Carbon Monoxide Concentrations Research Articles

A Field Study of Carbon Monoxide Levels in Snow Caves During Short-Term Stove Use.

While the use of camping stoves in poorly ventilated areas is discouraged, the need to address dehydration challenges in harsh arctic conditions has led to their unconventional use inside snow caves for snow melting, subjecting occupants to unknown carbon monoxide (CO) levels. This study, located at sea level in northeastern Greenland, aimed to assess CO levels and dynamics during short cooking sessions in newly constructed emergency snow caves. In 5 snow caves, constructed according to the same design principles by 4 different individuals, a single MSR Whisperlite multifuel burner, primed with ethanol and burning white gas, was used to melt snow. CO concentrations were monitored every minute until all the snow in a 5-L pot was converted to water and CO levels returned to below 10 ppm. A total of 16 experiments conducted showed that the priming phase generated the highest CO peaks, with a maximum of 120 ppm. Time-weighted averages ranged from 14 ppm to 67 ppm, with trial durations of 15 to 21 min. A single trial with a dirty burner resulted in up to a 10-fold increase in CO levels. While single, short cooking sessions of less than 10 min burn time in newly constructed snow caves may be tolerated under specific conditions, the study highlighted substantial variation between caves and the importance of using clean burners, emphasizing the need for further research to gain a comprehensive understanding of CO exposure dynamics in snow caves.

Dynamic evacuation path planning for subway station fire based on IACO

Fire in subway stations is the most serious type of accident causing casualties, and traditional static evacuation paths are no longer able to guarantee evacuation safety. To solve this problem, this study proposes an improved ant colony algorithm (IACO), which dynamically adjusts the evacuation paths by combining the simulation results of simulation software (PyroSim), and extends the available safe evacuation time of the personnel in a limited way. First, PyroSim is used to simulate the subway station hall and platform fire scenarios with the temperature, carbon monoxide (CO) concentration, and visibility at each location, obtained from twelve sets of monitors. Improvements in the pheromone concentration and update strategy of the ant colony algorithm (ACO) can improve the accuracy and convergence speed of the algorithm. Optimal path planning is achieved by the IACO, and then evacuation paths are dynamically adjusted by comparing the time at which each monitoring point on the path reaches the human hazard thresholds. Finally, until no adjustments can be made to obtain an evacuation path that extends the available safe evacuation time for personnel in a limited way. Compared to the traditional static evacuation path, this study provides a dynamic evacuation path that takes into account the real-time changes of fires in subway stations. This dynamic evacuation path planning approach will be put into use in subway stations before the accident to provide guidance on fire safety in subway stations and to provide contingency plans for emergency evacuation.

Evaluation of CI Engine Performance Using Compression Ratio for Palm Oil Biodiesel

The experiment explores vegetable oils as potential future fuels for internal combustion engines, particularly compression ignition engines designed for diesel. However, these oils have distinct properties from diesel, requiring modifications for direct use. Integration approaches include adjusting oil properties or adapting engines. Commonly, transesterification aligns oil properties, but using biodiesel often affects engine performance. In this study palm oil biodiesel as a diesel substitute, evaluating engine performance and emissions. In a short-term test, engine performance and emission traits by employing biodiesel compression ratio of 12:1, 14:1 and 18:1 with diesel at full load. Findings indicated that 14:1 and 18:1 exhibited 1.01 % and 1.02 % higher brake power than pure diesel. Volumetric efficiency percentages were 82.33 for 14:1, 82.36 for 18:1, and 82.30 for 12:1. Notably, 14:1 and 18:1 showed 5.44% and 10.90 % increased brake thermal efficiency compared to diesel. Interestingly, 14:1 and 18:1 displayed 2.35 % and 2.61 % higher mechanical efficiency than diesel. The exhaust gas temperature was notably decreased in 12:1 to 18:1 compression ratio. Nitric oxide concentrations were 65.07 ppm for 12:1, 124.44 ppm for 14:1, and 126.48 ppm for 18:1. Carbon dioxide levels increased by 9.79 % for 14:1 and 62.47 % for 18:1 relative to diesel. Carbon monoxide concentrations were 0.07 % for 14:1 and 0.05% for 18:1, in contrast to diesel's 0.12%.

