Creating Building Blocks for a More Dynamic Air Quality Management Framework

K. Demerjian (PI), H. Mao (co-PI), J. Yun, M. Beauharnois, M. Ku, K. Civerolo, and R. Bielawa

EPA STAR Grant:Dynamic Air Quality Management-Progress Review WebinarMarch 31, 2016

1

Acknowledgement

Thanks to Christian Hogrefe (U.S. EPA), principal scientist who collaborated with our research team under this co-operative agreement and to Sergey Napelenok for his contributions in resolving complier issues associated with the CMAQ-DDM computations.

2

Presentation Outline• Research Objectives

• Description of CMAQ-DDM Computation Data Base

• O3 Sensitivity to EGU Peaking units

• Sensitivity analysis of predicted O3 concentrations with respect to emission source categories and emission domains

• Summary of Findings

• Regional and hemispheric influences on northeastern US baseline carbon monoxide and ozone over 2001 – 2010

• The impact of emission reductions and transport on Hgin Bronx, NY over 2008 – 2015

• Trends in northeastern U.S. background Hg

• Summary of Findings 3

Research Objectives

1) Develop a prototype system for providing real-time information on the contribution of short-term emission sources to air quality in relation to other source categories and the potential air quality benefits from episodic control measures.2) Perform a comprehensive multi-pollutant air quality assessment that examine trends in pollutant concentrations versus emission controls, co-pollutant effects, and develop possible indicators that may aid in improved tracking of the effect of emission controls.

4

DDM computational database created from CMAQ, (running CMAQ-DDM), NOX, VOC, for O3 sensitivity (May-Sep, 2007)

May Jun Jul Aug Sep May Jun Jul Aug Sep May Jun Jul Aug Sep May Jun Jul Aug Sep

1 x x x x x x x x x x x x x x x x x x x x

2 x x x x x x x x x x x x x x x x x x x x

3 x x x x x x x x x x x x x x x x x x x x

4 x x x x x x x x x x

5 x x x x x x x x x x x x x x x x x x x x

6 x x x x x x x x x x x x x x x x x x x x

7 x x x x x x x x x x x x x x x x x x x x

BC x x x x x x x x x x x x x x x x x x x x

no model runs no model runs

NYCONLY MVNONYC LADCEN SESARMEmission

Category

CMAQ-DDM 12-km modeling domain and emission regions

Computational production runs (above table) by emission category: 1) all anthropogenic emission sources; 2) mobile source emissions; 3) combined area and non-road emissions; 4) “peaking unit” electric generating unit (EGU) point sources emissions; 5) all EGU point sources emissions; 6) other point sources emissions; 7) biogenic emissions; and boundary conditions (BC) and the respective emission domains as shown on the right.

5

EGU/ PEAKING UNITS O3

SENSITIVITY

6

Selected monitoring sites and locations of peaking units for sum of NOx greater than 80 tons in MANEVU

7

1) MANEVU region

1) 2)

Holtsville

QC

Babylon

Westport

White Plains

2) NYCONLY region

Yun et al. EM 2013

Peaking units high demand days (21 days) - Daily total NOx from peaking units in MANEVU greater than 80 tons/day (Holtsville)

8

Modeled O3 hourly distribution

(21 days exceeding 80 tons/day) Daily total NOx emissions from all peaking units

(5/15-9/15/2007) (21 days exceeding 80 tons/day)

Jun Jul Aug Sep2007

0

100

200

300

NO

x (

ton

s)

MANEVU.NOX.tons

NYS.NOX.tons

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

0

40

80

120

160

mod O

3 (

ppb)

O3 sensitivity to NOx/VOC emissions from all sources at Holtsville (21 days)

9

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-10

0

10

20

30

O3

se

nsitiv

ity t

o N

Ox

a) MVNONYC

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-10

0

10

20

30

O3

se

nsitiv

ity t

o V

OC

b) MVNONYC

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-80

-60

-40

-20

0

20

40

O3

se

nsitiv

ity t

o N

Ox

c) NYCONLY

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-10

0

10

20

30

40

50

O3

se

nsitiv

ity t

o V

OC

d) NYCONLY

O3 sensitivity to NOx emissions at Holtsville (21 days)

