Climate Change Extremes and Air Pollution in California Michael J. Kleeman Department of Civil and...

Climate Change Extremes and Air Pollution in California

Michael J. KleemanDepartment of Civil and Environmental

EngineeringUC Davis

Air Pollution Taxonomy

Primary Pollutants• Emitted directly from a source to

the atmosphere

• Concentration determined by emissions rate, wind speed, PBL height, and precipitation

Secondary Pollutants• Produced by chemical reactions in

the atmosphere

• Concentration determined by emissions, wind speed, PBL height, precipitation, temperature, humidity, UV, etc.

wind(m/sec)

emissions (kg/sec)

Conc = emissions / (wind * height) height (m)

http://acmg.seas.harvard.edu/people/faculty/djj/book/bookchap11.html

Major Air Pollutants: Ozone and Particles

• Ozone (O3) is a chemical oxidant (reactive!)– Produced by CxHyOz + NOx -> O3

– inflammation of lung tissue, pulmonary and nasal congestion, coughing and wheezing, aggravates asthma, decreases resistance to pneumonia and bronchitis

• Airborne Particles (PM10, PM2.5)– Emitted directly or formed by chemical reaction– Associated with increased death rate even at low

concentrations (15 µg m-3)

Early Extreme Air Pollution Event: The London Fog

Source: http://www.portfolio.mvm.ed.ac.uk/studentwebs/session4/27/greatsmog52.htm

Source: DOCKERY DW, POPE CA, XU XP, et al. “AN ASSOCIATION BETWEEN AIR-POLLUTION AND MORTALITY IN 6 UNITED-STATES CITIES”, NEW ENGLAND JOURNAL OF MEDICINE 329 (24): 1753-1759 DEC 9 1993.

More Recent Health Effects Data for Airborne Particles: The Six Cities Study

PM2.5 Concentrations in the US:30,000 – 50,000 deaths each year

Source: US EPA (http://www.epa.gov/air/airtrends/2007/report/particlepollution.pdf).

California’s Major Air

Basins

San Joaquin Valley

South Coast Air Basin

PM10 Time Trends 1980-2003 Riverside (Southern California)

Emissions changes make this a “non-stationary” signal.

We spend a lot of money to purchase this decrease over time.

Will climate change reduce the effectiveness of our emissions control programs?

Parallel Climate Model (PCM) - provides initial and boundary conditions for the Weather

Research and Forecasting (WRF) model.

CIT-UCD 3D Photochemical Model - calculates transport and chemistry of gas- and particle-phase

species, and uses dry and wet deposition schemes

WRF Preprocessing System (WPS) - processes PCM outputs into the format used by WRF

Weather Research and Forecasting (WRF) version 2.2 - generates hourly fields for a 264x264x10 grid-cell domain

with 4-km horizontal resolution, and variable vertical spacing extending to 5000 m above the ground

WRF Output Processing -extracts and processes 2D

and 3D meteorological fields for the air quality

model

Emissions Processing - processes source oriented typed emissions for area,

point, mobile and biogenic sources

Initial conditions, seasonal boundary conditions, and

land use data

- hourly mixing ratios of gas-phase and concentrations of particle-phase species using SAPRC chemical mechanism

Source: A. Mahmud, M. Hixson, J. Hu, Z. Zhao, S.H. Chen, M.J. Kleeman, “Climate impact on airborne particulate matter concentrations in California using seven year analysis periods”, Atmos. Chem. Phys. Discuss., 10, 1–35, 2010.

Over-view of Climate Air Quality Modeling System

Statistical downscaling for PM doesn’t work well in California.

Dynamic downscaling studies are expensive. How well do they work?

Seven-year Average PM2.5 Concentrations in California

PM2.5 Organic Compounds PM2.5 Nitrate

0.00

5.00

10.00

15.00

20.00

25.00

CELA SJ4 FSF M14 VCS S13

Site

To

tal M

ass

(mg

m-3

)

Mod

Obs

Observed data obtained from the California Air Resources Board (CARB )

Six sites in California: Central Los Angeles (CELA), San Jose (SJ4), Fresno (FSF), Modesto (M14), Visalia (VCS), and Sacramento (S13)

Comparison: Modeled vs Observed (2000-06)

PM2.5 Total Mass Comparison


WRF Over-predicts Wind Speed During Pollution Events in California

Are WRF Predictions for Wind Speed Too “Noisy” During Pollution Events?

Simulation with PCM

Simulation with GFS

Are WRF PBL Height Predictions Reliable Enough?

