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The formation of secondary particles in the atmosphere accounts for more than half of global cloud condensation nuclei. Experiments at the CERN CLOUD (Cosmics Leaving OUtdoor Droplets) chamber have underlined the importance of ions for new particle formation, but quantifying their effect in the atmosphere remains challenging. By using a novel instrument setup consisting of two nano-particle counters, one of them equipped with an ion filter, we were able to further investigate the ion-related mechanisms of new particle formation. In autumn 2015, we carried out experiments at CLOUD on four systems of different chemical compositions involving monoterpenes, sulfuric acid, nitrogen oxides, and ammonia. We measured the influence of ions on the nucleation rates under precisely controlled and atmospherically relevant conditions. Our results indicate that ions enhance the nucleation process when the charge is necessary to stabilize newly formed clusters, i.e. in conditions where neutral clusters are unstable. For charged clusters that were formed by ion-induced nucleation, we were able to measure, for the first time, their progressive neutralization due to recombination with oppositely charged ions. A large fraction of the clusters carried a charge at 1.2 nm diameter. However, depending on particle growth rates and ion concentrations, charged clusters were largely neutralized by ion–ion recombination before they grew to 2.2 nm. At this size, more than 90 % of particles were neutral. In other words, particles may originate from ion-induced nucleation, although they are neutral upon detection at diameters larger than 2.2 nm. Observations at Hyytiälä, Finland, showed lower ion concentrations and a lower contribution of ion-induced nucleation than measured at CLOUD under similar conditions. Although this can be partly explained by the observation that ion-induced fractions decrease towards lower ion concentrations, further investigations are needed to resolve the origin of the discrepancy.
Highlights
• We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series.
• The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios.
• The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33.
Abstract
High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements.
Nucleation and growth of aerosol particles from atmospheric vapors constitutes a major source of global cloud condensation nuclei (CCN). The fraction of newly formed particles that reaches CCN sizes is highly sensitive to particle growth rates, especially for particle sizes <10 nm, where coagulation losses to larger aerosol particles are greatest. Recent results show that some oxidation products from biogenic volatile organic compounds are major contributors to particle formation and initial growth. However, whether oxidized organics contribute to particle growth over the broad span of tropospheric temperatures remains an open question, and quantitative mass balance for organic growth has yet to be demonstrated at any temperature. Here, in experiments performed under atmospheric conditions in the Cosmics Leaving Outdoor Droplets (CLOUD) chamber at the European Organization for Nuclear Research (CERN), we show that rapid growth of organic particles occurs over the range from −25 ∘C to 25 ∘C. The lower extent of autoxidation at reduced temperatures is compensated by the decreased volatility of all oxidized molecules. This is confirmed by particle-phase composition measurements, showing enhanced uptake of relatively less oxygenated products at cold temperatures. We can reproduce the measured growth rates using an aerosol growth model based entirely on the experimentally measured gas-phase spectra of oxidized organic molecules obtained from two complementary mass spectrometers. We show that the growth rates are sensitive to particle curvature, explaining widespread atmospheric observations that particle growth rates increase in the single-digit-nanometer size range. Our results demonstrate that organic vapors can contribute to particle growth over a wide range of tropospheric temperatures from molecular cluster sizes onward.
PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms
(2022)
Highlights
• We present PolarCAP, a deep learning model that can classify the polarity of a waveform with a 98% accuracy.
• The first-motion polarity of seismograms is a useful parameter, but its manual determination can be laborious and imprecise.
• We demonstrate that in several cases the model can assign trace polar-ity more accurately than a human analyst.
Abstract
The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder architecture to identify first-motion polarities of earth-quake waveforms. PolarCAP is trained in a supervised fashion using more than 130,000 labelled traces from the Italian seismic dataset (INSTANCE) and is cross-validated on 22,000 traces to choose the most optimal set of hyperparameters. We obtain an accuracy of 0.98 on a completely unseen test dataset of almost 33,000 traces. Furthermore, we check the model generalizability by testing it on the datasets provided by previous works and show that our model achieves a higher recall on both positive and negative polarities.
Nontarget screening exhibits a seasonal cycle of PM2.5 organic aerosol composition in Beijing
(2022)
The molecular composition of atmospheric particulate matter (PM) in the urban environment is complex, and it remains a challenge to identify its sources and formation pathways. Here, we report the seasonal variation of the molecular composition of organic aerosols (OA), based on 172 PM2.5 filter samples collected in Beijing, China, from February 2018 to March 2019. We applied a hierarchical cluster analysis (HCA) on a large nontarget-screening data set and found a strong seasonal difference in the OA chemical composition. Molecular fingerprints of the major compound clusters exhibit a unique molecular pattern in the Van Krevelen-space. We found that summer OA in Beijing features a higher degree of oxidation and a higher proportion of organosulfates (OSs) in comparison to OA during wintertime, which exhibits a high contribution from (nitro-)aromatic compounds. OSs appeared with a high intensity in summer-haze conditions, indicating the importance of anthropogenic enhancement of secondary OA in summer Beijing. Furthermore, we quantified the contribution of the four main compound clusters to total OA using surrogate standards. With this approach, we are able to explain a small fraction of the OA (∼11–14%) monitored by the Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM). However, we observe a strong correlation between the sum of the quantified clusters and OA measured by the ToF-ACSM, indicating that the identified clusters represent the major variability of OA seasonal cycles. This study highlights the potential of using nontarget screening in combination with HCA for gaining a better understanding of the molecular composition and the origin of OA in the urban environment.