CO2 Sensor Research Articles

Building Ventilation Optimization Through Occupant-Centered Computer Vision Analysis

Abstract Buildings consume about 40 % of all energy. Ventilation plays a significant role in both the energy consumption of buildings and the comfort of occupants. To achieve energy efficiency and comfort, smarter ventilation control algorithms can be employed, such as those with feedback based on CO2 levels. Furthermore, by knowing the current number of people in a space, ventilation can theoretically be adjusted to maintain a constant CO2 level without wasting energy when people are not present. An additional benefit of such control could arise due to occupants’ habits. For example, if a person senses elevated CO2 levels, even if the ventilation system has started operating more intense, they might choose to open a window, potentially compromising energy efficiency. Therefore, if the control algorithm were to maintain a constant CO2 level, occupants may be less likely to open windows. In our work, we explore a model in combination with a custom monitoring system based on computer vision to implement such control. The monitoring system combines outside and inside CO2 sensors with precise people counting based on computer vision to provide data to the model. The model relies on the mass balance equation for CO2 and considers the historical data of the number of occupants and their activities to estimate the overall CO2 generation in indoor spaces. The results suggest that the model can effectively forecast CO2 dynamics with an absolute deviation of 40 ppm. However, it was observed that the analysis of the actual air exchange level could be compromised by several factors.

Dynamic Data Enhancing Battery Efficiency Through Collection Scheduling in IQRF Wireless Sensor Networks

Abstract In this study, we explore innovative strategies for enhancing energy efficiency in Wireless Sensor Networks (WSNs), with a focus on the IQRF network. Our approach integrates dynamic sleep scheduling and data collection methods to optimize battery usage and extend the network’s operational lifespan. We introduce a battery life estimation model, taking into account various factors such as data collection frequency and network size. This model is instrumental in predicting battery longevity under different operational scenarios. Additionally, we develop a practical tool in the form of an API and an online calculator, aimed at assisting network designers in planning and maintaining energy-efficient WSNs. Our results, derived from a case study involving a CO2 sensor network, demonstrate the effectiveness of our methodologies in real-world applications. The study concludes that implementing dynamic data collection and sleep scheduling significantly enhances battery life, offering a valuable contribution to the sustainability and reliability of WSNs.

Diel variability of carbon dioxide concentrations and emissions in a largest urban lake, Central China: Insights from continuous measurements

Accurately quantifying the carbon dioxide (CO2) emissions from lakes, especially in urban areas, remains challenging due to constrained temporal resolution in field monitoring. Current lake CO2 flux estimates primarily rely on daylight measurements, yet nighttime emissions is normally overlooked. In this study, a non-dispersive infrared CO2 sensor was applied to measure dissolved CO2 concentrations over a 24-h period in a largest urban lake (Tangxun Lake) in Wuhan City, Central China, yielding extensive data on diel variability of CO2 concentrations and emissions. We showed the practicality and efficiency of the sensor for real-time continuous measurements in lakes. Our findings revealed distinct diurnal variations in CO2 concentrations (Day: 38.58 ± 23.8 μmol L−1; Night: 42.01 ± 20.2 μmol L−1) and fluxes (Day: 7.68 ± 10.34 mmol m−2 d−1; Night: 9.68 ± 9.19 mmol m−2 d−1) in the Tangxun Lake. The balance of photosynthesis and respiration is of utmost importance in modulating diurnal CO2 dynamics and can be influenced by nutrient loadings and temperature. A diel variability correction factor of 1.14 was proposed, suggesting that daytime-only measurements could underestimate CO2 emissions in urban lakes. Our data suggested that samplings between 11:00 and 12:00 could better represent the average diel CO2 fluxes. This study offered valuable insights on the diel variability of CO2 fluxes, emphasizing the importance of in situ continuous measurements to accurately quantify CO2 emissions, facilitating selections of sampling strategies and formulation of management strategies for urban lakes.

