ALBERT

Description: Primary biogenic organic aerosols (PBOAs) represent a major fraction of coarse organic matter (OM) in air. Despite their implication in many atmospheric processes and human health problems, we surprisingly know little about PBOA characteristics (i.e., composition, dominant sources, and contribution to airborne particles). In addition, specific primary sugar compounds (SCs) are generally used as markers of PBOAs associated with bacteria and fungi, but our knowledge of microbial communities associated with atmospheric particulate matter (PM) remains incomplete. This work aimed at providing a comprehensive understanding of the microbial fingerprints associated with SCs in PM10 (particles smaller than 10 µm) and their main sources in the surrounding environment (soils and vegetation). An intensive study was conducted on PM10 collected at a rural background site located in an agricultural area in France. We combined high-throughput sequencing of bacteria and fungi with detailed physicochemical characterizations of PM10, soil, and plant samples and monitored meteorological and agricultural activities throughout the sampling period. Results show that in summer SCs in PM10 are a major contributor of OM in air, representing 0.8 % to 13.5 % of OM mass. SC concentrations are clearly determined by the abundance of only a few specific airborne fungal and bacterial taxa. The temporal fluctuations in the abundance of only four predominant fungal genera, namely Cladosporium, Alternaria, Sporobolomyces, and Dioszegia, reflect the temporal dynamics in SC concentrations. Among bacterial taxa, the abundance of only Massilia, Pseudomonas, Frigoribacterium, and Sphingomonas is positively correlated with SC species. These microbes are significantly enhanced in leaf over soil samples. Interestingly, the overall community structure of bacteria and fungi are similar within PM10 and leaf samples and significantly distinct between PM10 and soil samples, indicating that surrounding vegetation is the major source of SC-associated microbial taxa in PM10 in this rural area of France.

Description: A temperature- and stress-dependent failure criterion for ice-filled rock (limestone) joints was proposed in 2018 as an essential tool to assess and model the stability of degrading permafrost rock slopes. To test the applicability to other rock types, we conducted laboratory tests with mica schist and gneiss, which provide the maximum expected deviation of lithological effects on the shear strength due to strong negative surface charges affecting the rock–ice interface. Retesting 120 samples at temperatures from −10 to −0.5 ∘C and normal stress of 100 to 400 kPa, we show that even for controversial rocks the failure criterion stays unaltered, suggesting that the failure criterion is transferable to mostly all rock types.

Description: A growing number of studies are using specific primary sugar species, such as sugar alcohols or primary saccharides, as marker compounds to characterize and apportion primary biogenic organic aerosols (PBOAs) in the atmosphere. To better understand their annual cycles, as well as their spatiotemporal abundance in terms of concentrations and sources, we conducted a large study focusing on three major atmospheric primary sugar compounds (i.e., arabitol, mannitol, and glucose) measured in various environmental conditions for about 5300 filter samples collected at 28 sites in France. Our results show significant atmospheric concentrations of polyols (defined here as the sum of arabitol and mannitol) and glucose at each sampling location, highlighting their ubiquity. Results also confirm that polyols and glucose are mainly associated with the coarse rather than the fine aerosol mode. At nearly all sites, atmospheric concentrations of polyols and glucose display a well-marked seasonal pattern, with maximum concentrations from late spring to early autumn, followed by an abrupt decrease in late autumn, and a minimum concentration during wintertime. Such seasonal patterns support biogenic emissions associated with higher biological metabolic activities (sporulation, growth, etc.) during warmer periods. Results from a previous comprehensive study using positive matrix factorization (PMF) based on an extended aerosol chemical composition dataset of up to 130 species for 16 of the same sample series have also been used in the present work. The polyols-to-PMPBOA ratio is 0.024±0.010 on average for all sites, with no clear distinction between traffic, urban, or rural typology. Overall, even if the exact origin of the PBOA source is still under investigation, it appears to be an important source of particulate matter (PM), especially during summertime. Results also show that PBOAs are significant sources of total organic matter (OM) in PM10 (13±4 % on a yearly average, and up to 40 % in some environments in summer) at most of the investigated sites. The mean PBOA chemical profile is clearly dominated by contribution from OM (78±9 % of the mass of the PBOA PMF on average), and only a minor contribution from the dust class (3±4 %), suggesting that ambient polyols are most likely associated with biological particle emissions (e.g., active spore discharge) rather than soil dust resuspension.

Description: Inhaled aerosolized particulate matter (PM) induces cellular oxidative stress in vivo, leading to adverse health outcomes. The oxidative potential (OP) of PM appears to be a more relevant proxy of the health impact of the aerosol rather than the total mass concentration. However, the relative contributions of the aerosol sources to the OP are still poorly known. In order to better quantify the impact of different PM sources, we sampled aerosols in a French city for one year (2014, 115 samples). A coupled analysis with detailed chemical speciation (more than 100 species, including organic and carbonaceous compounds, ions, metals and aethalometer measurements) and two OP assays (ascorbic acid, AA, and dithiothreitiol, DTT) in a simulated lung fluid (SLF) were performed in these samples. We present in this study a statistical framework using a coupled approach with positive matrix factorization (PMF) and multiple linear regression to attribute a redox-activity to PM sources. Our results highlight the importance of the biomass burning and vehicular sources to explain the observed OP for both assays. In general, we see a different contribution of the sources when considering the OP AA, OP DTT or the mass of the PM10. Moreover, significant differences are observed between the DTT and AA tests which emphasized chemical specificities of the two tests and the need of a standardized approach for the future studies on epidemiology or toxicology of the PM.

