Dataset Information

Estimating vehicle carbon dioxide emissions from Boulder, Colorado, using horizontal path-integrated column measurements.

ABSTRACT: We performed 7.5 weeks of path-integrated concentration measurements of CO2, CH4, H2O, and HDO over the city of Boulder, Colorado. An open-path dual-comb spectrometer simultaneously measured time-resolved data across a reference path, located near the mountains to the west of the city, and across an over-city path that intersected two-thirds of the city, including two major commuter arteries. By comparing the measured concentrations over the two paths when the wind is primarily out of the west, we observe daytime CO2 enhancements over the city. Given the warm weather and the measurement footprint, the dominant contribution to the CO2 enhancement is from city vehicle traffic. We use a Gaussian plume model combined with reported city traffic patterns to estimate city emissions of on-road CO2 as (6.2 ± 2.2) × 105 metric tons (t) CO2 yr-1 after correcting for non-traffic sources. Within the uncertainty, this value agrees with the city's bottom-up greenhouse gas inventory for the on-road vehicle sector of 4.5 × 105 t CO2 yr-1. Finally, we discuss experimental modifications that could lead to improved estimates from our path-integrated measurements.

SUBMITTER: Waxman EM

PROVIDER: S-EPMC6759866 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Estimating vehicle carbon dioxide emissions from Boulder, Colorado, using horizontal path-integrated column measurements.

Waxman Eleanor M EM Cossel Kevin C KC Giorgetta Fabrizio F Truong Gar-Wing GW Swann William C WC Coddington Ian I Newbury Nathan R NR

Atmospheric chemistry and physics 20190101

We performed 7.5 weeks of path-integrated concentration measurements of CO2, CH4, H2O, and HDO over the city of Boulder, Colorado. An open-path dual-comb spectrometer simultaneously measured time-resolved data across a reference path, located near the mountains to the west of the city, and across an over-city path that intersected two-thirds of the city, including two major commuter arteries. By comparing the measured concentrations over the two paths when the wi ...[more]

PMID: 31555337

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Project description:BackgroundAutonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and actuation, communication and safety measures, among others.MethodsIn this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning.ResultsThe reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.

Project description:Exposure to vehicular emissions has been linked to numerous adverse health effects. In response to the arising concerns, near-road monitoring is conducted to better characterize the impact of mobile source emissions on air quality and exposure in the near-road environment. An intensive measurement campaign measured traffic-related air pollutants (TRAPs) and related data (e.g., meteorology, traffic, regional air pollutant levels) in Atlanta, along one of the busiest highway corridors in the US. Given the complexity of the near-road environment, the study aimed to compare two near-road monitors, located in close proximity to each other, to assess how observed similarities and differences between measurements at these two sites inform the siting of other near-road monitoring stations. TRAP measurements, including carbon monoxide (CO) and nitrogen dioxide (NO2), are analyzed at two roadside monitors in Atlanta, GA located within 325m of each other. Both meteorological and traffic conditions were monitored to assess the temporal impact of these factors on traffic-related pollutant concentrations. The meteorological factors drove the diurnal variability of primary pollutant concentration more than traffic count. In spite of their proximity, while the CO and NO2 concentrations were correlated with similar diurnal variations, pollutant concentrations at the two closely sited monitors differed, likely due to the differences in the siting characteristics reducing the dispersion of the primary emissions out of the near-road environment. Overall, the near-road TRAP concentrations at all sites were not as elevated as seen in prior studies, supporting that decreased vehicle emissions have led to significant reductions in TRAP levels, even along major interstates. Further, the differences in the observed levels show that use of single near-road observations will not capture pollutant levels representative of the local near-road environment and that additional approaches (e.g., air quality models) are needed to characterize exposures.

Dataset Information

Estimating vehicle carbon dioxide emissions from Boulder, Colorado, using horizontal path-integrated column measurements.

Publications

Estimating vehicle carbon dioxide emissions from Boulder, Colorado, using horizontal path-integrated column measurements.

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