Observation Network Research Articles

Recent Spatiotemporal Changes of Subhourly Extreme Rainfall Events in Seoul

This study aims to examine the spatiotemporal variations of extreme rainfall events in Seoul, South Korea, utilizing subhourly rainfall data collected from an extensive observation network. Employing the Mann–Kendall trend detection test and Moran’s I index, this study analyzed temporal trends and spatial distributions in the extreme rainfall time series at 37 weather stations. The findings revealed that the annual maximum precipitation (AMP) series for various durations in Seoul generally exhibited a decreasing trend, with a marked decline observed for durations of 300 and 360 min. Furthermore, the study identified notable changes in the spatial patterns of AMP events, particularly for subhourly AMP events. The results indicate an intensification in the spatial concentration of AMP distribution. Consequently, despite the overall reduction in AMP, the likelihood of urban flooding may increase. This suggests that existing hydraulic design criteria, based on past concepts that temporal and spatial variability are constant, may no longer adequately represent extreme rainfall events. Therefore, revising these criteria to account for the evolving nature of extreme rainfall patterns is imperative.

Using the long-term genetic monitoring network ARMS-MBON to detect marine non-indigenous species along the European coasts

The increasing prevalence of non-indigenous species (NIS) in marine ecosystems poses significant challenges for biodiversity conservation and ecosystem management. Advances in molecular techniques enable early detection and long-term monitoring of such taxa, especially when coupled with spatio-temporally wide sampling by networks such as the European ARMS Marine Biodiversity Observation Network (ARMS-MBON). This initiative performs standardised sampling campaigns using autonomous reef monitoring structures (ARMS) along European coasts and adjacent regions, providing open-access DNA metabarcoding data sets. We tested the potential of genetic observatory networks to detect and monitor marine NIS by analysing all publicly available ARMS-MBON cytochrome c oxidase subunit I (COI) and 18S rRNA amplicon sequencing data as of February 2024 using a customised bioinformatic pipeline. Screening against the World Register of Introduced Marine Species (WRiMS) and applying manual curation, we identified 63 marine taxa considered non-indigenous at one or more locations. This included widespread taxa and potential new introductions, such as Eucheilota menoni in the Adriatic Sea. We found no significantly higher number of NIS in samples from locations particularly impacted by maritime traffic compared to other areas. Our results suggest that the genetic observatory network approach is powerful for detecting and monitoring marine NIS, and that manual curation still is an essential step for obtaining reliable results. We recommend key improvements including more spatially intense sampling across diverse environments as well as enhancement of NIS reference checklists and genetic databases to ensure accurate identification of both known and unknown NIS across Europe.

Revising the coal mining CH4 emission factor based on multiple inventories and atmospheric inversion approach at one of the world's largest coal production areas: Shanxi province, China.

Methane (CH4) emissions from the coal industry represent a substantial portion of anthropogenic CH4 emissions from energy-related activities. China ranks as the world's largest coal producer, where Shanxi Province is one of its major coal production regions and accounts for 20.7% of the national total coal production. The inherent variability in coal properties, geological conditions, and mining techniques across coal mines introduces significant fluctuations in CH4 emission characteristics and emission factors (EFs), creating considerable uncertainty when estimating CH4 emissions in this major coal mining region using traditional emission inventories, thereby introducing large bias in estimating national total CH4 emissions of China. In this study, we applied a top-down approach to estimate CH4 emissions in the Taiyuan-Jinzhong Metropolitan (TJM) area of Shanxi Province, using atmospheric CH4 concentration observed from a 30-meter tower between March 2018 and February 2019. Building upon our previous work, we integrated five emission inventories-EDGAR, GFEI-coal, PRO-coal, GFEI-fuel, and a satellite-based CH4 emission product-with two inversion methods. Additionally, satellite xCH4 data were utilized to identify significant emission outliers, which were then calibrated when estimating the CH4 emission and EF for the region. Our results revealed notable disparities in the magnitude of CH4 emissions among the five inventories for the TJM region. After applying the MSF and SFBI methods to constrain the prior emission inventories, the posterior CH4 emissions for the TJM region were estimated at 1.1×106t, 1.0×106t, 1.1×106t, 1.3×106t, and 1.5×106t, respectively, across the five inventories. The derived coal mine CH4 EF for the TJM region was 9.6 (±1.35) m3/t, significantly lower than the previously reported value of 23.2 (±4.9) m3/t, highlighting the substantial impact of emission outliers on posterior CH4 emissions. A comparative analysis with EFs from other studies demonstrated that this value closely aligns with the EF values for coal with low CH4 content. However, it is important to note that substantial regional variability of coal mining activities can result in significant uncertainty in EFs across different areas. Therefore, we underscore the necessity of establishing a more extensive atmospheric CH4 observation network to enhance the assessment of regional variations in CH4 EFs and emissions from coal mining activities.

