Ozone Monitoring Instrument Observations of Interannual Increases in SO2 Emissions from Indian Coal-Fired Power Plants during 2005–2012

2013 ◽  
Vol 47 (24) ◽  
pp. 13993-14000 ◽  
Author(s):  
Zifeng Lu ◽  
David G. Streets ◽  
Benjamin de Foy ◽  
Nickolay A. Krotkov
Keyword(s):  
Power Plants ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Monitoring Instrument ◽  
Indian Coal ◽  
Ozone Monitoring

scholarly journals A global catalogue of large SO<sub>2</sub> sources and emissions derived from the Ozone Monitoring Instrument

2016 ◽  
Author(s):  
Vitali E. Fioletov ◽  
Chris A. McLinden ◽  
Nickolay Krotkov ◽  
Can Li ◽  
Joanna Joiner ◽  
...  
Keyword(s):  
Power Plants ◽  
Oil And Gas ◽  
Principal Component ◽  
Oil And Gas Industry ◽  
Point Sources ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Gas Industry ◽  
Monitoring Instrument ◽  
Ozone Monitoring

Abstract. Sulphur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new Principal Component Analysis (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr−1 to more than 4000 kt yr−1 of SO2 per year have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emissions detection algorithm, and their evolution during the 2005–2014 period was traced by estimating annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30 % of total SO2 emissions estimated from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly constant. Anthropogenic emissions from the USA declined by 80 % over the 2005–2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly constant. In total, OMI-based estimates account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr−1 and not detected by OMI.

scholarly journals A global catalogue of large SO<sub>2</sub> sources and emissions derived from the Ozone Monitoring Instrument

2016 ◽  
Vol 16 (18) ◽  
pp. 11497-11519 ◽  
Author(s):  
Vitali E. Fioletov ◽  
Chris A. McLinden ◽  
Nickolay Krotkov ◽  
Can Li ◽  
Joanna Joiner ◽  
...  
Keyword(s):  
Power Plants ◽  
Oil And Gas ◽  
Principal Component ◽  
Oil And Gas Industry ◽  
Point Sources ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Gas Industry ◽  
Monitoring Instrument ◽  
Ozone Monitoring

Abstract. Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new principal component analysis (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr−1 to more than 4000 kt yr−1 of SO2 per year have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emission detection algorithm, and their evolution during the 2005–2014 period was traced by estimating annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30 % of total SO2 emissions estimated from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly constant. Anthropogenic emissions from the USA declined by 80 % over the 2005–2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly constant. In total, OMI-based estimates account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr−1 and not detected by OMI.

scholarly journals New-generation NASA Aura Ozone Monitoring Instrument (OMI) volcanic SO<sub>2</sub> dataset: Algorithm description, initial results, and continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

2016 ◽  
Author(s):  
Can Li ◽  
Nickolay A. Krotkov ◽  
Simon Carn ◽  
Yan Zhang ◽  
Robert J. D. Spurr ◽  
...  
Keyword(s):  
Lower Troposphere ◽  
Climate Impacts ◽  
Wavelength Shift ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Monitoring Instrument ◽  
Spectral Fitting ◽  
Ozone Monitoring ◽  
Pca Algorithm ◽  
New Generation

