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Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R2) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts. [ABSTRACT FROM AUTHOR]

The study evaluates the impacts of India's COVID-19 lockdown and unlocking periods on the country's ambient air quality. India experienced three strictly enforced lockdowns followed by unlocking periods where economic and social restrictions were gradually lifted. We have examined the in situ and satellite data of NO2 emissions for several Indian cities to assess the impacts of the lockdowns in India. Additionally, we analyzed NO2 data acquired from the Sentinel-5P TROPOMI sensor over a few districts of the Punjab state, as well as the National Capital Region. The comparisons between the in situ and satellite NO2 emissions were performed for the years 2019, 2020 and up to July 2021. Further analysis was conducted on the satellite data to map the NO2 emissions over India during March to July for the years of 2019, 2020 and 2021. Based on the in situ and satellite observations, we observed that the NO2 emissions significantly decreased by 45–55% in the first wave and 30% in the second wave, especially over the Northern Indian cities during the lockdown periods. The improved air quality over India is indicative of reduced pollution in the atmosphere due to the lockdown process, which slowed down the industrial and commercial activities, including the migration of humans from one place to another. Overall, the present study contributes to the understanding of the trends of the ambient air quality over large geographical areas using the Sentinel-5P satellite data and provides valuable information for regulatory bodies to design a better decision support system to improve air quality. [ABSTRACT FROM AUTHOR]

Large-scale debris cover glacier mapping can be efficiently conducted from high spatial resolution (HSR) remote sensing imagery using object-based image analysis (OBIA), which works on a group of pixels. This paper presents the spectral and spatial capabilities of OBIA to classify multiple glacier cover classes using a multisource approach by integrating multispectral, thermal, and slope information into one workflow. The novel contributions of this study are effective mapping of small yet important geomorphological features, classification of shadow regions without manual corrections, discrimination of snow/ice, ice-mixed debris, and supraglacial debris without using shortwave infrared bands, and an adaptation of an area-weighted error matrix specifically built for assessing OBIA's accuracy. The large-scale glacier cover map is produced with a high overall accuracy of ≈94% (area-weighted error matrix). The proposed OBIA approach also proved to be effective in mapping minor geomorphological features such as small glacial lakes, exposed ice faces, debris cones, rills, and crevasses with individual class accuracies in the range of 96.9–100%. We confirm the portability of our proposed approach by comparing the results with reference glacier inventories and applying it to different sensor data and study areas. [ABSTRACT FROM AUTHOR]

In September 2014, Kashmir witnessed a catastrophic flood resulting in a significant loss of lives and property. Such massive losses could have been avoided if any structural support such as dams were constructed in the Jhelum basin, which has a history of devastating floods. The GIS-based multicriteria analysis (MCA) model provided three suitability zones for dam locations. The final suitable dam sites were identified within the highest suitability zone based on topography (cross-sections), stream order, high suitable zone, minimum dam site interval, distance from roads, and protected area distance to the dam site. It was discovered that 10.98% of the total 4347.74 km2 area evaluated falls in the high suitability zone, 28.88% of the area falls in the medium suitability zone, and 60.14% of the area falls in the low suitability zone. Within the study area, four viable reservoir sites with a holding capacity of 4,489,367.55 m3 were revealed. [ABSTRACT FROM AUTHOR]

Water scarcity is a global challenge, especially in arid regions, including Middle Eastern and North African countries. The distribution of water around the earth is not even. Trading water in the form of an embedded commodity, known as the water footprint (WF), from water-abundant regions to water-scarce regions, is a viable solution to water scarcity problems. Agricultural products account for approximately 85% of the earth's total WF, indicating that importing water-intense crops, such as cereal crops, can partially solve the local water scarcity problem. This study investigated water, energy, and food nexus dynamics for the trades of a few major crops, specifically considering Saudi Arabia. It analyzed the trade of crops and its impact on WF, energy, and carbon dioxide (CO2) emission savings. The findings revealed that importing major cereal crops to Saudi Arabia could significantly reduce the local WF. The imports of wheat, maize, rice, and barley reduced approximately 24 billion m3 per year of consumable WF (i.e., blue and green water footprint) in the global scale. Similarly, the trade of major crops had a significant impact on energy and CO2 emission savings. The energy savings from the wheat, maize, and barley trades in Saudi Arabia was estimated to be approximately 9 billion kWh. It also saved about 7 million tons per year of CO2 emissions. The trades of cereal crops in Saudi Arabia reduced water consumption, energy usage, and CO2 emissions significantly. [ABSTRACT FROM AUTHOR]