A Long-Term Ecological Research Data Set From the Marine Genetic Monitoring Program ARMS-MBON 2018-2020.

Molecular methods such as DNA/eDNA metabarcoding have emerged as useful tools to document the biodiversity of complex communities over large spatio-temporal scales. We established an international Marine Biodiversity Observation Network (ARMS-MBON) combining standardised sampling using autonomous reef monitoring structures (ARMS) with metabarcoding for genetic monitoring of marine hard-bottom benthic communities. Here, we present the data of our first sampling campaign comprising 56 ARMS units deployed in 2018-2019 and retrieved in 2018-2020 across 15 observatories along the coasts of Europe and adjacent regions. We describe the open-access data set (image, genetic and metadata) and explore the genetic data to show its potential for marine biodiversity monitoring and ecological research. Our analysis shows that ARMS recovered more than 60 eukaryotic phyla capturing diversity of up to ~5500 amplicon sequence variants and ~1800 operational taxonomic units, and up to ~250 and ~50 species per observatory using the cytochrome c oxidase subunit I (COI) and 18S rRNA marker genes, respectively. Further, ARMS detected threatened, vulnerable and non-indigenous species often targeted in biological monitoring. We show that while deployment duration does not drive diversity estimates, sampling effort and sequencing depth across observatories do. We recommend that ARMS should be deployed for at least 3-6 months during the main growth season to use resources as efficiently as possible and that post-sequencing curation is applied to enable statistical comparison of spatio-temporal entities. We suggest that ARMS should be used in biological monitoring programs and long-term ecological research and encourage the adoption of our ARMS-MBON protocols.

Radar and profiler observations of the kinematic and thermodynamic structure of a shallow bore in a low-shear environment

Abstract Observations obtained from scanning radars, two profiling systems, surface, and radiosonde instruments are utilized to describe the weakening stages of a long-lived (>4 h), shallow bore that evolved within a low-shear environment on 23 August 2013 over northern Alabama. RHI scans from a scanning X-band radar provided details on the bore shape and surrounding mesoscale perturbations in airflow, while a Doppler lidar afforded high-resolution vertical motion profiles at a location 24 km away. During passage over both profiling sites, updrafts (~2 m s−1) were sampled over the lowest several hundred meters AGL, and aerosol backscatter showed upward displacements of 300-400 m near the 1.2 km AGL level. The surface pressure rise of ~0.3 hPa at multiple locations over the observational network corroborates the shallowness of both the bore and the surface-based inversion. Several other unique features were documented, including bore movement in the direction of the weak low-level flow; a gust front structure with a well-defined feeder flow trailing the bore gust front, both confined to the lowest 500 m AGL; the presence of waves preceding and following the bore disturbance, with a 12-21 min period within the 0.5-2.5 km layer above the bore at both profiler locations; and variations in horizontal flow in the form of a weak jet near 1.4 km AGL, with winds in the jet aligned in the same direction as bore movement, and located more than 10 km ahead of the bore gust front.

Propagation Characteristics of Tsunamis in an Atmosphere-Ocean Coupled System Revealed by Wave Gradiometry: The 2022 Hunga Tonga–Hunga Ha’apai Eruption Case