In2O3-based thin-film transistors with a (400) polar surface for CO2 gas detection at 150 °C

Because of increasing demand for CO2 detection, CO2 sensors capable of low-temperature operation are desired. However, conventional oxide semiconductor sensors still require high operating temperatures. In this study, we fabricated a CO2 sensor that can operate at 150 °C. The sensor is based on an open-channel-type thin-film transistor (TFT) structure with an In2O3(400) polar plane as a channel. Because the In2O3(400) surface is polar, many OH groups are adsorbed onto the as-prepared surface. When CO2 gas is introduced, CO2 molecules react with the OH groups and the TFT characteristics change. As a result, CO2 can be detected with a sensitivity 2.9 times greater than that under an inert N2 atmosphere despite the operating temperature of only 150 °C. In a TFT with nonpolar In2O3(222) planes, the sensitivity remains at 1.3 times. We therefore believe that TFTs fabricated with the polar surface of an oxide semiconductor are useful for gas-sensing applications.

A neural network filter based high-sensitive MIR CO2 sensor

A low-power consumption, small-size and high measurement sensitivity mid-infrared (MIR) carbon dioxide (CO2) sensor was developed based on wavelength modulation spectroscopy (WMS) technology, utilizing a mid-infrared distributed feedback interband cascade laser (DFB-ICL) operating around 2682 nm. The sensor incorporated two key components, a home-made multi-pass gas cell with base length of 10 cm achieving 1.5 m optical path and a highly integrated FPGA board, which were used to reduce the size of the sensor. A novel second harmonic-neural network filter (SH-NNF) was also proposed and implemented in this sensor. The sensor’s performance was evaluated through a 20 mins continuous experiment using calibrated gas with 500 ppm concentration. The results, before and after SH-NNF, demonstrated an improvement in sensitivity and detection precision from 1.68 ppm (1 σ) to 1.17 ppm and from 2.14 ppm to 1.45 ppm, respectively. The optimum limit of detection of 0.392 ppm with integration time of 218 s was obtained. The ppm detection and compact characteristics of the developed MIR sensor show its potential for detecting CO2 in the ambient air.

A multimodal lithium niobate-based SAW gas sensor for accurate detection of carbon dioxide at elevated temperature

Carbon dioxide (CO2) detection is crucial for safety control, monitoring of the environment, and other industrial applications. The diverse fields of applications make the detection of CO2 a challenging task. In this paper, a study on a multimodal surface acoustic wave (SAW) CO2 sensing system was conducted to scrutinize the sensitivity of the lithium niobate-based multimodal SAW sensor towards CO2 with temperature compensation. The study focused on developing and evaluating a SAW temperature-compensated gas sensor using time-of-flight measurements. The sensor exhibited good repeatability and sensitivity toward different concentrations of CO2.

Network Optimization of Carbon Monoxide Sensor Nodes in the Metropolitan Region of São Paulo

Air pollution is one of the biggest problems affecting large urban areas. Better monitoring of regions suffering from this type of pollution is in the interest of public health. Although many cities employ sensors to monitor air pollution, a current concern is how to establish the ideal number of sensors to monitor a given geographical region. To address this concern, this research proposes a method to optimize the number of sensors in an air pollution monitoring network to cover a given region efficiently and precisely and uses the metropolitan region of São Paulo, Brazil, and CO sensors as an example. The model of Fragmentation into Groups via Routes is proposed to distribute sensors within micro-regions that display similar air pollution characteristics. A network of virtual sensors is created, and the output of each sensor is established using a method of spatial interpolation called IDW. To identify the optimum sensor configuration, a genetic algorithm is used to assess the topology with the lowest variance of data spread. A lesser number of sensor stations to be treated leads to faster responses to sudden changes in urban conditions. Therefore, municipality authorities can take quick measures to improve the population’s wellness.

