Climate Change Extremes and Air
Pollution in California
Michael J. Kleeman
Department of Civil and Environmental
Engineering
UC Davis
Air Pollution Taxonomy
Primary Pollutants
Secondary Pollutants
• Emitted directly from a source to
the atmosphere
• Produced by chemical reactions
in the atmosphere
wind
(m/sec)
Conc = emissions /
(wind * height)
height (m)
emissions (kg/sec)
http://acmg.seas.harvard.edu/people/faculty/djj/book/bookchap11.html
• Concentration determined by
emissions rate, wind speed, PBL
height, and precipitation
• Concentration determined by
emissions, wind speed, PBL
height, precipitation,
temperature, humidity, UV, etc.
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
More Recent Health Effects Data for Airborne
Particles: The Six Cities Study
Source: DOCKERY DW, POPE CA, XU XP, et al. “AN ASSOCIATION BETWEEN AIR-POLLUTION AND MORTALITY IN 6 UNITEDSTATES CITIES”, NEW ENGLAND JOURNAL OF MEDICINE 329 (24): 1753-1759 DEC 9 1993.
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?
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?
Parallel Climate Model (PCM)
- provides initial and boundary conditions for the Weather
Research and Forecasting (WRF) model.
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
Emissions Processing
- processes source oriented
typed emissions for area,
point, mobile and biogenic
sources
WRF Output Processing
-extracts and processes 2D
and 3D meteorological
fields for the air quality
model
Initial conditions, seasonal
boundary conditions, and
land use data
CIT-UCD 3D Photochemical Model
- calculates transport and chemistry of gas- and particle-phase
species, and uses dry and wet deposition schemes
- 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.
Seven-year Average PM2.5
Concentrations in California
PM2.5 Organic Compounds
PM2.5 Nitrate
Comparison: Modeled vs Observed (2000-06)
 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)
PM2.5 Total Mass Comparison
-3
Total Mass (m gm )
25.00
20.00
Mod
Obs
15.00
10.00
5.00
0.00
CELA
SJ4
FSF
M14
VCS
S13
Site
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.
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=Increased
Blue=Decreased
Probability that Calculations show a
Statistically Significant Change
Green=Less Certain
Blue=More Certain
Analysis of Extreme Events: 99th Percentile Days
in 2000-06 vs. 2047-53
120
2000-2006
Sample (n) = 1008
Mean (m ) = 8.06
Std (s) = ±2.87
(a)
100
80
60
20
0
120
100
80
0.35 3.13
(b)
5.91
8.69
11.48 14.26 17.04 19.82
2047-2053
Bin mid-point
(ug/m3)
Sample (n) = 1008
Mean (m) = 7.92
Std (s) =±3.08
60
40
20
0
0.
35
1.
74
3.
13
4.
52
5.
91
7.
30
8.
6
10 9
.0
11 9
.4
12 8
.8
14 7
.2
15 6
.6
17 5
.0
18 4
.4
19 3
.8
21 2
.2
1
Frequency (#)
40
Bin mid-point (mgm-3)
Increase in Airborne Particle
Concentrations During Future Extreme
Events Due to Climate
99th Percentile PM2.5
2047-53 (max=58 µg/m3)
99th Percentile PM2.5
2000-06 (max=46µg/m3)
Difference in 99th
Percentile PM2.5
Caused by Climate
7
26
12
8
16
8
-28
28
6
-3
-10
-31
-6
SV
38
45
35
56
39
47
44
40
35
15
25
8
-14
-27
-54
SJV
50
55
47
68
49
49
47
-30
53
57
-2
20
30
24
16
-9
-13
-23
39
-37
PM2.5 category/species
-7
Miscellaneous
High Sulfur
Content Fuels
Meat Cooking
-34
Diesel
Combustion
-5
Shipping
-5
Dust
-6
METL
-36
N(-III)
-7
S(VI)
-6
3
Wood Smoke
4
Gasoline
Combustion
SoCAB
N(V)
200
150
100
50
0
-50
-100
-150
-200
19
OC
200
150
100
50
0
-50
-100
-150
-200
10
EC
200
150
100
50
0
-50
-100
-150
-200
CA
TOT MASS
Future change from present-day (%)
Changing
Population
Exposure
During
Extreme
Events
200
150
100
50
0
-50
-100
-150
-200
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
300
Daily 1-hr max ozone (ppb)
250
1980-1989: Slope=8.55 ppb/K
1990-1999: Slope=5.96 ppb/K
2000-2004: Slope=3.24 ppb/K
200
150
Los Angeles
100
50
0
275
280
285
290
295
Daily max T850 (K)
300
305
310
Decreasing Ozone Climate Penalty Can Be
Understood Using An “Isopleth” Diagram
Solid black lines mark
contours of constant
ozone concentrations
Dashed line shows
our emissions
trajectory between
1990 - 2020
43ppb
75ppb
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 Changes
2000-06 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
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.
