scholarly journals Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO<sub>2</sub> and CO

2022 ◽  
Author(s):  
Dien Wu ◽  
Junjie Liu ◽  
Paul O. Wennberg ◽  
Paul I. Palmer ◽  
Robert R. Nelson ◽  
...  
Keyword(s):  
Los Angeles ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Combustion Efficiency ◽  
Heavy Industry ◽  
Spatially Resolved ◽  
Urban Land Cover ◽  
Emission Estimate ◽  
The Difference ◽  
Carbon Dioxide Co2

Abstract. Carbon dioxide (CO2) and air pollutants such as carbon monoxide (CO) are co-emitted by many combustion sources. Previous efforts have combined satellite-based observations of multiple tracers to calculate their emission ratio (ER) for inferring combustion efficiency at regional to city scale. Very few studies have focused on burning efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for deriving spatially-resolved ERs from asynchronous satellite measurements including 1) variations in meteorological conditions induced by different overpass times, 2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), and 3) interferences from the biosphere and biomass burning. In this study, we extended an established emission estimate approach to arrive at spatially-resolved ERs based on retrieved column-averaged CO2 (XCO2) from the Snapshot Area Mapping (SAM) mode of the Orbiting Carbon Observatory-3 (OCO-3) and column-averaged CO from the TROPOspheric Monitoring Instrument (TROPOMI). To evaluate the influence of the confounding factors listed above and further explain the intra-urban variations in ERs, we leveraged a Lagrangian atmospheric transport model and an urban land cover classification dataset and reported ERCO from the sounding level to the overpass- and city- levels. We found that the difference in the overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially-resolved ERCO. Specifically, a time difference of > 3 hours typically led to dramatic changes in the wind direction and shape of urban plumes and thereby making the calculation of accurate sounding-specific ERCO difficult. After removing those cases from consideration and applying a simple plume shift method when necessary, we discovered significant contrasts in combustion efficiencies between 1) two megacities versus two industry-oriented cities and 2) different regions within a city, based on six to seven nearly-coincident overpasses per city. Results suggest that the combustion efficiency for heavy industry in Los Angeles is slightly lower than its overall city-wide value (< 10 ppb-CO / ppm-CO2). In contrast, ERs related to the heavy industry in Shanghai are found to be much higher than Shanghai’s city-mean and more aligned with city-means of the two industry-oriented Chinese cities (approaching 20 ppb-CO / ppm-CO2). Although investigations based on a larger number of satellite overpasses are needed, our first analysis provides guidance for estimating intra-city gradients in combustion efficiency from future missions, such as those that will map column CO2 and CO concentration simultaneously with high spatiotemporal resolutions.

scholarly journals Study of the main processes driving atmospheric CH<sub>4</sub> variability in a rural Spanish region

2017 ◽  
Author(s):  
Claudia Grossi ◽  
Felix R. Vogel ◽  
Roger Curcoll ◽  
Alba Àgueda ◽  
Arturo Vargas ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Daily Basis ◽  
Atmospheric Variability ◽  
Absolute Deviation ◽  
Monthly Basis ◽  
Bottom Up ◽  
Methane Fluxes ◽  
Carbon Dioxide Co2 ◽  
Atmospheric Concentrations

Abstract. Atmospheric concentrations of the two main greenhouse gases (GHGs), carbon dioxide (CO2) and methane (CH4), are continuously measured since November 2012 at the Spanish rural station of Gredos (GIC3), within the climate network ClimaDat, together with atmospheric radon (222Rn) tracer and meteorological parameters. The atmospheric variability of CH4 concentrations measured from 2013 to 2015 at GIC3 has been analyzed in this study. It is interpreted in relation to the variability of measured 222Rn concentrations, modelled 222Rn fluxes and modelled heights of the planetary boundary layer (PBLH) in the same period. In addition, nocturnal fluxes of CH4 were estimated using two methods: the Radon Tracer Method (RTM) and one based on the EDGARv4.2 bottom-up emission inventory. Both previous methods have been applied using the same footprints, calculated with the atmospheric transport model FLEXPARTv6.2. Results show that daily and seasonal changes in atmospheric concentrations of 222Rn (and the corresponding fluxes) can help to understand the atmospheric CH4 variability. On daily basis, the variation in the PBLH mainly drives changes in 222Rn and CH4 concentrations while, on monthly basis, their atmospheric variability seems to depend on changes in their emissions. The median value of RTM based methane fluxes (FR_CH4) is 0.17 mg CH4 m−2 h−1 with an absolute deviation of 0.08 mg CH4 m−2 h−1. Median methane fluxes based on bottom-up inventory (FE_CH4) is of 0.32 mg CH4 m−2 h−1 with an absolute deviation of 0.06 mg CH4 m−2 h−1. Monthly FR_CH4 flux shows a seasonality which is not observed in the monthly FE_CH4 flux. During January–May FR_CH4 fluxes present a median value of 0.08 mg CH4 m−2 h−1 with an absolute deviation of 0.05 mg CH4 m−2 h−1 and a median value of 0.19 mg CH4 m−2 h−1 with an absolute deviation of 0.06 mg CH4 m−2 h−1 during June–December. This seasonal doubling of the median methane fluxes calculated by RTM at the GIC3 area seems to be mainly related to the alternate presence of transhumant livestock in the GIC3 area. The results obtained in this study highlight the benefit of applying independent RTM to improve the seasonality of the emission factors from bottom-up inventories.

