scholarly journals Evaluation of 4 years of continuous <i>δ</i><sup>13</sup>C(CO<sub>2</sub>) data using a moving Keeling plot method

2016 ◽  
Vol 13 (14) ◽  
pp. 4237-4251 ◽  
Author(s):  
Sanam Noreen Vardag ◽  
Samuel Hammer ◽  
Ingeborg Levin
Keyword(s):  
Transport Model ◽  
Co2 Concentration ◽  
High Temporal Resolution ◽  
Model Data ◽  
Additional Information ◽  
Keeling Plot ◽  
Data Points ◽  
The Mean ◽  
Carbon Dioxide Co2 ◽  
End Members

Abstract. Different carbon dioxide (CO2) emitters can be distinguished by their carbon isotope ratios. Therefore measurements of atmospheric δ13C(CO2) and CO2 concentration contain information on the CO2 source mix in the catchment area of an atmospheric measurement site. This information may be illustratively presented as the mean isotopic source signature. Recently an increasing number of continuous measurements of δ13C(CO2) and CO2 have become available, opening the door to the quantification of CO2 shares from different sources at high temporal resolution. Here, we present a method to compute the CO2 source signature (δS) continuously and evaluate our result using model data from the Stochastic Time-Inverted Lagrangian Transport model. Only when we restrict the analysis to situations which fulfill the basic assumptions of the Keeling plot method does our approach provide correct results with minimal biases in δS. On average, this bias is 0.2 ‰ with an interquartile range of about 1.2 ‰ for hourly model data. As a consequence of applying the required strict filter criteria, 85 % of the data points – mainly daytime values – need to be discarded. Applying the method to a 4-year dataset of CO2 and δ13C(CO2) measured in Heidelberg, Germany, yields a distinct seasonal cycle of δS. Disentangling this seasonal source signature into shares of source components is, however, only possible if the isotopic end members of these sources – i.e., the biosphere, δbio, and the fuel mix, δF – are known. From the mean source signature record in 2012, δbio could be reliably estimated only for summer to (−25.0 ± 1.0) ‰ and δF only for winter to (−32.5 ± 2.5) ‰. As the isotopic end members δbio and δF were shown to change over the season, no year-round estimation of the fossil fuel or biosphere share is possible from the measured mean source signature record without additional information from emission inventories or other tracer measurements.

scholarly journals Evaluation of four years continuous δ<sup>13</sup>C(CO<sub>2</sub>) data using a running Keeling approach

2016 ◽  
Author(s):  
Sanam Noreen Vardag ◽  
Samuel Hammer ◽  
Ingeborg Levin
Keyword(s):  
Fossil Fuel ◽  
Co2 Concentration ◽  
High Temporal Resolution ◽  
Data Set ◽  
Additional Information ◽  
Hourly Data ◽  
The Mean ◽  
Carbon Dioxide Co2 ◽  
End Members ◽  
Different Sources

Abstract. As different carbon dioxide (CO2) emitters have different carbon isotope ratios, measurements of atmospheric δ13C(CO2) and CO2 concentration contain information on the CO2 source mix in the catchment area of an atmospheric measurement site. Often, this information is illustratively presented as mean isotopic source signature. Recently an increasing number of continuous measurements of δ13C(CO2) and CO2 have become available, opening the door to quantification of CO2 shares from different sources at high temporal resolution. Here, we present a method to compute the CO2 source signature (δS) continuously without introducing biases and evaluate our result using model data. We find that biases in δS are smaller than 0.2 ‰ with uncertainties of about 1.2 ‰ for hourly data. Applying the method to a four year data set of CO2 and δ13C(CO2) measured in Heidelberg, Germany, yields a distinct seasonal cycle of δS. Disentangling this seasonal source signature into its source components is, however, only possible if the isotopic end members of these sources, i.e., the biosphere, δbio, and the fuel mix, δF, are known. From the mean source signature record in 2012, δbio could be reliably estimated only for summer to (−25 ± 1) ‰ and δF only for winter to (−32.5 ± 2.5) ‰. As the isotopic end members δbio and δF were shown to change over the season, no year-round estimation of the fossil fuel or biosphere share is possible from the measured mean source signature record without additional information from emission inventories or other tracer measurements, such as Δ14C(CO2).

