CO2 Signal Research Articles

Reactive oxygen species signaling and stomatal movement in plant responses to drought stress and pathogen attack.

Stomata, the pores formed by a pair of guard cells, are the main gateways for water transpiration and photosynthetic CO2 exchange, as well as pathogen invasion in land plants. Guard cell movement is regulated by a combination of environmental factors, including water status, light, CO2 levels and pathogen attack, as well as endogenous signals, such as abscisic acid and apoplastic reactive oxygen species (ROS). Under abiotic and biotic stress conditions, extracellular ROS are mainly produced by plasma membrane-localized NADPH oxidases, whereas intracellular ROS are produced in multiple organelles. These ROS form a sophisticated cellular signaling network, with the accumulation of apoplastic ROS an early hallmark of stomatal movement. Here, we review recent progress in understanding the molecular mechanisms of the ROS signaling network, primarily during drought stress and pathogen attack. We summarize the roles of apoplastic ROS in regulating stomatal movement, ABA and CO2 signaling, and immunity responses. Finally, we discuss ROS accumulation and communication between organelles and cells. This information provides a conceptual framework for understanding how ROS signaling is integrated with various signaling pathways during plant responses to abiotic and biotic stress stimuli.

4D vertical seismic profile modeling of CO2 injection scenarios to evaluate field results at Aquistore

The Aquistore carbon capture and storage project (Saskatchewan, Canada) is currently employing 3D vertical seismic profiles to monitor CO2 injected in a geological reservoir at a depth of >3200 m. Distributed acoustic sensing has previously been shown to be an effective tool for imaging the CO2 plume at the research site, and this study presents a continued effort in characterizing its trajectory, location, and size via fluid flow simulations, fluid substitution calculations, and forward modeling of seismic data. Time-lapse synthetic seismic volumes corresponding to two injection quantities, 36 and 97 kt, are compared alongside distributed acoustic sensing field data with nearly identical acquisition geometries and processing flows. The modeling results support the interpretation, based on field data, that most of the CO2 signal originates from the lower Deadwood reservoir unit. However, it is apparent that more effort is required to predict the trajectory of the plume, and the seismic amplitude contrasts caused by fluid replacement.

The eMLR(C*) Method to Determine Decadal Changes in the Global Ocean Storage of Anthropogenic CO2

The determination of the decadal change in anthropogenic CO2 in the global ocean from repeat hydrographic surveys represents a formidable challenge, which we address here by introducing a seamless new method. This method builds on the extended multiple linear regression (eMLR) approach to identify the anthropogenic CO2 signal, but in order to improve the robustness of this method, we fit C∗ rather than dissolved inorganic carbon and use a probabilistic method for the selection of the predictors. In order to account for the multiyear nature of the surveys, we adjust all C∗ observations of a particular observing period to a common reference year by assuming a transient steady state. We finally use the eMLR models together with global gridded climatological distributions of the predictors to map the estimated change in anthropogenic CO2 to the global ocean. Testing this method with synthetic data generated from a hindcast simulation with an ocean model reveals that the method is able to reconstruct the change in anthropogenic CO2 with only a small global bias (<5%). Within ocean basins, the errors can be larger, mostly driven by changes in ocean circulation. Overall, we conclude from the model that the method has an accuracy of retrieving the column integrated change in anthropogenic CO2 of about ±10% at the scale of whole ocean basins. We expect that this uncertainty needs to be doubled to about ±20% when the change in anthropogenic CO2 is reconstructed from observations.