scholarly journals Measuring Regional Atmospheric CO2 Concentrations in the Lower Troposphere with a Non-Dispersive Infrared Analyzer Mounted on a UAV, Ogata Village, Akita, Japan

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 487 ◽  
Author(s):  
Takashi Chiba ◽  
Yumi Haga ◽  
Makoto Inoue ◽  
Osamu Kiguchi ◽  
Takeshi Nagayoshi ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Lower Troposphere ◽  
Co2 Concentration ◽  
Measuring System ◽  
Japan Meteorological Agency ◽  
Co2 Concentrations ◽  
Monthly Pattern ◽  
Carbon Dioxide Co2 ◽  
Atmospheric Co2 Concentrations

We have developed a simple measuring system prototype that uses an unmanned aerial vehicle (UAV) and a non-dispersive infrared (NDIR) analyzer to detect regional carbon dioxide (CO2) concentrations and obtain vertical CO2 distributions. Here, we report CO2 measurement results for the lower troposphere above Ogata Village, Akita Prefecture, Japan (about 40° N, 140° E, approximately −1 m amsl), obtained with this UAV system. The actual flight observations were conducted at 500, 400, 300, 200, 100, and 10 m above the ground, at least once a month during the daytime from February 2018 to February 2019. The raw CO2 values from the NDIR were calibrated by two different CO2 standard gases and high-purity nitrogen (N2) gas (as a CO2 zero gas; 0 ppm). During the observation period, the maximum CO2 concentration was measured in February 2019 and the minimum in August 2018. In all seasons, CO2 concentrations became higher as the flight altitude was increased. The monthly pattern of observed CO2 changes is similar to that generally observed in the Northern Hemisphere as well as to surface CO2 changes simulated by an atmospheric transport model of the Japan Meteorological Agency. It is highly probable that these changes reflect the vegetation distribution around the study area.

scholarly journals A comparison of atmospheric CO<sub>2</sub> flux signals obtained from GEOS-Chem flux inversions constrained by in situ or GOSAT observations

2018 ◽  
Author(s):  
Saroja M. Polavarapu ◽  
Feng Deng ◽  
Brendan Byrne ◽  
Dylan B. A. Jones ◽  
Micheal Neish
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Flux ◽  
Boreal Summer ◽  
Spatial Structures ◽  
Independent Observations ◽  
In Situ Observations ◽  
The Difference ◽  
Observing Systems

Abstract. The CO2 flux signal is defined as the difference of the four-dimensional CO2 field obtained by integrating an atmospheric transport model with posterior fluxes and that obtained with prior fluxes. It is a function of both the model and the prior fluxes and it can provide insight into how posterior fluxes inform CO2 distributions. Here, we use the GEOS-Chem transport model constrained by either GOSAT or in situ observations to obtain two sets of posterior flux estimates in order to compare the flux signals obtained from the two different observing systems. Flux signals are also computed using two different models. The global flux signal in the troposphere primarily reflects the northern extratropics whereas the global flux signal in the stratosphere mainly reflects tropical contributions. While both observing systems constrain the global budget for 2010 equally well, stronger seasonal variations of the flux signal are obtained with GOSAT. Posterior CO2 distributions obtained with in situ observations better agree with TCCON measurements over an 18-month time period, but GOSAT-informed posterior fluxes better constrain the seasonal cycle at northern extratropical sites. Zonal standard deviations of the flux signal exceed the minimal value (defined by uncertainty in meteorological analyses) through most of the year when GOSAT observations are used, but when in situ observations are used, the minimum value is exceeded only in boreal summer. This indicates a potential for flux estimates constrained by GOSAT data to retrieve spatial structures within a zonal band throughout the year in the tropics and through most of the year in the northern extratropics. Verification of such spatial structures will require a dense network of independent observations.