Primary productivity (PP) has been recently investigated using remote sensing-based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models—the vertically generalized production model (VGPM), the Eppley-VGPM, and the carbon-based production model (CbPM)—produced over the Red Sea domain for the 1998–2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization, and moving averages correlative analysis to uncover the models’ responses to various climate factors. Here, we use the models’ eight-day composite and monthly averages compared with satellite-based variables, including chlorophyll-a (Chla), mixed layer depth (MLD), and sea-surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO), and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI, and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models’ connection to oceanographic and atmospheric parameters, as well as the trends in the southern Red Sea, thus calling for further validation efforts. [ABSTRACT FROM AUTHOR]

This is an integrated study to classify Tamil Nadu Coast in terms of its vulnerability to coastal processes. It is accomplished through estimating Coastal Vulnerability Index (CVI) for the entire stretch of the said coast using weighted averages of six parameters, viz., geomorphology, shoreline changes, coastal slope, and relative sea level rise, mean significant wave height and mean tidal range. Thus, CVI evaluates the state of the coast in terms of level of risk hovering on it as and when it is affected by oceanic hazards. The target of this study is to evaluate the vulnerability status of coastal stretches of 35 taluks of Tamil Nadu. Twenty one percent of the coastal stretch is under very high vulnerability level. Following this is 36% stretch under high vulnerability state. Another 34% coastal stretch is in moderate setting. Low vulnerability state prevails about 6%. Further, it is learnt that there are only 2% of Tamil Nadu coastal region coming under the least vulnerable category. The study revealed the very high vulnerability of Nagapattinam coastal region. The present study may also help create awareness among people about marine natural hazards such as coastal erosion, inundation, loss of life and their properties. [ABSTRACT FROM AUTHOR]

Actinyl ions can self-assemble in aqueous solution to form closed cage clusters ranging from 1.5 to 4.0 nm in diameter. The self-assembly, stability, and behavior of the nanoclusters depend on the nature of the aqueous environment, such as the pH and cations present. In this work, a classical force field for [(UO2)20(O2)30]20− (U20) peroxide nanoclusters in aqueous solution was developed from quantum-mechanical calculations. Using molecular dynamics simulations, the preferred binding sites of six cations (Li+, Na+, K+, Rb+, Cs+, and Ca2+) to the nanocluster were determined. Replica exchange molecular dynamics was used to equilibrate the structure and determine the equilibrium distribution of cations and water with respect to the nanocluster cage. In addition, the free energy barriers associated with cations entering the cluster were computed. Finally, the association of two cages was investigated by computing the free energy as a function of intercage distance. The free energy profiles reveal that the nanoclusters prefer to be associated when neutralized with divalent cations, but do not associate when neutralized with monovalent cations. This could explain the formation of tertiary structures observed experimentally. [ABSTRACT FROM AUTHOR]

Speed Up Robust Feature Algorithm (SURF) has been a very useful technique in the advancement of image feature algorithm. The strategy offers an extremely decent agreement between the runtime and accuracy, especially at object borders and fine structures. It has a wide scope of applications in remote sensing like getting computerized surface models from UAV and satellite images. In this paper, SURF algorithm has been discussed in details to enhance the capability of the system for image feature extraction technique to detect and obtain the maximum feature points from aerial imagery. The algorithms are developed depending upon such phenomena in which a maximum result can be obtained in very less time. [ABSTRACT FROM AUTHOR]

An optical algorithm is developed for the retrieval of the downwelling diffuse attenuation coefficient $K_{d}$ (490) in the Red Sea using a comprehensive hydrolight simulated data set ($N = 5000$). We found a robust relationship between the $K_{d}$ (490) and the ratio of remote sensing reflectance $R_{\text {rs}}$ (443)/ $R_{\text {rs}}$ (555), with an excellent determination coefficient ($R^{2} = 0.999$) and a low root-mean-square error (RMSE = 0.00033). The performance of the developed algorithm is evaluated with in situ data collected in the Red Sea by comparing obatined model output with existing empirical (NASA, Morel et al., Zhang and Fell, Tiwari and Shanmugam) and semianalytical (Lee et al.) algorithms. On the used in situ data from the Red Sea, the new algorithm shows good retrievals of $K_{d}$ (490) with a low bias, and a low RMSE compared to that of the existing algorithms. For satellite application, we applied our algorithm to selected MODIS–Aqua images acquired over the Red Sea, which captured spatial features of phytoplankton blooms and physical processes (e.g., cyclonic and anticyclonic circulations) in the Red Sea. The new algorithm has the potential to improve our understanding of water transparency and photosynthetic processes that rely on the availability of solar radiation. [ABSTRACT FROM AUTHOR]