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 IRIS analyser assessment reveals sub-hourly variability of isotope ratios in carbon dioxide at Baring Head, New Zealand's atmospheric observatory in the Southern Ocean

2021 ◽  
Author(s):  
Peter Sperlich ◽  
Gordon W. Brailsford ◽  
Rowena C. Moss ◽  
John McGregor ◽  
Ross J. Martin ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Southern Ocean ◽  
Atmospheric Carbon Dioxide ◽  
Isotope Ratios ◽  
Limiting Factor ◽  
Analytical Technique ◽  
Plot Analysis ◽  
Keeling Plot ◽  
Carbon Dioxide Co2 ◽  
End Members

Abstract. We assess the performance of an Isotope Ratio Infrared Spectrometer (IRIS) to measure carbon (δ13C) and oxygen (δ18O) isotope ratios in atmospheric carbon dioxide (CO2) and report observations from a 26 day field deployment trial at Baring Head, New Zealand, NIWA's atmospheric observatory for Southern Ocean baseline air. Our study describes an operational method to improve the performance in comparison to previous publications on this analytical technique. By using a calibration technique that reflected the principle of identical treatment of sample and reference gases, we achieved a reproducibility of 0.07 ‰ for δ13C-CO2 and 0.06 ‰ for δ18O-CO2 over multiple days. This performance is within the "extended compatibility goal" of 0.1 ‰ for both δ13C-CO2 and δ18O-CO2, which was recommended by the World Meteorological Organisation (WMO) for studies of regional or urban CO2 fluxes. One goal of this study was to assess the capabilities and limitations of the IRIS analyser to resolve δ13C-CO2 and δ18O-CO2 variations under field conditions. Therefore, we selected multiple events within the 26 day record for Keeling Plot Analysis. This resolved the isotopic composition of end members with an uncertainty of ≤ 1 ‰ when the magnitude of CO2 signals is larger than 10 ppm. The uncertainty of the Keeling Plot Analysis strongly increased for smaller CO2 events (2–7 ppm), where the instrument performance is the limiting factor and may only allow for the distinction between very different end members, such as the role of terrestrial versus oceanic carbon cycle processes. Further improvement in measurement performance is desirable to meet the WMO "network compatibility goal" of 0.01 ‰ for δ13C-CO2 and 0.05 ‰ for δ18O-CO2, which is needed to resolve the small variability that is typical for background air observatories such as Baring Head.

scholarly journals Comparison of TROPOMI/Sentinel-5 Precursor NO<sub>2</sub> observations with ground-based measurements in Helsinki

2020 ◽  
Vol 13 (1) ◽  
pp. 205-218 ◽  
Author(s):  
Iolanda Ialongo ◽  
Henrik Virta ◽  
Henk Eskes ◽  
Jari Hovila ◽  
John Douros
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
A Priori ◽  
Quality Data ◽  
High Temporal Resolution ◽  
Chemical Transport Model ◽  
Early Evaluation ◽  
The Mean ◽  
Urban Sites

