CO2 Concentration Measurements Research Articles

Fast, furious, and gassy: Etna's explosive eruption from the mantle

The 3930 BP Fall Stratified (FS) eruption at Mt. Etna is a rare example of a highly explosive eruption of primitive (picritic) magma directly from the mantle. The eruption produced ash plumes up to an estimated 20 km height, leading to a volcanic explosivity index (VEI) 4 (subplinian). Given its volatile-rich and primitive nature, the FS magma may have ascended rapidly from great depths to avoid fractionation and mixing within the extensive plumbing system beneath Etna. To determine the pressures from which the FS magma derived, we perform rehomogenization experiments on melt inclusions hosted in Fo90–91 olivines to resorb shrinkage bubbles and determine the initial H2O and CO2 in the melt. With measured CO2 concentrations of up to 9600 ppm, volatile solubility models yield magma storage pressures of 630–800 MPa. These correspond to depths of 24–30 km, which are comparable to the seismologically estimated Moho. Therefore, the magma's high CO2 concentration must come from carbon in the mantle (likely from subducted carbonates), as opposed to assimilation of shallow (<10 km) crustal carbonates.Diffusion modeling of H2O and forsterite zonation profiles in clear, euhedral, and crystallographically oriented olivines indicates rapid ascent of magma directly from its source region to the surface. Forsterite profiles exhibit a narrow rim of growth zoning but no detectable diffusional zoning, reflecting maximum ascent times of 1–5 days. Eighteen measured H2O profiles result in remarkably uniform decompression rates of 0.47 MPa/s (95% confidence interval of 0.16–1.28 MPa/s), which is among the fastest measured for basaltic-intermediate magmas. These decompression rates indicate that the final stage of magma ascent over the region in which H2O degasses (between the surface and ∼ 15 km) occurred extremely fast at ∼ 17.5 m/s. This eruption may provide a link between primary magma composition and eruption intensity: we propose that the unusually explosive nature of this picritic eruption was driven by high H2O and CO2 concentrations, which led to continuously rapid ascent without stalling, all the way from the Moho.

Potential of 14C-based vs. ΔCO-based ΔffCO2 observations to estimate urban fossil fuel CO2 (ffCO2) emissions

Abstract. Atmospheric transport inversions are a powerful tool for independently estimating surface CO2 fluxes from atmospheric CO2 concentration measurements. However, additional tracers are needed to separate the fossil fuel CO2 (ffCO2) emissions from non-fossil CO2 fluxes. In this study, we focus on radiocarbon (14C), the most direct tracer of ffCO2, and the continuously measured surrogate tracer carbon monoxide (CO), which is co-emitted with ffCO2 during incomplete combustion. In the companion paper by Maier et al. (2024), we determined discrete 14C-based and continuous ΔCO-based estimates of the ffCO2 excess concentration (ΔffCO2) compared with a clean-air reference for the urban Heidelberg observation site in southwestern Germany. The ΔCO-based ΔffCO2 concentration was calculated by dividing the continuously measured ΔCO excess concentration by an average 14C-basedΔCO/ΔffCO2 ratio. Here, we use the CarboScope inversion framework adapted for the urban domain around Heidelberg to assess the potential of both types of ΔffCO2 observations to investigate ffCO2 emissions and their seasonal cycle. We find that, although they are more precise, 14C-based ΔffCO2 observations from almost 100 afternoon flask samples collected in the 2 years of 2019 and 2020 are not well suited for estimating robust ffCO2 emissions in the main footprint of this urban area, which has a very heterogeneous distribution of sources including several point sources. The benefit of the continuous ΔCO-based ΔffCO2 estimates is that they can be averaged to reduce the impact of individual hours with an inadequate model performance. We show that the weekly averaged ΔCO-based ΔffCO2 observations allow for a robust reconstruction of the seasonal cycle of the area source ffCO2 emissions from temporally flat a priori emissions. In particular, the distinct COVID-19 signal – with a steep drop in emissions in spring 2020 – is clearly present in these data-driven a posteriori results. Moreover, our top-down results show a shift in the seasonality of the area source ffCO2 emissions around Heidelberg in 2019 compared with the bottom-up estimates from the Netherlands Organization for Applied Scientific Research (TNO). This highlights the huge potential of ΔCO-based ΔffCO2 to validate bottom-up ffCO2 emissions at urban stations if the ΔCO/ΔffCO2 ratios can be determined without biases.

