120 years of Modeling Emission Inventories in Support of Climate and Air Quality Studies over Asia

Bujeon Jung1, Ki-Chul Choi1, Ji-Hyun Seo1, Taehyung Kim2 , Jung-Hun Woo1*1Department of Advanced Technology Fusion, Konkuk University, Seoul, Korea, 2Department of Environmental Engineering, Konkuk University, Seoul, Korea

Department ofAdvanced Technology

Fusion

Selection of Global and Regional Emission Inventories

region SRESID

B40_1980 B40_2000 variationemission % emission %Canada 1 7.15 0 14.63 1 7.48

USA 2 33.82 1 86.38 4 52.55 Central 3 12.57 0 64.50 3 51.92

South America 4 40.55 1 213.99 10 173.44 Northern Africa 5 2380.88 53 143.60 7 -2237.28 Western Africa 6 152.20 3 93.25 4 -58.95 Eastern Africa 7 5.60 0 18.67 1 13.07

Southern Africa 8 32.34 1 72.89 3 40.55 OECD Europe 9 18.30 0 51.45 2 33.16 Eastern Europe 10 13.36 0 33.32 2 19.96 Former USSR 11 7.70 0 35.25 2 27.55 Middle East 12 1657.50 37 212.11 10 -1445.38 South Asia 13 16.47 0 277.68 13 261.21 East Asia 14 41.19 1 524.29 25 483.09

South East Asia 15 66.70 1 237.25 11 170.55 Oceania 16 0.87 0 3.18 0 2.32 Japan 17 0.06 0 0.24 0 0.18

Variation of regional BC emissionVariation of regional BC emission Unit: Gg

• B40 : biofuel, residential

IPCC SRES A1B Emissions ScenarioIPCC SRES A1B Emissions Scenario

Northern Africa

Middle East

B101%

B200%

B301%

B4062%

B510%

F101%

F200%

F300%

F4018%

F512%

F543%

F571%

F581%

F801%

I106%

I200%

I500%

W403%

B10:Industry

B20:Power generation

B30:Charcoal production

B40:Residential

B51:Transport road

F10:Industry

F20:Power generation

F30:Other transformation

F40:Residentials

F51:Transport road

F54:Transport land non-road

F57:Transport air

F58:Transport international Shipping

F80:Oil production

I10:Iron and steel

I20:Non-ferro metals

I50:Pulp&paper

W40:Waste incineration

Sectoral BC emissionsSectoral BC emissions

Importance of Asian Emissions (Black Carbon case)Importance of Asian Emissions (Black Carbon case)

• Northern Africa, Middle East : Decreasing about 90%• South Asia, East Asia : Increasing 11~16 times

Region Sector Reference

Korea, Japan, Taiwan

Power plants EDGAR temporal variationIndustrial comb. EDGAR temporal variationIndustrial process EDGAR temporal variationSolvent use EDGAR temporal variation

Temporal allocation• EDGAR monthly allocation profiles were mostly used for the allocation• For Korea, Japan, and Taiwan, D.Y. Kim(1998)’s profiles were used to allocate annual residential combustion to months, and monthly profiles for mobile sources in SMOKE were used to allocate residential and mobile

References for Temporal Allocation

IntroductionThe global climate change may affect regional air quality, especially over the high pollutant emission regions such as Asia. An integrated modeling system of climate and air quality should play important role to understand and to predict those impacts. The more comprehensive modeling emissions inventories with emphasis on Asian emissions, therefore, were developed in support of the NIER (National Institute of Environmental Research, Korea) integrated modeling framework.

We developed an emission processing system that can process various emissions inventories, such as EDGAR-HYDE, EDGAR FT2000, Zhanget al. (2009), Streets et al. (2004), and Bond et al. (2004). Using this system, we prepared historical modeling emission inventories of CO, BC,OC, NH3, NOx, SO2, NMVOC (speciated) and GHGs (CO2, N2O, CH4) for 21 years (1980~2000). we are now generating future modelingemission inventories of two IPCC SRES scenarios (A2, B1) for 100 years (2001~2100) .