Abstract A densely cabled-type ocean-bottom pressure observation network is suitable for array analyses that enable derivation of the details of the tsunami propagation as well as early detection of tsunamis. The 2022 eruption of the Hunga Tonga–Hunga Ha’apai volcano excited the atmospheric Lamb and Pekeris waves and accompanying sea-surface disturbances, and the coupled atmospheric and sea-surface disturbances traveled worldwide. To understand the propagation characteristics of sea-surface disturbances in an atmosphere-ocean coupled system, we applied wave gradiometry, one array analysis technique, to the records of the dense, offshore, ocean-bottom pressure gauge array, and a land-based array of barometers on the Japanese archipelago. We examined the propagation properties of the disturbances using spatial variations of amplitudes as well as propagation directions and velocities. The wave gradiometry analyses provided several interesting results. Coherent sea-surface disturbances accompanied by the atmospheric Lamb and Pekeris waves propagated mainly in directions and velocities that reflected the bathymetry, that is, they propagated as tsunamis, whereas the barometric disturbances propagated as simple plane waves. When the atmospheric Lamb waves propagated, we observed spatially heterogeneous variations of the amplitudes of tsunamis within the observation network that reflected the different stages during the process of splitting the forcibly excited tsunamis into two wave trains. In contrast, the amplitudes of the tsunamis generally increased during the atmospheric Pekeris waves. Considering the propagation characteristics of barometric disturbances, these results may have reflected the amplification in accordance with Green’s law and the resonance between atmospheric and sea-surface disturbances. Wave gradiometry with dense ocean-bottom pressure records provides information that can facilitate monitoring tsunami propagation even if the propagation process is different from that of oceanic free waves.

A changepoint approach to modelling nonstationary soil moisture dynamics

Abstract Soil moisture dynamics provide an indicator of soil health that scientists model via drydown curves. The typical modelling process requires the soil moisture time series to be manually separated into drydown segments and then exponential decay models are fitted to them independently. Sensor development in recent years means that experiments that were previously conducted over a few field campaigns can now be scaled to months or years at a higher sampling rate. To better meet the challenge of increasing data size, this paper proposes a novel changepoint-based approach to automatically identify structural changes in the soil drying process and simultaneously estimate the drydown parameters that are of interest to soil scientists. A simulation study is carried out to demonstrate the performance of the method in detecting changes and retrieving model parameters. Practical aspects of the method such as adding covariates and penalty learning are discussed. The method is applied to hourly soil moisture time series from the National Ecological Observatory Network data portal to investigate the temporal dynamics of soil moisture drydown. We recover known relationships previously identified manually, alongside delivering new insights into the temporal variability across soil types and locations.

Indian Land Carbon Sink Estimated from Surface and GOSAT Observations

The carbon sink over land plays a key role in the mitigation of climate change by removing carbon dioxide (CO2) from the atmosphere. Accurately assessing the land sink capacity across regions should contribute to better future climate projections and help guide the mitigation of global emissions towards the Paris Agreement. This study estimates terrestrial CO2 fluxes over India using a high-resolution global inverse model that assimilates surface observations from the global observation network and the Indian subcontinent, airborne sampling from Brazil, and data from the Greenhouse gas Observing SATellite (GOSAT) satellite. The inverse model optimizes terrestrial biosphere fluxes and ocean-atmosphere CO2 exchanges independently, and it obtains CO2 fluxes over large land and ocean regions that are comparable to a multi-model estimate from a previous model intercomparison study. The sensitivity of optimized fluxes to the weights of the GOSAT satellite data and regional surface station data in the inverse calculations is also examined. It was found that the carbon sink over the South Asian region is reduced when the weight of the GOSAT data is reduced along with a stricter data filtering. Over India, our result shows a carbon sink of 0.040 ± 0.133 PgC yr−1 using both GOSAT and global surface data, while the sink increases to 0.147 ± 0.094 PgC yr−1 by adding data from the Indian subcontinent. This demonstrates that surface observations from the Indian subcontinent provide a significant additional constraint on the flux estimates, suggesting an increased sink over the region. Thus, this study highlights the importance of Indian sub-continental measurements in estimating the terrestrial CO2 fluxes over India. Additionally, the findings suggest that obtaining robust estimates solely using the GOSAT satellite data could be challenging since the GOSAT satellite data yield significantly varies over seasons, particularly with increased rain and cloud frequency.

Congeneric Rodents Differ in Immune Gene Expression: Implications for Host Competence for Tick-Borne Pathogens.

