Km2 Area Research Articles

A crustal Pb isotope map of southeastern Australia.

Lead isotopes are a powerful geochemical tracer and a popular tool applied across a broad range of scientific fields, e.g., earth sciences, archaeology, and forensic sciences. Here we present a Pb isotope dataset collected from 232 igneous samples, spanning a ca. 2.3 million km2 area in southeastern Australia, and over 3 billion years of Earth history. This contribution provides a range of isotopic maps showing the spatial variability of Pb isotopes (206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb), magma source U/Pb and Th/U, and model ages. The samples selected for this study encompass U- and Th-poor media (i.e., K-feldspar), and U- and Th-bearing sampling media (i.e., whole-rock), providing a temporally and spatially resolved image of U and Th distribution in the crust, and their influence on crustal Pb through radiogenic ingrowth. This dataset has the potential to benefit a wide variety of different disciplines and is an important resource for addressing earth science questions ranging from unravelling crustal differentiation and architecture, through tracing magma source U- and Th-enrichment, to mineral deposit genesis.

Can neotectonic faults influence soil air radon levels in the Upper Rhine Graben? An exploratory machine learning assessment

Radon (Rn) is a naturally occurring radioactive gas that poses a significant lung cancer risk. Subsurface fault zones can act as pathways for fluid and gas migration, potentially amplifying Rn accumulation. This study investigates the impact of fault zones on Rn concentrations within a 25 km2 area in the Northern Upper Rhine Graben, Germany — a region with available detailed geophysical exploration data and active neotectonic faulting. We conducted 597 Rn soil air measurements along precisely located fault zones, integrating a comprehensive range of environmental parameters. Utilizing the advanced machine learning model eXtreme Gradient Boosting (XGBoost) in conjunction with SHapley Additive exPlanations (SHAP) values, we dissected the influence of soil types, environmental factors, and proximity to fault zones on soil air Rn concentrations at a 1-meter depth. Our results reveal that clay-rich soils and cumulative 30-day precipitation are the primary drivers of elevated Rn levels. Proximity to fault zones also significantly influences Rn concentrations, though its impact is less pronounced than the factors mentioned above. Additionally, environmental factors such as wind speed, air pressure, and temperature exhibited lesser effects on Rn levels. The negligible influence of measuring devices and operating personnel increases confidence in data integrity in extensive environmental studies. This study demonstrates the effectiveness of integrating XGBoost with SHAP values to identify and quantify key factors influencing Rn concentrations. By providing a robust framework for enhancing Rn prediction models through machine learning, our findings contribute to improved risk assessments and mitigation strategies, thereby advancing public health and environmental management.

Easy yield mapping for precision agriculture

ABSTRACT Within precision agriculture, yield mapping is important in the evaluation of crop management and delineation of management zones. It can also be used to assess within-field yield potential, in order to guide different precision agriculture practices. However, some farmers do not have a yield monitoring system, and some who do may obtain incomplete or erroneous yield data. This study examined the accuracy with which winter wheat (Triticum aestivum L.) yield could be mapped in 18 fields in southern Sweden using a simple empirical relationship between Sentinel-2 (ESA, Paris, France) data, vegetation index (VI) maps and combined harvester data collected in nearby fields. The results showed that a decrease in map resolution to 40 m reduced the error in the yield maps obtained. Normalised difference water index (NDWI) was the most efficient VI, while a combination of satellite data from earlier and later plant development (booting and milk development stages) performed slightly better than data for other development stages and combinations. The best-performing model at a within-field scale (40-m resolution) had an average mean absolute error (MAE) of 0.40 tonnes ha−1 in a leave-one-field-out cross validation. When the prediction model at field-means scale was applied on 69 farms in a 1055 km2 area, MAE was 0.75 tonnes ha−1 when comparing predictions with mean yields reported by farmers in a phone survey. Therefore, if adequate combined harvester and/or mean yield data are available, a modelling framework that translates satellite imagery into yield maps on-the-fly could be made available for different stakeholders via decision support systems for precision agriculture.

