Fill Data Gaps Research Articles

Prediction of Sea Level in the Arabian Gulf Using Artificial Neural Networks

Creating an efficient model for predicting sea level fluctuations is essential for climate change research. This study examined the effectiveness of utilizing Artificial Neural Networks (ANNs), particularly the recurrent network approach. ANNs were chosen for their capacity to learn from extensive and intricate data and their ability to handle nonlinear correlations. The Long Short-Term Memory (LSTM) algorithm was employed to fill data gaps and predict future sea level records in the Arabian Gulf, especially in Mina Salman. The results were promising, with LSTM successfully filling a 6-year data gap while maintaining low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. The first phase of the model yielded a RMSE value of 63.4 mm and a MAPE value of 3.14%. The same approach was used to retrain the model with a mix of real and predicted values, preserving historical patterns and yearly rates with an RMSE of 66.5 mm and a MAPE of 3.07%. These findings highlight LSTM’s advantages when considering only historical information for predicting the future sea level changes. The research provides valuable insights into predicting sea level changes in regions with limited field data, such as the Arabian Gulf, and emphasizes the potential for further research to enhance sea level prediction models through improved optimization techniques.

Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations

Satellite-derived aerosol optical depth (AOD) has been extensively utilized for retrieving ground-level PM2.5 distributions. However, the presence of non-random missing data gaps in AOD poses a challenge to directly obtaining the gap-free AOD-derived PM2.5, thereby impeding accurate exposure risk assessment. Here, this study presents a novel and flexible framework that couples stacking and flexible spatiotemporal data fusion (FSDAF) approaches. By integrating multiple models and data sources, this framework aims to generate hourly (24-h) gap-free PM2.5 estimates for the Beijing–Tianjin–Hebei (BTH) region in 2018. This study effectively reconstructed data at least three times more effectively than the original AOD-derived PM2.5, achieving the Pearson coefficient (r), the coefficient determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) values of 0.91, 0.84, 19.38 µg/m3, and 12.17 µg/m3, respectively, based on entire samples. Such strong predictive performance was also exhibited in spatial-based (r: 0.92–0.93, R2: 0.85–0.87, RMSE: 18.13 µg/m3–20.18 µg/m3, and MAE: 11.21 µg/m3–12.52 µg/m3) and temporal-based (r: 0.91–0.98, R2: 0.82–0.96, RMSE: 3.8 µg/m3–21.89 µg/m3, and MAE: 2.71 µg/m3–14.00 µg/m3) validations, indicating the robustness of this framework. Additionally, this framework enables the assessment of annual and seasonal PM2.5 concentrations and distributions, revealing that higher levels are experienced in the southern region, while lower levels prevail in the northern part. Winter exhibits the most severe levels, followed by spring and autumn, with comparatively lower levels in summer. Notably, the proposed framework effectively mitigates bias in calculating population-weighted exposure risk by filling data gaps with calculated values of 51.04 µg/m3, 54.17 µg/m3, 56.24 µg/m3, and 55.00 µg/m3 in Beijing, Tianjin, Hebei, and the BTH region, respectively.

