Gakuji KURATA Pichnaree Lalitaporn, Minna Guo, Ken Senoo, Naoya - PowerPoint PPT Presentation

1 Kyoto University 19 th AIM International Workshop 13 th – 14 th December, 2013 NIES, Tsukuba, JAPAN Gakuji KURATA Pichnaree Lalitaporn, Minna Guo, Ken Senoo, Naoya Kuramoto Kyoto University

2 Kyoto University Model Simulation of PM 2.5 during January to March 2013 from China to Japan. Observation and model simulation of severe haze event in June 2013 Development of personal exposure model to estimate the health impact 15years trend analysis of Satellite retrieval NO 2 and Aerosol Optical Depth (AOD) around Asian region. Design of framework to estimate co-benefits, especially for residential sector and urban activities.

3 Kyoto University Model Simulation of PM 2.5 during January to March 2013 from China to Japan. • From January to March 2013, US Embassy in Beijing recorded very high PM 2.5 concentration in Beijing. (over 800 μg /m 3 ). • Also, PM 2.5 level at the major cities in China showed Hazardous level (over 250 μg /m 3 ) • During this period, relatively high PM 2.5 concentration was observed in Japan, especially western part of Japan. 900 800 700 Environmental 600 Standard (75 μg /m 3 ) ( μg /m 3 ) 500 400 300 200 100 0 JAN/01 JAN/08 JAN/15 JAN/22 JAN/29 Source: US embassy at Beijing

4 Kyoto University Description of Simulation  Meteorological Model WRF version 3.4 36km x 36km input Meteorological : NCEP FNL  Chemical Transport Model CMAQ version 5.0 Chemical Solver: CB5-Aero4  Period 20 th December 2012 ～ 31 st March 2013  Emission Inventory Combined Emission : EDGAR 4.2 , GEIA and REAS Reference year of Emission: 2008 (apply no Adjustment)

5 Kyoto University 2.5

Kyoto University Daily average of PM 2.5 [ μg /m 3 ] Daily average of PM 2.5 [ μg /m 3 ] Daily average of PM 2.5 [ μg /m 3 ] 6

7 Kyoto University 1. Northern China 2. South-East China 3. South-West China 4. Other Asia Contributions from above four area were calculated . Period: 26 th January 2013 - 4 th February 2013 contribution of Emission from China (Black Carbon) Other SW-China SE-China N-China Obs at W-Japan PM2.5 concentration (ug/m3) Other SW-China SE-China N-China PM 2.5 Contribution (ug/m3) Black Carbon

8 Kyoto University 8 Northern China Coastal China  Contribution from Northern China is large for Korea and Japan.  However, Contribution from Southen inland China reach to Japan, periodically. Southern inland China

9 Kyoto University Observation and model simulation of severe haze event in June 2013 Marina Bay Hotel (Singapore) (BBC news) Causeway （ Malaysia - Singapore ） (Alter net) 21 June 19 June 22 June

10 Kyoto University GRIMM EDM164 PM ： 0.25μm -34um , 31 ch. 1 ～ 2,000,000 particles/L dust mass: 0.1 ～ 6,000μg/m 3 Time resolution : ～ 1 min. Meteorology: wind, temperature, precipitation, RH Operating under SATREPS project 600 ■～ 1μm PM2.5 concentrtion Total PM (Nov 2012 – June 2013) 500 ■ 1 ～ 2.5μm [ μg /m 3 ] ■ 2.5 ～ 4μm 400 ■ 4 ～ 10μm 10 June – 24 June ■ 10μm ～ 300 200 100 11 月 12 月 1 月 2 月 3 月 4 月 5 月 6 月 0

11 Kyoto University  NASA LANCE-FIRMS Database Location of Forest Fire and estimated fire intensity (Based on Satellite Monitoring) [semi-realtime dataset] Fire location during 1 June 2013 ～ 10 July Johor Singapore

12 Kyoto University Description of Simulation WRF version 3.4 & CMAQ version 5.0 Grid size: 16km x 16km, Period: June 1 st - 30 th June 2013 Emission Inventory Anthropogenic and Biogenic EDGAR 4.2 , GEIA and REAS + Forest Fire: NASA LANCE-FIRMS Chemical Component of Biomass Burning at Indonesia

13 Kyoto University Simulation Result ( PM 2.5 ) Simulation Result 20- 23 June 2013 at UTM location OC Major Nitrate components Observation at UTM campus [ μg /m 3 ] ６００ ■～ 1μm ■ 1 ～ 2.5μm ４００ ■ 2.5 ～ 4μm ■ 4 ～ 10μm ２００ ■ 10μm ～ ０

14 Kyoto University Development of personal exposure model to estimate the health impact DALYs attributable to household air pollution Source: WHO(2011)

