modeling of wet deposition in chemical transport...

MODELING OF WET DEPOSITION IN CHEMICAL TRANSPORT

SIMULATION

Toshihiro KITADA

National Institute of Technology,

Gifu College

Kamimakuwa, Motosu 441-8580, Japan

Transport/chemistry/deposition model for atmospheric trace chemical species

• An important tool:

(1) for understanding of the effects of various human activities, such as fuel combustion and deforestation, on human health, eco-system, and climate, and

(2) for planning of appropriate control of emission sources.

“Comprehensive” models such as RADM (Chang, et al., 1987); STEM-II (Carmichael, et al.,

1986); and CMAQ, WRF-Chem etc. for public

use

• “Comprehensive” models include not only gas/aerosol phase chemistry but also aqueous phase chemistry in cloud/rain water in addition to the processes of advection, diffusion, wet deposition (mass transfer between aqueous and gas/aerosol phases), and dry deposition.

Types of Modeling of Wet Deposition

1. Simple Modeling using Scavenging Coefficient.

2. Dynamic Modeling using One Dimensional Cloud Microphysics Model (Ex. RSM – RADOM scavenging module, Berkowitz et al. 1989; PLUVIUS, Hales 1981, CMAQ, WRF-Chem, others) → Hydrometeors themselves are not transported over horizontal grid cells.

3. Dynamic Modeling using Three/Two Dimensional Cloud Microphysics Model (Ex. Hegg et al. 1986, Rutledge et al. 1986, Kitada et al. 1993, others) → Allow transport of hydrometeors over horizontal grid cells.

One of the Important Factors: Wet Deposition Modeling:

Target for the “Comprehensive” Model Development

• the model which can correctly reproduce mass balance of various chemical species in the atmosphere with keeping adequate accuracy for calculated concentration distributions of chemical species.

• In the Fukushima case, radioactive species are the target.

Examples

• Cloud-Resolving Modeling

• Non Cloud-Resolving Modeling: Simple Modeling of Wet Deposition

ＭＭ５(NCAR Mesoscale

Model)

Out puts: Flow,

Temperature, Water

vapor,

Hydrometeors, Eddy

diffusivity.

Cloud Micro

Physics

Out put：

Cloud water/ice,

Rain, Snow,

Graupel

Turb. Model

k-εModel

Out put： k, ε,

Eddy

diffusivity

Boundary & Initial

GCM out puts 、 GPV 、 Obs.

Meteorol.

Surface Boundary

（Geography, Topo. ）

Elevation, Land Use, etc.

Gas Phase Species

Process: Advec., Diffusion,

Chem. Rxns., Mass Transfer

between Gas and Hydro.

Phases, Dry Deposition

Chem. Species：NOx, HNO3,

NH3, Hydrocarbons, O3,

H2O2, DMS, SO2, H2SO4,

etc.

Aerosol ： NH4NO3,

(NH4)2SO4, NaCl, BC, OC,

etc.

Species in Hydrometeors

（ Hydrometeors ： Cloud

Water, Cloud Ice, Rain Water,

Snow, Graupel） Process：Adv., Diff., Aqueous Phase

Chem. Rex, Mass Transfer between

gas and hydrometeor phases, Wet

Deposition

Chem. Species：S(IV), SO4=, NO3

-,

H2O2, O3, OH, HO2, Ca2+, K+, Na+,

Cl- , etc.

TRANPORT/CHEMISTRY/DEPOSITION MODEL

METEOROLOGICAL MODEL

Role of Cloud in Transport/Transformation of Trace Chemical

Species

Mass Conservation Eqs. of Chemical Species in hydrometeor Phases (Kitada et al., 1993)

𝑞𝑗 : water content of j th hydrometeor such as cloud

water, cloud ice, rain water, snow, and graupel. : concentration of i-th species in j-th hydrometeor. : chemical reaction rate of i-th species in j-th hydrometeor. : mass transfer rate of i-th species between j-th and k-th hydrometeors. : mass transfer rate of i-th species between gas/aerosol phase and j-th hydrometeor.