Shifting terrains: Understanding residential contaminants after flood disasters

Flood disasters can induce the mass transport of soils and sediments. This has the potential to distribute contaminants and present novel combinations to new locations – including residential neighborhoods. Even when soil contaminants cannot be directly attributed to the disaster, data on bacterial and heavy metal(loids) can facilitate an environmentally just recovery by enabling reconstruction decisions that fill data gaps to minimize future exposure. These data-gathering interventions may be especially useful in poor, rural, and racially diverse communities where there is a high probability of exposure to multiple hazards and a potential dependency on the financial resources of disaster aid as a means of reducing chronic exposures to other environmental pollutants. At the same time, entering these post-disasters spaces is ethically complex. To acknowledge this complexity, we pilot a framework for work that gathers social-ecological hazard information while retaining a fair-minded approach to transdisciplinary work. Assembled a transdisciplinary team to recruit participants from 90 households subjected to flooding in the southeastern US. Participating households agreed to interviews to elicit flood experience and environmental health concerns, soil sampling for fecal bacteria (E. coli) and soil sampling for selected heavy metals and metalloids (Pb, As, Cd) at their flooded residence. Soil sampling found a wide range of E. coli concentrations in soil (0.4–1115.7 CFU/ dry gram). Heavy metal(loid)s were detected at most residences (As 97.9 %; Ca 25.5 %; Pb 100 %). Individually, heavy metal(loid) concentrations did not exceed regulatory thresholds. Hazard, risk, and mitigation concerns expressed during interviews reveal that integrated human-nature concepts complicate common understandings of how hazard perceptibility (smell, sight, touch, and information) affects research-action spaces. Qualitative analysis of interviews and field notes revealed that soil-related hazards addressed by our biophysical protocols were less salient than changes with direct causal associations with flooding. We conclude by discussing the potential for the social-ecological hazard information that is fair-minded and transdisciplinary (SHIFT) framework to advance environmentally just approaches to research-action spaces after disasters.

Functional diversity metrics can perform well with highly incomplete data sets

Abstract Characterising changes in functional diversity at large spatial scales provides insight into the impact of human activity on ecosystem structure and function. However, the approach is often based on trait data sets that are incomplete and unrepresentative, with uncertain impacts on functional diversity estimates. To address this knowledge gap, we simulated random and biased removal of data from three empirical trait data sets: an avian data set (9579 species), a plant data set (2185 species) and a crocodilian data set (25 species). For these data sets, we assessed whether functional diversity metrics were robust to data incompleteness with and without using imputation to fill data gaps. We compared two metrics each calculated with two methods: functional richness (calculated with convex hulls and trait probabilities densities) and functional divergence (calculated with distance‐based Rao and trait probability densities). Without imputation, estimates of functional diversity (richness and divergence) for birds and plants were robust when 20%–70% of species had missing data for four out of 11 and two out of six continuous traits, respectively, depending on the severity of bias and method used. However, when missing traits were imputed, functional diversity metrics consistently remained representative of the true value when 70% of bird species were missing data for four out of 11 traits and when 50% of plant species were missing data for two out of six traits. Trait probability densities and distance‐based Rao were particularly robust to missingness and bias when combined with imputation. Convex hull‐based estimations of functional richness were less reliable. When applied to a smaller data set (crocodilians, 25 species), all functional diversity metrics were much more sensitive to missing data. Expanding global morphometric data sets to represent more taxa and traits, and to quantify intraspecific variation, remains a priority. In the meantime, our results show that widely used methods can successfully quantify large‐scale functional diversity even when data are missing for half of species, provided that missing traits are estimated using imputation. We recommend the use of trait probability densities or distance‐based Rao when working with large incomplete data sets and filling data gaps with imputation.

Validating a Tethered Balloon System and Optical Technologies for Marine Wildlife Detection and Tracking

The interactions between marine wildlife and marine energy devices are not well understood, leading to regulatory delays for device deployments and testing. Technologies that enable marine wildlife observations can help to fill data gaps and reduce uncertainties about animal–device interactions. A validation test conducted in Galveston Bay near La Porte, Texas, in December 2022 used a technology package consisting of a tethered balloon system and three independent sensor systems, including three-band visible, eight-band multispectral, and single-band thermal to detect three marine-mammal-shaped surrogates. The field campaign aimed to provide an initial step to evaluating the use of the TBS and the effectiveness of the sensor suite for marine wildlife observations and detection. From 2 December to 7 December 2022, 6 flights were conducted under varying altitudes and environmental conditions resulting in the collection of 5454 images. A subset of the images was classified and analyzed with two collection criteria including Beaufort wind force scale and TBS altitude to assess a range of observations of a surrogate from near-shore to offshore based on pixel count. The results of this validation test demonstrate the potential for using TBSs and imaging sensors for marine wildlife observations and offer valuable information for further development and application of this technology for marine energy and other blue economy sectors.

