Methods for Global Survey of Natural Gas Flaring from Visible Infrared Imaging Radiometer Suite Data

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1 Article Methods Global Survey Natural Gas Flaring from Visible Infrared Imaging Radiometer Suite Data Chrispher D. Elvidge 1, *, Mikhail Zhizhin 2,3, Kimberly Baugh 2, Feng-Chi Hsu 2 Tilottama Ghosh 2 Received: 28 Ocber 2015; Accepted: 14 December 2015; Published: 25 December 2015 Academic Edir: Richard B. Cfin 1 Earth Observation Group, National Centers Environmental Inmation, National Oceanic Atmospheric Administration, 325 Broadway, Boulder, CO 80205, USA 2 Cooperative Institute Research in Environmental Sciences, University Colorado, Boulder, CO 80303, USA; mikhail.zhizhin@noaa.gov (M.Z.); kim.baugh@noaa.gov (K.B.); feng.c.hsu@noaa.gov (F.-C.H.); tilottama.ghosh@noaa.gov (T.G.) 3 Russian Space Research Institute, Moscow , Russia * Correspondence: chris.elvidge@noaa.gov; Tel.: Abstract: A set methods are presented survey natural using data collected by National Aeronautics Space Administration/National Oceanic Atmospheric Administration NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The accuracy flared volume estimates is rated at 9.5%. VIIRS is particularly well suited detecting measuring radiant emissions from flares through collection shortwave near-infrared data at night, recording peak radiant emissions from flares. In 2012, a tal 7467 individual flare sites were identified. The tal flared volume is estimated at 143 ( 13.6) billion cubic meters (BCM), corresponding 3.5% production. While USA has largest number flares, Russia leads in terms flared volume. Ninety percent flared volume was found in upstream production areas, 8% at refineries 2% at liquified natural (LNG) terminals. The results confirm that bulk natural occurs in upstream production areas. VIIRS data can provide site-specific tracking natural use in evaluating efts reduce eliminate routine. Keywords: Visible Infrared Imaging Radiometer Suite (VIIRS); Nightfire; ; carbon intensity; carbon dioxide emissions 1. Introduction Flaring is widely used dispose natural produced at oil facilities that lack sufficient infrastructure capture all that is produced ( 1). The term associated refers natural that emerges when crude oil is brought Earth s surface. This is largest source. Smaller quantities occur at oil refineries natural processing facilities. Because is a waste disposal process, re is no systematic reporting locations flared volumes. Additionally, where flare volume data are reported, data are typically self-reported by flare operars, estimated from difference between natural volume produced quantity used or sold. It is ree difficult assess reliability accuracy reported data. There are four distinct applications site-specific estimates flared volumes. First, re are carbon cycle analyses that rely on site-specific knowledge locations magnitudes greenhouse emissions atmosphere [1]. Second is tracking activities reduce ; doi: /en

2 [2]. Third is identification potentially attractive locations utilization. Fourth is calculation carbon intensity fuels, such as Calinia Low Carbon Fuel Stard [3]. In this paper, we we present present a series a series methods methods that produce that produce site-specific site specific estimation estimation flared volumes flared worldwide volumes using worldwide data collected using by data National collected Aeronautics by National Space Aeronautics Administration/National Space Administration/National Oceanic AtmosphericOceanic Administration Atmospheric NASA/NOAA Administration Visible Infrared NASA/NOAA Imaging Radiometer Visible Infrared Suite Imaging (VIIRS). Using Radiometer data collected Suite (VIIRS). in 2012, Using we conducted data collected a in 2012, survey we conducted a sites survey separated se in sites upstream separated (production se sites) in upstream downstream (production refineries sites) liquefied downstream natural refineries (LNG) liquefied terminals. natural We developed (LNG) aterminals. calibrationwe developed estimating a calibration flared volumes estimating flared applied this volumes individual applied sites. this The results individual have been sites. aggregated The results have national been level, aggregated tallies national number level, tallies sites estimates number tal sites flared estimates volume in tal flared volume in Large quantities radiant energy are produced by flares. 1. Large quantities radiant energy are produced by flares. 2. Satellite Observation Gas Flares 2. Satellite Observation Gas Flares Because lack systematic reporting from flare operars remote nature many Because lack systematic reporting from flare operars remote nature many flare locations, satellite sensors are an attractive option moniring flares. However, flare locations, satellite sensors are an attractive option moniring flares. However, none existing sensors have been designed specifically detection moniring none existing sensors have been designed specifically detection moniring flares. Systems that collect at high spatial resolution are not well suited collect data on large flares. Systems that collect at high spatial resolution are not well suited collect data on large numbers flare sites, lacking repeat cycle suitable cloud clearing capturing variability numbers flare sites, lacking a repeat cycle suitable cloud clearing capturing variability in activity. In addition, high spatial resolution sensors need be tasked collect data at in activity. In addition, high spatial resolution sensors need be tasked collect data at specific sites, data are sold commercially, significantly raising cost complexity any specific sites, data are sold commercially, significantly raising cost complexity any potential eft moniring. For moniring flares, option potential eft moniring. For moniring flares, option analyze data from moderate spatial resolution (~1 km 2 ) polar orbiting sensors has considerable analyze data from moderate spatial resolution (~1 km 2 ) polar orbiting sensors has considerable merit. merit. Here, data are free coverage is every 24 hours. The challenge this style Here, data are free coverage is every 24 h. The challenge this style data data is that flares are subpixel sources requiring specialized analysis identify flares is that flares are subpixel sources requiring specialized analysis identify flares extract extract radiant emissions. radiant emissions. There have been several published studies describing detection using satellite systems. There have been several published studies describing detection using satellite systems. NASA s moderate resolution imaging spectroradiometer (MODIS) data were used invenry NASA s moderate-resolution imaging spectroradiometer (MODIS) data were used invenry flares μm spectral b estimate flared volumes in Nigeria [4]. Nighttime flares 4 µm spectral b estimate flared volumes in Nigeria [4]. Nighttime shortwave infrared (SWIR) data from advanced along track scanning radiometer (AATSR) have shortwave infrared (SWIR) data from advanced along track scanning radiometer (AATSR) have been been used map flares ly based on temporal persistence flares [5]. A survey used map flares ly based on temporal persistence flares [5]. A survey sites was developed a portion Canada using daytime data collected by Lsat 8 [6]. sites was developed a portion Canada using daytime data collected by Lsat 8 [6]. To date, most extensive time series, national estimates flared volumes, comes from low light imaging nighttime data acquired by Operational Linescan System (OLS) operated by U.S. Air Force Defense Meteorological Satellite Program (DMSP) [7]. Gas flares were identified visually in DMSP data because sensor detects electric lights from cities