Design and optimization of a combined heat and power system with a fluidized-bed combustor and stirling engine

In this study, a fluidized-bed biofuel system was integrated with a Stirling engine (SE) to create a combined heat and power (CHP) system with high energy efficiency. Methods for ensuring the stability of the SE and fluidized-bed combustion were investigated and implemented. The proposed design was tested experimentally, and the system successfully produced electricity from the heat of the flue gas generated during biomass combustion. The Taguchi method was employed to maximize the temperature of the fluidized bed by modifying three parameters: the sand height, air flow rate, and biomass feed rate. The biomass used in this study was discarded mushroom sawdust waste. The biomass feed rate was varied from 9.5 to 17 g/min, and the air flow rate was varied from 50 to 60 L/min. The integrated CHP system was found to yield 90−100 W of electric power and 1077.3 W of heat energy for producing hot water. The oxygen, carbon dioxide, carbon monoxide, and nitric oxide concentrations of the flue gas produced under the optimized system parameters were analyzed, and minimal emissions of carbon monoxide and nitric oxides, which are harmful gases, were discovered. In most experiments on SEs in the literature, liquid or gaseous fuels have been used to power the engine. However, this study successfully used solid biomass fuel to power a practical SE, thus widening the knowledge on the utilization of this renewable energy source for electricity generation. The results of this study indicate that the proposed CHP system is stable, clean, and efficient. This system potentially provides a solution for two problems, namely the disposal of mushroom sawdust waste and the eco-friendly generation of heat and electricity. Therefore, the proposed system is promising for green and sustainable power generation in the future.

Carbon Monoxide Pollution and Limited Health Service Access in Third-world Countries

Despite its complexity, carbon monoxide is a key component of indoor and outdoor air pollution. While carbon monoxide arises from natural sources as well as human activities, the most detrimental exposure risks are within the domestic environment. In third-world countries like Guatemala, a notable positive correlation has been observed between severe respiratory diseases and the concentration of carbon monoxide in the air. This correlation can be attributed to these regions' housing conditions and daily lifestyle practices. Additionally, the limited healthcare services accessibility in third-world nations exacerbated the severity of domestic carbon monoxide poisoning cases. It is evident that effective healthcare interventions still need to be fully utilized in third-world countries, and thus, there is a need to move beyond identifying broad strategies and specific policies. Addressing the issues of carbon monoxide exposure and its impact on public health in these regions requires comprehensive approaches that take into account the challenges faced by these countries.
 To access carbon monoxide concentration in the household, EsayLog USB has been used in this study. The study reviews the amount of carbon monoxide present in certain villages with different conditions and high carbon monoxide concentrations, especially near fires using combustible fuels and houses at high altitudes. The study found that third-world countries' decision-makers should educate communities, build the environment to provide better medical access to the public and implement appropriate regulations to deliver better quality health care to people.

A Data-Driven Approach to Identify Major Air Pollutants in Shanghai Port Area and Their Contributing Factors