Evaluation of Geospatial Interpolation Techniques for Enhancing Spatiotemporal Rainfall Distribution and Filling Data Gaps in Asir Region, Saudi Arabia

Providing an accurate spatiotemporal distribution of rainfall and filling data gaps are pivotal for effective water resource management. This study focuses on the Asir region in the southwest of Saudi Arabia. Given the limited accuracy of satellite data in this arid/mountain-dominated study area, geospatial interpolation has emerged as a viable alternative approach for filling terrestrial records data gaps. Furthermore, the irregularity in rain gauge data and the yearly spatial variation in data gaps hinder the creation of a coherent distribution pattern. To address this, the Centered Root Mean Square Error (CRMSE) is employed as a criterion to select the most appropriate geospatial interpolation technique among 51 evaluated methods for maximum and total yearly precipitation data. This study produced gap-free maps of total and maximum yearly precipitation from 1966 to 2013. Beyond 2013, it is recommended to utilize ordinary Kriging with a J-Bessel semivariogram and simple Kriging with a K-Bessel semivariogram to estimate the spatial distribution of maximum and total yearly rainfall depth, respectively. Additionally, a proposed methodology for allocating additional rain gauges to improve the accuracy of rainfall spatial distribution is introduced based on a cross-validation error (CVE) assessment. Newly proposed gauges in the study area resulted in a significant 21% CVE reduction.

Estimating runoff from pan-Arctic drainage basins for 2002–2019 using an improved runoff-storage relationship

The Arctic is undergoing dramatic climate and environmental changes. The long-term alterations in river discharges from the boreal catchments, which serve as vital links between the ocean and land, are having a profound impact on various environmental factors, particularly ocean circulation and sea-ice content. However, comprehensive and continuous monitoring of Arctic river discharge at seasonal or higher temporal resolutions remains challenging. In this study, we propose a new approach to estimate runoff by generating monthly runoff time series at the basin scale. Our method is based on changes in water storage observed by the gravimetric satellites Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission since 2002. The method utilizes an empirical runoff-storage (R-S) relationship, offering simplicity and low computational burden while maintaining good accuracy in estimating runoff. To validate our method, we utilize in-situ runoff measurements from the seven largest boreal drainage basins spanning the period from 2002 to 2019, encompassing a total of 18 years. We divide these 18 years of observations into two phases: a training phase (8 years) and a testing phase (10 years). The results indicate that the R-S method established during the training phase yields monthly Nash-Sutcliffe efficiency (NSE) values ranging from 0.65 to 0.92 when compared to in-situ runoff measurements. Moreover, the method demonstrates a consistent performance in estimating runoff during the testing phase (monthly NSE: 0.67–0.84). With the exception of the Ob and Mackenzie basins, which exhibit distinct climatic conditions and hydrological networks, the R-S models are interchangeable across basins. This makes it suitable for both temporal and spatial extrapolation to fill data gaps, provided that accurate water and snow storage data are available. All in all, our method enables the reconstruction of monthly surface runoff across the entire boreal basins between 2002 and 2019. The results indicate an average annual runoff of 3200 ± 160 Gt over the study area of 1.58 × 107 km2. To evaluate the accuracy of our estimates, we compare the total runoff estimates obtained using the R-S method with those from 12 model estimates. Our estimates exhibit the highest correlation with available in-situ runoff measurements and yield monthly NSE values >0.57 for five out of the twelve model estimates. This study presents a convenient method to address the urgent need for comprehensive, continuous, and monthly temporal resolution of runoff estimates throughout the entire boreal region.

