Monitoring Campaign Research Articles

The Calm Before the (Next) Storm: No Third Outburst in 2019–2020, and Ongoing Monitoring of the Transient AGN IC 3599

We report on follow-up observations of the Seyfert 1.9 galaxy IC 3599 with the NASA Neil Gehrels Swift mission. The detection of a second X-ray outburst in 2010 by Swift after the first discovery of a bright X-ray outburst in 1990 by ROSAT led to the suggestion of two very different explanations. The first one assumed that IC 3599 exhibits outbursts due to repeated partial tidal stripping of a star, predicting another outburst of IC 3599 in 2019/2020. The second, alternative scenario assumed that the event observed in X-rays is due to an accretion-disk instability, which would suggest a much longer period between the large outbursts. Our continued monitoring campaign by Swift allowed us to test the first scenario that predicted a repetition of high-amplitude flaring activity in 2019/2020. We do not find any evidence of dramatic flaring activity with factors of 100 since the last X-ray outburst seen in 2010. These observations support the accretion-disk scenario. Further, while IC 3599 remains in low-emission states, the long-term X-ray light curve of IC 3599 reveals ongoing strong variability of a factor of a few. The most remarkable event is a miniflare of a factor of 10 in X-rays in 2022 December. After that flare, the otherwise supersoft X-ray spectrum shows an exceptional hardening, reminiscent of a temporary corona formation.

Enhancing Roads Infrastructure Monitoring with FBG Sensors and Multi-Threshold Signal Tracking

Accurate monitoring techniques are needed to improve pavement durability by ensuring their structural integrity and longevity. This study developed an innovative real-time monitoring system utilizing fiber Bragg grating (FBG) sensors embedded in asphalt layers. The data collected by FBG sensors includes a baseline primarily influenced by temperature and peak shape data associated with short-term loading from passing vehicles. Therefore, analyzing FBG data and identifying statistical patterns poses a challenge. To address this challenge, employing a signal tracker proves to be a valuable option for monitoring the baseline and scanning signals for subsequent analysis. Moreover, considering the varying loading weights on FBG sensors due to diverse vehicles, tracking FBG signals across multiple thresholds enhances precision. A MATLAB-based program was developed incorporating a multi-threshold signal tracker capable of efficiently processing FBG data by tracking signals in both positive and negative multi-thresholds. This tracker effectively distinguishes between different loading events and extracts the baseline signal after tracking. Subsequently, the system provides outputs, including loading distribution, number of loading events, traffic, and temperature effects. Finally, to validate the system, real FBG data collected during an extensive monitoring campaign was processed using the developed program. The obtained outputs contribute to a comprehensive assessment of pavement performance, thereby facilitating the development of more accurate maintenance and rehabilitation strategies.

Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

ANALYSIS OF MOVEMENT DATA FROM GPS-MONITORED LIVESTOCK GUARDIAN DOGS (LGDS) AT TWO SHEEPFOLDS IN THE NORTH-WESTERN MARAMURES LAND, ROMANIA USING THE KERNEL DENSITY ESTIMATION METHOD. IMPLICATIONS FOR OUTDOOR TOURISM

The Carpathians and other mountainous regions around the world are renowned for their specific landscapes shaped by pastoralism, a millennia-old traditional and sustainable economic system. In Romania, this traditional occupation has an established place within the Romanian culture. In an environment where large predators are present, the livestock owners and shepherds have traditionally relied, and still do, on livestock guardian dogs (LGDs) to protect the flock against carnivores or theft, therefore, the dogs are perceived as an integral component of the traditional pastoral system. However, from late April until the end of September, many outdoor recreational activities like hiking, mountain trail running, or biking overlap with the pastoral calendar, creating a potential for conflict between two, very different categories of landscape users, with recurring incidents happening over the years. In this study, a winter GPS monitoring campaign was proposed, between November 2023 and January 2024 that used GPS professional collars to track the movements of two livestock guardian dogs stationed at two sheepfolds located at their winter bases in the hills at the foot of Ignis Mountains (part of the Romanian northern Carpathians) from northwestern Maramureș Land, Romania. The campaign generated point-based spatiotemporal data processed and analyzed in M. Excel and QGIS using Kernel density estimation as the main method to generate metrics and identify potential clusters of LGD activity in their usual environment. The results highlight high observational clusters near the winter folds but also lower observational clusters in areas situated hundreds of meters distance around the main compound, in certain locations. Although temporally limited, the results have the potential to help the understanding of the animal's preferred zone of habitation and substantiate future win-win cohabitation solutions that minimize conflictual encounters between the shepherds and their guardian dogs on one side, as primary land users, and outdoor recreationists as other landscape users.