Air pollution is a growing concern in metropolitan areas worldwide, and Shanghai, as one of the world’s busiest ports, faces significant challenges in local air pollution control. Assessing the contribution of a specific port to air pollution is essential for effective environmental management and public health improvement, making the analysis of air pollution contributions at a selected port in Shanghai a pertinent research focus. This study aims to delve into the distribution patterns of atmospheric pollutants in port areas and their influencing factors, utilizing a data-driven approach to unveil the relationship between pollution sources and dispersion. Through a comparative analysis of pollution levels in the port’s interior, surrounding regions, and urban area concentrations, we ascertain that carbon monoxide (CO) and nitric oxide (NO) are the primary pollutants in the port, with concentrations significantly exceeding those of the surrounding areas and urban area levels. These two pollutants exhibit an hourly pattern, with lower levels during the day and higher concentrations at night. Employing a random forest model, this study quantitatively analyzes the contribution rates of different factors to pollutant concentrations. The results indicate that NO concentration is primarily influenced by operational intensity and wind speed, while CO concentration is mainly affected by meteorological factors. Further, an orthogonal experiment reveals that maintaining daily operational vehicle numbers within 5000 effectively controls NO pollution, especially at low wind speeds. Additionally, humidity and temperature exhibit similar trends in influencing NO and CO, with heightened pollution occurring within the range of 75% to 90% humidity and 6 °C to 10 °C temperature. Severe pollution accumulates under stagnant wind conditions with wind speeds below 0.2 m/s. The results help to explore the underlying mechanisms of port pollution further and use machine learning for early pollution prediction, aiding timely warnings and emission reduction strategy formulation.

Carbon monoxide and carboxyhemoglobin contents in exhaled air and blood pressure levels with diff erent nicotine delivery pathways

Aim. To assess the content of CO and COHb in exhaled air and the level of blood pressure (BP) in the working-age population of Yuzhno-Sakhalinsk when using various means of nicotine delivery.Materials and methods. The study participants (n = 503) were surveyed using a questionnaire. The BP and oxygen saturation parameters were assessed. The content of CO and COHb content in exhaled air was measured using a smokerlyzer. Statistical processing was performed using nonparametric methods.Results. The study group was dominated by males (58.3%). A high incidence of smoking was observed among females, with up to 42% in the general group and 48.8% in the group of consumers of alternative nicotine delivery systems (ANDS). A correlation between the content of CO and COHb and the smoking experience of more than 10 years was established, regardless of the means of nicotine delivery. Arterial hypertension was registered among 39.8% of the surveyed participants, with a higher frequency in cigarette smokers (28.8%, p = 0.02). This was associated with a greater proportion of individuals (62%) with a long smoking experience of more than 10 years (p = 0.003). A weak direct correlation was established between an increase in blood pressure and the content of CO and COHb (p < 0.001).Conclusion. The share of females as active consumers of tobacco products has grown, particularly in the form of ANDS. Among smokers, the average high level of CO and COHb prevails, which correlates with the smoking experience of more than 10 years and does not depend on the means of nicotine delivery. A direct relationship between the intensity of smoking and the risk of hypertension was established.

PERBANDINGAN MODEL PREDIKSI DATA MINING DALAM MEMPREDIKSI KONSENTRASI POLUTAN KARBON MONOKSIDA (CO) DI JAKARTA

DKI Jakarta, as the capital of Indonesia, faces serious challenges in terms of air quality. Carbon monoxide (CO) is one of the main air pollutants in Jakarta that is harmful to human health and the environment. Data mining is a method that can be used to predict situations based on a model. The study aims to compare data mining models with the best-performing methods to predict carbon monoxide pollutants in Jakarta. The predictive data mining model of the python library is tested and evaluated based on the evaluation metrics of MASE, RMSSE, MAE, RMSE, MAPE and SMAPE values. The model test results showed that K Neighbors with the Conditional Deseasonalize & Detrending model had the best metric evaluation value to predict CO concentration with the value evaluation metrics of MASE 0.2942, RMSSE 0.2483, MAE 2.7362, RMSE 3.3863, MAPE 0.1975 and SMAPE 0.01993. Overall, K Neighbors with the Conditional Deseasonalize & Detrending model shows good performance to predict CO concentrations in Jakarta, but further adjustments are needed to improve accuracy.