Greenhouse Gas Emission Estimate Using a Fully-automated Permanent Sensor Network in Munich

2020 ◽  
Author(s):  
Florian Dietrich ◽  
Jia Chen ◽  
Benno Voggenreiter ◽  
Xinxu Zhao
Keyword(s):  
Sensor Network ◽  
Urban Areas ◽  
Greenhouse Gas Emission ◽  
Atmospheric Transport ◽  
Chem Phys ◽  
Solar Absorption ◽  
Spatially Resolved ◽  
Solar Tracking ◽  
The Difference ◽  
The City

&lt;p&gt;To effectively mitigate climate change, it is indispensable to know the locations of the emission sources and their respective emission strength. As the majority of greenhouse gases (GHG) such as carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;), methane (CH&lt;sub&gt;4&lt;/sub&gt;) and carbon monoxide (CO) are generated in cities, our focus lies in the emission determination of urban areas. For this reason, we established a fully-automated sensor network in Munich, Germany to permanently measure GHGs.&lt;/p&gt;&lt;p&gt;Our permanent network is based on the differential column measurement principle [1] and measures the city emissions using five FTIR spectrometer systems (EM27/SUN from Bruker [2]). For these spectrometers we built a self-developed enclosure system and equipped them with several sensors (e.g. computer vision based solar radiation sensor) to measure the column-averaged concentrations of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt; and CO in a fully-automated way. The difference between the column amounts inside and outside of the city reflects the pollutants abundance generated in the city. Four stations are placed at the city outskirts to capture the inflow/outflow column amounts in arbitrary wind conditions. One inner-city station, which has already been operating successfully since 2016 [3], is serving as a permanent downwind site for half of the city.&lt;/p&gt;&lt;p&gt;With the help of atmospheric transport models, combined with a Bayesian inverse modelling approach, those concentration differences are transferred into spatially resolved emission estimates of the city. After testing the network in two campaigns (2017 and 2018), the network is finally long-term operating since summer 2019 and continuously measures the GHG concentrations in Munich. We will show both the hardware achievements and first measurement and emission results after ten month of operation.&lt;/p&gt;&lt;p&gt;[1] Chen et al.: Differential column measurements using compact solar-tracking spectrometers. Atmos. Chem. Phys., 16: 8479&amp;#8211;8498, 2016.&lt;/p&gt;&lt;p&gt;[2] Gisi et al.: XCO2-measurements with a tabletop FTS using solar absorption spectroscopy, Atmos. Meas. Tech., 5, 2969-2980, 2012&lt;/p&gt;&lt;p&gt;[3] Heinle and Chen: Automated Enclosure and Protection System for Compact Solar-Tracking Spectrometers, Atmos. Meas. Tech., 11, 2173-2185, 2018&lt;/p&gt;

scholarly journals Detecting changes in Arctic methane emissions: limitations of the inter-polar difference of atmospheric mole fractions

2018 ◽  
Author(s):  
Oscar B. Dimdore-Miles ◽  
Paul I. Palmer ◽  
Lori P. Bruhwiler
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Random Noise ◽  
Monitoring Network ◽  
The Arctic ◽  
Polar Regions ◽  
Annual Growth Rate ◽  
The Difference ◽  
The Antarctic

