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T. Vlemmix
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12 records found
1
Nitrogen Oxide Emissions from U.S. Oil and Gas Production
Recent Trends and Source Attribution
Journal article
(2020)
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Barbara Dix, Joep de Bruin, Pieternel Levelt, Joost de Gouw, Esther Roosenbrand, Tim Vlemmix, Colby Francoeur, Alan Gorchov-Negron, Brian McDonald, Mikhail Zhizhin, Christopher Elvidge, Pepijn Veefkind
U.S. oil and natural gas production volumes have grown by up to 100% in key production areas between January 2017 and August 2019. Here we show that recent trends are visible from space and can be attributed to drilling, production, and gas flaring activities. By using oil and gas activity data as predictors in a multivariate regression to satellite measurements of tropospheric NO2 columns, observed changes in NO2 over time could be attributed to NOx emissions associated with drilling, production and gas flaring for three select regions: the Permian, Bakken, and Eagle Ford basins. We find that drilling had been the dominant NOx source contributing around 80% before the downturn in drilling activity in 2015. Thereafter, NOx contributions from drilling activities and combined production and flaring activities are similar. Comparison of our top-down source attribution with a bottom-up fuel-based oil and gas NOx emission inventory shows agreement within error margins.
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U.S. oil and natural gas production volumes have grown by up to 100% in key production areas between January 2017 and August 2019. Here we show that recent trends are visible from space and can be attributed to drilling, production, and gas flaring activities. By using oil and gas activity data as predictors in a multivariate regression to satellite measurements of tropospheric NO2 columns, observed changes in NO2 over time could be attributed to NOx emissions associated with drilling, production and gas flaring for three select regions: the Permian, Bakken, and Eagle Ford basins. We find that drilling had been the dominant NOx source contributing around 80% before the downturn in drilling activity in 2015. Thereafter, NOx contributions from drilling activities and combined production and flaring activities are similar. Comparison of our top-down source attribution with a bottom-up fuel-based oil and gas NOx emission inventory shows agreement within error margins.
Poster
(2019)
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Arnoud Apituley, Karin Kreher, Michael Van Roozendael, Ankie Piters, Henk Eskes, Tim Vlemmix, Herman Russchenberg, Christine Unal, Mirjam den Hoed, More authors...
A Sentinel-5p/TROPOMI validation campaign was held in the Netherlands based at the Cabauw Experimental Site for Atmospheric Research during September 2019. The TROpomi vaLIdation eXperiment (TROLIX) consisted of active and passive remote sensing platforms in conjunction with several balloon-borne, airborne and surface chemical measurements. The goal of this geophysical validation campaign was to make intensive observations to establish the quality of TROPOMI L2 main data products (UVAI, Aerosol Layer Height, NO2, O3, HCHO, Clouds) under realistic non-idealized conditions with varying cloud cover and a wide range of atmospheric conditions. Since TROPOMI is a hyperspectral imager with a very high spatial resolution of 3.5x7 km2, understanding local effects such as inhomogeneous sources of pollution, sub-pixel clouds and variations in ground albedo is important to interpret TROPOMI results. Therefore, the campaign included sub-pixel resolution local networks of sensors, involving Pandora and MAXDOAS instruments, around Cabauw (51.97° N, 4.93° E) and within the city of Rotterdam. Cabauw is considered rural while Rotterdam is densely populated and industrialized. These focal areas were connected through airborne as well as ground based mobile observations. Cabauw, using its comprehensive in-situ and remote sensing observation program in and around the 213 m meteorological tower, was the main site of the campaign with focus on vertical profiling using lidar instruments for aerosols, clouds, water vapor, tropospheric and stratospheric ozone, as well as balloon-borne sensors for NO2 and ozone. The data set collected can be directly compared to the TROPOMI L2 data products, while measurements of parameters related to a-priori data and auxiliary parameters that influence the quality of the L2 products such as aerosol and cloud profiles and in-situ aerosol and atmospheric chemistry were also collected. This paper gives an overview of the campaign, and an overview of the participating main and ancillary instrumentation. Furthermore, an overview of the meteorological and atmospheric conditions observed during the campaign is given from the respective perspectives of the participating instruments, including satellite observations and the support by atmospheric modeling (CAMS).
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A Sentinel-5p/TROPOMI validation campaign was held in the Netherlands based at the Cabauw Experimental Site for Atmospheric Research during September 2019. The TROpomi vaLIdation eXperiment (TROLIX) consisted of active and passive remote sensing platforms in conjunction with several balloon-borne, airborne and surface chemical measurements. The goal of this geophysical validation campaign was to make intensive observations to establish the quality of TROPOMI L2 main data products (UVAI, Aerosol Layer Height, NO2, O3, HCHO, Clouds) under realistic non-idealized conditions with varying cloud cover and a wide range of atmospheric conditions. Since TROPOMI is a hyperspectral imager with a very high spatial resolution of 3.5x7 km2, understanding local effects such as inhomogeneous sources of pollution, sub-pixel clouds and variations in ground albedo is important to interpret TROPOMI results. Therefore, the campaign included sub-pixel resolution local networks of sensors, involving Pandora and MAXDOAS instruments, around Cabauw (51.97° N, 4.93° E) and within the city of Rotterdam. Cabauw is considered rural while Rotterdam is densely populated and industrialized. These focal areas were connected through airborne as well as ground based mobile observations. Cabauw, using its comprehensive in-situ and remote sensing observation program in and around the 213 m meteorological tower, was the main site of the campaign with focus on vertical profiling using lidar instruments for aerosols, clouds, water vapor, tropospheric and stratospheric ozone, as well as balloon-borne sensors for NO2 and ozone. The data set collected can be directly compared to the TROPOMI L2 data products, while measurements of parameters related to a-priori data and auxiliary parameters that influence the quality of the L2 products such as aerosol and cloud profiles and in-situ aerosol and atmospheric chemistry were also collected. This paper gives an overview of the campaign, and an overview of the participating main and ancillary instrumentation. Furthermore, an overview of the meteorological and atmospheric conditions observed during the campaign is given from the respective perspectives of the participating instruments, including satellite observations and the support by atmospheric modeling (CAMS).
