CO diffusion during high-altitude and high-ground temperature tunnel blasting based on field monitoring and numerical simulation

Study on CO diffusion law and concentration distribution function under ventilation after blasting in high-altitude tunnel

Migration characteristics of CO under forced ventilation after excavation roadway blasting: A case study in a plateau mine

IlluminationsA simple model to demonstrate perfusion and diffusion limitation of gasesPraghalathan Kanthakumar, and Vinay OommenPraghalathan KanthakumarDepartment of Physiology, Christian Medical College, Vellore, Tamil Nadu, India, and Vinay OommenDepartment of Physiology, Christian Medical College, Vellore, Tamil Nadu, IndiaPublished Online:01 Dec 2012https://doi.org/10.1152/advan.00077.2012MoreSectionsPDF (400 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations transfer of gases across the respiratory membrane is limited by either perfusion or diffusion (1). This concept is usually explained in terms of whether a gas reaches its partial pressure equilibrium in the pulmonary capillaries. If it does, the gas is considered to be “perfusion limited,” and if it does not, it is considered to be “diffusion limited.” To facilitate the understanding of this concept, we designed an analogical model using paper cups and water to simulate the physiological principles behind gas transport.MATERIALS AND METHODSMaterials RequiredThe following materials are required: paper cups (∼200-ml volume), a long frame in which to stick the paper cups (this should be able to hold the weight of the cups filled with water), instant glue, and a sink with a tap where the experiment is to be performed.Construction of the ModelTen identical paper cups were stuck on a plastic wire casing ∼2 in. wide and 40 in. long using instant glue. Sufficient space was given between the cups to prevent water overflowing from one cup to another. The final assembled model is shown in Fig. 1A.Fig. 1.A: model showing paper cups stuck on a long plastic frame. B: the cups are moved under a running tap. The flow of water represents the diffusion of a gas across the respiratory membrane. The speed of movement of the cups represents the rate of perfusion. The volume of the cups represents the capacity of the blood for the gas at its alveolar partial pressure.Download figureDownload PowerPointPhysiological BasisIn this analogy, each cup stood for the capacity of the blood for the gas at its alveolar partial pressure. The capacity of the blood for a gas includes dissolved gas as well as other forms, such as bound to hemoglobin. The demonstration of perfusion limitation used oxygen as the example, whereas the demonstration of diffusion limitation used carbon monoxide (CO) as the example. These gases were chosen as they have nearly identical solubility coefficients and both bind to hemoglobin. For both gases, the hemoglobin-bound form is the major determinant of the total gas content in the blood. This enabled the use of similarly sized paper cups in this analogy.The speed at which the cups are moved stood for the perfusion rate. The flow of water through the tap was equivalent to the diffusion of a gas across the respiratory membrane (Fig. 1B). Cups filled up to the brim were equivalent to the pulmonary capillary partial pressure of the gas reaching its equilibrium with alveolar air.RESULTSPresentation of the ModelThis model was presented to first-year medical students in groups of 15 students. Students were first shown the model, and the components of the model were then explained. Students were told that the cups would be positioned under a running tap and moved at a constant speed.Two volunteers from each group were asked to perform the experiment in front of the others. While one student timed the process, the other student moved the cups under the running tap. The experiment was conducted in an area where the sink had sufficient space to maneuver the setup.Two experiments were conducted. The first experiment was designed to convey the physiological principles underlying oxygen transfer that make it “perfusion limited.” The second experiment was designed to simulate the physiological factors that make CO transfer “diffusion limited.”At the start, students were given a set of instructions to perform the experiment. A set of questions was discussed during each experiment.Experiment 1: model of oxygen transfer across the respiratory membrane.Students were given written instructions to fill the water cups to approximately three-quarters of their capacity. They were to adjust the water flow through the tap such that the remaining volume filled in ∼4 s. While one student moved the cups, the other student told the first student when to do so. The first cup was placed under the tap for 12 s. After 12 s had elapsed, the next cup was to be brought under the tap. This continued for 60 s (Fig. 2A).Fig. 2.Model of oxygen transfer across the respiratory membrane. A: the cups, representing the oxygen carrying capacity of the blood at 100 mmHg (alveolar Po2), are three-quarters full to begin with, to represent 75% saturation of hemoglobin with oxygen in mixed venous blood (75% hemoglobin saturation approximately equals 75% of the oxygen carrying capacity of the blood). They are moved at a speed of 1 cup every 12 s, which represents the rate of perfusion. Water flow, representing the rate of diffusion, is adjusted such that the remaining one-quarter of the cup is filled in 4 s. The equilibrium of Po2 between the alveolus and blood, represented by a full cup, is reached in one-third of the transit time. At the end of 1 min, the total amount of water collected, representing the total oxygen transferred across the membrane, amounts to 1.25 cups. B: the cups are moved at a faster rate of 1 cup every 6 s to simulate increased perfusion. The water collected at the end of 1 min has increased to 2.5 cups, showing that the speed of the moving cups (perfusion) limits the water collected. C: the flow of water through the tap is increased to more than the initial flow in A and B while the speed of the cups remains at 1 cup every 12 s. The amount of water collected remains at 1.25 cups in 1 