Accurate measurements of CO2 mole fraction in the atmospheric surface layer by an affordable instrumentation

We aimed to assess the feasibility of an affordable instrumentation, based on a non-dispersive infrared analyser, to obtain atmospheric CO2 mole fraction data for background CO2 measurements from a flux tower site in southern Finland. The measurement period was November 2006 to December 2011. We describe the instrumentation, calibration, measurements and data processing and a comparison between two analysers, inter-comparisons with a flask sampling system and with reference gas cylinders and a comparison with an independent inversion model. The obtained accuracy was better than 0.5 ppm. The inter-comparisons showed discrepancies ranging from -0.3 ppm to 0.06 ppm between the measured and reference data. The comparison between the analyzers showed a 0.1 +/- 0.4 ppm difference. The trend and phase of the measured and simulated data agreed generally well and the bias of the simulation was 0.2 +/- 3.3 ppm. The study highlighted the importance of quantifying all sources of measurement uncertainty

An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations

We present a statistical framework to identify regional signals in station-based CO2 time series with minimal local influence. A curve-fitting function is first applied to the detrended time series to derive a harmonic describing the annual CO2 cycle. We then combine a polynomial fit to the data with a short-term residual filter to estimate the smoothed cycle and define a seasonally adjusted noise component, equal to 2 standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which surpass this +/- 2 sigma threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale atmospheric transport events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts.Peer reviewe

Neural Correlates of Auditory Perceptual Awareness under Informational Masking

Our ability to detect target sounds in complex acoustic backgrounds is often limited not by the ear's resolution, but by the brain's information-processing capacity. The neural mechanisms and loci of this “informational masking” are unknown. We combined magnetoencephalography with simultaneous behavioral measures in humans to investigate neural correlates of informational masking and auditory perceptual awareness in the auditory cortex. Cortical responses were sorted according to whether or not target sounds were detected by the listener in a complex, randomly varying multi-tone background known to produce informational masking. Detected target sounds elicited a prominent, long-latency response (50–250 ms), whereas undetected targets did not. In contrast, both detected and undetected targets produced equally robust auditory middle-latency, steady-state responses, presumably from the primary auditory cortex. These findings indicate that neural correlates of auditory awareness in informational masking emerge between early and late stages of processing within the auditory cortex

Une surveillance des flux de gaz à effet de serre et incertitudes associées avec un réseau instrument européen ICOS

International audienc

Evaluation of SF6_6, C2_2Cl4_4, and CO to approximate fossil fuel CO2_2 in the Northern Hemisphere using a chemistry transport model

International audienceThe distribution of the fossil fuel component in atmospheric CO$_2$ cannot be measured directly at a cheap cost. Could anthropogenic tracers with source patterns similar to fossil fuel CO$_2$ then be used for that purpose? Here we present and evaluate a methodology using surrogate tracers, CO, SF$_6$, and C$_2$Cl$_4$, to deduce fossil fuel CO$_2$. A three-dimensional atmospheric chemistry transport model is used to simulate the relationship between each tracer and fossil fuel CO$_2$. In summertime the regression slopes between fossil fuel CO$_2$ and surrogate tracers show large spatial variations for chemically active tracers ( CO and C$_2$Cl$_4$), although C$_2$Cl$_4$ presents less scatter than CO. At two tall tower sites in the United States ( WLEF, Wisconsin, and WITN, North Carolina), we found that in summertime the C$_2$Cl$_4$ (CO) versus fossil CO$_2$ slope is on average up to 15% ( 25%) higher than in winter. We show that for C$_2$Cl$_4$ this seasonal variation is due to OH oxidation. For CO the seasonal variation is due to both chemistry and mixing with nonanthropogenic CO sources. In wintertime the three surrogate tracers SF$_6$, C$_2$Cl$_4$, and CO are about equally as good indicators of the presence of fossil CO$_2$. However, our model strongly underestimates the variability of SF$_6$ at both towers, probably because of unaccounted for emissions. Hence poor knowledge of emission distribution hampers the use of SF$_6$ as a surrogate tracer. From a practical point of view we recommend the use of C$_2$Cl$_4$ as a proxy of fossil CO$_2$. We also recommend the use of tracers to separate fossil CO$_2$. Despite the fact that the uncertainty on the regression slope is on the order of 30%, the tracer approach is likely to have less bias than when letting one model with one inventory emission map calculate the fossil CO$_2$ distribution