Overcoming challenges in life cycle assessment of smart energy systems – A map of solution approaches

Current research underlines the benefits of digitalization to integrate renewable energy sources (RES) efficiently and to coordinate generation and supply. This development towards smart energy systems (SES) includes emerging use cases at the distribution grid level, e.g., smart charging strategies for electric vehicles or energy management systems in buildings. An environmental assessment of such, however, is challenging. The standardized Life Cycle Assessment (LCA) approaches seem insufficient since SES are emerging systems at the interface of digitalization and energy with multiple intended and unintended environmental effects. This article aims to derive solution approaches for LCA professionals that investigate use cases enabled by information and communication technology (ICT), specifically in SES. To identify challenges during the first two phases of an LCA and solution approaches, we examine seven recent research projects and conduct a real-time Delphi workshop with LCA experts in the field of SES. As a result, the article provides approaches on setting up an LCA, filling data gaps, and methods to draw upon the long-term impacts of use cases in SES. Key findings include a modified taxonomy of potential environmental effects. Results show that systemic effects in SES have potentially the highest impact on the LCA, followed by effects caused by user behavior. Assessing these effects of ‘higher-order’ poses an increased complexity. Determined solution approaches include combining the standardized LCA with energy system modeling or surveys on user behavior. Overall, this article reveals orientation strategies and recommendations to guide LCA professionals toward a holistic environmental assessment of use cases in SES.

Characterizing human health damage from ionizing radiation in life cycle assessment

PurposeAlthough a wide number of industrial processes routinely release radionuclides into the environment, the resulting potential impacts on human health have been largely overlooked in life cycle assessment (LCA). As part of the Life Cycle Initiative project on Global Guidance for Life Cycle Impact Assessment Indicators and Methods (GLAM), we aim to develop a consensus-based source-to-damage framework and factors for characterizing human health damage from ionizing radiation in LCA.MethodsOur framework comprises four modules. The fate and exposure modules are based on UCrad, an earlier developed compartment-based environmental model for radionuclides. The focus of the present work is on the dose response and severity modules, which are based on most recent data from the International Committee on Radiological Protection and the Global Burden of Disease project series. The characterization factors are expressed in terms of DALY per kBq released.Results and discussionsWe obtain characterization factors for 115 radionuclides and 8 environmental compartments. To evaluate our approach, we compare both effect factors (combining dose response and severity) and characterization factors with those proposed in earlier studies. Our analysis demonstrates that differences are explicable by the different approaches used in the fate and exposure modelling. We also test the sensitivity of our factors to different approaches for filling data gaps, suggesting that our factors are robust. Finally, we apply our factors in an illustrative case study on rice production and consumption under various scenarios to identify dominant radionuclides and how these differ when other approaches are used.ConclusionsOur framework is aligned with widely adopted methodologies for human health impact assessment, thus enabling robust comparisons, and covers nearly all radionuclides released by anthropogenic activities, including those that may arise from disposal of nuclear waste. Our factors are readily applicable for assessing radionuclide emissions in LCA. As next step we recommend (i) incorporating decay products into the fate model and (ii) integrating a model for indoor emissions of radon and indoor exposure to naturally occurring radionuclides (NORM).

Drone-based meteorological observations up to the tropopause – a concept study

Abstract. The main in situ database for numerical weather prediction currently relies on radiosonde and airliner observations, with large systematic data gaps: horizontally in certain countries, above the oceans and in polar regions, and vertically in the rapidly changing atmospheric boundary layer, as well as up to the tropopause in areas with low air traffic. These gaps might be patched by measurements with drones. They provide a significant improvement towards environment-friendly additional data, avoiding waste and without the need for helium. So far, such systems have not been regarded as a feasible alternative for performing measurements up to the upper troposphere. In this article, the development of a drone system that is capable of sounding the atmosphere up to an altitude of 10 km with its own propulsion is presented, for which Antarctic and mid-European ambient conditions were taken into account: after an assessment of the environmental conditions at two exemplary radiosounding sites, the design of the system and the instrumentation are presented. Further, the process to get permissions for such flight tests even in the densely populated continent of Europe is discussed, and methods to compare drone and radiosonde data for quality assessment are presented. The main result is the technical achievement of demonstrating the feasibility of reaching an altitude of 10 km with a small meteorologically equipped drone using its own propulsion. The first data are compared to radiosonde measurements, demonstrating an accuracy comparable to other aircraft-based observations, despite the simplistic sensor package deployed. A detailed error discussion is given. The article closes with an outlook on the potential use of drones for filling data gaps in the troposphere.