Deccan Volcanism and Related Seismic Unrest in the Koyna–Warna Region, India

Abstract Koyna–Warna is a region of low-tectonic deformation and normal surface heat flow (∼40 mW/m2) in the Deccan volcanic province, India, where low-to-moderate magnitude earthquakes have continued to occur in the last 60 yr. These earthquakes are uniquely restricted to an 11×16 km2 area and confined to the upper crust between 3 and 9 km depth. Located at the last phase of the interaction of India with the Reunion mantle plume ∼65 Ma ago responsible for extensive volcanism, the cause of sustained seismicity in Koyna region is debated. Using the shear-wave velocity model derived through the joint inversion of the receiver function and surface-wave data from the seismic zone, we propose that earthquakes in the Koyna region occur due to stress concentration arising because of the high-density magma intrusions in the shallow crust at 3–9 km. The high-density mafic-ultramafic body exerts gravitationally induced stress of about ∼12 to 15 MPa. The continuation of earthquakes in the deeper part is inhibited by the possible fluid-filled mush zone imaged as a low-velocity layer at a 9–17 km depth. The magma intrusion as dike can induce a cycle of normal faulting in the overlying rock mass, as observed in the Warna region. We present the first evidence of an extremely high velocity (>4.7 km/s) layer at 40–50 km below Moho, interpreted as the presence of eclogite–peridotite responsible for producing Deccan magma in large volume.

Resource abundance of cobalt-rich ferromanganese crusts in the KC-8 seamount, West Pacific

We have estimated the resource potential of cobalt-rich ferromanganese crusts (CFCs) on the summit area of the KC-8 seamount in the West Pacific, based on the acoustic backscatter intensity (ABI), ROV video footage, and CFC thickness measurements. To estimate the spatial distribution of CFC across 13 equally divided blocks within a 360 km2 area, shipboard and deep-tow ABIs were compared with visual data. The presence of CFCs is evident in the areas where ABI is higher than average, validating the use of ABI data to delineate the exposure of CFCs. With high accuracy in distinguishing between CFCs and sediment-dominant areas, the distribution area of CFCs was precisely calculated. The variation in CFC thickness across the KC-8 seamount summit was measured through CFC samples retrieved using a CFC corer. CFC thickness is highly variable even between adjacent sites, but it shows a roughly increasing trend from south to northwest. Based on the CFC distribution area associated with thickness, we calculated a CFC resource abundance. Of the estimation, 82% have remained on the gentle slope (<10°) areas, which can be accessible for future mining. Overall, our study suggests that the combination of an acoustic survey with reliable thickness information is highly effective in estimating CFC abundance.

A Multicriteria Approach for Landslide Hazard Zonation in the Lesser Kumaun Himalaya

ABSTRACTThe socioeconomic prosperity of a region is intricately tied to its infrastructure development, particularly the connectivity provided by highways. Preserving the economic well‐being of an area demands meticulous planning and the execution of construction projects with minimal risk. Landslide hazard zonation (LHZ) is a crucial tool for assessing the likelihood of landslide occurrences in specific regions. This study delves into the LHZ of a 171.2 km2 area, focusing on critical causative factors such as slope inclination and aspect, slope mass material, Land Use/Land Cover (LULC), distance from fault lines, roads, and drainage systems. The classification resulted in four distinct zones along the National Highway (NH‐109) stretch from Bhowali to Almora. Notably, the moderate‐hazard zone encompasses 60.4% of the area, followed by the high‐hazard zone at 24.4%, the low‐hazard zone at 11.9%, and the very high‐hazard zone at 3.3%. To enhance the reliability of the study, 63 previously identified landslide locations were marked based on diverse sources, including field studies and landslide inventory data from the Geological Survey of India (GSI). In the validation process, 12 past landslide occurrences were situated within the very high‐hazard zone, 32 in the high‐hazard zone, 18 in the moderate‐hazard zone, and only one in the low‐hazard zone. The receiver operating characteristic (ROC) curve yielded a commendable quality with a % area under the curve (AUC) of 71.2%. In summary, this research underscores the importance of LHZ in assessing and mitigating landslide risks along a stretch of the NH‐109 corridor. The findings provide valuable insights for informed decision‐making in infrastructure development and risk management, contributing to the sustainable growth and protection of the region's livelihood.