The Potential of Low-Cost Tin-Oxide Sensors Combined with Machine Learning for Estimating Atmospheric CH₄ Variations around Background Concentration

International audienceContinued developments in instrumentation and modeling have driven progress in monitoring methane (CH4) emissions at a range of spatial scales. The sites that emit CH4 such as landfills, oil and gas extraction or storage infrastructure, intensive livestock farms account for a large share of global emissions, and need to be monitored on a continuous basis to verify the effectiveness of reductions policies. Low cost sensors are valuable to monitor methane (CH4) around such facilities because they can be deployed in a large number to sample atmospheric plumes and retrieve emission rates using dispersion models. Here we present two tests of three different versions of Figaro® TGS tin-oxide sensors for estimating CH4 concentrations variations, at levels similar to current atmospheric values, with a sought accuracy of 0.1 to 0.2 ppm. In the first test, we characterize the variation of the resistance of the tin-oxide semi-conducting sensors to controlled levels of CH4, H2O and CO in the laboratory, to analyze cross-sensitivities. In the second test, we reconstruct observed CH4 variations in a room, that ranged from 1.9 and 2.4 ppm during a three month experiment from observed time series of resistances and other variables. To do so, a machine learning model is trained against true CH4 recorded by a high precision instrument. The machine-learning model using 30% of the data for training reconstructs CH4 within the target accuracy of 0.1 ppm only if training variables are representative of conditions during the testing period. The model-derived sensitivities of the sensors resistance to H2O compared to CH4 are larger than those observed under controlled conditions, which deserves further characterization of all the factors influencing the resistance of the sensor

Reconstruction of high-frequency methane atmospheric concentration peaks from measurements using metal oxide low-cost sensors

International audienceDetecting and quantifying CH 4 gas emissions at industrial facilities is important goal for being able to reduce these emissions. The nature of CH 4 emissions through 'leaks' is episodic and spatially variable, making their monitoring a complex task, being partly addressed by atmospheric surveys with various types of instruments. Continuous records are preferable to snapshot surveys for monitoring a site, and one solution would be to deploy a permanent network of sensors. Deploying such a network with research-level instruments being expensive, low-cost and low-power sensors could be a good alternative. However, low cost entails usually lower accuracy and the existence of sensors drifts and cross-sensitivity to other gases and environmental parameters. Here we present four tests conducted with two types of Figaro TGS sensors on a laboratory experiment. The sensors were exposed to ambient air and peaks of CH 4 concentrations. We assembled four chambers, each containing one TGS sensor of each type. The first test consisted in comparing parametric and non-parametric models to reconstruct the CH 4 peaks signal from observations of the voltage variations of TGS sensors. The obtained relative accuracy is higher than 10% to reconstruct the maximum amplitude of peaks (RMSE ≤ 2 ppm). Polynomial regression and multilayer perceptron (MLP) models gave the highest performances for one type of sensor (TGS 2611C, RMSE = 0.9 ppm) and for the combination of two sensors (TGS 2611C + TGS 2611E, RMSE = 0.8 ppm) with a training set size of 70% of the total observations. In the second test, we compared the performance of the same models with a reduced training set. To reduce the size of the training set, we have employed a stratification of the data into clusters of peaks that allowed us to keep the same model performances with only 25% of the data to train the models. The third test consisted of detecting the effects of age in the sensors after six months of continuous measurements. We observed performance degradation through our models, of between 0.6 and 0.8 ppm. In the final test, we assessed the capability of a model to be transferred between chambers on the same type of sensor, and found that it is possible to transfer models only if the target range of variation of CH 4 is similar to the one on which the model was trained