MOF-derived Co3O4 nanoparticles over direct grown ZnO nanoflower on ceramic for CO sensor with high selectivity

ZnO nanoflower was directly fabricated on ceramic and then Co3O4 was grown in-situ on ZnO nanoflower by a MOF-derived method. Co3O4 nanoparticles were successfully coated on the surface of ZnO nanoflower to develop a sensor for the detection of carbon monoxide. Notably, the Co3O4/ZnO sensor exhibits an outstanding response of 38 % to 100 ppm CO at 210 ℃ with a fast response/recovery time (38 s/50 s). In addition, the sensor showed excellent selectivity for CO, which is 14 times better than for the second gas (500 ppm CO and NH3). In the meantime, the sensor's response remained a stable value when the 100 ppm CO is mixed with 100 ppm H2, CH4 and HN3, respectively. The prominent CO sensing performance of the sensor is attributed to its unique structure charaterastics of nanoflower and the constructed heterostructure, which provides a large surface area and abundant active sites. Therefore, the sensor based on MOF-derived Co3O4 nanoparticles directly on ZnO nanoflower have promising application for constructing high performance CO sensor in the detection domain.

2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces.

The International Liaison Committee on Resuscitation engages in a continuous review of new, peer-reviewed, published cardiopulmonary resuscitation and first aid science. Draft Consensus on Science With Treatment Recommendations are posted online throughout the year, and this annual summary provides more concise versions of the final Consensus on Science With Treatment Recommendations from all task forces for the year. Topics addressed by systematic reviews this year include resuscitation of cardiac arrest from drowning, extracorporeal cardiopulmonary resuscitation for adults and children, calcium during cardiac arrest, double sequential defibrillation, neuroprognostication after cardiac arrest for adults and children, maintaining normal temperature after preterm birth, heart rate monitoring methods for diagnostics in neonates, detection of exhaled carbon dioxide in neonates, family presence during resuscitation of adults, and a stepwise approach to resuscitation skills training. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces list priority knowledge gaps for further research. Additional topics are addressed with scoping reviews and evidence updates.

2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces

The International Liaison Committee on Resuscitation engages in a continuous review of new, peer-reviewed, published cardiopulmonary resuscitation and first aid science. Draft Consensus on Science With Treatment Recommendations are posted online throughout the year, and this annual summary provides more concise versions of the final Consensus on Science With Treatment Recommendations from all task forces for the year. Topics addressed by systematic reviews this year include resuscitation of cardiac arrest from drowning, extracorporeal cardiopulmonary resuscitation for adults and children, calcium during cardiac arrest, double sequential defibrillation, neuroprognostication after cardiac arrest for adults and children, maintaining normal temperature after preterm birth, heart rate monitoring methods for diagnostics in neonates, detection of exhaled carbon dioxide in neonates, family presence during resuscitation of adults, and a stepwise approach to resuscitation skills training. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces list priority knowledge gaps for further research. Additional topics are addressed with scoping reviews and evidence updates.

PSXI-7 Sensing of Defecation and Urination Events Through Carbon Dioxide Monitoring

Abstract The continued success of livestock production in grazing systems depends on the ability to manage lands sustainably. Non-point-source emissions, particularly defecation/urination events (DUE), are a major cause of soil degradation, runoff into water sources, and nutrient management issues. Monitoring of these emissions from livestock through wearable sensing could support more precision management, but is complicated by the need for individualized sensors, variable signal strength across topographies, and unreliable data transmission in remote locations. The objective of this study was to explore opportunities to use CO2 sensing as an indicator of DUE from cattle on pasture using data obtained from an open-source sensor. The use of tail sensors was implemented to monitor the DUE of horses at various times of the day, ranging from 8 am to 5 pm. The tail sensors were developed using a neoprene base attached to velcro straps that were placed around the tailhead. The sensors themselves were equipped with a SparkFun ESP32 Thing Plus microprocessor connected to a SparkFun CO2 sensor and a lithium ion rechargeable battery that was charged via micro USB (SparkFun Electronics, Niwot, CO). Sensors were placed on 4 horses for 6 hours each collection period and data collection occurred over 9 days. Horses were observed in real time and DUE were recorded at the time of occurrence. Data were collected from the sensors via a Dragino LoRa gateway and a mobile hotspot for wireless connection. Data analysis relied on visual data exploration and targeted assessment of associations using random forest regression, support vector machines, and extreme gradient boosting. The visual data exploration revealed that DUE occurred in much lower frequency than non-events, and so random oversampling and undersampling were used for each classification approach to attempt to improve accuracy. Although CO2 peaks often corresponded to DUE, they also frequently occurred in the absence of DUE. All classification algorithms showed poor accuracies (0.50 to 0.51), which were only marginally improved by over- (< 0.51) and undersampling (< 0.69). This preliminary assessment revealed considerable noise in sensing CO2 emissions in production settings, which may preclude usefulness in DUE sensing.

