Samenvatting
Atmospheric flask samples are either collected at atmospheric pressure by opening a valve of a pre-evacuated flask or pressurized with the help of a pump to a few bar above ambient pressure. Under humid conditions, there is a risk that water vapor in the sample leads to condensation on the walls of the flask, notably at higher than ambient sampling pressures. Liquid water in sample flasks is known to affect the CO2 mixing ratios and also alters the isotopic composition of oxygen (17O and 18O) in CO2 via isotopic equilibration. Hence, for accurate determination of CO2 mole fractions and its stable isotopic composition, it is vital to dry the air samples to a sufficiently low dew point before they are pressurized in flasks to avoid condensation. Moreover, the drying system itself should not influence the mixing ratio and the isotopic composition of CO2 or that of the other constituents under study. For the Airborne Stable Isotopes of Carbon from the Amazon (ASICA) project focusing on accurate measurements of CO2 and its singly substituted stable isotopologues over the Amazon, an air-drying system capable of removing water vapor from air sampled at a dew point lower than -2 °C, flow rates up to 12 L min-1 and without the need for electrical power was needed. Since to date no commercial air-drying device that meets these requirements has been available, we designed and built our own consumable-free, power-free and portable drying system based on multitube Nafion™ gas sample driers (Perma Pure, Lakewood, USA). The required dry purge air is provided by feeding the exhaust flow of the flask sampling system through a dry molecular sieve (type 3A) cartridge. In this study we describe the systematic evaluation of our Nafion™-based air sample dryer with emphasis on its performance concerning the measurements of atmospheric CO2 mole fractions and the three singly substituted isotopologues of CO2 (16O13C16O, 16O12C17O and 16O12C18O), as well as the trace gas species CH4, CO, N2O and SF6. Experimental results simulating extreme tropical conditions (saturated air at 33 °C) indicated that the response of the air dryer is almost instantaneous and that approximately 85 L of air, containing up to 4 % water vapor, can be processed staying below a -2 °C dew point temperature (at 275 kPa). We estimated that at least eight flasks can be sampled (at an overpressure of 275 kPa) with a water vapor content below -2 °C dew point temperature during a typical flight sampling up to 5 km altitude over the Amazon, whereas the remaining samples would stay well below 5 °C dew point temperature (at 275 kPa). The performance of the air dryer on measurements of CO2, CH4, CO, N2O, and SF6 and the CO2 isotopologues 16O13C16O and 16O12C18O was tested in the laboratory simulating real sampling conditions by compressing humidified air from a calibrated cylinder, after being dried by the air dryer, into sample flasks. We found that the mole fraction and the isotopic composition difference between the different test conditions (including the dryer) and the base condition (dry air, without dryer) remained well within or very close to, in the case of N2O, the World Meteorological Organization recommended compatibility goals for independent measurement programs, proving that the test condition induced no significant bias on the sample measurements.
Originele taal-2 | English |
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Pagina's (van-tot) | 4051-4064 |
Aantal pagina's | 14 |
Tijdschrift | Atmospheric Measurement Techniques |
Volume | 13 |
Nummer van het tijdschrift | 7 |
DOI's | |
Status | Published - 28-jul.-2020 |
Vingerafdruk
Duik in de onderzoeksthema's van 'Evaluation of a field-deployable Nafion (TM)-based air-drying system for collecting whole air samples and its application to stable isotope measurements of CO2'. Samen vormen ze een unieke vingerafdruk.Datasets
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Evaluation of a field-deployable Nafion™-based air drying system for collecting whole air samples and its application to stable isotope measurements of CO2
Paul, D. (Contributor), University of Groningen, 1-jan.-2020
DOI: 10.34894/vbvvwn, https://doi.org/10.34894%2Fvbvvwn
Dataset