Upload
kitra-conrad
View
24
Download
0
Tags:
Embed Size (px)
DESCRIPTION
Response of fine particles to the reduction of precursor emissions in Yangtze River Delta (YRD), China. Juan Li 1 , Joshua S. Fu 1 , Yang Gao 1 , Yun-Fat Lam 1 Guoshun Zhuang 2 , Kan Huang 1,2 , Ying Zhou 3 1. The University of Tennessee, Knoxville, U.S.A. 2. Fudan University, China - PowerPoint PPT Presentation
Citation preview
Response of fine particles to the reduction of precursor emissions in Yangtze River Delta
(YRD), China
Juan Li1, Joshua S. Fu1, Yang Gao1, Yun-Fat Lam1 Guoshun Zhuang2, Kan
Huang1,2, Ying Zhou3
1. The University of Tennessee, Knoxville, U.S.A.
2. Fudan University, China3. Emory University, U.S.A.
99thth Annual CMAS Conference, October 11-13, 2010 Chapel Hill, NC Annual CMAS Conference, October 11-13, 2010 Chapel Hill, NC
Outline
• Introduction
• Objective
• Model description and performance
• Sensitivity study
VOC emission reduction
NOx emission reduction
• Implication on emission control in YRD
Shanghai CityPopulation: 18,884,600Population Density: 2,700 inhabitants/km²
Yangtze River Delta
Area: 99600 km2
Population: over 80 million people in 2007
50 million are urban.
Introduction
ShanghaiShanghai
Current issue (O3 & PM)
Haze
Shanghai
True color-satellite image on January 18, 2007
YRD is one of the four regions in China, which experiences severe visibility impairment. (Record: PM10 = 512 g/m3)
However, very limited regional modeling have been performed in YRD.
Objective
To study the response of O3 and PM2.5 over YRD to the changes of NOx and VOC emissions using CMAQ.
Reveal the atmospheric nitrate chemistry over YRD to provide effective suggestions about emission control.
Modeling Configuration
27 km
9 km
3 km
CMAQ V4.6 with CB05AE4
• Meteorological Input:MM5 V3.7
• Domain:27km, 9km & 3km
• Vertical Grid Spacing: 24 layers
• Emission:INTEX-B with local emission adjustments
• Simulation Period: 2006
• IC/BC: GEOS-Chem Discussion will be mainly on 3 km domain
Emissions Development
• Regional Emission Inventory
– INTEX-B & TRACE-P
• GIS program
– Spatial Allocation
– Spatial Allocation Factor
• FORTRAN Program
– Emission Vertical distribution
– Temporal Allocation Domain
• Regional Re-adjustment of Emissions
Area
INTEX-B VOC 43.56
Ref VOC 57.42
INTEX-B NOX 50.06
Ref NOX 46.39
Unit: 1000 tons/year
Emissions Comparison
3.3%
27.2%
69.5%
32.4%
18.0%
49.6%
20.7%
34.5%
44.8%
55.2%
30.3%
14.4%
0%
20%
40%
60%
80%
100%
Per
cent
age
(%)
VOC VOC NOX NOX
INTEX-B Ref INTEX-B Ref
Point Tran Area
INTEX-B: Intercontinental Chemical Transport Experiment-Phase B
Ref. Local report
Emissions Comparison (Cont.)
0
500
1000
1500
2000
Un
it:
Th
ou
san
to
n/y
ear
PM10 PM10 PM2.5 PM2.5 SO2 SO2 CO CO NH3 NH3
INTEX-B
Ref INTEX-B
Ref INTEX-B
Ref INTEX-B
Ref INTEX-B
Ref
Point Tran Area
Examples of CMAQ Emissions Input
Methanol PNO3 g/smole/s
MM5 Wind and Temperature
Dec. 2006, Shanghai
Jul. 2006, Shanghai
JULY
Wind rose plot in Shanghai
JANUARY
CMAQ scenarios
Scenario Sector Description Reduction Pct
0 Base Base case -
1 Power NOx alone (SCR alone) ~85%
2 Power NOx + SO2 (SCR + FGD) ~85% for NOx + ~90% for SO2
3 Traffic NOx alone 20%
4 Traffic NOx + VOC 20%
5 Traffic NOx + VOC + PM 20%
6 Traffic NOx + VOC + PM 50%, sensitivity run
7 Traffic VOC alone 20%
8 industry NOx alone 20%
9 industry NOx + any important co-pollutants 20%
10 combined Additional sensitivity runs
Observational Site
Red color: A represent O3 observational site;
Blue color: B represent PM2.5 NH4
+, NO3- observational site
Observational site locate in Fudan University, a representative of residence area in downtown of Shanghai
Ozone Time Series in Site A
0
40
80
120
160
Time(GMT)
O3
(pp
bv
)
MB NMB NME MNBa MNEa RRMS
E
Daily_max_8hr Ozone -6.4 -14.0% 24.7% -4.3% 28.1% 0.8 16.7