3 3 15 To date, most extensive time series, national estimates flared volumes, comes from low light imaging nighttime data acquired by Operational Linescan System (OLS) operated by U.S. Air Force Defense Meteorological Satellite Program (DMSP) [7]. Gas flares were identified visually in DMSP data because sensor detects electric lights from cities wns, as well as flares. Estimation volumes using DMSP data ended in 2012 due orbit degradation, wns, resulting as well as in solar flares. contamination. Estimation volumes using DMSP data ended in 2012 This due study orbit was degradation, conductedresulting using VIIRS in solar data contamination. Suomi National Polar Partnership (SNPP) satellite, launchedthis in study The was VIIRS conducted is operated using VIIRS in an unusual data on way Suomi that National fers a substantial Polar Partnership advantage (SNPP) observation satellite, launched At night, The VIIRS VIIRS is operated continues an unusual record data way in that three fers near- a substantial short-wave infrared advantage channels designed observation daytime. imaging At night, ( 2). VIIRS Atcontinues night, record only features data in three detected in se near channels short wave are combustion infrared channels sources designed [8]. The SWIR daytime channel, imaging at ( 1.6 µm, 2). At is night, at wavelength only features detected in se channels are combustion sources [8]. The SWIR channel, at 1.6 μm, is at region where peak radiant emissions from flares occur. The 4 µm channel, widely used in fire wavelength region where peak radiant emissions from flares occur. The 4 μm channel, widely detection [9], only detects large flares due fact that it falls on trailing edge flare used in fire detection [9], only detects large flares due fact that it falls on trailing edge radiant emissions observes a mixture flare plus background radiant emissions. Typically, flare radiant emissions observes a mixture flare plus background radiant emissions. flare Typically, radiant emissions flare radiant in emissions 4 µm channel in 4 are μm about channel a third are about a third emissions emissions at 1.6 µm. at 1.6 This μm. has a dramatic This has effect, a dramatic limitingeffect, detection limiting detection smaller flares smaller in stard flares in stard satellite satellite fire products fire products based on channels based set on at channels 4 µmset wavelength. at 4 μm wavelength. 2. Relative 2. Relative spectral spectral response response visible infrared imaging imaging radiometer radiometer suite suite (VIIRS) (VIIRS) bs bs typical flares at 1800 K. Day night b: DNB. typical flares at 1800 K. Day night b: DNB. By detecting flare radiances in multiple spectral bs, it is possible model s By detecting flares. The flare temperature radiances in multiple hot source spectral is calculated bs, using it is Wien s possible displacement modellaw: s flares. The temperature hot source is calculated using Wien s displacement law: T = b/ʎmax (1) where T is temperature in kelvin (K), b is Wien s T b{l displacement max constant = K*μm ʎmax is (1) wavelength peak radiant emissions. where T is The temperature flares appear in kelvin as graybodies (K), b is Wien s because displacement y are sub pixel constant sources. = The emission K*µm scaling L max is wavelength facr (ε) is defined peakas radiant ratio emissions. between observed radiances radiances an object at The that temperature flares appear filling as entire graybodies field because view. The y source are area sub-pixel (S) is calculated sources. by The multiplying emission ε by scaling size pixel footprint. Radiant heat (RH) is calculated using Stefan Boltzmann law: facr (ε) is defined as ratio between observed radiances radiances an object at that temperature filling entire field view. The RH source = σt 4 S area (S) is calculated by multiplying (2) ε by size where pixel RH = radiant footprint. heat Radiant in megawatts heat (RH) (MW), isσ calculated = Stefan Boltzmann using Stefan Boltzmann constant, T is temperature law: in K S = source area in square meters. 2 shows spectral bs collected RH by VIIRS σt 4 S at night. Bs M7 10 are daytime spectral (2) bs that continue be collected at night, enabling unambiguous detection combustion sources where at RH 0.87 = μm, radiant 1.24 μm heat in megawatts 1.6 μm. Nighttime (MW), collection σ = Stefan Boltzmann M11 b constant, at 2.2 μm, T which is temperature will in K improve S = source detection area inquantification square meters. flares, has been approved VIIRS is expected commence 2 shows in spectral The solid bs red line collected is by VIIRS at night. an 1800 Bs K object, M7-10 typical are daytime a flare. spectral bs The that M10 continue spectral b be collected records atpeak night, radiant enabling emissions unambiguous from typical detection flare. combustion sources 3