10

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-1

4

9

14

O3

se

nsitiv

ity t

o N

Ox

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-50

-30

-10

10

O3

se

nsitiv

ity t

o N

Ox

O3 sensitivity to mobile

source emissions

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-0.5

-0.1

0.3

0.7

O3

se

nsitiv

ity t

o N

Ox

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-20

-15

-10

-5

0

O3

se

nsitiv

ity t

o N

Ox

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-1

1

3

5

7

9

O3

se

nsitiv

ity t

o N

OX

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-20

-15

-10

-5

0

5

O3

se

nsitiv

ity t

o N

Ox

O3 sensitivity to peaking

EGU source emissionsO3 sensitivity to all EGU

source emissions

a) MVNONYC

b) NYCONLY b) NYCONLY

a) MVNONYC a) MVNONYC

b) NYCONLY

Daily max 8hr avg. O3 (base) and new daily max 8hr avg O3 for each emission/region (peaking unit sources) change scenario:

1) Holtsville 2) Queens College

11

5/31 6/

16/

186/

196/

266/

276/

28 7/97/

107/

31 8/1

8/2

8/3

8/4

8/7

8/8

8/98/

25 9/7

9/89/

10

50

100

O3.p

pb

5/31 6/

16/

186/

196/

266/

276/

28 7/97/

107/

31 8/1

8/2

8/3

8/4

8/7

8/8

8/98/

25 9/7

9/89/

10

50

100

O3.p

pb

O3.base

newO3 with MVNYnox 30% reduction

newO3 with MVnox 30% reduction

newO3 with NYnox 30% reduction

1)

2)

SENSITIVITY ANALYSIS OF PREDICTED O3

CONCENTRATIONS WITH RESPECT TO EMISSION SOURCE CATEGORIES AND EMISSION DOMAINS

12

8/2/2007

13

3 year moving average of 4th highest daily max 8 hour average O3 at monitoring sites

14

Pfizer lab, QC, Holtsville, and Babylon in New York

White Plains and Westport

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

0

40

80

120

pp

b

Pfizer.lab

QC

Holtsville

Babylon

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

0

40

80

120

pp

b

Westport

WhitePlains

Daily max 8 hour average O3 (base) of 10 worst days and new daily max 8 hour average O3 for each emission/region (all

sources) change scenario at the Holtsville site

15

based on 2007 MARAMA V3 and 2011 EPA V1 inventories

Emission domain NOX VOC

MVNONYC/NYCONLY -30% -20%

SESARM -30% +40%

LADCEN -15% +15%

0 2 4 6 8 10

60

70

80

90

100

110

120

O3.p

pb

Worst day #

O3 mod

newO3: MV/NY NOX & VOC control

newO3: MV/NY NOX control

newO3: MV/NY VOC control

newO3: MV NOX & VOC control

newO3: MV NOX control

newO3: MV VOC control

newO3: NY NOX & VOC control

newO3: NY NOX control

newO3: NY VOC control

newO3: all region NOX & VOC control

O3 obs 2007

O3 obs 2011

Hourly average distributions of O3 sensitivity to emissions/regions at the Holtsville site for 10 worst days of daily

max 8 hour average O3

16

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

0

5

10

15

pp

b

a)

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-1

4

9

14

pp

b

b)

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-22

-17

-12

-7

-2

3

pp

b

c)

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

0

5

10

15

20

25

pp

b

mobile

area.nonroad

allEGU

all.other.point

biogenic

d)

a) NOx from the MVNONYC region b) VOC from the MVNONYC regionc) NOx from the NYCONLY region d) VOC from the NYCONLY region

Hourly average distributions of O3 sensitivity to emissions/regions at the Holtsville site for 10 worst days of daily

max 8 hour average O3

17

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

0

2

4

6

pp

b

e)

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-2

0

2

4

pp

b

f)

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

0.0

0.5

1.0

1.5

2.0

pp

b

g)

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

hour

-0.1

0.3

0.7

1.1

pp

b

mobile

area.nonroad

allEGU

all.other.point

biogenic

h)

e) NOx from the SESARM region f) VOC from the SESARM regiong) NOx from the LADCEN region h) VOC from the LADCEN region

