Studies of the Dispersed Composition of Atmospheric Aerosol and Its Relationship with Small Gas Impurities in the Near-Water Layer of Lake Baikal Based on the Results of Ship Measurements in the Summer of 2020

The atmosphere over Lake Baikal covers a vast area (31,500 square meters) and has more significant differences in the composition and variability of gaseous and aerosol components in atmospheric air than in coastal continental areas and is still a poorly studied object. In recent years, the anthropogenic impact on the ecosystem of Lake Baikal has been increasing due to the development of industry in the region, the expansion of tourist infrastructure and recreational areas of the coastal zone of the lake. In addition, one of the significant sources of atmospheric pollution in the Baikal region is the emissions of smoke aerosol and trace gases from forest fires, the number of which is increasing in the region. This article presents the results of experimental studies of the dispersed composition of aerosols and gas impurities, such as ozone, sulfur dioxide, and nitrogen oxides during route ship measurements in the water area of Lake Baikal in the summer of 2020.

Versatile soil gas concentration and isotope monitoring: optimization and integration of novel soil gas probes with online trace gas detection

Gas concentrations and isotopic signatures can unveil microbial metabolisms and their responses to environmental changes in soil. Currently, few methods measure in situ soil trace gases such as the products of nitrogen and carbon cycling or volatile organic compounds (VOCs) that constrain microbial biochemical processes like nitrification, methanogenesis, respiration, and microbial communication. Versatile trace gas sampling systems that integrate soil probes with sensitive trace gas analyzers could fill this gap with in situ soil gas measurements that resolve spatial (centimeters) and temporal (minutes) patterns. We developed a system that integrates new porous and hydrophobic sintered polytetrafluoroethylene (sPTFE) diffusive soil gas probes that non-disruptively collect soil gas samples with a transfer system to direct gas from multiple probes to one or more central gas analyzer(s) such as laser and mass spectrometers. Here, we demonstrate the feasibility and versatility of this automated multiprobe system for soil gas measurements of isotopic ratios of nitrous oxide (δ18O, δ15N, and the 15N site preference of N2O), methane, carbon dioxide (δ13C), and VOCs. First, we used an inert silica matrix to challenge probe measurements under controlled gas conditions. By changing and controlling system flow parameters, including the probe flow rate, we optimized recovery of representative soil gas samples while reducing sampling artifacts on subsurface concentrations. Second, we used this system to provide a real-time window into the impact of environmental manipulation of irrigation and soil redox conditions on in situ N2O and VOC concentrations. Moreover, to reveal the dynamics in the stable isotope ratios of N2O (i.e., 14N14N16O, 14N15N16O, 15N14N16O, and 14N14N18O), we developed a new high-precision laser spectrometer with a reduced sample volume demand. Our integrated system – a tunable infrared laser direct absorption spectrometry (TILDAS) in parallel with Vocus proton transfer reaction mass spectrometry (PTR-MS), in line with sPTFE soil gas probes – successfully quantified isotopic signatures for N2O, CO2, and VOCs in real time as responses to changes in the dry–wetting cycle and redox conditions. Broadening the collection of trace gases that can be monitored in the subsurface is critical for monitoring biogeochemical cycles, ecosystem health, and management practices at scales relevant to the soil system.

WOMBAT v1.0: a fully Bayesian global flux-inversion framework

WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

Highly Sensitive Sphere-Tube Coupled Photoacoustic Cell Suitable for Detection of a Variety of Trace Gases: NO2 as an Example

The concentration of trace gases in the atmospheric environment is extremely low, but it has a great impact on the living environment of organisms. Photoacoustic spectroscopy has attracted extensive attention in the field of trace gas detection because of its high sensitivity, good selectivity, and fast response. As the core of a photoacoustic detection setup, the photoacoustic cell has a significant impact on detection performance. To improve detection sensitivity, a sphere-tube coupled photoacoustic cell (STPAC) was developed, which was mainly composed of a diffuse-reflective sphere and an acoustic resonance tube. Modulated light was reflected multiple times in the sphere to increase optical path, and photoacoustic (PA) signals were further amplified by the tube. Based on STPAC, a PA gas detection setup was built with a laser diode (LD) at 450 nm as the light source. The experimental results showed that the minimum detection limit (noise equivalent concentration, NEC) of NO2 was ~0.7 parts per billion (ppb). Compared with the T-type PA cell (TPAC) in which the modulated light passed through the sphere, the signal-to-noise ratio of STPAC was increased by an order of magnitude at the same concentration of the NO2 sample.

Evaluation des EarthCare Cloud Profiling Radars durch bodengebundene Radare

<p>Radarmessungen liefern für die Erforschung von Niederschlag, Wolken und der involvierten Prozesse einen signifikanten Beitrag. Dazu tragen auch Netzwerke wie ACTRIS (Aerosol, Cloud and Trace Gases Research Infrastructure) bei, in welchen nicht nur die Zahl bodengebundener Wolkenradarsysteme stetig wächst, sondern auch deren Datennutzung, z. B. durch Anwendung im synergistischen Verbund mit anderen Messsystemen bei der Wolkenklassifizierung. Europa verfügt somit über ein dichtes bodengebundenes Netzwerk, um Wolken zu untersuchen, für die globale Betrachtung sind allerdings Satelliten notwendig. Mittels satellitengestützter Wolkenradarsysteme, wie z. B. CloudSat, ist es möglich, ein globales Bild zu erhalten. Satellitengestützte Cloud Profiling Radare (CPR) können allerdings hinsichtlich ihrer meist geringeren Sensitivität und aufgrund des sehr starken Bodenechos gegenüber bodengebundenen Systemen im Nachteil sein. Somit sind beispielsweise die Beobachtung bodennaher Wolken, z.B. Grenzschichtbewölkung, oder das Quantifizieren von bodennahem Niederschlag für CPR problematisch.</p> <p>In den kommenden Jahren wird die ESA/JAXA Mission EarthCare ein neues CPR mit verbesserter Performance in Umlauf bringen. Um bereits vor dem Start des Satelliten die Performance des CPR zu evaluieren, werden in dieser Arbeit bodengebundene Messdaten mit simulierten CPR-Daten verglichen. Hierzu werden Datensätze von bodengebundenen Radaren mittels Vorwärtsoperator in einen komplementären Radarsatellitendatensatz umgewandelt. Im Anschluss werden die Datensätze verglichen und ausgewertet.</p> <p>Die Datengrundlage für diese Arbeit liefern die W-Band-Radare des ACTRIS Netzwerks. Die zeitlich langen ACTRIS-Datensätze liefern eine optimale Datengrundlage für eine statistische Analyse der CPR-Performance. Diese Analyse macht es möglich, das neue CPR im Bezug auf die Beobachtung bodennaher Wolken und des bodennahen Niederschlags zu evaluieren.</p>