4 4 15 at 0.87 µm, 1.24 µm 1.6 µm. Nighttime collection M11 b at 2.2 µm, which will improve detection quantification flares, has been approved VIIRS is expected commence in The solid red line is an 1800 K object, typical a flare. The M10 spectral b records peak radiant emissions from typical flare. 3. Methods 3. Methods 3.1. Visible Infrared Imaging Radiometer Suite Nightfire Processing 3.1. Visible Infrared Imaging Radiometer Suite Nightfire Processing The VIIRS Nightfire (VNF) algorithm [8] was applied all usable nighttime VIIRS data The VIIRS Nightfire (VNF) algorithm [8] was applied all usable nighttime VIIRS data from The usable record started 1 March 2012 ran end December, This from The usable record started 1 March 2012 ran end December, This processing included detection subpixel hot sources in five spectral bs (M7, M8, M10, M12 processing included detection subpixel hot sources in five spectral bs (M7, M8, M10, M12 M13). Redundant bow-tie pixels found along outer portions swath were marked M13). Redundant bow tie pixels found along outer portions swath were marked exclusion in output. The location (latitude, longitude) hot pixels, spectral radiances, satellite exclusion in output. The location (latitude, longitude) hot pixels, spectral radiances, zenith angle cloud state were recorded in output. While all detected pixels were recorded in satellite zenith angle cloud state were recorded in output. While all detected pixels were output, local maxima were marked. As cloud cover falsely provides a no signal, pixel recorded in output, local maxima were marked. As cloud cover falsely provides a no cloud states were extracted from VIIRS using a cloud mask (VCM) [10], so that se non-detections signal, pixel cloud states were extracted from VIIRS using a cloud mask (VCM) [10], so that se can be ignored. While VCM generally successfully identifies cloud cover, it sometime identifies non detections can be ignored. While VCM generally successfully identifies cloud cover, flares as clouds due spectral confusion in mid-wave infrared. To address this, VNF removes it sometime identifies flares as clouds due spectral confusion in mid wave infrared. isolated patches cloud where se coincide M10 detections. To address this, VNF removes isolated patches cloud where se coincide M10 detections. fitting fitting was was applied applied detected detected pixels. pixels. Pixels Pixels detection detection in M12 in M12 M13 M13 were were processed processed dual dual fitting, fitting, one one background background one one hot hot source. source. From From fits, fits, temperature temperature (K) (K) source source area area (m (m 2 ) were calculated. 2 ) were calculated. A A single single hot hot source source fit fit is is developed developed pixels pixels that that lack lack detection detection in in M12 M12 M13 M13 spectral spectral bs. bs. fitting fittingcannot cannot be be permed permed on on pixels pixels detection detection in only in only a single a single spectral spectral b. b. This This a iscommon a common occurrence occurrence small small flares flares detected detected only in only in M10 spectral M10 spectral b. The b. M10 only The M10-only pixel detections pixel detections were treated were treated using using a different a different set set processing processing steps steps incorporation incorporation in flared flared estimates estimates (Section (Section 3.5). 3.5). Examination results results on on a temperature a versus versus source source area area basis basis reveals reveals that that re re were were two two primary data dataclusters ( 3). 3). There There is is a low a lowtemperature detection detection set, set, peaking peaking in K K range, dominated by bybiomass burning. The The high high temperature set set (above (above K) K) is dominated is by by flares, peak numbers flares flares in in K Krange Temperature hisgram a single a single day day VIIRS VIIRS Nightfire Nightfire (VNF) (VNF) data. data. The The distribution distribution is bimodal, majority majority flares flares falling falling in range range from from K K K. Biomass K. Biomass burning, burning, industrial sites sites volcanoes have have temperatures in in K range. K range. The The range range from from K K K Kis is a a crossover zone zone between between flares flares biomass biomass burning. burning Analyzing Atmospheric Effects The VIIRS measures p atmosphere (TOA) radiances. In some spectral bs, re can be substantial losses in radiance from Earth s surface TOA from atmospheric absorption scatter. A study was conducted analyze effects atmospheric variations on VNF data. The

