Deep learning and process understanding for data-driven Earth system science.

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

AEDT: A new concept for ecological dynamics in the ever-changing world.

The important concept of equilibrium has always been controversial in ecology, but a new, more general concept, an asymptotic environmentally determined trajectory (AEDT), overcomes many concerns with equilibrium by realistically incorporating long-term climate change while retaining much of the predictive power of a stable equilibrium. A population or ecological community is predicted to approach its AEDT, which is a function of time reflecting environmental history and biology. The AEDT invokes familiar questions and predictions but in a more realistic context in which consideration of past environments and a future changing profoundly due to human influence becomes possible. Strong applications are also predicted in population genetics, evolution, earth sciences, and economics.

Geospatial analysis and distribution patterns of home nursing care in a metropolitan area - a large-scale analysis.

AIMS: This study focuses on home nursing care distribution in an urban setting in Germany. BACKGROUND: A shortage of nursing care workforce is present in Germany. METHODS: A geospatial analysis was performed to examine distribution patterns at the district level in Frankfurt, Germany (n = 46 districts) and factors were analysed influencing the location choice of home nursing care providers (n = 151). Furthermore, within the analysis we focused on the population aged over 65 years to model the demand for nursing care. RESULTS: The analysis revealed a tendency of home nursing care providers to be located near the city centre (centripetal distribution pattern). However, the demand for care showed more inconsistent patterns. Still, a centripetal distribution pattern of demand could be stated. Compared with the control groups (e.g. acute hospitals and pharmacies) similar geographical distribution patterns were present. However, the location of home nursing care providers was less influenced by demand compared with the control groups. CONCLUSION: The supply of nursing care was unevenly distributed in this metropolitan setting, but still matched the demand for nursing care. IMPLICATION FOR NURSING MANAGEMENT: Due to the rapidly changing health care environments policy, regulations must be (re-)evaluated critically to improve the management and delivery of nursing care provision.

Organic Carbon Burial in Lakes and Reservoirs of the Conterminous United States.

Organic carbon (OC) burial in lacustrine sediments represents an important sink in the global carbon cycle; however, large-scale OC burial rates are poorly constrained, primarily because of the sparseness of available data sets. Here we present an analysis of OC burial rates in water bodies of the conterminous U.S. (CONUS) that takes advantage of recently developed national-scale data sets on reservoir sedimentation rates, sediment OC concentrations, lake OC burial rates, and water body distributions. We relate these data to basin characteristics and land use in a geostatistical analysis to develop an empirical model of OC burial in water bodies of the CONUS. Our results indicate that CONUS water bodies sequester 20.8 (95% CI: 9.4-65.8) Tg C yr(-1), and spatial patterns in OC burial are strongly influenced by water body type, size, and abundance; land use; and soil and vegetation characteristics in surrounding areas. Carbon burial is greatest in the central and southeastern regions of the CONUS, where cultivation and an abundance of small water bodies enhance accumulation of sediment and OC in aquatic environments.

Mass spectrometry in Earth sciences: the precise and accurate measurement of time.

Precise determinations of the isotopic compositions of a variety of elements is a widely applied tool in Earth sciences. Isotope ratios are used to quantify rates of geological processes that occurred during the previous 4.5 billion years, and also at the present time. An outstanding application is geochronology, which utilizes the production of radiogenic daughter isotopes by the radioactive decay of parent isotopes. Geochronological tools, involving isotopic analysis of selected elements from smallest volumes of minerals by thermal ionization mass spectrometry, provide precise and accurate measurements of time throughout the geological history of our planet over nine orders of magnitude, from the accretion of the proto-planetary disk, to the timing of the last glaciation. This article summarizes the recent efforts of the Isotope Geochemistry, Geochronology and Thermochronology research group at the University of Geneva to advance the U-Pb geochronological tool to achieve unprecedented precision and accuracy, and presents two examples of its application to two significant open questions in Earth sciences: what are the triggers and timescales of volcanic supereruptions, and what were the causes of mass extinctions in the geological past, driven by global climatic and environmental deterioration?

How to tackle complexity in urban climate resilience? Negotiating climate science, adaptation and multi-level governance in India.

