CO2 Concentration Measurements Research Articles

Carbfix 2: A transport model of long-term CO2 and H2S injection into basaltic rocks at Hellisheidi, SW-Iceland

The Carbfix 2 project was developed to demonstrate the cost effectiveness and technical maturity of CO2 and H2S direct capture from emission sources and permanent geological storage by in-situ mineralization in basalts. CO2 and H2S emissions of the Hellisheiði Geothermal Power Plant are separated from the other non condensable gases and captured by water dissolution. It is then transported to an injection well where it is co-injected with geothermal brine from the power station. The injection takes place in the Húsmúli re-injection area also known as the Carbfix demonstration site and has been an integral part of the operations of the power station since 2014. Field scale three-dimensional transport models of the injection at Húsmúli are developed to characterize the storage reservoir and constrain the flow paths between the Carbfix re-injection site and the nearby Skarðsmýrarfjall production zone. The model shows a good match to the tracer recovery and enthalpy from the monitoring wells and suggests a strong structural control at Húsmúli associated with faults acting as preferential pathways for the fluid. Implementation of boron, calcium, CO2, and H2S as solutes in the transport model and comparison between the modelled and the measured concentrations in the samples from the monitoring wells allows to assess the conservative and reactive behavior of each solute. By comparing expected (modelled) and measured CO2 concentrations we can estimate the fraction of dissolved gas mineralization between the injection well and monitoring wells. The model confirms values recorded in the literature of a mineralization degree in excess of 70% for the well closest to the injection and 95% for the furthest well. This suggests that non-reactive simulation schemes can be used to effectively monitor in-situ mineralization, CO2 containment and long term storage security of the gelogical reservoir. Modelling the long-term injection of fluids at Húsmúli suggests a drop in electricity generation from the closest well to the injection zone by 2040 and provides a theoretical maximum estimate of the storage capacity of the reservoir of 300 megatons of CO2 by 2050 if 10% of the space available is filled.

Monitoring CO2 storage using well-casing source electromagnetics

Successful geologic storage of CO2, whether in a carbon capture, utilization, and storage process or as part of enhanced oil recovery (EOR) operations, requires careful monitoring of the concentration of CO2 within the reservoir. Because injected CO2 is more electrically resistive than the in-situ brines and pore fluids in most storage sites, electromagnetic (EM) methods are well suited to map the distribution of injected CO2 over time. In particular, utilizing existing metallic well casings as electrodes to channel current to formation depth and measuring the resulting electrical and magnetic fields at the surface is an effective technique to image the deep and thin reservoirs of interest. This paper describes the theory behind the well-casing source EM method and discusses a series of field surveys carried out over a three-year period using the method to measure CO2 concentration at an EOR site in West Hastings, Texas. The surveys identify resistive zones that agree with the injection locations and geologic structure of the reservoir and show an increase in reservoir resistivity related to CO2 concentration between the initial field status and the middle survey. However, they also show an unexpected decrease in the resistivity within the reservoir between the last two surveys, which may be due to changes in survey parameters or CO2 injection patterns during that interval.

Carbon Monoxide Emissions from the Washington, DC, and Baltimore Metropolitan Area: Recent Trend and COVID-19 Anomaly.

We analyze airborne measurements of atmospheric CO concentration from 70 flights conducted over six years (2015-2020) using an inverse model to quantify the CO emissions from the Washington, DC, and Baltimore metropolitan areas. We found that CO emissions have been declining in the area at a rate of ≈-4.5 % a-1 since 2015 or ≈-3.1 % a-1 since 2016. In addition, we found that CO emissions show a "Sunday" effect, with emissions being lower, on average, than for the rest of the week and that the seasonal cycle is no larger than 16 %. Our results also show that the trend derived from the NEI agrees well with the observed trend, but that NEI daytime-adjusted emissions are ≈50 % larger than our estimated emissions. In 2020, measurements collected during the shutdown in activity related to the COVID-19 pandemic indicate a significant drop in CO emissions of 16 % relative to the expected emissions trend from the previous years, or 23 % relative to the mean of 2016 to February 2020. Our results also indicate a larger reduction in April than in May. Last, we show that this reduction in CO emissions was driven mainly by a reduction in traffic.

Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment.