Experimental studies of air ozonization influence on pollutants emission in vortex ignition-stabilizing modules of gas turbine combustion chambers

Reduction of pollutant emissions into the atmosphere is an urgent task of modern gas turbine engineering development. Such substances that have a negative impact on the environment include nitrogen oxides and carbon monoxide. The article provides a review of the existing methods for reducing the emissions of pollutants used in the combustion of hydrocarbon fuels in the combustion chambers of gas turbine units, as well as indicates promising technologies developed based on these methods. Justification is presented of the necessity to study the method of ozonization of air supplied to the fuel combustion zone to reduce the content of pollutants in its combustion products. The purpose of the study was to develop recommendations for reducing pollutant emissions within a given range of variations regime parameters. To perform the necessary set of experimental studies, a specialized laboratory stand was created, the combined circuit and measurement scheme of which is given in the article. A rather detailed description is given of the structural elements of the stand and, in particular, of the subject of research — a full-scale vortex ignitionstabilizing module of the combustion chamber of a gas turbine unit, an electric ozonator, air and combustible gas supply systems, an exhaust system with a central fan, control and measuring instruments and fittings. The tests were carried out in accordance with specially developed methods of controlling the stand operation in three modes: startup; transfer from one steady-state mode to another; stop. The influence of air ozonization on combustion processes in the vortex ignition-stabilizing module was determined by the content of nitrogen oxides and carbon monoxide in it. The factors that influence the decrease in the concentration of carbon monoxide in the combustion products during the combustion of air in ozonized air have been determined. The methods of calculation determination of combustible gas and air parameters — mass flow rates, flow velocities and dynamic heads — are described. The results of the performed experimental studies are presented in graphical interpretation, their detailed analysis is given, on the basis of which recommendations on practical use of the method of air ozonization are given.

The location analysis and efficient control of hidden coal spontaneous combustion disaster in coal mine goaf: A case study

Coal spontaneous combustion significantly threatens the mine production safety. This paper focuses on the practical aspects of the disaster within the sealed goaf of Pingmei No. 11 mine. Obtained the characteristic parameters of spontaneous combustion in goaf by arranging high-level drilling field and drilling method. The grey correlation analysis method was applied to analyze the determination index of the effective characterization of the hidden spontaneous combustion area range, and interpolation aided in assessing these indicators before and after the increase amount of N2 injection. All showed that the hidden spontaneous combustion area was determined to be 70–80 m away from the return air side and 10–15 m away from the stop line, and the comprehensive treatment technologies are executed to control the spontaneous combustion hazards, including injecting inert gas to cool the high-temperature area, injecting the aqueous foam to cover the fire source, and plugging leakage to isolation the O2. Finally, the O2 concentration in goaf dropped below 5.0%, and carbon monoxide concentration fell below 0.001% after implementing these methods, effectively controlling the coal spontaneous combustion disaster. The successful management of concealed coal spontaneous combustion disasters offers a valuable reference for identifying and efficiently addressing such incidents in goaf areas.

Coke Formation and Zeolite Catalyst Effects on Products from Co‐pyrolysis of Waste Tyre and Poplar Wood in a Semi‐Batch Reactor under N2 Atmosphere

Herein, it is aimed to investigate the effect of zeolite catalyst on the co‐pyrolysis process of waste tyre and poplar wood. A laboratory‐sized reactor is used to pyrolyze 15 g of sample at 500 °C under atmospheric pressure. In the results, it is indicated that co‐pyrolysis affects the properties of the products. Comparing to single feedstock, the co‐pyrolysis enhances the yields of bio‐oil, while reducing the formation of char. The pyrolysis of poplar wood produces 23.3 wt% of bio‐oil, while the pyrolysis of tyre yields 25 wt% of bio‐oil. However, it is 31.29 wt% for the co‐pyrolysis process. Higher content of valuable compounds are formed in the bio‐oil, which improve the fuel properties of the bio‐oil. The zeolite catalyst alters the characteristics of the products obtained from the co‐pyrolysis process. The gaseous products show a decrease in the concentrations of carbon monoxide and carbon dioxide when the catalyst is present. The liquid product analysis reveals the presence of light aromatics such as ethyl benzene and light linear compounds such as alkanes including octane, hexane, etc. The analysis of the catalysts suggests that a very small amount of coke deposits on the catalyst due to the slight increase in carbon content.