min, showing that water flow (diffusion) does not limit the water collected.Download figureDownload PowerPointThe questions given to aid the discussion were as follows: 1. Why were the cups three-quarters full at the beginning of the experiment?2. Why does one wait for 12 s even though the time taken for the cups to fill was only 4 s?3. Would increasing the speed at which the cups were moved increase the amount of water collected in a given time? [Students were instructed to repeat the experiment by increasing the speed of cup movement to one cup every 6 s instead of 12 s (Fig. 2B).]4. Would increasing the water flow through the tap increase the amount of water collected in a given time? [Students were asked to repeat the experiment with the normal speed of cup movement and a higher water flow (Fig. 2C).]5. What limited the amount of water carried in a given time in the above experiment and what was the physiological equivalent of this factor?It was explained to the students that the mixed venous blood having a Po2 of 40 mmHg is ∼75% saturated with oxygen. Therefore, the cups that stood for the oxygen carrying capacity of the blood at 100 mmHg (alveolar Po2) were three-fourths full at the beginning of the experiment. (At this point, the contribution of dissolved and hemoglobin-bound forms of oxygen can be introduced to the students. Pulmonary capillary Po2 is determined by the dissolved form. The difference in partial pressures between the alveoli and capillaries is one of the factors that determine the rate of diffusion. However, the dissolved form contributes very little to total oxygen carrying capacity. The oxygen carrying capacity is nearly equal to the amount of oxygen bound to hemoglobin.)The time taken for the pulmonary capillary blood to equilibrate with alveolar Po2 at rest is about one-thirds the transit time across the alveoli. Thus, although the cups filled in 4 s, students were asked to wait for 12 s.When the students filled water at the normal rate, five cups were full at the end of 1 min (Fig. 2A). However, when the students moved the cups at twice the rate, it was apparent that although the cups were moving faster, all the cups leaving the tap were still getting filled to the brim. At the end of 1 min, students had 10 full cups, showing that the amount of water collected in a given time had increased (Fig. 2B).When only the flow of water was increased with the rate of movement of the cups kept constant, students noticed that there was more overflow of water. At the end of 1 min, only five cups were full. The total amount of water collected did not increase (Fig. 2C).Increasing the rate of cup movement increased the amount of water collected, but increasing the water flow through the tap did not. Therefore, the limiting factor was identified as the “rate of cup movement.” As the rate of cup movement stood for the rate of perfusion, this was explained as a model of perfusion limitation.Experiment 2: model of CO transfer across the respiratory membrane.Students were instructed to use empty water cups for this experiment. The water flow through the tap was adjusted such that each cup filled in ∼30 s. One student held the row of cups while another student timed the process. The first cup was to be placed under the tap for 12 s. After 12 s had elapsed, the next cup was positioned under the tap. This was repeated for 1 min (Fig. 3A).Fig. 3.Model of carbon monoxide (CO) transfer across the respiratory membrane. A: the cups, representing the capacity of the blood for CO, are empty as there is no CO in the blood to start with. (CO can completely displace oxygen from hemoglobin, and thus all the hemoglobin in the blood can bind CO.) They are moved at a speed of 1 cup every 12 s, which represents the rate of perfusion. Water flow, representing the rate of diffusion, is adjusted such that each cup is filled in 30 s. The equilibrium of the partial pressure of CO between the alveolus and blood, represented by a full cup, is not reached. At the end of 1 min, the total amount of water collected, representing the total CO transferred across the membrane, amounts to 2 cups. B: the cups are moved at a faster rate of 1 cup every 6 s to simulate increased perfusion. Although the speed of the cup movement has doubled, the water collected in each cup has halved. The water collected at the end of 1 min has remained at 2 cups, showing that the speed of the moving cups (perfusion) does not limit the amount of water collected. C: the flow of water through the tap is increased to more than the initial flow in A and B while the speed of the cups remains at 1 cup every 12 s. The amount of water collected is >2 cups, showing that the water flow (diffusion) limits the total amount of water collected.Download figureDownload PowerPointThe questions given to aid the discussion were as follows: 1. Why were the cups empty at the beginning of the experiment as opposed to the earlier experiment?2. Why was the water flow through the tap set at a low flow?3. Would increasing the speed at which the cups were moved increase the amount of water collected in a given time? [Students were asked to repeat the experiment by increasing the speed of cup movement, once every 6 s, and note the amount of water collected (Fig. 3B).]4. Would increasing the water flow through the tap increase the amount of water collected in a given time? [Students were asked to repeat the experiment with the normal speed of cup movement and a higher water flow (Fig. 3C).]5. What limited the amount of water carried in a given time in this experiment and what was the physiological equivalent of this factor?It was explained to the students that the capacity of the blood to carry CO was shown by the volume of the cups. As CO can displace oxygen from hemoglobin, all the hemoglobin molecules are available for CO to bind. Therefore, the cups that were moved under the tap were empty. (At this point, students can be reminded of the fact that as in the case of oxygen, the hemoglobin-bound form of CO is the major contributor to the CO content of blood. Although the dissolved form determines the partial pressure of the gas, the contribution of the dissolved form