Revealing caprine schistosomiasis and its One Health importance in Malawi: A molecular epidemiological investigation augmented with a praziquantel treatment and GPS animal tracking pilot sub-study

To shed first light on caprine schistosomiasis and its zoonotic potential in Malawi, we conducted a molecular epidemiological investigation, sampling goats (n = 230) across three districts, using faecal miracidia hatching test. Molecular genotyping of miracidia later revealed the prevalence of Schistosoma mattheei to be 0.0 % in Nsanje District (n = 30), 16.7 % in Chikwawa District (n = 30) and 25.3 % in Mangochi District (n = 170). Notably, a miracidium of Schistosoma haematobium was observed from a single goat in Chikwawa. Inspection of carcasses (n = 51) at two local abattoirs in Mangochi District did not find any evidence of caprine schistosomiasis where only a single herd, at Mangochi 3, was infected. Here, despite sampling several other herds nearby, the prevalence was 87.7 % (n = 49), with an animal found excreting 1000 miracidia per 5 g of faeces. At this location, our praziquantel treatment (n = 14) and GPS animal tracking (n = 2) pilot sub-study compared two local goat herds over a three-month period. The daily foraging ranges across a 10 km2 area were recorded, alongside targeted schistosome surveillance within local freshwater intermediate snail hosts. Analysis of GPS data revealed only one herd (infected) to have regular daily water contact with Lake Malawi whereas the other herd (not infected) totally avoided the lake. One week after praziquantel treatment administered at 40 mg/kg, anthelminthic cure rate was 92.3 % while at three months approximately a third of treated animals were shedding schistosome miracidia. Cercariae from several field-caught snails locally were genotyped, inclusive of finding a Schistosoma haematobium-mattheei hybrid. Our findings reveal the focalized nature of caprine schistosomiasis, signposting a novel alert for S. haematobium transmission, and highlight where zoonotic transmission can be intense. To better address zoonotic spill-over from S. mattheei (and/or S. haematobium), the national control programme for schistosomiasis should formally develop targeted surveillance of caprine schistosomiasis and where appropriate, attempt an integrated One Health intervention in future.

The potential of gene drives in malaria vector species to control malaria in African environments

Gene drives are a promising means of malaria control with the potential to cause sustained reductions in transmission. In real environments, however, their impacts will depend on local ecological and epidemiological factors. We develop a data-driven model to investigate the impacts of gene drives that causes vector population suppression. We simulate gene drive releases in sixteen ~ 12,000 km2 areas of west Africa that span variation in vector ecology and malaria prevalence, and estimate reductions in vector abundance, malaria prevalence and clinical cases. Average reductions in vector abundance ranged from 71.6–98.4% across areas, while impacts on malaria depended strongly on which vector species were targeted. When other new interventions including RTS,S vaccination and pyrethroid-PBO bednets were in place, at least 60% more clinical cases were averted when gene drives were added, demonstrating the benefits of integrated interventions. Our results show that different strategies for gene drive implementation may be required across different African settings.

Impacts of a marine engineering project on hydrodynamics and diffusion characteristics of fluvial pollutants in Laizhou Bay, China

IntroductionThe rapid advances in marine engineering projects are exacerbating environmental pressures on bay ecosystems. This study utilized the MIKE 21 model to evaluate the impacts of such projects in Laizhou Bay (LZB) on hydrodynamic conditions and the spread of dissolved inorganic nitrogen (DIN) from riverine inputs.ResultsThe results indicated an expansion of 80.77 km2 in areas with DIN concentrations surpassing 0.5 mg/L 2 months after input from the Yellow River, with increased levels in the southern Yellow River Delta. Decreased flow velocities adjacent to the wave barriers at the Xiaoqing River estuary impeded lateral DIN dispersion, resulting in a 0.93 mg/L increase in DIN concentrations at the river mouth. After the construction of marine engineering projects (2020), significant alterations in the coastline of LZB have markedly modified hydrodynamic characteristics near marine structures, altering DIN dispersion patterns.ConclusionThis study provides crucial information for the management of pollutants at estuaries, understanding dispersion mechanisms, and evaluating the feasibility and environmental compatibility of marine engineering projects.

Serpentoviruses in Free-Ranging Shingleback Skinks (Tiliqua rugosa) in Western Australia and South Australia, Australia.