As the world's population is expected to be over 2/3rd urban by 2050, climate action in cities is a growing area of interest in the inter-disciplines of development policy, disaster mitigation and environmental governance. The climate impacts are expected to be quite severe in the developing world, given its urban societies are densely packed, vastly exposed to natural elements while possessing limited capabilities. There is a notable ambiguity and complexity that inhibits a methodical approach in identifying urban resilience measures. The complexity is due to intersection of large number of distinct variables in climate geoscience (precipitation and temperature anomalies at different locations, RCPs, timeline), adaptation alternatives (approach, priority, intervention level) and urban governance (functional mandate, institutional capacity, and plans & policies). This research examines how disparate and complex knowledge and information in these inter-disciplines can be processed for systematic 'negotiation' to situate, ground and operationalize resilience in cities. With India as a case, we test this by simulating mid-term and long-run climate scenarios (2050 & 2080) to map regional climate impacts that shows escalation in the intensity of climate events like heat waves, urban flooding, landslides and sea level rise. We draw on suitable adaptation measures for five key urban sectors- water, infrastructure (including energy), building, urban planning, health and conclude a sleuth of climate resilience building measures for policy application through national/ state policies, local urban plans and preparation of city resilience strategy, as well as advance the research on 'negotiated resilience' in urban areas.

Urban seismic monitoring in Brasília, Brazil.

Urban seismology has gained scientific interest with the development of seismic ambient noise monitoring techniques and also for being a useful tool to connect society with the Earth sciences. The interpretation of the sources of seismic records generated by sporting events, traffic, or huge agglomerations arouses the population's curiosity and opens up a range of possibilities for new applications of seismology, especially in the area of urban monitoring. In this contribution, we present the analysis of seismic records from a station in the city of Brasilia during unusual episodes of silencing and noisy periods. Usually, cultural noise is observed in high-fequency bands. We showed in our analysis that cultural noise can also be observed in the low-frequency band, when high-frequency signal is attenuated. As examples of noisy periods, we have that of the Soccer World Cup in Brazil in 2014, where changes in noise are related to celebrations of goals and the party held by FIFA in the city, and the political manifestations in the period of the Impeachment trial in 2016, which reached the concentration of about 300,000 protesters. The two most characteristic periods of seismic silence have been the quarantine due to the COVID-19 pandemic in 2020, and the trucker strike that occurred across the country in 2018, both drastically reducing the movement of people in the city.

Mapping soil textural fractions across a large watershed in north-east Florida.

Assessment of regional scale soil spatial variation and mapping their distribution is constrained by sparse data which are collected using field surveys that are labor intensive and cost prohibitive. We explored geostatistical (ordinary kriging-OK), regression (Regression Tree-RT), and hybrid methods (RT plus residual Sequential Gaussian Simulation-SGS) to map soil textural fractions across the Santa Fe River Watershed (3585 km(2)) in north-east Florida. Soil samples collected from four depths (L1: 0-30 cm, L2: 30-60 cm, L3: 60-120 cm, and L4: 120-180 cm) at 141 locations were analyzed for soil textural fractions (sand, silt and clay contents), and combined with textural data (15 profiles) assembled under the Florida Soil Characterization program. Textural fractions in L1 and L2 were autocorrelated, and spatially mapped across the watershed. OK performance was poor, which may be attributed to the sparse sampling. RT model structure varied among textural fractions, and the model explained variations ranged from 25% for L1 silt to 61% for L2 clay content. Regression residuals were simulated using SGS, and the average of simulated residuals were used to approximate regression residual distribution map, which were added to regression trend maps. Independent validation of the prediction maps showed that regression models performed slightly better than OK, and regression combined with average of simulated regression residuals improved predictions beyond the regression model. Sand content >90% in both 0-30 and 30-60 cm covered 80.6% of the watershed area.

Arthropod entombment in weathering-formed opal: new horizons for recording life in rocks.