to total CO content of the blood is small.) The flow of water was kept low as the diffusion rate for CO is lower than that of oxygen. This is because there is a low concentration of CO in inspired air and therefore a smaller concentration gradient across the diffusing membrane.Collecting water at the normal rate for 1 min resulted in five partially filled cups (Fig. 3A). Moving the cups every 6 s doubled the speed at which the cups were moved. However, each cup carried only half the amount of water as before. Therefore, the amount of water collected in a given time remained the same (Fig. 3B). However, when only the flow of water was increased, the amount of water collected in each cup increased, and the total amount of water collected in 1 min was more than the previous two occasions (Fig. 3C).In this experiment, increasing the rate of cup movement did not increase the amount of water collected in a given time, but increasing the water flow through the tap did. Therefore, the factor that limited the amount of water collected was the “flow of water through the tap.” This was analogous to the diffusion of a gas across the respiratory membrane. Hence, this model simulates the diffusion limitation of a gas.DISCUSSIONBoth the experiments described above were designed to incorporate different physiological factors that, under normal circumstances, make oxygen perfusion limited and CO diffusion limited. It was also emphasized that although oxygen transfer is known to be perfusion limited and CO is known to be diffusion limited, these are not intrinsic properties of the gases themselves.The factors that make oxygen perfusion limited that were modeled were as follows: 1. A high rate of diffusion as a result of a large partial pressure difference. This was modeled using a higher water flow as opposed to that of CO.2. A lower capacity of the blood to bind new oxygen (compared with CO) as hemoglobin is 75% saturated with oxygen in the mixed venous blood. This was modeled using cups that were three-quarters full.The factors that make CO diffusion limited that were modeled were as follows: 1. A low rate of diffusion due to a low concentration of the gas, such as used in diffusion studies. This was modeled using a low water flow compared with that of oxygen.2. A high capacity of the blood to bind CO, as CO can easily displace oxygen from hemoglobin due to its high affinity. This was modeled using empty cups.This model can also be used to illustrate the behavior of gases, such as nitrous oxide, that do not bind to hemoglobin. However, in this case, it must be clearly emphasized to students that the volume of the cup, which represents the gas carrying capacity at the alveolar partial pressure, mainly consists of the gas in the dissolved form.LimitationsIn our model, the water flow representing oxygen diffusion was kept at one cup every 12 s and the flow representing CO diffusion was kept lower, at one cup every 30 s. However, physiologically, the CO diffusion rate is proportionately much lower considering the low partial pressures of CO used in diffusion studies. To accurately reflect this would have required a very low water flow, which was not practical.The rate of diffusion in our model was represented by the flow of water through the tap. Equilibrium of partial pressures is represented by a full cup, after which water overflows. In the lungs, however, the rate of diffusion keeps decreasing as the partial pressure equilibrium is approached due to a decreasing partial pressure gradient, until it stops at equilibrium. Ideally, to model this, the flow of water through the tap should gradually decrease as the cup fills and stop once the cup is full. This can be modeled using a siphon and a water reservoir, but the design would be complex and the time to fill each cup would be very long.This model specifically focuses on the concepts of diffusion and perfusion limitation of gases in the lung. Care must be taken while integrating this model with other aspects of respiratory physiology.ConclusionsThe model described above can be constructed in a short time, using materials that are inexpensive, and the experiments can be done in any place having a tap and a sink. The experiments enabled the students understand the concept of diffusion and perfusion limitation of gases easily. Apart from imparting an intuitive understanding of this concept, this experiment also served to reinforce other physiological principles such as the oxygen-hemoglobin dissociation curve, partial pressure of gases, factors that govern the diffusion rates of gases, and the pathophysiology of CO poisoning.We believe that this experiment simplifies the concept of perfusion and diffusion limitation of gases, which is otherwise a difficult concept for medical students.DISCLOSURESNo conflicts of interest, financial or otherwise, are declared by the author(s).AUTHOR CONTRIBUTIONSAuthor contributions: P.K. and V.T.O. conception and design of research; P.K. and V.T.O. performed experiments; P.K. and V.T.O. prepared figures; P.K. drafted manuscript; P.K. and V.T.O. edited and revised manuscript; P.K. and V.T.O. approved final version of manuscript.REFERENCE1. Boron WF. Gas exchange in the lungs. In: Medical Physiology, edited by , Boron WF , Boulpaep EL. Philadelphia, PA: Saunders Elsevier, 2009, p. 690–692.Crossref | Google ScholarAUTHOR NOTESAddress for reprint requests and other correspondence: P. Kanthakumar, Dept. of Physiology, Christian Medical College, Vellore, Tamil Nadu 632002, India (e-mail: [email protected]com). Download PDF Previous Back to Top Next FiguresReferencesRelatedInformation Cited By“Seeing red” reflects hemoglobin’s saturation state: a discovery-based activity for understanding the science of pulse oximetryHeidi L. Lujan and Stephen E. DiCarlo28 July 2022 | Advances in Physiology Education, Vol. 46, No. 3 More from this issue > Volume 36Issue 4December 2012Pages 352-355 Copyright & PermissionsCopyright © 2012 the American Physiological Societyhttps://doi.org/10.1152/advan.00077.2012PubMed23209018History Received 29 May 2012 Accepted 27 August 2012 Published online 1 December 2012 Published in print 1 December 2012 Metrics