The Paris metropolitan area, the largest urban region in the European Union, has experienced two national COVID-19 confinements in 2020 with different levels of restrictions on mobility and economic activity, which caused reductions in CO2 emissions. To quantify the timing and magnitude of daily emission reductions during the two lockdowns, we used continuous atmospheric CO2 monitoring, a new high-resolution near-real-time emission inventory, and an atmospheric Bayesian inverse model. The atmospheric inversion estimated the changes in fossil fuel CO2 emissions over the Greater Paris region during the two lockdowns, in comparison with the same periods in 2018 and 2019. It shows decreases by 42-53% during the first lockdown with stringent measures and by only 20% during the second lockdown when traffic reduction was weaker. Both lockdown emission reductions are mainly due to decreases in traffic. These results are consistent with independent estimates based on activity data made by the city environmental agency. We also show that unusual persistent anticyclonic weather patterns with north-easterly winds that prevailed at the start of the first lockdown period contributed a substantial drop in measured CO2 concentration enhancements over Paris, superimposed on the reduction of urban CO2 emissions. We conclude that atmospheric CO2 monitoring makes it possible to identify significant emission changes (>20%) at subannual time scales over an urban region.

Subjective and objective sensory assessments of indoor air quality in college dormitories in Nanjing

Indoor air quality (IAQ) is closely related to human health and work efficiency. The use of either subjective or objective assessments alone cannot adequately reflect IAQ and thus, is more reasonable to assess IAQ by combining them. In this study, the IAQ of 10 college dormitories was investigated using two subjective methods: field and laboratory sensory assessments, combined with objective measurements of CO2, formaldehyde, and total volatile organic compound (TVOC) concentrations. Two typical conditions, occupied and unoccupied, were studied. The results showed that 60% of dormitories under occupied conditions exhibited subjectively unacceptable odor. Further, 90% and 10% of dormitories failed to meet the objective assessments of CO2 and TVOC. Similarly, 80% of dormitories exhibited subjectively unacceptable odor under unoccupied conditions, whereas only 10% of dormitories failed to meet the objective assessment of TVOC concentration. Significant differences were observed between different IAQ assessment approaches, proving the necessity of combined objective and subjective methods. The difference between field and laboratory subjective odor assessments was significantly correlated with the field audiovisual environment dissatisfaction, indicating that differences in the audiovisual environment affected odor assessments. Furthermore, the adverse symptoms experienced by residents were correlated with the results of the laboratory odor assessments, indicating that odor may be an important indicator of the adverse effects of IAQ on human health.

Transient Effects Caused by Gas Depletion during Carbon Dioxide Electroreduction.

The novel use of carbon dioxide (CO2) electroreduction to generate carbon-based products which do not contribute to the greenhouse effect has promoted the vision of carbon dioxide as a renewable feedstock for future clean fuel production. Depending on the material choice for the electrocatalysis, a certain variety of products is expected from the carbon dioxide reduction reaction (CO2RR). However, as the CO2 concentration in areas close to the working electrode (relative to the diffusive boundary layer) decreases as it is being consumed and transformed into other products, the generation of H2 is favored to the detriment of CO2 electroreduction. Therefore, the extent to which H2 is produced can be used as a metric to evaluate the efficiency of CO2RR. This article proposes a model that accounts for the modes in which aqueous gas depletion evolves over time and affects the long-term CO2 electroreduction and the corresponding pH evolution near the electrode's surface. For the latter, two main contributions are distinguished: gas depletion due to CO2 consumption and ion generation in areas close to the electrocatalyst surface. pH is then suggested as an accurate and indirect means to measure CO2 concentration in a liquid electrolyte. We conclude that CO2 depletion causes a strong decay in the electrochemical reaction efficiency. In the end, we discuss several methods which may delay the onset of the adverse effects caused by gas depletion, such as the utilization of pulsed electroreduction, cycling the applied current to electrodes on and off periodically.

Calculation of Intake Oxygen Concentration through Intake CO2 Measurement and Evaluation of Its Effect on Nitrogen Oxide Prediction Accuracy in a Heavy-Duty Diesel Engine

A new procedure, based on measurement of intake CO2 concentration and ambient humidity was developed and assessed in this study for different diesel engines in order to evaluate the oxygen concentration in the intake manifold. Steady-state and transient datasets were used for this purpose. The method is very fast to implement since it does not require any tuning procedure and it involves just one engine-related input quantity. Moreover, its accuracy is very high since it was found that the absolute error between the measured and predicted intake O2 levels is in the ±0.15% range. The method was applied to verify the performance of a previously developed NOx model under transient operating conditions. This model had previously been adopted by the authors during the IMPERIUM H2020 EU project to set up a model-based controller for a heavy-duty diesel engine. The performance of the NOx model was evaluated considering two cases in which the intake O2 concentration is either derived from engine-control unit sub-models or from the newly developed method. It was found that a significant improvement in NOx model accuracy is obtained in the latter case, and this allowed the previously developed NOx model to be further validated under transient operating conditions.

