Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics
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Estimating food consumption, micronutrient intake and the contribution of large-scale food fortification to micronutrient adequacy in Tanzania
Get PDFTo assess the potential contribution of large-scale food fortification (LSFF) towards meeting dietary micronutrient requirements in Tanzania.
Design:
We used household food consumption data from the National Panel Survey 2014–15 to estimate fortifiable food vehicle coverage and consumption (standardised using the adult female equivalent approach) and the prevalence at risk of inadequate apparent intake of five micronutrients included in Tanzania’s fortification legislation. We modelled four LSFF scenarios: no fortification, status quo (i.e. compliance with current fortification contents) and full fortification with and without maize flour fortification.
Setting:
Tanzania.
Participants:
A nationally representative sample of 3290 Tanzanian households.
Results:
The coverage of edible oils and maize and wheat flours (including products of wheat flour and oil such as bread and cakes) was high, with 91 percent, 88 percent and 53 percent of households consuming these commodities, respectively. We estimated that vitamin A-fortified oil could reduce the prevalence of inadequate apparent intake of vitamin A (retinol activity equivalent) from 92 percent without LSFF to 80 percent with LSFF at current fortification levels. Low industry LSFF compliance of flour fortification limits the contribution of other micronutrients, but a hypothetical full fortification scenario shows that LSFF of cereal flours could substantially reduce the prevalence at risk of inadequate intakes of iron, zinc, folate and vitamin B 12 .
Conclusions:
The current Tanzania LSFF programme likely contributes to reducing vitamin A inadequacy. Policies that support increased compliance could improve the supply of multiple nutrients, but the prominence of small-scale maize mills restricts this theoretical benefit
The journey of plastics: historical development, environmental challenges, and the emergence of bioplastics for single-use products
Get PDFThis paper explores the historical development of conventional plastics, tracing their evolution from early forms to their pervasive use in modern society. Its observations include the rise of mass plastic production during World War II and the post-war development, showcasing plastics’ economic and societal impact. The environmental repercussions of plastic pollution have led to increased global awareness and calls for sustainable alternatives. The emergence of bioplastics is investigated, including their classification, properties, applications, and challenges in scaling. This paper emphasises the urgency of adopting bioplastics for a sustainable future and discusses efforts towards homogenisation and standardisation across global markets
Using causal diagrams and superpopulation models to correct geographic biases in biodiversity monitoring data
Get PDF•1. Biodiversity monitoring schemes periodically measure species' abundances and distributions at a sample of sites to understand how they have changed over time. Often, the aim is to infer change in an average sense across some wider landscape. Inference to the wider landscape is simple if the species' abundances and distributions are similar at sampled to non‐sampled locations. Otherwise, the data are geographically biased, and some form of correction is desirable.
•2. We combine causal diagrams with ‘superpopulation models’ to correct time‐varying geographic biases in biodiversity monitoring data. For a given time‐period, expert‐derived causal diagrams are used to deduce the set of variables that explain the geographic bias, and superpopulation models adjust for these variables to produce a corrected estimate of a landscape‐wide mean of for example abundance or occupancy. Estimating a time trend in the variable of interest is achieved by fitting models for multiple time‐periods and, if the drivers of bias are suspected to change over time, by constructing per period causal diagrams. We test the approach using simulated data then apply it to real data from the UK Butterfly Monitoring Scheme (UKBMS).
•3. If the variables that explain the geographic bias are known and measured without error, our method is unbiased. Introducing measurement error reduces the method's efficacy, but it is still an improvement on using the sample mean. When applied to data from the UKBMS, the approach gives different results to the scheme's current method, which assumes no geographic bias.