Subsurface flow pathway and hydrochemistry of a geographically isolated depression wetland in the Serra Geral Diabase Formation, São Paulo State, Brazil

Connectivity among water bodies is crucial for hydrological and ecological processes and fundamental for water management. Hydrochemistry and potentiometric studies on an isolated wetland allow to assess landscape influence, flow pathways, and subsurface connection. This study focuses on an isolated wetland over diabase on a topographic depression, with no visible river connection, and aims to determine the influence of rainfall and landscape on water composition and investigate subsurface flow toward the Santa Gertrudes stream. Water levels were monitored, and samples were collected from four wells during wet and dry seasons. Physical and chemical water parameters were measured, and ion concentrations determined. Pearson’s correlations highlighted codependence between physical and chemical parameters and ions and Piper’s diagram classified water composition. Six potentiometric maps were generated using interpolated hydraulic loads from monitoring campaigns. The findings show fluctuations in water level (0.11 to 0.93 m during wet periods and 1.25 to 1.79 m during dry periods), indicating rainwater directly affects water availability. Water composition was categorized as Sodium Bicarbonate and electrical conductivity values (greater than 100 µS/cm) may indicate that upstream wells are retaining salts from the surrounding crops. Strong positive correlation between sodium, bicarbonate and carbonate, and sulfate with electrical conductivity suggests simultaneous release of ions due to rock and soil mineral alteration. Potentiometric data reinforce the hypothesis of a subsurface connection between the studied area and the Santa Gertrudes stream. Future studies could benefit from expanding the monitoring network to include more GIWs and applying additional methods, such as isotope tracers.

Validation of cheap axial passive sampler and procedure suitable for atmospheric NO2 community-based monitoring

Nitrogen dioxide (NO2) is an air pollutant highly impacting on human health, and its measurement is crucial for air quality assessment. Use of passive samplers for long-term large-scale monitoring is a reasonably reliable and economic alternative to more sophisticated and expensive equipment employed in active air sampling by environmental control authorities. In recent years the Citizen Science approach, based on low-cost devices, is spreading more and more in environmental control. Passive samplers available on the market (like the consolidated “Palmes” tubes) are often used in community-based monitoring campaigns. We describe validation of a new cheap axial diffusion tube for NO2 monitoring, used in combination with a new user-friendly App for smartphone that represents an innovation to speed up recording of geo-localization and exposition period data. Affordability and availability of materials, simple construction protocol and easy App procedure, allow possible self-production by school students and non-expert users, making the proposed tube a potential tool to realize extended Citizen Science monitoring campaigns. Accuracy within 25% and precision within 20%, evidenced in validation, show comparability of the tube performance with Palmes-type tubes and agreement with the official monitoring station results. Two small-scale trial monitoring campaigns, involving high school students, were performed to test the efficacy of the proposed “tube-App” system in combining educational impact and community value of air quality monitoring.