Hourly Ozone60
b -2.2 -25.7% 29.0%
-25.3% 28.9% 0.6 27.5
Ozone performance statistics (based on 4 months of data)
PM2.5 Daily Average Distribution
MB NMB NME MNBa MNEa R RMSE
Daily_24hr Avg -6.1 0.1% 44.53% 0.5% 47.2% 0.43 21.67
PM2.5performance statistics (based on 4 months of data)
2006_AUG_PM2.5_OBS_MODEL
0
5
10
15
20
25
30
35
40
45
50
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Date
PM
2.5_C
on
c(
g/m
3)
Obs
Model
Model Performance - Temporal Distribution
Model Performance - Statistics
• NMB—the normalized mean bias; NME—the normalized mean error;
Variable Data # Mean Obs. Mean Simulation NMB NME
SO2 (ppb) 365 45.13 50.14 11.1% 42.7%
NO2 (ppb) 365 35.91 42.22 17.6% 37.5%
O3 (ppb) 166 30.55 27.15 -11.1% 37.2%
NH4 (μg/m3) 99 2.83 3.52 24.5% 71%
NO3 (μg/m3) 99 2.21 1.87 -15.7% 77.8%
PM10 (μg/m3) 365 66.62 71.13 6.8% 40.7%
Sensitivity StudyResponse of PM2.5 to 20% reduction of NOx and VOC, respectively
-1.5
-1
-0.5
0
0.5
1
01/0
1/06
02/0
1/06
03/0
1/06
04/0
1/06
05/0
1/06
06/0
1/06
07/0
1/06
08/0
1/06
09/0
1/06
10/0
1/06
11/0
1/06
12/0
1/06
PM
2.5
(μ
g/m
3 )
Reduction in 20%NOx Reduction in 20% VOC
-0.12
-0.08
-0.04
0
0.04
Spring Summer Fall Winter
PM
2.5 (
μg m
-3)
Response of NH4+, NO3
- to the reduction in 20% NOx and 20% VOC emission
NH4+NH4
+ NO3-NO3
-
-0.09
-0.07
-0.05
-0.03
-0.01
0.01
0.03
Sen 1 Sen 2
Con
c. (
μg/
m3 )
Spring Summer Fall Winter
Reduction in 20%VOC Reduction in 20%NOx
Correlation between PAN and NH4+, NO3
-
PAN were well correlated with NH4+and NO3
-; the slopes in four seasons were in the order of
winter>fall>spring>summer, which was coincident with the seasonal variation of temperature, indicating that lower temperature is in favor of the formation of PAN, Peroxyacetyl nitrate (PAN) may play a key role in the formation of NO3
- and NH4+ in response to the reduction of NOx emission.
r = 0.89
r = 0.71
r = 0.78
r = 0.65
0
10
20
30
40
50
60
0 0.5 1 1.5 2PAN (ppbv)
NO
3- (μg
m-3
)
Fall
Summer
Spring
Winterr = 0.76
r = 0.76
r = 0.81
r = 0.54
0
5
10
15
20
25
30
0 0.5 1 1.5 2
PAN (ppbv)
NH
4+ (μ
g m
-3)
Fall
Summer
Spring
Winter
PAN (Peroxyacetyl nitrate)
CH3C(O)OO· + NO2 CH3C(O)OONO2
(PAN)
HNO3 + NH3 NH4NO3
Response of O3 to reduction in NOx and VOC emission by 20%
-1
-0.5
0
0.5
1
1.5
2
2.5
O3
(ppb
v)
Sen2 Sen1Reduction in 20%NOx Reduction in 20%VOC
Response at Other Sites
-0.45-0.3
-0.150
0.150.3
0.45
1/1/
2006
2/1/
2006
3/1/
2006
4/1/
2006
5/1/
2006
6/1/
2006
7/1/
2006
8/1/
2006
9/1/
2006
10/1
/200
6
11/1
/200
6
12/1
/200
6
NH
4+ (μg/
m3) Suzhou Nanjing Hangzhou Ningbo
-2-1.5
-1-0.5
00.5
11.5
1/1/
2006
2/1/
2006
3/1/
2006
4/1/
2006
5/1/
2006
6/1/
2006
7/1/
2006
8/1/
2006
9/1/
2006
10/1
/200
6
11/1
/200
6
12/1
/200
6
NO
3- (μg/
m3)
-2
-1
0
1
2
1/1/
2006
2/1/
2006
3/1/
2006
4/1/
2006
5/1/
2006
6/1/
2006
7/1/
2006
8/1/
2006
9/1/
2006
10/1
/200
6
11/1
/200
6
12/1
/200
6
PM
2.5
(μg/
m3)
-0.5
0
0.5
1
1.5
2
2.5
O3
(ppb
v)
-0.6
-0.45
-0.3
-0.15
0
0.15
NH
4+ (
μg/m
3) Suzhou Nanjing Hangzhou Ningbo
-1.8
-1.2
-0.6
0
0.6
NO
3- (μg/
m3)
-2.4
-1.8
-1.2
-0.6
0
0.6
PM
2.5
(μ
g/m
3)
-1.6
-1.2
-0.8
-0.4
0
O3
(ppb
v)
Reduction in 20%VOC Reduction in 20%NOx
PAN may play a key role in the formation of NO3- and
NH4+in response to the reduction of NOx emission.
Emission reduction of VOC in YRD is more effective than NOx in terms of reducing O3 and PM2.5.
Summary
Acknowledgement
Energy Foundation
Harvard School of Public Health (Grant No. G-0910-10653).
National Key Project of Basic Research of China (Grant No.
2006CB403704),
National Natural Science Foundation of China (Grant Nos. 20877020,
40575062, and 40599420).
The National Institute for Computational Sciences at the University of
Tennessee provides CPU time on the Kraken supercomputer to conduct the
simulations.
Question