関東地域の冬季における高濃度大気汚染の数値シミュ

•URBAN POLLUTION MODELING IN WINTER
– JAPAN EXPERIENCE
Toshimasa Ohara
Yuki Otsuka
Seiji Sugata
Tatsuko Morikawa
(Shizuoka University)
(Shizuoka University)
(National Inst. for Environ. Studies)
(Petroleum Energy Center )
Background
High wintertime concentrations of aerosol particles are
serious atmospheric environment problem in Japan,
•
especially in the Tokyo Metropolitan Area (TMA).
Trends of air quality in Tokyo
Significant 90
improvement
SO2(ppb)
80
CONCENTRATION
70
NO2(ppb)
CO(0.1ppm)
OX(ppb)
NMHC(pphmC)
SPM(μg/m3)
Small improvement
60
50
40
30
20
10
0
75
80
85
YEAR
90
95
00
In the past three decades,
the urban air pollution in
Japan has gradually
improved due to a series of
emission control.
Still however, the TMA
experiences unacceptable
air quality with high levels
of particle matter during
winter.
Objectives
●To clarify the mechanism of urban pollution formation in the
• TMA, an intensive field study was conducted in December
1999 covering the TMA by the Japan Clean Air Program
(JCAP)*. These observations were successful in detecting
typical episodes of urban pollution in the TMA during winter.
* Japan Clean Air Program (JCAP), which was launched in 1997 by the Petroleum Energy Center, in
collaboration with automobile and oil industries in Japan.
●This presentation focused on the current application results of
regional three-dimensional model to the JCAP field campaign
in December 1999 in order to analyze the formation
mechanism of the heavy air pollution of the aerosol particles
and those precursors in winter in the TMA.
Characteristics of urban pollution in the TMA
(1) Complicated meteorology by terrain complexities
• The terrain complexities generate complex local wind
circulation such as land/sea and mountain/valley flows, and
these wind circulation system play a significant role for the
urban air quality.
(2) Trans-boundary pollution
The trans-boundary pollution from Asian countries influence
urban pollution.
(3) Major emission is automobile
Major emission source is related in automobiles likewise in
many mega-cities in the world.
Characteristic of the urban pollution in the TMA (1)
The terrain complexities generate complex local wind circulation
such as land/sea and mountain/valley flows, and these wind
circulation
system play a significant role for the urban air quality.
•
Meso-front
Strong inversion layer
Mizuno and Kondo, 1992
Kanto Plain
Tsukuba
Calm
Meso-front
Tokyo
Topography around the TMA
Ohara and Uno, 1997
Characteristic of the urban pollution in the TMA (2)
The trans-boundary pollution from Asian countries
influence urban pollution.
Ammonium
Nitrate
EC
OC
Annual spatial distribution
for surface sulfate (μg/m3)
Average surface aerosols in spring, 2001
Characteristic of the urban pollution in the TMA (3)
Major source is the automotive emission likewise
•in many mega-cities in the world.
Line sources from automobile emissions
Contribution of emissions
related in automobiles
Emission map around the TMA
NOx
SO2
NMVOC
CO
59%
25%
40%
90%
EC
OC
90%
95%
•
2. JCAP field campaign in winter, 1999
Overview of observations
To clarify the mechanism of urban pollution formation in the TMA, an intensive field study,
including aircraft flights and continuous surface measurements of aerosol mass
at •18 sites, was conducted in December 1999 covering the TMA by the JCAP.
Surface observations
Upper Observations
Intensive study days: 7 to 11, December
Time variations for surface NO2
Two typical pattern of
severe pollution
(a) Meso-front type
Northern part of the front
・ Stagnation
and inversion layer
・ Heavy pollution
. Southern part of the front
・ Strong wind
・ Clean
09 December, 1999
(a) Meso-front type
Meso-front
0600 JST
10 December, 1999
(b) Wide stagnation type
Over the wide area of TMA
・ Weak wind
・ Heavy pollution
0900 JST
1200 JST
1800 JST
(b) Wide stagnation type
1500 JST
2100 JST
Typical heavy pollution episodes
(a) Meso-front type
airflow
SPM
1800 JST 9 Dec., 1999
Aerosol
composition
12/9 Average per Day
60.0
Polluted
PM2.5 Cl-　50.0
SPM(μｇ/m3)
F
O
K
Y
Clean
PM2.5 Others
40.0
PM10-2.5 Others
PM2.5 OC
30.0
PM2.5 EC
20.0
PM2.5 NH4+
PM2.5 SO42-
10.0
PM2.5 NO3-
(b) Wide stagnation type
airflow
2100 JST 10 Dec., 1999
fukaya