Journal article
(2019)
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Frederik Tack, Alexis Merlaud, Andreas C. Meier, Tim Vlemmix, Thomas Ruhtz, Marian Daniel Iordache, Xinrui Ge, Len Van Der Wal, Dirk Schuettemeyer, More Authors...
We present an intercomparison study of four airborne imaging DOAS instruments, dedicated to the retrieval and high-resolution mapping of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs). The AROMAPEX campaign took place in Berlin, Germany, in April 2016 with the primary objective to test and intercompare the performance of experimental airborne imagers. The imaging DOAS instruments were operated simultaneously from two manned aircraft, performing synchronised flights: APEX (VITO–BIRA-IASB) was operated from DLR's DO-228 D-CFFU aircraft at 6.2 km in altitude, while AirMAP (IUP-Bremen), SWING (BIRA-IASB), and SBI (TNO–TU Delft–KNMI) were operated from the FUB Cessna 207T D-EAFU at 3.1 km. Two synchronised flights took place on 21 April 2016. NO2 slant columns were retrieved by applying differential optical absorption spectroscopy (DOAS) in the visible wavelength region and converted to VCDs by the computation of appropriate air mass factors (AMFs). Finally, the NO2 VCDs were georeferenced and mapped at high spatial resolution. For the sake of harmonising the different data sets, efforts were made to agree on a common set of parameter settings, AMF look-up table, and gridding algorithm. The NO2 horizontal distribution, observed by the different DOAS imagers, shows very similar spatial patterns. The NO2 field is dominated by two large plumes related to industrial compounds, crossing the city from west to east. The major highways A100 and A113 are also identified as line sources of NO2. Retrieved NO2 VCDs range between 1×1015 molec cm−2 upwind of the city and 20×1015 molec cm−2 in the dominant plume, with a mean of 7.3±1.8×1015 molec cm−2 for the morning flight and between 1 and 23×1015 molec cm−2 with a mean of 6.0±1.4×1015 molec cm−2 for the afternoon flight. The mean NO2 VCD retrieval errors are in the range of 22 % to 36 % for all sensors. The four data sets are in good agreement with Pearson correlation coefficients better than 0.9, while the linear regression analyses show slopes close to unity and generally small intercepts.
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We present an intercomparison study of four airborne imaging DOAS instruments, dedicated to the retrieval and high-resolution mapping of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs). The AROMAPEX campaign took place in Berlin, Germany, in April 2016 with the primary objective to test and intercompare the performance of experimental airborne imagers. The imaging DOAS instruments were operated simultaneously from two manned aircraft, performing synchronised flights: APEX (VITO–BIRA-IASB) was operated from DLR's DO-228 D-CFFU aircraft at 6.2 km in altitude, while AirMAP (IUP-Bremen), SWING (BIRA-IASB), and SBI (TNO–TU Delft–KNMI) were operated from the FUB Cessna 207T D-EAFU at 3.1 km. Two synchronised flights took place on 21 April 2016. NO2 slant columns were retrieved by applying differential optical absorption spectroscopy (DOAS) in the visible wavelength region and converted to VCDs by the computation of appropriate air mass factors (AMFs). Finally, the NO2 VCDs were georeferenced and mapped at high spatial resolution. For the sake of harmonising the different data sets, efforts were made to agree on a common set of parameter settings, AMF look-up table, and gridding algorithm. The NO2 horizontal distribution, observed by the different DOAS imagers, shows very similar spatial patterns. The NO2 field is dominated by two large plumes related to industrial compounds, crossing the city from west to east. The major highways A100 and A113 are also identified as line sources of NO2. Retrieved NO2 VCDs range between 1×1015 molec cm−2 upwind of the city and 20×1015 molec cm−2 in the dominant plume, with a mean of 7.3±1.8×1015 molec cm−2 for the morning flight and between 1 and 23×1015 molec cm−2 with a mean of 6.0±1.4×1015 molec cm−2 for the afternoon flight. The mean NO2 VCD retrieval errors are in the range of 22 % to 36 % for all sensors. The four data sets are in good agreement with Pearson correlation coefficients better than 0.9, while the linear regression analyses show slopes close to unity and generally small intercepts.
Abstract
(2018)
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J.A. de Gouw, C. Warneke, M. Trainer, J. Peischl, T.B. Ryerson, Joep de Bruin, Pieternel Felicitas Levelt, Joris Pepijn Veefkind, Tim Vlemmix, More authors...