Daily max 8hr average O3 with biogenic (MANEVU) VOC removal

185/2

05/2

55/3

06/4 6/9

6/146/1

96/2

46/2

97/4 7/9

7/147/1

97/2

47/2

98/3 8/8

8/138/1

88/2

38/2

89/2 9/7

9/12

date

0

20

40

60

80

100

120

O3

(ppb

)

O3 mod. 2007

O3 obs. 2007

O3 mod. biogenic VOC removal

sensitivity analysis of predicted O3 conc. with respect to emission source categories and emission domain

10 worst day at the Holtsville site

19

0

20

40

60

80

100

120

140

-50

-40

-30

-20

-10

0

10

20

30

40

5/2

5

5/3

1

6/1

6/2

6

7/8

7/9

8/2

8/4

8/8

8/3

0

mo

d. O

3(p

pb

)

O3

sen

siti

vity

to

N

YCO

NLY

NO

x/V

OC

(p

pb

)

-5

05

101520

25303540

5/2

5

5/3

1

6/1

6/2

6

7/8

7/9

8/2

8/4

8/8

8/3

0

O3

sen

siti

vity

to

M

VN

ON

YC N

Ox/

VO

C (

pp

b)

-5

0

5

10

15

20

25

30

35

40

5/2

5

5/3

1

6/1

6/2

6

7/8

7/9

8/2

8/4

8/8

8/3

0

O3

sen

siti

vity

to

SE

SAR

M N

Ox/

VO

C (

pp

b)

-505

10152025303540

5/2

5

5/3

1

6/1

6/2

6

7/8

7/9

8/2

8/4

8/8

8/3

0

O3

sen

siti

vity

to

LA

DC

EN N

Ox/

VO

C (

pp

b)

O3 reductions (ppb) associated with each emission change scenario on selected sites in NY for 10 max 8hr average O3 worst days

20

O3 reductions associated with each emission changes scenario

MVNY MVNY MVNY MV MV MV NY NY NY SE SE SE LAD LAD LAD allreg

site site ID

base

O3 nox.voc nox voc nox.voc nox voc nox.voc nox voc nox.voc nox voc nox.voc nox voc nox.voc

All sources emissions

Babylon 361030002 mean 99.8 4.6 -2.9 7.5 5.8 3.3 2.4 -1.1 -6.2 5.1 -0.5 1.4 -1.9 0.0 0.3 -0.3 4.2

SD 6.5 4.0 7.1 4.1 2.6 1.8 1.6 5.5 7.0 2.8 1.3 1.5 1.6 0.2 0.4 0.2 4.4

Holtsville 361030009 mean 99.3 7.1 1.6 5.5 5.2 3.6 1.7 1.9 -1.9 3.8 -0.3 1.0 -1.4 0.0 0.2 -0.2 6.8

SD 11.7 4.8 6.0 2.2 1.2 1.5 0.9 4.9 5.5 1.6 1.4 0.9 1.8 0.1 0.2 0.1 5.7

NYBG 360050133 mean 90.0 -0.1 -8.3 8.3 4.1 2.2 1.9 -4.1 -10.5 6.4 -1.1 1.4 -2.6 0.0 0.2 -0.2 -1.2

SD 3.5 2.2 5.1 3.8 1.7 1.1 1.4 3.7 5.3 2.6 2.0 1.3 2.5 0.1 0.2 0.2 4.0

QC 360810124 mean 90.0 0.4 -6.2 6.6 4.8 3.0 1.8 -4.4 -9.2 4.8 0.1 1.5 -1.3 -0.1 0.4 -0.4 0.5

SD 10.9 2.9 4.7 3.0 1.9 1.6 0.9 3.3 4.4 2.3 1.8 1.6 1.2 0.4 0.4 0.4 4.0

Mobile sources emissions

Babylon 361030002 mean 99.8 -2.3 -3.1 0.7 1.6 1.5 0.1 -4.0 -4.6 0.6 0.6 0.6 0.0 0.1 0.1 0.0 -1.6

SD 6.5 3.4 3.9 0.5 0.9 0.8 0.1 3.6 4.0 0.5 0.7 0.7 0.0 0.2 0.2 0.0 3.6

Holtsville 361030009 mean 99.3 0.5 0.1 0.4 1.7 1.6 0.1 -1.2 -1.5 0.3 0.5 0.5 0.0 0.1 0.1 0.0 1.1

