CO2 Concentration Measurements Research Articles

Prediction of Carbon Dioxide Concentrations in Strawberry Greenhouse by Using Time Series Models

Carbon dioxide (CO2) concentrations play an important role in plant production, as they have a direct impact on both plant growth and yield. Therefore, the objectives of this study were to predict CO2 concentrations in the greenhouse by applying time series models using five datasets. To estimate the CO2 concentrations, this study was conducted over a four-month period from 1 December 2023 to 31 March 2024, in a strawberry-cultivating greenhouse. Fifteen sensors (MCH-383SD, Lutron, Taiwan) were installed inside the greenhouse to measure CO2 concentration at 1-min intervals. Finally, the dataset was transformed into intervals of 1, 5, 10, 30, and 60 min. The time-series data were analyzed using the autoregressive integrated moving average (ARIMA) and the Prophet Forecasting Model (PFM), with performance assessed through root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The evaluation indicated that the best model performance was achieved with data collected at 1-min intervals, while model performance declined with longer intervals, with the lowest performance observed at 60-min intervals. Specifically, the ARIMA model outperformed across all data collection intervals while comparing with the PFM. The ARIMA model, with data collected at 1-min intervals, achieved an R2 of 0.928, RMSE of 7.359, and MAE of 2.832. However, both ARIMA and PFM exhibited poorer performances as the interval of data collection increased, with the lowest performance at 60-min intervals where ARIMA had an R2 of 0.762, RMSE of 19.469, and MAE of 11.48. This research underscores the importance of frequent data collection for precise environmental control in greenhouse agriculture, emphasizing the critical role of short-interval data collection for accurate predictive modeling.

Measurement report: Urban ammonia and amines in Houston, Texas

Abstract. Ammonia and amines play critical roles in secondary aerosol formation, especially in urban environments. However, fast measurements of ammonia and amines in the atmosphere are very scarce. We measured ammonia and amines with a chemical ionization mass spectrometer (CIMS) at the urban center in Houston, Texas, the fourth most populated urban site in the United States, during October 2022. Ammonia concentrations were on average four parts per billion by volume (ppbv), while the concentration of an individual amine ranged from several parts per trillion by volume (pptv) to hundreds of pptv. These reduced nitrogen compounds were more abundant during weekdays than on weekends and correlated with measured CO concentrations, implying they were mostly emitted from pollutant sources. Both ammonia and amines showed a distinct diurnal cycle, with higher concentrations in the warmer afternoon, indicating dominant gas-to-particle conversion processes taking place with the changing ambient temperatures. Studies have shown that dimethylamine is critical for new particle formation (NPF) in the polluted boundary layer, but currently there are no amine emission inventories in global climate models (as opposed to ammonia). Our observations made in the very polluted area of Houston, as well as a less polluted site (Kent, Ohio) from our previous study (You et al., 2014), indicate there is a consistent ratio of dimethylamine over ammonia at these two sites. Thus, our observations can provide a relatively constrained proxy of dimethylamine using 0.1 % ammonia concentrations at polluted sites in the United States to model NPF processes.

A Sensor Probe with Active and Passive Humidity Management for In Situ Soil CO2 Monitoring.

Soil CO2 concentration and flux measurements are important in diverse fields, including geoscience, climate science, soil ecology, and agriculture. However, practitioners in these fields face difficulties with existing soil CO2 gas probes, which have had problems with high costs and frequent failures when deployed. Confronted with a recent research project's need for long-term in-soil CO2 monitoring at a large number of sites in harsh environmental conditions, we developed our own CO2 logging system to reduce expense and avoid the expected failures of commercial instruments. Our newly developed soil probes overcome the central challenge of soil gas probes-surviving continuous exposure to soil moisture while remaining open to soil gases-via three approaches: a 3D printed housing (economical for small-scale production) following design principles that correct the usual water permeability flaw of 3D printed materials; passive moisture protection via a hydrophobic, CO2-permeable PTFE membrane; and active moisture protection via a low-power micro-dehumidifier. Our CO2 instrumentation performed well and yielded a high-quality dataset that includes signals related to a prescribed fire as well as seasonal and diel cycles. We expect our technology to support underground CO2 monitoring in fields where it is already practiced and stimulate its expansion into diverse new fields.