5 A database was built hold time series detections from individual VIIRS observations. The purpose database is enable rapid extraction time series VNF data individual sites. The database has three tables, each spatial extensions. Table 1 is used sre all VNF detections as individual points. This includes latitude longitude pixel Analyzing Atmospheric Effects The VIIRS measures p--atmosphere (TOA) radiances. In some spectral bs, re can be substantial losses in radiance from Earth s surface TOA from atmospheric absorption Energies 2016, scatter. 9, 14 A study was conducted analyze effects atmospheric variations on VNF data. The study was based on a flare in Iraq (dry atmosphere) two flares in Nigeria (moist onshore atmosphere, fshore). onshore Data fshore). included Data in included analysis inspanned analysis from spanned 1 March from March 31 December December The analysis The included analysis all included available allsatellite availablezenith satellite angles. zenithan angles. atmospheric An atmospheric correction correction was developed was developed each each VIIRS VIIRS observations observations using using MODerate MODerate Resolution Atmospheric TRANsmission (MODTRAN) model model [11] [11] parameterized by by atmosphere temperature, pressure water watervapor priles, as as well well as ground as ground temperature temperature derived derived from simultaneously-acquired from simultaneously acquired advanced advanced technology technology microwave microwave sounder sounder (ATMS) data (ATMS) data a surface elevation a surface model. elevation Formodel. each observation, For each observation, RH (MW) RH was(mw) calculated was calculated out out atmospheric atmospheric correction. correction. It was found It was that found re that is re a strongly is a strongly coherent coherent linear relationship linear relationship between between TOA TOA atmospherically-corrected atmospherically corrected RH data RH ( data ( 4). We 4). attribute We attribute this this fact that fact that M10 spectral M10 spectral b isb in a clear is in a atmospheric clear atmospheric window. window. Based on Based se on results, se results, study was study conducted was conducted uncorrected uncorrected TOA radiances. TOA radiances. 4. Radiant 4. Radiant heat heat (RH) (RH) three three flares, flares, out out atmospheric atmospheric correction. correction. Sites Sites include include onshore onshore fshore fshore flares flares in in Nigeria Nigeria (moist (moist atmosphere) atmosphere) a flare a flare in in Iraq Iraq (dry (dry atmosphere). atmosphere) Identifying Gas Flaring Sites 3.3. Identifying Gas Flaring Sites It is not possible adequately separate flares from or hot sources based solely on It is not possible adequately separate flares from or hot sources based solely on temperature due overlap between high temperature biomass burning low temperature temperature due overlap between high temperature biomass burning low temperature ( 3). To separate flares from fires, we use both temperature persistence. ( 3). To separate flares from fires, we use both temperature persistence. To To accomplish this, we built a 15 arc second grid tallying number times an M10 accomplish this, we built a 15 arc second grid tallying number times an M10 detection detection occurred during year. The vast majority biomass burning events could be filtered occurred during year. The vast majority biomass burning events could be filtered out by out by excluding single double detections. Manual editing was used mask out few excluding single double detections. Manual editing was used mask out few remaining remaining biomass burning events. biomass burning events. The remaining sites were divided in three classes based on ir temperature records: (1) sites The remaining sites were divided in three classes based on ir temperature records: (1) sites where maximum temperature exceeded 1400 K; se were taken be flares; (2) sites where where maximum temperature exceeded 1400 K; se were taken be flares; (2) sites where temperatures never exceeded 1400 K; se are primarily industrial sites; (3) sites single temperatures never exceeded 1400 K; se are primarily industrial sites; (3) sites single b, M10 detections, where temperature could not be calculated. If no temperature sites were b, M10 detections, where temperature could not be calculated. If no temperature sites were in 10 kilometers a flare, y were classed as potential flares were subsequently in 10 kilometers a flare, y were classed as potential flares were subsequently resolved by visual inspection satellite images. Finally, a water shedding algorithm was used resolved by visual inspection satellite images. Finally, a water-shedding algorithm was used separate conjoined flare features. A raster vecr algorithm was used draw vecrs defining separate conjoined flare features. A raster vecr algorithm was used draw vecrs defining outlines individual sites. outlines individual sites Building a Global Gas Flaring Database

6 Building a Global Gas Flaring Database A database was built hold time series detections from individual VIIRS observations. The purpose database is enable rapid extraction time series VNF data individual sites. The database has three tables, each spatial extensions. Table 1 is used sre all VNF detections as individual points. This includes latitude longitude pixel center, radiances from individual spectral bs, view geometry specification local maxima in clusters adjacent pixels. Table 2 sres boundaries individual sites polygon geometry. The third table sres date/time cloud state conditions all satellite observations centroid points individual sites. A typical database query will select all VNF detections in an individual site polygon fill gaps when flare was not detected dates cloud state observed at site center Assigning Temperatures M10-Only Flare Detections Small flares ten have detection only in a single b, M10, location peak radiant emissions typical flares. In se cases, it is not possible model a derive temperature source area, re is insufficient inmation ree calculate RH Stefan Boltzmann law. We develop two methods assigning temperatures M10-only flare detections. The first method is used in cases where flare site had observations on or nights fits. In this method, M10-only detections were assigned average temperature flare observations from same site. The second method is used cases where flare site never has observations fits. In this case, a temperature is assigned from nearest flare site having fits. Using se methods assigning temperatures, it was possible calculate source areas RH values weak flare detections based on M10 radiance Adjusting Flare Area Estimates View Angle Differences VIIRS observes Earth at satellite zenith angles ranging from zero (nadir) 70 degrees (edge scan). In examining observed signals from flares, we found that flares tend have higher radiance when viewed at high satellite zenith angles. Our interpretation this phenomena is that flares are typically taller than y are wide due buoyancy hot relative surrounding air ( 1). Thus, flare footprints appear larger when viewed from side smaller when viewed from straight above ( nadir view). The expression this in VIIRS data is that flares have higher radiance when viewed at an oblique angle when compared nadir, yet temperature remains stable across all viewing angles. This results in larger source areas flares when viewed at edge--scan. The three-dimensional shape flares can be modeled as an ellipsoid, based on apparent size flares versus satellite zenith angle ( 5). Using approach developed by Jekrard [12], it is possible derive ellipticity or height (H) versus width (R) ratios individual flares. For a vertically-sting flare, footprint viewed by satellite from a zenith angle α will be: S pα, H, Rq πrb`h2 ` R 2 `H 2 R 2 cos2α {2 (3) Non-linear regression Equation (3) set average flare footprints S(α i ) from different satellite zenith angles α i can estimate flare shape H/R. Typical flares have ellipticities in range 1 4, an average 1.6. Testing indicated that calibration flared volumes had a higher coefficient determination (R 2 ) if source size was adjusted side view. For frequently-observed flares, it is generally possible calculate ellipticity, in which cases, source sizes were adjusted a horizontal side view or satellite zenith angle 90 degrees. For flares infrequent detection, we used average ellipticity 1.6.