Measurement of Trace CO2 Concentration with Open-path TDLAS-WMS Technology

Measurement of Trace CO2 Concentration with Open-path TDLAS-WMS Technology

Development of SHACS: Progress on Channel-1 Prototype Demonstrator

Finding solutions to the continuous concerns over climate change has been of increased interest in recent times with efforts targeted at mitigating its effects. Satellite observations and in situ terrestrial networks play key roles in the understanding and management of the problem. Whilst detecting carbon dioxide (CO₂) optically is relatively straightforward, and has been achieved with small satellites, accurate quantitative mapping of CO₂ requires very high precision (~1 ppm uncertainty or better) measurements of gas concentration. This is usually achieved through identifying CO₂ by its spectral absorption bands at 1.56-1.62 and 1.92-2.06 μm wavelength by using high-resolution spectrometers (e.g., 0.27 cm⁻¹ resolution at a signal-to-noise ratio (SNR) of >300:1). This normally requires high performance, large and complex instruments whose high cost, mass, volume, and power requirements preclude their use on small satellites. In this article, we describe the progress made in the design and development of a compact precision spatial heterodyne atmospheric carbon dioxide spectrometer (SHACS), which utilizes the spatial heterodyne spectroscopy (SHS) technique to form a robust, compact, no-moving-part Fourier transform spectroscopy (FTS). We also present the developmental stage and terrestrial atmospheric CO₂ testing of a single channel (1.6 μm) of SHACS instrument. This instrument achieves a high spectral resolution of 0.22 cm⁻¹ at a high SNR of >900:1 and can fit into a micro-satellite platform. With this performance, high-quality measurements of atmospheric CO₂ concentration with an average measurement precision of 1.27 ppm have been achieved.

Study on the dynamic natural ventilation rates of occupied residence

The residential natural ventilation rates have a significant impact on indoor thermal comfort and air quality and building energy consumption. The characteristics of the indoor-outdoor temperature difference and wind pressure change over time, as well as the occupants' window opening behavior and the use of HVAC systems, resulting in the residential air change rate being dynamic with time. Many previous studies of residential ventilation measured the steady-state air change rates, which does not reflect the actual dynamic characteristics. In this study, a field measurement was conducted in a bedroom of one natural ventilation residential building in Beijing with continuously monitoring the CO2 concentration, indoor air temperature, and outdoor meteorological parameters for one year. Using the CO2 released by occupants as a tracer gas, the extended Kalman filter based on the Transient Mass Balance Equation (TMBE) was adopted to calculate the dynamic air change rate. This method can effectively filter the CO2 concentration measurement noise. The trend of air change rate with each influencing factor was analyzed. This study is expected to lay the foundation for future studies of dynamic air change rates in residential buildings.

CO2 emission rates from sedentary subjects under controlled laboratory conditions

We determined carbon dioxide (CO2) emission rates from sedentary subjects performing light work on tablets or smartphones in controlled chamber exposures. Five groups, each consisting of four people (four groups with two females and two males and one with three males and one female), stayed in a 22.5 m3 stainless steel chamber under different environmental conditions for 3 h in the morning and then 2,5 h in the afternoon after having a light lunch. Three groups consisted of young adults (college students), one of seniors, and one of teenagers. The chamber was ventilated with outdoor air at 3.2 h−1 (per person rate was 5 L/s). The CO2 emission rates per person were calculated using a single-zone mass-balance equation and the measured CO2 concentration once steady state had been reached. Per person emission rates varied between 14.1 and 16.8 L/h in the morning and 15.9–17.8 L/h in the afternoon; higher levels in the afternoon were probably caused by the increased metabolism from diet-induced thermogenesis (DIT). Emission rates were higher with increased temperature when the participants felt warm, but did not change with increased relative humidity or ozone concentration. They differed to some extent from those estimated using ISO 8996 and ASTM DS 6245, but were similar to published measured CO2 emission rates. The present results require confirmation with more people and measurements of CO2 emission rates using the calorimetric method.