OFF/ON LineOFF/ON LineCouplingCoupling

Climate Change ModelClimate Change Model

Atmos. Chemistry ModelAtmos. Chemistry ModelRegionalRegional

Atmos. Chemistry ModelAtmos. Chemistry Model

Regional Regional Climate Change ModelClimate Change Model

Global ScaleGlobal Scale Regional ScaleRegional Scale

Regional ScaleRegional ScaleGlobal ScaleGlobal Scale

OFF/ON LineOFF/ON LineCouplingCoupling

DownscalingDownscaling

DownscalingDownscaling

Integrated System OFF/ON LineOFF/ON Line

CouplingCoupling

MM5MM5

CMAQCMAQ

CCSMCCSM

GEOSGEOS--ChemChem

Select the best available emission inventories from existing researches, mosaic and merge inventories, project inventories to the past/future, temporally allocate emissions to months, and speciate pollutants to GEOS-Chem model species are the processes that were needed in support of Global chemistry modeling.

GLOBAL EMISSIONS PROCESSING (KU-EPS *)

REGIONAL EMISSIONS PROCESSING (SMOKE-Asia)

Since we focus on East-Asia air pollution, further emissions processing of the developed emissions inventory for the East-Asia domain was conducted. We developed a SMOKE-based emissions modeling system for Asia(SMOKE-Asia) to accomplish it.

* KU-EPS : Konkuk University Emission Processing System

Selection of Global and Regional Emission Inventories

In this study, we extensively reviewed and analyzed a number ofexisting global(upper left) and regional(upper right) scale emissioninventories. After that, we developed a most up-to-date base yearemissions inventory. The evaluation was based on the availability andaccessibility of inventory data, spatial-temporal coverage andresolution, and so on. We tried to develop 11-years (1997-2007)transient emissions inventory to conduct initial- and near-termatmospheric chemistry modeling. Results from EDGAR(Olivier etal., 2002), RIVM IMAGE model(Bouwman et al., 2006), TRACE-P2000 (Streets et al., 2003), INTEX 2006 (Zhang et al., 2009), REAS(Ohara et al., 2007), and other literatures were used for it.

Global Emission Inventories Regional Emission Inventories

Global and Regional Emission Domains

Asia

Schematic Diagram of Global and Regional Emissions Processing System SMOKE-AsiaSMOKE-AsiaKU-EPSKU-EPS

Solvent use EDGAR temporal variationResidential D. Y. Kim(1998), Ph. D. thesisTransportation SMOKE temporal allocation factor

China

Power plants Wang et al.(2005)Industry Streets et al.(2003), Wang et al.(2005)Transportation Streets et al.(2003), Wang et al.(2005)Residential (F+B) Streets et al.(2003)

Southeast Asia

Power plants EDGAR temporal variationIndustrial comb. EDGAR temporal variationIndustrial process EDGAR temporal variationSolvent use EDGAR temporal variation

Residential Streets et al.(2003) using South China average value

Transportation EDGAR temporal variation

India

Power plants EDGAR temporal variationIndustrial comb. EDGAR temporal variationIndustrial process EDGAR temporal variationSolvent use EDGAR temporal variation

Residential Streets et al.(2003) using South China average value

Transportation EDGAR temporal variation

Other Asia Country

Power plants EDGAR temporal variationIndustrial comb. EDGAR temporal variationIndustrial process EDGAR temporal variationSolvent use EDGAR temporal variationResidential EDGAR temporal variationTransportation EDGAR temporal variation

emissions

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

1 2 3 4 5 6 7 8 9 10 11 12

EDGAR EDGAR & Streets et al.,(2003) Streets et al.(2003)

(Month)

(Fra

ctio

n of

Ann

ual E

mis

sion

)

NH3 Temporal Variation Updates NH3 Temporal Variation Updates

Regional Emission Processing ResultRegional Emission Processing Result 2006/01/01(holiday) 2006/02/01(weekday)