Serpentoviruses are strongly associated with upper respiratory tract disease in captive and free-ranging bluetongued skinks (Tiliqua spp.). In Australia, bluetongue serpentoviruses were first reported in shingleback skinks (Tiliqua rugosa) with upper respiratory tract disease that presented to wildlife rehabilitation facilities in Perth, Western Australia. Since then, serpentoviruses have been detected commonly in captive bluetongued skinks from most areas of Australia, yet knowledge about the prevalence and distribution of these viruses in free-ranging bluetongued skinks, and other skink species, remains limited. Oral swabs were collected from 162 shingleback skinks from four areas in Western Australia and neighboring South Australia to screen for bluetongue serpentoviruses by PCR. The proportions of PCR positives were 0% (0/4) for Rottnest Island (a small island west of Perth, Western Australia), 3% (1/32) for the Shire of Kent (∼5,600 km2 in the southwest of Western Australia), 1% (1/91) from an approximately 250,000 km2 area across South Australia and Western Australia, and 0% (0/35) from Mount Mary (∼150 km2 in the mid north of South Australia). Neither of the two PCR-positive shingleback skinks had overt signs of upper respiratory tract disease. These results are consistent with serpentoviruses occurring at a relatively low crude prevalence of 1.4% (95% confidence interval, 0.2-4.9%) across these areas, although the potential bias from sampling active and apparently healthy individuals may mean that this estimate is lower than the true prevalence. This contrasts with the high proportion of PCR positives reported in captive individuals. In the absence of experimental or observational data on viral clearance and recovery, Tiliqua spp. skinks that are intended for release into the wild should be housed with strict biosecurity to prevent interactions with captive individuals and screened to ensure that they are not PCR positive before release.

A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks

Our understanding of the impact of climate change on water availability and natural hazards in high-mountain regions is limited due to the spatial and temporal scarcity of ground observations of precipitation and snow. Freely available, satellite-based information about the snowpack is currently mainly limited to indirect measurements of snow-covered area or very coarse-scale snow water equivalent (SWE), but only for flat areas in lowlands without vegetation cover. Novel space-based laser altimeters, such as ICESat-2, have the potential to provide high-resolution snow depth data in worldwide mountain regions where no ground observations exist. However, these space-based laser altimeters come with spatial gaps between ground tracks, obtained without repetition at a give location. To overcome these drawbacks, here, we present a combined probabilistic data assimilation and deep learning approach to reconstruct spatio-temporal SWE from observations of snow depth along ground tracks, imitating ICESat-2 tracks in view of a potential future global application.Our approach is based on assimilating SWE and snow cover information in a degree-day model with an iterative ensemble smoother (IES) which allows temporally reconstructing SWE along hypothetical ground tracks separated by 3 km. As input, the degree-day model uses daily precipitation and downscaled air temperature from the ERA5 reanalysis. A feedforward neural network (FNN) is then used for spatial propagation of the daily mean and standard deviation of the updated SWE ensemble members obtained from the IES. The combined IES-FNN approach provides uncertainty-aware spatio-temporally continuous estimates of SWE.We tested our approach in the alpine Dischma valley (Switzerland) using high-resolution snow depth maps obtained from photogrammetric techniques mounted on airplanes and unmanned aerial system observations. Our results show that the IES-FNN model provides reliable estimates at a resolution of approximately 100 m. Even assimilating only one SWE observation during the year (combined with satellite-based melt-out date estimates) produces satisfying results when evaluating the IES-FNN SWE reconstructions on independent dates and smaller (<4 km2) areas: mean absolute error of 86 mm (78 mm) at Schürlialp (Latschüelfurgga) for average SWE of 180 mm (254 mm), and average spatial linear correlation with the reference SWE of 0.51 (0.48). However, the assimilated SWE observation must not be too early in the accumulation season or too late in the melt season when the snowpack is starting or ending to accumulate or melt, respectively. Smaller distances between ground tracks (1500 m and 500 m) show improved performance of the IES-FNN approach in space, with no significant improvement in terms of temporal reconstruction.Applying the IES-FNN approach to e.g., real ICESat-2 data, remains challenging due to the higher uncertainties associated with these data. However, the approach we propose remains potentially very helpful in addressing the problem of scarcity of ground observations of precipitation and snow in high-mountain regions.