•4. Where the goal is to estimate change in some variable of interest at the landscape level (e.g. biodiversity indicators), models that do not adjust for geographic bias implicitly assume it does not exist. Our approach makes the weaker assumption that there is no geographic bias conditional on the adjustment variables, so it should yield more accurate estimates of time trends in many circumstances. The method does require assumptions about the drivers of bias, but these are codified explicitly in the causal diagrams. Operationalising our approach should be less costly than full probability sampling, which would be needed to satisfy the assumptions of conventional approaches
Ensuring representative sample volume predictions in microplastic monitoring
Get PDFA large body of literature is available quantifying microplastic contamination in freshwater and marine systems across the globe. “Microplastics” do not represent a single analyte. Rather, they are usually operationally defined based on their size, polymer and shape, dependent on the sample collection method and the analytical range of the measurement technique. In the absence of standardised methods, significant variability and uncertainty remains as to how to compare data from different sources, and so consider exposure correctly. To examine this issue, a previously compiled database containing 1603 marine observations and 208 freshwater observations of microplastic concentrations from across the globe between 1971 and 2020 was analysed. Reported concentrations span nine orders of magnitude. Investigating the relationship between sampling methods and reported concentrations, a striking correlation between smaller sample unit volumes and higher microplastic concentrations was observed. Close to half of the studies reviewed scored poorly in quality scoring protocols according to the sample volume taken. It is critical that sufficient particles are measured in a sample to reduce the errors from random chance. Given the inverse relationship with particle size and abundance, the volume required for a representative sample should be calculated case-by-case, based on what size microplastics are under investigation and where they are being measured. We have developed the Representative Sample Volume Predictor (RSVP) tool, which standardises statistical prediction of sufficient sample volumes, to ensure microplastics are detected with a given level of confidence. Reviewing reports in freshwater, we found ~ 12% of observations reported sample volumes which would have a false negative error rate > 5%. Such sample volumes run the risk of wrongly concluding that microplastics are absent in samples and are not sufficient to be quantitative. The RSVP tool also provides a harmonised Poisson point process estimation of confidence intervals to test whether two observations are likely to be significantly different, even in the absence of replication. In this way, we demonstrate application of the tool to evaluate historic data, but also to assist in new study designs to ensure that environmental microplastic exposure data is relevant and reliable. The tool can also be applied to other data for randomly dispersed events in space or time, and so has potential for transdisciplinary use
Combining the 15N gas flux method and N2O isotopocule data for the determination of soil microbial N2O sources
Get PDF•Rationale: The analysis of natural abundance isotopes in biogenic N2O molecules provides valuable insights into the nature of their precursors and their role in biogeochemical cycles. However, current methodologies (for example, the isotopocule map approach) face limitations, as they only enable the estimation of combined contributions from multiple processes at once rather than discriminating individual sources. This study aimed to overcome this challenge by developing a novel methodology for the partitioning of N2O sources in soil, combining natural abundance isotopes and the use of a 15 N tracer (15N Gas Flux method) in parallel incubations.
•Methods: Laboratory incubations of an agricultural soil were conducted to optimize denitrification conditions through increased moisture and nitrate amendments, using nitrate that was either 15 N‐labeled or unlabeled. A new linear system combined with Monte Carlo simulation was developed to determine N2O source contributions, and the subsequent results were compared with FRAME, a Bayesian statistical model for stable isotope analysis.
•Results: Our new methodology identified bacterial denitrification as the dominant process (87.6%), followed by fungal denitrification (9.4%), nitrification (1.5%), and nitrifier denitrification (1.6%). Comparisons with FRAME showed good agreement, although FRAME estimated slightly lower bacterial denitrification (80%) and higher nitrifier‐denitrification (9%) contributions.
•Conclusions: This approach provides an improved framework for accurately partitioning N2O sources, enhancing understanding of nitrogen cycling in agroecosystems, and supporting broader environmental applications
Deep learning phase pickers: how well can existing models detect hydraulic-fracturing induced microseismicity from a borehole array?
Get PDFDeep learning (DL) phase picking models have proven effective in processing large volumes of seismic data, including successfully detecting earthquakes missed by other standard detection methods. Despite their success, the applicability of existing extensively trained DL models to high-frequency borehole data sets is currently unclear. In this study, we compare four established models [Generalized Seismic Phase Detection (GPD), U-GPD, PhaseNet and EQTransformer] trained on regional earthquakes recorded at surface stations (100 Hz) in terms of their picking performance on high-frequency borehole data (2000 Hz) from the Preston New Road (PNR) unconventional shale gas site, in the United Kingdom (UK). The PNR-1z data set, which we use as a benchmark, consists of continuously recorded waveforms containing over 38 000 seismic events previously catalogued, ranging in magnitudes from −2.8 to 1.1. Remarkably, all four DL models can detect induced seismicity in high-frequency borehole data and two might satisfy the monitoring requirements of some users without any modifications. In particular, PhaseNet and U-GPD demonstrate exceptional recall rates of 95 and 76.6 per cent, respectively, and detect a substantial number of new events (over 15 800 and 8300 events, respectively). PhaseNet’s success might be attributed to its exposure to more extensive and diverse instrument data set during training, as well as its relatively small model size, which might mitigate overfitting to its training set. U-GPD outperforms PhaseNet during periods of high seismic rates due to its smaller window size (400 samples compared to PhaseNet’s 3000-sample window). These models start missing events below −0.5, suggesting that the models could benefit from additional training with microseismic data-sets. Nonetheless, PhaseNet may satisfy some users’ monitoring requirements without further modification, detecting over 52 000 events at PNR. This suggests that DL models can provide efficient solutions to the big data challenge of downhole monitoring of hydraulic-fracturing induced seismicity as well as improved risk mitigation strategies at unconventional exploration sites