Ending violence against children: A new international standard to foster data availability

Interest in data on violence against children has been gathering momentum in recent years. Nevertheless, data collection efforts overall are sporadic and national data systems remain underdeveloped. What is more, definitions of violence are inconsistent and unclear. What ‘counts’ as violence against children varies across data collection efforts, negatively impacting data quality. Significant investment – in the form of guidance as well as tools and other resources for capacity-building – is urgently required to respond to countries' data needs. The newly released International Classification of Violence against Children (ICVAC) holds potential for bringing the world one step closer to filling data gaps and thus promoting accountability towards the ambitious global goal of ending violence against children.

Predicting building age from urban form at large scale

To stay within 1.5 °C of global warming, reducing energy-related emissions in the building sector is essential. Rather than generic climate recommendations, this requires tailored, low-carbon urban planning solutions and spatially explicit methods that can inform policy measures at urban, street and building scale. Here, we propose a scalable method that is able to predict building age information in different European countries using only open urban morphology data. We find that spatially cross-validated regression models are sufficiently robust to generalize and predict building age in unseen cities with a mean absolute error (MAE) between 15.3 years (Netherlands) and 19.9 years (Spain). Our experiments show that large-scale models improve generalization for predicting across cities, but are not needed to infer missing data within known cities. Filling data gaps within known cities is possible with a MAE between 9.6 years (Netherlands) and 16.7 years (Spain). Overall, our results demonstrate the feasibility of generating missing age data in different contexts across Europe and informing climate mitigation policies such as large-scale energy retrofits. For the French residential building stock, we find that using age predictions to target retrofit efforts can increase energy savings by more than 50% compared to missing age data. Finally, we highlight challenges posed by data inconsistencies and urban form differences between countries that need to be addressed for an actual roll-out of such methods.

Comparison of gap-filling methods for producing all-weather daily remotely sensed near-surface air temperature

Thermal infrared remotely sensed near-surface air temperature (Ta) can provide gridded temperature information at relatively high spatial and temporal resolutions, and such measurements are thus widely used as an essential environmental parameter in numerous fields. However, data gaps caused by clouds highly restrict the applicability of remotely sensed Ta. Only a few studies have explored the production of seamless remotely sensed Ta products, and all of them estimated Ta from the gap-filled land surface temperature (LST). This study first estimates the daily minimum, average and maximum Ta of clear-sky pixels from remote sensing data over the Yangtze River Delta (YRD) and the Ningxia Autonomous Region (NAR), China, during 2016–2020, and then applies five gap-filling methods, including spatial, temporal, spatiotemporal and two multisource fusion-based gap-filling methods, to fill the data gaps in the remotely sensed Ta data. The performances of these methods under different cloud, terrain and landscape conditions are also assessed. The validation results indicate that the Temporal Fourier analysis (TFA) method exhibits high accuracy, good robustness under various cloud and surface conditions, and good ability to describe spatial details of Ta of cloud-cover areas. It is the most suitable method to fill data gaps in remotely sensed Ta. This study provides a valuable reference for selecting appropriate methods to develop seamless remotely sensed Ta products.

Collecting volunteered geographic information from the Global Navigation Satellite System (GNSS): experiences from the CAMALIOT project

ABSTRACT Raw observations (carrier-phase and code observations) from the Global Navigation Satellite System (GNSS) can now be accessed from Android mobile phones (Version 7.0 onwards). This paves the way for GNSS data to be utilized for low-cost precise positioning or in ionospheric or tropospheric applications. This paper presents results from data collection campaigns using the CAMALIOT mobile app. In the first campaign, 116.3 billion measurements from 11,828 mobile devices were collected from all continents. Although participation decreased during the second campaign, data are still being collected globally. In this contribution, we demonstrate the potential of volunteered geographic information (VGI) from mobile phones to fill data gaps in geodetic station networks that collect GNSS data, e.g. in Brazil, but also how the data can provide a denser set of observations than current networks in countries across Europe. We also show that mobile phones capable of dual-frequency reception, which is an emerging technology that can provide a richer source of GNSS data, are contributing in a substantial way. Finally, we present the results from a survey of participants to indicate that participation is diverse in terms of backgrounds and geography, where the dominant motivation for participation is to contribute to scientific research.

