Figure 1.1 Summary estimates of relative risks of mortality caused by different air
pollutants. The error bars indicate 95% confidence intervals. Left part: PM10, black smoke
(BS) and ground-level ozone from European studies; right part: PM2.5 from North American
32
Figure 1.2 Correlations between visibility and PM2.5 concentration in Beijing [Zhao et al.,
33
Figure 1.3 Components of RF for emissionsof principal aerosols and aerosol precursors. Values represent RF in 2005 due to emissions since 1750. [Adopted from Forster et al., 2007]. The details of RF estimates are shown in Forster et al. [2007].
34
Figure 1.4 Probability distribution function (PDFs) from combining anthropogenic RFs. Three cases are shown: the total of all anthropogenic RF terms (red area, a combination of those effects represented by red and blue dashed lines), long-lived GHGs and ozone only (red dashed line), and aerosol direct and indirect (cloud albedo) RFs (blue dashed line) only [Forster et al., 2007].
35
Figure 1.5 Intercontinental transport pathways in the Northern Hemisphere. The colored boxes indicate the four source and receptor regions used in HTAP intercomparison Experiment Set. The arrows approximate the magnitude of main transport pathways in summer and winter, based on modeled average CO transport over 8-10 day periods. Light arrows indicate transport generally near ground level (less than 3km above the surface) and the dark arrows indicate transport higher in the atmosphere (more than 3km above the surface) [HTAP, 2007].
36
Figure 1.6 Estimated total contributions (in oC) to global warming since 1750 by different sources. The second bar shows that of BC-containing soot particles. Data included through 2005. (Figure 1.1. from Bice et al. [2009]; data source: Jacobson [2004]).
37 Residential biofuel 19% Residential coal 6% Industrial coal 8% Gasoline transport 2% Diesel on- road 10% Diesel off-road 7% Open biomass burning 41% Others 7%
Figure 1.7 Distribution of global BC emissions by major contributing source types (Figure 2. A. 2 in Chapter 2 of UNEP [2011], adapted from Bond et al. [2004]).
38
Figure 1.8 Projected Chinese vehicle stock under the mid vehicle growth scenario [Figure 13 from Wang et al., 2006]. RV means rural vehicle and MC means motorcycle. Wang et al. [2006] project vehicle stocks based on China Automotive Industry Yearbook data. The real data end in 2001 for RV, and in 2004 for the other vehicle types.
39
Figure 1.9 Sectoral contribution of black carbon emissions from contained combustion in 1996. (Figure 4 from Bond et al. [2004]). The gray bars behind the colored bars represent the fraction of emissions from “contained” combustion (that undertaken for energy use,
excluding open burning) in each region. The green bars to the left indicate the relative contribution of each region to the total. The contributions are based on the central values.
40
Figure 1.10 Typical diesel engine exhaust particle size distribution; both mass and number weightings are shown [Kittelson, 1998].
41
Table 1.1 Anthropogenic and natural emissions for the year 2005 (Units: Tg/year; Table 2.1 from UNEP, 2011). Sector BC OC PM2.5 Anthropogenic Large-scale combustion a 0.1 0.15 8.1 Industrial processes b 0.43 0.66 4.5 Residential-commercial combustion 2.7 9.6 17.8 Transport c 1.6 1.4 3.4
Fossil-fuel extraction and distribution d 0.28 0.06 0.51
Waste/landfill e 0.1 0.75 1.3 Agriculture f 0.31 1.2 3.4 Total anthropogenic g 5.5 13.8 39 Natural h 3-3.7 33-38 6000i Global total 8.5-9.2 47-52 6039 a
Includes power plants and industrial boilers. b Includes cement, lime and brick kilns.
c Includes international shipping and aviation that come from Representative Concentration Pathway 8.5 [Lamarque et
al., 2010]
d
Includes coal, oil, gas production, storage and distribution. e Includes open garbage burning.