High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city

The development of high-resolution spatial and spatiotemporal models of air pollutants is essential for exposure science and epidemiological applications. While fixed-site sampling has conventionally provided input data for statistical predictive models, the evolving mobile monitoring method offers improved spatial resolution, ideal for measuring pollutants with high spatial variability such as ultrafine particles (UFP).The Quebec Air Pollution Exposure and Epidemiology (QAPEE) study measured and modelled the spatial and spatiotemporal distributions of understudied pollutants, such as UFPs, black carbon (BC), and brown carbon (BrC), along with fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in Quebec City, Canada. We conducted a combined fixed-site (NO2 and O3) and mobile monitoring (PM2.5, BC, BrC, and UFPs) campaign over 10-months. Mobile monitoring routes were monitored on a weekly basis between 8am–10am and designed using location/allocation modelling. Seasonal fixed-site sampling campaigns captured continuous 24-h measurements over two-week periods. Generalized Additive Models (GAMs), which combined data on pollution concentrations with spatial, temporal, and spatiotemporal predictor variables were used to model and predict concentration surfaces.Annual models for PM2.5, NO2, O3 as well as seven of the smallest size fractions in the UFP range, had high out of sample predictive accuracy (range r2: 0.54–0.86). Varying spatial patterns were observed across UFP size ranges measured as Particle Number Counts (PNC). The monthly spatiotemporal models for PM2.5 (r2 = 0.49), BC (r2 = 0.27), BrC (r2 = 0.29), and PNC (r2 = 0.49) had moderate or moderate-low out of sample predictive accuracy. We conducted a sensitivity analysis and found that the minimum number of ‘n visits’ (mobile monitoring sessions) required to model annually representative air pollution concentrations was between 24 and 32 visits dependent on the pollutant.This study provides a single source of exposure models for a comprehensive set of air pollutants in Quebec City, Canada. These exposure models will feed into epidemiological research on the health impacts of ambient UFPs and other pollutants.

Correction to: Characterizing X-ray, UV, and optical variability in NGC 6814 using high-cadence Swift observations from a 2022 monitoring campaign

Correction to: Characterizing X-ray, UV, and optical variability in NGC 6814 using high-cadence <i>Swift</i> observations from a 2022 monitoring campaign

Evaluating the Urban Heat Mitigation Potential of a Living Wall in Milan: One Year of Microclimate Monitoring

Urban heat island (UHI) mitigation and adaptation are urgent needs in a built environment, which requires us to search for sustainable solutions to limit the urban heat island effect and improve the energy efficiency of building envelopes. Among these solutions, vertical green structures (VGSs) have recently attracted significant attention for their potential to mitigate adverse effects, especially in densely built areas. This study presents a comprehensive data analysis of the microclimate of a living wall in Milan, Italy. Our aim was to evaluate this VGS’s performance in mitigating temperature increases caused by the UHI effect. In the literature, similar studies are limited to shorter monitoring periods (mostly in cooling seasons) and specific orientations (mostly facing south). However, the VGS presented in this case study here faces northwest and was continuously monitored for one calendar year. During this continuous in situ monitoring campaign, air temperature data from sensors either embedded in vegetation or exposed on a bare wall were collected and analysed over a whole calendar year, which is a novelty compared to the existing literature focused on VGSs due to the long duration. The findings indicate that the studied VGS has the ability to influence the outdoor microclimate depending on the season, the precipitation events, the wall exposure, the type of vegetation, and the vegetation’s phenological attributes. The analysis showed that the VGS consistently maintained cooler temperatures than the bare wall, with mean temperature differences ranging from 2.8 °C in autumn to 0.8 °C in spring through the winter. The vegetation acted as a natural insulator by reducing the air temperature during the hot summer and in early autumn, corresponding to the growing period of the vegetation. Thus, VGSs show potential to mitigate the global warming effect. These findings provide valuable insights on vegetation’s capability to act as a thermal regulator for sustainable urban planning and energy-efficient building design and retrofitting.

Embracing epistemic uncertainty: a risk evaluation method for pollutants in stormwater.

