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.

[1]  Thomas W Kirchstetter,et al.  Measurement of black carbon and particle number emission factors from individual heavy-duty trucks. , 2009, Environmental science & technology.

[2]  Julio Lumbreras,et al.  Computation of uncertainty for atmospheric emission projections from key pollutant sources in Spain , 2009 .

[3]  Alexei G. Sankovski,et al.  Special report on emissions scenarios : a special report of Working group III of the Intergovernmental Panel on Climate Change , 2000 .

[4]  Stefano Tarantola,et al.  Uncertainty and global sensitivity analysis of road transport emission estimates , 2004 .

[5]  David G. Streets,et al.  Influences of man-made emissions and climate changes on tropospheric ozone, methane, and sulfate at 2030 from a broad range of possible futures , 2006 .

[6]  B. DeAngelo,et al.  Bounding the role of black carbon in the climate system: A scientific assessment , 2013 .

[7]  G. Bishop,et al.  A decade of on-road emissions measurements. , 2008, Environmental science & technology.

[8]  D. Streets,et al.  Global emission projections for the transportation sector using dynamic technology modeling , 2013 .

[9]  M. Chin,et al.  Anthropogenic and natural contributions to regional trends in aerosol optical depth, 1980–2006 , 2009 .

[10]  Kaarle Kupiainen,et al.  Integrated Assessment of Black Carbon and Tropospheric Ozone , 2011 .

[11]  Allan J. Lichtman Ecological Regression Analysis in the Los Angeles County Case and Beyond , 1991 .

[12]  H. Christopher Frey,et al.  Quantification of Variability and Uncertainty in Air Pollutant Emission Inventories: Method and Case Study for Utility NOx Emissions , 2002, Journal of the Air & Waste Management Association.

[13]  Leonidas Ntziachristos,et al.  An empirical method for predicting exhaust emissions of regulated pollutants from future vehicle technologies , 2001 .

[14]  S. G. Fritz,et al.  Exhaust Particulate Matter Emission Factors and Deterioration Rate for In-Use Motor Vehicles , 2003 .

[15]  John A. Sokolowski,et al.  Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains , 2010 .

[16]  Tami C. Bond,et al.  Global emission projections of particulate matter (PM): I. Exhaust emissions from on-road vehicles , 2011 .

[17]  Jiming Hao,et al.  Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China , 2010 .

[18]  Arthur Petersen,et al.  Agreeing to disagree: uncertainty management in assessing climate change, impacts and responses by the IPCC , 2009 .

[19]  T. Berntsen,et al.  Global temperature responses to current emissions from the transport sectors , 2008, Proceedings of the National Academy of Sciences.

[20]  John H. Seinfeld,et al.  Development of a second-generation mathematical model for urban air pollution—II. Evaluation of model performance , 1983 .

[21]  David G. Streets,et al.  Dissecting Future Aerosol Emissions: warming Tendencies and Mitigation Opportunities , 2007 .

[22]  Kaarle Kupiainen,et al.  Scenarios of global anthropogenic emissions of air pollutants and methane until 2030 , 2007 .

[23]  John M. Reilly,et al.  Uncertainty in emissions projections for climate models , 2002 .

[24]  Zbigniew Klimont,et al.  Uncertainty analysis of emission estimates in the RAINS integrated assessment model , 2005 .

[25]  Robin Smit,et al.  A new method to compare vehicle emissions measured by remote sensing and laboratory testing: high-emitters and potential implications for emission inventories. , 2011, The Science of the total environment.

[26]  Mathieu Vrac,et al.  Future air quality in Europe: a multi-model assessment of projected exposure to ozone , 2012 .

[27]  Nguyen Thi Kim Oanh,et al.  Climate-relevant properties of diesel particulate emissions: results from a piggyback study in Bangkok, Thailand. , 2009, Environmental science & technology.

[28]  D. Streets,et al.  A technology‐based global inventory of black and organic carbon emissions from combustion , 2004 .

[29]  Qiang Zhang,et al.  Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996-2010 , 2011 .

[30]  Koulis Pericleous,et al.  Model sensitivity and uncertainty analysis using roadside air quality measurements , 2002 .

[31]  Nicholas Z. Muller,et al.  Global Air Quality and Health Co-benefits of Mitigating Near-Term Climate Change through Methane and Black Carbon Emission Controls , 2012, Environmental health perspectives.

[32]  S R Hanna,et al.  Air quality model evaluation and uncertainty. , 1988, JAPCA.

[33]  Michael Q. Wang,et al.  Modeling future vehicle sales and stock in China , 2012 .

[34]  Kirsten L. Findell,et al.  Strong sensitivity of late 21st century climate to projected changes in short-lived air pollutants , 2008 .

[35]  Eri Saikawa,et al.  Author ' s personal copy Present and potential future contributions of sulfate , black and organic carbon aerosols from China to global air quality , premature mortality and radiative forcing , 2009 .

[36]  Nadine Unger,et al.  Climate forcing by the on-road transportation and power generation sectors , 2009 .

[37]  D. Dockery,et al.  Health Effects of Fine Particulate Air Pollution: Lines that Connect , 2006, Journal of the Air & Waste Management Association.

[38]  Douglas R. Lawson,et al.  “Passing the Test” – Human Behavior and California’s Smog Check Program , 1993 .

[39]  Jens Borken,et al.  Global and Country Inventory of Road Passenger and Freight Transportation , 2007 .

[40]  David Vose,et al.  Quantitative Risk Analysis: A Guide to Monte Carlo Simulation Modelling , 1996 .

[41]  Luc Int Panis,et al.  An uncertainty analysis of air pollution externalities from road transport in Belgium in 2010. , 2004, The Science of the total environment.