Predicting thermal satisfaction as a function of indoor CO[formula omitted] levels: Bayesian modelling of new field data

In the pursuit of mitigating the performance gap between model-predicted thermal comfort and measurements of perceived thermal comfort, recent studies have investigated the multi-domain nature of thermal comfort and IEQ. This paper presents an update to a recent work that applied Bayesian logistic regression to examine possible independent correlations between perceived thermal comfort of building occupants and metrics of indoor environmental quality (IEQ) such as CO2 concentrations. The prior work made use of the COPE dataset, a 20-year-old field database of objective and subjective IEQ measurements collected from approximately 800 occupants of open-plan offices in large Canadian and US cities. This study updates that work with a new IEQ field study of 141 workstations carried out at the University of British Columbia across 2019 and 2020. In addition, a new, hierarchical (multi-level) Bayesian logistic regression model is formulated and applied. The new results not only increase the credibility of observing measurements of CO2 concentrations to improve predictions of thermal satisfaction in the original COPE dataset, but that the credibility of this evidence is strengthened on the addition of the new data. At indoor temperatures of 23.5 °C, the probability of an occupant feeling thermally satisfied at measured CO2 levels of 550 ppm was 0.62 [0.54 - 0.69, 95% CrI]. This decreased to 0.28 [0.17-0.42, 95% CrI] at 750 ppm.This study does not suggest these observations are generalizable outside the COPE and UBC datasets, nor does it suggest that CO2 must be affecting occupants physiologically or psychologically to cause these observations. However, it is suggested that CO2 levels may remain overlooked as a contextual, or latent variable for the real-time prediction of perceived thermal comfort in buildings. For instance, prior research has observed a relationship between indoor CO2 levels and perceived air quality as well as a relationship between multiple perceptual factors of IEQ, such as air quality and thermal comfort. If the findings of this paper continue to be found in future studies, measurements of CO2 concentrations may be used to improve the accuracy of thermal comfort prediction models. The core recommendation for future work is therefore to expand measurements in future thermal comfort field studies to include, at least, measurements of indoor CO2 levels. A greater dataset is needed to determine whether the findings of this study are possibly universally applicable, and/or whether knowledge of indoor CO2 concentrations may improve personalized models of thermal comfort that are building- and context-specific.

Molecular Detection of Airborne Mycobacterium tuberculosis in South African High Schools.

Rationale: South African adolescents carry a high tuberculosis disease burden. It is not known if schools are high-risk settings for Mycobacterium tuberculosis (MTB) transmission. Objectives: To detect airborne MTB genomic DNA in classrooms. Methods: We studied 72 classrooms occupied by 2,262 students in two South African schools. High-volume air filtration was performed for median 40 (interquartile range [IQR], 35-54) minutes and assayed by droplet digital PCR (ddPCR)-targeting MTB region of difference 9 (RD9), with concurrent CO2 concentration measurement. Classroom data were benchmarked against public health clinics. Students who consented to individual tuberculosis screening completed a questionnaire and sputum collection (Xpert MTB/RIF Ultra) if symptom positive. Poisson statistics were used for MTB RD9 copy quantification. Measurements and Main Results: ddPCR assays were positive in 13/72 (18.1%) classrooms and 4/39 (10.3%) clinic measurements (P = 0.276). Median ambient CO2 concentration was 886 (IQR, 747-1223) ppm in classrooms versus 490 (IQR, 405-587) ppm in clinics (P < 0.001). Average airborne concentration of MTB RD9 was 3.61 copies per 180,000 liters in classrooms versus 1.74 copies per 180,000 liters in clinics (P = 0.280). Across all classrooms, the average risk of an occupant inhaling one MTB RD9 copy was estimated as 0.71% during one standard lesson of 35 minutes. Among 1,836/2,262 (81.2%) students who consented to screening, 21/90 (23.3%) symptomatic students produced a sputum sample, of which one was Xpert MTB/RIF Ultra positive. Conclusions: Airborne MTB genomic DNA was detected frequently in high school classrooms. Instantaneous risk of classroom exposure was similar to the risk in public health clinics.

