Download Satellite remote sensing of pollution with application to the

Satellite remote sensing of
pollution with application to the
Arctic
Chris McLinden
Environment Canada
([email protected])
27 July, 2012
CREATE summer school, Alliston, Ontario
Introduction
The forward problem:
Given the
dragon, what can
be inferred about
its tracks?
Given the state of
the atmosphere,
what can be
inferred about the
radiation field?
[RT modeling]
The inverse problem:
Given its tracks,
what can be
inferred about
the dragon?
Taken from Bohren & Huffman, 1983
Given measurements
of the radiation field,
what can be inferred
about the
atmospheric state?
[remote sensing]
Introduction
Remote sensing is the acquisition of information about an
object or phenomenon, without making physical contact
with the target.
More simply: measurement at a distance
This is done by collecting electromagnetic radiation and
measuring a portion of the spectrum.
Thus remote sensing instruments measure spectra;
geophysical properties are only inferred.
Introduction
A remote sensing instrument must:
1. Capture electromagnetic
radiation (EMR) over some welldefined region
2.
Isolate the wavelength interval of
interest
3.
Measure the power captured in
this spectral interval, and convert
it to absolute radiance

th
rE
Satellite remote sensing of Air Quality
The first meteorological satellites were launched in the
1960s; the first air quality satellites ones in the 1990s
Air quality instruments are in low earth orbit, so 1-2
measurements over a given location per day
The near-surface atmosphere can only
be detected using nadir, or downlooking, viewing geometry
These instruments measure over a
volume of air, which generally includes
the entire atmospheric column.
Nadir geometry
air quality = near surface atmospheric composition
Satellite remote sensing of Air Quality
Satellites measure
Air quality satellite instruments derive the vertical
column density
vertical column density, or VCD
The VCD represents the vertically
integrated number density profile and
or number of
molecules per cm
has units of molecules per unit area
(e.g., molecules/cm2, or cm-2)
• The primary air quality data product is the
tropospheric VCD – the vertically-integrated number
density between the surface and the tropopause (~10
km)
• This may require the removal of the stratospheric
portion of the total VCD
2
Satellite remote sensing of Air Quality
Strengths:
• provide large-scale coverage / an integrated view
• measures over otherwise inaccessible areas
Limitations:
• only a handful of pollutants may be detected
• “moderate” spatial resolution, 10x10 km2 at best
• provides limited (or no) information on where the
pollutant is located in the atmospheric column
• only one or two measurements per day; cannot see
below cloud tops
Some Air Quality satellite sensors
UV/vis spectral
aerosol
thermal IR
Quantity Measured
GOME
OMI
= Ozone Monitoring Instrument
SCIAMACHY
NO2, SO2
OMI
NO2, SO2
GOME2 / MetOp-A
NO2, SO2
GOME2 / MetOp-B
NO2, SO2
TropOMI
NO2, SO2
MODIS /Terra
Aerosol optical depth
MODIS / Aqua
Aerosol optical depth
MOPITT
CO
TES
CO, NH3
IASI / MetOp-A
CO
IASI / MetOp-B
CO
1996
2000
2004
Year
2008
2012
2016
Ozone Monitoring Experiment (OMI)
• Dutch/Finish instrument, launched in 2004 on
the NASA Aura satellite, still operational
• Measures sunlight reflected from Earth’s
surface and atmosphere back out into space
(nadir geometry)
• A spectrometer that measures near-UV and
visible light (280 to 600 nm)
• Horizontal resolution roughly 15 by 30 km2
(best in its class)
• Uses a 2D array detector that simultaneously
measures many wavelengths and across-track
positions
• Air quality gases: NO2 and SO2
OMI
~15 km
(2 sec.)
Data Inversion
Extra-terrestrial
reflected
OMI measures spectra – composition
obtained through a careful analysis of
the spectra accounting for all relevant
atmospheric and instrument effects
Converting raw data to VCDs (called “inversion”) is a complex process
that requires the use of atmospheric computer models that
simulate the chemical and physical processes
Raw
Spectra
(Level 0)
Calibrated,
geolocated
Spectra (L1)
Spectral
Fit
Removal of
stratosphere
Convert Slant
to vertical column
Tropospheric
Vertical Column
Density VCD (L2)
OMI Processing Sequence
The models supply additional information necessary for the proper
interpretation of the satellite data
Data Inversion
The high-frequency absorption structure is exploited to determine
amount of absorber in the path.