7 yet temperature remains stable across all viewing angles. This results in larger source areas flares when viewed at edge scan. The three dimensional shape flares can be modeled as an ellipsoid, based on apparent size flares versus satellite zenith angle ( 5). Using approach developed by Jekrard [12], it is possible derive ellipticity or height (H) versus width (R) ratios individual flares. Energies 2016, For 9, a 14 vertically sting flare, footprint viewed by satellite from a zenith angle α will be: 7 15 α,, π cos 2α /2 (3) 5. Variation in flare area estimates as a function satellite zenith angle. The red line is 5. Variation in flare area estimates as a function satellite zenith angle. The red line is best best fit line based on an elliptical model flare shape. fit line based on an elliptical model flare shape Discrimination Flare Types Flare sites were divided in upstream (or production) sites, downstream processing sites flares at LNG terminals. This was based on spatial databases [13] containing 658 refineries 35 LNG terminals combined visual inspection high spatial resolution images available in Google Earth. The upstream sites include oil natural production facilities. Downstream sites are primarily refineries, identified eir from database or based on spatial extent infrastructure large numbers circular srage tanks. In some cases, oil refineries are adjacent LNG terminals, each site having flares. In this case, LNG section was identified based on presence LNG carrier loading infrastructure LNG srage tanks Calibration Estimate Gas Flaring Volumes RH units MW, is calculated from temperature source size using Stefan Boltzmann law (Equation (2)). Although efficiency combustion at flare variations in composition ( heating value) somewhat affect relationship between volume entering a flare RH emitted, re should be a reasonably consistent relationship between reported flare volumes estimated RH. We attempted develop a calibration estimating flared volumes based on monthly sum RH estimates (normalized cloud cover number valid nighttime observations) versus reported from individual sites in Nigeria, Texas North Dakota. Over limited range data available, calibration appeared linear. When applied ly, however, linear calibration resulted in unrealistically high flared volumes largest ~100 flares out >7000 detected ly. This result implied that re is a non-linear relationship between RH flared volumes, RH growing in a logarithmic fashion relative flared volume. To address this issue, we developed a non-linear calibration using national-level reporting upstream (plus venting) 47 countries provided by Cedigaz [14], plus state-level reporting Texas North Dakota. Non-linearity was introduced by applying an exponent source area in calculation a modified RH estimate, RH', each flare. As part calibration process, value exponent was tuned achieve highest possible R 2 coefficient determination between reported flare volumes RH' ( 6). As vented does not contribute RH emissions, we assumed that quantity reported venting was negligible. This was reasonable, as venting is rare generally not reported by oil field operars. For calibration, annual RH' estimates from all upstream sites in national boundaries were summed, normalization cloud cover number valid nighttime observations.

8 8 15 Cedigaz includes only flare volumes at oil fields in its reported data. In Russia, in particular, re is a substantial volume at non-associated condensate fields, which is not included in Cedigaz estimates. Indeed, RH' in all Russia is high relative Cedigaz reported number ( 7). To remedy this, Russian flares were associated vecr maps oil fields, natural fields condensate fields, only RH' flares related oil fields were used calibration. To determine optimal exponent modulating source areas estimating RH', source areas were modulated using exponents ranging from evaluating coefficient determination (R 2 ) between RH' reported data. The highest R 2 occurred an exponent 0.7 ( 6). 6. The 6. The exponent exponent applied applied flare flare source source area was area tuned was tuned 0.7 yield 0.7 yield highest highest coefficient coefficient determination (R 2 ). determination 6. The exponent (R 2 ). applied flare source area was tuned 0.7 yield highest coefficient determination (R 2 ). The resulting calibration is shown in 7. To estimate slope in linear equation BCM The= resulting slope RH calibration we use a stard is shownlinear regression 7. To through estimate origin. slope The in confidence linear intervals equation The resulting calibration is shown 7. To estimate slope in linear equation BCM BCM = = slope slope tal ˆ RH sum RH we we ± BCM use use a a stard are stard derived linear from regression regression 95% through confidence through origin. intervals origin. The confidence The confidence regression intervals intervals slope multiplied tal tal by sum ± tal BCM BCMsum are are derived derived observed from from RHʹ. 95% 95% confidence confidence intervals intervals regression regression slope slope multiplied by tal sum observed RHʹ. RH'. 7. Calibration estimating flared volumes from VIIRS derived RH based on Cedigaz reported 7. Calibration data. The dashed estimating red lines flared indicate volumes positions from VIIRS derived 95% confidence RH based intervals on Cedigaz reported 7. billion cubic data. Calibration The meter dashed estimating (BCM) red prediction lines indicate flared errors. Note positions volumes that from Russian VIIRS-derived 95% data confidence RH used in intervals based regression on Cedigaz was reported billion filtered cubic data. remove meter The(BCM) dashed flares prediction in red natural lineserrors. indicate Note that condensate positions Russian fields, data since 95% used confidence in regression in intervals se areas was is billion filtered not represented cubic remove meter in flares (BCM) Cedigaz in prediction natural errors. estimates. Note condensate that fields, Russian since data used in in se regression areas is was filtered not represented remove in flares Cedigaz in natural estimates. condensate fields, since in se areas is not 3.9. represented Calculation in Flared Cedigaz Gas Volumes estimates Calculation Flared Gas Volumes The calibration from 7 was applied each individual flare site worldwide. In addition The calibration flare volume, from 7 location, was applied average each temperature, individual flare average site worldwide. source area In addition percent frequency flare detection volume, were calculated. location, average temperature, average source area percent frequency detection were calculated. 4. Results

