportation projects. The original VT-Micro model was developed using chassis dynamometer data on nine light duty vehicles. The VT-Micro model is expanded by including data from 60 light duty vehicles and trucks. Statistical clustering techniques are applied to group vehicles into homogenous categories. Specifically, classification and regression tree algorithms are utilized to classify the 60 vehicles into 5 LDV and 2 LDT categories. In addition, the framework accounts for temporal lags between vehicle operational variables and measured vehicle emissions. The VT-Micro model is validated by comparing against laboratory measurements with prediction errors within 17%.

Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions

Power-based electric vehicle energy consumption model: Model development and validation

Motorists typically select routes that minimize their travel time or generalized cost. This may entail traveling on longer but faster routes. This raises questions concerning whether traveling along a longer but faster route results in energy and/or air quality improvements. We investigate the impacts of route choice decisions on vehicle energy consumption and emission rates for different vehicle types using microscopic and macroscopic emission estimation tools. The results demonstrate that the faster highway route choice is not always the best from an environmental and energy consumption perspective. Specifically, significant improvements to energy and air quality can be achieved when motorists utilize a slower arterial route although they incur additional travel time. The study also demonstrates that macroscopic emission estimation tools (e.g., MOBILE6) can produce erroneous conclusions given that they ignore transient vehicle behavior along a route. The findings suggest that an emission- and energy-optimized traffic assignment can significantly improve emissions over the standard user equilibrium and system optimum assignment formulations. Finally, the study demonstrates that a small portion of the entire trip involves high engine-load conditions that produce significant increases in emissions; demonstrating that by minimizing high-emitting driving behavior, air quality can be improved significantly.

The effects of route choice decisions on vehicle energy consumption and emissions

Existing automobile fuel consumption and emission models suffer from two major drawbacks; they produce a bang–bang control through the use of a linear power model and the calibration of model parameters is not possible using publicly available data thus necessitating in-laboratory or field data collection. This paper develops two fuel consumption models that overcome these two limitations. Specifically, the models do not produce a bang–bang control and are calibrated using US Environmental Protection Agency city and highway fuel economy ratings in addition to publicly available vehicle and roadway pavement parameters. The models are demonstrated to estimate vehicle fuel consumption rates consistent with in-field measurements. In addition the models estimate CO2 emissions that are highly correlated with field measurements.

Kyoungho Ahn

Papers

Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels

Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions

Power-based electric vehicle energy consumption model: Model development and validation

The effects of route choice decisions on vehicle energy consumption and emissions

Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model Development and Testing