Evaluating Humidity and Sea Salt Disturbances on CO2 Flux Measurements

AbstractGlobal oceans are an important sink of atmospheric carbon dioxide (CO2). Therefore, understanding the air–sea flux of CO2 is a vital part in describing the global carbon balance. Eddy covariance (EC) measurements are often used to study CO2 fluxes from both land and ocean. Values of CO2 are usually measured with infrared absorption sensors, which at the same time measure water vapor. Studies have shown that the presence of water vapor fluctuations in the sampling air potentially results in erroneous CO2 flux measurements resulting from the cross sensitivity of the sensor. Here measured CO2 fluxes from both enclosed-path Li-Cor 7200 sensors and open-path Li-Cor 7500 instruments from an inland measurement site are compared with a marine site. Also, new quality control criteria based on a relative signal strength indicator (RSSI) are introduced. The sampling gas in one of the Li-Cor 7200 instruments was dried by means of a multitube diffusion dryer so that the water vapor fluxes were close to zero. With this setup the effect that cross sensitivity of the CO2 signal to water vapor can have on the CO2 fluxes was investigated. The dryer had no significant effect on the CO2 fluxes. The study tested the hypothesis that the cross-sensitivity effect is caused by hygroscopic particles such as sea salt by spraying a saline solution on the windows of the Li-Cor 7200 instruments during the inland field test. The results confirm earlier findings that sea salt contamination can affect CO2 fluxes significantly and that drying the sampling air for the gas analyzer is an effective method for reducing this signal contamination.

Gaussian-Accelerated Molecular Dynamics Modeling Leads to Identification of SLAC1 Anion Channel Residues for CO2 Signaling in Arabidopsis Guard Cell

Gaussian-Accelerated Molecular Dynamics Modeling Leads to Identification of SLAC1 Anion Channel Residues for CO2 Signaling in Arabidopsis Guard Cell

Automatic Quantitative Analysis of Human Respired Carbon Dioxide Waveform for Asthma and Non-Asthma Classification Using Support Vector Machine

Currently, carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> waveforms measured by capnography are used to estimate respiratory rate and end-tidal CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (EtCO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) in the clinic. However, the shape of the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> signal carries significant diagnostic information about the asthmatic condition. Previous studies have shown a strong correlation between various features that quantitatively characterize the shape of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> signal and are used to discriminate asthma from non-asthma using pulmonary function tests, but no reliable progress was made, and no translation into clinical practice has been achieved. Therefore, this paper reports a relatively simple signal processing algorithm for automatic differentiation of asthma and non-asthma. CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> signals were recorded from 30 non-asthmatic and 43 asthmatic patients. Each breath cycle was decomposed into subcycles, and features were computationally extracted. Thereafter, feature selection was performed using the area ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A_{z}$ </tex-math></inline-formula> ) under the receiver operating characteristics curve analysis. A classification was performed via a leave-one-out cross-validation procedure by employing a support vector machine. Our results show maximum screening capabilities for upward expiration ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AR_{1}$ </tex-math></inline-formula> ), downward inspiration ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AR_{2}$ </tex-math></inline-formula> ), and the sum of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AR_{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AR_{2}$ </tex-math></inline-formula> , with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A_{z}$ </tex-math></inline-formula> of 0.892, 0.803, and 0.793, respectively. The proposed method obtained an average accuracy of 94.52%, sensitivity of 97.67%, and specificity of 90% for discrimination of asthma and non-asthma. The proposed method allows for automatic classification of asthma and non-asthma condition by analyzing the shape of the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> waveform. The developed method may possibly be incorporated in real-time for assessment and management of the asthmatic conditions.

Recognition of leak CO 2 with wavelet analysis based on correlation monitoring between CO 2 and O 2 in atmosphere

Abstract It is necessary to monitor and identify CO2 leakage from geosequestration project. Some experiments were designed to test the feasibility of characterization and recognition method for CO2 leakage based on correlation analysis between atmospheric CO2 and O2. The monitoring results showed that atmospheric CO2 had a good linear relationship with O2, especially in the day time. The variations of CO2 and O2 were influenced by the weather condition. The results of emission experiments proved the capability of coupling monitoring atmospheric CO2 and O2 to recognize the abnormal CO2 signal due to leakage. Moreover, two parameters, ALF (apparent leakage flux) and ALFs (apparent leakage flux based on surplus variable) were used to distinguish CO2 leakage in real time. The monitoring results verified that it was feasible to identify CO2 leakage with these two parameters. Compared with the results after median filter, the abrupt leakage signal can be more easily recognized with that after wavelet filter. ALFs can be applied to estimate the elevated concentration due to leakage, which can also be predicted by interpolating with related variation of atmospheric O2. ALF reflects the relative leakage rate. Therefore, it is practicable to identify leakage event in real time with the method proposed in this paper.