Abstract. We present a comparison between satellite-based TROPOMI (TROPOspheric Monitoring Instrument) NO2 products and ground-based observations in Helsinki (Finland). TROPOMI NO2 total (summed) columns are compared with the measurements performed by the Pandora spectrometer between April and September 2018. The mean relative and absolute bias between the TROPOMI and Pandora NO2 total columns is about 10 % and 0.12×1015 molec. cm−2 respectively. The dispersion of these differences (estimated as their standard deviation) is 2.2×1015 molec. cm−2. We find high correlation (r = 0.68) between satellite- and ground-based data, but also that TROPOMI total columns underestimate ground-based observations for relatively large Pandora NO2 total columns, corresponding to episodes of relatively elevated pollution. This is expected because of the relatively large size of the TROPOMI ground pixel (3.5×7 km) and the a priori used in the retrieval compared to the relatively small field-of-view of the Pandora instrument. On the other hand, TROPOMI slightly overestimates (within the retrieval uncertainties) relatively small NO2 total columns. Replacing the coarse a priori NO2 profiles with high-resolution profiles from the CAMS chemical transport model improves the agreement between TROPOMI and Pandora total columns for episodes of NO2 enhancement. When only the low values of NO2 total columns or the whole dataset are taken into account, the mean bias slightly increases. The change in bias remains mostly within the uncertainties. We also analyse the consistency between satellite-based data and in situ NO2 surface concentrations measured at the Helsinki–Kumpula air quality station (located a few metres from the Pandora spectrometer). We find similar day-to-day variability between TROPOMI, Pandora and in situ measurements, with NO2 enhancements observed during the same days. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as a result of reduced emissions from traffic and industrial activities (as expected in urban sites). The TROPOMI NO2 maps reveal also spatial features, such as the main traffic ways and the airport area, as well as the effect of the prevailing south-west wind patterns. This is one of the first works in which TROPOMI NO2 retrievals are validated against ground-based observations and the results provide an early evaluation of their applicability for monitoring pollution levels in urban sites. Overall, TROPOMI retrievals are valuable to complement the ground-based air quality data (available with high temporal resolution) for describing the spatio-temporal variability of NO2, even in a relatively small city like Helsinki.

scholarly journals Towards understanding the variability in biospheric CO<sub>2</sub> fluxes: using FTIR spectrometry and a chemical transport model to investigate the sources and sinks of carbonyl sulfide and its link to CO<sub>2</sub>

2016 ◽  
Vol 16 (4) ◽  
pp. 2123-2138 ◽  
Author(s):  
Yuting Wang ◽  
Nicholas M. Deutscher ◽  
Mathias Palm ◽  
Thorsten Warneke ◽  
Justus Notholt ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Transport Model ◽  
Carbonyl Sulfide ◽  
Co2 Concentration ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Solar Absorption ◽  
Ftir Spectrometry ◽  
Carbon Dioxide Co2 ◽  
Biosphere Model

Abstract. Understanding carbon dioxide (CO2) biospheric processes is of great importance because the terrestrial exchange drives the seasonal and interannual variability of CO2 in the atmosphere. Atmospheric inversions based on CO2 concentration measurements alone can only determine net biosphere fluxes, but not differentiate between photosynthesis (uptake) and respiration (production). Carbonyl sulfide (OCS) could provide an important additional constraint: it is also taken up by plants during photosynthesis but not emitted during respiration, and therefore is a potential means to differentiate between these processes. Solar absorption Fourier Transform InfraRed (FTIR) spectrometry allows for the retrievals of the atmospheric concentrations of both CO2 and OCS from measured solar absorption spectra. Here, we investigate co-located and quasi-simultaneous FTIR measurements of OCS and CO2 performed at five selected sites located in the Northern Hemisphere. These measurements are compared to simulations of OCS and CO2 using a chemical transport model (GEOS-Chem). The coupled biospheric fluxes of OCS and CO2 from the simple biosphere model (SiB) are used in the study. The CO2 simulation with SiB fluxes agrees with the measurements well, while the OCS simulation reproduced a weaker drawdown than FTIR measurements at selected sites, and a smaller latitudinal gradient in the Northern Hemisphere during growing season when comparing with HIPPO (HIAPER Pole-to-Pole Observations) data spanning both hemispheres. An offset in the timing of the seasonal cycle minimum between SiB simulation and measurements is also seen. Using OCS as a photosynthesis proxy can help to understand how the biospheric processes are reproduced in models and to further understand the carbon cycle in the real world.

scholarly journals Assimilation of atmospheric CO2 observations from space can support national CO2 emission inventories

2021 ◽  
Author(s):  
Thomas Kaminski ◽  
Marko Scholze ◽  
Peter Rayner ◽  
Michael Voßbeck ◽  
Michael Buchwitz ◽  
...  
Keyword(s):  
Co2 Emission ◽  
Fossil Fuel ◽  
Greenhouse Gas Emission ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Additional Information ◽  
Emission Estimate ◽  
Carbon Dioxide Co2 ◽  
Assimilation System ◽  
Fossil Fuel Emissions

