Methane, carbon monoxide, and carbon dioxide concentrations measured in the atmospheric surface layer over continental Russia in the TROICA experiments

2006 ◽  
Vol 42 (1) ◽  
pp. 46-59 ◽  
Author(s):  
I. B. Belikov ◽  
C. A. M. Brenninkmeijer ◽  
N. F. Elansky ◽  
A. A. Ral’ko
1961 ◽  
Vol 41 (2) ◽  
pp. 187-196 ◽  
Author(s):  
J. M. McArthur ◽  
J. E. Miltimore

Methods are described for sampling and analysing rumen gases. The analysis requires less than 15 minutes for the determination of hydrogen, oxygen, nitrogen, methane, carbon monoxide, carbon dioxide, and hydrogen sulphide, i.e., for all gases occurring in the rumen. The method is sensitive and requires only a small quantity of sample, and the sample volume need not be known. The presence of water or other vapours in the sample does not influence the results. Relative thermal detector responses have been determined for gases which occur in the rumen. These eliminate the necessity for the calibration of gas chromatographs using thermal detection. The first complete analysis of rumen gas is presented.


Author(s):  
Ю.А. Тунакова ◽  
С.В. Новикова ◽  
А.Р. Шагидуллин ◽  
В.С. Валиев

Снижение углеродного следа в настоящее время является одной из приоритетных задач мировой экономики. Для достижения этой цели необходимо с одной стороны снижать выбросы парниковых газов, с другой стороны развивать методы мониторинга парниковых газов в атмосферном воздухе для обеспечения контроля эффективности принимаемых решений.Учитывая сложность процессов рассеивания газов в атмосферном воздухе, значительными преимуществами в вопросах определения концентраций атмосферных примесей обладают нейросетевые методы моделирования. В данной статье представлен метод расчета концентраций углекислого газа в атмосферном воздухе с помощью спроектированной и обученной каскадной нейросетевой модели, позволяющей при расчете концентраций учитывать сложное влияние метеорологических факторов и локальных условий рассеивания. Первым уровнем модели является расчет концентрации оксида углерода по известным параметрам источников выбросов этого вещества с использованием регламентированной методики расчета рассеивания примесей в атмосфере в Унифицированной программе расчета рассеивания «Эколог-Город». Вторым уровнем является нейронная сеть, которая корректирует рассчитанную на первом шаге концентрацию по заданным метеорологическим параметрам для увеличения точности моделирования. Третьим уровнем является нейронная сеть, позволяющая по полученной на предыдущем шаге концентрации оксида углерода, а также измеренным значениям коэффициента химической трансформации и концентрации атмосферного озона производить расчет концентрации углекислого газа.Полученная каскадная модель апробирована на территории г. Нижнекамск. Достигнутая точность расчета концентрации углекислого составила более 95%. Таким образом, представленная технология позволяет расширить возможности локальной системы мониторинга в условиях недостаточного количества измерений диоксида углерода. Reducing the carbon footprint is currently one of the priorities for the world economy. To do this, it is necessary to reduce greenhouse gas emissions, as well as to develop methods for monitoring greenhouse gases in the atmospheric air to ensure control over the effectiveness of decisions taken.Considering the complexity of the processes of dispersion of gases in the atmospheric air, neural network modeling methods have significant advantages in determining the concentrations of atmospheric impurities. This article presents a method for calculating the concentration of carbon dioxide in the atmospheric air using a designed and trained cascade neural network model, which makes it possible to take into account the complex influence of meteorological factors and local dispersion conditions when calculating concentrations. The first level of the model is the calculation of the concentration of carbon monoxide according to the known parameters of the emission sources of this substance using the regulated method for calculating the dispersion of impurities in the atmosphere in the Unified program for calculating dispersion "Ecolog-City". The second level is a neural network, which corrects the concentration calculated at the first step according to the specified meteorological parameters to increase the modeling accuracy. The third level is a neural network that allows calculating the concentration of carbon dioxide based on the concentration of carbon monoxide obtained at the previous step, as well as the measured values of the coefficient of chemical transformation and concentration of atmospheric ozone.The resulting cascade model was tested on the territory of Nizhnekamsk. The achieved accuracy of calculating the concentration of carbon dioxide was more than 95%. Thus, the presented technology makes it possible to expand the capabilities of the local monitoring system in conditions of an insufficient number of measurements of carbon dioxide.


2020 ◽  
Author(s):  
Marina Loskutova ◽  
Alexander Makshtas ◽  
Tuomas Laurila ◽  
Eija Asmi

<p>The Arctic region is one of the main areas of greenhouse gases sources due to large amount of biomass, carbon stocks in the soil and extensive wetlands. Large resources of previously inactive organic carbon may take part in atmospheric chemical reactions under melting permafrost conditions. In this case, carbon dioxide concentrations will increase in the atmosphere. Since 2015 Arctic and Antarctic Research Institute in cooperation with Finnish Meteorological Institute have been measuring the continuous concentrations of water vapor, methane, carbon dioxide and carbon monoxide at Research Station "Ice Base Cape Baranova" (79° 18´ N, 101° 48´ E, 30 m asl.) using cavity ringdown spectroscopy (CRDS) analyzer Picarro G2401. The sampling inlet is located at 10 m height.</p><p>Data preprocessing consists of deleting values obtained during power failures and 2 minutes after calibration. The values for wind directions corresponding to the transfer from diesel power station (90 - 145 °) and for wind speeds less than 3 m/s were also discarded because in this case polluted air may be distributed over the station homogeneously. After that data were adjusted taking into account the nearest calibration values by linear interpolation. The archive of carbon dioxide concentrations data averaged over each hour from October 2015 to December 2019 was used for further analysis.</p><p>CO<sub>2</sub> time series are characterized by a pronounced annual variation with concentration decreasing in summer months. The absorption by sea phytoplankton in the absence of sea ice cover causes the annual variability of carbon dioxide. Besides, the predominant presence of stable stratification of the atmospheric surface layer throughout the polar night contributes to accumulation of the gas in the surface layer in winter. The annual amplitude is 18–20 ppm approximately, which is consistent with the data of Alert and Barrow polar stations.</p><p>The analysis of the dependence of registered concentration distribution on the wind direction shows that the highest values are observed during the air-mass transfer from the south-western and northern directions. If the first case can be explained by the anthropogenic impact and presence of extensive wetlands in the summer, the reason for the second one requires a more detailed analysis. Applying the HYSPLIT trajectory model for cases of elevated values of greenhouse gas concentrations did not allow us to obtain an unambiguous answer. Although elevated values were observed, as a rule, when air masses transferred from the regions of Norilsk, Yamal, the Kola Peninsula, and Lena estuary, however, there were cases of elevated concentrations during the transfer of air masses from the Arctic Ocean. This may be due to the action of any local sources, but their detection requires additional data analysis. The work had been executed in frame of CNTP Roshydromet 1.5.3.3.</p>


Sign in / Sign up

Export Citation Format

Share Document