Unveiling spatial variations in atmospheric CO2 sources: a case study of metropolitan area of Naples, Italy

In the lower atmosphere, CO2 emissions impact human health and ecosystems, making data at this level essential for addressing carbon-cycle and public-health questions. The atmospheric concentration of CO2 is crucial in urban areas due to its connection with air quality, pollution, and climate change, becoming a pivotal parameter for environmental management and public safety. In volcanic zones, geogenic CO2 profoundly affects the environment, although hydrocarbon combustion is the primary driver of increased atmospheric CO2 and global warming. Distinguishing geogenic from anthropogenic emissions is challenging, especially through air CO2 concentration measurements alone. This study presents survey results on the stable isotope composition of carbon and oxygen in CO2 and airborne CO2 concentration in Naples’ urban area, including the Campi Flegrei caldera, a widespread hydrothermal/volcanic zone in the metropolitan area. Over the past 50 years, two major volcanic unrests (1969–72 and 1982–84) were monitored using seismic, deformation, and geochemical data. Since 2005, this area has experienced ongoing unrest, involving the pressurization of the underlying hydrothermal system as a causal factor of the current uplift in the Pozzuoli area and the increased CO2 emissions in the atmosphere. To better understand CO2 emission dynamics and to quantify its volcanic origin a mobile laboratory was used. Results show that CO2 levels in Naples’ urban area exceed background atmospheric levels, indicating an anthropogenic origin from fossil fuel combustion. Conversely, in Pozzuoli's urban area, the stable isotope composition reveals a volcanic origin of the airborne CO2. This study emphasizes the importance of monitoring stable isotopes of atmospheric CO2, especially in volcanic areas, contributing valuable insights for environmental and public health management.

Automatic detection of CO2 rebreathing during BiPAP ventilation

Carbon dioxide rebreathing (CO2 rebreathing) significantly influences respiratory drive and the work of breathing during BiPAP ventilation. We analyzed CO2 movement during BiPAP ventilation to find a method of real time detection of CO2 rebreathing without the need of CO2 concentration measurement sampled from the circuit (method expensive and not routinely used). Observational study during routine care in 15 bed university hospital ICU. At 18 patients who required BiPAP ventilation, intubated or during noninvasive ventilation, during weaning period airflow, pressure and CO2 concentration signals were registered on both sides of venting port and 17 respiratory parameters were measured or calculated for each of 4747 respiratory cycles analyzed. Based on CO2 movement (expiration–inspiration sequences) 3 types of cycle were identified, type I and II do not induce rebreathing but type III does. To test differences between the 3 types ANOVA, t-tests, and canonical discriminant analysis (CDA) were used. Then a multilayer perceptron (MLP) network, a type of artificial neural network, using the above parameters (excluding CO2 concentration) was applied to automatically identify the three types of respiratory cycles. Of the 4747 respiratory cycles, 1849 were type I, 1545 type II, and 1353 type III. ANOVA and t-tests showed significant differences between the types of respiratory cycles. CDA confirmed a correct apportionment of 93.9% of the cycles; notably, of 97.9% of type III. MLP automatically classified the respiratory cycles into the three types with 98.8% accuracy. Three types of respiratory cycles could be distinguished based on CO2 movement during BiPAP ventilation. Artificial neural networks can be used to automatically detect respiratory cycle type III, the only inducing CO2 rebreathing.

Relationship between Exhaled Aerosol and Carbon Dioxide Emission Across Respiratory Activities.