f Includes the burning of agricultural residues.
g Anthropogenic emissions are from GAINS model, website:
http://gains.iiasa.ac.at/index.php/home-page/241-on-line-access-to-gains.
h Includes the open burning of all biomass other than agricultural residues, and emissions of sea salt and dust. According to IPCC [2007a], the submicron shares of sea salt and dust are 15% and 7-20%, respectively. It should be noted that emissions caused by forest and savanna fires are not always natural burning. Bond et al. [2004] shows that the total BC and OC emissions from open-burning of forest and savanna are 2.95 and 23.39 Tg/yr .
i Note that UNEP [2011] may overestimate natural PM
2.5 emissions. Luo et al. [2003] estimate PM2.5 from mineral dust of about 660 Tg/yr. Jaeglé et al. [2011] show an estimate of PM10 from sea salt of 4600-5200 Tg/yr, with PM2.5 less than half of that. Average global fire PM2.5 emissions according to version 3 of the Global Fire Emissions Database (GFED3) are 31 Tg/yr.
42
Table 1.2 Review of emission projections.
Citation Regions Sectors Modeled
time Species
Methods and key
assumptions Major findings
Streets et al., 2004 Global Residential, industrial, power plants, transport, and biomass burning 2030, 2050 BC and OC
1. Driven by four IPCC scenarios (A1B, A2, B1 and B2);
2. The emission factors were based on a simple technology stock model.
1. Global BC and OC emissions will decline from 1996;
2. BC emissions from transport sector increase under most scenarios. Cofala et al., 2007 Global Domestic, industrial, power plants, transport and others 2030 SO2, NOx, CO, BC, OC, and CH4
1. The global version of the RAINSa model was applied; 2. Two scenarios were used: CLEb and MRFc ;
3. Emission factors were affected mainly by the implementation of emission control legislation in each country, which is based on the current national
expectations of development.
1. In CLEb scenario, all emissions decrease, except CH4;
2. In MRFc scenario, all emissions decrease.
43
Table 1.2 (cont.)
Citation Regions Sectors Modeled
time Species
Methods and key
assumptions Major findings
Rao et al., 2005 Global
Fossil fuel and biomass combustion in residential, industrial, transportation and electricity sectors 2000-2100 BC and OC
1. Two IPCC scenarios (A2 and B1) were used;
2. Emission factors decline based on GDP per capita of the different regions in various scenarios.
Significant co-benefits from local air pollution policies on reducing aerosol emissions, especially in the transportation and residential sectors which largely drive the long-term trends in BC and OC. Representative Concentration Pathways (RCPs)k Global Residential, industrial, transport, power plant, agriculture, savannah burning and land-use change 2000-2100 BC, OC, CO2, CH4, N2O, ODS, sulfur, CO, NOx, VOC and NH3, HFC, PFC, SF6 RCP 3-PD, 4.5, 6.0 and 8.5 are developed by IMAGEl, MiniCAMm, AIMn and MESSAGEo respectively.
Long-term emissions are represented by database; results are shown in papers, e.g., Moss et al. [2010] and van Vuuren et al. [2007].
44
Table 1.2 (cont.)
Citation Regions Sectors Modeled
time Species
Methods and key
assumptions Major findings
GAINSj Global Residential, industrial, power plant, transport and agriculture 1990-2030 TSP, BC, OC, PM1p, PM2.5, PM10, SO2, NOx, VOC, NH3,CO, CO2, non-CO2 greenhouse gases.
Control strategies are defined to project
emissions; they can be seen as legal frameworks that specify for all emission sources in a country the required emission controls, independent from the extent to which such emission sources exist in a particular country at a time.
Emissions are represented by database. Ohara et al., 2007 Asia Combustion and non-combustion sources 1980-2020 SO2, NOx, CO, NMVOC, BC and OC
1. Three emission scenarios were included: PFCd, REFe, PSCf;
2. Emission factors change based on assumptions in RAINSa-Asia model, neighboring countries, and GDP change rate. 1. In REFe, all emissions increase except OC from 2000 to 2020; 2. Asian total
emissions are strongly influenced by the emission scenarios.