It has been reported that exhaled carbon monoxide (CO) concentrations and arterial carboxyhemoglobin (CO-Hb) concentration in blood may be increased in critically ill patients. However, there was no study that examined correlation among amount of CO in exhaled air, CO-Hb concentrations in erythrocytes, and bilirubin IXalpha (BR) in serum, i.e., the three major indexes of heme catabolism, within the same subject. Here, we examined CO concentrations in exhaled air, CO-Hb concentrations in arterial blood, and BR levels in serum in 29 critically ill patients. Measurements of exhaled CO, arterial CO-Hb, and serum total BR have been done in the intensive care unit. As control, exhaled CO concentration was also measured in eight healthy volunteers. A median exhaled CO concentration was significantly higher in critically ill patients compared with control. There was significant correlation between CO and CO-Hb and CO and total BR level. We also found CO concentrations correlated with indirect BR but not direct BR. Multivariate linear regression analysis for amount of exhaled CO concentrations also showed significant correlation with CO-Hb and total BR, despite the fact that respiratory variables of study subjects were markedly heterogeneous. We found no correlation among exhaled CO, patients' severity, and degree of inflammation, but we found a strong trend of a higher exhaled CO concentration in survivors than in nonsurvivors. These findings suggest there is an increased heme breakdown in critically ill patients and that exhaled CO concentration, arterial CO-Hb, and serum total BR concentrations may be useful markers in critically ill conditions.