In March and April of 2015, the NOAA WP-3D research aircraft made airborne measurements over several different oil and natural gas production regions in the central and western U.S. ranging from North Dakota to Texas. The study was conducted at a time when the domestic production of natural gas was at an all-time high and the production of crude oil near an all-time high, but also when drilling activity had abruptly decreased due to a drop in the price of oil. In this presentation, we will give a summary of the measurement results obtained in the different production regions. Emission fluxes of greenhouse gases (CH4) and air pollutants (VOCs, NOx, air toxics) were determined through mass balance and from enhancement ratios versus methane. While photochemistry was generally weak during the flights, some trace gases showed evidence for secondary formation. Measurements by mass spectrometry showed the presence of some less commonly observed trace gases including nitrogen heterocyclic compounds. Emissions of pollutants are expressed as a fraction of the produced natural gas and crude oil. Such metrics can be compared with emission factors for fossil fuel combustion by other sources (motor vehicles and power plants) and allow a comparison of emissions from different stages in the lifecycle of fossil fuels. We have also studied NOx emissions from oil and natural gas production through trend analysis of the NO2columns from the Ozone Monitoring Instrument. This analysis shows that the drilling of new wells and the extraction of crude oil and natural gas both lead to NOx emissions. These results are compared with a new fuel-based emission inventory for NOx emissions from oil and natural gas production.
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In March and April of 2015, the NOAA WP-3D research aircraft made airborne measurements over several different oil and natural gas production regions in the central and western U.S. ranging from North Dakota to Texas. The study was conducted at a time when the domestic production of natural gas was at an all-time high and the production of crude oil near an all-time high, but also when drilling activity had abruptly decreased due to a drop in the price of oil. In this presentation, we will give a summary of the measurement results obtained in the different production regions. Emission fluxes of greenhouse gases (CH4) and air pollutants (VOCs, NOx, air toxics) were determined through mass balance and from enhancement ratios versus methane. While photochemistry was generally weak during the flights, some trace gases showed evidence for secondary formation. Measurements by mass spectrometry showed the presence of some less commonly observed trace gases including nitrogen heterocyclic compounds. Emissions of pollutants are expressed as a fraction of the produced natural gas and crude oil. Such metrics can be compared with emission factors for fossil fuel combustion by other sources (motor vehicles and power plants) and allow a comparison of emissions from different stages in the lifecycle of fossil fuels. We have also studied NOx emissions from oil and natural gas production through trend analysis of the NO2columns from the Ozone Monitoring Instrument. This analysis shows that the drilling of new wells and the extraction of crude oil and natural gas both lead to NOx emissions. These results are compared with a new fuel-based emission inventory for NOx emissions from oil and natural gas production.
Spatial distribution analysis of the OMI aerosol layer height
A pixel-by-pixel comparison to CALIOP observations
A global picture of atmospheric aerosol vertical distribution with a high temporal resolution is of key importance not only for climate, cloud formation, and air quality research studies but also for correcting scattered radiation induced by aerosols in absorbing trace gas retrievals from passive satellite sensors. Aerosol layer height (ALH) was retrieved from the OMI 477 nm O2 − O2 band and its spatial pattern evaluated over selected cloud-free scenes. Such retrievals benefit from a synergy with MODIS data to provide complementary information on aerosols and cloudy pixels. We used a neural network approach previously trained and developed. Comparison with CALIOP aerosol level 2 products over urban and industrial pollution in eastern China shows consistent spatial patterns with an uncertainty in the range of 462–648 m. In addition, we show the possibility to determine the height of thick aerosol layers released by intensive biomass burning events in South America and Russia from OMI visible measurements. A Saharan dust outbreak over sea is finally discussed. Complementary detailed analyses show that the assumed aerosol properties in the forward modelling are the key factors affecting the accuracy of the results, together with potential cloud residuals in the observation pixels. Furthermore, we demonstrate that the physical meaning of the retrieved ALH scalar corresponds to the weighted average of the vertical aerosol extinction profile. These encouraging findings strongly suggest the potential of the OMI ALH product, and in more general the use of the 477 nm O2 − O2 band from present and future similar satellite sensors, for climate studies as well as for future aerosol correction in air quality trace gas retrievals.
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A global picture of atmospheric aerosol vertical distribution with a high temporal resolution is of key importance not only for climate, cloud formation, and air quality research studies but also for correcting scattered radiation induced by aerosols in absorbing trace gas retrievals from passive satellite sensors. Aerosol layer height (ALH) was retrieved from the OMI 477 nm O2 − O2 band and its spatial pattern evaluated over selected cloud-free scenes. Such retrievals benefit from a synergy with MODIS data to provide complementary information on aerosols and cloudy pixels. We used a neural network approach previously trained and developed. Comparison with CALIOP aerosol level 2 products over urban and industrial pollution in eastern China shows consistent spatial patterns with an uncertainty in the range of 462–648 m. In addition, we show the possibility to determine the height of thick aerosol layers released by intensive biomass burning events in South America and Russia from OMI visible measurements. A Saharan dust outbreak over sea is finally discussed. Complementary detailed analyses show that the assumed aerosol properties in the forward modelling are the key factors affecting the accuracy of the results, together with potential cloud residuals in the observation pixels. Furthermore, we demonstrate that the physical meaning of the retrieved ALH scalar corresponds to the weighted average of the vertical aerosol extinction profile. These encouraging findings strongly suggest the potential of the OMI ALH product, and in more general the use of the 477 nm O2 − O2 band from present and future similar satellite sensors, for climate studies as well as for future aerosol correction in air quality trace gas retrievals.
Aerosol layer height from OMI and neural network
Evaluation and possibility of a 13-year time series?
