Air Pollution Scenarios towards 2030 in China: Emissions and Air Quality

Transcription

1 Air Pollution Scenarios towards 2030 in China: Emissions and Air Quality Prof. Kebin HE Department of Environmental Science and Engineering, Tsinghua University, Beijing , China Workshop on Hemispheric Transport of Air Pollution: Emission Inventories and Future projections Beijing, China October 18 th -20 th, 2006

2 Outlines Background Working framework Scenario development Modeling methodology Modeling results Discussion

3 Outlines Background Working framework Scenario development Modeling methodology Modeling results Discussion

4 Historical emissions Activities Energy Consumption Emissions tons 10 4 vehi cl es St eel Cement Chemical Fertilizer MTCE Vehi cl e Popul at i on tons fertilizer Hydr o NG oi l coal Tg SO2 PM

5 Why emission projection is needed Rapid development in 10th five years, but Only Environment target is not met SO 2 : 25490Gg 19950Gg (Emitted in 2005) (2000) 18000Gg (Target in 2005) Aggressive environment target for 11th five years Projection of emissions is needed to support policy making

6 Outlines Background Working framework Scenario development Modeling methodology Modeling results Discussion

7 Work Scope Purpose: To simulate energy consumption and emission of air pollutants up to 2030 Spatial Scope: China National Level Time Step: 2000 (Base Year), 2005, 2010, 2020 and 2030 EI sectoral scope: power, industry, domestic, transportation and biomass burning Pollutants: SO 2, NO x, CO, VOC, BC and OC

8 Framework Energy Technology Options Environmental Technology Options Activities/Energy Use Tool: LEAP model Emissions Tool: Updated Trace-P EI Air Quality Tool: Models-3/CMAQ Energy Policy Options Environmental Policy Options

9 Tech based EI methodology Energy consumption Tech character Key Environmental of emission From projection LEAP Policies / tech Fuel Quality Unabated Emission Factor Activities Tech split Abatement Efficiency Implementation Rate Emission Factor Emissions

10 Outlines Background Working framework Scenario development Modeling methodology Modeling results Discussion

11 Scenario Definition BAU Sustainable Energy Scenarios towards 2020 developed by ERI Long term environmental program by government Policies and regulations to ensure sustainable development

12 Scenario Definition EP Policies Households energy saving Industry energy saving Building energy saving Vehicle energy efficiency improvement Indicators Energy saving lamp increases to 45% in 2010, and 70% in 2030 in urban households. 24% in 2010, and 53% in 2030 in rural households. Energy intensity decreases: Iron and steel: 1.72% per year, Nonmetal minerals: 3.2% per year, Chemical products: 3.5% per year, Manufacturing and processing: 3.5% per year, etc. Public building: Terminal heating loading (W/m 2 ) decreases to 54.4% of current value in 2010, and 32.8% in Residential building: Terminal heating loading (W/m 2 ) decreases to 55.5% of current value in 2010, and 36.1% in Energy efficiency of light buses and cars increase 87% before 2030 Indicators in BAU Urban: 34% in 2010, and 45% in Rural: 20% in 2010 and 40% in 2030 Iron and steel: 1.62% per year, Nonmetal minerals: 2.9% per year, Chemical products: 3.25% per year, Manufacturing and processing: 2.5% per year, etc. Public building: 68.4% in 2010 and 51.8% in Residential building: 70.6% in 2010 and 54.0% in Energy efficiency increase 46% before 2030

13 Scenario Definition PCP Policies Improvement of rural cooking condition Switching heating boilers and stoves Vehicle emission standard Two control zone policy PM control in industry Indicators Biomass consumption in cooking decreases to 50% in 2010 and 19% in 2030 Urban: 24% of heating boilers uses natural gas in 2010, and 50% in Rural: Biomass stoves contribute 50% in 2010, and 10% in 2030 Implement EURO IV in 2010, and EURO V in 2015 New power plants install FGD (flue gas desulfurization), and eliminate power plants over 30 years old. New power plants install SCR (Selective Catalytic Reduction) from Bag house installed with 20% of CFB (Circulating Fluidized Bed) boiler in 2010, and 75% in 2030; with 2% of grate furnace in 2010, and 30% in 2030 Indicators in BAU 53% in 2010 and 26% in Urban: 20% in 2010, and 45% in Rural: Biomass stoves contribute 45% in 2010, and 5% in 2030 EURO IV in 2012, and EURO V in 2018 New power plants install FGD. New power plants install SCR (Selective Catalytic Reduction) from With CFB: 15% in 2010 and 60% in 2030; with grate furnace: 1% in 2010, and 20% in 2030

14 Scenario Design Scenario Definition Business As Usual (BAU) Scenario I (BAU+EP) Scenario II (BAU+EP+PCP)

15 Outlines Background Working framework Scenario development Modeling methodology Modeling results Discussion

16 Development of EI Tech/Fuel options in energy consumption Power: Pulverized coal Industry: Coal/kiln, Coal/boiler (CFB, auto grate furnace, handfed grate furnace), coke, heavy oil, diesel Transportation: LDGV, LDDV, LDGT1, LDGT2, LDDT, HDGV, HDDV, MC Residential: Options of abatement tech Coal (Raw coal, briquette), biomass, LPG, NG Power: FGD, SCR Industry boiler: Fabric, ESP, wet scrubber, cyclone, none Transportation: EURO-I ~ EURO-V

17 Example of Emission projection: SO2 and NOx from electricity El ect r i ci t y by coal - f i r ed pl ant s (Billion gigajoule) BAU Scenar i o I Scenar i o I I Capaci t y( MW) Coal - f i r ed pl ant s FGD i n BAU and Scenar i o I FGD in Scenario II Capaci t y( MW) Coal - f i r ed pl ant s SCR in BAU and Scenario I SCR in Scenario II BAU S- I S- II SO2 By Power Gg BAU S- I S- II Nox by Power