Assessment of Different Frameworks for Addressing Climate Change Impact on Crop Production and Water Requirement

Various methodologies are used to estimate the impact of changing climatic factors, such as precipitation, temperature, and solar radiation, on crop production and water demand. In this study, the changes in rice yield, water demand, and crop phenology were estimated with varying CO2 concentration and an ensemble of general circulation models (GCMs), using a decision support system for agrotechnology transfer (DSSAT), a crop growth model. The measured CO2 concentration of 400 ppm from the Keeling curve, was used as the default CO2 concentration to estimate yield, water demand, and phenology. These outputs, obtained with the default concentration, were compared with the results from climate change scenarios’ concentrations. Further, the outputs corresponding to the ensembled GCMs’ climate data were obtained, and the results were compared with the ensembled crop model outputs simulated with each GCM. The yield was found to increase with the increase in CO2 concentration up to a certain threshold, whereas water demand and phenology were observed to decrease with the increase in CO2 concentration. The two approaches of the ensemble technique to obtain final outputs from DSSAT results did not show a large difference in the predictions.

Integrating Internet of Things (IoT) Approach to Post-Occupancy Evaluation (POE): An Experimental At-the-Moment Occupant Comfort Control System

This paper describes an empirical experiment of Internet of Things (IoT)’s integration in the Post-Occupancy Evaluation (POE) process. The experiment aimed to trial a novel IoT approach to enabling building user responsiveness to prevalent IEQ for individualised comfort. The purpose is to provide a system that mitigates a common issue of centralised air conditioning that limits occupants’ control over their immediate environment. To achieve this, an IoT platform was developed with smart IEQ monitoring sensors and wearable devices and trialled with PhD researchers in a shared university workspace. The findings provided empirical evidence of IoT’s enhanced benefits to improving user control over their individual comfort and enabling positive energy behaviour in buildings. Specifically, the IoT system provided real-time insight into CO2 concentration data while enabling responsive occupant interaction with their immediate environment and at-the-moment mitigation actions. Outputs of the experiment showed that the perceptions of participants about the stuffiness of the air, productivity, and healthy environment were significantly better after taking the mitigation action compared to before. Also, we found a significant relationship between measured CO2 concentration readings and perceived air stuffiness (p = 0.004) and productivity (p = 0.006) and a non-significant relationship between CO2 concentration readings and perceived healthy environment (p = 0.058). Interestingly, we observed that irrespective of the similarities in recorded CO2 concentration readings being within acceptable ranges (632–712 ppm), the perception of air stuffiness significantly differed (p = 0.018) before and after the mitigation actions. The effectiveness of the developed IoT platform was evidenced as most of the participants found the process very easy to participate in with little interruptions to their work as little time was consumed. The results are useful in modifying approaches to building occupant comfort and energy behaviour in commercial and residential settings.

Integrating Internet of Things (IoT) Approach to Post-Occupancy Evaluation (POE): An Experimental At-the-Moment Occupant Comfort Control System