45
Table 1.2 (cont.)
Citation Regions Sectors Modeled
time Species
Methods and key
assumptions Major findings
Xing et al., 2011 China Power plant, industrial, domestic and transportation 2020 SO2, NOx, PM10, NMVOC and NH3
1. Four scenarios are developed based on energy scenarios and emission control strategies;
2. Emission factors change with the application of air pollution control
legislations.
1. SO2, NOx, NMVOC and NH3 will increase, while PM will decrease under current control legislation and proposed control; 2. Strict emission control policy will reduce SO2 and PM10, but increase VOC.
Wei et al., 2011 China Combustion and non-combustion sources 2010-2020 VOC 1. Fuel consumption is projected by local energy model and
growth rates of GDP; 2. Two scenariosi based on emission control are applied to estimate emission factors. 1. VOCs emission increase continuously from 2005 to 2020; 2. Emissions of road vehicles and stationary fuel combustion decreases.
46
Table 1.2 (cont.)
Citation Regions Sectors Modeled
time Species
Methods and key
assumptions Major findings
Song et al.,
2011 China on-road vehicles 1990-2030 BC
1. Eight scenarios are designed based on
implementation of emission standards, share of
biodiesel, and sulfur contents in fuel;
2. Emission factors follow the reduction targets
proposed in the regulations.
1. Emission standards to reduce PM result in decrease BC
emissions;
2. Emissions continue to decline but start to increase from 2015 under a business as usual scenario. Winther and Nielsen, 2011 Denmark, Greenland and the Faroe Islands Residential, road transport (exhaust and non-exhaust) and other sources 1990-2030 BC and OC
The relevant BC and OC emission factor information is based primarily on the European models GAINSj and COPERT 4h.
1. Residential sources are the largest
contributor to TSP, BC and OC
emissions.
2. The total emissions of TSP, BC and OC decrease from 1990 to 2030.
47
Table 1.2 (cont.)
Citation Regions Sectors Modeled
time Species
Methods and key
assumptions Major findings
Kousoulidou
et al., 2008 Europe Road transport 2020
Exhaust emissions of NOx, NO2 and PM2.5;
non-exhaust (tire and brake wear) PM2.5 and PM10
1. The stock of vehicles in 2000 and 2005 estimated by TREMOVEg model; 2. Exhaust emissions was performed with the COPERT 4h; 3. Emissions were projected following two scenarios: CLEb and MRFc.
NOx and PM exhaust and one-exhaust emissions will decrease from 2000 to 2020. Zhao et al., 2008 China coal-fired power plants 2005-2020 SO2, NOx and PM 1. A unit-based method is developed;
2. Emission factors are estimated based on
emission control scenarios and the application rate of emission control
technologies.
1. SO2 and PM of power sector could be controlled with losing small units and application FGD and dust collectors; 2. NOx emission of power sector would increase along with the energy growth
48
Table 1.2 (cont.)
Citation Regions Sectors Modeled
time Species
Methods and key
assumptions Major findings
Zhang et al., 2008 Hangzhou, China transport 2004-2030 CO, VOCs, NOx, PM10, CO2
1. The travel-distance based emission factors of each year are evaluated with IVE model;
2. The vehicle fleet
distribution is projected by a scenario-based energy environment modeling platform called "long-range Energy Alternative
Planning" (LEAP) system, which is based on an economic elasticity method relating per capita vehicle ownership to per capita GDP.