Abstract. We consider the utility of the annual inter-polar difference (IPD) as a metric for changes in Arctic emission of methane (CH4). The IPD has been previously defined as the difference between weighted annual means of CH4 mole fraction data collected at polar stations (−53° > latitude > 53°). This subtraction approach (IPDΔ) implicitly assumes that extra-polar CH4 emissions arrive within the same calendar year at both poles. Using an analytic approach we show that a comprehensive description of the IPD includes terms corresponding to the atmospheric transport of air masses from lower latitudes to the polar regions. We show the importance of these transport flux terms in understanding the IPD using idealized numerical experiments with the TM5 global 3-D atmospheric chemistry transport model run from 1980 to 2010. A northern mid-latitude pulse in January 1990, which increases prior emission distributions, arrives at the Arctic with a higher mixing ratio and ≃ 12 months earlier than at the Antarctic. The perturbation at the poles subsequently decays with an e-folding lifetime of ≃ 4 years. A similarly timed pulse emitted from the tropics arrives with a higher value at the Antarctic ≃ 11 months earlier than at the Arctic. This perturbation decays with an e-folding lifetime of ≃ 7 years. These simulations demonstrate that the assumption of symmetric transport of extra-polar emissions to the poles is not realistic, resulting in considerable IPDΔ variations due to variations in emissions and atmospheric transport. We assess how well the annual IPD can detect a constant annual growth rate of Arctic emissions for three scenarios, 0.5 %, 1 %, and 2 %, superimposed on signals from lower latitudes, including random noise. We find that it can take up to 16 years to detect the smallest prescribed trend in Arctic emissions at the 95 % confidence level. Scenarios with higher, but likely unrealistic, growth in Arctic emissions are detected in less than a decade. We argue that a more reliable measurement-driven IPD metric would include data collected from all latitudes, emphasizing the importance of maintaining a global monitoring network to observe decadal changes in atmospheric greenhouse gases.

scholarly journals The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products

2017 ◽  
Vol 10 (1) ◽  
pp. 59-81 ◽  
Author(s):  
David Crisp ◽  
Harold R. Pollock ◽  
Robert Rosenberg ◽  
Lars Chapsky ◽  
Richard A. M. Lee ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Atmospheric Transport ◽  
Resolving Power ◽  
Sources And Sinks ◽  
Spatially Resolved ◽  
Yield And Quality ◽  
Data Products ◽  
High Measurement ◽  
Science Community ◽  
Carbon Dioxide Co2

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O2) A-band at 0.765 microns and the carbon dioxide (CO2) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO2 dry-air mole fraction, XCO2. Variations of XCO2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO2. This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small (< 0.25 %) changes in the background XCO2 field. High measurement precision is therefore essential to resolve these small variations, and high accuracy is needed because small biases in the retrieved XCO2 distribution could be misinterpreted as evidence for CO2 fluxes. To meet its demanding measurement requirements, each OCO-2 spectrometer channel collects 24 spectra s−1 across a narrow (< 10 km) swath as the observatory flies over the sunlit hemisphere, yielding almost 1 million soundings each day. On monthly timescales, between 7 and 12 % of these soundings pass the cloud screens and other data quality filters to yield full-column estimates of XCO2. Each of these soundings has an unprecedented combination of spatial resolution (< 3 km2/sounding), spectral resolving power (λ∕Δλ > 17 000), dynamic range (∼ 104), and sensitivity (continuum signal-to-noise ratio > 400). The OCO-2 instrument performance was extensively characterized and calibrated prior to launch. In general, the instrument has performed as expected during its first 18 months in orbit. However, ongoing calibration and science analysis activities have revealed a number of subtle radiometric and spectroscopic challenges that affect the yield and quality of the OCO-2 data products. These issues include increased numbers of bad pixels, transient artifacts introduced by cosmic rays, radiance discontinuities for spatially non-uniform scenes, a misunderstanding of the instrument polarization orientation, and time-dependent changes in the throughput of the oxygen A-band channel. Here, we describe the OCO-2 instrument, its data products, and its on-orbit performance. We then summarize calibration challenges encountered during its first 18 months in orbit and the methods used to mitigate their impact on the calibrated radiance spectra distributed to the science community.

scholarly journals A Bayesian inversion estimate of N<sub>2</sub>O emissions for western and central Europe and the assessment of aggregation errors

2011 ◽  
Vol 11 (7) ◽  
pp. 3443-3458 ◽  
Author(s):  
R. L. Thompson ◽  
C. Gerbig ◽  
C. Rödenbeck
Keyword(s):  
Central Europe ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Temporal Correlation ◽  
Bayesian Inversion ◽  
Bias Error ◽  
Mixing Ratios ◽  
Spatially Resolved ◽  
Atmospheric Observations ◽  
N2o Fluxes