omiya2
omiya1
Meso-front
kudan
yokosuka
0.0
Y K O1 O2 Fpoint
South
SPM
60.0
Front North
Aerosol
composition
12/10 Average
PM2.5 Cl-　50.0
PM2.5 Others
F
O
K
Y
SPM(μｇ/m3)
40.0
PM10-2.5 Others
30.0
PM2.5 OC
PM2.5 EC
20.0
PM2.5 NH4+
10.0
PM2.5 SO42PM2.5 NO3-
fukaya
omiya2
omiya1
Heavy pollution in
the wide area
kudan
0.0
yokosuka
Calm
Y K O 1 O2 F
point
75 m height
•
3. Model and simulation conditions
To simulate the urban pollution in the TMA
(1) Complicated meteorology by terrain complexities
Coupled model of RAMS and CMAQ with horizontal and
vertical nesting
(2) Trans-boundary pollution
Three nested grids ( East Asia + Japanese Island + TMA )
(3) Major emission is automobile
Emission inventory for automobile source developed by the
JCAP
Model Domains
East Asia (Grid1)
Central Japan (Gird2)
Kanto Plain (Grid3)
< Number of grid points >
66(x)×58(y)×20(z)
< Horizontal grid interval >
44.8 km (x, y)
< Depth of 1st layer >
100 m
50(x)×50(y)×20(z)
11.2 km (x, y)
40(x)×50(y)×26(z)
5.6 km (x, y)
100 m
Top of vertical level : 10 km
Simulation period
: December 6 to 11, 1999
Evaluation area
：Tokyo Metropolitan Area (TMA)
20 m
Height above S.L. (m)
ＴＭＡ
Simulation model
Regional Met. Model
(CSU-RAMS 4.3)
3-D High Frequency Met. Data Set
FDDA
ECMWF Met. data
Input Parameters
（SST, Topography ・・）
Emission Data
Chemical Transport Model
(CMAQ)
Gases, Aerosols
NOx, SO2, CO, NH3, EC, OC,
NMVOC (including biogenic)
Grid 1: Carmichael & Streets (2001) etc.
Grid 2, 3 : JCAP
Boundary Concentrations
Initial Concentrations
Description of meteorological model
CSU-RAMS Ver.4.3
（Colorado State University - Regional Atmospheric Modeling System)
・ Map projection: Polar-stereographic
・ Vertical coordinate system: σz terrain-following
・ Non-hydrostatic
・ Cloud: Kuo-type scheme
・ Surface layer: Louis scheme
・ Vertical diffusivity: Mellor & Yamada scheme (2.5level)
・ Four-dimensional data assimilation:
ECMWF Data Sets (Analysis, Lon.-Lat.=0.5deg, Δt=6 hours)
・ Two-way nesting
Description of Chemical Transport Model
Models-3/CMAQ（Models-3 Community Multiscale Air Quality)
developed by Byun and Ching (1999) of U.S. EPA
・Advection with piece-wise parabolic method (PPM)
・Vertical diffusion with K-theory parameterization
・Deposition flux as bottom boundary condition for the vertical
diffusion
・Mass conservation adjustment scheme
・Horizontal diffusion with scale dependent diffusivity
・Carbon Bond 4 (CB-4) chemistry mechanism with isoprene
chemistry
・QSSA gas-phase reaction solver
・Emissions injected in the vertical diffusion module
・Aqueous-phase reactions and convective cloud mixing
・Modal approach aerosol size distribution and dynamics
・One-way nesting
Aerosol species in Models-3/CMAQ
16 components
ASO4J
ASO4I
ANH4J
ANH4I
ANO3J
ANO3I
AORGAJ
AORGAI
AORGPAJ
AORGPAI
AORGBJ
AORGBI
AECJ
AECI
A25J
A25I
Sulfate
(Accumulation mode)
(Aitken mode)
Ammonium
(Accumulation mode)
(Aitken mode)
Nitrate
(Accumulation mode)
(Aitken mode)
Anthropogenic secondary organic (Accumulation mode)
(Aitken mode)
Primary organic
(Accumulation mode)
(Aitken mode mode)
Secondary biogenic organic
(Accumulation mode)
(Aitken mode)
Elemental carbon
(Accumulation mode)
(Aitken mode)
Unspecified anthropogenic mass (Accumulation mode)
(Aitken mode)
4. Results and discussion
•
4-1 Comparison with observations
4-2 Vertical SPM structure
4-3 Aerosol composition
4-4 OC formation processes
Comparison with observations
(Meteorological fields)
○：Observation
：model
12/9 1800 JST
12/10 2100 JST
Meso-front
Calm
Calm
Thin regularly spaced: model
Thicker: observation
RAMS with the data nudging using ECMWF reanalysis data set could reproduce
well the meteorological fields in the TMA through the field campaign.
However, it was difficult to simulate exactly the wind field, for example, the
location of meso-front and the strong stagnant air condition.
Comparison with observations (Air quality)
CMAQ with RAMS could reproduce reasonably well the temporal variations of the
observed concentrations of the aerosol particles and their precursor gases.
Comparison with observations (SPM distribution)
1800 JST 9 Dec.
Observation
Model
2000 JST 10 Dec.
Observation
Model
(1) CMAQ with RAMS could reproduce reasonably well the observed spatial distribution.
