A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Global emission projections of particulate matter (PM): II. Uncertainty analyses of on-road vehicle exhaust emissions
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 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.
Highlights Global PM emissions from on-road vehicle are 1050 Gg in 2030 and 1260 Gg in 2050. Emissions are most sensitive to parameters in the retirement rate function. 95% confidence interval of global emissions is −42% to 47% of baseline in 2030. Uncertainty in the future technology mix is significant.
Global emission projections of particulate matter (PM): II. Uncertainty analyses of on-road vehicle exhaust emissions
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 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.
Highlights Global PM emissions from on-road vehicle are 1050 Gg in 2030 and 1260 Gg in 2050. Emissions are most sensitive to parameters in the retirement rate function. 95% confidence interval of global emissions is −42% to 47% of baseline in 2030. Uncertainty in the future technology mix is significant.
Global emission projections of particulate matter (PM): II. Uncertainty analyses of on-road vehicle exhaust emissions
Yan, Fang (author) / Winijkul, Ekbordin (author) / Bond, Tami C. (author) / Streets, David G. (author)
Atmospheric Environment ; 87 ; 189-199
2014-01-27
11 pages
Article (Journal)
Electronic Resource
English
EXHAUST EMISSIONS - Quantifying particulate emissions
Online Contents | 1997
Particulate matter emission by a vehicle running on unpaved road
| Elsevier | 2008