Bridging soil biodiversity and human well-being: an actionable framework to measure links between the natural capital and plural value of soils

Human activities contribute to soil degradation globally, endangering life belowground and services like food production and climate regulation. To reverse this situation, an actionable framework to connect soil health and soil biodiversity status with human well-being, integrating the biophysical, economic, and social domains, is urgently needed. Here, learning from previous generalist and soil-specific frameworks, we introduce the Soil Biodiversity and Well-being Framework, which creates the conceptual architecture to quantifiably link soil natural capital with human beneficiaries, soil management, environmental pressures, and societal responses. Furthermore, we outline the requirements for its operationalization, based on a flexible set of measurable indicators for soil natural capital assets, plural valuation of soil-mediated nature’s contributions to people, and human well-being. The implementation of the framework by multiple stakeholders (e.g., scientists, farmers, or policymakers) can generate the multidimensional and quantitative evidence to support action toward transformative change for sustainable soil management and soil biodiversity conservation

Development of an integrated modeling framework for visibility and air quality forecasting in Delhi

Rapid urbanization has subjected the megacities of developing countries to various environmental stresses. Delhi, a major Indian megacity, faces increasing urban stress leading to reduction in air quality, and visibility. These challenges necessitate an integrated modeling framework to mitigate adverse environmental impacts on public health. Therefore, we have developed an advanced version of the high-resolution Delhi Model with Chemistry and Aerosol framework (DM-Chem) at the National Centre for Medium Range Weather Forecasting (NCMRWF) under the Weather and Climate Science for Service Partnership India (WCSSP India) project. This collaborative initiative between India and the UK aims to provide real-time forecasts of visibility and fine particulate matter (PM 2.5 ) for Delhi and neighbouring regions during the winter season. The DM Chem framework is unique due to its detailed urban canopy scheme and realistic aerosol representation, making it well-suited for city-scale forecasts. It is designed to predict extreme fog and pollution events in the winter season. Here we discuss the major physical parameterization improvements for the model, along with its skill and deficiencies in predicting extreme events. Notably, irrigation effects on surrounding agricultural areas have significantly improved fog and visibility forecasts, but have degraded the wind forecasts. We demonstrate the applicability of this modeling framework to study aerosol-radiation interaction during fog holes and discuss its potential to be applied or adapted to other megacities worldwide

Investigation of the October effect in very low-frequency (VLF) signals

Subionospheric very low-frequency (VLF) radio signals are reflected by free electrons in the ionospheric D-region at about 60–90 km altitude and can propagate over long distances, which makes them useful for monitoring the state of the D-region or perturbations due to solar flares. At the D-region height, the ionosphere is mainly ionized by solar Lyman-α radiation. The reflection characteristics of VLF signals depend on the state and dynamics of the D-region, which is highly influenced by Lyman-α radiation. Although the amplitude of the received terrestrial VLF signal changes as a function of solar zenith angle over the course of the year, the VLF amplitude shows a distinctive sharp decrease around October, which is hence called the “October effect”. This study investigates the occurrence of the October effect and its dependencies on latitude and longitude. We developed a method to detect the occurrence of the October effect in the long-term VLF data and derive key parameters characterizing (start and end date, intensity) the sudden decrease in the signal amplitude. This investigation using a network of VLF stations distributed over low-, middle-, and high-latitude regions shows that the occurrence of the October effect has a clear latitudinal dependency, occurring earlier in high-latitude regions than at midlatitudes. No low-latitude signature is found

Could underground disposal of carbon dioxide help to reduce India’s emissions?

BGS geologists have partnered with research institutes in India to explore the potential for carbon capture and storage, with an emphasis on storage

The prevalence of Phytophthora in British plant nurseries; high‐risk hosts and substrates and opportunities to implement best practice

Invasive Phytophthora species infect a very broad range of herbaceous and woody hosts globally. The UK alone has experienced a particularly damaging series of outbreaks and epidemics of new, invasive Phytophthora species affecting the nation's trees over the last 30 years. The link between Phytophthora outbreaks and the importation and spread of infected nursery stock is well established across many countries worldwide. To understand better the pathways of spread of Phytophthora in the nursery trade in Britain, we applied a standardized nursery sampling method combined with a refined metabarcoding detection method to capture the diversity of Phytophthora species at 134 British plant nurseries representing a range of biosecurity and trading practices over multiple sampling years between 2016 and 2022. This included root and water samples collected from 17 nurseries sampled seasonally and root samples collected from 117 nurseries sampled once as part of plant health inspections. Based on analyses of 1894 pooled samples, DNA barcodes of 85 Phytophthora species or complexes were detected, with variation in species' relative frequencies across nurseries. We present the top 20 host– Phytophthora associations ranked by relative frequency and report five novel Phytophthora records for the UK. We identified surprisingly high‐risk hosts (such as Douglas fir) with the greatest number of Phytophthora associations and revealed Phytophthora nursery niche preferences for water or roots. We discuss the implications of our findings in terms of pathogen diversity and abundance, high‐risk hosts, our information dissemination approach and resulting advice on nursery practices aimed at reducing risk