In this study, we show that pollutants of emerging concern are, by nature, prone to the emergence of epistemic uncertainty. We also show that the current uncertainty quantification methods used for pollutant modelling rely almost exclusively on parameter uncertainty, which is not adequate to tackle epistemic uncertainty affecting the model structure. We, therefore, suggest a paradigm shift in the current pollutant modelling approaches by adding a term explicitly accounting for epistemic uncertainties. In a proof-of-concept, we use this approach to investigate the impact of epistemic uncertainty in the fluctuation of pollutants during wet-weather discharge (input information) on the distribution of mass of pollutants (output distributions). We found that the range of variability negatively impacts the tail of output distributions. The fluctuation time, associated with high covariance between discharge and concentration, is a major driver for the output distributions. Adapting to different levels of epistemic uncertainty, our approach helps to identify critical unknown information in the fluctuation of pollutant concentration. Such information can be used in a risk management context and to design smart monitoring campaigns.

Cases of sensor fault assessment

Abstract The field of structural health monitoring (SHM) is based on collecting data, and according to that, useful information about the monitored structures is obtained. Therefore, it is essential that the signals from the sensors used in the monitoring campaign can be trusted. However, some of these sensors will sometimes fail or will, at least, pick up excessive noise that might influence the quality of the information extracted from the data. In this paper, a new technique of sensor fault detection is illustrated that is based on constraints between multiple output signals from the monitored structures.

Unravelling the Enigma of Scrub Typhus: A Critical Review and Insights Into Epidemiology, Clinical Features, Diagnostic Advances, and Emerging Trends.

Scrub typhus (ST) is caused by the bacterium Orientia tsutsugamushi, which exhibits significant antigenic diversity and is prevalent in the Asia-Pacific region. Its clinical presentation is characterized by non-specific symptoms such as headache, myalgia, sweating, and vomiting, along with the abrupt onset of fever and chills. An eschar is often visible in the axilla, groin, or inguinal area and is present in around half of the confirmed cases. The Weil-Felix test is the earliest detection technique, though it is not highly specific. Diagnostic procedures include biopsy, culture, serology, and PCR. The molecularly detectable amount of Orientiaein the bloodstreamoccasionally reaches levels that are identified during acute illness and vanish after the first round of appropriate antibiotic treatment. This study offers a thorough review of ST, a disease carried by vectors caused by Orientia tsutsugamushi.We emphasize on the significance of monitoring and awareness campaigns, diagnostic problems, and geographical differences. It is essential to use multidisciplinary techniques combining epidemiologists, public health authorities, and doctors. Sustained observation and research are essential for developing successful preventative and control measures. When participating in outdoor activities in areas known for such infections or at particular times of the year when ticks or mites carrythe rickettsia infection, people should take great precautions to prevent getting bitten by ticks or mites. Prompt medical evaluation is advised if suspicious symptoms or signs appear, especially in elderly individuals.

Cepheids with giant companions

Context. Binary Cepheids with giant companions are crucial for studying the physical properties of Cepheid variables, in particular providing the best means to measure their masses. Systems composed of two Cepheids are even more important, but to date, only one such system has been identified, in the Large Magellanic Cloud (LMC). Aims. Our current aim is to increase the number of these systems known tenfold and to provide their basic characteristics. The final goal is to obtain the physical properties of the component Cepheids, including their masses and radii, and to learn about their evolution in the multiple systems, also revealing their origin. Methods. We started a spectroscopic monitoring campaign of nine unresolved pairs of Cepheids from the OGLE catalog to check if they are gravitationally bound. Two of these so-called double Cepheids are located in the LMC, five are in the Small Magellanic Cloud (SMC), and two are in the Milky Way (MW). Results. We report a spectroscopic detection of the binarity of all nine of these double Cepheids with orbital periods ranging from 2 to 18 years. This increases the number of known binary double (BIND) Cepheids from 1 to 10 and triples the number of all confirmed double-lined binary (SB2) Cepheids. For five BIND Cepheids, the disentangled pulsational light curves of the components show anti-correlated phase shifts due to orbital motion. We show the first empirical evidence that typical period–luminosity relations (PLRs) are rather binary Cepheid PLRs, as they include light of the companion. Conclusions. The statistics of pulsation period ratios of BIND Cepheids do not agree with those expected for pairs of Cepheids of the same age. These ratios together with the determined mass ratios far from unity suggest a merger origin of at least one component for about half of the systems. The SMC and MW objects are the first found in SB2 systems composed of giants in their host galaxies. The Milky Way BIND Cepheids are also the closest such systems, being located at about 11 and 26 kpc.