Actinyl ions (AnO2n+), the form in which actinides are commonly found in aqueous solution, are important species in the nuclear fuel cycle. These ions can form stable complexes with ionic ligands such as OH−, NO3−, Cl−, and even other actinyl ions in the aqueous phase. Knowledge of the relative stabilities of these complexes is important for the efficient design of separation processes used in recycling. These complexes also play a major role in the formation of actinide nanoclusters. A quantitative treatment of the stability of these actinyl ion complexes is therefore warranted. In the present work, molecular dynamics (MD) simulations have been performed to calculate the potential of mean force (PMF) between two actinyl ions (UO22+ and NpO2+) and various ligands (F−, Cl−, OH−, NO3−, SO42−, CO32−, Na+, and H2O) in explicitly-modeled aqueous solution. Equilibrium constants were calculated from the PMFs, and are consistent with experimental trends. Dication actinyls show stronger affinity for anions compared to monocation actinyls, whereas the opposite is true for cation–cation interactions between actinyls. Finally, the dynamics of actinyl–ligand contact ion pair (CIP) dissociation were characterized by calculating rate constants from transition state theory. The transmission coefficient, a dynamical correction factor used to correct for reaction barrier recrossing, was calculated for each actinyl–ligand CIP dissociation event. [ABSTRACT FROM AUTHOR]

We report the dielectric and electrical behavior of thermally evaporated lithium fluoride (LiF)-tungsten trioxide (WO) ( x wt% WO-doped LiF; x = 0, 1, 3, 5, 7, 10) nano-composite thin films at room temperature over a wide range of frequencies from 0.1 Hz to 1 MHz. Among the various doping concentrations of WO, the 5 wt% WO-doped LiF nano-composite thin film reaches to the maximum dielectric constant ~ 25, compared with those of pure LiF ~ 9 thin film. The electrical studies of the films have been done by complex impedance spectroscopy and show the presence of grain and grain boundaries contribution in the films for all doping concentrations. The relaxation behavior in the nano-composite films has been observed in the dielectric curve at low frequencies which is superimposed by electrode polarization. The non-Debye type of relaxation in the films has been observed in the impedance and modulus curve. The peaks appearing at low frequencies for each doping concentration in loss tangent spectrum show the presence of relaxing dipoles in the films. An increment in the ac conductivity has been observed with doping concentration up to 5%. The ac conductivity curves obey the jump relaxation power law where an electrode polarization effect can be seen at low-frequency region. [ABSTRACT FROM AUTHOR]

Despite the importance of diffuse attenuation coefficient of downwelling irradiance (K d) to physical, chemical and biological processes in the euphotic zone, accurate determination of this apparent optical property from satellite ocean color data for coastal oceanic waters is still challenging with most of the existing algorithms. This paper presents a simple and robust algorithm developed using the relationship (R² = 0.995) of the ratio of remote sensing reflectances at 490 and 670 nm to the K d at a wavelength of 490 nm derived from a large number of radiometric profiles from various regional waters. When tested with three independent data sets from relatively clear and turbid waters, accuracy of K d (490) prediction by the present algorithm is statistically good compared to that of the existing algorithms. The effectiveness of the present algorithm is also tested on MODIS-Aqua images from complex waters around India. It is found that the present algorithm makes better retrievals of Kd (490) capturing the spatial characteristics of bloom and turbid water features. These results suggest that the present algorithm has the potential to provide more accurate Kd (490) from satellite ocean color data for coastal oceanic waters. [ABSTRACT FROM PUBLISHER]