High-resolution flow and phosphorus forecasting using ANN models, catering for extremes in the case of the River Swale (UK)

The forecasting of river flows and pollutant concentrations is essential in supporting mitigation measures for anthropogenic and climate change effects on rivers and their environment. This paper addresses two aspects receiving little attention in the literature: high-resolution (sub-daily) data-driven modeling and the prediction of phosphorus compounds. It presents a series of artificial neural networks (ANNs) to forecast flows and the concentrations of soluble reactive phosphorus (SRP) and total phosphorus (TP) under a wide range of conditions, including low flows and storm events (0.74 to 484 m3/s). Results show correct forecast along a stretch of the River Swale (UK) with an anticipation of up to 15 h, at resolutions of up to 3 h. The concentration prediction is improved compared to a previous application of an advection–dispersion model

The Ronne Ice Shelf survived the last interglacial

The fate of the West Antarctic Ice Sheet (WAIS) is the largest cause of uncertainty in long-term sea-level projections. In the last interglacial (LIG) around 125,000 years ago, data suggest that sea level was several metres higher than today, and required a significant contribution from Antarctic ice loss, with WAIS usually implicated. Antarctica and the Southern Ocean were warmer than today, by amounts comparable to those expected by 2100 under moderate to high future warming scenarios. However, direct evidence about the size of WAIS in the LIG is sparse. Here we use sea salt data from an ice core from Skytrain Ice Rise, adjacent to WAIS, to show that, during most of the LIG, the Ronne Ice Shelf was still in place, and close to its current extent. Water isotope data are consistent with a retreat of WAIS, but seem inconsistent with more dramatic model realizations in which both WAIS and the large Antarctic ice shelves were lost. This new constraint calls for a reappraisal of other elements of the LIG sea-level budget. It also weakens the observational basis that motivated model simulations projecting the highest end of projections for future rates of sea-level rise to 2300 and beyond

Increasing importance of regional emission controls for further reduction of PM2.5 in Beijing

Over the past decade, Beijing has achieved positive results in controlling fine particulate matter (PM2.5) pollution. However, it remains a challenge to further reduce PM2.5 concentrations to a lower level, such as the World Health Organization's air quality guidelines (5 μg/m3). In this study, PM2.5 concentrations and emission reductions over eight years covering two policy periods of air pollution abatement (2013–2017 and 2018–2020) were compared to investigate the efficiency of emission controls in Beijing and surrounding areas. An approach based on observational data, particularly including data from a regional atmospheric background station, was employed to calculate the relative contributions of the local emissions and regional transport. Results show that local emission reductions play a more important role in decreasing PM2.5 in Beijing. However, following a substantial decrease in local emissions over the first period, the relative contribution of regionally transported PM2.5 reached more than 50 % during the second period. The results indicate that joint regional prevention and control of air pollution are needed for Beijing in the future. In addition, the background PM2.5 concentrations over the North China Plain show an increasing trend in recent years, which may be attributed to the increased atmospheric oxidation capacity, thereby posing a challenge for further regional air quality management

User guide : BGS Heat Flow V1 (United Kingdom)

The British Geological Survey (BGS) heat flow model version 1 (United Kingdom) is derived from a catalogue of legacy heat flow data points and ground-heat modelling. The model portrays heat flow as a continuous modelled surface, interpolated between observation and estimation points. Heat flow data is typically used to determine temperatures beneath the ground surface, and model how much thermal energy may be recovered from or stored within. The dataset is presented in three forms: 1) as a raster model covering the extent of the United Kingdom (UK), each cell of the raster model defined by a square polygon of area 4km2 (2km per side) attributed with heat flow as milli-Watts per square metre (mWm-2) 2) as a cellular, vector grid. Each raster cell/ record in the model is defined by a square polygon of area 4km2 (2km per side) describing heat flow as milli-Watts per square metre (mWm-2). This model directly vectorises the raster model. 3) as an alternative cellular vector grid: BGS Heat Flow V1_ Hex (United Kingdom), represented by hexagonal polygons of area 2.6 km2 (1 Km side length) describing summarised (minimum, maximum and average) heat flow per cell as milli-Watts per square metre (mWm-2). This model spatially resamples the raster data. The v1 release is based on legacy data and models last summarised in Busby et al. (2009). It does not incorporate the approximation of a regional paleoclimate correction presented in Busby and Terrington (2017). Users should note the limitation that the heat flow values presented in this model are underestimated, which would result in underestimation of geothermal resources at depth. This user guide provides the information required to enable the reader to understand and use this BGS product

Disturbance and disease [Voices article]

The world has witnessed an alarming rise in the emergence of infectious diseases, posing profound challenges to public health, ecological balance, and global well-being. Displacement of people and the destabilization of ecosystems create conditions that significantly amplify the risk of disease outbreaks. In this Voices, we ask: how has anthropogenic environmental disturbance impacted the likelihood of disease outbreaks