SD 11.7 2.7 3.1 0.4 0.4 0.4 0.1 2.7 3.0 0.3 0.4 0.5 0.0 0.1 0.1 0.0 2.6

NYBG 360050133 mean 90.0 -4.2 -4.9 0.7 1.1 1.0 0.1 -5.3 -6.0 0.6 0.7 0.7 0.0 0.1 0.1 0.0 -3.5

SD 3.5 2.4 2.7 0.4 0.6 0.5 0.0 2.6 2.8 0.4 0.7 0.7 0.0 0.1 0.1 0.0 2.8

QC 360810124 mean 90.0 -4.5 -5.0 0.6 1.3 1.3 0.1 -5.8 -6.3 0.5 0.7 0.7 0.0 0.2 0.2 0.0 -3.6

SD 10.9 2.8 3.0 0.3 0.7 0.7 0.0 2.8 3.0 0.3 0.8 0.8 0.0 0.2 0.2 0.0 3.2

All EGU point sources emissions

Babylon 361030002 mean 99.8 0.5 0.5 0.0 0.7 0.7 0.0 -0.1 -0.1 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.8

SD 6.5 0.8 0.9 0.0 0.4 0.4 0.0 0.7 0.7 0.0 0.2 0.2 0.0 0.0 0.0 0.0 1.0

Holtsville 361030009 mean 99.3 0.1 0.1 0.0 0.6 0.6 0.0 -0.5 -0.5 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.3

SD 11.7 0.8 0.8 0.0 0.3 0.4 0.0 0.8 0.8 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.8

NYBG 360050133 mean 90.0 -0.3 -0.3 0.0 0.4 0.4 0.0 -0.7 -0.7 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.0

SD 3.5 0.7 0.7 0.0 0.3 0.3 0.0 0.8 0.8 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.8

QC 360810124 mean 90.0 0.1 0.1 0.0 0.6 0.6 0.0 -0.5 -0.5 0.0 0.3 0.3 0.0 0.1 0.1 0.0 0.4

SD 10.9 0.8 0.8 0.0 0.4 0.4 0.0 0.6 0.6 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.9

Summary Findings

• The CMAQ-DDM Computation Data Base provides exceptional data mining opportunities for assessing air quality management options.

• NOx emissions from EGU peaking units can be estimated for real-time operational AQ forecasts, but their impact on modeled ozone production is small.

• Modeled 2007 ozone concentrations for the 10 worst ozone days are over predicted by as much as 20 ppb at monitoring sites downwind of NYC.

• Sensitivity analysis of predicted O3 concentrations with respect to source emission categories indicates a substantial contribution from biogenic VOC emissions which warrants further study.

21

Impacts of increasing Asian emissions, NOx emissions from the Northeast Urban corridor, global biomass burning emissions, and meteorological conditions (incl. cyclone activity, AO, and NAO) were found to be major factors influencing the baseline ozone and CO in the Northeastern US.

Regional and Hemispheric Influences on Northeastern US Baseline Carbon Monoxide and Ozone over 2001 – 2010

Zhou et al., 2016, acpd

Baseline: Monthly 10th percentile values of CO; the median value of the ozone data corresponding to CO < monthly 10th percentile.

a)

b)

c)

d)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

80

120

160

200

240

80

120

160

200

240

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

80

120

160

200

240

80

120

160

200

240

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

15

30

45

60

15

30

45

60

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

15

30

45

60

15

30

45

60

Baseline C

O (

ppbv)

AI

CS

PM

TF

PSP

Baseline C

O (

ppbv) MWO

WFM

Baseline O

3 (

ppbv) AI

CS

PM

TF

PSP

Baseline O

3 (

ppbv) MWO

WFM

• Decreasing trends in baseline CO at most sites, −4.3 to −2.5 ppbv yr−1

• No overall significant trends in baseline O3 due possibly to worldwide increasing NOx and constant CH4 in the 2000s

Zhou et al., 2016, acpd

• Insignificant trends in baseline CO at high elevation sites in spring and winter due possibly to decreasing US emissions and increasing Asian emissions.