Correlation of Respiratory Aerosols and Metabolic Carbon Dioxide

Respiratory aerosols from breathing and talking are an important transmission route for viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Previous studies have found that particles with diameters ranging from 10 nm to 145 μm are produced from different regions in the respiratory system and especially smaller particles can remain airborne for long periods while carrying viral RNA. We present the first study in which respiratory aerosols have been simultaneously measured with carbon dioxide (CO2) to establish the correlation between the two concentrations. CO2 concentrations are easily available through low-cost sensors and could be used to estimate viral exposure through this correlation, whereas source-specific aerosol measurements are complicated and not possible with low-cost sensors. The increase in both respiratory aerosols and CO2 was linear over ten minutes in a 2 m3 chamber for all participants, suggesting a strong correlation. On average, talking released more particles than breathing, with 14,600 ± 16,800 min−1 (one-σ standard deviation) and 6210 ± 5630 min−1 on average, respectively, while CO2 increased with 139 ± 33 ppm min−1 during talking and 143 ± 29 ppm min−1 during breathing. Assuming a typical viral load of 7×106 RNA copies per mL of oral fluid, ten minutes of talking and breathing are estimated to produce 1 and 16 suspended RNA copies, respectively, correlating to a CO2 concentration of around 1800 ppm in a 2 m3 chamber. However, viral loads can vary by several orders of magnitude depending on the stage of the disease and the individual. It was therefore concluded that, by measuring CO2 concentrations, only the number and volume concentrations of released particles can be estimated with reasonable certainty, while the number of suspended RNA copies cannot.

Numerical Model for Prediction of Indoor COVID-19 Infection Risk Based on Sensor Data

In addition to infection with SARS-CoV-2 via direct droplet transmission or contact with contaminated surfaces, infection via aerosol transport is a predominant pathway in indoor environments. The developed numerical model evaluates the risk of a COVID-19 infection in a particular room based on measurements of temperature, humidity, CO2 and particle concentration, the number of people and instances of speech, coughs and sneezing using a dedicated low-cost sensor system. The model can dynamically provide the predicted risk of infection to the building management system or people in the room. The effect of temperature, humidity and ventilation intensity on the infection risk is shown. Coughing and especially sneezing greatly increase the probability of infection in the room; therefore distinguishing these events is crucial for the applied measurement system.

Research on the Inversion Algorithm of Carbon Dioxide Detection By the Non-Dispersive Infrared Technology

Carbon dioxide (CO2) of the automobile exhausts is not only direct atmospheric contaminants, but also contributes to the formation of the greenhouse effect and frequent natural disasters, directly threatening human health and the ecological environment. This work is focused on the monitoring of CO2 by non-dispersive infrared (NDIR) technology. The NDIR instrument is non-poisonous, robust and durable. It has the characteristics of long life, easy maintenance and operation. Accurate measurement of CO2 concentration is very important for monitoring the quality of automobile exhausts. The absorption line of CO2, centered at 4.26μm has been selected due to the very strong absorption peak. The inversion algorithm of CO2 detection is researched in the wide dynamic range of 0-20% for CO2 on NDIR. According to the Beer-Lambert law, the calibration curve are obtained. The least squares method is used to respectively fit the logarithmic function, exponential function, polynomial, etc. to establish three inversion models. After analyzing and verifying these three models, the measurement error is less than 7% in the range of 0-20% for CO2 on NDIR. The results of the experiment shows that three inversion models are effective. They not only overcomes the shortcomings of the detection sensitivity and wide dynamic measurement range on NDIR technology, but also have a good measurement linearity for CO2 detection. They provide a necessary precondition for the interference correction of gas analysis. In addition, this method also provides a solution for the measurement of other gases (such as SO2 and NO2) with high precision and wide dynamic range.

An investigation on CO2 concentration based on field survey and simulation in naturally ventilated Nepalese school buildings during summer

Natural ventilation is usually adopted to provide required indoor air quality (IAQ) and thermal comfort. IAQ to be represented by CO2 concentration and ventilation is correlated with the academic performance of students. Most Nepalese school buildings are relying on natural ventilation through windows and have no glass sheets within them. This study is assessing the ventilation performance in those buildings that has not yet been known, through the measurement of CO2 concentration and questionnaire survey during the summer of 2019 in three schools located in the temperate climate of Nepal. The indoor CO2 concentration was much lower than the acceptable limit. Most students accepted the IAQ under such condition of classrooms. Based on the transient mass balance, a numerical simulation method was developed for estimating the number of air change per hour (ACH) and the indoor CO2 concentration. The estimated ACHs were found higher than 25 h−1 and thereby confirmed that ventilation was performed very well. The indoor CO2 concentrations simulated using the estimated ACHs were compared and found well fit with the measured CO2 concentration. The simulation of how the reduction of the ACH to a half or a quarter affects the indoor CO2 concentrations was conducted and found that the CO2 concentration would be still quite low in these schools. The findings in this study should be useful for assessing the ventilation performance after the installation of glass windows to be necessary at one of the highest priorities is made in the future.