Animal fossils preserved in various geological materials, such as limestone, claystone, or amber, provide detailed information on extinct species that is indispensable for retracing the evolution of terrestrial life. Here, we present the first record of an animal fossil preserved in opal formed by weathering with such high-resolution details that even individual cuticle hairs are observed. The fossil consists of the exoskeleton of a nymphal insect belonging to the order Hemiptera and either the family Tettigarctidae or the Cicadidae. This identification is based on anatomical details such as the tibial and femoral morphology of the forelegs. The exoskeleton of the insect was primarily zeolitized during the alteration of the host rocks and later sealed in opal deposited by silica-rich fluids derived from the continental weathering of the volcanic host rocks. Organic matter is preserved in the form of amorphous carbon. This finding makes opal formed by rocks weathering a new, complementary source of animal fossils, offering new prospects for the search for ancient life in the early history of Earth and possibly other terrestrial planets such as Mars, where weathering-formed opal occurs.

Integration of geoscience frameworks into digital pathology analysis permits quantification of microarchitectural relationships in histological landscapes.

Although gold-standard histological assessment is subjective it remains central to diagnosis and clinical trial protocols and is crucial for the evaluation of any preclinical disease model. Objectivity and reproducibility are enhanced by quantitative analysis of histological images but current methods require application-specific algorithm training and fail to extract understanding from the histological context of observable features. We reinterpret histopathological images as disease landscapes to describe a generalisable framework defining topographic relationships in tissue using geoscience approaches. The framework requires no user-dependent training to operate on all image datasets in a classifier-agnostic manner but is adaptable and scalable, able to quantify occult abnormalities, derive mechanistic insights, and define a new feature class for machine-learning diagnostic classification. We demonstrate application to inflammatory, fibrotic and neoplastic disease in multiple organs, including the detection and quantification of occult lobular enlargement in the liver secondary to hilar obstruction. We anticipate this approach will provide a robust class of histological data for trial stratification or endpoints, provide quantitative endorsement of experimental models of disease, and could be incorporated within advanced approaches to clinical diagnostic pathology.

Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach.

The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve above problems, a kernel principal component wavelet neural network (KPCWNN) trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study. An additional kernel principal component (KPC) layer is applied to reduce the dimensionality of apparent resistivity data and increase the computational efficiency of wavelet neural network (WNN). Meanwhile, a novel ISFLA algorithm is adopted for improving the learning ability and inversion quality of WNN. In the proposed ISFLA, a hybrid LC mutation attractor is used to enhance the exploitation ability and a differential updating rule is used to enhance the exploration ability. Four groups of experiments are considered to demonstrate the feasibility of the proposed inversion method. The inversion results of the synthetic and field examples show that the introduced method is superior to other algorithms in terms of prediction accuracy and computational efficiency, which contribute to better inversion results.

Geospatial analysis of residential proximity to open-pit coal mining areas in relation to micronuclei frequency, particulate matter concentration, and elemental enrichment factors.

During coal surface mining, several activities such as drilling, blasting, loading, and transport produce large quantities of particulate matter (PM) that is directly emitted into the atmosphere. Occupational exposure to this PM has been associated with an increase of DNA damage, but there is a scarcity of data examining the impact of these industrial operations in cytogenetic endpoints frequency and cancer risk of potentially exposed surrounding populations. In this study, we used a Geographic Information Systems (GIS) approach and Inverse Distance Weighting (IDW) methods to perform a spatial and statistical analysis to explore whether exposure to PM2.5 and PM10 pollution, and additional factors, including the enrichment of the PM with inorganic elements, contribute to cytogenetic damage in residents living in proximity to an open-pit coal mining area. Results showed a spatial relationship between exposure to elevated concentrations of PM2.5, PM10 and micronuclei frequency in binucleated (MNBN) and mononucleated (MNMONO) cells. Active pits, disposal, and storage areas could be identified as the possible emission sources of combustion elements. Mining activities were also correlated with increased concentrations of highly enriched elements like S, Cu and Cr in the atmosphere, corroborating its role in the inorganic elements pollution around coal mines. Elements enriched in the PM2.5 fraction contributed to increasing of MNBN but seems to be more related to increased MNMONO frequencies and DNA damage accumulated in vivo. The combined use of GIS and IDW methods could represent an important tool for monitoring potential cancer risk associated to dynamically distributed variables like the PM.

Investigation of small-scale preferential flow with a forced-gradient tracer test.