• TF was the only site with increasing baseline O3 at significant rates in spring and winter over 2001–2010, likely a result of NOx emission reductions in the Northeast.

• Siberian and Canadian forest fires contributed 37% & 22%, respectively, to summertime baseline CO variability over 2001-2010

a)

b)

c)

d)

2001 2002 2003 2004 2005 2006 2007 2008 2009 20100.0

0.6

1.2

1.8

2.4

3.0

0.0

0.6

1.2

1.8

2.4

3.0

2001 2002 2003 2004 2005 2006 2007 2008 2009 20100.10

0.15

0.20

0.25

0.30

0.35

0.10

0.15

0.20

0.25

0.30

0.35

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

75

100

125

150

175

200

75

100

125

150

175

200

2001 2002 2003 2004 2005 2006 2007 2008 2009 201020

25

30

35

40

45

50

20

25

30

35

40

45

50

GF

ED

(1

013g

/mo

nth

)

Russia

Alaska

Canada

California

MO

PIT

T (

10

18 m

ole

cu

les/c

m2

/mo

nth

)

Russia

Alaska

Canada

California

Ba

se

line

CO

(p

pb

v)

AI

CS

MWO

PM

TF

PSP

WFM

Ba

se

line

O3 (

pp

bv)

AI

CS

MWO

PM

TF

PSP

WFM

• Decreasing biomass burning emissions in Russia likely contributed to decreasing baseline CO and O3 in summer.

Fire emissions of CO

MOPITT CO column

Summertime baseline CO

Summertime baseline O3

A case in point was summer 2009 with the largest cyclone count (20) and the strongest negative AO phase (−0.92) of the decade

Impact of cyclone activity and Arctic Oscillation

2000 2002 2004 2006 2008 20100

5

10

15

20

Co

unt

of

Cyclo

nes

-1.0

-0.5

0.0

0.5

1.0

AO

In

dex

Unusually strong northeasterly

2002 2004 2006 2008 2010

40

60

80

100

120

140

160

180

B

aselin

e C

O (

pp

bv)

30

32

34

36

38

40

10

20

30

40

50

Baselin

e O

3 (

pp

bv)

CO

em

issio

ns (

Tg

)

f)

b)

c)

a)

d)

e) Lowest fire emissions in summer 2009

The effect of biomass burning may dominate over AO and cyclone activity during some summers (e.g., 2003), while the two worked in concert during others (e.g., 2009).

Negative correlation between springtime baseline O3 and NAO

2002 2004 2006 2008 20100

10

20

30

40

50

60

CS

MWO

PM

TF

PSP

WFM

Mean

10yr mean

NAO

Ba

se

line

O3 (

pp

bv)

-1

0

1

2

3

4

5

NA

O Index

2002 2004 2006 2008 2010-40

-20

0

20

40

60

80

100

CS

PM

TF

PSP

WFM

Solar

Rela

tive H

um

idity

450

600

750

900

1050

1200

Sola

r R

adia

tion

(W

/m2)

Impact of NAO in spring (March and April)

Lower NE US baseline O3 linked to positive NAO with less solar radiation flux, weakened stratospheric intrusion, and intensified continental export.

PV

• Regional contributions to NYC ambient concentrations of Hg were estimated to be more than a factor of two larger than the local using the HYSPLIT dispersion model simulations.

• The effect of Hg emission reductions can be obscured by interannual variability in circulation.

The Impact of Regional Transport and Emission Reductionson Hg in Bronx, NY over 2008 – 2015

2009 2010 2011 2012 2013 2014

Co

ntr

ibu

tio

n in

%

20

30

40

50

60

70

80

Winter- NYC emissions only

Winter - No NYC emissions

a)b)

2008 2011 Notes

NYC 165 199 Increases in misc. & waste ; little decrease in fuel comb

E US 64124 55187 Major decreases in fuel combustion

Mercury emissions (lbs)

Emission changes not reflected in temporal variability in GEM concentrations

• The 2009 – 2010 annual cycles not reproduced in the other years.• Lowest warm season concentrations in 2011 disrupted the 2009-2010 annual cycles• Largest concentrations in 2014, in both cool and warm seasons

Winter 2010 the lowest Hg0 vs 2014 the highest Hg0

largely associated with the interannual variability in circulation patterns

Summer 2011 the lowest Hg0 vs 2014 the highest Hg0

201420111980-2010

2010 20141980-2010 Summer 2011 the lowest Hg0 vs 2014 the highest Hg0

Trends in Northeastern U.S. Background Hg

• A decreasing trend of 3.8±0.9 ppqv yr-1 found at an elevated rural site (Pac Monadnock), possibly associated with decreasing anthropogenic emission in North America.