Air Flow Experiments on a Train Carriage—Towards Understanding the Risk of Airborne Transmission

A series of experiments was undertaken on an intercity train carriage aimed at providing a “proof of concept” for three methods in improving our understanding of airflow behaviour and the accompanied dispersion of exhaled droplets. The methods used included the following: measuring CO2 concentrations as a proxy for exhaled breath, measuring the concentrations of different size fractions of aerosol particles released from a nebuliser, and visualising the flow patterns at cross-sections of the carriage by using a fog machine and lasers. Each experiment succeeded in providing practical insights into the risk of airborne transmission. For example, it was shown that the carriage is not well mixed over its length, however, it is likely to be well mixed along its height and width. A discussion of the suitability of the fresh air supply rates on UK train carriages is also provided, drawing on the CO2 concentrations measured during these experiments.

An Assessment of Air Quality within Facilities of Municipal Solid Waste Management (MSWM) Sites in Lahore, Pakistan

The pollutants emission during the process of municipal solid waste management (MSWM) is of great concern due to its hazardous effect on the environment and living organisms. An assessment of the air quality of MSWM sites was made after having 16 repetitive visits at solid waste disposal sites and transfer stations of Lahore during wet and dry seasons. Pollution parameters such as fine particulate matter (PM2.5) and greenhouse gases (GHG) were measured along with meteorological parameters. PM2.5 measurement was made by using particle counter Dylos and TSI’s Dust Trak. Both of these instruments were positioned simultaneously at the source site and downwind (50 m). CH4 and meteorological parameters were measured by Aeroqual 500 series, while the Extech CO220 monitor was used to measure CO2 concentration. An assessment of air quality showed the levels of their mean values as CH4 and CO2 ranged between 1.5–13.7 ppm and 443.4–515.7 ppm, respectively. The PM2.5 ranged between 127.1 and 307.1 µg/m3 at sources and 172.3 and 403.8 µg/m3 downwind (50 m). GHG showed lower levels than the proposed limit value, which could not cause any health issues, while PM2.5 was 6–10 times higher than the Pak-EPA established standards. Higher pollutant concentration was recorded in the dry season than the wet season. Regression analysis was performed to predict correlation of PM2.5 with GHG and meteorological parameters. GHG as well as meteorological parameters also exhibited a correlation with PM2.5. It was estimated that the ambient air of such sites is not safe for public health. So, it is necessary to use safe practices for MSWM and its emission control to prevent nearby communities and the environment.

Detectability of COVID-19 global emissions reductions in local CO2 concentration measurements

It is estimated that global anthropogenic carbon dioxide (CO2) emissions reduced by up to 12% at the start of 2020 compared to recent years due to the COVID-19 related downturn in economic activity. Despite the large decrease in CO2 emissions, no reduction in the trend in background atmospheric CO2 concentrations has been detected. So, how long would it take for sustained COVID-19 CO2 emission reductions to be detected in daily and monthly averaged local CO2 concentration measurements? CO2 concentration measurements for five measurement sites in the UK and Ireland are combined with meteorological numerical weather prediction data to build statistical models that can predict future CO2 concentrations. It is found that of the observed daily variability can be explained by these simple models. Emission reduction scenario experiments using these simple models illustrate that large daily and seasonal variability in local CO2 concentrations precludes the rapid emergence of a detectable signal. COVID-19 magnitude emissions reductions would only be detectable in the daily CO2 concentrations after at least 38 months and in monthly CO2 concentrations after 11 months of sustained reductions. For monthly CO2 concentrations the time of emergence is similar for all sites since the seasonal variability is largely driven by non-local fluxes of CO2 between the terrestrial biosphere and the atmosphere. The COVID-19 CO2 anthropogenic emissions reductions are similar in magnitude to those that are required to meet the Paris Agreement target of keeping global temperatures below C. This study demonstrates that, using measurements alone, there will be a considerable lag between changes in global anthropogenic emissions and a detected signal in local CO2 concentration trends. Thus, there is likely to be a delay of several years between changes in policy designed to meet CO2 anthropogenic emissions targets and our ability to detect the impact of these policies on CO2 concentrations using atmospheric measurements alone.