This paper describes an empirical experiment of Internet of Things (IoT)’s integration in the Post-Occupancy Evaluation (POE) process. The experiment aimed to trial a novel IoT approach to enabling building user responsiveness to prevalent IEQ for individualised comfort. The purpose is to provide a system that mitigates a common issue of centralised air conditioning that limits occupants’ control over their immediate environment. To achieve this, an IoT platform was developed with smart IEQ monitoring sensors and wearable devices and trialled with PhD researchers in a shared university workspace. The findings provided empirical evidence of IoT’s enhanced benefits to improving user control over their individual comfort and enabling positive energy behaviour in buildings. Specifically, the IoT system provided real-time insight into CO2 concentration data while enabling responsive occupant interaction with their immediate environment and at-the-moment mitigation actions. Outputs of the experiment showed that the perceptions of participants about the stuffiness of the air, productivity, and healthy environment were significantly better after taking the mitigation action compared to before. Also, we found a significant relationship between measured CO2 concentration readings and perceived air stuffiness (p = 0.004) and productivity (p = 0.006) and a non-significant relationship between CO2 concentration readings and perceived healthy environment (p = 0.058). Interestingly, we observed that irrespective of the similarities in recorded CO2 concentration readings being within acceptable ranges (632–712 ppm), the perception of air stuffiness significantly differed (p = 0.018) before and after the mitigation actions. The effectiveness of the developed IoT platform was evidenced as most of the participants found the process very easy to participate in with little interruptions to their work as little time was consumed. The results are useful in modifying approaches to building occupant comfort and energy behaviour in commercial and residential settings.

Surface plasmon resonance sensor of hollow microstructure fiber based on ZIF-8 thin film

An annular hollow microstructured fiber surface plasmon resonance (SPR) sensor based on ZIF-8 film is proposed for the measurement of CO2 concentration in air. Internal detection is adopted to enhance the spectral response to CO2 gas, and nano-gold pillars are embedded on both sides of the channel. The geometric parameters of the sensor are optimized to achieve an average sensitivity of 1.01 nm/% over the range of CO2 concentration from 0% to 100%. The results indicate that the sensor is a promising SPR gas sensor with great potential in CO2 detection applications.

CFD simulation study and experimental analysis of indoor air stratification in an unventilated classroom: a case study in Spain

CFD simulation study and experimental analysis of indoor air stratification in an unventilated classroom: a case study in Spain

In situ air change rate estimation from metabolic CO2 measurement. Summer experimental campaign in a single-family test house

Night ventilation is one of the most promising approaches for reducing the cooling-energy consumption of buildings while improving the summer thermal comfort of occupants. More accurate measurement of the air change rate (ACR) through window openings may help promote this approach. Indeed this is still an active research topic that requires well-controlled experimental data. This article aims to expand the application scope of the tracer gas methodology for dynamic estimation of ACR in residential buildings. Controlled inputs, such as human metabolic CO2 emissions and various window-opening configurations, are chosen to represent the actual use of a single-family house during the cooling season. The measurement dataset – including weather on site, wind on the walls, indoor air speed, temperature, relative humidity, CO2 concentration at various positions and at a time resolution of 1 min – is processed for sharing. Wind fluctuation on the external wall emphasizes the dynamic nature of ACR. Indoor temperature and CO2 concentration measurements showed a mean spatial heterogeneity of 1.0 °C and 625 ppm, respectively. Indoor CO2 levels are low (<2000 ppm), which is a limitation in the use of the tracer gas equation. Thus, the potential of the dataset for dynamic ACR estimation is discussed by introducing and analyzing two indicators. Finally, the ability of the dataset to compute dynamic ACR is demonstrated through deterministic solving of the tracer gas equation.

High CO2 exposure due to facemask wear is unlikely to impair cognition even in a warm environment after a long-term adaptation

There is a lack of consensus regarding the impact of elevated indoor CO2 exposure on cognition. COVID-19 provided an opportunity to study responses to long-term elevated CO2 exposure from facemask wear. Such an opportunity allows us to avoid exposing participants to elevated CO2 levels not typically experienced in real-life settings, potentially increasing the ecological validity of our experimental design. Further, the worsening warmness during summer necessitates studies and understanding of peoples’ cognition with combined stressors of both heat and CO2 accumulation. In this work, we recruited 60 college students to understand whether facemask wear elevates local CO2 levels and, if so, the extent to which it impacts cognition in warm conditions. Subjects remained in a controlled summer environmental room (temperature 31.5 °C, relative humidity 30 %) for 90 min with or without facemasks. Participants completed six cognitive tests in a random order and answered surveys using computer-based software. Ten experimental subjects had a second 30 min visit to measure CO2 concentration at the ala of the nose with and without surgical masks. The results show that wearing a surgical mask sharply increased CO2 concentration near the nose by 15,000 ppm. Our analysis showed that the experimental group with facemask wear did not exhibit significantly different cognition performance except for short-term memory which was higher instead of lower than the control group. Participants with facemask wear showed significantly lower risk-taking, possibly attributed to thermal discomfort. Nevertheless, no significance in cognition or decision-making was observed after controlling the familywise error rate using the Bonferroni correction. We hypothesize that the insignificant difference might be caused by adaptation to long-term wear and high CO2 exposure in daily life during COVID-19, which cannot be revealed in the studies prior to the pandemic.