High-cadence monitoring of the emission properties of magnetar XTE J1810−197 with the Stockert radio telescope

Context. Since the detection of a burst resembling a fast radio burst (FRB) from the Galactic magnetar SGR 1935+2154, magnetars have joined the set of favourable candidates for FRB progenitors. However, the emission mechanism of magnetars remains poorly understood. Aims. Observations of magnetars with a high cadence over extended timescales have allowed for their emission properties to be determined, in particular, their temporal variations. In this work, we present the results of the long-term monitoring campaign of the magnetar XTE J1810−197 since its second observed active phase from December 2018 until November 2021, with the Stockert 25 m radio telescope. Methods. We present a single pulse search method, improving on commonly used neural network classifiers thanks to the filtering of radio frequency interference based on its spectral variance and the magnetar’s rotation. Results. With this approach, we were able to lower the signal to noise ratio (S/N) detection threshold from 8 to 5. This allowed us to find over 115 000 spiky single pulses – compared to 56 000 from the neutral network approach. Here, we present the temporal variation of the overall profile and single pulses. Two distinct phases of different single pulse activity can be identified: phase 1 from December 2018 to mid-2019, with a few single pulses per hour, and phase 2 from September 2020 with hundreds of single pulses per hour (with a comparable average flux density). We find that the single pulse properties and folded profile in phase 2 exhibit a change around mid-March 2021. Before this date, the folded profile consists of a single peak and single pulses, with fluences of up to 1000 Jyms and a single-peaked width distribution at around 10 ms. After mid-March 2021, the profile consists of a two peaks and the single pulse population shows a bimodal width distribution with a second peak at 1 ms and fluences of up to 500 Jyms. We also present asymmetries in the phase-resolved single pulse width distributions beginning to appear in 2020, where the pulses arriving earlier in the rotational phase appear wider than those appearing later. This asymmetry persists despite the temporal evolution of the other single pulse and emission properties. Conclusions. We argue that a drift in the emission region in the magnetosphere may explain this observed behaviour. Additionally, we find that the fluence of the detected single pulses depends on the rotational phase and the highest fluence is found in the centre of the peaks in the profile. While the majority of the emission can be linked to the detected single pulses, we cannot exclude another weak mode of emission. In contrast to the pulses from SGR 1935+2154, we have not found any spectral feature or bursts with energies in the order of magnitude of an FRB during our observational campaign. Therefore, the question of whether this magnetar is capable of emitting such highly energetic bursts remains open.

Structural Health Monitoring of the Roman bridge S. Angelo on the Savuto river (Scigliano, Italy), by integration of a new sensor and geomatic techniques

The Roman Bridge of S. Angelo, located along the road that connected Capua to Reggio Calabria (Via ab Regio ad Capuam), constitutes an important remain of Roman engineering works and is the best-preserved bridge along the road itself. Located in the middle valley of the Savuto river, this bridge is currently subject to structural monitoring by using various integrated techniques. The monitoring is carried out as part of a series of initiatives aimed at gaining in-depth knowledge and safeguarding a series of historic buildings in the same area. A new type of sensor, characterized by features not offered at the same time by those currently on the market, has been developed to aid structural and environmental monitoring. Previous surveys had been carried out with geomatic techniques; one of the products of these surveys is a 3D model of the bridge, used as input to carry out a Finite Element Modeling (FEM) analysis. The results of this FEM analysis will be compared with those obtained by the measurements carried out with the new sensor. The monitoring campaign, which has just begun, and the expected results are described and commented on. The main results obtained to date are the model of the bridge, a structural analysis and the settlements that have occurred over time as well as the creation of the new sensor.