Mice in the genus Peromyscus are abundant and geographically widespread in North America, serving as reservoirs for zoonotic pathogens, including Borrelia burgdorferi (B. burgdorferi), the causative agent of Lyme disease, transmitted by Ixodes scapularis ticks. While the white-footed mouse (Peromyscus leucopus (P. leucopus)) is the primary reservoir in the United States, the deer mouse (P. maniculatus), an ecologically similar congener, rarely transmits the pathogen to biting ticks. Understanding the factors that allow these similar species to serve as a poor and competent reservoir is critical for understanding tick-borne disease ecology and epidemiology, especially as climate change expands the habitats where ticks can transmit pathogens. Our study investigated immunological differences between these rodent species. Specifically, we compared the expression of six immune genes (i.e., TLR-2, IFN-γ, IL-6, IL-10, GATA-3, TGF-β) broadly involved in bacterial recognition, elimination, and/or pathology mitigation in ear biopsies collected by the National Ecological Observatory Network (NEON) as part of their routine surveillance. A principal components analysis indicated that immune gene expression in both species varied in two dimensions: TLR2, IFN-γ, IL-6, and IL-10 (comprising PC1) and TGF-β and GATA3 (comprising PC2) expression tended to covary within individuals. However, when we analyzed expression differences of each gene singly between species, P. maniculatus expressed more TLR2, IL-6, and IL-10 but less IFN-γ and GATA3 than P. leucopus. This immune profile could partly explain why P. leucopus is a better reservoir for bacterial pathogens such as B. burgdorferi.

Storm Peak Laboratory: A Research and Training Facility for the Atmospheric Sciences

Abstract Storm Peak Laboratory, located on the Steamboat Springs Ski Resort in Colorado on the west summit of Mount Werner at 10,532 ft (3220 m) ASL, is an internationally recognized high-elevation atmospheric research station that has been in use for over 40 years. This article provides a brief history of the Storm Peak Laboratory and the major research themes it has supported and discusses opportunities to leverage mountain observatory measurements to advance our understanding of the atmospheric processes. This facility provides long-term measurements of meteorology, clouds, aerosols, snow hydrology and atmospheric gases, and serves as a “proving ground” for instrument development and testing. Storm Peak Laboratory is part of multiple national and international observational networks. Due to the unique capabilities of Storm Peak Laboratory, there is a long history of targeted field campaigns primarily within the following research areas: mixed phase cloud microphysics, atmospheric chemistry pertaining to the formation, characterization, and hygroscopicity of aerosols; and transport and transformation of atmospheric mercury. Research training has been central to the mission of SPL over the last 40 years. Currently, SPL hosts both undergraduate and graduate level courses in atmospheric science and snow hydrology organized by numerous institutions. Examples of these unique research training opportunities are provided.

Plant diversity across dimensions: Coupling biodiversity measures from the ground and the sky.

Tracking biodiversity across biomes over space and time has emerged as an imperative in unified global efforts to manage our living planet for a sustainable future for humanity. We harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US. Our findings show strong and positive associations between diversity metrics based on spectral species and ground-based plant species richness and other dimensions of plant diversity, whereas metrics based on distance matrices did not. We found that spectral diversity consistently predicts analogous metrics of plant taxonomic, functional, and phylogenetic dimensions of biodiversity across biomes. The approach demonstrates promise for monitoring dimensions of biodiversity globally by integrating ground-based measures of biodiversity with imaging spectroscopy and advances capacity toward a Global Biodiversity Observing System.

Evaluation of multi-source precipitation products for monitoring drought across China

Accurate precipitation data are crucial for effective drought monitoring, especially in China’s complex and diverse climatic regions. This study evaluates the performance of six multisource precipitation products-ERA5-Land, CMORPH CRT, GSMaP MVK, IMERG Late, and IMERG Final-in detecting drought across China from 2009 to 2019, using ground station observations for validation. By applying various drought and evaluation indices across various timescales, this analysis captures short and long-term climate variations, assessing each product’s accuracy across diverse regions. Spatial and temporal analyses revealed that IMERG Final closely aligns with observed precipitation, particularly in the high-rainfall areas like the Yangtze River Basin, while GSMaP MVK and ERA5 tend to overestimate precipitation in arid and semi-arid regions. Discrepancies are most pronounced in complex terrains such as the Qinghai-Tibet Plateau and southwestern mountains, where sparse observational networks exacerbate errors. Drought indices, including SPEI-3 and SPI-1, were used to measure each product’s effectiveness in detecting drought intensity, frequency, and duration. IMERG Final consistently showed the highest correlation with ground data across all drought levels (Light, Moderate, and Severe), while GSMaP MVK and ERA5 tended to overestimate drought occurrences in certain drought-prone areas. Hotspot analyses of indices such as CDD, PRCPTOT, and R95p further confirmed IMERG Final’s accuracy in identifying drought and wet event patterns, closely reflecting ground measurements, whereas ERA5 and GSMaP MVK occasionally overestimated drought frequencies. In summary, IMERG Final emerged as a relatively accurate and reliable product for drought monitoring, showing strong applicability across China’s diverse climatic regions. These findings aid in data correction, enhances understanding of regional drought variability, and integration strategies to improve water resource management and extreme event monitoring.