A new tracer experiment (referred to as MADE-5) was conducted at the well-known Macrodispersion Experiment (MADE) site to investigate the influence of small-scale mass-transfer and dispersion processes on well-to-well transport. The test was performed under dipole forced-gradient flow conditions and concentrations were monitored in an extraction well and in two multilevel sampler (MLS) wells located at 6, 1.5, and 3.75 m from the source, respectively. The shape of the breakthrough curve (BTC) measured at the extraction well is strongly asymmetric showing a rapidly arriving peak and an extensive late-time tail. The BTCs measured at seven different depths in the two MLSs are radically different from one another in terms of shape, arrival times, and magnitude of the concentration peaks. All of these characteristics indicate the presence of a complex network of preferential flow pathways controlling solute transport at the test site. Field-experimental data were also used to evaluate two transport models: a stochastic advection-dispersion model (ADM) based on conditional multivariate Gaussian realizations of the hydraulic conductivity field and a dual-domain single-rate (DDSR) mass-transfer model based on a deterministic reconstruction of the aquifer heterogeneity. Unlike the stochastic ADM realizations, the DDSR accurately predicted the magnitude of the concentration peak and its arrival time (within a 1.5% error). For the multilevel BTCs between the injection and extraction wells, neither model reproduced the observed values, indicating that a high-resolution characterization of the aquifer heterogeneity at the subdecimeter scale would be needed to fully capture 3D transport details.

Brown diamonds from an eclogite xenolith from Udachnaya kimberlite, Yakutia, Russia.

We have performed petrographic and spectroscopic studies of brown diamonds from an eclogite xenolith from the Udachnaya pipe (Yakutia, Russia). Brown diamonds are randomly intermixed with colorless ones in the rock and often located at the grain boundaries of clinopyroxene and garnet. Brown diamonds can be characterized by a set of defects (H4, N2D and a line at 490.7 nm) which are absent in colorless diamonds. This set of defects is typical for plastically deformed diamonds and indicates that diamonds were likely annealed for a relatively short period after deformation had occurred. Excitation of brown colored zones with a 632.8 nm He-Ne laser produced the typical diamond band plus two additional bands at 1730 cm(-1) and 3350 cm(-1). These spectral features are not genuine Raman bands, and can be attributed to photoluminescence at ∼710 nm (1.75 eV) and ∼802 nm (1.54 eV). No Raman peak corresponding to graphite was observed in regions of brown coloration. Comparison with previous reports of brown diamonds from eclogites showed our eclogitic sample to have a typical structure without signs of apparent deformation. Two mechanisms with regard to diamond deformation are proposed: deformation of eclogite by external forces followed by subsequent recrystallization of silicates or, alternatively, deformation by local stress arising due to decompression and expansion of silicates during ascent of the xenolith to surface conditions.

Slug tests in the presence of background head trends.

We extend Bouwer and Rice (1976) slug test theory to incorporate background head trends that may be important in incompressible material of low permeability k. The extension, which features a convolution integral of the background head, is closed form for linear trends. A sensitivity study suggests that a rising background head can diminish the head changes associated with a slug-out test and underestimate k if it is ignored, as does falling background trend with a slug-in test. A falling background head can reinforce slug-in test head change and, if ignored, can overestimate k, as does a rising background head with a slug-out test. The simple extension is verified by field tests in glacial till and stratified drift deposits in eastern Massachusetts.

Geostatistical modeling of a heterogeneous site bordering the Venice lagoon, Italy.

Geostatistical methods are well suited for analyzing the local and spatial uncertainties that accompany the modeling of highly heterogeneous three-dimensional (3D) geological architectures. The spatial modeling of 3D hydrogeological architectures is crucial for polluted site characterization, in regards to both groundwater modeling and planning remediation procedures. From this perspective, the polluted site of Porto Marghera, located on the periphery of the Venice lagoon, represents an interesting example. For this site, the available dense spatial sampling network, with 769 boreholes over an area of 6 km(2), allows us to evaluate the high geological heterogeneity by means of indicator kriging and sequential indicator simulation. We show that geostatistical methodologies and ad hoc post processing of geostatistical analysis results allow us to effectively analyze the high hydrogeological heterogeneity of the studied site.