An Informed and Systematic Method to Identify Variable Mid-L Dwarfs

Most brown dwarfs show some level of photometric or spectral variability. However, finding the most variable dwarfs more suited for a thorough variability monitoring campaign remained a challenge until a few years ago with the design of spectral indices to find the most likely L and T dwarfs using their near-infrared (NIR) single-epoch spectrum. In this work, we designed and tested NIR spectral indices to preselect the most likely variable L4–L8 dwarfs, complementing the indices presented by Ashraf et al. and Oliveros-Gomez et al. We used time-resolved NIR Hubble Space Telescope Wide Field Camera 3 spectra of an L6.0 dwarf, LP 261–75b, to design our novel spectral indices. We tested these spectral indices on 75 L4.0–L8.0 NIR SpeX/IRTF spectra, providing 27 new variable candidates. Our indices have a recovery rate of ∼80% and a false negative rate of ∼25%. All the known nonvariable brown dwarfs were found to be nonvariable by our indices. We estimated the variability fraction of our sample to be 51−38+4 %, which agrees with the variability fractions provided by Buenzli et al., Radigan et al., and Metchev et al. for L4–L8 dwarfs. These spectral indices may support the future selection of the most likely variable directly imaged exoplanets for studies with the James Webb Space Telescope and as well as the 30 m telescopes.

Low-cost UAV coordinated carbon observation network: Carbon dioxide measurement with multiple UAVs

Improving the mitigation of global greenhouse effects requires drastic reductions in carbon emissions. Effective carbon management at different spatial scales is therefore crucial to a more sustainable path of economic development. Regional and local carbon management currently rely on the traditional bottom-up method of emission reports, which are known for their opaque and complex procedures. Novel top-down carbon monitoring methods including space-based, aerial and ground-based observations are tantalising alternatives to more reliable data sources of carbon emissions as well as sinks. Unmanned Aerial Vehicles (UAVs) based carbon monitoring is an upcoming field because of its lower costs, higher spatial-temporal resolution and operability at a wider range of weather conditions compared to satellite observations. UAVs equipped with Non-dispersive Infrared (NDIR) carbon dioxide (CO2) sensors can measure in the air with faster response and can intelligently navigate themselves within areas of greater interest. With the help of lighter NDIR payloads and multiple UAVs flying in synchrony, larger cross sections or areas for maximising information acquisition of emission plumes can be covered. In this article, we first introduce the design of a miniaturised NDIR payload, which is compatible with a lightweight UAV to achieve a single system weighing less than 4 kg. Then, we test our design via comparisons against another NDIR payload to determine its accuracy (−0.82 ppm and −0.44 ppm) and precision (0.12 ppm and −0.03 ppm) in measurements of two vertical concentration profiles. Finally, we deployed four UAVs on two major anthropogenic emission source monitoring campaigns with coordinated flight patterns. To fully understand the benefit of multi-UAV carbon monitoring, we attempted two flight patterns and computed power plant emission rates via the cross-sectional flux method. While all flights successfully captured the concentration enhancements downwind of the power plants, the single-sectional flight pattern measured an emission rate of 10ktCO2/day which is 44% less than that reported monthly and the multi-sectional flight pattern yielded relatively varied results (8.5ktCO2/day on average, which is 54% less) for the respective sections due to wind underestimations, plume section undersampling and wind fluctuations.

Systematic underestimation of polycyclic aromatic hydrocarbon aqueous concentrations in rivers