Blue light and CO2 signals converge to regulate light-induced stomatal opening

Stomata regulate gas exchange between plants and atmosphere by integrating opening and closing signals. Stomata open in response to low CO2 concentrations to maximize photosynthesis in the light; however, the mechanisms that coordinate photosynthesis and stomatal conductance have yet to be identified. Here we identify and characterize CBC1/2 (CONVERGENCE OF BLUE LIGHT (BL) AND CO2 1/2), two kinases that link BL, a major component of photosynthetically active radiation (PAR), and the signals from low concentrations of CO2 in guard cells. CBC1/CBC2 redundantly stimulate stomatal opening by inhibition of S-type anion channels in response to both BL and low concentrations of CO2. CBC1/CBC2 function in the signaling pathways of phototropins and HT1 (HIGH LEAF TEMPERATURE 1). CBC1/CBC2 interact with and are phosphorylated by HT1. We propose that CBCs regulate stomatal aperture by integrating signals from BL and CO2 and act as the convergence site for signals from BL and low CO2.

Residential load management in an energy hub with heat pump water heater

Today, as a consequence of the growing installation of efficient technologies, such as combined heat and power (CHP) as a co-generation, the integration of electricity through grid supply, Photovoltaic (PV) and energy storage systems as an integrated network is attracting a lot of attention in smart grid applications. To model the interaction among electricity and natural gas, the energy hub framework is adopted to determine a modelling procedure for such multi-carrier energy systems. This paper presents a residential energy hub model for a smart home as a modified framework of conventional energy hubs in a smart grid with consideration of heat pump water heater, coordination of sources and carbon emissions. Therefore, this study is twofold; the first part optimizes the operation of the combined CHP, Photovoltaic and storage system under dynamic pricing. Since residential load management plays a key role in realizing household demand response programs in a smart grid, performing optimal load management in the proposed residential energy hub model is also studied in this paper. To achieve this, the optimization problem is extended by considering modelling of a heat pump water heater. It is also found out that CO2 signal could give customers an environmental motivation to shift or reduce loads during peak hours, as it would enable co-optimization of electricity consumption costs and carbon emissions reductions.

Heavy fuel oil pyrolysis and combustion: Kinetics and evolved gases investigated by TGA-FTIR

Heavy fuel oil (HFO) obtained from crude oil distillation is a widely used fuel in marine engines and power generation technologies. In the present study, the pyrolysis and combustion of a Saudi Arabian HFO in nitrogen and in air, respectively, were investigated using non-isothermal thermo-gravimetric analysis (TGA) coupled with a Fourier-transform infrared (FTIR) spectrometer. TG and DTG (differential thermo-gravimetry) were used for the kinetic analysis and to study the mass loss characteristics due to the thermal degradation of HFO at temperatures up to 1000°C and at various heating rates of 5, 10 and 20°C/min, in air and N2 atmospheres. FTIR analysis was then performed to study the composition of the evolved gases. The TG/DTG curves during HFO combustion show the presence of three distinct stages: the low temperature oxidation (LTO); fuel decomposition (FD); and high temperature oxidation (HTO) stages. The TG/DTG curves obtained during HFO pyrolysis show the presence of two devolatilization stages similar to that seen in the LTO stage of HFO combustion. Apart from this, the TG/DTG curves obtained during HFO combustion and pyrolysis differ significantly. Kinetic analysis was also performed using the distributed activation energy model, and the kinetic parameter (E) was determined for the different stages of HFO combustion and pyrolysis processes, yielding a good agreement with the measured TG profiles. FTIR analysis showed the signal of CO2 as approximately 50 times more compared to the other pollutant gases under combustion conditions. Under pyrolytic conditions, the signal intensity of alkane functional groups was the highest followed by alkenes. The TGA-FTIR results provide new insights into the overall HFO combustion processes, which can be used to improve combustor designs and control emissions.