CMAQ-ready modeling NOx

emissions inventory for the first day of

each monthin the year 2006

JAN FEB MAR APR

MAY JUN JUL AUG

2006 2020 2050 2100

NO

x

A2

B1

Rea

ctiv

eVO

C A2

B1

• KU-EPS processes emission processing components, except spatial allocation•An emission modeling and processing system using Sparse Matrix Operator Kernel Emissions(SMOKE) for Asia region was developed in this study • Higher resolution regional EI developed using GIS information were developed in support of SMOKE-Asia • INTEX2006 source sectors were further divided into EDGAR source sectors, then were mapped to US EPA SCCs in support of SMOKEprocessing

Unit : Tg 1980 (a) 2000 (b) Difference (c) % (d)

CO 454.7 559.5 104.8 23.0

SO2 122.9 127.2 4.2 3.5

NOx 69.2 98.9 29.7 42.9

NH3 40.2 56.4 16.1 40.1

BC 7.3 4.0 -3.2 -45.1

OC 7.5 7.1 -0.4 -5.5

NMVOC 112.8 138.6 25.8 22.9

CO2 20305.9 27618.2 7312.32 36.01

CH4 254.2 301.6 47.5 18.7

N2O 9.6 11.3 1.7 17.1

After switching the Asia emissions

Unit : Tg 1980 (a) 2000 (b) Difference (c) % (d)

CO 424.3 532.9 108.6 25.6

SO2 126.9 145.3 18.4 14.5

NOx 67.3 101.9 34.6 51.4

NH3 44.9 67.4 22.5 50.1

NMVOC 126.2 137.6 11.4 9.0

CO2 20694.8 27136.2 6441.4 31.1

CH4 261.7 297.7 36.0 13.7

N2O 9.6 11.3 1.7 17.1

Before switching the Asia emissions (c) (b) – (a)(d) (c) / (a) × 100

Emission Trend (1980 -2100)

• BC, OC : Using Bond et al.(2004) emission data in ARCTAS Pre-mission Inventory

• N2O : EDGAR 3.2 FT 2000 emissions data (Not switched)

- East-Asia emissions for the first day of each month in the year 2006, GMT 7:00 (Korea Standard Time PM 4:00)

- Winter season got a higher NOx emissions than summer season- The daily profile of holiday was applied to the first day of JAN, MAR, APR, JUL, OCT which represents lower emission.

Holiday

SEP OCT NOV DEC

ON-GOING WORK• The GEOS-Chem/CMAQ air chemistry modeling using CCSM3/MM5 meteorological fields and transient emissions data are being conducted to understand impact of emissions change on global and regional air quality • Further analysis of the activity data and emission factors which causing emission discrepancies between global and regional emissions•Generating future modeling emission inventories of other IPCC SRES scenarios (A1B, A1FI, A1T, B2) for 100 years (2001~2100)

This research was performed under the support of "National Comprehensive Measures against Climate Change(III)"(Grant No. 1700-1737-322-210-13)

ACKNOWLEDGEMENT

Historical emissionsHistorical emissionsForecasting emissionsForecasting emissions

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CO

CMAQ-ready modeling NOx andreactive VOC emissions inventory

(Annual-average)

• Emission growth of NOx and NMVOC (compare to year 2006)- A2 Scenario : NOx emission and VOC emission in year 2100 increase 215% and 65% respectively- B1 Scenario : NOx emission and VOC emission in year 2100 decrease 71% and 60% respectively.

•The change of dominant source (portion, unit:%)

2006 2020 2050 2100

NO

x Mobile Sources

(≒ 34%)

A2 Mobile

(≒ 29%)

Power generation(≒27%)

Power generation(≒30%)Electric Utility

(≒30%)

B1 Mobile

(≒29%)

Power generation(≒20%)Electric Utility

(≒20%)

Industrial combustion

(≒22%)

NM

VO

C

Residential Source

(≒ 36%) Mobile Sources

(≒ 33%)

A2

Residential Source(≒ 34%) Mobile Sources

(≒ 34%)

Mobile Sources

(≒ 34%)

Residential Source

(≒ 23%) Mobile Sources

(≒ 21%)

B1

Mobile Sources

(≒ 36%)

Industrial processes(≒35%)

Wastetreatment(≒ 56%)

Ozone Precursor Emission for Asia regionOzone Precursor Emission for Asia region