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