9 Calculation Flared Gas Volumes The calibration from 7 was applied each individual flare site worldwide. In addition flare volume, location, average temperature, average source area percent frequency detection were calculated. 4. Results The analysis produced results identifying locations flares sites flared volume estimates These can be aggregated national level underst distribution Energies 2016, 9, national 14 potential reducing CO 2 emissions through reductions in Number Flaring Sites 4.1. Number Flaring Sites A tal 7467 flare sites were identified in 2012 ( 8). Of se, 6802 were upstream flares, A tal 7467 flare sites were identified 2012 ( 8). Of se, 6802 were upstream flares, 628 were downstream sites (predominantly refineries) 37 flares were found at LNG terminals. 628 were downstream sites (predominantly refineries) The USA had largest number flare sites, flares ( 9). were 9). Russia found had at LNG second terminals. largest The USA number had flare largest sites number (1053), less flare than sites, half USA 2399 tally. Flare ( 9). Russia numbers by had country second trail f largest rapidly number below flare Russia, sites (1053), Canada less than (332), half Nigeria USA (325) tally. Flare China numbers by country trail f rapidly (309). below Russia, Canada (332), Nigeria (325) China (309). 8. Spatial distribution natural in Spatial distribution natural in Flare tally by country Flare Flare tally tally by by country country Flared Gas Volume Estimates Individual Flaring Sites 4.2. Flared Gas Volume Estimates Individual Flaring Sites An output file was generated listing annual flared volume estimates all detected An output file was generated listing annual flared volume estimates all detected flares. The output lists each flare site s latitude longitude, average flare temperature, percent flares. The output lists each flare site s latitude longitude, average flare temperature, percent frequency detection, normalized sum RH annual flared volume estimate. frequency detection, normalized sum RH annual flared volume estimate. In addition tabular data, a Keyhole Markup Language (KML) file was produced enable a In addition tabular data, a Keyhole Markup Language (KML) file was produced enable a review results in Google Earth. The KML 2012 is included as a supplemental file

10 Flared Gas Volume Estimates Individual Flaring Sites An output file was generated listing annual flared volume estimates all detected flares. The output lists each flare site s latitude longitude, average flare temperature, percent frequency detection, normalized sum RH annual flared volume estimate. In addition tabular data, a Keyhole Markup Language (KML) file was produced enable a review results in Google Earth. The KML 2012 is included as a supplemental file this publication. 10 shows estimated flare volumes each flare sites identified VIIRS data. It is a classic exponential distribution, high flare volumes concentrated in a relatively small percentage flare sites large numbers sites small flared volumes. Half flared volume is concentrated at fewer than 400 flares, 90% occurs at Energies just 2016, 30% 9, 14 sites. The largest flare found ( 10), located 7 km souast Punta de Mata in Venezuela, had an estimated flared volume 1.13 billion cubic meters (BCM). The smallest flare had a had 2012 a 2012 volume 28,431 cubic meters, located in in Lekhwair oil oil field, field, Oman. Oman. Thus, Thus, from from smallest smallest largest, flared volumes mapped by by VIIRS span span five five orders orders magnitude VIIRS VIIRS estimate estimate flared flared volumes 7467 flares, flares, worldwide. This This includes includes both both upstream, upstream, downstream LNG terminal flare sites National 4.3. National- Global Level Global-Level Estimates National level National-level flared flared volumes were calculated by summing estimates from from individual individual flare flare sites sites in in national national boundaries associated exclusive economic economic zones. zones. The The tal tal flared flared volume volume is estimated is estimated at at ± BCM. BCM. The results results can can be be furr furr divided divided in in upstream, upstream, downstream LNG terminals. Upstream was found in 88 countries; tal flared downstream LNG terminals. Upstream was found in 88 countries; tal flared volume in 2012 estimated at BCM ( 11). Russia leads in estimated upstream volume in 2012 estimated at ± 12.2 BCM ( 11). Russia leads in estimated upstream 24.6 BCM, followed by Iraq (11.9), Iran (10.7), Nigeria (10.5), Venezuela (8.1) USA (6.5). Downstream 24.6 BCM, followed ( by Iraq 12) (11.9), was found Iran (10.7), in 85 countries Nigeria (10.5), a tal Venezuela 10.7 (8.1) 1.0 BCM. Algeria USA (6.5). Downstream leads in estimated ( downstream 12) was found in BCM, countries followed bya Iran, tal Qatar, 10.7 Mexico ± 1.0 BCM. Saudi Algeria leads Arabia. in estimated Estimated downstream at LNG liquefaction 1.4 plants BCM, taled followed 3.1 by 0.29Iran, BCM, Qatar, Algeria Mexico leading Saudi Arabia. Estimated 0.82 BCM ( 13). at LNG liquefaction plants taled 3.1 ± 0.29 BCM, Algeria leading 0.82 BCM ( 13).