Description: The primary sugar compounds (SCs, defined as glucose, arabitol, and mannitol) are widely recognized as suitable molecular markers to characterize and apportion primary biogenic organic aerosol emission sources. This work improves our understanding of the spatial behavior and distribution of these chemical species and evidences their major effective environmental drivers. We conducted a large study focusing on the daily (24 h) PM10 SC concentrations for 16 increasing space scale sites (local to nationwide), over at least 1 complete year. These sites are distributed in several French geographic areas of different environmental conditions. Our analyses, mainly based on the examination of the short-term evolutions of SC concentrations, clearly show distance-dependent correlations. SC concentration evolutions are highly synchronous at an urban city scale and remain well correlated throughout the same geographic regions, even if the sites are situated in different cities. However, sampling sites located in two distinct geographic areas are poorly correlated. Such a pattern indicates that the processes responsible for the evolution of the atmospheric SC concentrations present a spatial homogeneity over typical areas of at least tens of kilometers. Local phenomena, such as the resuspension of topsoil and associated microbiota, do no account for the major emissions processes of SC in urban areas not directly influenced by agricultural activities. The concentrations of SC and cellulose display remarkably synchronous temporal evolution cycles at an urban site in Grenoble, indicating a common source ascribed to vegetation. Additionally, higher concentrations of SC at another site located in a crop field region occur during each harvest periods, indicating resuspension processes of plant materials (crop detritus, leaf debris) and associated microbiota for agricultural and nearby urbanized areas. Finally, ambient air temperature, relative humidity, and vegetation density constitute the main effective drivers of SC atmospheric concentrations.

Description: Understanding rock slope kinematics in steep, fractured bedrock permafrost is a challenging task. Recent laboratory studies have provided enhanced understanding of rock fatigue and fracturing in cold environments but were not successfully confirmed by field studies. This study presents a unique time series of fracture kinematics, rock temperatures and environmental conditions at 3500 m a. s. l.  on the steep, strongly fractured Hörnligrat of the Matterhorn (Swiss Alps). Thanks to 8 years of continuous data, the longer-term evolution of fracture kinematics in permafrost can be analyzed with an unprecedented level of detail. Evidence for common trends in spatiotemporal pattern of fracture kinematics could be found: a partly reversible seasonal movement can be observed at all locations, with variable amplitudes. In the wider context of rock slope stability assessment, we propose separating reversible (elastic) components of fracture kinematics, caused by thermoelastic strains, from the irreversible (plastic) component due to other processes. A regression analysis between temperature and fracture displacement shows that all instrumented fractures exhibit reversible displacements that dominate fracture kinematics in winter. Furthermore, removing this reversible component from the observed displacement enables us to quantify the irreversible component. From this, a new metric – termed index of irreversibility – is proposed to quantify relative irreversibility of fracture kinematics. This new index can identify periods when fracture displacements are dominated by irreversible processes. For many sensors, irreversible enhanced fracture displacement is observed in summer and its initiation coincides with the onset of positive rock temperatures. This likely indicates thawing-related processes, such as meltwater percolation into fractures, as a forcing mechanism for irreversible displacements. For a few instrumented fractures, irreversible displacements were found at the onset of the freezing period, suggesting that cryogenic processes act as a driving factor through increasing ice pressure. The proposed analysis provides a tool for investigating and better understanding processes related to irreversible kinematics.

Description: The particulate matter (PM) induces cellular oxidative stress in vivo and so lead to adverse health outcome. The oxidative potential (OP) of the PM appears to be a more relevant proxy of the health impact of the aerosol rather than the total mass concentration. However, the relative contributions of the emission’s sources of aerosols to the OP are still poorly known. In order to better quantify the impact of each PM source to the air quality, we sampled aerosols in a French city for one year (year 2014, 115 samples). A coupled analysis with detailed chemical speciation (more than 100 species, including organic and carbonaceous compounds, ions, metals and aethalomether measurements) and two OP assays (ascorbic acid (AA) and dithiothreitiol (DTT)) in a simulated lung fluid (SLF) were performed in these samples. We developed in this study a new statistical model using a coupled approach with Positive Matrix Factorisation (PMF) and multiple linear regressions to attribute a redox-activity per PM sources. Our results highlight the importance of the Biomass burning and Vehicular sources to explain the observed OP for both assays. In general, we see a different contribution of the sources when considering the OP AA, OP DTT or the mass of the PM10. Moreover, some significant differences are observed between the DTT and AA tests that emphasized the chemical specificities of the two tests and the need of a standardized approach for the future studies on epidemiology or toxicology of the PM.

