Global emission projections of particulate matter (PM): II. Uncertainty analyses of on-road vehicle exhaust emissions

Fang Yan, Ekbordin Winijkul, Tami C. Bond, David G. Streets

Research output: Contribution to journalArticlepeer-review

Abstract

Estimates of future emissions are necessary for understanding the future health of the atmosphere, designing national and international strategies for air quality control, and evaluating mitigation policies. Emission inventories are uncertain and future projections even more so, thus it is important to quantify the uncertainty inherent in emission projections.This paper is the second in a series that seeks to establish a more mechanistic understanding of future air pollutant emissions based on changes in technology. The first paper in this series (Yan etal., 2011) described a model that projects emissions based on dynamic changes of vehicle fleet, Speciated Pollutant Emission Wizard-Trend, or SPEW-Trend. In this paper, we explore the underlying uncertainties of global and regional exhaust PM emission projections from on-road vehicles in the coming decades using sensitivity analysis and Monte Carlo simulation.This work examines the emission sensitivities due to uncertainties in retirement rate, timing of emission standards, transition rate of high-emitting vehicles called "superemitters", and emission factor degradation rate. It is concluded that global emissions are most sensitive to parameters in the retirement rate function. Monte Carlo simulations show that emission uncertainty caused by lack of knowledge about technology composition is comparable to the uncertainty demonstrated by alternative economic scenarios, especially during the period 2010-2030.

Original languageEnglish (US)
Pages (from-to)189-199
Number of pages11
JournalAtmospheric Environment
Volume87
DOIs
StatePublished - Apr 2014
Externally publishedYes

Keywords

  • Emission projection
  • Monte Carlo simulations
  • On-road
  • Particulate matter (PM)
  • Uncertainty analysis

ASJC Scopus subject areas

  • General Environmental Science
  • Atmospheric Science

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