Gakuji KURATA Kyoto University 1 2 Mortality ( 1000 persons ) - PowerPoint PPT Presentation

18 th AIM International Workshop 14 th – 16 th December, 2012 NIES, Tsukuba, JAPAN Gakuji KURATA Kyoto University 1

2 Mortality ( × 1000 persons ) (WHO Global Health Risks Report, 2009)  At the Least Developed Countries, Air Pollution is still major threat to human health. 2

Emission of Black Carbon Emission of Sulfur Simulated Global dimming at the surface due to ABCs 3

To quantify the co- benefit of LCS countermeasure to reduction of health impact of air pollution Indoor Death Health Health Exposure Exposure LCS policies LCS policies Disease Impact Impact Emission inventory Micro Micro Emission Co-benefit Co-benefit (Mesh data) Environment Environment Inventory Analysis Analysis (Regional) Downscaling Outdoor Impact Calculated ArcGIS Assessment Concentration Time variation Boundary (Annual, Daily) Emission Condition Mesh data Chemical GCM CMAQ Output Transport Meteo. WRF Field Model Landuse Terrain Meteorological Model 4

To quantify the co-benefit of LCS countermeasure to reduction of health impact of air pollution Roadside Using the Satellite Developing the monitoring of Developing the retrieval of trace Asian extension of PM 2.5 and Gaseous Indoor Air Quality species to improve SMOKE emission species and Exposure an emission Inventory system model in Iskandar information of Air Pollutants Malaysia 5

SATELLITE OBSERVATIONS NO 2 CH 2 O CO Ozone Aerosol SO 2 .............. ............... Monthly average of NO 2 Vertical Column concentration (November, 2012) by OMI 6

SATELLITE OBSERVATIONS: Temporal & seasonal variability of NO 2 columns Mid-latitude zone Low-latitude zone Equator zone Mid/Low – latitude zone:  Maximum: wintertime (Nov-Feb)  Minimum: summertime (Jun-Aug) Equator zone:  Maximum: dry season (Jun-Aug)  Minimum: rainy season (Dec-Feb) 15 December 2012 7

NO x satellite data REAS NO x emission vs. satellite NO 2 columns MACCity NO x emission vs. satellite NO 2 columns  Most of the cities located in mainland  give relatively good relationship (r > 0.7)  The cities located near coastal area  r is quite low  the inaccuracy of the emission & effects of met. 8

Model simulation Satellite data OMI vs. GEOS-Chem simulated NO 2 columns Mid-latitude zone OMI GEOS-Chem - Year 2005 - Year 2005 - 13:40LT - 12:00-14:00LT - Monthly data - Monthly data Model results underestimate satellite data by the factor around 3-5 Mid/Low – latitude zone:  Maximum: wintertime (Nov-Jan)  Minimum: summertime (Jun-Aug) Equator zone:  Maximum: dry season  Minimum: rainy season Low-latitude zone Equator zone 15 December 2012 9 9

Development of Thailand Emission inventory Thailand Emission Inventory for year 2005  Developed by:  Chatchawan Vongmahadlek , Pham Thi Bich Thao, Narisara Thongboonchoo Joint Graduate School of Energy and Environment, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand  Boonsong Satayopas Department of Civil Engineering, Chiang Mai University, Chiang Mai, Thailand  Spatial Allocation Profiles: a 1- by 1-km resolution Emission Sources Anthropogenic Sources  - Industrial stationary source: power plants, industrial facilities and industrial processes - Mobile source: on-road & nonroad sources - Nonindustrial stationary source: residential households, biomass burning, NH3 sources, incinerators, gas stations, and smoking tobacco Natural Sources  - NMVOC emissions from vegetation - NO x emissions from: the soil of forestry, the soil of agricultural farms and lightening strikes  Most emissions species are dominant in anthropogenic sources (92–99%)  Except NMVOC emissions  highly contributed by natural sources (53.5%). 10

Thailand emission inventory 2005 SO 2 CO MNVOC NH 3 PM 10 PM 2.5 BC OC 11

Thailand NO x emissions 2005 Area Source Point Source Mobile Source Resolution: 1x1 km 2 Resolution: 1x1 km 2 Resolution: 1x1 km 2 12

Thailand NO x emissions Satellite NO 2 columns 13 15 December 2012 13

Ground monitoring NO 2 Satellite NO 2 columns NORTH NORTHEAST CENTRAL EAST SOUTH EAST SOUTH WEST 14