Ecotoxicity and Accumulation of Perfluorononanoic Acid in the Fathead Minnow (Pimephales promelas) and an Approach to Developing Protective Thresholds in the Aquatic Environment Through Species Sensitivity Distribution.

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous in the environment. Locations where PFAS-containing aqueous film-forming foam (AFFF) has been used or accidentally released have resulted in persistently high concentrations of PFAS, including in surface water that may be adjacent to release sites. Perfluorooctane sulfonic acid (PFOS) is most frequently measured near AFFF release sites; however, other PFAS are being quantified more frequently and, of those, perfluorononanoic acid (PFNA) is common. The goal of our study was to fill data gaps on PFNA toxicity to freshwater fish using the fathead minnow (Pimephales promelas). We aimed to understand how PFNA may impact apical endpoints following a 42-day exposure to mature fish and a 21-day exposure to second-generation larval fish. Exposure concentrations were 0, 124, 250, 500, and 1000 µg/L for both adult (F0) and larval (F1) generations. The most sensitive endpoint measured was development in the F1 generation at concentrations of ≥250 µg/L. The 10% and 20% effective concentration of the tested population for the F1 biomass endpoint was 100.3 and 129.5 µg/L, respectively. These data were collated with toxicity values from the primary literature on aquatic organisms exposed to PFNA for subchronic or chronic durations. A species sensitivity distribution was developed to estimate a screening-level threshold for PFNA. The resulting hazard concentration protective of 95% of the freshwater aquatic species was 55 µg PFNA/L. Although this value is likely protective of aquatic organisms exposed to PFNA, it is prudent to consider that organisms experience multiple stressors (including many PFAS) simultaneously; an approach to understand screening-level thresholds for PFAS mixtures remains an uncertainty within the field of ecological risk assessment. Environ Toxicol Chem 2023;42:2229-2236. © 2023 SETAC.

Patient journey for hypertension and dyslipidemia in Saudi Arabia: highlighting the evidence gaps

BackgroundIn recent years, Saudi Arabia has witnessed staggering rates of hypertension and dyslipidemia-related cardiovascular (CV) deaths, overburdening the healthcare ecosystem of the country. Appropriate public health interventions can be devised through quantitative mapping of evidence. Identification of potential data gaps can prioritize future research needs and develop a ‘best-fit’ framework for patient-centric management of hypertension and dyslipidemia.MethodsThis review quantified data gaps in the prevalence and key epidemiological touchpoints of the patient journey including awareness, screening, diagnosis, treatment, adherence, and control in patients with hypertension and dyslipidemia in Saudi Arabia. Studies published in English between January 2010 and December 2021 were identified through a structured search on MEDLINE, Embase, BIOSIS, and PubMed databases. An unstructured search on public and government websites, including Saudi Ministry of Health, without date limits was carried out to fill data gaps. After exclusion of studies based on predefined criteria, a total of 14 studies on hypertension and 12 studies and one anecdotal evidence for dyslipidemia were included in the final analyses.ResultsThe prevalence of hypertension was reported to be 14.0%–41.8% while that for dyslipidemia was 12.5%–62.0%. The screening rate for hypertension was 100.0% as revealed by the nationwide surveys. Among hypertensive patients, only 27.6%–61.1% patients were aware of their condition, 42.2% patients underwent diagnosis, 27.9%–78.9% patients received antihypertensive treatment, 22.5% patients adhered to treatment medication, while blood pressure (BP) control was achieved in 27.0%–45.0% patients. Likewise, among patients with dyslipidemia, 10.5%–47.3% patients were aware of their condition, 34.6% patients were screened, and 17.8% underwent diagnosis. Although high treatment rates ranging from 40.0%–94.0% were reported, medication adherence recorded was 45.0%–77.4% among the treated patients. The overall low control rates ranged from 28.0%–41.5%.ConclusionsThe study findings highlight evidence gaps along key touchpoints of patient journey. Reinforcing the efforts for high-quality evidence-based research at a national level may pave a path for better resource utilization and provide guidance to practice and amend health policies for patients, healthcare practitioners (HCPs), and healthcare policy makers for better patient outcomes in Saudi Arabia.