Abstract. Since the fall of 2004, the Ozone Monitoring Instrument (OMI) has been providing global monitoring of volcanic SO2 emissions, helping to understand their climate impacts and to mitigate aviation hazards. Here we introduce a new generation OMI volcanic SO2 dataset based on a principal component analysis (PCA) retrieval technique. To reduce retrieval noise and artifacts as seen in the current operational linear fit (LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic features extracted directly from OMI radiances in the spectral fitting, thereby helping to minimize interferences from various geophysical processes (e.g., O3 absorption) and measurement details (e.g., wavelength shift). To solve the problem of low bias for large SO2 total columns in the LF product, the OMSO2VOLCANO algorithm employs a table lookup approach to estimate SO2 Jacobians (i.e., the instrument sensitivity to a perturbation in the SO2 column amount) and iteratively adjusts the spectral fitting window to exclude shorter wavelengths where the SO2 absorption signals are saturated. To first order, the effects of clouds and aerosols are accounted for using a simple Lambertian equivalent reflectivity approach. As with the LF algorithm, OMSO2VOLCANO provides total column retrievals based on a set of pre-defined SO2 profiles from the lower troposphere to the lower stratosphere, including a new profile peaked at 13 km for plumes in the upper troposphere. Examples given in this study indicate that the new dataset shows significant improvement over the LF product, with at least 50 % reduction in retrieval noise over the remote Pacific. For large eruptions such as Kasatochi in 2008 (~ 1700 kt total SO2) and Sierra Negra in 2005 (> 1100 DU maximal SO2), OMSO2VOLCANO generally agrees well with other algorithms that also utilize the full spectral content of satellite measurements, while the LF algorithm tends to underestimate SO2. We also demonstrate that, despite the coarser spatial and spectral resolution of the Suomi National Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite (OMPS) instrument, application of the new PCA algorithm to OMPS data produces highly consistent retrievals between OMI and OMPS. The new PCA algorithm is therefore capable of continuing the volcanic SO2 data record well into the future using current and future hyperspectral UV satellite instruments.

scholarly journals New-generation NASA Aura Ozone Monitoring Instrument (OMI) volcanic SO<sub>2</sub> dataset: algorithm description, initial results, and continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

2017 ◽  
Vol 10 (2) ◽  
pp. 445-458 ◽  
Author(s):  
Can Li ◽  
Nickolay A. Krotkov ◽  
Simon Carn ◽  
Yan Zhang ◽  
Robert J. D. Spurr ◽  
...  
Keyword(s):  
Lower Troposphere ◽  
Climate Impacts ◽  
Wavelength Shift ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Monitoring Instrument ◽  
Spectral Fitting ◽  
Ozone Monitoring ◽  
Pca Algorithm ◽  
New Generation

Abstract. Since the fall of 2004, the Ozone Monitoring Instrument (OMI) has been providing global monitoring of volcanic SO2 emissions, helping to understand their climate impacts and to mitigate aviation hazards. Here we introduce a new-generation OMI volcanic SO2 dataset based on a principal component analysis (PCA) retrieval technique. To reduce retrieval noise and artifacts as seen in the current operational linear fit (LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic features extracted directly from OMI radiances in the spectral fitting, thereby helping to minimize interferences from various geophysical processes (e.g., O3 absorption) and measurement details (e.g., wavelength shift). To solve the problem of low bias for large SO2 total columns in the LF product, the OMSO2VOLCANO algorithm employs a table lookup approach to estimate SO2 Jacobians (i.e., the instrument sensitivity to a perturbation in the SO2 column amount) and iteratively adjusts the spectral fitting window to exclude shorter wavelengths where the SO2 absorption signals are saturated. To first order, the effects of clouds and aerosols are accounted for using a simple Lambertian equivalent reflectivity approach. As with the LF algorithm, OMSO2VOLCANO provides total column retrievals based on a set of predefined SO2 profiles from the lower troposphere to the lower stratosphere, including a new profile peaked at 13  km for plumes in the upper troposphere. Examples given in this study indicate that the new dataset shows significant improvement over the LF product, with at least 50 % reduction in retrieval noise over the remote Pacific. For large eruptions such as Kasatochi in 2008 (∼ 1700 kt total SO2) and Sierra Negra in 2005 (> 1100 DU maximum SO2), OMSO2VOLCANO generally agrees well with other algorithms that also utilize the full spectral content of satellite measurements, while the LF algorithm tends to underestimate SO2. We also demonstrate that, despite the coarser spatial and spectral resolution of the Suomi National Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite (OMPS) instrument, application of the new PCA algorithm to OMPS data produces highly consistent retrievals between OMI and OMPS. The new PCA algorithm is therefore capable of continuing the volcanic SO2 data record well into the future using current and future hyperspectral UV satellite instruments.

scholarly journals Improving automated global detection of volcanic SO<sub>2</sub> plumes using the Ozone Monitoring Instrument (OMI)