Ground monitoring NO 2 vs. Satellite NO 2 columns Central Thailand: BKK (1) Northeast: Khonkaen (12) East: Rayong (6) Southwest coast: Phuket (13) North: Chiangmai (8) Southeast coast: Songkhla (14) 15

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Counting of Road transportation Counted the number of transportation at 3 locations along the major highway in Johor Bahru, Malaysia Loction1 Loction2 Loction3 Vehicle Classification 17

Equipments 20m DUSTTRAK Ⅱ HAZ Grimm Camera DUSTTRAK Ⅱ 8532 6000 EDM164 8532 30m PM:1.0~10 μ m HAZ6000 CO 2 : 0-5,000 ppm CO : 0-100 ppm 50m NO2 : 0- 5,000 ppb SO2 : 0-5,000 ppb Ozone : 0-1000 ppb VOC : 0-100ppm GRIMM EDM164 PM ： 0.25 μ m-34um , 31 channels Move DUSTTRAK Ⅱ 8532 Meteorology: wind, temperature, per 1 hour(total 3 hours) precipitation, RH 18

Johor to Skudai Skudai to Johor Loction1 Loction2 Loction3 19

Sample of Observed data PM1 0.08 0.075 20m 50m 0.07 100m 0.065 Mass (mg/m 3 ) 0.06 PM concentration 0.055 0.05 from DUSTTRAK Ⅱ 0.045 0.04 0.035 (20m,50m and 100m point) 0.03 0.025 0.02 0.015 0.01 0.005 0 10:39:16 10:46:46 10:54:16 11:01:46 11:09:16 11:16:46 11:24:16 11:31:46 11:39:16 11:46:46 11:54:16 12:01:46 12:09:16 12:16:46 12:24:16 12:31:46 12:39:16 12:46:46 12:54:16 13:01:46 13:09:16 13:16:46 13:24:16 13:31:46 Time(hour,min,sec) Next step PM10 We will use the Gaussian 0.08 0.075 Plume model from line source 0.07 20m 0.065 50m Mass (mg/m 3 ) to reproduce the 0.06 100m 0.055 concentration variation and 0.05 0.045 compare with the observation. 0.04 0.035 0.03 0.025 0.02 0.015 0.01 0.005 0 11:10:44 11:18:14 11:25:44 11:33:14 11:40:44 11:48:14 11:55:44 12:03:14 12:10:44 12:18:14 12:25:44 12:33:14 12:40:44 12:48:14 12:55:44 13:03:14 13:10:44 13:18:14 13:25:44 13:33:14 13:40:44 13:48:14 13:55:44 14:03:14 evaluate the emission factor of PM2.5 from the road transportation. Time(hour,min,sec) 20

We have developed the emission inventory for atmospheric pollutants for Asian • countries. To use these data for the input of Chemical Transport Model (Air Quality Model), • following information is not enough.  Spatial distribution ( Spatial Downscalling)  Seasonal and Diurnal variation of emission  disaggregation of NMVOC to model chemical species. Asian extension of SMOKE system 21

Seasonal / Disaggregation Diurnal of Chemical Inventory Variation Species GIS Spatial Distribution MIMS SMOKE BenMAP CMAQ WRF Exposure Model Meteorological Model 22

MIMS input Shapefiles focused on south Malay Peninsula Road Network Urban Area Landuse (Cattle) and Population  Current GIS input is not enough ...  Road Network is only covers major highway.  Population mesh is coarse and not so accurate  replace the GIS data for input to MIMS processor. 23 23

Emission Processing SMOKE-Asia v1.1 CO SO 2 System (Konkuk Univ) Spatial Domain East Asia SMOKE Processing 5/26/2008 ~ Period 6/01/2008 Emission Inventory Data 2008 Chemical Mechanism Carbon Bond Mechanism IV (CB04) Meteorological Data WRF-MCIP Processing Target CO, NOX, SO 2 , VOC, Materials PM 10 NO NO 2 PM 10 24 24

To complete current project in next several months. Indoor Death Health Health Exposure Exposure LCS policies LCS policies Disease Impact Impact Emission inventory Micro Micro Emission Co-benefit Co-benefit (Mesh data) Environment Environment Inventory Analysis Analysis (Regional) Downscaling Outdoor Impact Calculated ArcGIS Assessment Concentration Time variation Boundary (Annual, Daily) Emission Condition Mesh data Chemical GCM CMAQ Output Transport Meteo. WRF Field Model Landuse Terrain Meteorological Model 25