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

被引:23
|
作者
Yan, Fang [1 ,2 ]
Winijkul, Ekbordin [1 ]
Bond, Tami C. [1 ]
Streets, David G. [2 ]
机构
[1] Univ Illinois, Dept Civil & Environm Engn, Urbana, IL 61801 USA
[2] Argonne Natl Lab, Decis & Informat Sci Div, Argonne, IL 60439 USA
关键词
Emission projection; On-road; Particulate matter (PM); Uncertainty analysis; Monte Carlo simulations; AIR-QUALITY; AEROSOL EMISSIONS; BLACK CARBON; POLLUTANTS; MODEL; CHINA; TRANSPORTATION; INVENTORIES; SENSITIVITY; POLLUTION;
D O I
10.1016/j.atmosenv.2014.01.045
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 et al., 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. (C) 2014 Published by Elsevier Ltd.
引用
收藏
页码:189 / 199
页数:11
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