Spectral fit: a multi-linear regression is performed using laboratory
measured absorption spectra of all relevant gases
0.015
0
SO2 window
NO2 window
10
NO2
NO
2
0.005
0
420
0.015
440
SO2
0.01
460
Optical Depth
0.01
-1
10
SO
Strong ozone
absorption interferes
with SO2 signal
2
O3
-2
10
0.005
-3
0
310
315
320
325
330
10
300
350
400
Wavelength [nm]
450
500
NO2 over the GTA
4900
2500
1600
900
400
Population Density
3600
50
40
30
20
100
0
OMI 2005-2007 summertime average
10
NO2 Tropospheric VCD [1014 cm-2]
60
6400
Nanticoke Generating Station
15
Nanticoke power plant
VCD [cm -2] or emissions
x 10
6
4
2
VCD
Annual NO emissions (scaled)
x
Power Generated (scaled)
0
2005
2006
2007
2008
Year
Reported to
Gov’t database
2009
2010
The Nanticoke Generating Station is the largest coal-fired power plant in North
America delivering 4000 MW at peak capacity.
Ontario attempting to phase out coal burning by 2014; four of its units have been
retired.
2011
NO2 over the GTA
10
20
30
40
50
60
NO2 Tropospheric VCD [1014 cm-2]
Weekends
Weekdays
40% increase
NO2 over the GTA
10
20
30
40
50
60
NO2 Tropospheric VCD [1014 cm-2]
2005-2011, summer, all wind directions
2005-2011, summer, Southerly winds
Windspeed and direction from ECMWF reanalysis tied to OMI observations
Global SO2 emission source catalogue (~200 sources)
Example: Volcanoes in Japan
Asama
Suwanose-jima
Kikai
Sakura-jima
Aso
Miyake-jima
SO2 Pollution Controls Bring Results
December 2, 2011
Fioletov et al., GRL., 2011
Scientists using the Ozone Monitoring Instrument (OMI) on NASA’s Aura satellite observed
major reductions in sulfur dioxide (SO2) between 2005 and 2010 in Alabama, Georgia, Indiana,
Kentucky, North Carolina, Ohio, Pennsylvania, and West Virginia. Led by Vitali Fioletov of
Environment Canada, the research team found that sulfur dioxide levels near the region’s coalfired power plants fell by nearly half since 2005.
See NASA Earth Observatory, http://earthobservatory.nasa.gov/IOTD/view.php?id=76571#
The
sourceininthe
theArctic:
Arctic:Norilsk,
Norilsk,
Russia,
70N.
Thelargest
largest SO
SO22 source
Russia,
70N.
Norilsk
70N
-0.3
-1.0
0.0
0.0
0.3
1.0
2.0
0.6
DU
3.0 DU
Copper, nickel smelting
1% of Russia’s GDP
2% of Russia’s industrial production
3% of Russia’s export
… and 2,400 kT of SO2 per year
(Canada <2,000 kT/yr)
Application to oil sands monitoring
• “Oil sands”, or “tar sands”, refer to a type of petroleum
deposit in which the oil is very thick and sticky (called
“bitumen”) and mixed with sand, water, and clay
• Only in recent years has it been profitable to extract and
refine oil from these deposits
• Canada has a proven reserve of ~170 billion barrels
Province
of Alberta
surface mining region
from Energy Resources Conservation Board, 2011
• Bitumen found close to the
surface may be mined;
deeper deposits need to be
heated and then pumped to
surface
Application to oil sands monitoring
Surface mining & upgrading processes emit NOx and SO2
into the atmosphere
OMI well suited to study these pollutants
NO
 data products - NO2: Dutch TEMIS version 2
SO2: NASA OMSO2 V003
SO
X
2
Mining & Transport
Extraction
from The Oil Sands Process, CNRL
Separation
Steps in Surface Mining
Primary Upgrading
Secondary Upgrading
Alberta
Surface Mining
Region
OMI NO2 2005-2010
tropospheric VCD
[0.25 0.25 grid]
Oil Sands
Edmonton
Toronto
a
0
1
2
3
4
Vertical Column Density (x1015 cm-2)
5
6
Surface Mining Area
LandSat
OMI NO2 (2005-2010)
90 km