Drainage effects on carbon budgets of degraded peatlands in the north of the Netherlands

Peatlands store vast amounts of carbon (C). However, land-use-driven drainage causes peat oxidation, resulting in CO2 emission. There is a growing need for ground-truthing CO2 emission and its potential drivers to better quantify long-term emission trends in peatlands. This will help improve National Inventory Reporting and ultimately aid the design and verification of mitigation measures. To investigate regional drivers of CO2 emission, we estimated C budgets using custom-made automated chamber systems measuring CO2 concentrations corrected for carbon export and import. Chamber systems were rotated among thirteen degraded peatland pastures in Friesland (the Netherlands). These peatlands varied in water table depth (WTD), drainage-irrigation management (fixed regulated ditch water level (DWL), subsurface irrigation, furrow irrigation, or dynamic regulated DWL), and soil moisture. We investigated (1) whether drainage-irrigation management and related hydrological drivers could explain variation in C budgets, (2) how nighttime ecosystem respiration (Reconight) related to hydrological drivers, and (3) how C budgets compared with estimates from Tier 1 and Tier 2 models regularly used in National Inventory Reporting. Deep-drained peatlands largely overlapped with C budgets from shallow-drained peatlands. The variation in C budgets could not be explained with drainage-irrigation measures or annual WTD, likely because of high variation between sites. Reconightincreased from 85 to 250 kg CO2 ha−1 day−1 as the WTD dropped from 0 to 50 cm across all sites. A deeper WTD had no apparent effect on Reconight, which could be explained by the unimodal relationship we found between Reconight and soil moisture. Finally, C budgets estimated by Tier 1 emission factors and Tier 2 national models mismatched the between-site and between-year variation found in chamber-based estimated NECBs. To conclude, our study showed that shallow WTDs greatly determine C budgets and that regional C budgets, which can be accurately measure with periodic automated chamber measurements, are instrumental for model validation.

Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO2 and CH4 in air

The pressing issue of atmospheric pollution has prompted the exploration of affordable methods for measuring and monitoring air contaminants as complementary techniques to standard methods, able to produce high-density data in time and space. The main challenge of this low-cost approach regards the in-field accuracy and reliability of the sensors. This study presents the development of low-cost stations for high-time resolution measurements of CO2 and CH4 concentrations calibrated via an in-field machine learning-based method. The calibration models were built based on measurements parallelly performed with the low-cost sensors and a CRDS analyzer for CO2 and CH4 as reference instrument, accounting for air temperature and relative humidity as external variables.To ensure versatility across locations, diversified datasets were collected, consisting of measurements performed in various environments and seasons. The calibration models, trained with 70 % for modeling, 15 % for validation, and 15 % for testing, demonstrated robustness with CO2 and CH4 predictions achieving R2 values from 0.8781 to 0.9827 and 0.7312 to 0.9410, and mean absolute errors ranging from 3.76 to 1.95 ppm and 0.03 to 0.01 ppm, for CO2 and CH4, respectively. These promising results pave the way for extending these stations to monitor additional air contaminants, like PM, NOx, and CO through the same calibration process, integrating them with remote data transmission modules to facilitate real-time access, control, and processing for end-users.

Sustained Reductions of Bay Area CO2 Emissions 2018-2022.

Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.