Exploring the dynamics and future projections of land use land cover changes by exploiting geospatial techniques; A case study of the Kabul River Basin

Global land cover change has caused significant environmental degradation and biodiversity loss. It affects ecosystem functions, livelihoods, and climate variation and has drawn substantial attention in recent decades. In the Kabul River Basin (KRB), there are limited studies on the historical Land Use/Land Cover (LULC) pattern, transition, intensity and future perspective. Therefore, this study aims to investigate long-term LULC changes and major drivers of LULC in the KRB over the past thirty years (1990–2020) and then to project the future LULC pattern for the years 2030, 2040 and 2050. Landsat Imageries of (1990–2020) were used as input data by utilizing the Random Forest Classifier algorithm (RF) in the Google Earth Engine (GEE) to classify the LULC. The LULC was then projected for the future, using the Cellular Automata Markov Chain Model (CA-MCM). The results demonstrated drastic LULC changes, controlled primarily by urbanization and agriculture expansion, which expanded from 467 Km2 (0.7 %) to 2312 km2 (3.4 %) and 6528 km2 (9.6 %) to 10812 (15.9 %), between 1990 and 2020. In contrast, bare land decreased from 70606 km2 (82.1 %) to 48212 km2 (70.9 %) between 1990 and 2020. In addition, the study depicts that the expansion in built-up and vegetation areas in the KRB during the study period were at the utilization of bare land. Future LULC predictions indicated that between 2020 and 2050, bare land would trend downward from 48212 km2 (70.9 %) to 46172 km2 (67.9 %), while vegetation and built-up areas would trend upward from 2312 km2 (3.4 %) to 3640 km2 (5.3 %), 10812 km2 (15.9 %) to 11622 km2 (17.1 %), and water bodies and snowcover would slightly vary from 1.2 % to 0.9 % and 7.9 %–9.0 %. In addition, the results of LULC dynamics reveal a significant strong positive correlation between population and built, as well as population and vegetation. Conversely, there is a strong negative correlation between population and bare land. Our results provide precise insights on LULC patterns and trends in the KRB, which could be employed to design a sustainable framework for land use and ecosystem protection.

Morphometric analysis and LULC change dynamics of Nayar watershed for the sustainable watershed management

The morphometric analysis of the watersheds is essential for the conservation of natural resources, including soil, water, and vegetation. The Morphometric analysis defines the linear, areal and relief aspects of the watershed. It involves the comprehensive analysis of various factors such as drainage network, surface water flow, and other topographical features. The aim of this study is to develop a sustainable watershed management strategy for the Nayar watershed based on morphometry and Land Use Land Cover (LULC) change dynamics. The Nayar watershed has a total area of 1956.33 km2. The multispectral satellite imagery, Digital Elevation Model (DEM) data, and Survey of India (SOI) Toposheets were used for understanding the topographical and morphological characteristics along with the LULC change dynamics. The findings of this research conclude that the Nayar watershed has parallel and dendritic drainage patterns with high relief. According to the LULC change dynamics, the Nayar watershed's area change comprising 57.60 km2 and 57.15 km2, are from Agricultural Land to Wasteland and from Forest Cover to Wasteland, respectively. This shift in the hilly region will increase the risks of landslides and erosion. Furthermore, the change of 110.03 km2 area of Forest Cover to Agricultural Land raises further challenges due to loss of natural vegetation in long run. In summary the change in LULC of Nayar watershed is vulnerable to risk of natural calamities like landslide, erosion. This work would be helpful to many experts and decision-makers for sustainable watershed management and natural resource management.

Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia.

The Gedeo zone agroforestry systems are the main source of Ethiopia's coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km2, the mapped coffee coverage is 583 km2. The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km2), sub-suitable (596.1 km2), and suitable (347.1 km2) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity.

Establishing a Landslide Traces Inventory for the Baota District, Yan’an City, China, Using High-Resolution Satellite Images

The Baota District of Yan’an City, located in the Loess Plateau, is an important patriotic education base in China. The region’s fragile geological environment and frequent geological disasters pose significant threats to the production and livelihood of residents. Establishing a landslide traces inventory can provide crucial assistance for studying regional land disaster distribution patterns and implementing disaster prevention and mitigation measures. However, the Baota District has not yet established a comprehensive and detailed landslide traces inventory, resulting in a lack of clear understanding and comprehensive knowledge regarding the threats and impacts of landslide disasters in the area. Therefore, this study employed high-resolution satellite images, applying a human–computer interactive visual interpretation method in conjunction with field survey verifications, to develop the most detailed and comprehensive landslide traces inventory for the Baota District to date. The results indicate that within the 3556 km2 area of the Baota District, there are 73,324 landslide traces, with an average landslide density of 20.62 km-2 and a total landslide area of 769.12 km2, accounting for 21.63% of the total land area. These landslides are relatively evenly distributed throughout the district, with a higher concentration in the east compared to the west. Most of the landslides are small in size. This study can support disaster prevention and mitigation efforts in the Baota District and serve as a reference for establishing landslide inventories in other regions of the Loess Plateau.