Respiratory particles produced during vocalized and nonvocalized activities such as breathing, speaking, and singing serve as a major route for respiratory pathogen transmission. This work reports concomitant measurements of exhaled carbon dioxide volume (VCO2) and minute ventilation (VE), along with exhaled respiratory particles during breathing, exercising, speaking, and singing. Exhaled CO2 and VE measured across healthy adult participants follow a similar trend to particle number concentration during the nonvocalized exercise activities (breathing at rest, vigorous exercise, and very vigorous exercise). Exhaled CO2 is strongly correlated with mean particle number (r = 0.81) and mass (r = 0.84) emission rates for the nonvocalized exercise activities. However, exhaled CO2 is poorly correlated with mean particle number (r = 0.34) and mass (r = 0.12) emission rates during activities requiring vocalization. These results demonstrate that in most real-world environments vocalization loudness is the main factor controlling respiratory particle emission and exhaled CO2 is a poor surrogate measure for estimating particle emission during vocalization. Although measurements of indoor CO2 concentrations provide valuable information about room ventilation, such measurements are poor indicators of respiratory particle concentrations and may significantly underestimate respiratory particle concentrations and disease transmission risk.

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.

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.

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.

Portraying on-road CO2 concentrations using street view panoramas and ensemble learning

A significant reduction in carbon dioxide (CO2) emissions caused by transportation is essential for attaining sustainable urban development. Carbon concentrations from road traffic in urban areas exhibit complex spatial patterns due to the impact of street configurations, mobile sources, and human activities. However, a comprehensive understanding of these patterns, which involve complex interactions, is still lacking due to the human perspective of road interface characteristics has not been taken into account. In this study, a mobile travel platform was constructed to collect both on-road navigation Street View Panoramas (OSVPs) and the corresponding CO2 concentrations. >100 thousand sample pairs that matched “street view-CO2 concentration” were obtained, covering 675.8 km of roads in Shenzhen, China. In addition, four ensemble learning (EL) models were utilized to establish nonlinear connections between the semantic and object features of streetscapes and CO2 concentrations. After performing EL fusion modeling, the predictive R2 in the test set exceeded 90 %, and the mean absolute error (MAE) was <3.2 ppm. The model was applied to Baidu Street View Panoramas (BSVPs) in Shenzhen to generate a map of average on-road CO2 with a 100 m resolution, and the Local Indicator of Spatial Association (LISA) was then used to identify high CO2 intensity spatial clusters. Additionally, the Light Gradient Boost-SHapley Additive exPlanation (LGB-SHAP) analysis revealed that vertically planted trees can reduce CO2 emissions from on-road sources. Moreover, the factors that affect on-road CO2 exhibit interaction and threshold effects. Street View Panoramas (SVPs) and Artificial Intelligence (AI) were adopted here to enhance the spatial measurement of on-road CO2 concentrations and the understanding of driving factors. Our approach facilitates the assessment and design of low-emission transportation in urban areas, which is critical for promoting sustainable traffic development.

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

Health problems and respiratory diseases are associated with poor indoor air ventilation. We investigated the air quality inside a classroom-laboratory where no ventilation is provided. The case of study, consisting of an internal enclosure, is located at the Escuela Técnica Superior de Edificación (ETSEM) of Madrid (Spain). The high height favours air stratification which is analysed in terms of temperature and CO2 spatial distribution. Temperature, air humidity, atmospheric pressure and CO2 concentration measurements were taken in time at three different height locations. A CFD numerical model was established to analyse air quality. Flow circulation is derived by solving full 3D Navier – Stokes governing equations, coupled with the thermal problem. The diffusion problem of the CO2 produced by the inner occupants is then derived from the kinematics solution. Three scenarios were taken into account: occupants seated (1), standing (2), half seated, half standing (3). Results clearly show the air stratification as a result of density gradient, which is in turn determined by temperature difference between the occupants and the surrounding air. Temperature prediction maximum relative error is contained to 3.5 %. As expected, CO2 concentration increases over time, reaching maximum values depending on the configuration considered and height location.

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.