𝐸𝑥𝑎𝑚𝑝𝑙𝑒: 𝐺𝑗i for Gas Absorption/

Desorption of i-th species between Gas Phase and Cloud Water and Rain Water Phases

𝐺𝑗i =

Mass Conservation Eqs. for Chemical Species in Gas/Aerosol Phase

SO2-Sulfate

(used in Kitada et al.,1993)

Aqueous Phase Chemical Reactions

Examples

• Cloud-Resolving Modeling

• Non Cloud-Resolving Modeling: Simple Modeling of Wet Deposition

CTM Equations System for Large Scale

Below top of cloud layer: scavenging of gas/aerosol phase species by hydrometeors,

Simple Modeling of Wet Deposition: Ex. For Particulate Sulfate (derived and summarized in Kitada, 1994) Formulation by Scavenging Rate Coefficient Λ:

Λ: Capture rate of sulfate by rain with collection efficiency “η” of sulfate particle (radius R) by rain drop (radius, a)

Mass conc. of “sulfate in rain phase”.

Mass conc. of “sulfate in gas/aerosol phase”.

Simple Modeling of Wet Deposition: Ex. For Particulate Sulfate (derived and summarized in Kitada, 1994) (continued)

Simplified Expression of Λ using volume averaged radius of rain drop "𝑎𝑚" (Slinn, 1977) :

Precipitation intensity P:

Simplified form of Λ (C=3/4, constant):

If we choose, for example, η = 0.05,

Empirical relation of 𝑎𝑚 and P (Mason, 1971):

=6x10−4η𝑃0.75 (𝑠−1)

(C≡3

4, 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡)

(continued)

From semi-empirical expression by Slinn (1977), sample value of η for particle with its radius 0.1 ~ 3μm →

V(am)

Cloud

H

By a variable transformation of , Eq. 2.11 can be written as follows:

Integrating from z=0 ~H :

(continued)

dz

Wet Deposition at Ground Level, Fw, can be obtained by evaluating the right hand side of Eq. (2.20):

+

: Sulfate in rain at height of cloud base H; may not be zero at z= H.

: Sulfate mass in rain water phase at ground level.

H ≡

In gas absorption/desorption case, conc. in rain water must be considered in wet dep.:

(continued)

Calculated S and N Deposition for March 1 to 15, 1994

S-total Deposition N-total Deposition

25 Acid Rain Monitoring Sites, Environ. Agency, Japan in 1994:

S-total Dep.: Ob vs Cal at 25 Acid Rain Sites

N-total Dep.: Ob vs Cal at 25 Acid Rain Sites

S-Wet Dep.: Ob vs Cal at 25 Acid Rain Sites

N-Wet Dep.: Ob vs Cal at 25 Acid Rain Sites

S-total Dep.: Ob vs Cal

N-total Dep.: Ob. Vs Cal.

Observation Point in China

PM Simulation for March 1 to 31, 2001

Beijing & South or East of Beijing: 北京、斉南、青島、大連

20 済南のPM濃度変化 (rwdp621_rr-emrad)

0

200

400

600

800

1000

1200

3/1

3/2

3/3

3/4

3/5

3/6

3/7

3/8

3/9

3/10

3/11

3/12

3/13

3/14

3/15

3/16

3/17

3/18

3/19

3/20

3/21

3/22

3/23

3/24

3/25

3/26

3/27

3/28

3/29

3/30

3/31

Mas

s C

onc. (μ

g/m

3)

SOIL_Coarse

SOIL_Fine

OCV

BCB

OCB

BCF

OCF

NO3

SO4

TSP(OBS)

21青島のPM濃度変化 (rwdp621_rr-emrad)

0

200

400

600

800

1000

1200

3/1

3/2

3/3

3/4

3/5

3/6

3/7

3/8

3/9

3/10

3/11

3/12

3/13

3/14

3/15

3/16

3/17

3/18

3/19

3/20

3/21

3/22

3/23

3/24

3/25

3/26

3/27

3/28

3/29

3/30

3/31

Mas

s C

onc. (μ

g/m

3)

SOIL_Coarse

SOIL_Fine

OCV

BCB

OCB

BCF

OCF

NO3

SO4

TSP(OBS)

７　大連を含むグリッドののPM濃度変化 (rwdp821_rr-emrad)