Flow dynamics and channel changes at Yamuna River in Delhi-National Capital Region, India

ABSTRACT The present work was carried out on River Yamuna passing through Delhi-National Capital Region (Delhi-NCR) of India for a stretch of about 125 km to assess the fluvial characteristics in the highly human-dominated region for a period of 200 years with the help of historical maps, topographical maps and satellite data with integration of geographic information system (GIS) environment. Erosion, deposition and channel stability data were analyzed for 1977–1986, 1986–1996, 1996–2006, 2006–2016, and 2016–2022 period. Digital Shoreline Analysis System (DSAS) application was utilized to quantify river channel movement with average channel migration of 22.8 m/year. Westward migration is 4149.2 m maximum in length and eastward migration is about 4083.8 m. The river has migrated a total of 32.26 km2 of area during 1955-2022. The findings indicate that various human activities, such as engineering structures, sand mining, embankments, urbanization, land use/land cover changes, and canal networks, have significantly impacted the river. The DSAS application was also used to predict the position of river channel centerline in future for 2032 and 2042 with channel length of 132.5 and 141.6 km respectively.

Oil pollution near the ports of Aktau, Kuryk and Ersai&amp;nbsp;(the Caspian Sea): Satellite and in-situ data

To study oil pollution on the surface of the Caspian Sea in the area of the ports of Aktau, Kuryk and Ersai, the archives of radar data obtained from the synthetic aperture radars of the Sentinel-1 satellite constellation belonging to the European Space Agency for the period 2013 - 2022 were selected. For this purpose, a specialized satellite information service “See the Caspian Sea” (STCS) (http://caspian.geosmis.ru) created in 2023 jointly by Space Research Institute and Shirshov Institute of Oceanology of Russian Academy of Sciences was used. This system is specifically designed for the Caspian Sea and is created on the basis of the existing “See the Sea” (STS) service, which is part of the “IKI-Monitoring” Center for Collective Use established in Space Research Institute. For a given polygon in the region of the ports of Aktau, Kuryk and Ersai, in the STCS system, time series archives consisting of more than 3,700 radar images were examined. Each image was manually analyzed to identify dark areas of reduced radar signal that could be identified as oil contamination. A total of 15 oil slicks were discovered, up to 89 km in length and up to 8 km2 in area. A satellite image of each oil slick is presented in the article, as well as its main characteristics. In addition, as part of the project “Satellite monitoring of the water areas of the ports of Aktau and Kuryk”, sea water samples were taken in the area of the ports of Aktau and Kuryk, which showed the content of oil products in the range of 0.014-0.1 mg/dm3. Thus, the resulting concentrations turned out to be less than the maximum permissible concentration of 0.1 mg per liter.

Forecasting Appropriate Habitats for Rare and Endangered Indocalamus Species in China in Response to Climate Change

China’s rare and endangered bamboo species belonging to the Indocalamus genus, specifically Indocalamus decorus Q. H. Dai, Indocalamus hirsutissimus Z. P. Wang &amp; P. X. Zhang, and Indocalamus pedalis (Keng) P. C. Keng, possess notable value in biodiversity conservation and have significant potential for use in landscape design. Using an enhanced MaxEnt model, this study forecasted shifts in the species’ potential range under four separate climate scenarios (SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0, and SSP5-RCP8.5), considering both the historical period (1970–2000, referred to as “the current period”) and upcoming timeframes (2021–2040, 2041–2060, 2061–2080, and 2081–2100). The analysis disclosed that the present total potential habitat area for these species is approximately 251.79 × 104 km2, with high, medium, and low suitability areas occupying 0.15 × 104 km2, 125.39 × 104 km2, and 126.26 × 104 km2, respectively. The minimum temperature of the coldest month emerged as the critical determinant of their potential habitat distribution. Expected climate changes are likely to increase the suitable habitat for these species, although areas with low suitability might slightly diminish, with Guizhou and Chongqing showing the least impact. The distribution centers of suitable habitats for the three plant species consistently exhibit a westward shift under various climate scenarios. These results contribute valuable insights for the spatial distribution, continuous monitoring, sustainable management, and ex situ conservation in response to climate change.