Application of portable multi-component gas analyzer to mineral exploration in semi-arid steppes of northern China: A case study from the Qinjiaying Ag–Pb–Zn prospect

Soil gases, particularly sulfur gases, have not been widely used in mineral exploration due to their high mobility and reactivity. However, our team developed a portable multi-component gas analyzer (PMGA) capable of on-site and real-time measurement of H2S, SO2, CH4, and CO2 concentrations in soil gas. This study showcases the applicability and effectiveness of PMGA in the geochemical exploration of concealed ore deposits in the Qinjiaying Ag–Pb prospect in semi-arid steppes. Comparison tests between different analyzers and periods were conducted in the Qinjiaying Ag–Pb prospect. The results showed that PMGA has high stability, consistency, and reproducibility. Then, soil gas geochemical surveys were carried out by applying PMGA along eight prospecting lines, with three prospecting lines passing through the known mineralized zone and the remaining five prospecting lines focusing on the covered areas. Three composite anomalies of H2S, SO2, CH4, and CO2 were identified. The No. 1 soil gas anomaly corresponds well to the known No. I mineralized zone. The No. 3 gas anomaly is characterized by large-scale and strong multi-gas anomalies, indicating the presence of great prospecting potentials. Finally, drilling verification revealed a medium-sized Ag–Pb–Zn deposit in the No. 3 gas anomaly. The results demonstrate that PMGA is a powerful tool for mineral exploration in the study area. This successful practice provides a paradigm for applying PMGA in mineral exploration in semi-arid steppes.

A non-destructive measuring device in the mid-infrared range for measuring the CO2 concentration in the headspace of food packaging

Carbon dioxide (CO2) is the most common gas used in modified atmosphere packaging (MAP) to protect packaged foods from spoilage and pathogenic microorganisms and thus extend the shelf life of food products. Non-destructive measurement of CO2 concentration of the packaging headspace is of great interest as it could be used at various stages of the value chain, from outgoing goods inspection to storage tests and possible monitoring of the modified atmosphere at retail. Therefore, the aim of this work was to develop a measuring device operating non-destructively on closed MAP trays. Absorption measurement in the mid-infrared range at four different wavelengths proved to be a suitable principle for determining the CO2 gas concentration in a closed packaging system: 4.26 μm (2347 cm−1) and 4.27 μm (2342 cm−1) in the range of the antisymmetric stretching vibration, as well as 4.45 μm (2247 cm−1) at the edge of the antisymmetric stretching vibration, and 3.95 μm (2532 cm−1) as a reference measurement outside the absorption band with a thermal infrared source. Measurement principle and setup - the measurement in the absorbing and non-absorbing range and the guiding of the thermal infrared (IR) emitter at a 45° angle through the corner of the tray - allows a measurement largely independent of tray shape, height, and packaged product. The measurement can also be used for pigmented and printed trays - except for carbon black pigments. Optical impairments of the packaging and labeled areas of the lidding film appeared to have the greatest influence on the measurement accuracy. The applicability of the measurement system was evaluated and successfully demonstrated using commercially available food packaging from a local supermarket by comparing the CO2 gas concentrations yielded with those obtained using a destructive measurement device.

Post-COVID breathlessness: a mathematical model of respiratory processing in the brain.

Breathlessness is among the most common post-COVID symptoms. In a considerable number of patients, severe breathlessness cannot be explained by peripheral organ impairment. Recent concepts have described how such persistent breathlessness could arise from dysfunctional processing of respiratory information in the brain. In this paper, we present a first quantitative and testable mathematical model of how processing of respiratory-related signals could lead to breathlessness perception. The model is based on recent theories that the brain holds an adaptive and dynamic internal representation of a respiratory state that is based on previous experiences and comprises gas exchange between environment, lung and tissue cells. Perceived breathlessness reflects the brain's estimate of this respiratory state signaling a potentially hazardous disequilibrium in gas exchange. The internal respiratory state evolves from the respiratory state of the last breath, is updated by a sensory measurement of CO2 concentration, and is dependent on the current activity context. To evaluate our model and thus test the assumed mechanism, we used data from an ongoing rebreathing experiment investigating breathlessness in patients with post-COVID without peripheral organ dysfunction (N = 5) and healthy control participants without complaints after COVID-19 (N = 5). Although the observed breathlessness patterns varied extensively between individual participants in the rebreathing experiment, our model shows good performance in replicating these individual, heterogeneous time courses. The model assumes the same underlying processes in the central nervous system in all individuals, i.e., also between patients and healthy control participants, and we hypothesize that differences in breathlessness are explained by different weighting and thus influence of these processes on the final percept. Our model could thus be applied in future studies to provide insight into where in the processing cascade of respiratory signals a deficit is located that leads to (post-COVID) breathlessness. A potential clinical application could be, e.g., the monitoring of effects of pulmonary rehabilitation on respiratory processing in the brain to improve the therapeutic strategies.