0

100

200

300

400

500

600

700

3/1

3/2

3/3

3/4

3/5

3/6

3/7

3/8

3/9

3/10

3/11

3/12

3/13

3/14

3/15

3/16

3/17

3/18

3/19

3/20

3/21

3/22

3/23

3/24

3/25

3/26

3/27

3/28

3/29

3/30

3/31

Mas

s C

onc. (μ

g/m

3)

SOIL_Coarse

SOIL_Fine

OCV

BCB

OCB

BCF

OCF

NO3

SO4

TSP(Dalian OBS)

TSP(OBS Ave.)

(b) Jinang

(c) Qingdao (d) Dailian

0

200

400

600

800

1000

1200

1400M

ass

Conc. (

OB

S

μg/

m3)

北京を含むグリッドの濃度比較 (rwdp721_rr-emrad)

SOIL_Coarse

SOIL_Fine

OCV

BCB

OCB

BCF

OCF

HNO3

SO4

TSP (Ave. OBS)

TSP (Beijing OBS)

(a) Beijing

PM: Cal. Vs Obs. for March 1 to 31, 2001

Southeastern Mountainous Area: Kunming(昆明)

0

50

100

150

200

250

Mas

s C

onc. (

μg/

m3)

36 昆明のPM濃度変化 (rwdp821_rr-emrad)

SOIL_Co

arseSOIL_Fin

eOCV

BCB

OCB

BCF

OCF

HNO3

SO4

TSP(OB

S)

(a) KUNMING

0

50

100

150

200

250

300

350

400

450

Mas

s C

onc. (μ

g/m

3)

37 ラサのPM濃度変化 (rwdp721_rr-emrad) SOIL_Co

arseSOIL_Fin

eOCV

BCB

OCB*1.5

BCF

OCF

HNO3

SO4

TSP(OB

S)

(b) LHASA

Southern Highland Area (Tibet): Lhasa(ラサ)

PM: Cal. Vs Obs. for March 1 to 31, 2001

Japan: 東京、大阪

0

50

100

150

200

250

Mas

s C

onc. (μ

g/m

3)

大阪のPM濃度変化 (rwdp721_rr-emrad)

SOIL_Coarse

SOIL_Fine

OCV

BCB

OCB

BCF

OCF

HNO3

SO4

SPM10(OBS)

0

20

40

60

80

100

120

140

Mas

s C

onc

. (μ

g/m

3)

東京のPM濃度変化 (rwdp721_rr-emrad)

SOIL_Coars

eSOIL_Fine

OCV

BCB

OCB

BCF

OCF

HNO3

SO4

SPM10(OB

S)

(a) TOKYO

(b) OSAKA, Japan

SPM: Cal. Vs Obs. for March 1 to 31, 2001

Summary and concluding remarks • Introduced two examples of wet deposition modeling;

• (1) Cloud-resolving

• (2) Non Cloud-resolving simplified approach

• (3) Further investigation of the cloud-resolving study may be necessary for improvement of wet deposition prediction; the research will also improve parameterization in the non cloud-resolving approach.

• (4) Difficulty for mass balance study in the Fukushima case: Almost no information on atmospheric concentration fields of the discharged radioactive materials(?). Only deposition distribution is available. → Reduce other uncertainties such as met fields as much as possible.

One of the important factors: reliable Wet Deposition calculation. • Three types of modeling; (1) Dynamic Modeling using Three/Two

Dimensional Cloud Transport Model: Cloud-Resolving Method → Allow trans-horizontal-grids mass transport of hydrometeors and chemical species with PDEs (Ex. Hegg et al. 1986, Rutledge et al. 1986, Kitada et al. 1993, others)

(2) Dynamic Modeling using One Dimensional Cloud Transport Model: Semi-Cloud-Resolving Method → Hydrometeors themselves are not transported over horizontal grid cells. (Ex. RSM – RADOM scavenging module, Berkowitz et al. 1989; PLUVIUS, Hales 1981, others); completes cloud processes within each vertical column.

One of the important factors: reliable Wet Deposition calculation. (continued)

(3) Simple Modeling using Scavenging Coefficient.