Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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, 70N. Thelargest largest SO SO22 source Russia, 70N. Norilsk 70N -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 60N, 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