Description: A growing number of studies are using specific primary sugar species, such as sugar alcohols or primary saccharides, as marker compounds to characterize and apportion primary biogenic organic aerosols (PBOA) in the atmosphere. To better understand their annual cycles, as well as their spatio-temporal abundance in terms of concentrations and sources, we conducted a large study focusing on three major atmospheric primary sugar compounds (i.e. arabitol, mannitol and glucose) measured in various environmental conditions on about 5,300 filter samples collected at 28 sites in France. Our results show significant atmospheric concentrations of polyols (defined here as the sum of arabitol and mannitol) and glucose at each sampling location, highlighting their ubiquity. Results also confirm that polyols and glucose are mainly associated with the coarse rather than the fine aerosol mode. At nearly all sites, atmospheric concentrations of polyols and glucose display a well-marked seasonal pattern, with maximum concentrations from late spring to early autumn, followed by an abrupt decrease in late autumn, and a minimum concentration during wintertime. Such seasonal patterns support biogenic emissions associated with higher biological metabolic activities (e.g. sporulation, growth, etc.) during warmer periods. Results from a previous comprehensive study using Positive Matrix Factorization (PMF) based on an extended aerosol chemical composition dataset of up to 130 species for 16 of the same sample series has also been used in the present work. Results show that PBOA are significant sources of total OM in PM10 (13±4% on a yearly average, and up to 40% in some environments in summer) at most of the investigated sites. The mean PBOA chemical profile is clearly dominated by OM (78±9% of the mass of the PBOA PMF factor on average), suggesting that ambient polyols are most likely associated with biological particle emissions (e.g. active spore discharge) rather than soil dust resuspension.

Description: The primary sugar compounds (SC, defined as glucose, arabitol and mannitol) are widely recognized as suitable molecular markers to characterize and apportion primary biogenic organic aerosol emission sources. This work improves our understanding of the spatial behavior and distribution of these chemical species and evidences their major effective environmental drivers. We conducted a large study focusing on the daily (24 h) PM10 SC concentrations for 16 increasing space scale sites (local to nation-wide), over at least one complete year. These sites are distributed in several French geographic areas of different environmental conditions. Our analyses, mainly based on the examination of the short-term evolutions of SC concentrations, clearly show distance-dependent correlations. SC concentration evolutions are highly synchronous at an urban city-scale and remain well correlated throughout the same geographic regions, even if the sites are situated in different cities. However, sampling sites located in two distinct geographic areas are poorly correlated. Such pattern indicates that the processes responsible for the evolution of the atmospheric SC concentrations present a spatial homogeneity over typical areas of at least tens of kilometers. Local phenomena, such as resuspension of topsoil and associated microbiota, do no account for the major emissions processes of SC in urban areas not directly influenced by agricultural activities. The concentrations of SC and cellulose display remarkably synchronous temporal evolution cycles at an urban site in Grenoble, indicating a common source ascribed to vegetation. Additionally, higher concentrations of SC at another site located in a crop field region occur during each harvest periods, pointing out resuspension processes of plant materials (crop detritus, leaf debris) and associated microbiota for agricultural and nearby urbanized areas. Finally, ambient air temperature, relative humidity and vegetation density constitute the main effective drivers of SC atmospheric concentrations.

Description: Passive monitoring of ground motion can be used for geophysical process analysis and natural hazard assessment. Detecting events in microseismic signals can provide responsive insights into active geophysical processes. However, in the raw signals, microseismic events are superimposed by external influences, for example, anthropogenic or natural noise sources that distort analysis results. In order to be able to perform event-based geophysical analysis with such microseismic data records, it is imperative that negative influence factors can be systematically and efficiently identified, quantified and taken into account. Current identification methods (manual and automatic) are subject to variable quality, inconsistencies or human errors. Moreover, manual methods suffer from their inability to scale to increasing data volumes, an important property when dealing with very large data volumes as in the case of long-term monitoring. In this work, we present a systematic strategy to identify a multitude of external influence sources, characterize and quantify their impact and develop methods for automated identification in microseismic signals. We apply the strategy developed to a real-world, multi-sensor, multi-year microseismic monitoring experiment performed at the Matterhorn Hörnligrat (Switzerland). We develop and present an approach based on convolutional neural networks for microseismic data to detect external influences originating in mountaineers, a major unwanted influence, with an error rate of less than 1 %, 3 times lower than comparable algorithms. Moreover, we present an ensemble classifier for the same task, obtaining an error rate of 0.79 % and an F1 score of 0.9383 by jointly using time-lapse image and microseismic data on an annotated subset of the monitoring data. Applying these classifiers to the whole experimental dataset reveals that approximately one-fourth of events detected by an event detector without such a preprocessing step are not due to seismic activity but due to anthropogenic influences and that time periods with mountaineer activity have a 9 times higher event rate. Due to these findings, we argue that a systematic identification of external influences using a semi-automated approach and machine learning techniques as presented in this paper is a prerequisite for the qualitative and quantitative analysis of long-term monitoring experiments.