Expansion of wastewater-based disease surveillance to improve health equity in California's Central Valley: sequential shifts in case-to-wastewater and hospitalization-to-wastewater ratios.

Over a third of the communities (39%) in the Central Valley of California, a richly diverse and important agricultural region, are classified as disadvantaged-with inadequate access to healthcare, lower socio-economic status, and higher exposure to air and water pollution. The majority of racial and ethnic minorities are also at higher risk of COVID-19 infection, hospitalization, and death according to the Centers for Disease Control and Prevention. Healthy Central Valley Together established a wastewater-based disease surveillance (WDS) program that aims to achieve greater health equity in the region through partnership with Central Valley communities and the Sewer Coronavirus Alert Network. WDS offers a cost-effective strategy to monitor trends in SARS-CoV-2 community infection rates. In this study, we evaluated correlations between public health and wastewater data (represented as SARS-CoV-2 target gene copies normalized by pepper mild mottle virus target gene copies) collected for three Central Valley communities over two periods of COVID-19 infection waves between October 2021 and September 2022. Public health data included clinical case counts at county and sewershed scales as well as COVID-19 hospitalization and intensive care unit admissions. Lag-adjusted hospitalization:wastewater ratios were also evaluated as a retrospective metric of disease severity and corollary to hospitalization:case ratios. Consistent with other studies, strong correlations were found between wastewater and public health data. However, a significant reduction in case:wastewater ratios was observed for all three communities from the first to the second wave of infections, decreasing from an average of 4.7 ± 1.4 over the first infection wave to 0.8 ± 0.4 over the second. The decline in case:wastewater ratios was likely due to reduced clinical testing availability and test seeking behavior, highlighting how WDS can fill data gaps associated with under-reporting of cases. Overall, the hospitalization:wastewater ratios remained more stable through the two waves of infections, averaging 0.5 ± 0.3 and 0.3 ± 0.4 over the first and second waves, respectively.

Filling the data gaps: Transferring models from data-rich to data-poor deep-sea areas to support spatial management

Spatial management of the deep sea is challenging due to limited available data on the distribution of species and habitats to support decision making. In the well-studied North Atlantic, predictive models of species distribution and habitat suitability have been used to fill data gaps and support sustainable management. In the South Atlantic and other poorly studied regions, this is not possible due to a massive lack of data. In this study, we investigated whether models constructed in data-rich areas can be used to inform data-poor regions (with otherwise similar environmental conditions). We used a novel model transfer approach to identify to what extent a habitat suitability model for Desmophyllum pertusum reef, built in a data-rich basin (North Atlantic), could be transferred usefully to a data-poor basin (South Atlantic). The transferred model was built using the Maximum Entropy algorithm and constructed with 227 presence and 3064 pseudo-absence points, and 200 m resolution environmental grids. Performance in the transferred region was validated using an independent dataset of D. pertusum presences and absences, with assessments made using both threshold-dependent and -independent metrics. We found that a model for D. pertusum reef fitted to North Atlantic data transferred reasonably well to the South Atlantic basin, with an area under the curve of 0.70. Suitable habitat for D. pertusum reef was predicted on 20 of the assessed 27 features including seamounts. Nationally managed Marine Protected Areas provide significant protection for D. pertusum reef habitat in the region, affording full protection from bottom trawling to 14 of the 20 suitable features. In areas beyond national jurisdiction (ABNJ), we found four seamounts that provided suitable habitat for D. pertusum reef to be at least partially protected from bottom trawling, whilst two did not fall within fisheries closures. There are factors to consider when developing models for transfer including data resolution and predictor type. Nevertheless, the promising results of this application demonstrate that model transfer approaches stand to provide significant contributions to spatial planning processes through provision of new, best available data. This is particularly true for ABNJ and areas that have previously undergone little scientific exploration such as the global south.