Abstract
(2018)
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Julien Chimot, Pepijn Veefkind, Gijs van Ouwerkerk, Tim Vlemmix, Pieternel Felicitas Levelt
Sunlight scattering and absorption by aerosols perturb the atmospheric radiation. This brings both scientific and technical challenges to the research community. Aerosols are an important player in the climate system (Boucher et al., IPCC report, Chapter 5: Clouds and aerosols, 2015), and also affect satellite measurements of reflected sunlight. Because long-time series of OMI trace gas observations are so crucial for analysing the air pollution trends between urban and regional scales, the uncertainties due to aerosols must be reduced. While, overall, the horizontal distribution of aerosol optical thickness (AOT) can be relatively well described, uncertainties in aerosol layer height (ALH) significantly contribute to the overall uncertainty. In the absence of clouds, ALH remains the most important error source on the observations of trace gases in the troposphere (e.g. NO2, HCHO, SO2) from UV-Vis air quality space-borne sensors over areas dominated by high AOT (i.e. > 0.5), and absorbing particles (Krotkov et al., 2008; Boersma et al., 2011; Barkley et al., 2012; Hewson et al., 2015; Castellanos et al., 2015; Chimot et al., 2016). Because atmospheric models still have large uncertainties on the vertical distribution of aerosols the use of passive satellite measurements with global coverage, such as OMI, becomes advantageous. We have developed a unique Multilayer Perceptron Neural Network (NN) algorithm (i.e. machine learning) to retrieve ALH from the OMI 477 nm O2-O2 visible absorption band and over cloud-free scenes (Chimot et al., 2017a). This band has high sensitivity to strong aerosol loading and has fewer challenges than other bands (e.g. O2-A). This retrieval approach strongly benefits from a synergy with MODIS as prior AOT information is needed. We will present the evaluation of the OMI ALH performances over cloud-free scenes. A 3-year time series (2005- 2007) over the urban and large industrialized area of north-east China shows accuracy in the range of 260-800 m for scenes with AOT (550 nm) > 0.5 (Chimot et al., 2017a). In addition, comparisons with CALIOP aerosol observations demonstrate a high correlation of the spatial distributions (Chimot et al., 2017b). Furthermore, analyses of biomass burning episodes over South America and Russia suggest the strong potential of UV-visible passive satellite sensors to probe thick smoke layer height (Chimot et al., 2017b). Improvement of these retrievals can be obtained by reducing uncertainties on the assumed aerosol type and surface albedo. Preliminary yearly global maps of OMI aerosol layer height will be shown. Finally, we will discuss how this first development may support the correction of aerosol radiation effect on the retrieved OMI tropospheric NO2 product, a key element of fossil-fuel emissions. This work illustrates the possibility, in the future, to derive a long-time series aerosol layer height with global coverage and to analyse the aerosol radiative effects, over cloud-free scenes, from current and nextgeneration UV-visible satellites: e.g. OMI, TROPOMI on-board Sentinel-5-Precurosr, Sentinel-4, Sentinel-5. Moreover, the proposed machine learning technique is a promising asset for facing the challenge of big satellite data.
...
Sunlight scattering and absorption by aerosols perturb the atmospheric radiation. This brings both scientific and technical challenges to the research community. Aerosols are an important player in the climate system (Boucher et al., IPCC report, Chapter 5: Clouds and aerosols, 2015), and also affect satellite measurements of reflected sunlight. Because long-time series of OMI trace gas observations are so crucial for analysing the air pollution trends between urban and regional scales, the uncertainties due to aerosols must be reduced. While, overall, the horizontal distribution of aerosol optical thickness (AOT) can be relatively well described, uncertainties in aerosol layer height (ALH) significantly contribute to the overall uncertainty. In the absence of clouds, ALH remains the most important error source on the observations of trace gases in the troposphere (e.g. NO2, HCHO, SO2) from UV-Vis air quality space-borne sensors over areas dominated by high AOT (i.e. > 0.5), and absorbing particles (Krotkov et al., 2008; Boersma et al., 2011; Barkley et al., 2012; Hewson et al., 2015; Castellanos et al., 2015; Chimot et al., 2016). Because atmospheric models still have large uncertainties on the vertical distribution of aerosols the use of passive satellite measurements with global coverage, such as OMI, becomes advantageous. We have developed a unique Multilayer Perceptron Neural Network (NN) algorithm (i.e. machine learning) to retrieve ALH from the OMI 477 nm O2-O2 visible absorption band and over cloud-free scenes (Chimot et al., 2017a). This band has high sensitivity to strong aerosol loading and has fewer challenges than other bands (e.g. O2-A). This retrieval approach strongly benefits from a synergy with MODIS as prior AOT information is needed. We will present the evaluation of the OMI ALH performances over cloud-free scenes. A 3-year time series (2005- 2007) over the urban and large industrialized area of north-east China shows accuracy in the range of 260-800 m for scenes with AOT (550 nm) > 0.5 (Chimot et al., 2017a). In addition, comparisons with CALIOP aerosol observations demonstrate a high correlation of the spatial distributions (Chimot et al., 2017b). Furthermore, analyses of biomass burning episodes over South America and Russia suggest the strong potential of UV-visible passive satellite sensors to probe thick smoke layer height (Chimot et al., 2017b). Improvement of these retrievals can be obtained by reducing uncertainties on the assumed aerosol type and surface albedo. Preliminary yearly global maps of OMI aerosol layer height will be shown. Finally, we will discuss how this first development may support the correction of aerosol radiation effect on the retrieved OMI tropospheric NO2 product, a key element of fossil-fuel emissions. This work illustrates the possibility, in the future, to derive a long-time series aerosol layer height with global coverage and to analyse the aerosol radiative effects, over cloud-free scenes, from current and nextgeneration UV-visible satellites: e.g. OMI, TROPOMI on-board Sentinel-5-Precurosr, Sentinel-4, Sentinel-5. Moreover, the proposed machine learning technique is a promising asset for facing the challenge of big satellite data.