The dynamics of actinyl ions (AnO2n+) in aqueous solutions is important not only for the design of advanced separation processes but also for understanding the fate of actinides in the environment. The hazardous nature of actinides makes it difficult to measure transport and thermodynamic properties experimentally, so predictive simulations are an attractive method for studying these systems. Here, we report the results of atomistic-level molecular dynamics simulations of actinyl ions (of U, Np, Pu, and Am) in their mono- and dication states in aqueous solution. Quantum mechanically derived force field parameters are used to compute self-diffusion coefficients of the actinyl ions, water exchange mechanisms, and residence times of water molecules in the first solvation shell of the actinyl ions. We find that monocation actinyl ions diffuse slightly faster than their dication counterparts. Our simulations suggest that there are two distinct water exchange mechanisms for mono and dications. An associative interchange pathway is observed for water exchange involving dication actinyls, while in monocation actinyls the exchange occurs via a dissociative mechanism. The residence time of water molecules in the first solvation shell depends on the water exchange mechanism. In the case of dications, a stiffer actinyl bond angle results in a longer residence time, while for monocations, a shorter water coordination distance leads to a longer residence time. The simulations predict much faster water exchange for UO22+ than what is observed experimentally with NMR, but other properties are consistent with experiments. [ABSTRACT FROM AUTHOR]

Several semi-analytical models for the satellite-based estimation of absorption coefficients of phytoplankton aph (\lambda ) have been used to routinely produce aph (\lambda ) product from satellite ocean color data. However, these models are generally applicable for clear ocean waters where they produce aph (\lambda ) values only at a few wavelengths in the blue-green domain; this causes the main difficulty in making these models more usable with any suite of wavelengths. Further, recent studies have shown the performance of these models to be highly questionable in optically complex waters. This emphasized the need for developing a more accurate model for the satellite-based estimation of aph (\lambda ) in a wide range of oceanic waters. In our previous study, we developed an empirical model (hereafter referred as Tiwari-Shanmugam model – “TS model”) based on the relationship of the in situ remote sensing reflectance ratio R rs (670)/R rs (490) and in situ aph(\lambda) which is best fit to a third order polynomial. In the present study, we rigorously test this model along with three global inversion models (e.g., Constrained Linear Matrix (LM) model, Quasi-analytical algorithm (QAA), and GSM semi-analytical model which are often used by the ocean color community) using three independent in situ data sets from clear to turbid coastal waters and satellite match-ups data from global waters. When applied to these data sets, the TS model produces more accurate aph values across the entire visible wavelengths (400–700 nm) in all these waters, whereas LM, QAA and GSM models yield significant errors in addition to being restricted to produce aph values only in the blue-green wavelengths (LM and GSM). Though the TS model is mathematically simple, it overcomes such limitations and yields excellent results in terms of reproducing measured aph spectra that are highly desired by the ocean color community for inputting in various bio-optical models and studying the spatial structure of the different phytoplankton communities from satellite remote sensing observations in global waters. [ABSTRACT FROM PUBLISHER]

Intra- and intermolecular force field parameters for the interaction of actinyl ions (AnO2n+, where, An = U, Np, Pu, Am and n = 1, 2) with water have been developed using quantum mechanical calculations. Water was modeled with the extended simple point charge potential (SPC/E). The resulting force field consists of a simple form in which intermolecular interactions are modeled with pairwise Lennard-Jones functions plus partial charge terms. Intramolecular bond stretching and angle bending are treated with harmonic functions. The new potentials were used to carry out extensive molecular dynamics simulations for each hydrated ion. Computed bond lengths, bond angles and coordination numbers agree well with known values and previous simulations. Hydration free energies, computed from molecular dynamics simulations as well as from quantum simulations with a solvation model, were in reasonable agreement with estimated experimental values. [ABSTRACT FROM AUTHOR]

Semi-analytical algorithms (SAAs) invert spectral remote sensing reflectance (R r s (λ) ,   s r − 1 ) to Inherent Optical Properties (IOPs) of an aquatic medium (λ is the wavelength). Existing SAAs implement different methodologies with a range of spectral IOP models and inversion methods producing concentrations of non-water constituents. Absorption spectrum decomposition algorithms (ADAs) are a set of algorithms developed to partition a n w (λ) ,   m − 1 (i.e., the light absorption coefficient without pure water absorption), into absorption subcomponents of phytoplankton (a p h (λ) ,   m − 1 ) and coloured detrital matter (a d g (λ) ,   m − 1 ) . Despite significant developments in ADAs, their applicability to remote sensing applications is rarely studied. The present study formulates hybrid inversion approaches that combine SAAs and ADAs to derive absorption subcomponents from R r s (λ) and explores potential alternatives to operational SAAs. Using R r s (λ) and concurrent absorption subcomponents from four datasets covering a wide range of optical properties, three operational SAAs, i.e., Garver–Siegel–Maritorena (GSM), Quasi-Analytical Algorithm (QAA), Generalized Inherent Optical Property (GIOP) model are evaluated in deriving a n w (λ) from R r s (λ) . Among these three models, QAA and GIOP models derived a n w (λ) with lower errors. Among six distinctive ADAs tested in the study, the Generalized Stacked Constraints Model (GSCM) and Zhang's model-derived absorption subcomponents achieved lower average spectral mean absolute percentage errors (MAPE) in the range of 8–38%. Four hybrid models, GIOPGSCM, GIOPZhang, QAAGSCM and QAAZhang, formulated using the SAAs and ADAs, are compared for their absorption subcomponent retrieval performance from R r s (λ) . GIOPGSCM and GIOPZhang models derived absorption subcomponents have lower errors than GIOP and QAA. Potential uncertainties associated with datasets and dependency of algorithm performance on datasets were discussed. [ABSTRACT FROM AUTHOR]

