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

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Gakuji KURATA

18th AIM International Workshop 14th – 16th December, 2012 NIES, Tsukuba, JAPAN

Kyoto University

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Mortality ( ×1000 persons ) (WHO Global Health Risks Report, 2009)

At the Least Developed Countries, Air Pollution is still major threat to human

health.

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

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To quantify the co-benefit of LCS countermeasure to reduction of health impact of air pollution

Downscaling Emission Inventory (Regional) GCM Output Landuse Terrain

ArcGIS WRF

Emission Mesh data Meteo. Field Calculated Concentration

LCS policies LCS policies

Health Impact Health Impact

Boundary Condition

Emission inventory (Mesh data) Meteorological Model Chemical Transport Model

CMAQ

Time variation (Annual, Daily)

Co-benefit Analysis Co-benefit Analysis

Death Disease

Impact Assessment

Exposure Exposure

Outdoor

Micro Environment Micro Environment

Indoor

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Roadside monitoring of PM2.5 and Gaseous species in Iskandar Malaysia Using the Satellite retrieval of trace species to improve an emission information Developing the Asian extension of SMOKE emission Inventory system

f Air Pollutants

Developing the Indoor Air Quality and Exposure model

To quantify the co-benefit of LCS countermeasure to reduction of health impact of air pollution

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SATELLITE OBSERVATIONS

Monthly average of NO2 Vertical Column concentration (November, 2012) by OMI

NO2 CH2O CO Ozone Aerosol SO2

.............. ...............

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SATELLITE OBSERVATIONS:

Temporal & seasonal variability of NO2 columns

15 December 2012

Mid/Low – latitude zone:  Maximum: wintertime (Nov-Feb)  Minimum: summertime (Jun-Aug) Equator zone:  Maximum: dry season (Jun-Aug)  Minimum: rainy season (Dec-Feb)

Mid-latitude zone Low-latitude zone Equator zone

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NOx satellite data

REAS NOx emission vs. satellite NO2 columns MACCity NOx emission vs. satellite NO2 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.

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Model simulation Satellite data

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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

OMI vs. GEOS-Chem simulated NO2 columns

GEOS-Chem

Year 2005
12:00-14:00LT
Monthly data

OMI

Year 2005
13:40LT
Monthly data

Mid-latitude zone Low-latitude zone Equator zone

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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

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Natural Sources

NMVOC emissions from vegetation
NOx emissions from: the soil of forestry, the soil of agricultural farms and lightening strikes

Development of Thailand Emission inventory

 Most emissions species are dominant in anthropogenic sources (92–99%)  Except NMVOC emissions  highly contributed by natural sources (53.5%).

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

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Thailand emission inventory 2005

SO2 CO MNVOC PM10 PM2.5 BC OC NH3

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Thailand NOx emissions 2005

Area Source Resolution: 1x1 km2 Point Source Resolution: 1x1 km2 Mobile Source Resolution: 1x1 km2

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Thailand NOx emissions Satellite NO2 columns

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Ground monitoring NO2 Satellite NO2 columns

NORTH NORTHEAST CENTRAL EAST SOUTH WEST SOUTH EAST

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Ground monitoring NO2 vs. Satellite NO2 columns

Central Thailand: BKK (1) East: Rayong (6) North: Chiangmai (8) Northeast: Khonkaen (12) Southwest coast: Phuket (13) Southeast coast: Songkhla (14)

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Loction1 Loction3 Loction2

Counted the number of transportation at 3 locations along the major highway in Johor Bahru, Malaysia

Counting of Road transportation

Vehicle Classification

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Grimm EDM164 HAZ 6000 DUSTTRAKⅡ 8532 Camera GRIMM EDM164

PM：0.25μm-34um , 31 channels Meteorology: wind, temperature, precipitation, RH

HAZ6000

CO2 : 0-5,000 ppm CO : 0-100 ppm NO2 : 0- 5,000 ppb SO2 : 0-5,000 ppb Ozone : 0-1000 ppb VOC : 0-100ppm

DUSTTRAKⅡ 8532

PM:1.0~10μm

Move DUSTTRAKⅡ8532 per 1 hour(total 3 hours)

20m 30m 50m

Equipments

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Loction1 Loction3 Loction2 Johor to Skudai Skudai to Johor

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0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055 0.06 0.065 0.07 0.075 0.08 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

Mass (mg/m3) Time(hour,min,sec) PM1

0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055 0.06 0.065 0.07 0.075 0.08 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

Mass (mg/m3) Time(hour,min,sec) PM10

Sample of Observed data

20m 50m 100m

PM concentration from DUSTTRAKⅡ (20m,50m and 100m point)

20m 50m 100m Next step We will use the Gaussian Plume model from line source to reproduce the concentration variation and compare with the observation. evaluate the emission factor of PM2.5 from the road transportation.

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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

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WRF CMAQ Exposure Model BenMAP

Meteorological Model

MIMS GIS SMOKE Inventory Seasonal / Diurnal Variation

Spatial Distribution

Disaggregation

f Chemical

Species

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MIMS input Shapefiles focused on south Malay Peninsula

Road Network Landuse (Cattle) Urban Area 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.

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Emission Processing System SMOKE-Asia v1.1 (Konkuk Univ) Spatial Domain East Asia SMOKE Processing Period 5/26/2008 ~ 6/01/2008 Emission Inventory Data 2008 Chemical Mechanism Carbon Bond Mechanism IV (CB04) Meteorological Data WRF-MCIP Processing Target Materials CO, NOX, SO2, VOC, PM10

CO SO2 NO2 NO PM10

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To complete current project in next several months.