Improving the ventilation of a classroom to achieve heating energy savings and better indoor air quality

Indoor air quality and heating energy consumption are closely linked, especially in densely occupied spaces, as classrooms. In a classroom of Rennes University, a transient thermal simulation highlights the impact of ventilation on heating consumptions for different cases: (1) without controlled mechanical ventilation (CMV), (2) with a single flow CMV (SF CMV) of a regulatory flow of 18 m3.h−1. person−1 during periods of occupancy, and (3) a double flow CMV (DF CMV) whose flow is during periods of occupancy either equal to 18 m3.h−1. person−1 or (4) to 35 m3.h−1.person−1. The infiltration flowrate was evaluated experimentally using CO2 concentration measurements in the first case, which corresponds to the actual situation. In the normal use of the classroom with 30 persons inside, CO2 concentration exceeds 5000 ppm and the heating energy consumption is equal to 52 kWh.m−2.year−1. In case (2) and (3), CO2 concentration has a maximum level of 1350 ppm and the energy consumption is equal to 65 and 53 kWh.m−2.year−1, respectively. In case (4), the maximum CO2 concentration stays in the range 800-900 ppm without excessive heating consumption (58 kWh.m−2.year−1). Finally, by limiting the fresh air flowrate to 18 m3.h−1.person−1 in DF CMV and coupling it to a façade insulation (extra Case 5), the heating consumption is reduced to 32 kWh.m−2.year−1. These results are valuable within the context of the upcoming energy renovation strategy of the campus. Practical application In most schools, in France and elsewhere, insufficient ventilation flowrates are often observed compared to regulatory requirements. If compliance with ventilation standards and the subsequent revision of CMV installations are necessary to ensure the health of users, it is important to evaluate the energy cost of these renovations, and to identify the best choice to reduce heating consumptions. Our contribution is to compare different renovation choices in terms of two indicators: energy consumption and indoor air quality evaluated using CO2 concentration.

Combined leaf gas-exchange system for model assessment.

Leaf gas-exchange measurements are useful in assessing plant environmental responses. However, uncertainties in the leaf gas-exchange model potentially limit its application. The main challenge in the model-dependent calculations is to detect violations of assumptions. Here, we developed a system that integrates into one instrument the direct measurement of leaf intercellular CO2 concentration and the standard open-flow (OF) and novel open-diffusion (OD) systems for flux measurement. In the OD system, a gas-permeable membrane between the leaf ambient air and outside air creates CO2 and H2O differentials, rather than the air flow in the OF chamber. We measured hypostomatous and amphistomatous leaves of several species with different photosynthetic capacities [sunflower (Helianthus annuus), grape (Vitis vinifera), lemon (Citrus limon), and cherry (Prunus avium)]. The CO2 and H2O differentials in the OD system strictly depend on the flux measured by the OF system. The lower permeability of the membrane resulted in a larger differential per flux, indicating that the OD system can increase the resolution for a small flux. An analysis of the conductance model along with observations suggested that cuticle and leaf intercellular conductances and the unsaturation of leaf humidity contributed to discrepancies between the direct measurement and standard calculation. The combined system developed here provides an opportunity to address these overlooked concepts in leaf gas exchange.