1. Regulated vehicle emission limits of National I and II standard of China are not effective to reduce the emission factors of vehicles except motorcycles; 2. Only National III and IV can reduce overall emission factors of all type vehicles with other gasoline and diesel.
a
Regional Air Pollution INformation and Simulation, website: http://www.iiasa.ac.at/Research/TAP/rains_europe/intro.html.
b A scenario used in RAINS, which already decided emission control legislation in each country based on the current national expectations of economic development.
c A scenario used in RAINS, which assumes the lowest emission levels that could be achieved with the most advanced emission control technologies that are on the market today.
49
Table 1.2 (cont.)
e
Reference scenario (a sustainable scenario) with moderate emission rates. f Policy Succeed Case (an optimistic case) with low emission rates.
g A policy assessment model, designed to study the effects of different transport and environment policies on the transport sector, website: http://www.tremove.org/. h A software tool used world-wide to calculate air pollutant and greenhouse gas emissions from road transport, website: http://www.emisia.com/copert/.
i
Scenario I assumes that standards for VOCs emission limits that have been promulgated would continue to be implemented effectively beyond the year of implementation, scenario II considers advanced control measures for the important emission sources distinguished by 2005 inventories besides the control actions required by the legislative standards described in scenario I.
j Greenhouse Gas and Air Pollution Interactions and Synergies, website: http://gains.iiasa.ac.at/index.php/home-page/241-on-line-access-to-gains. k
Database and website: http://www.iiasa.ac.at/web-apps/tnt/RcpDb/dsd?Action=htmlpage&page=about.
l Integrated Model to Assess the Global Environment, developed the Netherlands Environmental Assessment Agency
m Global Change Assessment Model, developed by the Pacific Northwest National Laboratory's Joint Global Change Research Institute (JGCRI). n Asian-Pacific Integrated model, developed by the National Institute for Environmental Studies (NIES), Japan.
o
Model for Energy Supply Strategy Alternatives and their General Environmental Impact, developed by Integrated Assessment Framework at the International Institute for Applied Systems Analysis (IIASA).
50
Table 1.3 Approximate relative net radiative forcing of BC and co-emitted OC from different sources [Bice et al., 2009]. Values are expressed in terms of equivalent amount of pure BC necessary to produce the same forcing through its direct and indirect effects as 1g of BC from the source.
Sources
Relative Radiative Forcing Approx. 1996 world emissions (Gg BC/yr) Approx. Global Average OC:BC1 Direct + indirect effects2 Including globally averaged snow albedo effect2 Unweighted emissions3 Weighted emissions by direct + indirect effects Open Burning Crop residues 4.8 -0.3 (28%) 0.2 (43%) 330 -70 Forest 9.1 -1.5 (7%) -0.9 (13%) 1240 -1600 Savannah 7.1 -0.9 (13%) -0.4 (22%) 1720 -1300 Waste 1.3 0.6 (83%) 1.1 (96%) 40 30 Contained combustion Agricultural waste 3.8 0.0 (39%) 0.4 (56%) 390 20 Animal waste 3.6 0.0 (42%) 0.5 (59%) 210 20 Coal 0.8 0.8 (90%) 1.2 (97%) 1130 90 Diesel fuel 0.4 0.9 (95%) 1.3 (99%) 1460 1300 Gasoline fuel 7.2 -0.9 (12%) -0.4 (22%) 130 -100 Wood 4 -0.1 (37%) 0.4 (53%) 880 0
1 OC/BC ratios are from Bond et al. [2004].
2 Percentages in parentheses indicate the probability that the radiative forcing of carbonaceous aerosols from a source is positive. These percentages take into account only uncertainties in forcing, not in OC: BC ratios.
3
51
Table 1.4 Contributions to emission projections from the transportation sector in SPEW-Trend (FY stands for Fang Yan, and EW stands for Ekbordin Winijkul).
Category On-road Off-road
Demand of new vehicles FY EW
Retirement rate FY EW
Adoption of emission standards FY FY
Estimation of superemitters FY EW
Vehicle Activity FY EW
PM emission projections FY EW
Uncertainty analysis FY EW and FY
52