Emission projection and uncertainty analysis of primary particulate matter (PM) from the transportation sector

Yan, Fang

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Description

Title

Emission projection and uncertainty analysis of primary particulate matter (PM) from the transportation sector

Author(s)

Yan, Fang

Issue Date

2012-06-27T21:23:27Z

Director of Research (if dissertation) or Advisor (if thesis)

Bond, Tami C.

Doctoral Committee Chair(s)

Bond, Tami C.

Committee Member(s)

• Rood, Mark J.
• Streets, David G.
• Eheart, J. Wayland

Department of Study

Civil & Environmental Eng

Discipline

Environ Engr in Civil Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Emission projection
• Particulate Matter
• Transportation
• Uncertainty
• Mitigation

Abstract

Particulate matter (PM) emissions from the transportation sector have significant impacts on climate and human health. Global projections of future PM emissions are critical elements in understanding air quality impacts on regional and global scales, because they are needed to forecast future air quality and climate change and to examine the effects of mitigation options. This dissertation aims to develop new methods for projections of future global emission from transportation, to analyze the uncertainty in those projections, and to investigate the effectiveness of mitigation measures. A dynamic model of vehicle population linked to emission characteristics, SPEW-Trend, is used to make the emission projections. Unlike previous models of global emissions, this model incorporates considerable detail on the technology stock, including the vehicle type and age, and the number of emitters with very high emissions (termed “superemitters”). These features of the vehicle fleet alter over time and respond to economic growth and changes in regional income. Estimates of vehicle growth are driven by changes in predicted fuel consumption from macroeconomic scenarios, ensuring that PM projections are consistent with scenarios that are used to assess impacts of greenhouse-gas emissions. Changes in the vehicle fleet occur through retirement, new emission standards, and the change of normal vehicles to superemitters. Retirement rates and superemitter fractions depend on regional income levels. Adoption dates of emission standards are either estimated from planned implementation or from income levels. Projections of global emissions from on-road vehicles for the period 2010 to 2050 are made under four commonly-used global fuel-consumption scenarios. Common features of these scenarios are a projected emission decrease until 2035, as emission standards are implemented worldwide and older engines built to lower standards are phased out. However, superemitters have a considerable effect on emission totals. They can potentially contribute more than 50% of global emissions around 2020-2030, which suggests that they should be specifically addressed in modeling and mitigation measures. Although it is common to acknowledge uncertainties in future economic trajectories, most current emission projection models are deterministic. Sensitivity analysis and Monte Carlo simulations are performed to quantify the uncertainties in these emission projections. The current work examines the emission sensitivities due to uncertainties in vehicle retirement rate, timing of emission standards, superemitter transition rate, and emission degradation rate. It is concluded that global emissions are most sensitive to retirement rate. Monte Carlo simulations show that emission uncertainty caused by lack of knowledge about technology composition is about the same as the uncertainty demonstrated by alternative economic scenarios, especially during the period 2010 to 2030. Two mitigation measures, scrappage of vehicles and retrofit to advanced control technology, are explored to examine potential PM emission reductions from on-road vehicles. The simulations show that scrappage can provide more emission reduction as soon as the measure begins, while retrofit reduces more emissions in later years when very advanced technology becomes available in most regions. With the consideration of uncertainties, scrappage and retrofit reduce emissions by 22-49% and 9-23%, respectively, within 90% confidence interval under medium scenarios in the year 2030.

Graduation Semester

2012-05

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http://hdl.handle.net/2142/31991

Copyright and License Information

Copyright 2012 Fang Yan

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