Mycobacterium tuberculosis Senses Host-Derived Carbon Monoxide during Macrophage Infection

Air pollution is a growing concern in rural areas where agricultural production can be reduced by it. This article analyses data obtained as part of a research project. The aim of this study is to understand the influence of atmospheric pressure, air temperature, air relative humidity, longitude and latitude of the location, and indoor and outdoor environment on local rural workplace diversity of air pollutants such as carbon monoxide (CO) and suspended particulate matter (SPM), as well as the contribution of these variables to changes in such air pollutants. The focus is on four topics: motivation, innovation and creativity, leadership, and social responsibility. Furthermore, this study developed an artificial neural network (ANN) model to predict CO and SPM concentrations in the air based on data collected from the mentioned inputs. The related sensors were assembled on an Arduino Mega 2560 board to form a field-portable device to detect air pollutants and meteorological parameters. The sensors included an MQ7 sensor for CO concentration measurement, a Sharp GP2Y1010AU0F dust sensor for SPM concentration measurement, a DHT11 sensor for air temperature and air relative humidity measurement, and a BMP180 sensor for air pressure measurements. The longitude and latitude of the location were measured using a smartphone. Measurements were conducted from 20 December 2021 to 16 July 2022. Results showed that the overall average outdoor CO and SPM concentrations were 10.97 ppm and 231.14 μg/m3 air, respectively. The overall average indoor concentrations were 12.21 ppm and 233.91 μg/m3 air for CO and SPM, respectively. Results showed that the ANN model demonstrated acceptable performance in predicting CO and SPM in both the training and testing phases, exhibiting a coefficient of determination (R2) of 0.575, a root mean square error (RMSE) of 1.490 ppm, and a mean absolute error (MAE) of 0.994 ppm for CO concentrations when applying the testing dataset. For SPM concentrations, the R2, RMSE, and MAE using the test dataset were 0.497, 30.301 μg/m3 air, and 23.889 μg/m3 air, respectively. The most influential input variable was air pressure, with contribution rates of 22.88% and 22.82% in predicting CO and SPM concentrations, respectively. The acceptable performance of the developed ANN model provides potential advances in air quality management and agricultural planning, enabling a more accurate and informed decision-making process regarding air pollution. The results of short-term estimation of CO and SPM concentrations suggest that the accuracy of the ANN model needs to be improved through more comprehensive data collection or advanced machine learning algorithms to improve the prediction results of these two air pollutants. Moreover, as even lower cost devices can predict CO and SPM concentrations, this study could lead to the development some kind of virtual sensor, as other air pollutants can be estimated from measurements of particulate matters.