Conference paper
(2017)
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B. T.G. De Goeij, G. C.J. Otter, R. Jansen, J. M.O. Van Wakeren, J. P. Veefkind, T. Vlemmix, X. Ge, P. F. Levelt, B. P.F. Dirks, P. M. Toet, L. F. Van Der Wal
In recent years TNO has investigated and developed different innovative opto-mechanical designs to realize advanced spectrometers for space applications in a more compact and cost-effective manner. This offers multiple advantages: a compact instrument can be flown on a much smaller platform or as add-on on a larger platform; a low-cost instrument opens up the possibility to fly multiple instruments in a satellite constellation, improving both global coverage and temporal sampling (e.g. multiple overpasses per day to study diurnal processes); in this way a constellation of low-cost instruments may provide added value to the larger scientific and operational satellite missions (e.g. the Copernicus Sentinel missions); a small, lightweight spectrometer can easily be mounted on a small aircraft or high-altitude UAV (offering high spatial resolution).
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In recent years TNO has investigated and developed different innovative opto-mechanical designs to realize advanced spectrometers for space applications in a more compact and cost-effective manner. This offers multiple advantages: a compact instrument can be flown on a much smaller platform or as add-on on a larger platform; a low-cost instrument opens up the possibility to fly multiple instruments in a satellite constellation, improving both global coverage and temporal sampling (e.g. multiple overpasses per day to study diurnal processes); in this way a constellation of low-cost instruments may provide added value to the larger scientific and operational satellite missions (e.g. the Copernicus Sentinel missions); a small, lightweight spectrometer can easily be mounted on a small aircraft or high-altitude UAV (offering high spatial resolution).
Journal article
(2017)
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Julien Chimot, J.P. Veefkind, Tim Vlemmix, Johan F. De Haan, Vassilis Amiridis, Emmanouil Proestakis, Eleni Marinou, Pieternel F. Levelt
This paper presents an exploratory study on the aerosol layer height (ALH) retrieval from the OMI 477nm O2 O2 spectral band. We have developed algorithms based on the multilayer perceptron (MLP) neural network (NN) approach and applied them to 3-year (2005-2007) OMI cloud-free scenes over north-east Asia, collocated with MODIS Aqua aerosol product. In addition to the importance of aerosol altitude for climate and air quality objectives, our long-term motivation is to evaluate the possibility of retrieving ALH for potential future improvements of trace gas retrievals (e.g. NO2, HCHO, SO2) from UV-visible air quality satellite measurements over scenes including high aerosol concentrations. This study presents a first step of this long-term objective and evaluates, from a statistic point of view, an ensemble of OMI ALH retrievals over a long time period of 3 years covering a large industrialized continental region. This ALH retrieval relies on the analysis of the O2 O2 slant column density (SCD) and requires an accurate knowledge of the aerosol optical thickness,. Using MODIS Aqua(550nm) as a prior information, absolute seasonal differences between the LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS) and average OMI ALH, over scenes with MODIS(550nm) ≥ 1. 0, are in the range of 260-800m (assuming single scattering albedo 0 Combining double low line 0. 95) and 180-310m (assuming 0 Combining double low line 0. 9). OMI ALH retrievals depend on the assumed aerosol single scattering albedo (sensitivity up to 660 m) and the chosen surface albedo (variation less than 200 m between OMLER and MODIS black-sky albedo). Scenes with ≤ 0. 5 are expected to show too large biases due to the little impact of particles on the O2 O2 SCD changes. In addition, NN algorithms also enable aerosol optical thickness retrieval by exploring the OMI reflectance in the continuum. Comparisons with collocated MODIS Aqua show agreements between 0. 02 ± 0. 45 and 0. 18 ± g 0. 24, depending on the season. Improvements may be obtained from a better knowledge of the surface albedo and higher accuracy of the aerosol model. Following the previous work over ocean of Park et al.(2016), our study shows the first encouraging aerosol layer height retrieval results over land from satellite observations of the 477 nm O2 gO2 absorption spectral band.
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This paper presents an exploratory study on the aerosol layer height (ALH) retrieval from the OMI 477nm O2 O2 spectral band. We have developed algorithms based on the multilayer perceptron (MLP) neural network (NN) approach and applied them to 3-year (2005-2007) OMI cloud-free scenes over north-east Asia, collocated with MODIS Aqua aerosol product. In addition to the importance of aerosol altitude for climate and air quality objectives, our long-term motivation is to evaluate the possibility of retrieving ALH for potential future improvements of trace gas retrievals (e.g. NO2, HCHO, SO2) from UV-visible air quality satellite measurements over scenes including high aerosol concentrations. This study presents a first step of this long-term objective and evaluates, from a statistic point of view, an ensemble of OMI ALH retrievals over a long time period of 3 years covering a large industrialized continental region. This ALH retrieval relies on the analysis of the O2 O2 slant column density (SCD) and requires an accurate knowledge of the aerosol optical thickness,. Using MODIS Aqua(550nm) as a prior information, absolute seasonal differences between the LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS) and average OMI ALH, over scenes with MODIS(550nm) ≥ 1. 0, are in the range of 260-800m (assuming single scattering albedo 0 Combining double low line 0. 95) and 180-310m (assuming 0 Combining double low line 0. 9). OMI ALH retrievals depend on the assumed aerosol single scattering albedo (sensitivity up to 660 m) and the chosen surface albedo (variation less than 200 m between OMLER and MODIS black-sky albedo). Scenes with ≤ 0. 5 are expected to show too large biases due to the little impact of particles on the O2 O2 SCD changes. In addition, NN algorithms also enable aerosol optical thickness retrieval by exploring the OMI reflectance in the continuum. Comparisons with collocated MODIS Aqua show agreements between 0. 02 ± 0. 45 and 0. 18 ± g 0. 24, depending on the season. Improvements may be obtained from a better knowledge of the surface albedo and higher accuracy of the aerosol model. Following the previous work over ocean of Park et al.(2016), our study shows the first encouraging aerosol layer height retrieval results over land from satellite observations of the 477 nm O2 gO2 absorption spectral band.