Investigation of the accuracy of BAS-grade CO2 sensors for measuring infiltration rates

Carbon dioxide (CO2) sensors used in building automation systems (BAS) and occupant-generated CO2 allow for a low-cost and environmentally friendly method for continuously monitoring the air leakage of the building envelope in commercial buildings. However, the validity of this method is hindered by the potential inaccuracies of BAS-grade CO2 sensor measurements. This paper presents a comprehensive laboratory study that compares the accuracy of air changes per hour (ACH) measured using CO2 and BAS-grade CO2 sensors to that of SF6 and expensive lab-grade equipment (photoacoustic gas monitor). The results show that CO2 is a suitable alternative to SF6 when using lab-grade equipment. On average, ACH measured with lab-grade CO2 equipment were ±4.5 % of those measured with lab-grade SF6 measurements and 90 % of measurements were within ±10 %. However, the results also indicated that methods to correct for sensor drift are required to improve ACH measurement accuracy when using BAS-grade CO2 sensors that have not undergone routine calibration. By applying a method to correct for sensor drift, ACH measured with BAS-grade CO2 sensors were improved from ±10.9 % to ±5.1 % (on average) and the percentage of measurements within ±10 % increased from 48 % to 83 %. In addition, an environmental impact assessment of the tracer gas tests conducted in this study provides quantitative evidence to support the need to transition away from SF6-based tracer gas tests. The assessment showed that in this study releasing SF6 was ∼177 times worse for the environment than releasing CO2.

Four-color fiber-coupled mid-infrared laser-absorption sensor for temperature, CO, CO2, and NO at 5 kHz in internal combustion engine vehicle exhaust

A mid-infrared (MIR) laser absorption spectroscopy (LAS) sensor was developed for temperature, CO, NO, and CO2 measurements at 5 kHz in engine-out exhaust. It used fiber-coupled quantum cascade lasers (QCLs) for measuring CO and NO, and an interband cascade laser (ICL) for measuring CO2. Validation tests in a heated gas cell confirmed that the LAS measurements of CO, CO2, NO, and temperature are accurate to within 4.8%, 5.1%, 4.6%, and 3.1%, respectively, at 1–2 atm and 300–1000 K. The LAS sensor was applied to characterize the engine-out exhaust gas of an 8-cylinder gasoline engine in a light-duty truck at operating conditions where commercial instruments lack sufficient time response to quantify important emission dynamics.

Enhanced performance for simultaneous atmospheric N2O and CO measurements based on ICEEMDAN and GWO-LSSVM

A TDLAS-based sensor for simultaneous detection of N2O and CO has been developed by us, which is capable of monitoring the concentrations of both gases in real time with high stability and detection accuracy. Two distributed feedback (DFB) were deployed as laser sources, a 20 m multi-pass gas cell (MPGC) was installed for absorption, an InGaAs detector was used to receive the optical signals and graphical user interface (GUI) were designed based on the microcontroller. The modulation depth curves were obtained experimentally to obtain the lowest measured concentration. Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) was employed to increase signal-to-noise ratio (SNR) of the signals and GWO-LSSVM was performed to invert concentrations. The linearity of the sensor was analyzed by linearity experiments, which obtained the linearity of the sensor for N2O and CO, with linear correlation coefficients (R2) of 0.9990 and 0.998, respectively. The detection accuracy of the sensor was verified and the relative errors of the sensor were all less than 3.59 %. The stability and anti-interference ability of the sensor were verified, it was found that the standard deviation (SD) of N2O and CO measured by the sensor was 47.27 ppb and 48.31 ppb respectively. The limit of detection (LoD) for N2O and CO are 8.37 ppb and 5.91 ppb, respectively according to Allan-Werle deviation. The sensor has good performance in simultaneous detection of N2O and CO.