Towards a high-quality in situ observation network for oxygenated volatile organic compounds (OVOCs) in Europe: transferring metrological traceability to the field

Abstract. Volatile organic compounds (VOCs) have a large impact on the oxidising capacity of the troposphere and are major precursors of tropospheric ozone and secondary atmospheric aerosols. Accurate measurements and data comparability of VOCs among monitoring networks are essential to assessing the trends of these secondary air pollutants. Metrological traceability of the measurements to the International System of Units (SI traceability) contributes to both measurement consistency and data comparability. Accurate, stable and SI-traceable reference gas mixtures (RGMs) and working standards are needed to achieve SI traceability through an unbroken chain of calibrations of the analytical instruments used to monitor VOCs. However, for many oxygenated VOCs (OVOCs), such RGMs and working standards are not available at an atmospheric amount of substance fraction levels (< 10 nmol mol−1). Here, we present the protocols developed to transfer SI traceability to the field by producing two types of SI-traceable working standards for selected OVOCs. These working standards, based on RGMs diluted dynamically with dry nitrogen and on certified spiked whole-air samples, were then assessed using a thermal desorber–gas chromatograph–flame ionisation detector (TD–GC–FID) and proton transfer reaction–time of flight–mass spectrometer (PTR–ToF–MS) as analytical methods. For that purpose, we calibrated five analytical instruments using in-house calibration standards and treated the new SI-traceable working standards as samples. Due to analytical limitations, the assessment was only possible for acetaldehyde, acetone, methanol and methyl ethyl ketone (MEK). Relative differences between assigned and measured values were used to assess the working standards based on the dilution of RGMs. The relative differences were within the measurement uncertainty for acetone, MEK, methanol and acetaldehyde at an amount of substance fractions around 10 nmol mol−1. For the working standards based on certified spiked whole-air samples in pressurised cylinders, results showed a good agreement among the laboratories (i.e. differences within the measurement expanded uncertainty (U) ranging between 0.5 and 3.3 nmol mol−1) and with the certified amount of substance fraction for acetaldehyde (15.7 nmol mol−1 ± 3.6 (U) nmol mol−1), acetone (17 nmol mol−1 ± 1.5 (U) nmol mol−1) and MEK (12.3 nmol mol−1 ± 2.3 (U) nmol mol−1). Despite the promising results for the working standards based on the dilution of RGMs and on certified spiked whole-air samples filled into pressurised cylinders, the assessment must be considered with care due to the large measurement uncertainty, particularly for methanol. Active collaboration among the metrological, meteorological and atmospheric chemistry monitoring communities is needed to tackle the challenges of OVOC monitoring, such as the lack of stable and SI-traceable calibration standards (i.e. RGMs and working standards). Besides this collaboration, other research applications, such as modelling and remote sensing, may benefit from the transfer of SI traceability to monitoring stations.

Instruction-induced modulation of the visual stream during gesture observation.

Although gesture observation tasks are believed to invariably activate the action-observation network (AON), we investigated whether the activation of different cognitive mechanisms when processing identical stimuli with different explicit instructions modulates AON activations. Accordingly, 24 healthy right-handed individuals observed gestures and they processed both the actor's moved hand (hand laterality judgment task, HT) and the meaning of the actor's gesture (meaning task, MT). The main brain-level result was that the HT (vs MT) differentially activated the left and right precuneus, the left inferior parietal lobe, the left and right superior parietal lobe, the middle frontal gyri bilaterally and the left precentral gyrus. MT (vs HT) differentially activated the left and right calcarine cortex, the fusiform gyrus bilaterally, the left inferior temporal gyrus, the left and right hippocampus and parahippocampal gyri, and the temporal pole bilaterally. Processing the actor's moving hand modulates the dorsal action observation network (while processing gesture meaning modulates the ventral object recognition stream). The present results suggest instruction-induced modulation on the visual stream during gesture observation.