Aerosol height retrieval from satellite visible measurements
Application to OMI 477 nm O2-O2 spectral band, based on Neural Networks
The ability to monitor air quality and climate from UltraViolet-Visible (UV-Vis) satellite spectral measurements relies on accurate trace gas (e.g. NO2, SO2, HCHO, O3) columns combined with aerosol properties and vertical distribution. In the absence of clouds, the most important error source on the observations of trace gases in the troposphere are aerosols, since their scattering and absorbing properties modify the average light path followed by the detected photons. Large impacts due to their vertical distribution uncertainties remain when retrieving vertical column densities of trace gases from UV-Vis air quality space-borne sensors [Krotkov et al., 2008; Boersma et al., 2011; Barkley et al., 2012; Hewson et al., 2015; Castellanos et al., 2015; Chimot et al., 2016a]. Aerosols and trace gases share, over urban and industrialized areas, similar anthropogenic sources, and their concentrations, as shown by the satellite observations, often present significant correlations [Veefkind et al., 2011]. We have recently developed a Multilayer Perceptron Neural Network (NN) algorithm to retrieve Aerosol Layer Height (ALH) from the OMI 477 nm O2-O2 absorption band [Chimot et al., 2016b]. This algorithm represents aerosols in the troposphere as a single scattering layer defined by its mean altitude and homogeneous optical properties. This algorithm enables the link between the OMI O2-O2 slant column density derived from the 477 nm spectral measurements and the aerosol layer altitude. A prior information about the Aerosol Optical Thickness (AOT) is needed to distinguish the effects due to the amount of fine particles and their altitude. Therefore, the ALH retrieval strongly benefits from a synergy between OMI 477 nm O2-O2 spectral measurements and MODIS AOT product. Aerosol layer heights are currently retrieved with an uncertainty in the range of 260-800 m for scenes with AOT larger than 1. Improvement of these retrievals can be expected by improving assumptions on the aerosol single scattering albedo (impacts up to 600 m on average) and surface albedo (less than 200 m). We will present the results obtained for the first time on 3-year (2005-2007) cloud-free OMI observations over large urban and industrialized areas, in North-East Asia [Chimot et al., 2016b]. Some case studies, depicting the correlation between OMI ALH with collocated CALIPSO aerosol observations over some days of strong pollution in China and wildfires in 2012 in Russia, will also be presented. Finally, we will discuss how this product may be used for an explicit aerosol correction in the retrieval of trace gases from UV-Vis satellite sensor. The focus will be on tropospheric NO2 column, over scenes with a high aerosol loading.
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The ability to monitor air quality and climate from UltraViolet-Visible (UV-Vis) satellite spectral measurements relies on accurate trace gas (e.g. NO2, SO2, HCHO, O3) columns combined with aerosol properties and vertical distribution. In the absence of clouds, the most important error source on the observations of trace gases in the troposphere are aerosols, since their scattering and absorbing properties modify the average light path followed by the detected photons. Large impacts due to their vertical distribution uncertainties remain when retrieving vertical column densities of trace gases from UV-Vis air quality space-borne sensors [Krotkov et al., 2008; Boersma et al., 2011; Barkley et al., 2012; Hewson et al., 2015; Castellanos et al., 2015; Chimot et al., 2016a]. Aerosols and trace gases share, over urban and industrialized areas, similar anthropogenic sources, and their concentrations, as shown by the satellite observations, often present significant correlations [Veefkind et al., 2011]. We have recently developed a Multilayer Perceptron Neural Network (NN) algorithm to retrieve Aerosol Layer Height (ALH) from the OMI 477 nm O2-O2 absorption band [Chimot et al., 2016b]. This algorithm represents aerosols in the troposphere as a single scattering layer defined by its mean altitude and homogeneous optical properties. This algorithm enables the link between the OMI O2-O2 slant column density derived from the 477 nm spectral measurements and the aerosol layer altitude. A prior information about the Aerosol Optical Thickness (AOT) is needed to distinguish the effects due to the amount of fine particles and their altitude. Therefore, the ALH retrieval strongly benefits from a synergy between OMI 477 nm O2-O2 spectral measurements and MODIS AOT product. Aerosol layer heights are currently retrieved with an uncertainty in the range of 260-800 m for scenes with AOT larger than 1. Improvement of these retrievals can be expected by improving assumptions on the aerosol single scattering albedo (impacts up to 600 m on average) and surface albedo (less than 200 m). We will present the results obtained for the first time on 3-year (2005-2007) cloud-free OMI observations over large urban and industrialized areas, in North-East Asia [Chimot et al., 2016b]. Some case studies, depicting the correlation between OMI ALH with collocated CALIPSO aerosol observations over some days of strong pollution in China and wildfires in 2012 in Russia, will also be presented. Finally, we will discuss how this product may be used for an explicit aerosol correction in the retrieval of trace gases from UV-Vis satellite sensor. The focus will be on tropospheric NO2 column, over scenes with a high aerosol loading.
Impact of aerosols on the OMI tropospheric NO2 retrievals over industrialized regions
How accurate is the aerosol correction of cloud-free scenes via a simple cloud model?