OMI SO2 (2005-2010)
57.5
57.5
57
57
56.5
56.5
Surface Mining Operations
with on-site Upgraders
Fort McMurray
Fort McMurray
Fort McMurray
0
1
1015
2
molecules/cm2
3
0
0.1
0.2
0.3
0.4
Dobson Units
• NO2 and SO2 both show area of enhancement over surface mining;
some differences in distribution evident
• NO2 also shows secondary maximum further to the north
• Primary source of SO2 is thought to be upgrading, and the only onsite upgraders are at the location indicated
Evolution
A to E = location of in-situ NO2 measurements
2005-2007
OMI NO2
c
2008-2010
A
2
A
B
C
B
C
1
1
2.5
1.5
2
2
D
D
2003-2006
1
2007-2010
15
E
E
cm -2)
0.5
SCIAMACHY NO2
Vertical Column Density (x10
b
- NO2 in 2008-2010
clearly larger, and also
area of enhancement
also appears larger
- SCIAMACHY data is
consistent with OMI
0
b
2005-2007
c
2008-2010
OMI SO2
0.3
1
2
1
0.2
2
0.1
0
-0.1
Vertical Column Density (DU)
0.4
- SO2 in 2008-2010
appears to be larger, but
area of enhancement
slightly smaller
- uncertainties too large
to conclude an increase
Evolution of NO2 over Oil Sands
Examine NO2 from a seasonal perspective – less spatial
information
Use fit of 2D Gaussian to characterize seasonal NO2 VCD
data (DJF, MMA, JJA, SON); calculate trends
Max VCD
Widths
Total mass [t(NO2)]
of enhancement
Background VCD
Maximum VCD
Widths of distribution [km]
“Background” VCD
WBEA in-situ NO2 (average over sites A-D)
Production [millions of barrels per day]
Air Mass Factors
Raw
Spectra
(Level 0)
Calibrated,
geolocated
Spectra (L1)
Spectral
Fit
Removal of
stratosphere
Convert Slant
to vertical column
Tropospheric
Vertical Column
Density (Level 2)
UV/vis Processing Sequence
VCDtrop = (SCD – VCDstrat AMFstrat) / AMFtrop
measured
modelled
Air mass factor (AMF) describe the sensitivity of the satellite sensor to
absorbing layer. They are computed using a multiple-scattering
radiative transfer model and their accuracy relies in large part on
the validity of input parameters, including:
1. Shape of the absorbing profile
2. Surface reflectivity or albedo
Landsat 1993
Surface Albedo
Landsat 2005
Landsat 2010
Refl
0.03
AOD at 550 nm [-]
0.025
b
Complications: surface albedo
0.2
0.15
0.1
Reflectivity [-]
AOD at 550 nm [-] VCD correction, a [-]
m
AMF [-]
• AMFs are sensitive to the
reflectivity of the underlying surface
1.2 c
– measured light that is reflected from the surface will have
1
passed through the entire
atmosphere twice
0.8
1.2
0.04
a
d
-1.9
 0.3%/yr
MODIS
OMI (471 nm)
OMI (442 nm)
0.035
1
0.03
Bright
Some light from surface
0.025
0.8
2005b
0.2
2006
2007
2008
2009
2010
2011
Year
Currently,
a surface reflectivity “climatology” is used
0.15
and so does not take into account changes in land
use/cover.
0.1
c
AMF [-]
AMF sensitivity studies suggest this would impact the
calculated
trend in NO2 by 1%/year.
1
[-]
No light from surface
1.2
m
Dark
0.8
-1.9  0.3%/yr
Oil Sands
Context
The enhancements in NO2 and SO2 are
comparable to what OMI observes
over a “large” coal-burning power
plant
NO2
SO2 emissions from the Oil Sands are
about 100 kT/year. There are many
other (>50) industrial sources with the
same or larger level of emissions in
the world. The largest industrial
source produces >2000 kT/year.
SO2
Oil production:
It is also useful to contrast these results
with other oil-industry sources
1,600,000 bpd
Reported SO2 emissions:
about 115 kT/y
OMI-estimated SO2 emissions:
about 85 kT/y
Ufa, Russia (oil refineries, power plants, etc.)