Analysis of co-seismic ionospheric disturbance of the Qinghai Mw 7.4 earthquake on May 21, 2021

ABSTRACT In this study, the total electron content (TEC) was extracted from Global Navigation Satellite System (GNSS) data provided by the Crustal Movement Observation Network of China (CMONOC). The ionospheric disturbances related to the Qinghai earthquake on 21 May 2021 were analysed. Significant coseismic ionospheric disturbances (CIDs) were observed near the earthquake epicentre. To further investigate the propagation characteristics of ionospheric anomalies associated with this earthquake, the epicentral area was divided into eight regions, each corresponding to a 45° sector, to analyse the characteristics of ionospheric disturbances. Firstly, based on the spatial and temporal characteristics of the slant total electron content, different degrees of ionospheric disturbances were identified at various time intervals after the earthquake. Secondly, the earthquake generated two types of coseismic ionospheric disturbances at the epicentre. The first type, with velocities of approximately 1.4 km s− 1, 1.8 km s− 1, and 1.0 km s− 1, were triggered by acoustic waves generated by the ground surface rupture induced by the earthquake. The second type, with velocities of about 0.8 km s− 1, represented another form of ionospheric disturbance induced by acoustic waves from the ground surface rupture. Thirdly, the ionospheric anomaly was most pronounced within the 90°–135° sector relative to the epicentre, corresponding to the southeast direction from the epicentre. Finally, based on the data from 15 GNSS stations at the epicentre, the coseismic deformation displacement of the earthquake was determined, with a maximum displacement of 0.24 m, and the leptokurtic slip characteristics of the earthquake were revealed.

Wind–Wave Misalignment in Irish Waters and Its Impact on Floating Offshore Wind Turbines

This study examined the impact of wind–wave misalignment on floating offshore wind turbines (FOWTs) in Irish waters, analysing average weather and extreme events, including hurricane conditions. Using the ERA5 reanalysis dataset validated against Irish Marine Data Buoy Observation Network measurements, the results showed a satisfactory accuracy with an average wind speed error of −0.54 m/s and a strong correlation coefficient of 0.92. Wind–wave misalignment was found to be inversely correlated with wind speed (correlation coefficient: −0.41), with minimum misalignment occurring approximately seven hours after a change in wind direction. The study revealed that misalignment could exceed 30∘ during hurricanes, contradicting standard assumptions of alignment under extreme conditions. The investigation highlighted that in western coastal areas, average misalignment could reach 57.95∘, while sheltered Irish Sea regions experienced lower values, such as 23.06∘. Numerical simulations confirmed that these misalignment events amplified side-to-side turbine deflections significantly. This research underscores the need to incorporate misalignment effects into industry testing standards and suggests that current methodologies may underestimate fatigue loads by up to 50%. This work emphasizes improved design and testing protocols for FOWTs in complex marine environments and highlights the suitability of ERA5 for climate analysis in Ireland.

Enhanced sensor web services by incorporating IoT interface protocols and spatio-temporal data streams for edge computing-based sensing

ABSTRACT The Geospatial Sensor Web (GSW) integrates heterogeneous aerial and ground sensors via cloud-edge linkages and GIS-based approaches, forming a multi-dimensional observation network. However, existing systems struggle to support edge-side collaborative observation due to fragmented physical standards, incompatible protocols, and limited self-configuration. This study proposes an enhanced Sensor Web, integrating IoT protocols and spatio-temporal models for unified access, collaborative management, and dynamic planning. Validated through the City Sensing Base Station (CSBS), a pilot experiment demonstrated the framework integrates diverse sensing resources across over eight protocols, achieving autonomous alignment of more than five platforms with rapid aerial-ground network formation during emergencies. It also validated autonomous collaboration and coordination of aerial-ground resources, enabling dynamic task allocation and execution across heterogeneous systems. Compared with cloud-based architectures, this approach significantly improves resource accessibility and real-time processing. By extending SensorML and Sensor Observation Service (SOS), the framework bridges the gap between conventional Sensor Webs and edge computing demands. Results confirm its effectiveness in coordinating heterogeneous resources and managing dynamic spatio-temporal data. These findings show how Internet of Things (IoT) protocols advance earth observation, modeling and improve GSW efficiency.