Ozone Climate Penalty Is Decreasing Over Time
ΔO3 / Δ Temperature (ppb/K)
Ozone Response to Temperature in the
SoCAB (1980-2010)
10
Statistical Downscaling
(Mahmud et al., 2008)
8
6
Model Perturbation
(Kleeman, 2008)
4
2
0
1980
1990
2000
Emissions Year
2010
Model Perturbation
(Millsteinand Harley,
2009)
Ozone Climate Penalty Doesn’t Go to Zero In the
Future
ΔO3 / Δ Temperature (ppb/K)
6
Azusa
5
Claremont
4
Central LA
3
Long Beach
2
1
0
1985
1990
1995
2000
2005
Emissions Year
2010
2015
2020
-50
-3
-2
-6
-4
-3
-3
-4
-6
-4
METL
Dust
Shipping
Wood Smoke
Diesel
Combustion
Gasoline
Combustion
Meat Cooking
High Sulfur
Content Fuels
Miscellaneous
-1
PM2.5 category/species
-1
-2
-1
-2
-5
-1
0
1
0
0
-1
-2
-2
-5
-10
N(-III)
-2
5
-1
0
0
0
1
-1
0
4
-1
0
11
30
S(VI)
10
2
-10
-2
0
0
-3
-5
-4
-3
-2
-5
-6
-2
-2
-1
-2
-4
-2
-2
2
10
N(V)
-3
-30
OC
-10
-3
50
EC
10
0
10
-2
-10
TOT MASS
Change in
PopulationWeighted
PM Caused
by Climate
Future change from present-day (%)
50
30
CA
-30
-50
50
SV
-30
-50
50
30
SJV
-30
-50
30
SoCAB
Climate Impact May Be Stronger on
Severe Airborne Particle Events
Population Exposure During Extreme
Events
7
26
12
8
16
8
-28
28
6
-3
-10
-31
-6
SV
38
45
35
56
39
47
44
40
35
15
25
8
-14
-27
-54
SJV
50
55
47
68
49
49
47
-30
53
57
-2
20
30
24
16
-9
-13
-23
39
-37
PM2.5 category/species
-7
Miscellaneous
High Sulfur
Content Fuels
-34
Meat Cooking
-5
Diesel
Combustion
-5
Shipping
-6
Dust
-36
METL
-7
N(-III)
-6
3
Wood Smoke
4
Gasoline
Combustion
SoCAB
S(VI)
200
150
100
50
0
-50
-100
-150
-200
19
N(V)
200
150
100
50
0
-50
-100
-150
-200
10
OC
200
150
100
50
0
-50
-100
-150
-200
CA
EC
Future change from present-day (%)
200
150
100
50
0
-50
-100
-150
-200
TOT MASS
Increase in Airborne Particle Concentrations
During Future Extreme Events Due to Climate
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
PM
EC
OC
NOx SOx ROG NH3
Percentage Emission Change from BAU
2.0%
0.0%
-2.0%
-4.0%
-6.0%
-8.0%
-10.0%
-12.0%
-14.0%
-16.0%
-18.0%
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.
Climate Predictions For 2001 vs. 2050
Using Averaged Meteorology
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.