Machine Learning in the Analysis of Carbon Dioxide Flow on a Site with Heterogeneous Vegetation

The article presents the results of studies of carbon dioxide flow in the territory of section No. 5 of the Eurasian Carbon Polygon (Russia, Republic of Bashkortostan). The gas analyzer Sniffer4D V2.0 (manufactured in Shenzhen, China) with an installed CO2 sensor, quadrocopter DJI MATRICE 300 RTK (manufactured in Shenzhen, China) were used as control devices. The studies were carried out on a clear autumn day in conditions of green vegetation and on a frosty November day with snow cover. Statistical characteristics of experimental data arrays are calculated. Studies of the influence of temperature, humidity of atmospheric air on the current value of CO2 have been carried out. Graphs of the distribution of carbon dioxide concentration in the atmospheric air of section No. 5 on autumn and winter days were obtained. It has been established that when building a model of CO2 in the air, the parameters of the process of deposition by green vegetation should be considered. It was found that in winter, an increase in air humidity contributes to a decrease in gas concentration. At an ambient temperature of 21 °C, an increase in humidity leads to an increase in the concentration of carbon dioxide.

Investigation of low-temperature deposition of SnO2 thin film for improving sensitivity of LaOCl/SnO2 heterojunction CO2 sensor using electrochemical impedance spectroscopy

In this study, LaOCl/SnO2 heterojunction CO2 sensors were fabricated by spin-coating LaOCl onto a SnO2 thin film fabricated via RF magnetron sputtering. The sensitivity of LaOCl/SnO2 heterojunction CO2 sensors tended to improve with the deposition of SnO2 thin films without intentional heating (at approximately 35 °C). Electrochemical impedance spectroscopy measurements revealed that the formation of a LaOCl/SnO2 heterojunction interface is indispensable for high-sensitivity sensors, and LaOCl acted not only as a catalyst but also as a capacitive layer in the sensors. The capacitive element of the LaOCl/SnO2 heterojunction changed under a CO2 gas atmosphere. It is confirmed that the formation of heterojunction interfaces contributes to the improvement of sensitivity. The results of this study indicate the potential applications of heterojunction gas sensors.

Low-cost carbon dioxide concentration sensors for assessing air quality in the built environment: an on-site evaluation of their measurement performance

Carbon dioxide (CO2) concentration is commonly used to determine indoor air quality. However, wearable environmental monitoring devices have not considered CO2 content, because they have mainly focused on the outdoor environment. Our goal is to develop a new wearable monitoring system for indoor research purposes that also incorporates a CO2 concentration sensor. We evaluated the performance of two low-cost CO2 sensors, the Senseair K30 and the Sensirion SCD30, by comparing them with a professional reference sensor in two different environments: a Test Cell and the LabZEB of ITC-CNR. A first result shows that both low-cost sensors have acceptable performance, with the K30 showing a trend closer to that of the professional reference sensor and better overall performance. For this reason, we conducted a third test in which we encapsulated the selected K30 low-cost sensor in a 3D-printed, wearable housing that supports the sensor and control unit to see if the 3D-printed part could affect measurement performance. The result shows no interference in measurement performance. This suggests that low-cost sensors can be used in a wearable system to assess indoor air quality for environmental and human-centered research in the built environment.

Investigating Pathways to Minimize Sensor Power Usage for the Internet of Remote Things.