18 Emission process strategy Regional Inventory Top-down method Province-based Location of Large Point sources Urban & rural population Road network... Spatial distribution Gridding based on GIS High resolution gridded inventory Chemical speciation Temporal allocation Air quality model

19 Gridding Population Landcover Urban Pop. by Regions Rural Pop. by Regions Region Emis. Estimate Sector Chem. Species Road Network Shiplanes Region bndries LPSs Volcano Landcover by Regions Road by Regions Shiplane by Types LPSs by Regions Volcano by Locations Area Emis. by Fine Grid Road Emis. by Fine Grid Ship Emis. by Fine Grid LPSs Emis. Vocanic Emis. GIS Info. Alloc. Factor Emissions Figure from J.Woo

20 Chemical speciation VOC Source profile: Based on EPA-SPECIATE & Trace-P database Searching for local data PM Local source profile except BC & OC BC & OC: Trace-P inventory BC & OC emission from combustion: an ongoing project in our group

21 Temporal allocation Local temporal profile were used whenever possible Routinization of emission inventory process Finished the routine: from gridding results to model acceptable ASCII file Change from manual work in Excel to module & standard program

22 CMAQ Model ready emissions Showing differences in release height (SO 2 ) Upper-layer sources (power plants) Middle-layer sources (industry) Lower-layer sources (residential and transportation

23 Models-3/CMAQ System Framework Meteorology Processor Emission Processor Gridding Process Emission Inventory Air Quality Model PAVE Domain: 36km (164*97)

24 Outlines Background Working framework Scenario development Modeling methodology Modeling results Discussion

25 Sectoral Distribution of 2001 EI 100% 80% 60% 40% 20% others Agriculture Biomass Burning Power Generation Transport Domestic Industry 0% SO2 Nox BC OC CO NMVOC CH4 NH3 The distribution characteristics varies a lot among the different type of primary air pollutants.

26 Spatial Distribution of 2001 EI SO2 NOx VOC ,t/(km2 year) CO

27 Emission Trend: SO BAU S- I S- II By Power BAU S- I S- II By I ndust r y BAU S- I S- II SO2 Emission BAU S- I S- II By Domest i c % more FGD installation 2020 leads 2025 to 2030 less emission 15% less electricity demand (2030) leads to less emission Ignore SO 2 Emission from transport sector

28 Emission Trend: NOx Gg BAU S- I S- II by Power Gg S- II BAU S- I Gg NOx Emi ssi on BAU S- I S- II by Domest i c Gg BAU S- I S- II by I ndust r y Gg BAU S- I S- II by Tr anspor t

29 Emission Trend: CO and VOC Gg CO Emi ssi on BAU S- I S- II Gg VOC Emi ssi on BAU S- I S- II

30 Emission Trend: BC and OC 1200 Gg BC Emi ssi on BAU S- I S- II Gg BAU S- I S- II OC Emi ssi on

31 Sectoral distribution kt Ot her s Bi omass Bur ni ng Tr anspor t Domest i c Fossi l Fuel s Domest i c Bi of uel s I ndust r y Power kt Bi omass Bur ni ng Tr anspor t Domest i c Fossi l Fuel s Domest i c Bi of uel s I ndust r y Power BAU 2010 BAU 2020 BAU 2030 BAU SO BAU 2010 BAU 2020 BAU Largest emission reduction 2030 BAU NOx kt BAU 2010 BAU 2020 BAU 2030 BAU Ot her s Bi omass Bur ni ng Tr anspor t Domest i c Fossi l Fuel s Domest i c Bi of uel s I ndust r y Power CO kt BAU 2010 BAU 2020 BAU 2030 BAU Bi omass Bur ni ng Tr anspor t Domest i c I ndust r y Power VOC

32 Sectoral distribution kt BAU 2010 BAU 2020 BAU 2030 BAU Bi omass Bur ni ng Tr anspor t Domest i c I ndust r y Power kt BAU 2010 BAU 2020 BAU 2030 BAU Bi omass Bur ni ng Tr anspor t Domest i c Industry Power Gener at i on BC Largest emission reduction OC Most effective abatement technology/policy: SO 2 : FGD NOx: SCR CO, BC & OC: advanced stoves or gasification (especially, in rural)

33 Emission Summary Emissions in 2001 SO2 (kt) NOx (kt) BC (kt) OC (kt) CO (kt) NMVOC (kt) Emissions Trends: SO2 NOx BC OC CO NMVOC 2010 BAU/ S 1/ S 2/ BAU/ S 1/ S 2/

34 CMAQ result: SO BAU BAU

35 CMAQ result: NO BAU BAU

36 CMAQ result PM BAU BAU

37 Outlines Background Working framework Scenario development Modeling methodology Modeling results Discussion

38 Discussion Technology based EI Evaluation of enforcement Regional differences of technologies

39 Acknowledgement U.S.EPA for founding support (ICAP, IES) ERI s 2020 China Sustainable Energy Scenario program for energy data U.S.DOE s Dr. David Streets for TRACE-P Emission Inventory U.S.EPA s Dr. Carey Jang for technical support on Medels-3/CMAQ

40 Thanks

41 Approaches to Emission Factors Chemical balance Raw gas factors (SO 2, Primary PM) Technical reports and papers Fuel characteristics, Operating practice, Control equipments Field measurement Final emission factors, Removal efficiency International EF database When lack of local measurement data (BC/OC, VOC)

42 Technical Split for Coal Combustion Devices Lime Good Efficiency Moderate Efficiency Poor Efficiency