A CO concentration prediction method for electronic nose based on TrellisNet with gated recurrent unit and dilated convolution

As one of the harmful substances in the atmosphere, carbon monoxide (CO) is harmful to human beings. With the wide application of gas sensors and machine learning algorithms, the accuracy of concentration predicting of various gas such as CO is constantly improving. Now we apply TrellisNet, a network utilizing both recurrent and convolutional techniques, to gas concentration prediction, which is also a time-series prediction task, with the aim of improving its performance. To enhance TrellisNet’s ability to retain long time series information, we replaced the activation units in each layer of the model with gated recurrent unit (GRU). Compared to using long short-term memory (LSTM) as the activation unit, our approach has lower computational complexity and offers a more stable model. Additionally, we introduced dilated convolutions in each layer, allowing the model to establish connections with as many past time steps as possible at a given time point, even with fewer layers. This further enhances the preservation of long time series information. We named our improved technique trellis convolutional dilated network (TrelliSense). Due to the injection of the same input values in each layer, TrelliSense also exhibits superior training stability. Experimental results demonstrate that TrelliSense outperforms other time prediction networks, including temporal convolutional network (TCN), LSTM, GRU, Gaussian-TCN and bidrectional lstm (Bi-LSTM) in terms of all error metrics (MAE, RMSE, SMAPE). Therefore, we argue that TrelliSense is a better method for predicting CO concentration.

Heme iron and carbon monoxide concentrations in meat of four tuna fish species by body site

Heme iron and carbon monoxide concentrations in meat of four tuna fish species by body site

Indoor Air Pollution from Household Cooking and Associated Health Hazards of Selected Rural Communities of Madhupur Tract in Bangladesh

Indoor air pollution is deemed one of the most severe environmental pollutions that occur mainly due to the inefficient and incomplete combustion of solid cooking fuels, which emits different types of particulate matter (PM2.5, PM10). Therefore, this study was organized in the hilly rural area of Sagordighi of Tangail district in Bangladesh to monitor the atmospheric particulate matters PM2.5, PM10 and CO (carbon monoxide) concentrations from the rural kitchens as well as the possible health hazards due to exposure of the air pollutants. One hundred and thirty air samples were collected from the kitchens by using Airveda air quality monitor and Testo 317-3 - Ambient CO meter to monitor the particulate matter and carbon-monoxide concentrations respectively during cooking and non-cooking time. A survey was also conducted through questionnaire. The concentrations of PM2.5 fluctuated from 112-999 µgm-3 whereas the mean concentration was 401.88±232 µgm-3 during cooking time. On the other hand, PM10 concentration varied from 114-1999 µgm-3 were observed during cooking time with a mean concentration was 523.10±413 µgm-3. The concentration of PM2.5 varied from 32-362 µgm-3 at non-cooking period while the mean value was 81.38±41 µgm-3. The concentration of PM10 ranged between 55-429 µgm-3 at non-cooking period and the mean of PM10 was 109.51±51 µgm-3. The mean concentration of CO during cooking period was 51.52±17 ppm and it varied from 20 ppm to 96.4 ppm. On the contrary, the mean concentration of CO during non-cooking period was 6.60± 6 ppm and it fluctuated from 0 to 40 ppm. The concentration of atmospheric particulate matter showed the highest concentration during cooking time. Contemplation on the measured concentration of particulate matters, these values were greater than concentration found in many European cities and also surpassed the Bangladesh National Ambient Air Quality Standard. The monitored CO value exceeded the recommended value. Questionnaire survey result indicated that respondents suffered from various diseases due to household cooking activities such as facing eye irritation, headaches, dry cough, dizziness and nasal congestion during cooking time. This research suggests installation of improved cooking stoves and providing proper ventilation facilities in kitchen and using renewable energy as well as creating public awareness among rural communities for minimizing the impacts of pollutants emitted from cooking activities.