The Internet of Remote Things (IoRT) offers an exciting landscape for the development and deployment of remote wireless sensing nodes (WSNs) which can gather useful environmental data. Low Power Wide Area Networks (LPWANs) provide an ideal network topology for enabling the IoRT, but due to the remote location of these WSNs, the power and energy requirements for such systems must be accurately determined before deployment, as devices will be running on limited energy resources, such as long-life batteries or energy harvesting. Various sensor modules that are available on the consumer market are suitable for these applications; however, the exact power requirements and characteristics of the sensor are often not stated in datasheets, nor verified experimentally. This study details an experimental procedure where the energy requirements are measured for various sensor modules that are available for Arduino and other microcontroller units (MCUs). First, the static power consumption of continually powered sensors was measured. The impact of sensor warm-up time, associated with powering on the sensor and waiting for reliable measurements, is also explored. Finally, the opportunity to reduce power for sensors which have multiple outputs was investigated to see if there is any significant reduction in power consumption when obtaining readings from fewer outputs than all that are available. It was found that, generally, CO2 and soil moisture sensors have a large power requirement when compared with temperature, humidity and pressure sensors. Limiting multiple sensor outputs was shown not to reduce power consumption. The warm-up time for analog sensors and digital sensors was generally negligible and in the order of 10-50 ms. However, one CO2 sensor had a large overhead warm-up time of several seconds which added a significant energy burden. It was found that more, or as much, power could be consumed during warm-up as during the actual measurement phase. Finally, this study found disparity between power consumption values in datasheets and experimental measurements, which could have significant consequences in terms of battery life in the field.

Modeling of gas production during pyrolysis of biomass with triple gaussian function

To address the limitations of the inability to monitor the internal processing environment of pyrolysis, a triple Gaussian model was developed to effectively describe gas production during biomass pyrolysis at different dwelling temperatures and ramping rates. A CO2 and H2 gas sensor array was constructed to collect gas production data during the biomass pyrolysis which was fitted to the three gas production ranges of the triple Gaussian model, carbonization, gasification, and activation. The amplitude A (peak gas production), mean µ (time at gas production peak), and standard deviation σ (rate of gas production) components of the triple Gaussian function were studied for the three ranges. The results showed that as both ramping rate and dwelling temperature increased, the production of CO2 increased, while the H2 production decreased. The analysis of A, σ, and μ Gaussian components of the model showed that in the carbonization range, the peak CO2 increased and H2 production decreased. In the gasification range, the CO2 production decreased, while more H2 production happened as the ramping rate and dwelling temperature increased. The components of both H2 and CO2 models were compared. These relationships between Gaussian components and ramping rate and dwelling temperature, showed clear linear trends. The results showed that the carbonization and gasification ranges of the two models were inversed. ESEM was performed on the resulting carbons which showed that more degradation in the carbon structure occurred at higher temperatures and lower ramping rate.

A Low-Cost Wireless Sensor Network for Barn Climate and Emission Monitoring—Intermediate Results

A barn’s climate is vital for animal welfare and emissions control, including greenhouse gases like methane (CH4) and carbon dioxide (CO2) and environmental gases like ammonia (NH3). The goal of this study was to investigate a developed low-cost online tool for monitoring barn climate and air pollutant emissions (OTICE) in naturally ventilated barns. OTICE employed a wireless sensor network with low-cost sensors for gases and climate variables, allowing scalable use across multiple barns. We evaluated the sensors for CO2, NH3, and CH4 for accuracy, both in controlled lab conditions and in a dairy barn in Germany, where measurements were carried out continuously for a duration of 12 days. For the averaged concentration levels over the measurement period, the low-cost sensors agreed well with the reference system, with relative deviations lower than 7% for all three gases, with maximum peak deviations up to 32% for CO2, 67% for NH3, and 65% for CH4, with strong Spearman correlations for CO2 and NH3 (ρCO2 = 0.8, ρNH3 = 0.68) and a rather weak correlation for CH4 with ρCH4 = 0.24. Further calibration and stability investigations are required, especially for CH4 sensing. However, the overall good results for NH3 and especially CO2 measurements indicate a huge potential of the low-cost system as a valuable tool for monitoring relative NH3 emission levels and the measurement of air exchange rates in naturally ventilated barns.