• Changes in ecosystems could disrupt this decreasing trend .

1. Thompson Farm – TF, Durham, NH, sea level2. Pack Monadnock – PM, Peterborough, NH, 700 asl3. Huntington Wildlife Forest – HWF, Adirondacks, NY, 510 m asl

1/1/04 1/1/05 1/1/06 1/1/07 1/1/08 1/1/09 1/1/10

Hg

o A

no

maly

(p

pq

v)

-40

-20

0

20

40a) PM

1/1/04 1/1/05 1/1/06 1/1/07 1/1/08 1/1/09 1/1/10

Hg

O

An

om

aly

(p

pq

v)

-40

-20

0

20

40

b) TF

c) HWF

1/1/06 1/1/07 1/1/08 1/1/09 1/1/10 1/1/11 1/1/12 1/1/13 1/1/14

25th

perc

entile

anom

aly

Hg

o (

ppqv)

-50

0

50

100

• A decreasing trend of 3.8±0.9 ppqv yr-1 at PM, compared to Mace Head (3.1±1.1 ppqv yr-1), Cape Point (3.8±0.6 ppqv yr-1), and mid-latitude Canadian sites (~2.6-3.9 ppqv yr-1).

• At TF, an abrupt increase in the fall of 2006 resulted in no trends over 2003 – 2010.

• HWF, no trend was observed over February 2006 – August 2013.

1/1/03 1/1/04 1/1/05 1/1/06 1/1/07 1/1/08 1/1/09 1/1/10 1/1/11

Hg

o A

no

ma

ly (

pp

qv)

-20

0

20

40

60

a) TF

1/1/06 7/1/06 1/1/07 7/1/07 1/1/08 7/1/08 1/1/09 7/1/09 1/1/10 7/1/10 1/1/11

Hg

o A

no

ma

ly (

pp

qv)

-100

0

100

200

300

400

c) HWF

1/1/05 1/1/06 1/1/07 1/1/08 1/1/09 1/1/10 1/1/11

Hg

o A

no

maly

(p

pq

v)

-20

-10

0

10

20

30

40

50

b) PM

-2.1 ± 0.6 ppqv yr-1

-1.6 ± 0.6 ppqv yr-1

-22 ±6 ppqv yr-1

Anthropogenic Contributionestimated from the measurement data

1/1/02 1/1/03 1/1/04 1/1/05 1/1/06 1/1/07 1/1/08 1/1/09 1/1/10 1/1/11

Hg

o A

no

maly

(p

pq

v)

-40

-20

0

20

40

Pre

cip

ita

tio

n (

mm

)

0

100

200

300

400

500

TF Background GEM vs. Precipitation

• Abundant precipitation in the senescence months in 2006 followed by a lack of snow in the following winter in the northeastern U.S.

• An examination of long-term soil moisture data for a northeastern site (Lye Brook, NY) suggested soils to be the driest in the year of 2007 during the decade of 2001 – 2011.

Summary Findings• Major factors influencing Northeastern US baseline ozone

and CO include: 1) increasing Asian emissions; 2) decreasing NOx emissions in the Northeast Urban corridor; 3) global biomass burning emissions; and 4) meteorological conditions (incl. cyclone activity, AO, and NAO).

• Regional contributions to NYC ambient concentrations of Hg were more than a factor of two larger than local contributions; the effect of emission reductions can be obscured by interannual variability in circulation.

• A decreasing Hg trend of 3.8±0.9 ppqv yr-1 found at a southern New England elevated rural site, in agreement with trends in background locations, is possibly associated with decreasing anthropogenic emissions in North America. Changes in ecosystems could disrupt this decreasing trend.

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Thanks for your attention

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