A Real-time Environmental Air Pollution Predictor Model Using Dense Deep Learning Approach in IoT Infrastructure

<p>With the technical advancements in Deep Learning (DL), it is probable to construct the predictor model for monitoring and controlling pollution from real-time data. Here, IoT techniques are used for sensing the emission rate from various factors and the predictor model is constructed using the available data, for instance, carbon monoxide prediction. Modern sensors are embedded to evaluate the level of pollutants and using these modern techniques, the source of emission rate is identified and notified to the specific environment. Deep learning concepts are used for predicting the pollution level based on the current and previous data attained from the sensors. Here, we have implemented a learning solution to predict carbon monoxide concentration hourly using the novel Dense Residual Convolutional Network Model with Bi-LSTM (Bidirection-Long Short Term Memory) with the spatial and temporal features by integrating the features of the present and previous pollutant data. The side output from the residual network model is used to evaluate prediction quality. The performance is compared with existing approaches like standard LSTM, CNN, pre-trained network model, etc. The experimentation is done in a Python environment, and the proposed model facilitates more prediction accuracy for the pollutants CO,SO_2,O_3 and NO_2 than other conventional network models and establishes a better trade-off.</p>

Unraveling the Interplay of Dopamine, Carbon Monoxide, and Heme Oxygenase in Neuromodulation and Cognition.

The dopaminergic system plays important roles in neuromodulation, including prominent roles in complex neurological functions such as cognition, reward, motivation, and memory. Understandably, the highly complex nature of such physiological functions means that their regulation is intertwined with other signaling pathways, as has been demonstrated by numerous studies. Contrary to its public perception of being poisonous at all concentrations, carbon monoxide (CO) is produced endogenously from heme degradation by heme oxygenase (HO) as part of the physiological process of red blood cell turnover. Physiological concentrations of CO can reach high micromolar ranges in the hemoglobin bound form. Low-dose CO has shown therapeutic effects in numerous animal models, including traumatic brain injury via engaging various hemoprotein targets. As such, the HO-CO axis has been shown to offer beneficial effects in organ protection, anti-inflammation, and neuroprotection, among many others. Further, a large number of publications have shown the interactions among CO, HO, and the dopaminergic system. In this review, we critically examine such experimental evidence in a holistic fashion and in the context of a possible dopamine-HO-CO signaling axis. We hope that this Perspective will stimulate additional investigations into the molecular connectivity related to this possible axis and open doors to the development of novel therapeutics that impact the dopaminergic system.

Study of the effect of ignition crank angle and mixture composition on the performance of a spark-ignition engine fueled with ethanol

The publication presents the results of the measurements of the operating parameters of a spark-ignition engine fueled with 95-octane unleaded gasoline (ES95) and ethyl alcohol, approx. 92%. The measurements were carried out at a constant load: an engine speed of 1,500 rpm and a constant pressure in the intake system - MAP=0.45 bar. For each type of fuel, the measurements were carried out in two series for two variables. The ignition crank anglewas varied in the range of 0˚÷40˚ and the mixture composition λ in the range of 0.85÷1.25. The recorded engine performance parameters included torque, intake manifold pressure, intake air temperature, exhaust gas temperature and temporal fuel consumption; and exhaust gas composition was examined in terms of carbon monoxide, hydrocarbons and nitrogen oxides. The study showed that an ethanol-fueled engine has lower average efficiency compared to a gasoline one. The highest efficiency for ethanol was obtained for rich mixtures in the range λ=0.85÷1.0 and at high ignition advance angles. The use of alcohol fuel showed a very favorable effect on the composition of exhaust gas and a significantly lower content of harmful exhaust components was demonstrated. For the same operating points, carbon monoxide content was reduced by an average of 15%, and hydrocarbons and nitrogen oxides by an average of 80%.