Low correlation between household carbon monoxide and particulate matter concentrations from biomass-related pollution in three resource-poor settings

Air pollution has long been and is still a problem for humans and the environment, a priority in countries with high vehicle rates. The contribution of gas released reaches 60-70%. Motor vehicles produce one of the pollutants, namely Carbon Monoxide (CO) gas. This research was conducted to determine the level of CO concentration results from sampling using impinger and prediction models using Caline4 software. The results of the concentration between direct measurements with the Caline4 model were then compared. This research was conducted in three streets for three working days namely Jl.Urip Sumoharjo, Jl.Talasalapang, and Jl.Nusantara. The methodology used is direct measurement using impinger, calculating vehicle volume at each measurement point, and analyzing CO concentrations using Caline4. The results of sampling showed the highest CO concentration on Jl.Urip Sumoharjo at 5.80 ppm, Jl.Talasalapang at 1.06 ppm, and Jl.Nusantara 1.15 ppm. The highest estimation results of CO concentrations with Caline4 on Jl.Urip Sumoharjo were 5.7 ppm, Jl.Talasalapang by 1.1 ppm, and Jl. Nusantara was 1.4 ppm. High or low CO concentration value depends on vehicles volume, for instance, CO concentration increases with increasing vehicles volume. In addition, it is also depends on the meteorological factor, such as, the faster the wind increases, the faster the pollutants will increase. Then compare the results of CO impinger concentration and Caline4 using the t-Test to see the difference of the two CO concentrations. Comparisons were made using the t-Test to meet the t-stat < t-critical concluded that there was no significant difference between the two CO concentrations.

Generally, larger cities are characterized by traffic congestion, which is associated with higher concentrations of pollution, including Carbon Monoxide (CO) pollution. However, this convention requires empirical support on the basis of accurate and reliable measurements. In addition, the assessment of the effect of CO on the autonomic nervous system (ANS), as measured by heart rate variability (HRV), has yielded conflicting results. A majority of the (few) studies on the topic have shown that increases in CO concentration of up to about 10 parts per million (ppm) are associated with a decrease in stress and risk to health in subjects. Beyond the hypothesis postulating city size as a determinant of increased CO concentration, the hypothesis proposing a causal link between CO concentration and HRV balance also requires empirical support. This article compares CO concentrations in a large metropolis with those in a small town, analyzing the relationship between CO and the HRV responses of young women in terms of city size. Four different types of environments were compared, taking into account mediating variables. The study participants spent 35 min in selected environments (a city center, a residential environment, a park, and a home) wearing Polar devices to measure HRV, and portable devices to measure noise thermal load and CO. The average concentrations of CO in each environment were calculated, along with the time distribution of the CO concentration, and the regression slopes between the concentrations of CO and the ANS balance, as measured by the low frequency power/high frequency power ratio (LF/HF) expressed as an HRV index. The results show that, regardless of size, the cities measured were all characterized by low levels of CO, far below the maximal accepted threshold standards, and that urban residents were exposed to these concentrations for less than half of the daytime hours. Furthermore, in contrast to the common view, larger cities do not necessarily accumulate higher concentrations of CO compared to small cities, regardless of the level of transport congestion. This study confirms the findings of the majority of the other studies on the subject, which showed a decrease in stress (as measured by HRV) as a result of an increase in CO concentrations below 7 ppm. Finally, following the assessment of the differential contribution attributed to the different environmental factors, it appears that noise, thermal load, and congestion all contribute more to a higher level of HRV balance than CO. This finding highlights the importance of a multivariable approach to the study, and a remediation of the effect of environmental factors on stress in urban environments.

Ambient particulate matter has been associated consistently with an increased risk for mortality largely due to cardiovascular diseases.1 Although the relative risk estimates from epidemiological studies are small, they apply to almost the entire population of the United States. Consequently, exposure to ambient particles produces considerable burden of disease, and its mitigation offers the benefit of improving life expectancy.2 Articles see pp 941 and 949 Over the past decade, research has substantiated the understanding of the pathophysiological mechanisms linking ambient particles to the cardiovascular system3,4 once it was noted that ambient air pollution elicits systemic inflammatory responses in the general population.5 An update of the American Heart Association statement on air pollution and cardiovascular disease3 is under way. Mechanisms considered for active and secondhand smoke as well as ambient air pollution are strikingly similar.4,6,7 They include progression of atherosclerotic plaques to vulnerable forms, prothrombotic states, endothelial dysfunction, and altered autonomic nervous system control (Figure). Increased systemic oxidative stress is considered the key mechanism responsible for most of these pathophysiological changes. Increased risks for cardiovascular disease in general and coronary artery disease in particular have been documented for active and secondhand smoke as well as ambient particulate matter. Deep venous thrombosis has been added to this list recently.8 Figure. Overview on pathomechanism linking ambient air pollution,4 secondhand smoke,7 and active smoking to acute coronary syndromes. Nevertheless, the public health relevance of particulate matter in the light of the smoking literature remains hotly debated. Smokers are exposed to considerably higher cumulative doses of particulate matter than the general nonsmoking population. Mortality due to low doses of ambient particles may be considered counterintuitive compared with doses of particles tolerated by smoking individuals. A systematic assessment of the exposure-response function ranging from low doses of inhaled particles …