The Ozone Monitoring Instrument (OMI) has provided daily global measurements of tropospheric NO2 for more than a decade. Numerous studies have drawn attention to the complexities related to measurements of tropospheric NO2 in the presence of aerosols. Fine particles affect the OMI spectral measurements and the length of the average light path followed by the photons. However, they are not explicitly taken into account in the current operational OMI tropospheric NO2 retrieval chain (DOMINO – Derivation of OMI tropospheric NO2) product. Instead, the operational OMI O2 − O2 cloud retrieval algorithm is applied both to cloudy and to cloud-free scenes (i.e. clear sky) dominated by the presence of aerosols. This paper describes in detail the complex interplay between the spectral effects of aerosols in the satellite observation and the associated response of the OMI O2 − O2 cloud retrieval algorithm. Then, it evaluates the impact on the accuracy of the tropospheric NO2 retrievals through the computed Air Mass Factor (AMF) with a focus on cloud-free scenes. For that purpose, collocated OMI NO2 and MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua aerosol products are analysed over the strongly industrialized East China area. In addition, aerosol effects on the tropospheric NO2 AMF and the retrieval of OMI cloud parameters are simulated. Both the observation-based and the simulation-based approach demonstrate that the retrieved cloud fraction increases with increasing Aerosol Optical Thickness (AOT), but the magnitude of this increase depends on the aerosol properties and surface albedo. This increase is induced by the additional scattering effects of aerosols which enhance the scene brightness. The decreasing effective cloud pressure with increasing AOT primarily represents the shielding effects of the O2 − O2 column located below the aerosol layers. The study cases show that the aerosol correction based on the implemented OMI cloud model results in biases between −20 and −40 % for the DOMINO tropospheric NO2 product in cases of high aerosol pollution (AOT ≥ 0.6) at elevated altitude. These biases result from a combination of the cloud model error, used in the presence of aerosols, and the limitations of the current OMI cloud Look-Up-Table (LUT). A new LUT with a higher sampling must be designed to remove the complex behaviour between these biases and AOT. In contrast, when aerosols are relatively close to the surface or mixed with NO2, aerosol correction based on the cloud model results in an overestimation of the DOMINO tropospheric NO2 column, between 10 and 20 %. These numbers are in line with comparison studies between ground-based and OMI tropospheric NO2 measurements in the presence of high aerosol pollution and particles located at higher altitudes. This highlights the need to implement an improved aerosol correction in the computation of tropospheric NO2 AMFs.
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The Ozone Monitoring Instrument (OMI) has provided daily global measurements of tropospheric NO2 for more than a decade. Numerous studies have drawn attention to the complexities related to measurements of tropospheric NO2 in the presence of aerosols. Fine particles affect the OMI spectral measurements and the length of the average light path followed by the photons. However, they are not explicitly taken into account in the current operational OMI tropospheric NO2 retrieval chain (DOMINO – Derivation of OMI tropospheric NO2) product. Instead, the operational OMI O2 − O2 cloud retrieval algorithm is applied both to cloudy and to cloud-free scenes (i.e. clear sky) dominated by the presence of aerosols. This paper describes in detail the complex interplay between the spectral effects of aerosols in the satellite observation and the associated response of the OMI O2 − O2 cloud retrieval algorithm. Then, it evaluates the impact on the accuracy of the tropospheric NO2 retrievals through the computed Air Mass Factor (AMF) with a focus on cloud-free scenes. For that purpose, collocated OMI NO2 and MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua aerosol products are analysed over the strongly industrialized East China area. In addition, aerosol effects on the tropospheric NO2 AMF and the retrieval of OMI cloud parameters are simulated. Both the observation-based and the simulation-based approach demonstrate that the retrieved cloud fraction increases with increasing Aerosol Optical Thickness (AOT), but the magnitude of this increase depends on the aerosol properties and surface albedo. This increase is induced by the additional scattering effects of aerosols which enhance the scene brightness. The decreasing effective cloud pressure with increasing AOT primarily represents the shielding effects of the O2 − O2 column located below the aerosol layers. The study cases show that the aerosol correction based on the implemented OMI cloud model results in biases between −20 and −40 % for the DOMINO tropospheric NO2 product in cases of high aerosol pollution (AOT ≥ 0.6) at elevated altitude. These biases result from a combination of the cloud model error, used in the presence of aerosols, and the limitations of the current OMI cloud Look-Up-Table (LUT). A new LUT with a higher sampling must be designed to remove the complex behaviour between these biases and AOT. In contrast, when aerosols are relatively close to the surface or mixed with NO2, aerosol correction based on the cloud model results in an overestimation of the DOMINO tropospheric NO2 column, between 10 and 20 %. These numbers are in line with comparison studies between ground-based and OMI tropospheric NO2 measurements in the presence of high aerosol pollution and particles located at higher altitudes. This highlights the need to implement an improved aerosol correction in the computation of tropospheric NO2 AMFs.