Synergistic observation of FY-4A&4B to estimate CO concentration in China: combining interpretable machine learning to reveal the influencing mechanisms of CO variations

Accurately estimating the concentration of carbon monoxide (CO) with high spatiotemporal resolution is crucial for assessing its meteorological-environmental-health impacts. Although machine learning models have high predictive ability in environmental research, there are relatively few explanations for model outputs. Utilizing the top-of-atmosphere radiation data of China’s new generation geostationary satellites (FY-4A and FY-4B) and interpretable machine learning models, the 24-hour near-surface CO concentrations in China was conducted (resolution: 1 hour, 0.04°). The model improved by 6.6% when using the all-sky dataset (cloud-contained model, R2 = 0.759) compared to the clear-sky dataset (cloud-removed model). The interpretability analysis of the CO estimation model used two methods, namely ante-hoc (model feature importance) and post-hoc (SHapley Additive exPlanations). The importance of daytime meteorological factors increased by 51% compared to nighttime. Combining partial dependency plots, the impact of key meteorological factors on CO was elucidated to gain a deeper understanding of the spatiotemporal variations of CO.

Intercomparison of Air Quality Models in a Megacity: Toward an Operational Ensemble Forecasting System for São Paulo

AbstractAn intercomparison of four regional air quality models is performed in the tropical megacity of São Paulo with the perspective of developing a forecasting system based on a model ensemble. Modeled concentrations of the main regulated pollutants are compared with combined observations in the megacity center, after analyzing the spatial scale of representativeness of air monitoring stations. During three contrasting periods characterized by different types of pollution events, the hourly concentrations of carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter (PM2.5 and PM10) modeled by the ensemble are in moderate agreement with observations. The median of the ensemble provides the best performance (R ≈ 0.7 for CO, 0.7 for NOx, 0.5 for SO2, 0.5 for PM2.5, and 0.4 for PM10) because each model has periods and pollutants for which it has the best agreement. NOx concentration is modeled with a large inter‐model variability, highlighting potential for improvement of anthropogenic emissions. Pollutants transported by biomass burning events strongly affect the air quality in São Paulo and are associated with significant inter‐model variability. Modeled hourly concentration of ozone (O3) is overestimated during the day (≈20 ppb) and underestimated at night (≈10 ppb), while nitrogen dioxide (NO2) is overestimated at night (≈20 ppb). The observed O3 concentration is best reproduced by the median of the ensemble (R ≈ 0.8), taking advantage of the variable performance of the models. Therefore, an operational air quality forecast system based on a regional model ensemble is promising for São Paulo.

Comparison of helium and argon for the production of carbon monoxide (CO) by a plasma jet for biomedical applications

Carbon monoxide (CO) has anti-inflammatory properties and its production by plasma could be a significant advantage in the field of plasma medicine. We characterized a pulsed kHz-driven plasma jet to produce CO for biomedical applications. With no target interaction, the CO2 conversion into CO, the breakdown voltage and energy delivered to the plasma were investigated for two noble carrier gases: helium and argon. The breakdown voltage and the energy delivered to the plasma in argon gas were twice as high as in helium. The breakdown voltage was barely affected by the gas flow rate and the applied voltage, while it decreased slightly with the excitation frequency because the amount of residual charges increases with the frequency. However, the energy delivered to the plasma was not particularly affected by a change in frequency or gas flow rate, while it increased linearly with the applied voltage. CO production rose from a couple of ppm to about 2000 ppm for a specific energy input from 2 to 2000 J/L (5 × 10−4 to 5200 × 10−4 eV/(atom or molecule)), making this plasma source safe in terms of CO production for biomedical applications. Unlike literature results, the nature of the noble carrier gas did not have an impact on CO production. The CO concentration produced with 0.3% CO2 admixture increased linearly with the specific energy input (SEI) until reaching a plateau at about 2100ppm. This implies that loss processes were negligible and that CO2 dissociation was mainly due to energetic particles such as electrons and excited noble atoms. The conversion decreased with the ratio of CO2. Helium and argon as carrier gases are equivalent in terms of CO production and the CO concentration can be controlled by the SEI and the ratio of CO2.