Abstract The Paris Agreement establishes a transparency framework for anthropogenic carbon dioxide (CO2) emissions. It's core component are inventory-based national greenhouse gas emission reports, which are complemented by independent estimates derived from atmospheric CO2 measurements combined with inverse modelling. It is, however, not known whether such a Monitoring and Verification Support (MVS) capacity is capable of constraining estimates of fossil-fuel emissions to an extent that is sufficient to provide valuable additional information. The CO2 Monitoring Mission (CO2M), planned as a constellation of satellites measuring column-integrated atmospheric CO2 concentration (XCO2), is expected to become a key component of such an MVS capacity. Here we provide a novel assessment of the potential of a comprehensive data assimilation system using simulated XCO2 and other observations to constrain fossil fuel CO2 emission estimates for an exemplary 1-week period in 2008. We find that CO2M enables useful weekly estimates of country-scale fossil fuel emissions independent of national inventories. When extrapolated from the weekly to the annual scale, uncertainties in emissions are comparable to uncertainties in inventories, so that estimates from inventories and from the MVS capacity can be used for mutual verification. We further demonstrate an alternative, synergistic mode of operation, with the purpose of delivering a best fossil fuel emission estimate. In this mode, the assimilation system uses not only XCO2 and the other data streams of the previous (verification) mode, but also the inventory information. Finally, we identify further steps towards an operational MVS capacity.

scholarly journals Towards understanding the variability in biospheric CO<sub>2</sub> fluxes: using FTIR spectrometry and a chemical transport model to investigate the sources and sinks of carbonyl sulfide and its link to CO<sub>2</sub>

2015 ◽  
Vol 15 (18) ◽  
pp. 26025-26065
Author(s):  
Y. Wang ◽  
N. M. Deutscher ◽  
M. Palm ◽  
T. Warneke ◽  
J. Notholt ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Transport Model ◽  
Carbonyl Sulfide ◽  
Co2 Concentration ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Solar Absorption ◽  
Ftir Spectrometry ◽  
Carbon Dioxide Co2 ◽  
Biosphere Model

Abstract. Understanding carbon dioxide (CO2) biospheric processes is of great importance because the terrestrial exchange drives the seasonal and inter-annual variability of CO2 in the atmosphere. Atmospheric inversions based on CO2 concentration measurements alone can only determine net biosphere fluxes, but not differentiate between photosynthesis (uptake) and respiration (production). Carbonyl sulfide (OCS) could provide an important additional constraint: it is also taken up by plants during photosynthesis but not emitted during respiration, and therefore is a potential mean to differentiate between these processes. Solar absorption Fourier Transform InfraRed (FTIR) spectrometry allows for the retrievals of the atmospheric concentrations of both CO2 and OCS from measured solar absorption spectra. Here, we investigate co-located and quasi-simultaneous FTIR measurements of OCS and CO2 performed at three selected sites located in the Northern Hemisphere. These measurements are compared to simulations of OCS and CO2 using a chemical transport model (GEOS-Chem). The OCS simulations are driven by different land biospheric fluxes to reproduce the seasonality of the measurements. Increasing the plant uptake of Kettle et al. (2002a) by a factor of three resulted in the best comparison with FTIR measurements. However, there are still discrepancies in the latitudinal distribution when comparing with HIPPO (HIAPER Pole-to-Pole Observations) data spanning both hemispheres. The coupled biospheric fluxes of OCS and CO2 from the simple biosphere model (SiB) are used in the study and compared to measurements. The CO2 simulation with SiB fluxes agrees with the measurements well, while the OCS simulation reproduced a weaker drawdown than FTIR measurements at selected sites, and a smaller latitudinal gradient in the Northern Hemisphere during growing season. An offset in the timing of the seasonal cycle minimum between SiB simulation and measurements is also seen. Using OCS as a photosynthesis proxy can help to understand how the biospheric processes are reproduced in models and to further understand the carbon cycle in the real world.