Abstract. A Bayesian inversion approach was used to retrieve temporally and spatially resolved N2O fluxes for western and central Europe using in-situ atmospheric observations from the tall tower site at Ochsenkopf, Germany (50°01' N, 11°48' E). For atmospheric transport, the STILT (Stochastic Time-Inverted Lagrangian Transport) model was employed, which was driven with ECMWF analysis and short term forecast fields. The influence of temporal aggregation error, as well as the choice of spatial and temporal correlation scale length, on the retrieval was investigated using a synthetic dataset consisting of mixing ratios generated for the Ochsenkopf site. We found that if the aggregation error is ignored, then a significant bias error in the retrieved fluxes ensues. However, by estimating this error and projecting it into the observation space, it was possible to avoid bias errors in the retrieved fluxes. Using the real observations from the Ochsenkopf site, N2O fluxes were retrieved every 7 days for 2007 at 2 by 2 degrees spatial resolution. Emissions of N2O were strongest during the summer and autumn months, with peak emissions in August and September, while the regions of Benelux and northern United Kingdom had strongest annual mean emissions.

scholarly journals A Bayesian inversion estimate of N<sub>2</sub>O emissions for western and central Europe and the assessment of aggregation errors

2010 ◽  
Vol 10 (11) ◽  
pp. 26073-26115 ◽  
Author(s):  
R. L. Thompson ◽  
C. Gerbig ◽  
C. Rödenbeck
Keyword(s):  
Central Europe ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Temporal Correlation ◽  
Bayesian Inversion ◽  
Bias Error ◽  
Mixing Ratios ◽  
Spatially Resolved ◽  
Atmospheric Observations ◽  
N2o Fluxes

Abstract. A Bayesian inversion approach was used to retrieve temporally and spatially resolved N2O fluxes for western and central Europe using in-situ atmospheric observations from the tall tower site at Ochsenkopf, Germany (50°01´ N, 11°48´ E). For atmospheric transport, the STILT (Stochastic Time-Inverted Lagrangian Transport) model was employed, which was driven with ECMWF analysis and short term forecast fields. The influence of temporal aggregation error, as well as the choice of spatial and temporal correlation scale length, on the retrieval was investigated using a synthetic dataset consisting of mixing ratios generated for the Ochsenkopf site. We found that if the aggregation error is ignored, then a significant bias error in the retrieved fluxes ensues. However, by estimating this error and projecting it into the observation space, it was possible to avoid bias errors in the retrieved fluxes. Using the real observations from the Ochsenkopf site, N2O fluxes were retrieved every 7 days for 2007 at 2 by 2 degrees spatial resolution. Emissions of N2O were strongest during the summer and autumn months, with peak emissions in August and September, while the regions of Benelux and northern United Kingdom had the strongest annual mean emissions.

scholarly journals On the tuning of atmospheric inverse methods: comparisons with the European Tracer Experiment (ETEX) and Chernobyl datasets using the atmospheric transport model FLEXPART

2020 ◽  
Vol 13 (12) ◽  
pp. 5917-5934
Author(s):  
Ondřej Tichý ◽  
Lukáš Ulrych ◽  
Václav Šmídl ◽  
Nikolaos Evangeliou ◽  
Andreas Stohl
Keyword(s):  
Source Term ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Optimization Procedure ◽  
Sensitivity Study ◽  
Tracer Experiment ◽  
Additional Information ◽  
Regularization Parameters ◽  
Ill Posed ◽  
The Difference

Abstract. Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem wherein the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties, e.g., in measurements or atmospheric transport modeling; hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information: the prior source term, also known as the first guess, and regularization parameters for the shape of the source term. While the first guess is based on information independent of the measurements, such as the physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term and regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3.

scholarly journals The On-Orbit Performance of the Orbiting Carbon Observatory-2 (OCO-2) Instrument and its Radiometrically Calibrated Products

2016 ◽  
Author(s):  
David Crisp ◽  
Harold R. Pollock ◽  
Robert Rosenberg ◽  
Lars Chapsky ◽  
Richard A. M. Lee ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Dioxide ◽  
High Resolution ◽  
Molecular Oxygen ◽  
Atmospheric Transport ◽  
Emission Sources ◽  
Sources And Sinks ◽  
Dry Air ◽  
Spatially Resolved ◽  
Carbon Dioxide Co2

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O2) A-band at 0.765 microns and the carbon dioxide (CO2) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO2 dry air mole fraction, XCO2. Variations of XCO2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO2. This is particularly challenging remote sensing observations because the all but the largest emission sources and natural absorbers produce only small (

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