2016 ◽  
Author(s):  
Verity J. B. Flower ◽  
Thomas Oommen ◽  
Simon A. Carn
Keyword(s):  
Traffic Control ◽  
Service Providers ◽  
Volcanic Eruptions ◽  
Volcano Monitoring ◽  
Volcanic Plume ◽  
Human Populations ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Monitoring Instrument ◽  
Ozone Monitoring

Abstract. Volcanic eruptions pose an ever-present threat to human populations around the globe, but many active volcanoes remain poorly monitored. In regions where ground-based monitoring is present the effects of volcanic eruptions can be moderated through observational alerts to both local populations and service providers such as air traffic control. However, in regions where volcano monitoring is limited satellite-based remote sensing provides a global data source that can be utilised to provide near real time identification of volcanic activity. This paper details the development of an automated volcanic plume detection method utilizing daily, global observations of sulphur dioxide (SO2) by the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite. Following identification and classification of known volcanic eruptions in 2005–2009, the OMI SO2 data are analysed using a logistic regression analysis which permits the identification of volcanic events with an overall accuracy of over 80 %, and consistent plume identification when the volcanic plume SO2 loading exceeds ~ 400 tons. The accuracy and minimal user input requirements of the developed procedure provide a basis for the creation of an automated SO2 alert system providing volcanic alerts in regions where ground based volcano monitoring capabilities are limited. The technique could easily be adapted for use with satellite measurements of volcanic SO2 emissions from other platforms.

Science objectives of the ozone monitoring instrument

2006 ◽  
Vol 44 (5) ◽  
pp. 1199-1208 ◽  
Author(s):  
P.F. Levelt ◽  
E. Hilsenrath ◽  
G.W. Leppelmeier ◽  
G.H.J. van den Oord ◽  
P.K. Bhartia ◽  
...  
Keyword(s):  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Ozone Monitoring

scholarly journals Simulation of the Ozone Monitoring Instrument aerosol index using the NASA Goddard Earth Observing System aerosol reanalysis products

2017 ◽  
Vol 10 (11) ◽  
pp. 4121-4134 ◽  
Author(s):  
Peter R. Colarco ◽  
Santiago Gassó ◽  
Changwoo Ahn ◽  
Virginie Buchard ◽  
Arlindo M. da Silva ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Surface Pressure ◽  
Model Atmosphere ◽  
Ozone Monitoring Instrument ◽  
Aerosol Optical Property ◽  
Actual Surface ◽  
Monitoring Instrument ◽  
Earth Observing System ◽  
Ozone Monitoring ◽  
Aerosol Index

Abstract. We provide an analysis of the commonly used Ozone Monitoring Instrument (OMI) aerosol index (AI) product for qualitative detection of the presence and loading of absorbing aerosols. In our analysis, simulated top-of-atmosphere (TOA) radiances are produced at the OMI footprints from a model atmosphere and aerosol profile provided by the NASA Goddard Earth Observing System (GEOS-5) Modern-Era Retrospective Analysis for Research and Applications aerosol reanalysis (MERRAero). Having established the credibility of the MERRAero simulation of the OMI AI in a previous paper we describe updates in the approach and aerosol optical property assumptions. The OMI TOA radiances are computed in cloud-free conditions from the MERRAero atmospheric state, and the AI is calculated. The simulated TOA radiances are fed to the OMI near-UV aerosol retrieval algorithms (known as OMAERUV) is compared to the MERRAero calculated AI. Two main sources of discrepancy are discussed: one pertaining to the OMI algorithm assumptions of the surface pressure, which are generally different from what the actual surface pressure of an observation is, and the other related to simplifying assumptions in the molecular atmosphere radiative transfer used in the OMI algorithms. Surface pressure assumptions lead to systematic biases in the OMAERUV AI, particularly over the oceans. Simplifications in the molecular radiative transfer lead to biases particularly in regions of topography intermediate to surface pressures of 600 and 1013.25 hPa. Generally, the errors in the OMI AI due to these considerations are less than 0.2 in magnitude, though larger errors are possible, particularly over land. We recommend that future versions of the OMI algorithms use surface pressures from readily available atmospheric analyses combined with high-spatial-resolution topographic maps and include more surface pressure nodal points in their radiative transfer lookup tables.

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