Spatial Variability of Physical Parameters and Processes in Two Field Soils

1991 ◽  
Vol 22 (5) ◽  
pp. 327-340 ◽  
Author(s):  
K. Høgh Jensen ◽  
J. C. Refsgaard
Keyword(s):  
Transport Model ◽  
Flow Direction ◽  
Measurement Data ◽  
Flow Simulation ◽  
Solute Concentration ◽  
Conclusive Evidence ◽  
Physical Parameters ◽  
Effective Parameters ◽  
The Mean ◽  
Tracer Experiments

A numerical analysis of solute transport in two spatially heterogeneous fields is carried out assuming that the fields are composed of ensembles of one-dimensional non-interacting soil columns, each column representing a possible soil profile in statistical terms. The basis for the analysis is the flow simulation described in Part II (Jensen and Refsgaard, this issue), which serves as input to a transport model based on the convection-dispersion equation. The simulations of the average and variation in solute concentration in planes perpendicular to the flow direction are compared to measurements obtained from tracer experiments carried out at the two fields. Due to the limited amount of measurement data, it is difficult to draw conclusive evidence of the simulations, but reliable simulations are obtained of the mean behaviour within the two fields. The concept of equivalent soil properties is also tested for the transport problem in heterogeneous soils. Based on effective parameters for the retention and hydraulic conductivity functions it is possible to predict the mean transport in the two experimental fields.

scholarly journals The Synergistic Effect of PM2.5 and CO2 Concentrations on Occupant Satisfaction and Work Productivity in a Meeting Room

2021 ◽  
Vol 18 (8) ◽  
pp. 4109
Author(s):  
Jindong Wu ◽  
Jiantao Weng ◽  
Bing Xia ◽  
Yujie Zhao ◽  
Qiuji Song
Keyword(s):  
Air Quality ◽  
Synergistic Effect ◽  
Work Productivity ◽  
Co2 Concentration ◽  
Human Beings ◽  
Meeting Room ◽  
Negative Effect ◽  
Carbon Dioxide Co2 ◽  
Occupant Satisfaction ◽  
Pm2.5 Concentration

High indoor air quality is crucial for the health of human beings. The purpose of this work is to analyze the synergistic effect of particulate matter 2.5 (PM2.5) and carbon dioxide (CO2) concentration on occupant satisfaction and work productivity. This study carried out a real-scale experiments in a meeting room with exposures of up to one hour. Indoor environment parameters, including air temperature, relative humidity, illuminance, and noise level, were controlled at a reasonable level. Twenty-nine young participants were participated in the experiments. Four mental tasks were conducted to quantitatively evaluate the work productivity of occupants and a questionnaire was used to access participants’ satisfaction. The Spearman correlation analysis and two-way analysis of variance were applied. It was found that the overall performance declined by 1% for every 10 μg/m3 increase in PM2.5 concentration. Moreover, for every 10% increase in dissatisfaction with air quality, productivity performance decreased by 1.1% or more. It should be noted that a high CO2 concentration (800 ppm) has a stronger negative effect on occupant satisfaction towards air quality than PM2.5 concentration in a non-ventilated room. In order to obtain optimal occupant satisfaction and work productivity, low concentrations of PM2.5 (<50 μg/m3) and CO2 (<700 ppm) are recommended.

scholarly journals Improved Visual Localization via Graph Filtering

2021 ◽  
Vol 7 (2) ◽  
pp. 20
Author(s):  
Carlos Lassance ◽  
Yasir Latif ◽  
Ravi Garg ◽  
Vincent Gripon ◽  
Ian Reid
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Visual Localization ◽  
Localization Accuracy ◽  
Additional Information ◽  
Feature Representations ◽  
The Mean ◽  
Classical Image ◽  
Vision Based Localization ◽  
Improve Reliability

Vision-based localization is the problem of inferring the pose of the camera given a single image. One commonly used approach relies on image retrieval where the query input is compared against a database of localized support examples and its pose is inferred with the help of the retrieved items. This assumes that images taken from the same places consist of the same landmarks and thus would have similar feature representations. These representations can learn to be robust to different variations in capture conditions like time of the day or weather. In this work, we introduce a framework which aims at enhancing the performance of such retrieval-based localization methods. It consists in taking into account additional information available, such as GPS coordinates or temporal proximity in the acquisition of the images. More precisely, our method consists in constructing a graph based on this additional information that is later used to improve reliability of the retrieval process by filtering the feature representations of support and/or query images. We show that the proposed method is able to significantly improve the localization accuracy on two large scale datasets, as well as the mean average precision in classical image retrieval scenarios.

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