11 volume in 2012 estimated at ± 12.2 BCM ( 11). Russia leads in estimated upstream 24.6 BCM, followed by Iraq (11.9), Iran (10.7), Nigeria (10.5), Venezuela (8.1) USA (6.5). Downstream ( 12) was found in 85 countries a tal 10.7 ± 1.0 BCM. Algeria leads in estimated downstream 1.4 BCM, followed by Iran, Qatar, Mexico Saudi Arabia. Estimated at LNG liquefaction plants taled 3.1 ± 0.29 BCM, Algeria leading BCM ( 13). 11. Top 20 countries upstream in Top 20 countries upstream in Top in in Top 20 countries downstream in countries LNG terminal in countries LNG terminal in in Comparison Defense Meteorological Satellite Program Satellite Estimates 4.4. Comparison Defense Meteorological Satellite Program Satellite Estimates The last year where DMSP estimates flared volumes were produced was DMSP The last year where DMSP estimates flared volumes were produced was DMSP orbit degradation from 2013 onward resulted in solar contamination that made it impossible orbit degradation from 2013 onward resulted in solar contamination that made it impossible produce data estimating flared volumes. produce data estimating flared volumes. The 2012 estimate from VIIRS (143 BCM) is 4% higher than DMSP estimate

12 Comparison Defense Meteorological Satellite Program Satellite Estimates The last year where DMSP estimates flared volumes were produced was DMSP orbit degradation from 2013 onward resulted in solar contamination that made it impossible produce data estimating flared volumes. The 2012 estimate from VIIRS (143 BCM) is 4% higher than DMSP estimate same year (137.4 BCM). The DMSP VIIRS estimates individual countries are highly correlated in most cases ( 14). There are several facrs that may be contributing differences between flared volume estimates between DMSP VIIRS. Firstly, DMSP sensor sensitivity electric lighting made it impossible identify flares imbedded in urban areas. VIIRS does not suffer from this drawback has identified a substantial number sites that could not be identified in DMSP data. Secondly, DMSP s inability distinguish between flare radiant emissions electric lighting may have resulted in overestimates flared volumes at heavily lit locations. Third, center core many detections made DMSP were saturated, meaning that signal was truncated by limited dynamic range DMSP instrument. In contrast, no saturation was encountered in flare observations in M10 spectral b. Fourth, DMSP low light imaging data have no in-flight calibration. An empirical intercalibration was used reduce sensor differences in flared estimation [7]. This issue is resolved VIIRS, which is widely regarded as a well calibrated instrument. Fifth, DMSP Energies 2016, calibration 9, 14 estimating flared volumes excluded Russian data, since it was impossible separate in oil versus areas at that time due lack field-specific vecrs. Finally, multispectral VIIRS VIIRS data data provides provides samples across across 99% 99% modelling a flare s radiant energy, as compared 2% single DMSP b. 14. VIIRS versus DMSP flared estimates Conclusions Conclusions Using Using a series series processing processing steps, steps, it it is is now now possible possible conduct conduct surveys surveys identify identify sites sites estimate estimate flared flared volumes volumes using using nighttime nighttime VIIRS VIIRS data. data. A survey survey in in found found sites sites worldwide, worldwide, an an estimated estimated ( 13.6) (±13.6) BCM BCM flared flared volume. volume. The The quantity quantity upstream upstream (production (production related) related) was was estimated estimated at at ( 12.2) (±12.2) BCM, BCM, downstream downstream at at ( 1.01) (±1.01) BCM BCM at LNG at LNG liquefaction liquefaction plants plants estimated estimated at 3.07 at ( 0.29) 3.07 (±0.29) BCM. BCM. While While USA USA had had largest largest number number individual individual flare flare sites, sites, Russia Russia led led in terms in terms largest largest volume. volume. The The VIIRS VIIRS instrument instrument has has substantial substantial advantages advantages over over or or satellite satellite sensors sensors in in terms terms moniring moniring.. VIIRS VIIRS collects collects data data every every h, h, providing providing repeat repeat observations observations that that enable numerous cloud free observations each site be made over course a year. VIIRS is unique in collection near infrared SWIR data at night that has proven be extremely useful detecting flares measuring ir radiant output. The VIIRS M10 spectral b, centered at 1.6 μm, covers peak radiant emissions flares. The or two bs that collect at night are at μm, recording radiances on leading edge radiant emissions. At night, se three spectral bs record unambiguous radiant emissions from