Aerosols correction of the OMI tropospheric NO2 retrievals over cloud-free scenes
Different methodologies based on the O2-O2 477 nm band
Numerous studies have drawn attention to the complexities related to the retrievals of tropospheric NO2 columns derived from satellite UltraViolet-Visible (UV-Vis) measurements in the presence of aerosols. Correction for aerosol effects will remain a challenge for the next generation of air quality satellite instruments such as TROPOMI on Sentinel-5 Precursor, Sentinel-4 and Sentinel-5. The Ozone Monitoring Instrument (OMI) instrument has provided daily global measurements of tropospheric NO2 for more than a decade. However, aerosols are not explicitly taken into account in the current operational OMI tropospheric NO2 retrieval chain (DOMINO v2 [Boersma et al., 2011]). Our study analyses 2 approaches for an operational aerosol correction, based on the use of the O2-O2 477 nm band. The 1st approach is the cloud-model based aerosol correction, also named “implicit aerosol correction”, and already used in the operational chain. The OMI O2-O2 cloud retrieval algorithm, based on the Differential Optical Absorption Spectroscopy (DOAS) approach, is applied both to cloudy and to cloud-free scenes with aerosols present. Perturbation of the OMI cloud retrievals over scenes dominated by aerosols has been observed in recent studies led by [Castellanos et al., 2015; Lin et al., 2015; Lin et al., 2014]. We investigated the causes of these perturbations by: (1) confronting the OMI tropospheric NO2, clouds and MODIS AQUA aerosol products; (2) characterizing the key drivers of the aerosol net effects, compared to a signal from clouds, in the UV-Vis spectra. This study has focused on large industrialised areas like East-China, over cloud-free scenes. One of the key findings is the limitation due to the coarse sampling of the employed cloud Look-Up Table (LUT) to convert the results of the applied DOAS fit into effective cloud fraction and pressure. This leads to an underestimation of tropospheric NO2 amount in cases of particles located at elevated altitude. A higher sampling of the variation of O2-O2 SCD and continuum reflectance as a function of effective cloud parameters in case of low effective cloud fraction values is requested for applying an aerosol correction. The updates of the OMI O2-O2 cloud algorithm, based on the scheduled new OMI cloud LUT, will be presented in terms of impacts of the effective cloud retrievals and reduced biases of tropospheric NO2 columns over cloud-free scenes dominated by aerosols in China. A 2nd approach is investigated, assuming a more explicit aerosol correction. Previous analyses pointed out that the O2-O2 spectra contain information about aerosols: the continuum reflectance is primarily constrained by the Aerosol Optical thickness (AOT) while the O2-O2 Slant Column Density (SCD) mostly results from the combination of AOT and aerosols altitude. We have developed a first prototype algorithm allowing to retrieve information about AOT and aerosol altitude from the O2-O2 DOAS fit. We will discuss preliminary sensitivities and the potential accuracy of the associated explicit aerosol correction, without the use of effective cloud parameters.
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Numerous studies have drawn attention to the complexities related to the retrievals of tropospheric NO2 columns derived from satellite UltraViolet-Visible (UV-Vis) measurements in the presence of aerosols. Correction for aerosol effects will remain a challenge for the next generation of air quality satellite instruments such as TROPOMI on Sentinel-5 Precursor, Sentinel-4 and Sentinel-5. The Ozone Monitoring Instrument (OMI) instrument has provided daily global measurements of tropospheric NO2 for more than a decade. However, aerosols are not explicitly taken into account in the current operational OMI tropospheric NO2 retrieval chain (DOMINO v2 [Boersma et al., 2011]). Our study analyses 2 approaches for an operational aerosol correction, based on the use of the O2-O2 477 nm band. The 1st approach is the cloud-model based aerosol correction, also named “implicit aerosol correction”, and already used in the operational chain. The OMI O2-O2 cloud retrieval algorithm, based on the Differential Optical Absorption Spectroscopy (DOAS) approach, is applied both to cloudy and to cloud-free scenes with aerosols present. Perturbation of the OMI cloud retrievals over scenes dominated by aerosols has been observed in recent studies led by [Castellanos et al., 2015; Lin et al., 2015; Lin et al., 2014]. We investigated the causes of these perturbations by: (1) confronting the OMI tropospheric NO2, clouds and MODIS AQUA aerosol products; (2) characterizing the key drivers of the aerosol net effects, compared to a signal from clouds, in the UV-Vis spectra. This study has focused on large industrialised areas like East-China, over cloud-free scenes. One of the key findings is the limitation due to the coarse sampling of the employed cloud Look-Up Table (LUT) to convert the results of the applied DOAS fit into effective cloud fraction and pressure. This leads to an underestimation of tropospheric NO2 amount in cases of particles located at elevated altitude. A higher sampling of the variation of O2-O2 SCD and continuum reflectance as a function of effective cloud parameters in case of low effective cloud fraction values is requested for applying an aerosol correction. The updates of the OMI O2-O2 cloud algorithm, based on the scheduled new OMI cloud LUT, will be presented in terms of impacts of the effective cloud retrievals and reduced biases of tropospheric NO2 columns over cloud-free scenes dominated by aerosols in China. A 2nd approach is investigated, assuming a more explicit aerosol correction. Previous analyses pointed out that the O2-O2 spectra contain information about aerosols: the continuum reflectance is primarily constrained by the Aerosol Optical thickness (AOT) while the O2-O2 Slant Column Density (SCD) mostly results from the combination of AOT and aerosols altitude. We have developed a first prototype algorithm allowing to retrieve information about AOT and aerosol altitude from the O2-O2 DOAS fit. We will discuss preliminary sensitivities and the potential accuracy of the associated explicit aerosol correction, without the use of effective cloud parameters.
MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area
Comparison of two profile retrieval approaches
Journal article
(2015)
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T. Vlemmix, F Hendrick, G Pinardi, I De Smedt, C Fayt, C. Hermans, AJM Piters, P. Wang, P. Levelt, M van Roozendael
A 4-year data set of MAX-DOAS observations in the Beijing area (2008-2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2-4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ∼ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, -23 ± 28 and -8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.
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A 4-year data set of MAX-DOAS observations in the Beijing area (2008-2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2-4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ∼ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, -23 ± 28 and -8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.