(2) However, the model failed to simulate accurately the high concentration area and also
the simulated concentration tends to be low. This situation may be explained by
・ Lack of reproduction of the meteorological field simulated by the RAMS
・ Underestimation of anthropogenic emissions
(3) Especially, it was very important to reproduce the details of meteorological fields by a
meteorological model.
Comparison with observations
(Average particle composition for two-day period)
Observation
Model
50.0
PM10-2.5 Others
PM2.5 Others
SPM(μｇ/m3)
40.0
50.0
40.0
PM2.5 Cl-　30.0
PM2.5 OC
PM2.5 EC
20.0
30.0
20.0
PM2.5 NH4+
fukaya
North
(inland)
omiya2
fukaya
omiya2
omiya1
kudan
yokosuka
South
（sea）
point
0.0
omiya1
PM2.5 NO3-
0.0
10.0
kudan
PM2.5 SO42-
yokosuka
10.0
・ The model can calculate only the fine particles, sulfate, nitrate, ammonium, EC, and OC.
・ For five fine components, modeled compositions are almost similar to observations.
・ Model tends to underestimate the observed concentrations for components except EC.
・ Contribution of EC and OC to the total particles is very high.
Spatial distribution of SPM
(a) Meso-front type
Horizontal (surface)
1800 JST 9 Dec.
N-S vertical section along the line L
Modeled meso-front
Modeled meso-front
Polluted air mass
Clean
Polluted
< horizontal > 50 km
< vertical >
250 m
Very shallow by the surface
inversion layer.
Flight observation: below 300 m
L1
South
North
（Sea）
（Inland）
(b) Wide stagnation type
2000 JST 10 Dec.
Polluted air mass
< horizontal > 100 km
< vertical >
900 m
Flight observation: below 900 m
L2
South
（Sea）
Modeled depth of vertical polluted layer accorded with the flight observations.
North
（Inland）
Average fine particle composition
(for two-day period)
EC (%)
OC (%)
NO3- (%)
SO4 2- (%)
NH4+ (%)
Surface
Average in a
34%
whole domain
16.1%
19.6%
18.2%
12.0%
Column
(below 2000 m
height)
9.1%
6.4%
17.8%
49.3%
15.9%
(1) Percentage of Carbonaceous particles (EC+OC) is 50% at surface.
They mainly caused by the primary fine particles emitted from automobiles in the TMA.
(2) NO3- and NH4+ percentages around the TMA is higher than that in the TMA.
Average composition of fine particles at
surface in the TMA (for two-day period)
Modela
EC
OC
NO3- SO42- NH4+
3.6
2.3
1.9
10.9
4.5
( 16% ) ( 9% ) ( 8% ) ( 47% ) ( 19% )
4.3
3.3
3.2
9.8
9.1
( 14% ) ( 11% ) ( 11% ) ( 33% ) ( 31% )
μg/m3
(%)
Observationb μg/m3
(%)
a
Averaged in the area.
b
Averaged in the sixteen monitoring stations area.
(1) Modeled composition is agreement with the observations except the ratio of EC and OC.
(2) At surface, the total carbonaceous particles content (EC+OC) reaches to 66%.
(3) At aloft, the contributions of carbonaceous particles decrease with the altitude because
the effect of surface emissions is smaller; instead, the contribution for NO3- and SO42increase because they are secondary particles.
Average OC components (for two-day period)
Primary OC
(POC)
OC
Anthropogenic Secondary OC
(ASOC)
Biogenic Secondary OC
(BSOC)
μg/m3
TMA
OC conc
（μg/m3）
TMA
5.1
Surface
whole domain
2.0
TMA
0.3
Column
whole domain
0.3
POC
（%）
93
81
73
68
ASOC
（%）
2
5
10
13
BSOC
（%）
4
15
16
19
At surface,
<Primary OC> 80%
<Anthropogenic secondary OC> Small fraction (almost 5%)
<Biogenic secondary OC> Higher in the mountainous area; 15% ;
important component of OC in this area despite the low reactivity in winter.
Conclusions
(1) RAMS can reproduce qualitatively the meteorological fields.
However, it is difficult to simulate the exact wind field, for
• example, the location of meso-front and the strong stagnation.
(2) CMAQ with RAMS can reproduce reasonably well the particles
and gases. However, the model fails to simulate accurately the
high concentration because of lack of RAMS performance.
(3) The local wind systems play an important role in controlling the
formation of heavy particle pollution. Particularly important
factors are the meso-front and the strong stagnant air condition.
(4) The urban aerosol particles near surface in the TMA are
dominated by EC and primary OC.
(5) The secondary biogenic organic carbon is an important OC
component despite the low reactivity in winter.
Next steps
Improvement of model performance
• Important tasks are
To reproduce the details of meteorological field by
a meso-scale meteorological model.
To improve the emission inventory, especially for
carbonaceous particles from automobiles.
To estimate uncalculated particles, for example,
chloride and coarse particles.
Sharing lots of results and experiences of urban
modeling in many cities over the world in order to
improve the urban air quality

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