13 13 15 enable numerous cloud-free observations each site be made over course a year. VIIRS is unique in collection near-infrared SWIR data at night that has proven be extremely useful detecting flares measuring ir radiant output. The VIIRS M10 spectral b, centered at 1.6 µm, covers peak radiant emissions flares. The or two bs that collect at night are at µm, recording radiances on leading edge radiant emissions. At night, se three spectral bs record unambiguous radiant emissions from flares or hot sources. These are augmented radiance measurements from two traditional fire detection spectral bs in 4-µm region. fitting hot source radiances yields estimates temperature (K), source size (m 2 ), hence, RH (MW) hot sources. Flares can be distinguished from or hot sources based on ir high temperature persistence. While VIIRS has a substantial number favorable characteristics, it has two primary shortcomings which data users should be aware. The first shortcoming is inability derive temperature, source size RH weak detections made on small flares. The fitting requires detection radiances in at least two spectral bs. There is a class small flares where detection occurs only in M10 spectral b, location peak radiant emissions from most flares. These M10-only events can also arise from biomass burning industrial sites. With no fit, re is no direct calculation RH. We developed methods assign temperatures M10-only detections; however, se observations are not as good as multispectral fit observations. This single b detection problem on small flares will be resolved when nighttime M11 collections are added VIIRS data stream. The second shortcoming arises from temporal sampling limitations VIIRS instrument. VIIRS collects data every night, but dwell time VIIRS on a site is a fraction a second. For steady continuous flares, this temporal sampling appears be adequate. However, VIIRS under-samples intermittent or rarely active flares. This under-sampling lowers probability detection decreases accuracy flared estimates flares highly variable flared volumes. We investigated impact applying an atmospheric correction using two flares in a humid tropical environment (onshore fshore) one flare in a dry desert environment. The atmospheric correction boosted RH, but effects across three sites were highly linear. Our conclusion is that no atmospheric correction is required flare detection made Nightfire algorithm. We attribute this high transmissivity atmosphere in 1.6 µm window, where flares typically have ir highest radiant output. Gas flares tend be taller than y are wide due buoyancy heated air mass. Because VIIRS views sites over a wide range view angles, a substantial portion variation in RH estimates can be attributed viewed source size varying as a function view angle. We developed a method characterizing average ellipticity individual flares by analyzing source size as a function satellite zenith angle. We use ellipticity estimate source size all flares as viewed from horizontal, which presents largest apparent source area. This increases coefficient determination (R 2 ) in calibration estimate flared volumes. A calibration estimating flared volumes has been developed based on national-level data upstream reported by Cedigaz. For this calibration, VIIRS data Russia were filtered only include flares at oil production facilities, since this is only type reported in Cedigaz data. Despite good correlation many countries, re remains a considerable spread between Cedigaz reported data VIIRS observed RH ( 7). For instance, Cedigaz estimate Iran (17.55 BCM) is high relative observed RH'. As a result, VIIRS estimated flared volume Iran (12.54 BCM) is 29% lower than Cedigaz number. Or countries where VIIRS estimates are lower than Cedigaz numbers include Nigeria, Venezuela, USA, Angola Indonesia. Countries where VIIRS estimates are higher than Cedigaz numbers include Russia, Iraq, Libya Algeria. In future, it may be possible improve VIIRS calibration

14 14 15 using observations sites metered flare volumes spanning a substantial portion range found in VIIRS data. This paper reports on results year Results from 2013, will be available in near future. The VIIRS data on flared volumes should be useful in carbon cycle studies, identification sites natural utilization projects, calculating carbon intensities fuels tracking progress efts reduce. Such projects can rely on availability VIIRS data from NOAA next several decades. Because burns f a potentially valuable fuel commodity, it is one obvious places focus efts reduce carbon loading on atmosphere as it represents. Making use natural that would orwise be flared could reduce consumption or fuels, thus lowering tal volume carbon emissions atmosphere. Flared volume represents about 3.5% tal worldwide natural consumption 19.8% U.S. natural consumption in 2012 [15]. If used fuel vehicles, it could power 74 million aumobiles in USA based on an average 25 miles per gallon oline [16] 13,476 miles per year [17]. In summary, we have developed a systematic set algorithms methods that can be used on a repeated basis identify sites worldwide, estimation flared volumes. With this capability, VIIRS can provide detailed, site-specific data tracking efts reduce natural. Supplementary Materials: The following are available online at Acknowledgments: This study was jointly funded by NOAA Joint Polar Satellite System (JPSS) proving ground program World Bank Global Gas Flaring Reduction partnership (GGFR). Calibration data were provided by Cedigaz. Author Contributions: Chris Elvidge designed managed study served as lead author. Mikhail Zhizhin developed Nightfire stware, conducted water-shedding identify individual sites, developed flare database, calibration produced output files. Kim Baugh managed data processing made cloud-free composite used identify sites. Feng-Chi Hsu conducted atmospheric effects analysis. Tilo Ghosh assisted in download processing data. She also produced 8. Conflicts Interest: The authors declare no conflict interest. References 1. Peylin, P.; Law, R.M.; Gurney, K.R.; Chevallier, F.; Jacobson, A.R.; Maki, T.; Niwa, Y.; Patra, P.K.; Peters, W.; Rayner, P.J.; et al. Global Atmospheric Carbon Budget: Results from an ensemble atmospheric CO 2 inversions. Biogeosciences 2013, 10, [CrossRef] 2. Sonibare, J.A.; Akeredolu, F.A. Natural domestic market development tal elimination routine flares in Nigeria s upstream petroleum operations. Energy Policy 2006, 34, [CrossRef] 3. Sonia, Y.; Witcover, J.; Kessler, J. Status Review Calinia s Low Carbon Fuel Stard Spring 2013 Issue; Research Report UCD-ITS-RR-13-06; Institute Transportation Studies, University Calinia: Davis, CA, USA, Anejionu, O.C.D.; Blackburn, G.A.; Whyatt, J.D. Detecting flares estimating volumes at individual flow stations using MODIS data. Remote Sens. Environ. 2014, 158, [CrossRef] 5. Casadio, S.; Arino, O.; Serpe, D. Gas moniring from space using ATSR instrument series. Remote Sens. Environ. 2012, 116, [CrossRef] 6. Chowdhury, S.; Shipman, T.; Chao, D.; Elvidge, C.D.; Zhizhin, M.; Hsu, F.-C. Daytime flare detection using Lsat-8 multispectral data. In Proceedings IEEE International Geoscience Remote Sensing Symposium, Quebec City, QC, Canada, July 2014; pp Elvidge, C.D.; Ziskin, D.; Baugh, K.E.; Tuttle, B.T.; Ghosh, T.; Pack, D.W.; Erwin, E.H.; Zhizhin, M. A Fifteen Year Record Global Natural Gas Flaring Derived from Satellite Data. Energies 2009, 2, [CrossRef] 8. Elvidge, C.D.; Zhizhin, M.; Hsu, F.-C.; Baugh, K.E. VIIRS Nightfire: Satellite Pyrometry at Night. Remote Sens. 2013, 5, [CrossRef]

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