Measurement uncertainty and behaviour analysis of indoor air quality variables of thermal zones of an in-use mechanically ventilated building

Most monitoring systems and models in buildings assume homogeneous measurements of indoor variables such as temperature, relative humidity (RH), CO2 concentration or water vapour content (Pv) throughout a thermal zone. However, these indoor variables may have different values within a thermal zone, depending on the physical location of the sensor. This spatial variability, which is not an error per se, depends on factors that cannot be controlled or eliminated, mainly due to air stratification. Consequently, if a hypothetical homogeneous value of the variable is assumed, the measurement may contain random errors. In this research, we use a statistical methodology to analyse and quantify the Overall Uncertainty (UX) when measuring these indoor variables. The UX comprises the inherent inaccuracies of the monitoring system, indicated as Systematic Uncertainty (UX(s)), and the random errors associated with the spatial variability of the variables, evaluated as Spatial Uncertainty (UX(sp)). We have found that the UX(sp) of the temperature and RH measurements are considerably higher than their UX(s). However, the spatial variabilities of the CO2 concentration and Pv measurements are similar to the systematic uncertainty. Furthermore, we have compared the indoor and outdoor variable measurements. Spearman's correlation coefficient between the indoor and outdoor Pv indicates a strong positive correlation. This, together with the uncertainty analysis, confirms that the Pv (or RH) is not a suitable variable to detect presence in mechanically ventilated buildings. Conversely, as expected, there is no correlation between the indoor and outdoor CO2 concentrations, confirming the advantages of this variable for occupancy detection.

Inverse problem for retrieving greenhouse gas fluxes at the non-uniform underlying surface from measurements of their concentrations at several levels

The study focuses on the formulation, analysis, and solution of the remote sensing inverse problem to retrieve surface carbon dioxide (CO2) fluxes from measurements of CO2 concentrations at different levels within the atmospheric boundary layer. A three-dimensional hydrodynamic model of turbulent greenhouse gas (GHG) transport was used as a forward model to link the surface GHG fluxes to the drone observations of GHG concentrations. The 3D model provides a GHG concentration distribution by solving the diffusion-advection equation using information on wind speed, its direction, and turbulent exchange coefficients. The surface GHG fluxes are considered as a boundary condition. The spatial distributions of wind speed and turbulence coefficient “for a moment in time” are computed from the relaxation problem for the averaged Navier-Stokes and continuity equations, using a 1.5 order closure scheme (E-ω model). The inverse problem is to retrieve a surface GHG flux by minimizing the difference between the measured and modelled concentrations at several levels. The algorithm was applied to estimate CO2 fluxes over a non-uniform forest canopy at the Roshny-Chu experimental site in the foothills of the Greater Caucasus (Chechen Republic). To test the forward numerical problem, data on surface topography, vegetation height and density, spatial distribution of photosynthetically active solar radiation, as well as data on plant photosynthesis and soil CO2 fluxes were used.

Quantitative, multi-species gas sensing using broadband terahertz time-domain spectroscopy

The broadband terahertz wave, with its correspondence to the fingerprint spectra of gas molecules and relatively high transmittance through smoke, aerosol, and combustion environments, bears great potential for gas detection and combustion diagnostics. While access to rotational spectral fingerprints in the terahertz region provides opportunities for species-selective diagnostics with minimized background and cross interference, few studies have been devoted to direct, quantitative, and simultaneous analysis of multiple components exploiting the terahertz region. In this work, we achieve quantitative measurements of CO, NH3 and H2O gas concentrations at standard temperatures and pressures over a bandwidth of 1 THz, using direct absorption spectrum from femtosecond-laser-based terahertz time-domain spectroscopy. Spectral fitting of the fully resolved rotational lines yields good precision and accuracy with validation against calibrated mixtures. The estimated detection limits of the multi-species sensing system are 250 ppm m, 7 ppm m and 4 ppm m for CO, NH3 and H2O, respectively. The demonstration of quantitative, multi-species gas sensing indicates the feasibility and practical value of using broadband terahertz absorption spectroscopy for real-time, quantitative analysis and speciation of multicomponent gases in complicated practical environments such as combustion and multi-phase flows.