Voltage Oscillations in a Polymer Electrolyte Membrane Fuel Cell with Pd-Pt/C and Pd/C Anodes.

Polymer electrolyte membrane fuel cells (PEMFC) fed with H2 contaminated with CO may exhibit oscillatory behavior when operated galvanostatically. The self‐organization of the anodic overpotential is interesting because it can be accompanied by an increase in the average performance. Herein we report experimental studies of voltage oscillations that emerge in a PEMFC equipped with a Pd/C or PdPt/C anode and fed with H2 contaminated with CO (100 ppm). We used on‐line mass spectrometry to investigate how the mass fragments associated with CO2 and CO (m/z 44 and 28, respectively) varied with the voltage oscillations. Overall, we observed that oscillations in the anodic overpotential are in phase with that of the CO and CO2 signals. This fact is consistent with an autonomous adsorption–oxidation cyclic process. For both anodes, it has been observed that, in general, an increase in current density implies an increase in oscillatory frequency. By using CO stripping, we also discuss how the onset of CO oxidation is related to the maximum overpotential reached during a cycle, whereas the minimum overpotential can be associated with the catalytic activity of the electrode for H2 oxidation.

Using Bayes Theorem to Quantify and Reduce Uncertainties when Monitoring Varying Marine Environments for Indications of a Leak

Monitoring the marine environment for leaks from geological storage projects is a challenge due to the variability of the environment and the extent of the area that migrating CO2 might seep through the seafloor. Due to the environmental risk associated leaks should not be allowed to continue undetected. There is also a cost issue since marine operations are expensive, so false alarms should be avoided. The main question is then: how large a deviation in the monitoring data should cause mobilization of confirmation and localization procedures? Here Baye's theorem and Bayesian decision theory is suggested as a tool for quantifying certainties and to implement costs for false positives (false alarms) and false negatives (undetected leaks) in the decision procedure. The procedure is exemplified using modeled natural CO2 content variability and the predicted CO2 signal from a simulated leak.

Differential Effects of Phosphatidylinositol 4-Kinase (PI4K) and 3-Kinase (PI3K) Inhibitors on Stomatal Responses to Environmental Signals.

Specific cellular components including products of phosphatidylinositol (PI) metabolism play an important role as signaling molecules in stomatal responses to environmental signals. In this study, pharmacological inhibitors of a set of cellular components, including PI4-kinase (PI4K) and PI3K, were used to investigate stomatal closure in response to CO2, darkness, and abscisic acid (ABA). Treatment with PAO, a specific inhibitor of PI4K, specifically inhibited the stomatal response to CO2 compared with that to darkness and ABA. In contrast, treatment with LY294002, a PI3K-specific inhibitor, specifically inhibited the stomatal response to darkness compared with that to CO2 and ABA. The specific inhibitory effects of PAO and LY294002 were also observed as changes in the spatial density of dot-like structures labeled by green fluorescent protein-tagged PATROL1, a protein that controls stomatal aperture possibly via regulation of H+-ATPase amount in guard cell plasma membranes. Our results suggest an important role for PI4K and PI3K in the CO2 and darkness signal transduction pathways, respectively, that mediate PATROL1 dynamics.

The effect of climate–carbon cycle feedbacks on emission metrics

The Climate–Carbon cycle Feedback (CCF) affects emission metric values. In the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change metric values for Global Warming Potentials (GWP) and Global Temperature Potentials (GTP) are reported both with and without CCF for non-CO2 climate forcers, while CCF is always included for CO2. The estimation of CCF for non-CO2 climate forcers in AR5 is based on a linear feedback analysis. This study compares that approach with an explicit approach that uses a temperature dependent carbon cycle model. The key difference in the CCF results for non-CO2 climate forcers is that, with the approach used in AR5, a fraction of the CO2 signal induced by non-CO2 forcers will persist in the atmosphere basically forever, while, with the approach based on an explicit carbon cycle model, the atmospheric CO2 signal induced by non-CO2 forcers eventually vanishes. The differences in metric values between the two model approaches are within ±10% for all well-mixed greenhouse gases when the time horizon is limited to 100 yr or less, for both GWP and GTP. However, for long time horizons, such as 500 yr, metric values are substantially lower with the explicit CCF model than with the linear feedback approach, up to 30% lower for GWP and up to 90% lower for GTP.