Even though the contribution of the aviation sector to the global economy is very notable, it also has an adverse impact on climate change. Improvements have been made in different areas (i.e., technology, sustainable aviation fuel, and design) to mitigate these adverse effects. However, the rate of improvement is small compared to the increase in the demand for air transportation. Hence, greenhouse gas emissions in the aviation sector are steadily increasing and this trend is expected to continue unless adequately addressed. In this context, this study examined the following: (i) the factors that affect the growth of aviation, (ii) trends in greenhouse gas emissions in the sector, (iii) trends in energy demand, (iv) mitigation pathways of emissions, (v) mitigation challenges for the International Civil Aviation Organization, (vi) achievements in mitigating emissions, (vii) barriers against mitigating emissions, and (viii) approaches of overcoming barriers against emissions mitigation. This study finds that continued research and development efforts targeting aircraft fuel burn efficiency are crucial in reducing greenhouse gas emissions. Although biofuels are promising for the reduction of aviation emissions, techniques to reduce NOx emissions could enhance large-scale deployment. Pragmatic market-based mechanisms, such as the Emissions Trading Scheme (ETS) and/or carbon tax must be enforced on a global scale to capitalize on a collective stakeholder effort to curb CO2 emissions. The findings of this study will help in understanding the emissions and energy consumption scenarios, which will provide a comprehensive package of mitigation pathways to overcome future emissions reduction challenges in the aviation sector. [ABSTRACT FROM AUTHOR]

Drylands cover about 40% of the world's land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people's lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an integrative approach of remotely sensed and physical process-based numerical modeling (e.g., Global Land Data Assimilation System (GLDAS) and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) models) data. Interaction between hydrological and ecological indicators including precipitation, evapotranspiration, surface soil moisture and vegetation indices are presented in the global four types of arid and semi-arid areas. The trends followed by precipitation, evapotranspiration and surface soil moisture over the decade are also mapped using harmonic analysis. This study also shows that some hotspots in these global drylands, which exhibit different processes of land cover change, demonstrate strong coherency with noted groundwater variations. Various types of statistical measures are computed using the satellite and model derived values over global arid and semi-arid regions. Comparisons between satellite- (NASA-USDA Surface Soil Moisture and MODIS Evapotranspiration data) and model (FLDAS and GLDAS)-derived values over arid regions (BSh, BSk, BWh and BWk) have shown the over and underestimation with low accuracy. Moreover, general consistency is apparent in most of the regions between GLDAS and FLDAS model, while a strong discrepancy is also observed in some regions, especially appearing in the Nile Basin downstream hyper-arid region. Data-driven modelling approaches are thus used to enhance the models' performance in this region, which shows improved results in multiple statistical measures ((RMSE), bias (ψ), the mean absolute percentage difference ( | ψ |) ) and the linear regression coefficients (i.e., slope, intercept, and coefficient of determination (R2)). [ABSTRACT FROM AUTHOR]

Intra- and intermolecular force field parameters for the interaction of actinyl ions (AnO2(n+), where, An = U, Np, Pu, Am and n = 1, 2) with water have been developed using quantum mechanical calculations. Water was modeled with the extended simple point charge potential (SPC/E). The resulting force field consists of a simple form in which intermolecular interactions are modeled with pairwise Lennard-Jones functions plus partial charge terms. Intramolecular bond stretching and angle bending are treated with harmonic functions. The new potentials were used to carry out extensive molecular dynamics simulations for each hydrated ion. Computed bond lengths, bond angles and coordination numbers agree well with known values and previous simulations. Hydration free energies, computed from molecular dynamics simulations as well as from quantum simulations with a solvation model, were in reasonable agreement with estimated experimental values.