The challenge of land in a neural network ocean model
Get PDFMachine learning (ML) techniques have emerged as a powerful tool for predicting weather and climate systems. However, much of the progress to date focuses on predicting the short-term evolution of the atmosphere. Here, we look at the potential for ML methodology to predict the evolution of the ocean. The presence of land in the domain is a key difference between ocean modeling and previous work looking at atmospheric modeling. Here, we look to train a convolutional neural network (CNN) to emulate a process-based General Circulation Model (GCM) of the ocean, in a configuration which contains land. We assess performance on predictions over the entire domain and near to the land (coastal points). Our results show that the CNN replicates the underlying GCM well when assessed over the entire domain. RMS errors over the test dataset are low in comparison to the signal being predicted, and the CNN model gives an order of magnitude improvement over a persistence forecast. When we partition the domain into near land and the ocean interior and assess performance over these two regions, we see that the model performs notably worse over the near land region. Near land, RMS scores are comparable to those from a simple persistence forecast. Our results indicate that ocean interaction with land is something the network struggles with and highlight that this is may be an area where advanced ML techniques specifically designed for, or adapted for, the geosciences could bring further benefits
Management measures and trends of biological invasions in Europe: a survey-based assessment of local managers
Get PDFBiological invasions are a major threat to biodiversity, ecosystem functioning and nature's contributions to people worldwide. However, the effectiveness of invasive alien species (IAS) management measures and the progress toward achieving biodiversity targets remain uncertain due to limited and nonuniform data availability. Management success is usually assessed at a local level and documented in technical reports, often written in languages other than English, which makes such data notoriously difficult to collect at large geographic scales. Here we present the first European assessment of how managers perceive trends in IAS and the effectiveness of management measures to mitigate biological invasions. We developed a structured questionnaire translated into 18 languages and disseminated it to local and regional managers of IAS in Europe. We received responses from 1928 participants from 41 European countries, including 24 European Union (EU) Member States. Our results reveal substantial efforts in IAS monitoring and control, with invasive plants being the primary focus. Yet, there is a general perception of an increase in the numbers, occupied areas, and impacts of IAS across environment and taxonomic groups, particularly plants, over time. This perceived increase is consistent across both EU and non‐EU countries, with respondents from EU countries demonstrating more certainty in their responses. Our results also indicate a lack of data on alien vertebrates and invertebrates, reflecting a need for more targeted monitoring and knowledge sharing between managers and policymakers and between countries. Overall, our study suggests that Europe's current strategies are insufficient to substantially reduce IAS by 2030 and hence to meet the Kunming‐Montreal Global Biodiversity Framework target
Generating electron density archives using mainland EISCAT data between 2001-2021 at 10 minute and 1 hour integration
Get PDFThe mesosphere/lower-thermosphere/ionosphere (MLTI) region is a critical boundary in the coupling of the atmosphere, climate and space weather, however it is one of the least understood regions, making it hard to include in whole atmosphere models. The EISCAT radars at Tromsø, Norway (UHF and VHF) have been measuring ionospheric parameters, such as electron density, since 1985 making it an excellent resource to study changes in the ionosphere over a long time period. This paper details how we have combined high elevation data from both radars between 2001-2021, re-integrated at 10 minutes and 1 hour, to look at the different sources of variability in the MLTI region between 50-200 km. Day of year climatology’s of the electron density highlight that the VHF data are more prone to contamination from Polar Mesospheric summer Echos. The magnetic local time variation of the electron density shows seasonal and altitude dependence related to solar UV illumination and electron precipitation, as expected. We compare our archives to the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) and find the biggest differences during the winter months and below 100 km, where the model does not yet include the impact of high energy electron precipitation
COSMOS-UK. Soil moisture: January 2025
Get PDFThe COSMOS-UK soil moisture status report provides an insight into the current soil moisture conditions across the UK as monitored by the COSMOS-UK network. The network comprises approximately 50 sites at which a cosmic ray neutron sensor is deployed to monitor soil moisture within a footprint of about 12 hectares. The report is comprised of: maps of end of month soil moisture both as volumetric water content and as a soil moisture index; a short description of current status; and selected time series graphs showing data from the last three years
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