Implementing a pragmatic randomised controlled trial in a humanitarian setting: lessons learned from the TISA trial

IntroductionHigh-quality evidence is crucial for guiding effective humanitarian responses, yet conducting rigorous research, particularly randomised controlled trials, in humanitarian crises remains challenging. The TISA (“traitement intégré de la sous-nutrition aiguë”) trial aimed to evaluate the impact of a Water, Sanitation and Hygiene (WASH) intervention on the standard national treatment of uncomplicated Severe Acute Malnutrition (SAM) in children aged 6–59 months. Implemented in two northern Senegalese regions from December 22, 2021, to February 20, 2023, the trial faced numerous challenges, which this paper explores along with the lessons learned.MethodsThe study utilised trial documentation, including field reports, meeting minutes, training plans, operational monitoring data and funding proposals, to retrace the trial timeline, identify challenges and outline implemented solutions. Contributions from all TISA key staff—current and former, field-based and headquarters—were essential for collecting and interpreting information. Challenges were categorised as internal (within the TISA consortium) or external (broader contextual issues).ResultsThe TISA trial, executed by a consortium of academic, operational, and community stakeholders, enrolled over 2000 children with uncomplicated SAM across 86 treatment posts in a 28,000 km2 area. The control group received standard outpatient SAM care, while the intervention group also received a WASH kit and hygiene promotion. Initially planned to start in April 2019 for 12 months, the trial faced a 30-month delay and was extended to 27 months due to challenges like the COVID-19 pandemic, national strikes, health system integration issues and weather-related disruptions. Internal challenges included logistics, staffing, data management, funding and aligning diverse stakeholder priorities.Discussion and conclusionDespite these obstacles, the trial concluded successfully, underscoring the importance of tailored monitoring, open communication, transparency and community involvement. Producing high-quality evidence in humanitarian contexts demands extensive preparation and strong coordination among local and international researchers, practitioners, communities, decision-makers and funders from the study’s inception.Trial registrationClinicaltrials.gov NCT04667767.

Agricultural land suitability of Manimutha Nadhi watershed using AHP and GIS techniques

A precise land suitability assessment plays a vital role in modern agriculture to sustain production against population explosions worldwide without affecting natural resources or environmental factors. Further, recent urbanization has led to a reduction in arable land and forced the conversion of non-arable land to arable land. Therefore, developing land suitability with its potential and limitations is essential through advanced techniques like multi-criteria Decision Analysis for the particular area. The current research aimed to assess the land suitability of the Manimutha Nadhi watershed, Tamil Nadu, India, using geographical information system (GIS) and Analytic Hierarchy Process (AHP) methods. This study area, Manimutha Nadhi, covers a total area of 263.89 km2. Using a random sampling technique, 57 soil samples were obtained. The parameters for the AHP study were soil pH, EC, Soil organic carbon (SOC), available NPK, soil texture, and soil erosion. The climatic parameters, Annaula average rainfall (AAR) and Annual average temperature (AAT) and topographic parameter slope of the study area are also used as the criteria for generating final agricultural land suitability. The final suitability map through GIS and AHP was created using weighted overlay analysis. The land suitability class on the basis of FAO classification is highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2) for agriculture. The results of the final land suitability map by AHP and GIS revealed that 150.58 km2 (57.09%) of the research region is highly suitable for agriculture (S1). The 29.60 km2 (10.08%) area was moderately suitable (S2) for irrigated agriculture. The 53.37 km2 (20.20%) area was marginally suitable (S3) for irrigated agriculture. The 10.56 km2 (7.53%) area was currently unsuitable (N1) for agricultural uses. The area of about 13.45 km2 (5.10%) was permanently unsuitable (N2) for agriculture. The outcomes can be used as baseline information on the land that could strengthen agriculture production in the Manimutha Nadhi watershed.