TY - JOUR
T1 - Global emission projections of particulate matter (PM)
T2 - II. Uncertainty analyses of on-road vehicle exhaust emissions
AU - Yan, Fang
AU - Winijkul, Ekbordin
AU - Bond, Tami C.
AU - Streets, David G.
N1 - Funding Information:
This work at University of Illinois at Urbana-Champaign was funded by the Clean Air Task Force , and the U.S. Environmental Protection Agency under grant RD83428001 . Argonne National Laboratory is operated by UChicago Argonne, LLC, under contract No. DE-AC02-06CH11357 with the US Department of Energy.
PY - 2014/4
Y1 - 2014/4
N2 - 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.
AB - 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.
KW - Emission projection
KW - Monte Carlo simulations
KW - On-road
KW - Particulate matter (PM)
KW - Uncertainty analysis
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U2 - 10.1016/j.atmosenv.2014.01.045
DO - 10.1016/j.atmosenv.2014.01.045
M3 - Article
AN - SCOPUS:84896834539
SN - 1352-2310
VL - 87
SP - 189
EP - 199
JO - Atmospheric Environment
JF - Atmospheric Environment
ER -