CES1 Releases Oxylipins from Oxidized Triacylglycerol (oxTAG) and Regulates Macrophage oxTAG/TAG Accumulation and PGE2/IL-1β Production.

Triacylglycerols (TAGs) are storage forms of fat, primarily found in cytoplasmic lipid droplets in cells. TAGs are broken down to their component free fatty acids by lipolytic enzymes when fuel reserves are required. However, polyunsaturated fatty acid (PUFA)-containing TAGs are susceptible to nonenzymatic oxidation reactions, leading to the formation of oxylipins that are esterified to the glycerol backbone (termed oxTAGs). Human carboxylesterase 1 (CES1) is a member of the serine hydrolase superfamily and defined by its ability to catalyze the hydrolysis of carboxyl ester bonds in both toxicants and lipids. CES1 is a bona fide TAG hydrolase, but it is unclear which specific fatty acids are preferentially released during lipolysis. To better understand the biochemical function of CES1 in immune cells, such as macrophages, its substrate selectivity when it encounters oxidized PUFAs in TAG lipid droplets requires study. We sought to identify those esterified oxidized fatty acids liberated from oxTAGs by CES1 because their release can activate signaling pathways that enforce the development of lipid-driven inflammation. Gaining this knowledge will help fill data gaps that exist between CES1 and the lipid-sensing nuclear receptors, PPARγ and LXRα, which are important drivers of lipid metabolism and inflammation in macrophages. Oxidized forms of triarachidonoylglycerol (oxTAG20:4) or trilinoleoylglycerol (oxTAG18:2), which contain physiologically relevant levels of oxidized PUFAs (<5 mol %), were incubated with recombinant CES1 to release oxylipins and nonoxidized arachidonic acid (AA) or linoleic acid (LA). CES1 hydrolyzed each oxTAG, yielding regioisomers of hydroxyeicosatetraenoic acids (5-, 11-, 12-, and 15-HETE) and hydroxyoctadecadienoic acids (9- and 13-HODE). Furthermore, human THP-1 macrophages with deficient CES1 levels exhibited a differential response to extracellular stimuli (oxTAGs, lipopolysaccharide, and 15-HETE) as compared to those with normal CES1 levels, including enhanced oxTAG/TAG lipid accumulation and altered cytokine and prostaglandin E2 profiles. This study suggests that CES1 can metabolize oxTAG lipids to release oxylipins and PUFAs, and it further specifies the substrate selectivity of CES1 in the metabolism of bioactive lipid mediators. We suggest that the accumulation of oxTAGs/TAGs within lipid droplets that arise due to CES1 deficiency enforces an inflammatory phenotype in macrophages.

Developing Biodiversity Baselines to Develop and Implement Future Conservation Targets.

With the recent launch of the Kunming-Montreal global biodiversity framework (GBF), and the associated monitoring framework, understanding the framework and data needed to support it is crucial. Unfortunately, whilst the monitoring framework was meant to provide key data to monitor progress towards goals and targets, most indicators are too unclear for detection or marking progress. The most common datasets for this task, such as the IUCN redlist of species, have major spatial inaccuracies, and lack the temporal resolution to track progress, whilst point-based datasets lack data from many regions, in addition to species coverage. Utilising existing data will require the careful use of existing data, such as the use of inventories and projecting richness patterns, or filling data gaps before developing species-level models and assessments. As high-resolution data fall outside the scope of explicit indicators within the monitoring framework, using essential biodiversity variables within GEOBON (which are noted in the prelude of the monitoring framework) as a vehicle for data aggregation provides a mechanism for collating the necessary high-resolution data. Ultimately developing effective targets for conservation will require better species data, for which National Biodiversity Strategic Action Plans (NBSAPs) and novel mechanisms for data mobilisation will be necessary. Furthermore, capitalising on climate targets and climate biodiversity synergies within the GBF provides an additional means for developing meaningful targets, trying to develop urgently needed data to monitor biodiversity trends, prioritising meaningful tasks, and tracking our progress towards biodiversity targets.