(same latitude as oil sands, ~same obs. conditions)
NO2
NO2
SO2
SO2
Three oil refineries located in Ufa with a
combined capacity of >1,000,000 BPD
Ufa Population: ~1,000,000
Oil Sands
Oil production:
OMI-estimated SO2 emissions:
about 100 kT/y
1,600,000 bpd
Reported SO2 emissions:
about 115 kT/y
OMI-estimated SO2 emissions:
about 85 kT/y
Cantarell and Ku-Maloob-Zaap Oil Fields, Mexico
(Large North American source, growing rapidly)
Oil Sands
2005-2011
NO2
NO2
2005-2007
SO2
SO2
2007-2011
Oil production:
800,000+500,000 BPD
OMI estimated SO2 emissions:
about 200 kT/y in 2005-2007
about 330 kT/y in 2008-2011
Oil production:
SO2
1,600,000 bpd
Reported SO2 emissions:
about 115 kT/y
OMI estimated SO2 emissions:
about 85 kT/y
Oil refineries in Aruba and Venezuela
(near vacation site; SO2 source comparable to oil sands)
Oil Sands
NO2
NO2
SO2
SO2
The Aruba refinery processes lower-cost heavy sour crude
oil and produces a high yield of finished distillate products.
Total capacity of 235,000 bpd
Paraguaná Refinery Complex, Venezuela, one of the world
largest refinery complexes (940,000 bpd)
Oil production:
1,600,000 bpd
Reported SO2 emissions:
about 115 kT/y
OMI estimated SO2 emissions:
about 85 kT/y
Future of Space-based
Monitoring
TROPOMI (2015, Europe): OMI-like but 6+ times better
spatial resolution, better sensitivity, 10+ times more data
points
OMI (15 x 30 km2)
TropOMI (7 x 7 km2)
Future of Space-based
Monitoring
• GEO-CAPE (2018+, USA):
Geostationary platform, should observe
up to 60N, target resolution 4 x 4 km2;
hourly repeat
• PCW (2018+, Canada, Polar Communications
and Weather): A pair of satellites in highlyellipitical orbits that together provide neargeostationary coverage of Arctic/sub-Arctic;
target 8 x 8 km2 resolution, hourly repeat
PCW concept
References
Texts:
Remote Sensing of the Lower Atmosphere: An Introduction, G. L. Stephens,
Oxford University Press, 1994.
The Remote Sensing of Tropospheric Composition from Space, John P.
Burrows, Ulrich Platt, Peter Borrell (editors), Spring, 2011. *
Papers:
Martin, R. V., Satellite remote sensing of surface air quality, Atmospheric
Environment, 42, 7823–7843, 2008. *
McLinden, C. A., V. Fioletov, K. F. Boersma, N. Krotkov, C. E. Sioris, J. P.
Veefkind, and K. Yang, Air quality over the Canadian oil sands: A first
assessment using satellite observations, Geophys. Res. Lett., 39, L04804,
doi:10.1029/2011GL050273, 2012. *
* pdf available from ftp://exp-studies.tor.ec.gc.ca/pub/ftpcm/CREATE/
Data Inversion
• Stratosphere removed using simulations from a global chemicaltransport model
• There are many paths that involve reflection and/or one or more
scattering events; to interpret the measurements computer models
are used that simulate multiple-scattering and absorption
• Computer models are also used to provide an estimate of the profile
shape
Profile shape
(from model)
2
1
0
visible
0
0.1
0.2
VMR [ppb]
Higher probability of reaching surface
0.3
40
NO (440 nm)
2
35
SO (313 nm)
2
30
Altitude [km]
z [km]
3
sensitivity
25
20
15
10
UV
5
0
0
0.5
1
1.5
2
Air Mass Factor
2.5
3
3.5
Lower probability of reaching surface
Mapping
Surface Mining Area
Pixel-averaging method to better resolve
features in satellite data:
* need to use a large amount of data
Approximate size
of OMI footprint
The value assigned to a grid-box is the
average of all data within radius r
x
25 km
y
r
320 km
r
LandSat 2009
e.g.: x=y=1 km, r=8 km