Preliminary evaluation, using passive tubes, of carbon monoxide concentrations in outdoor and indoor air at street level shops in Genoa (Italy)

Carbon monoxide (CO) concentrations, sea-to-air fluxes and microbial consumption rate constants, along with atmospheric CO mixing ratios, were measured in the East China Sea (ECS) in autumn. Atmospheric CO mixing ratios varied from 96 to 256 ppbv, with an average of 146 ppbv (SD = 54 ppbv, n = 31). Overall, the atmospheric CO concentrations displayed a decreasing trend from inshore to offshore stations. The surface water CO concentrations in the investigated area ranged from 0.24 to 6.12 nmol L−1, with an average of 1.68 nmol L−1 (SD = 1.50 nmol L−1, n = 31). The surface water CO concentrations were affected significantly by sunlight. Vertical profiles showed that CO concentrations declined rapidly with depth, with the maximum appearing in the surface water. The surface CO concentrations were oversaturated, with the saturation factors ranging from 1.4 to 56.9, suggesting that the ECS was a net source of atmospheric CO. The sea-to-air fluxes of CO in the ECS ranged from 0.06 to 11.31 µmol m−2 d−1, with an average of 2.90 µmol m−2 d−1 (SD = 2.95µmol m−2 d−1, n = 31). In the incubation experiments, CO concentrations decreased exponentially with incubation time and the processes conformed to the first order reaction characteristics. The microbial CO consumption rate constants in the surface water (KCO) ranged from 0.063 to 0.22 h−1, with an average of 0.12 h−1 (SD = 0.062 h−1, n = 6). A negative correlation between KCO and salinity was observed in the present study.

Exhaled carbon monoxide (CO) concentrations were measured on a CO monitor by vital capacity maneuvers in asthmatic patients receiving or not receiving inhaled corticosteroids and in nonsmoking and smoking healthy control subjects. CO was detectable and measured reproducibly in the exhaled air of all subjects. The exhaled CO concentrations were higher in asthmatic patients not receiving inhaled corticosteroids (5.6+/-0.6 ppm, p < 0.001) and similar in asthmatic patients receiving inhaled corticosteroids (1.7+/-0.1 ppm) compared with those in nonsmoking healthy control subjects (1.5+/-0.1 ppm). Smoking healthy control subjects had the highest levels of exhaled CO concentration among the groups (21.6+/-2.8 ppm, p < 0.001). To examine whether inhaling corticosteroids reduce exhaled CO concentration in a given asthmatic patient, 12 patients with symptomatic asthma who were being treated by inhaled beta2-agonists alone underwent measurements of exhaled CO concentration before and 4 wk after the initiation of inhaled corticosteroid treatment. All patients had reductions in exhaled CO concentration (p < 0.001) and eosinophil cell counts in sputum (p < 0.01) that were accompanied by an improvement in airway obstruction. Changes in exhaled CO concentration were significantly related to those in the eosinophil cell counts in sputum (p < 0.001). The present study shows an elevation of exhaled CO in asthmatic patients that decreases with corticosteroid therapy. Increases in the exhaled CO levels therefore may reflect inflammation in the asthmatic lung.

Carbon monoxide degassing from seismic fault zones in the Basin and Range province, west of Beijing, China

Long-term spatial distributions and trends of ambient CO concentrations in the central Taiwan Basin