Change in Annual Average Airborne Particle Concentrations Due to Climate is Smaller than Inter-Annual Variability

Change in 7-Year Average Airborne Particle Concentrations Due to Climate

Red=IncreasedBlue=Decreased

Probability that Calculations show a Statistically Significant Change

Green=Less CertainBlue=More Certain

Analysis of Extreme Events: 99th Percentile Days in 2000-06 vs. 2047-53

0

20

40

60

80

100

120

0.35 3.13 5.91 8.69 11.48 14.26 17.04 19.82

Bin mid-point (ug/m3)

2000-2006Sample (n) = 1008Mean (m ) = 8.06Std (s) = ±2.87

0

20

40

60

80

100

120

0.35

1.74

3.13

4.52

5.91

7.30

8.69

10.0

911

.4812

.8714

.2615

.6517

.0418

.4319

.8221

.21

Bin mid-point (mgm-3)

2047-2053Sample (n) = 1008Mean (m) = 7.92Std (s) =±3.08

(b)

Fre

qu

en

cy (

#)

(a)

Increase in Airborne Particle Concentrations During Future Extreme Events Due to Climate

99th Percentile PM2.52000-06 (max=46µg/m3)

99th Percentile PM2.5 2047-53 (max=58 µg/m3)

Difference in 99th Percentile PM2.5 Caused by Climate

Changing Population Exposure During Extreme Events

6

-6-10

-32816

-31

8812

-28

2671910

-200-150

-100-50

0

50100

150200

CA

25

-14

8153540

-54

444739

-27

56354538

-200-150-100-50

050

100150200

SV

391624305753

-37

474949

-30

68475550

-200-150-100-50

050

100150200

SJVF

utu

re c

ha

ng

e fr

om

pre

sen

t-d

ay

(%)

-7-23-13-9

20-2

-34-5-5-6

-36

3

-7

4

-6

-200-150-100-50

050

100150200

TO

T M

AS

S

EC

OC

N(V

)

S(V

I)

N(-

III)

ME

TL

Dus

t

Shi

ppin

g

Woo

d S

mok

e

Die

sel

Com

bust

ion

Gas

olin

eC

ombu

stio

n

Mea

t Coo

king

Hig

h S

ulfu

rC

onte

nt F

uels

Mis

cella

neou

s

SoCAB

PM2.5 category/species

Wildfires Cause Extreme Air Quality Events

Ozone Concentrations in the US

Source: US EPA (http://www.epa.gov/air/airtrends/2007/report/groundlevelozone.pdf).

Ozone Time Trends 1980-2003

Ozone Formation Increases at Warmer Temperatures

Statistical Evidence from Measurements

Predictions from Reactive Chemical Transport Models

0

50

100

150

200

250

300

275 280 285 290 295 300 305 310Daily max T850 (K)

Dai

ly 1

-hr

max

ozo

ne (

ppb)

1980-1989: Slope=8.55 ppb/K

1990-1999: Slope=5.96 ppb/K

2000-2004: Slope=3.24 ppb/K

Los Angeles

Decreasing Ozone Climate Penalty Can Be Understood Using An “Isopleth” Diagram

Solid black lines mark contours of constant ozone concentrations

43ppb

75ppb

Dashed line shows our emissions trajectory between 1990 - 2020

Do Extreme Temperatures

Always Produce Extreme Ozone Concentrations?

Source: A. Steiner, A. Davis, S. Sillman, R. Owen, A. Michalak, and A. Fiore, “Observed Suppression of Ozone Formation at Extremely High Temperatures Due to Chemical and Biophysical Feedbacks”, PNAS, 107, P 19685-19690, 2010.

Conclusions• Air pollution events driven by emissions as well as

meteorology – we don’t have emissions models that can predict extreme events (traffic jams, factory upsets, etc)

• Regional climate models must accurately predict wind speed and PBL height during low wind speed “extreme events” – either summer or winter events

• Regional climate models must accurately predict high and low extreme temperatures – either summer or winter events

• Ozone “Climate-Penalty” is shrinking over time, but it likely won’t go to zero and it may rebound

• Climate does not strongly affect annual-average PM, but effects on extreme events may be stronger

EXTRA SLIDES

Temperature Changes2000-06 vs. 2047-53

ΔTemperature Summer (oC) Δ Temperature Winter (oC)

Source: Z. Zhao, S. Chen, and M. Kleeman, “The Impact of Climate Change on Air Quality Related Meteorological Conditions in California – Part II: Present versus Future Time Simulation Analysis”, Climate Change, submitted, 2010.