Polycyclic aromatic hydrocarbons (PAHs) are a widespread group of organic contaminants whose presence in water bodies is cause of severe concern. With few exceptions, the majority of PAHs is hydrophobic, presents a high adsorption affinity, and is thus primarily transported within river systems during high-flow events together with suspended particulate matter (SPM). Evidence exists of analytical challenges related to the incomplete extraction of PAHs adsorbed to solids and thus to a potential negative bias in the chemical analysis of PAHs in bulk water samples with high SPM content. Despite this, partly due to the elevated efforts required to collect representative samples containing sufficient SPM for the separate PAH analysis in this matrix, several investigations rely on the analysis of aqueous samples. This study tests the hypothesis that surveys based exclusively on bulk water may lead to a systematic underestimation of the real contamination level and transport of PAHs in rivers. Six high-turbidity events were examined in three Austrian rivers applying time-integrated sampling and simultaneously analyzing PAHs in total bulk water, filtered water, SPM, and supernatant. Despite an unavoidable degree of uncertainty in such challenging sampling scheme, the results indicate that measurements performed with best available standard methods in bulk water samples determined in average only about 40% of the theoretically expected total PAHs concentrations derived from the analyses in SPM. Such deviation has important implications for the reliable assessment of the compliance with environmental quality standards as well as for surveys aimed to estimate riverine loads, validate emission models, and understand the transport dynamics of PAHs in rivers. Whereas the first objective, e.g., in European countries, is alternatively achieved via monitoring in biota, the latter ones require efforts directed to complement monitoring campaigns with separate sampling of SPM, with monitoring of suspended solids transport to appropriately select and interpret the results of water samples and to improve the chemical analysis of PAHs in bulk water samples with high solids content.

Identification of problematic epochs in astronomical time series through transfer learning

We present a novel method for detecting outliers in astronomical time series based on the combination of a deep neural network and a k-nearest neighbor algorithm with the aim of identifying and removing problematic epochs in the light curves of astronomical objects. We used an EfficientNet network pretrained on ImageNet as a feature extractor and performed a k-nearest neighbor search in the resulting feature space to measure the distance from the first neighbor for each image. If the distance was above the one obtained for a stacked image, we flagged the image as a potential outlier. We applied our method to a time series obtained from the VLT Survey Telescope monitoring campaign of the Deep Drilling Fields of the Vera C. Rubin Legacy Survey of Space and Time

Community structure and function during periods of high performance and system upset in a full-scale mixed microalgal wastewater resource recovery facility

Microalgae have the potential to exceed current nutrient recovery limits from wastewater, enabling water resource recovery facilities (WRRFs) to achieve increasingly stringent effluent permits. The use of photobioreactors (PBRs) and the separation of hydraulic retention and solids residence time (HRT/SRT) further enables increased biomass in a reduced physical footprint while allowing operational parameters (e.g., SRT) to select for desired functional communities. However, as algal technology transitions to full-scale, there is a need to understand the effect of operational and environmental parameters on complex microbial dynamics among mixotrophic microalgae, bacterial groups, and pests (i.e., grazers and pathogens) and to implement robust process controls for stable long-term performance. Here, we examine a full-scale, intensive WRRF utilizing mixed microalgae for tertiary treatment in the US (EcoRecover, Clearas Water Recovery Inc.) during a nine-month monitoring campaign. We investigated the temporal variations in microbial community structure (18S and 16S rRNA genes), which revealed that stable system performance of the EcoRecover system was marked by a low-diversity microalgal community (DINVSIMPSON = 2.01) dominated by Scenedesmus sp. (MRA = 55 %-80 %) that achieved strict nutrient removal (effluent TP < 0.04 mg·L-1) and steady biomass concentration (TSSmonthly avg. = 400–700 mg·L−1). Operational variables including pH, alkalinity, and influent ammonium (NH4+), correlated positively (p < 0.05, method = Spearman) with algal community during stable performance. Further, the use of these parameters as operational controls along with N/P loading and SRT allowed for system recovery following upset events. Importantly, the presence or absence of bacterial nitrification did not directly impact algal system performance and overall nutrient recovery, but partial nitrification (potentially resulting from NO2− accumulation) inhibited algal growth and should be considered during long-term operation. The microalgal communities were also adversely affected by zooplankton grazers (ciliates, rotifers) and fungal parasites (Aphelidium), particularly during periods of upset when algal cultures were experiencing culture turnover or stress conditions (e.g., nitrogen limitation, elevated temperature). Overall, the active management of system operation in order to maintain healthy algal cultures and high biomass productivity can result in significant periods (>4 months) of stable system performance that achieve robust nutrient recovery, even in winter months in northern latitudes (WI, USA).