New Advances in Dial-Lidar-Based Remote Sensing of the Volcanic CO2 Flux

We report here on the results of a proof-of-concept study aimed at remotely sensing the volcanic CO2 flux using a Differential Adsorption lidar (DIAL-lidar). The observations we report on were conducted on June 2014 on Stromboli volcano, where our lidar (LIght Detection And Ranging) was used to scan the volcanic plume from ~ 3 km distance from the summit vents. The obtained results prove that a remotely operating lidar can resolve a volcanic CO2 signal of a few tens of ppm (in excess to background air) over km-long optical paths. We combine these results with independent estimates of plume transport speed (from processing of UV Camera images) to derive volcanic CO2 flux time-series of ≈16-33 minutes temporal resolution. Our lidar-based CO2 fluxes range from 1.8±0.5 to 32.1±8.0 kg/s, and constrain the daily averaged CO2 emissions from Stromboli at 8.3±2.1 to 18.1±4.5 kg/s (or 718-1565 tons/day). These inferred fluxes fall within the range of earlier observations at Stromboli. They also agree well with contemporaneous CO2 flux determinations (8.4-20.1 kg/s) obtained using a standard approach that combines Multi-GAS-based in-plume readings of the CO2/SO2 ratio (≈ 8) with UV-camera sensed SO2 fluxes (1.5-3.4 kg/s). We conclude that DIAL-lidars offer new prospects for safer (remote) instrumental observations of the volcanic CO2 flux.

The concept of thin film bulk acoustic resonators as selective CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; gas sensors

Abstract. Carbon dioxide (CO2) is a gas that well represents air quality in indoor environments as well as being an important greenhouse gas. However, the reliable and affordable sensing of environmental CO2 at room temperature, with techniques other than optical spectroscopy, remains an unsolved problem to this day. One major challenge for solid state sensors is the realisation of adequate selectivity, especially towards changing humidity. The thin film bulk acoustic resonator (FBAR) is a MEMS (Microelectromechanical systems) device that can not only detect gas-induced mass changes but also changes in the acoustic velocity and density of its layers. This multi-sensing provides a suitable platform for selective gas sensing. In this work we present studies done on polyaminosiloxane- and ethyl cellulose-functionalised FBARs regarding CO2 sensitivity, selectivity towards humidity, and stability. We demonstrate how CO2 and humidity signals can be separated and that CO2 can be sensed with a resolution of 50 ppm between 400 and 1000 ppm. Using the Mason model, we show how the acoustic velocity and density of an absorption layer can be determined and how changes in those parameters affect the resonance frequency shift. The understanding of these results ultimately presents a tool to theoretically separate any number of gas analytes.

A CO-based method to determine the regional biospheric signal in atmospheric CO2

Regional-scale inverse modeling of atmospheric carbon dioxide (CO2) holds promise to determine the net CO2 fluxes between the land biosphere and the atmosphere. This approach requires not only high fidelity of atmospheric transport and mixing, but also an accurate estimation of the contribution of the anthropogenic and background CO2 signals to isolate the biospheric CO2 signal from the atmospheric CO2 variations. Thus, uncertainties in any of these three components directly impact the quality of the biospheric flux inversion. Here, we present and evaluate a carbon monoxide (CO)-based method to reduce these uncertainties solely on the basis of co-located observations. To this end, we use simultaneous observations of CO2 and CO from a background observation site to determine the background mole fractions for both gases, and the regional anthropogenic component of CO together with an estimate of the anthropogenic CO/CO2 mole fraction ratio to determine the anthropogenic CO2 component. We apply this method to two sites of the CarboCount CH observation network on the Swiss Plateau, Beromünster and Lägern-Hochwacht, and use the high-altitude site Jungfraujoch as background for the year 2013. Since such a background site is not always available, we also explore the possibility to use observations from the sites themselves to derive the background. We contrast the method with the standard approach of isolating the biospheric CO2 component by subtracting the anthropogenic and background components simulated by an atmospheric transport model. These tests reveal superior results from the observation-based method with retrieved wintertime biospheric signals being small and having little variance. Both observation- and model-based methods have difficulty to explain observations from late-winter and springtime pollution events in 2013, when anomalously cold temperatures and northeasterly winds tended to bring highly CO-enriched air masses to Switzerland. The uncertainty of anthropogenic CO/CO2 emission ratios is currently the most important factor limiting the method. Further, our results highlight that care needs to be taken when the background component is determined from the site’s observations. Nonetheless, we find that future atmospheric carbon monitoring efforts would profit greatly from at least measuring CO alongside CO2.