Humidity Changes (%)2000-06 vs. 2047-53


Ozone Climate Penalty Is Decreasing Over Time

0

2

4

6

8

10

1980 1990 2000 2010

ΔO3

/ Δ

Tem

pera

ture

(ppb

/K)

Emissions Year

Ozone Response to Temperature in the SoCAB (1980-2010)

Statistical Downscaling (Mahmud et al., 2008)

Model Perturbation (Kleeman, 2008)

Model Perturbation (Millsteinand Harley, 2009)

Ozone Climate Penalty Doesn’t Go to Zero In the Future

0

1

2

3

4

5

6

1985 1990 1995 2000 2005 2010 2015 2020

ΔO3

/ ΔTe

mpe

ratu

re (p

pb/K

)

Emissions Year

Azusa

Claremont

Central LA

Long Beach

Change in Population-Weighted PM Caused

by Climate

-2 -2 -4

2

-2 -1 -2 -2 -6 -5 -2 -3 -4 -5 -3

-50

-30

-10

10

30

50CA

0 0 -111

-5

4

-2 -2

0 -1 1 0 0 0 -1

-50

-30

-10

10

30

50SV

0 0

-2

5

-5

-1 0 0 1

-2

0

-1

-1

-2 -1

-50

-30

-10

10

30

50SJV

-2 -3 -3

2

-2

-1

-3 -2 -6 -4 -3 -3 -4 -6 -4

-50

-30

-10

10

30

50

TO

T M

AS

S

EC

OC

N(V

)

S(V

I)

N(-

III)

ME

TL

Dus

t

Shi

ppin

g

Woo

d S

mok

e

Die

sel

Com

bust

ion

Gas

olin

eC

ombu

stio

n

Mea

t Coo

king

Hig

h S

ulfu

rC

onte

nt F

uels

Mis

cella

neou

s

SoCAB

Fut

ure

chan

ge fr

om p

rese

nt-d

ay (

%)


Climate Impact May Be Stronger on Severe Airborne Particle Events

Increase in Airborne Particle Concentrations During Future Extreme Events Due to Climate

Population Exposure During Extreme Events

6

-6-10

-32816

-31

8812

-28

2671910

-200-150

-100-50

0

50100

150200

CA

25

-14

8153540

-54

444739

-27

56354538

-200-150-100-50

050

100150200

SV

391624305753

-37

474949

-30

68475550

-200-150-100-50

050

100150200

SJV

Fu

ture

ch

an

ge

fro

m p

rese

nt-

da

y (%

)

-7-23-13-9

20-2

-34-5-5-6

-36

3

-7

4

-6

-200-150-100-50

050

100150200

TO

T M

AS

S

EC

OC

N(V

)

S(V

I)

N(-

III)

ME

TL

Dus

t

Shi

ppin

g

Woo

d S

mok

e

Die

sel

Com

bust

ion

Gas

olin

eC

ombu

stio

n

Mea

t Coo

king

Hig

h S

ulfu

rC

onte

nt F

uels

Mis

cella

neou

s

SoCAB


Criteria Pollutant Emissions Reductions Associated With Climate Mitigation: AB32

Level 1 – Industrial

Level 2 – Electric Utilities & Natural Gas

Level 3 – Agricultural

Level 4 – On-road vehicles

Level 5 – Off-road vehicles

AB32 Has Different Impact on Each Criteria Pollutant Emissions Rate

-18.0%

-16.0%

-14.0%

-12.0%

-10.0%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

PM EC OC NOx SOx ROG NH3

Perc

enta

ge Em

issio

n Ch

ange

from

BAU

Lvl 1: Industry

Lvl 2: Elec. & NG

Lvl 3: Agriculture

Lvl 4: On-road Mobile

Lvl 5: Other Mobile

AB32 Reduces Population Exposure to PM2.5 as a Co-Benefit of GHG Mitigation

(a) Change in population-weighted PM2.5 in California

(b) Change in population-weighted PM2.5 in Los Angeles

S-3-05: More Aggressive GHG Mitigation Strategies Have Bigger Co-Benefits

(a) Change in population-weighted PM2.5 in California

(b) Change in population-weighted PM2.5 in Los Angeles

Climate Predictions For 2001 vs. 2050 Using 36 km Resolution

Source:: Tagaris E, Liao KJ, Delucia AJ, Deck L, Amar P, Russell AG, “Potential Impact of Climate Change on Air Pollution-Related Human Health Effects”, Environ. Sci. Technol., 43, 4979-4988, 2009.

Source:: Millstein, D.E., and Harley, R.A. “Impact of Climate Change on Photochemical Air Pollution in southern California”, Atmos. Chem. Phys., 9, 3745-3754, 2009.

Climate Predictions For 2001 vs. 2050 Using Averaged Meteorology

Climate Change Extremes and Air Pollution in California Michael J. Kleeman Department of Civil and...

Documents

Transcript of Climate Change Extremes and Air Pollution in California Michael J. Kleeman Department of Civil and...