Development and Application of a Cost–Effective Analytical Method for Hydrofluorocarbons Using Preconcentrator–Gas Chromatograph–Mass Spectrometer

The expansion of atmospheric observation networks for hydrofluorocarbons (HFCs), which are closely related to climate change and contribute to global warming, significantly impacts our society and daily life. Their emissions are estimated to increase in the future, which is a major challenge. Observations of HFCs globally were performed using expensive GHG–specific equipment installed at AGAGE and other sites, but many research institutions find it difficult to install such equipment. Therefore, we successfully developed a measurement method for six components of HFCs (HFC–23, HFC–32, HFC–125, HFC–134a, HFC–143a, and HFC–152a) with high atmospheric concentrations in various parts of the world by optimizing measurement parameters such as the sample transfer volume and rate, module cooling temperatures, the injection time, the GC oven temperature program, and the monitored ions of a commercially available preconcentrator–GC–MS. Because this developed measurement method is cost–effective and simpler to operate than those of GHG–specific equipment, it is expected to provide an opportunity for many research institutes to measure HFCs. Furthermore, in addition to HFCs, we confirmed that simultaneous measurements can be performed for 97 volatile organic compounds (VOCs), including hazardous components. This research can contribute to the observation of HFCs in countries and regions where the actual status of emissions is unclear or where no or few atmospheric observations were conducted. The results of those observations can be used to formulate more detailed global warming countermeasures.

Influenza Immunization in Very-Low-Birth-Weight Infants: Epidemiology and Long-Term Outcomes.

Very-low-birth-weight infants (VLBWIs; birth weight < 1500 g) are at an increased risk of complicated influenza infection, which frequently includes pneumonia, encephalitis or even death. Data on influenza immunization and its outcome in VLBWIs are scarce. This study aimed to provide epidemiological data on influenza immunization for German VLBWIs and hypothesized that immunization would protect VLBWIs from infection-mediated neurodevelopmental impairment and preserves lung function at early school age. In this observational population-based German Neonatal Network (GNN) study, infants born between 2009 and 2015 were invited to partake in a 6-year follow-up investigation including lung function and developmental testing. Uni- and multivariate analyses were performed to evaluate the clinical characteristics and outcomes of influenza-immunized VLBWIs compared to non-immunized VLBWIs. Influenza immunization was performed in 871 out of the 3358 VLBWIs (26%) with six-year follow-up. Immunized infants were characterized by a low gestational age and higher rates of morbidity, particularly bronchopulmonary dysplasia. Although early immunization showed no safety signals and had protective effects on the long-term risk of bronchitis (OR: 0.2; CI: 0.1-0.6; p = 0.002), most VLBWIs (88.0%) were unimmunized in their first influenza season. Influenza immunization was not associated with improved lung function (forced expiratory volume in one second and forced vital capacity) or a better neurocognitive outcome (intelligence quotient and strengths and difficulties questionnaire) at early school age. In Germany, only one quarter of 6-year-old VLBWIs were immunized against influenza, particularly those born <28 gestational weeks and/or BPD. Specific influenza immunization guidelines that define evidence-based recommendations are needed for this vulnerable group.

Distributed observers for linear systems with communication noises over Markovian switching topologies

This paper studies the distributed state estimation problem for a linear system that is jointly observable with the consideration of communication noises and Markovian switching topologies. To address this issue, a network of observers is proposed, where each observer only measures partial system output, which may not be sufficient to observe the complete state of the system. Subsequently, all observers within the network collaborate by sharing their estimated states with their neighbours through the network, facilitating a cooperative reconstruction of the entire state at each local observer. To better reflect the communication environment, we consider a situation in which communication links may fail and rebuild over time, and communication noises may occur when observers exchange information through Markovian switching topologies. Parameters of observers are designed properly, under which each observer fulfills the estimation task in the mean square sense. Illustrative examples to demonstrate the effectiveness of the distributed observers are also provided.