Natural Variation in Arabidopsis Cvi-0 Accession Reveals an Important Role of MPK12 in Guard Cell CO2 Signaling.

Plant gas exchange is regulated by guard cells that form stomatal pores. Stomatal adjustments are crucial for plant survival; they regulate uptake of CO2 for photosynthesis, loss of water, and entrance of air pollutants such as ozone. We mapped ozone hypersensitivity, more open stomata, and stomatal CO2-insensitivity phenotypes of the Arabidopsis thaliana accession Cvi-0 to a single amino acid substitution in MITOGEN-ACTIVATED PROTEIN (MAP) KINASE 12 (MPK12). In parallel, we showed that stomatal CO2-insensitivity phenotypes of a mutant cis (CO2-insensitive) were caused by a deletion of MPK12. Lack of MPK12 impaired bicarbonate-induced activation of S-type anion channels. We demonstrated that MPK12 interacted with the protein kinase HIGH LEAF TEMPERATURE 1 (HT1)—a central node in guard cell CO2 signaling—and that MPK12 functions as an inhibitor of HT1. These data provide a new function for plant MPKs as protein kinase inhibitors and suggest a mechanism through which guard cell CO2 signaling controls plant water management.

A nonlinear merging method of analog and photon signals for CO2 detection in lower altitudes using differential absorption lidar

The current acquisition system of a lidar detects return signals in two modes (i.e., analog and photon counting); resulting in the lower (below 1500m) and upper (higher than 1100m) atmospheric parameters need analog and photon counting signal to retrieve, respectively. Hence, a lidar cannot obtain a continuous column of the concentrations of atmospheric components. For carbon cycle studies, the range-resolved concentration of atmospheric CO2 in the lower troposphere (below 1500m) is one of the most significant parameters that should be determined. This study proposes a novel gluing method that merges the CO2 signal detected by ground-based DIAL in the lower troposphere. Through simulation experiments, the best uniform approximation polynomial theorem is utilized to determine the transformation coefficient to correlate signals from the different modes perfectly. The experimental results (both simulation experiments and actual measurement of signals) show that the proposed method is suitable and feasible for merging data in the region below 1500m. Hence, the photon-counting signals whose SNRs are higher than those of the analog signals can be used to retrieve atmospheric parameters at an increased near range, facilitating atmospheric soundings using ground-based lidar in various fields.

The BErkeley Atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; Observation Network: initial evaluation

Abstract. With the majority of the world population residing in urban areas, attempts to monitor and mitigate greenhouse gas emissions must necessarily center on cities. However, existing carbon dioxide observation networks are ill-equipped to resolve the specific intra-city emission phenomena targeted by regulation. Here we describe the design and implementation of the BErkeley Atmospheric CO2 Observation Network (BEACO2N), a distributed CO2 monitoring instrument that utilizes low-cost technology to achieve unprecedented spatial density throughout and around the city of Oakland, California. We characterize the network in terms of four performance parameters – cost, reliability, precision, and systematic uncertainty – and find the BEACO2N approach to be sufficiently cost-effective and reliable while nonetheless providing high-quality atmospheric observations. First results from the initial installation successfully capture hourly, daily, and seasonal CO2 signals relevant to urban environments on spatial scales that cannot be accurately represented by atmospheric transport models alone, demonstrating the utility of high-resolution surface networks in urban greenhouse gas monitoring efforts.