15 Kyoto University Energy consumption in Household (MJ/year/person) Coal Wood Crop residue

16 Kyoto University Single-Compartment Mass Balance Model under steady-state assumption   1 Se = +   C F vC + m p o   v F V d  Formulation to calculate the concentration.  With Indoor emission ME A 、 B 、 C 1 S における大気汚染物質濃度 ( μg/m 3 ) : 微環境 Pollutant concentration at micro environment (m) ( μg /m 3 ) = + e C ( F vC ) ( ) + 0 m p Pollutant concentration at Outdoor ( μg /m 3 ) : 屋外大気汚染物質濃度 ( μg/m 3 ) v F V d : 浸透率 (-) Penetration Factor (-)  w/o indoor emission ME D : 換気回数(1/hr) Air Exchange Rate (1/hr) F v = p C C Deposition rate (1/hr) : 除去率(1/hr) + m o v F d : 一時間当たり燃料消費量 (KJ /hr) Energy consumption (KJ/hr)  Outdoor ME E Emission Factor ( μg /KJ) : 排出係数 ( μg/KJ) Volume of Micro Environment(m 3 ) : 部屋の体積(m 3 ) = C C m o 16

1000 100 200 300 400 500 600 700 800 900 Kyoto University 0 0 1-4 ・ Rural 5-14 ・ 15-19 ・ EMP 15-19 ・ UEM 1000 1200 20-24 ・ UEM 200 400 600 800 20-24 ・ EMP 0 25-29 ・ UEM Average PM2.5 exposure (Upper: Urban Lower: Rural) ME-A(Indoor-Cooking) ME-B(Indoor-Heating) ME-C(Indoor-Lighting) ME-D(Indoor-w/o-emission) ME-E(H)(Outdoor-High) ME-E(M)(Outdoor-Mid) ME-E(L)(Outdoor-Low) 25-29 ・ EMP Beijing 30-34 ・ UEM Tia ianjin in 30-34 ・ EMP He Hebe bei 35-39 ・ UEM 35-39 ・ EMP Shanxi 40-44 ・ UEM Inner… 40-44 ・ EMP 45-49 ・ UEM Liaoning 45-49 ・ EMP Jilin ilin 50-54 ・ UEM 50-54 ・ EMP Heilon… Male 55-59 ・ UEM Shanghai 55-59 ・ EMP 60-64 ・ UEM Jiangsu 60-64 ・ EMP Zhejiang 65-69 ・ UEM Anhui 65-69 ・ EMP 70-74 ・ UEM Fujian 70-74 ・ EMP Jiangxi 0 1-4 ・ Shand… 5-14 ・ Hena enan 15-19 ・ EMP 15-19 ・ UEM Hubei 20-24 ・ UEM Hunan Female 20-24 ・ EMP 25-29 ・ UEM Guang… 25-29 ・ EMP Guangxi 30-34 ・ UEM Hai ainan an 30-34 ・ EMP 35-39 ・ UEM Chong… 35-39 ・ EMP Sichuan 40-44 ・ UEM 40-44 ・ EMP Guizhou 45-49 ・ UEM Yunnan 45-49 ・ EMP 50-54 ・ UEM Tibe ibet 50-54 ・ EMP Shaanxi 55-59 ・ UEM 55-59 ・ EMP Gansu Rural 60-64 ・ UEM Qinghai 60-64 ・ EMP Ningxia 65-69 ・ UEM 65-69 ・ EMP Xinjiang 70-74 ・ UEM 17 70-74 ・ EMP

18 Kyoto University 15years trend analysis of Satellite retrieval NO 2 and Aerosol Optical Depth (AOD) around Asian region. Trend at Beijing Trend at Remote area Trend at Bangkok 50 0.4 12 10 40 0.3 8 30 0.2 6 20 4 0.1 10 2 0 0 0 1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010

19 Kyoto University Design of framework to estimate co-benefits, especially for residential sector and urban activities. To quantify the co-benefit of LCS countermeasure to reduction of health impact of air pollution Indoor Death Health Exposure Dise a se LCS policies Impact Emission inventory Micro Emission Co-benefit (Mesh data) Environment Inventory Analysis (Regional) Downscaling Outdoor Impact Calculated ArcGIS Assessment Concentration Time variation Boundary (Annual, Daily) Emission Condition Mesh data Chemical GCM CM CMAQ CM Output Transport Meteo. WRF Field Model Landuse Terrain Meteorological Model

20 Kyoto University Reduction by CO 2 measure. Additional Reduction by CH 4 + BC measure. Shindell et al ., (2012) • Health Impact of SLCP(expecially PM 2.5 and Ozone) is very large. • At the same time, SLCP contribute global radiative forcing. • Recently, It is said that the rapid reduction of CH 4 and BC can reduce the temperature increase around 0.5 ℃ soon after the reduction.

21 Kyoto University • Most BC-rich source emit OC simultaneously. So, net radiative forcing by such source negative. Reduction of BC-rich source enhances • global warming. Bond et. al ., JGR (2012) Open Burning Industrial Residential

22 Kyoto University  Further development of emission estimation method for residential sector. (Collect additional data for energy consumption and cooking and heating equipment)  Development of urban pollution model which can manage roadside high concentration to improve personal exposure model.  Future estimation under BaU case and LCS scenarios.  Validation of the model and historical health impact. Thank you for your attention.